research paper on social networking

ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

• 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
• 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
• 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
• 4 CETYS Universidad, Tijuana, Mexico
• 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
• 6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbas, R., and Mesch, G. (2018). Do rich teens get richer? Facebook use and the link between offline and online social capital among Palestinian youth in Israel. Inf. Commun. Soc. 21, 63–79. doi: 10.1080/1369118X.2016.1261168

CrossRef Full Text | Google Scholar

Adnan, M., and Anwar, K. (2020). Online learning amid the COVID-19 pandemic: students' perspectives. J. Pedagog. Sociol. Psychol. 2, 45–51. doi: 10.33902/JPSP.2020261309

PubMed Abstract | CrossRef Full Text | Google Scholar

Ali Qalati, S., Li, W., Ahmed, N., Ali Mirani, M., and Khan, A. (2021). Examining the factors affecting SME performance: the mediating role of social media adoption. Sustainability 13:75. doi: 10.3390/su13010075

Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94. doi: 10.1007/BF02723327

Bagozzi, R. P., Yi, Y., and Phillips, L. W. (1991). Assessing construct validity in organizational research. Admin. Sci. Q. 36, 421–458. doi: 10.2307/2393203

Bano, S., Cisheng, W., Khan, A. N., and Khan, N. A. (2019). WhatsApp use and student's psychological well-being: role of social capital and social integration. Child. Youth Serv. Rev. 103, 200–208. doi: 10.1016/j.childyouth.2019.06.002

Barbosa, B., Chkoniya, V., Simoes, D., Filipe, S., and Santos, C. A. (2020). Always connected: generation Y smartphone use and social capital. Rev. Ibérica Sist. Tecnol. Inf. E 35, 152–166.

Google Scholar

Bekalu, M. A., McCloud, R. F., and Viswanath, K. (2019). Association of social media use with social well-being, positive mental health, and self-rated health: disentangling routine use from emotional connection to use. Health Educ. Behav. 46(2 Suppl), 69S−80S. doi: 10.1177/1090198119863768

Brown, G., and Michinov, N. (2019). Measuring latent ties on Facebook: a novel approach to studying their prevalence and relationship with bridging social capital. Technol. Soc. 59:101176. doi: 10.1016/j.techsoc.2019.101176

Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105. doi: 10.1037/h0046016

Carlson, J. R., Zivnuska, S., Harris, R. B., Harris, K. J., and Carlson, D. S. (2016). Social media use in the workplace: a study of dual effects. J. Org. End User Comput. 28, 15–31. doi: 10.4018/JOEUC.2016010102

Chan, M. (2015). Mobile phones and the good life: examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 17, 96–113. doi: 10.1177/1461444813516836

Chappell, N. L., and Badger, M. (1989). Social isolation and well-being. J. Gerontol. 44, S169–S176. doi: 10.1093/geronj/44.5.s169

Chatterjee, S. (2020). Antecedents of phubbing: from technological and psychological perspectives. J. Syst. Inf. Technol. 22, 161–118. doi: 10.1108/JSIT-05-2019-0089

Chen, H.-T., and Li, X. (2017). The contribution of mobile social media to social capital and psychological well-being: examining the role of communicative use, friending and self-disclosure. Comput. Hum. Behav. 75, 958–965. doi: 10.1016/j.chb.2017.06.011

Choi, D.-H., and Noh, G.-Y. (2019). The influence of social media use on attitude toward suicide through psychological well-being, social isolation, and social support. Inf. Commun. Soc. 23, 1–17. doi: 10.1080/1369118X.2019.1574860

Chotpitayasunondh, V., and Douglas, K. M. (2016). How “phubbing” becomes the norm: the antecedents and consequences of snubbing via smartphone. Comput. Hum. Behav. 63, 9–18. doi: 10.1016/j.chb.2016.05.018

Chotpitayasunondh, V., and Douglas, K. M. (2018). The effects of “phubbing” on social interaction. J. Appl. Soc. Psychol. 48, 304–316. doi: 10.1111/jasp.12506

Cohen, J. (1998). Statistical Power Analysis for the Behavioural Sciences . Hillsdale, NJ: Lawrence Erlbaum Associates.

Davey, S., Davey, A., Raghav, S. K., Singh, J. V., Singh, N., Blachnio, A., et al. (2018). Predictors and consequences of “phubbing” among adolescents and youth in India: an impact evaluation study. J. Fam. Community Med. 25, 35–42. doi: 10.4103/jfcm.JFCM_71_17

David, M. E., Roberts, J. A., and Christenson, B. (2018). Too much of a good thing: investigating the association between actual smartphone use and individual well-being. Int. J. Hum. Comput. Interact. 34, 265–275. doi: 10.1080/10447318.2017.1349250

Dhir, A., Yossatorn, Y., Kaur, P., and Chen, S. (2018). Online social media fatigue and psychological wellbeing—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int. J. Inf. Manag. 40, 141–152. doi: 10.1016/j.ijinfomgt.2018.01.012

Dutot, V., and Bergeron, F. (2016). From strategic orientation to social media orientation: improving SMEs' performance on social media. J. Small Bus. Enterp. Dev. 23, 1165–1190. doi: 10.1108/JSBED-11-2015-0160

Ellison, N. B., Steinfield, C., and Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and college students' use of online social network sites. J. Comput. Mediat. Commun. 12, 1143–1168. doi: 10.1111/j.1083-6101.2007.00367.x

Fan, M., Huang, Y., Qalati, S. A., Shah, S. M. M., Ostic, D., and Pu, Z. (2021). Effects of information overload, communication overload, and inequality on digital distrust: a cyber-violence behavior mechanism. Front. Psychol. 12:643981. doi: 10.3389/fpsyg.2021.643981

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18, 39–50. doi: 10.1177/002224378101800104

Gökçearslan, S., Uluyol, Ç., and Sahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among University students: a path analysis. Child. Youth Serv. Rev. 91, 47–54. doi: 10.1016/j.childyouth.2018.05.036

Gong, S., Xu, P., and Wang, S. (2021). Social capital and psychological well-being of Chinese immigrants in Japan. Int. J. Environ. Res. Public Health 18:547. doi: 10.3390/ijerph18020547

Guazzini, A., Duradoni, M., Capelli, A., and Meringolo, P. (2019). An explorative model to assess individuals' phubbing risk. Fut. Internet 11:21. doi: 10.3390/fi11010021

Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24. doi: 10.1108/EBR-11-2018-0203

Hair, J. F., Sarstedt, M., Pieper, T. M., and Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plann. 45, 320–340. doi: 10.1016/j.lrp.2012.09.008

Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage.

Hajek, A., and König, H.-H. (2021). Social isolation and loneliness of older adults in times of the CoViD-19 pandemic: can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology 67, 121–123. doi: 10.1159/000512793

Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). “The use of partial least squares path modeling in international marketing,” in New Challenges to International Marketing , Vol. 20, eds R.R. Sinkovics and P.N. Ghauri (Bigley: Emerald), 277–319.

Holliman, A. J., Waldeck, D., Jay, B., Murphy, S., Atkinson, E., Collie, R. J., et al. (2021). Adaptability and social support: examining links with psychological wellbeing among UK students and non-students. Fron. Psychol. 12:636520. doi: 10.3389/fpsyg.2021.636520

Jeong, S.-H., Kim, H., Yum, J.-Y., and Hwang, Y. (2016). What type of content are smartphone users addicted to? SNS vs. games. Comput. Hum. Behav. 54, 10–17. doi: 10.1016/j.chb.2015.07.035

Jiao, Y., Jo, M.-S., and Sarigöllü, E. (2017). Social value and content value in social media: two paths to psychological well-being. J. Org. Comput. Electr. Commer. 27, 3–24. doi: 10.1080/10919392.2016.1264762

Jordan, P. J., and Troth, A. C. (2019). Common method bias in applied settings: the dilemma of researching in organizations. Austr. J. Manag. 45, 3–14. doi: 10.1177/0312896219871976

Karikari, S., Osei-Frimpong, K., and Owusu-Frimpong, N. (2017). Evaluating individual level antecedents and consequences of social media use in Ghana. Technol. Forecast. Soc. Change 123, 68–79. doi: 10.1016/j.techfore.2017.06.023

Kemp, S. (January 30, 2020). Digital 2020: 3.8 billion people use social media. We Are Social . Available online at: https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media .

Kim, B., and Kim, Y. (2017). College students' social media use and communication network heterogeneity: implications for social capital and subjective well-being. Comput. Hum. Behav. 73, 620–628. doi: 10.1016/j.chb.2017.03.033

Kim, K., Milne, G. R., and Bahl, S. (2018). Smart phone addiction and mindfulness: an intergenerational comparison. Int. J. Pharmaceut. Healthcare Market. 12, 25–43. doi: 10.1108/IJPHM-08-2016-0044

Kircaburun, K., Alhabash, S., Tosuntaş, S. B., and Griffiths, M. D. (2020). Uses and gratifications of problematic social media use among University students: a simultaneous examination of the big five of personality traits, social media platforms, and social media use motives. Int. J. Mental Health Addict. 18, 525–547. doi: 10.1007/s11469-018-9940-6

Leong, L.-Y., Hew, T.-S., Ooi, K.-B., Lee, V.-H., and Hew, J.-J. (2019). A hybrid SEM-neural network analysis of social media addiction. Expert Syst. Appl. 133, 296–316. doi: 10.1016/j.eswa.2019.05.024

Li, L., Griffiths, M. D., Mei, S., and Niu, Z. (2020a). Fear of missing out and smartphone addiction mediates the relationship between positive and negative affect and sleep quality among Chinese University students. Front. Psychiatr. 11:877. doi: 10.3389/fpsyt.2020.00877

Li, W., Qalati, S. A., Khan, M. A. S., Kwabena, G. Y., Erusalkina, D., and Anwar, F. (2020b). Value co-creation and growth of social enterprises in developing countries: moderating role of environmental dynamics. Entrep. Res. J. 2020:20190359. doi: 10.1515/erj-2019-0359

Li, X., and Chen, W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Comput. Hum. Behav . 35, 116–123. doi: 10.1016/j.chb.2014.02.012

Matthews, L., Hair, J. F., and Matthews, R. (2018). PLS-SEM: the holy grail for advanced analysis. Mark. Manag. J. 28, 1–13.

Meshi, D., Cotten, S. R., and Bender, A. R. (2020). Problematic social media use and perceived social isolation in older adults: a cross-sectional study. Gerontology 66, 160–168. doi: 10.1159/000502577

Mou, J., Shin, D.-H., and Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behav. Inf. Technol. 36, 125–139. doi: 10.1080/0144929X.2016.1203024

Nunnally, J. (1978). Psychometric Methods . New York, NY: McGraw-Hill.

Oghazi, P., Karlsson, S., Hellström, D., and Hjort, K. (2018). Online purchase return policy leniency and purchase decision: mediating role of consumer trust. J. Retail. Consumer Serv. 41, 190–200.

Pang, H. (2018). Exploring the beneficial effects of social networking site use on Chinese students' perceptions of social capital and psychological well-being in Germany. Int. J. Intercult. Relat. 67, 1–11. doi: 10.1016/j.ijintrel.2018.08.002

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879

Podsakoff, P. M., and Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. J. Manag. 12, 531–544. doi: 10.1177/014920638601200408

Preacher, K. J., and Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res. Methods 40, 879–891. doi: 10.3758/brm.40.3.879

Primack, B. A., Shensa, A., Sidani, J. E., Whaite, E. O., yi Lin, L., Rosen, D., et al. (2017). Social media use and perceived social isolation among young adults in the US. Am. J. Prev. Med. 53, 1–8. doi: 10.1016/j.amepre.2017.01.010

Putnam, R. D. (1995). Tuning in, tuning out: the strange disappearance of social capital in America. Polit. Sci. Polit. 28, 664–684. doi: 10.2307/420517

Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community . New York, NY: Simon and Schuster.

Qalati, S. A., Ostic, D., Fan, M., Dakhan, S. A., Vela, E. G., Zufar, Z., et al. (2021). The general public knowledge, attitude, and practices regarding COVID-19 during the lockdown in Asian developing countries. Int. Q. Commun. Health Educ. 2021:272684X211004945. doi: 10.1177/0272684X211004945

Reer, F., Tang, W. Y., and Quandt, T. (2019). Psychosocial well-being and social media engagement: the mediating roles of social comparison orientation and fear of missing out. New Media Soc. 21, 1486–1505. doi: 10.1177/1461444818823719

Ringle, C., Wende, S., and Becker, J. (2015). SmartPLS 3 [software] . Bönningstedt: SmartPLS.

Ringle, C. M., Sarstedt, M., and Straub, D. (2012). A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Q . 36, iii–xiv. doi: 10.2307/41410402

Roberts, J. A., and David, M. E. (2020). The social media party: fear of missing out (FoMO), social media intensity, connection, and well-being. Int. J. Hum. Comput. Interact. 36, 386–392. doi: 10.1080/10447318.2019.1646517

Salehan, M., and Negahban, A. (2013). Social networking on smartphones: when mobile phones become addictive. Comput. Hum. Behav. 29, 2632–2639. doi: 10.1016/j.chb.2013.07.003

Sarstedt, M., and Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. J. Mark. Anal. 7, 196–202. doi: 10.1057/s41270-019-00058-3

Schinka, K. C., VanDulmen, M. H., Bossarte, R., and Swahn, M. (2012). Association between loneliness and suicidality during middle childhood and adolescence: longitudinal effects and the role of demographic characteristics. J. Psychol. Interdiscipl. Appl. 146, 105–118. doi: 10.1080/00223980.2011.584084

Shi, S., Mu, R., Lin, L., Chen, Y., Kou, G., and Chen, X.-J. (2018). The impact of perceived online service quality on swift guanxi. Internet Res. 28, 432–455. doi: 10.1108/IntR-12-2016-0389

Shoukat, S. (2019). Cell phone addiction and psychological and physiological health in adolescents. EXCLI J. 18, 47–50. doi: 10.17179/excli2018-2006

Shrestha, N. (2021). Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat. 9, 4–11. doi: 10.12691/ajams-9-1-2

Stouthuysen, K., Teunis, I., Reusen, E., and Slabbinck, H. (2018). Initial trust and intentions to buy: The effect of vendor-specific guarantees, customer reviews and the role of online shopping experience. Electr. Commer. Res. Appl. 27, 23–38. doi: 10.1016/j.elerap.2017.11.002

Swar, B., and Hameed, T. (2017). “Fear of missing out, social media engagement, smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth ,” Paper presented at the 10th International Conference on Health Informatics (Porto).

Tangmunkongvorakul, A., Musumari, P. M., Thongpibul, K., Srithanaviboonchai, K., Techasrivichien, T., Suguimoto, S. P., et al. (2019). Association of excessive smartphone use with psychological well-being among University students in Chiang Mai, Thailand. PLoS ONE 14:e0210294. doi: 10.1371/journal.pone.0210294

Tateno, M., Teo, A. R., Ukai, W., Kanazawa, J., Katsuki, R., Kubo, H., et al. (2019). Internet addiction, smartphone addiction, and hikikomori trait in Japanese young adult: social isolation and social network. Front. Psychiatry 10:455. doi: 10.3389/fpsyt.2019.00455

Tefertiller, A. C., Maxwell, L. C., and Morris, D. L. (2020). Social media goes to the movies: fear of missing out, social capital, and social motivations of cinema attendance. Mass Commun. Soc. 23, 378–399. doi: 10.1080/15205436.2019.1653468

Tehseen, S., Qureshi, Z. H., Johara, F., and Ramayah, T. (2020). Assessing dimensions of entrepreneurial competencies: a type II (reflective-formative) measurement approach using PLS-SEM. J. Sustain. Sci. Manage. 15, 108–145.

Tehseen, S., Ramayah, T., and Sajilan, S. (2017). Testing and controlling for common method variance: a review of available methods. J. Manag. Sci. 4, 146–165. doi: 10.20547/jms.2014.1704202

Tonacci, A., Billeci, L., Sansone, F., Masci, A., Pala, A. P., Domenici, C., et al. (2019). An innovative, unobtrusive approach to investigate smartphone interaction in nonaddicted subjects based on wearable sensors: a pilot study. Medicina (Kaunas) 55:37. doi: 10.3390/medicina55020037

Twenge, J. M., and Campbell, W. K. (2019). Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatr. Q. 90, 311–331. doi: 10.1007/s11126-019-09630-7

Vallespín, M., Molinillo, S., and Muñoz-Leiva, F. (2017). Segmentation and explanation of smartphone use for travel planning based on socio-demographic and behavioral variables. Ind. Manag. Data Syst. 117, 605–619. doi: 10.1108/IMDS-03-2016-0089

Van Den Eijnden, R. J., Lemmens, J. S., and Valkenburg, P. M. (2016). The social media disorder scale. Comput. Hum. Behav. 61, 478–487. doi: 10.1016/j.chb.2016.03.038

Whaite, E. O., Shensa, A., Sidani, J. E., Colditz, J. B., and Primack, B. A. (2018). Social media use, personality characteristics, and social isolation among young adults in the United States. Pers. Indiv. Differ. 124, 45–50. doi: 10.1016/j.paid.2017.10.030

Williams, D. (2006). On and off the'net: scales for social capital in an online era. J. Comput. Mediat. Commun. 11, 593–628. doi: 10.1016/j.1083-6101.2006.00029.x

Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

Reviewed by:

Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Research article
• Open access
• Published: 28 March 2022

Social networking sites use and college students’ academic performance: testing for an inverted U-shaped relationship using automated mobile app usage data

• Wondwesen Tafesse   ORCID: orcid.org/0000-0002-1284-7167 1  

International Journal of Educational Technology in Higher Education volume  19 , Article number:  16 ( 2022 ) Cite this article

35k Accesses

8 Citations

4 Altmetric

Metrics details

With the widespread adoption of social networking sites among college students, discerning the relationship between social networking sites use and college students’ academic performance has become a major research endeavor. However, much of the available research in this area rely on student self-reports and findings are notably inconsistent. Further, available studies typically cast the relationship between social networking sites use and college students’ academic performance in linear terms, ignoring the potential moderating role of the intensity of social networking sites use. In this study, we draw on contrasting arguments in the literature predicting positive and negative effects of social networking sites use on college students’ academic performance to propose an inverted U-shaped relationship. We collected data on social networking sites use by having college students install a tracking app on their smartphones for 1 week and data on academic performance from internal college records. Our findings indicate that social networking sites use indeed exhibits an inverted U-shaped relationship with college students’ academic performance. Specifically, we find that spending up to 88.87 min daily on social networking sites is positively associated with academic performance, but beyond that, social networking sites use is negatively associated with academic performance. We discuss the implications of our findings.

Introduction

With the widespread adoption of social networking sites among college students, discerning the relationship between social networking sites use and college students’ academic performance has become a major research endeavor (Doleck & Lajoie, 2018 ; Koranteng et al., 2019 ; Liu et al., 2017 ; Tafesse, 2020 ). Numerous studies have been published on this topic to date and the relevant literature is accumulating rapidly (Doleck & Lajoie, 2018 ; Masrom et al., 2021 ). However, findings have been highly inconsistent (Astatke et al., 2021 ), with some studies documenting a negative relationship between social networking sites use and academic performance (e.g., Junco, 2015 ; Karpinski et al., 2013 ; Tafesse, 2020 ) and others documenting a positive relationship (e.g., Park et al., 2018 ; Samad et al., 2019 ; Sarwar et al., 2019 ).

Notably, much of the available research relies on student self-reports to measure social networking sites use (Astatke et al., 2021 ; Doleck & Lajoie, 2018 ). Students are asked to self-report the frequency or duration of their social networking sites use. Because students have been shown to substantially underestimate their social networking sites use, however, self-report data is prone to measurement error, thereby potentially biasing the magnitude and direction of reported findings (Felisoni & Godoi, 2018 ; Giunchiglia et al., 2018 ; Wang et al., 2015 ). To overcome these limitations, researchers have begun to employ software programs and mobile applications that can automatically track the frequency and duration of social networking sites use, which enables precise measurement (e.g., Felisoni & Godoi, 2018 ; Giunchiglia et al., 2018 ; Wang et al., 2015 ). Coupled with the use of institutional records to measure students’ academic performance, these latter studies have managed to overcome the measurement difficulties afflicting self-reported data. However, even these more recent efforts typically cast the relationship between social networking sites use and academic performance in linear terms. That is, social networking sites use is proposed to linearly co-vary with academic performance.

In the current study, we maintain that the linear relationship typically tested in the literature may not fully capture the complex interplay between social networking sites use and academic performance. We contend that the relationship between social networking sites use and academic performance can be characterized as an inverted U-shape. The fact that both positive and negative effects have been reported in the literature (Astatke et al., 2021 ; Masrom et al., 2021 ; Raza et al., 2020 ) points to the possibility that social networking sites use might produce both positive and negative academic outcomes depending on the intensity of their use. For instance, heavy use of social networking sites can be detrimental to academic performance by having college students reallocate time away from academic work or requiring them to multi-task (Alt, 2015 ; Junco, 2015 ; Kapriniski et al., 2013 ; Marker et al., 2018 ). Modest use of social networking sites, on the other hand, might contribute positively to academic performance by facilitating collaborative learning and offering informational and entertainment values (Al-Qaysi et al., 2021 ; Hoi, 2021 ; Lampe et al., 2015 ; Lemay et al., 2020 ; Raza et al., 2020 ). Prior studies have suggested that not all social networking sites use is maladaptive (Lemay et al., 2020 ).

We combine the positive and negative effects of social networking sites use reported in the literature into an inverted U-shaped relationship by positing the intensity of social networking sites use as a moderating variable. The inverted U-shaped model fits the data better than the linear model, highlighting the crucial role that the intensity of social networking sites use plays in shaping the relationship between social networking sites use and college students’ academic performance. By demonstrating that social networking sites can be associated with both negative and positive academic outcomes depending on their intensity of use, our approach serves to reconcile empirical inconsistencies observed in the literature (Astatke et al., 2021 ). Further, the findings serve to synthesize the contrasting theoretical perspectives offered in the literature––some arguing for a positive effect of social networking sites use, others arguing for a negative effect––into a coherent curvilinear relationship. Overall, our findings contribute to a more nuanced understanding of the relationship between social networking sites use and college students’ academic performance.

Literature review

Social networking sites: an overview.

Ellison and Boyd ( 2013 ) defined social networking sites as “a networked communication platform in which participants (1) have uniquely identifiable profiles that consist of user-supplied content, content provided by other users, and/or system-level data; (2) can publicly articulate connections that can be viewed and traversed by others; and (3) can consume, produce, and/or interact with streams of user-generated content provided by their connections on the site” (p. 180). This definition emphasizes three defining features of social networking sites.

First, social networking sites allow users to create uniquely identifiable profiles animated by both user- and system-supplied information. Examples of these user- and system-supplied information that define a user’s profile on social networking sites include biographic details, self-descriptions, photos, interests and activities (Ellison & Boyd, 2013 ). These pieces of information facilitate online peer-to-peer networking by revealing users’ identities (Kane et al., 2014 ; Zhang & Leung, 2015 ). Second, social networking sites allow users to articulate connections that can be viewed and traversed by others. These connections are typically manifested in the form of friends lists, followers lists, group memberships, liked pages and so on. These publicly stated connections enable users to discern other users’ social connections, further facilitating peer-to-peer networking activities on the platforms (Ellison & Boyd, 2013 ). Zhang and Leung ( 2015 ) maintained that the ability to traverse and view other users’ connections and activities is an innovative feature of social networking sites that is virtually unknown in traditional forms of communication. Finally, social networking sites allow users to consume, produce and interact with the streams of user-generated content provided by their connections (Kane et al., 2014 ). Users create their content by combining text, images, videos, emoticons, animations and so forth—all languages of social networking sites (Dumpit & Fernandez, 2017 ). As well as sharing their own content, users can consume and interact with other users’ content, by liking, sharing and commenting on them, thereby creating a dynamic and continuous cycle of online interaction and engagement, which is essential to the vitality of social networking sites (Masrom et al., 2021 ; Smith, 2017 ).

College students rely heavily on social networking sites for their daily communication, entertainment and information needs (Ansari & Khan, 2020 ; Doleck et al., 2018 ; Ifinedo, 2016 ; Lemay et al., 2020 ). Studies tracking college students’ social media habits have indicated that students spend a significant amount of time daily, switching between multiple social networking sites such as Facebook, Twitter, Instagram, YouTube and Snapchat (Alhabash & Ma, 2017 ; Dumpit & Fernandez, 2017 ; Felisoni & Godoi, 2018 ; Smith, 2017 ; Wang et al., 2015 ). College students use social networking sites for various purposes including opinion sharing, information acquisition, entertainment, self-documentation, self-expression and social interactions, among others (Alhabash & Ma, 2017 ; Chawinga, 2017 ; Lemay et al., 2020 ). Educational use of social networking sites, such as accessing course information, organizing group work, receiving feedback and interacting with instructors, have also been noted in the literature (Al-Qaysi et al., 2021 ; Al-Rahmi et al., 2020 ; Ansari & Khan, 2020 ; Hoi, 2021 ; Raza et al., 2020 ; Smith, 2017 ).

Review of the empirical literature

The pervasive adoption and use of social networking sites among college students have spurred a flurry of research into how social networking sites use influences academic performance (Masrom et al., 2021 ). Several studies have been published and the relevant literature has accumulated over the past years. In response, several systematic literature reviews (e.g., Astatke et al., 2021 ; Doleck & Lajoie, 2018 ; Masrom et al., 2021 ) and meta-analyses (e.g., Huang, 2018 ; Liu et al., 2017 ) have been carried out. Yet, these reviews and meta-analyses document major inconsistencies in the literature. Despite the expanding literature and efforts to consolidate it, results remain inconsistent. Below, we present a summary of representative works.

In an early study, Karpinski et al. ( 2013 ) looked at the relationship between social networking sites use and academic performance among college students in the USA and Europe. They find that social networking sites use is negatively associated with college students’ academic performance both in the US and European samples, but the association is stronger for the US sample. In another widely cited study, Junco ( 2015 ) investigated the relationship between social networking sites use and college students’ academic performance by considering class standing as a moderating variable. The researcher finds that freshmen suffered the highest decline in academic performance from increased social networking sites use, while seniors were less severely affected. Recently, Tafesse ( 2020 ) finds that increased use of social networking sites is negatively associated with academic performance both directly, and indirectly, via decreased student engagement.

In a study that examined the relationship between social networking sites use and student engagement among Korean college students, Park et al. ( 2018 ) reported a positive relationship. But when used for purposes such as image management and social pressure, social networking sites use tends to reduce student engagement. Similarly, Sarwar et al. ( 2019 ) find that social networking sites use contributes positively to college students’ academic performance both directly, and indirectly, by enabling collaborative learning. Finally, Al-Rahmi et al. ( 2020 ) find that college students’ increased perceptions of social presence, interest, perceived enjoyment and perceived usefulness of social networking sites are positively associated with collaborative learning.

Despite their contributions to a deeper understanding of how social networking sites use influence academic performance, the reviewed studies relied on student self-reports to measure both social networking sites use and academic performance, which might introduce measurement errors by, for instance, eliciting socially desirable answers or artificially inflating the correlation among measured variables due to common method bias (Podsakoff et al., 2003 ). To overcome these measurement issues, researchers have begun to deploy software programs and mobile applications that are installed on students’ PCs or smartphones to automatically track the frequency and duration of social networking sites use (Felisoni & Godoi, 2018 ; Giunchiglia et al., 2018 ; Wang et al., 2015 ). Increasingly also, researchers are obtaining data about students’ academic performance from institutional records instead of student self-reports. Collecting data from multiple sources is one of the most effective procedural remedies against common method bias (Podsakoff et al., 2003 ).

Pertinent among this latter group of studies is a pioneering investigation by Wang et al. ( 2015 ), which tracked the social media behavior of college students in the USA for one week by having them install a software program on their PCs and smartphones. The researchers subsequently divided their sample into heavy versus light users and compared their perceptions of how social networking sites use affect academic performance. Their findings suggest that heavy users felt more distracted and fell behind on schoolwork relative to light users. Although the researchers did not formally test the moderating effect of the intensity of social networking sites use, their findings reveal sharp differences in perceptions between heavy and light users.

In a more recent study, Giunchiglia et al. ( 2018 ) measured social networking sites use by having college students install a mobile usage tracking app on their devices and run it for a week. In addition, they employed time diaries to measure social networking sites use during lecture hours and study time. Their findings indicate that increased social networking sites use during lecture hours and study time is negatively predictive of semester GPA. Conversely, social networking sites inactivity during lecture hours and study time is positively predictive of semester GPA. In another study, Felisoni and Godoi ( 2018 ) tracked college students’ overall smartphone use for one week using a tracking app. They find a negative relationship between increased smartphone use and semester GPA.

Following the latter group of studies, we measured social networking sites use by having college students install a mobile usage tracking app on their smartphones and run it for one week and students’ academic performance using semester and cumulative GPAs obtained from internal college records. However, we departed from previous studies by testing for an inverted U-shaped relationship. Extant studies typically model the relationship between social networking sites use and academic performance linearly, which ignores the potential moderating role of the intensity of social networking sites use. By testing for an inverted U-shaped relationship, we demonstrate the moderating role of the intensity of social networking sites use in the relationship between social networking sites use and college students’ academic performance.

Theoretical perspectives

Two main theoretical perspectives are put forth in the literature to explain the relationship between social networking sites use and college students' academic performance: the time-displacement/multitasking argument; and the collaborative learning argument.

The first perspective holds that social networking sites distract students from attaining deeper engagement with their academic study (Alt, 2015 ; Astatke et al., 2021 ; Cao et al., 2018 ; Doleck et al., 2018 ; Junco, 2012 ; Karpinski et al., 2013 ). Two important theoretical mechanisms are proposed to explain this negative relationship: time displacement and multitasking. The time displacement explanation is based on the notion that time is inelastic and daily human activities are scheduled around a fixed, 24-h cycle. The introduction of a new activity, therefore, comes at the expense of other established activities as less time would be available for them (Nie, 2001 ; Tokunaga, 2016 ). According to the time displacement argument, time spent on social networking sites is time reallocated from important academic activities such as studying, attending classes or doing assignments (Doleck et al., 2018 ; Tafesse, 2020 ). By forcing the reallocation of time from academically productive to academically nonproductive tasks, social networking sites use is argued to adversely affect students’ academic performance (Alt, 2015 ; Cao et al., 2018 ; Tafesse, 2020 ).

The multitasking explanation, on the other hand, suggests that attending to two or more tasks at the same time can result in cognitive overload, which reduces students’ ability to correctly and completely execute the tasks at hand (Junco, 2012 ; Junco & Cotton, 2012 ; Karpinski et al., 2013 ; Lau, 2017 ). The multitasking argument implies that trying to accomplish academic tasks while staying on social networking sites reduces students’ attention span and their cognitive ability to effectively engage in academic work, thereby adversely affecting their academic performance (Junco, 2012 ; Karpinski et al., 2013 ; Lau, 2017 ; Lepp et al., 2015 ).

The second perspective holds that social networking sites can be harnessed to facilitate collaborative learning and motivate students into a more constructive learning engagement (Eid & Al-Jabri, 2016 ; Hoi, 2021 ; Lampe et al., 2015 ; Liu et al., 2017 ; Raza et al., 2020 ). Researchers subscribing to this perspective point to the fact that the interactive and social features of social networking sites can be utilized to exchange information, arrange group work, receive feedback and facilitate interaction with instructors (Al-Rahmi et al., 2020 ; Ansari & Khan, 2020 ; Chawinga, 2017 ; Lampe et al., 2015 ; Smith, 2017 ). Social networking sites emphasize collaboration and group engagement as opposed to individual learning, thereby allowing students to become active partners and socially engaged in the process of exchanging information, discovering knowledge and solving problems, which should increase their overall learning and academic performance (Ansari & Khan, 2020 ; Astatke et al., 2021 ; Lampe et al., 2015 ; Sarwar et al., 2019 ; Smith, 2017 ).

With the growing role of social networking sites as a platform for opinion sharing and information exchange at a societal level (Ellison & Boyd, 2013 ), exposure to social networking sites can further widen students’ perspectives and introduce them to diverse worldviews (Alloway et al., 2013 ; Chawinga, 2017 ; Park et al., 2018 ). Social networking sites could also offer students relief from demanding academic tasks by availing entertaining content, such as funny videos, jokes and memes, which can increase their motivation for subsequent tasks (Ansari & Khan, 2020 ; Eid & Al-Jabri, 2016 ; Phua et al., 2017 ; Raza et al., 2020 ).

We draw on the two contrasting perspectives presented above to propose an inverted U-shaped relationship between social networking sites use and college students’ academic performance. The proposed model anticipates a positive relationship between social networking sites use and academic performance when the intensity of social networking sites use is low and a negative relationship when the intensity of social networking sites use is high.

Methodology

Sampling and data collection.

The current study was carried out at a large public university in an Eastern African country in 2019. The study targeted undergraduate students studying business and economics subjects. Business and economics students were chosen for the simple reason that the researchers involved in the study were affiliated with the Business and Economics College. The necessary ethical clearance was obtained from the Office of the Vice-Dean to conduct the study.

Data on students’ social networking sites use was collected by asking voluntary students to install “App Usage”—a freely available mobile usage tracking app—on their smartphones in the Spring 2019 semester. Although we evaluated several candidate mobile usage tracking apps for the purpose of our study, we settled on App Usage for two reasons. First, App Usage offers an accurate measurement of users’ smartphone activities. We installed App Usage on our smartphones, personally checked its accuracy and we were satisfied with the result. Second, App Usage has an intuitive and convenient feature for downloading and sharing one’s app usage history either via email or messaging apps. Because usage history is rendered in a CSV file format, it facilitates faster data capture and processing. An example of custom reports produced by App Usage is presented in the Appendix.

Due to the sensitivity of the data we were after, we resorted to a snowball approach to recruit participants. We start by recruiting an initial batch of students based on personal rapport and solicited their voluntary participation. We then asked this initial batch to recruit additional participants. Through this process, we recruited about 51 voluntary participants. To minimize the effect of social desirability bias, we excluded students attending any one of our classes. Subsequently, we familiarized the participants with the basic functionality of App Usage and asked them to install it on their smartphones. To increase the number of valid responses from the participants, we took several confidence-building steps. First, we limited the applicable usage history to only one week. Second, we excluded weekends since social media use during weekends can be particularly personal relative to weekdays. Likewise, to minimize the potential impact of installing App Usage on students’ smartphone habits, we let the participants run App Usage for three weeks before asking them to submit their usage history in the fourth week. Further, we let students install App Usage after three weeks into the Spring semester. This allowed us to avoid tracking students’ smartphone activities during exam periods, which might underreport their smartphone behavior.

Eventually, 40 students submitted valid app usage data. The remaining 11 students failed to send in their usage data despite our best efforts. Although relatively small, the final sample (N = 40) was representative of the student population in terms of departmental affiliation (accounting = 47%; management = 28%; marketing = 25%), gender-mix (male = 60%; female = 40%) and academic year (second year = 62%; third year = 38%). Notably, first-year students were underrepresented in our data. This is because the initial batch of participants we approached were all second-and third-year students. However, the departmental affiliation and gender proportion in the data map well to the departmental affiliation and gender proportion of the student population. Table 1 summarizes the sample characteristics.

Measurement of variables

The usage history submitted by the students contained details including the names of the mobile apps they used, the amount of time they spent on each mobile app and the start and end dates of the usage history. We constructed two relevant variables from this data. The first was daily average minutes spent on social networking sites, which was used to measure the intensity of students’ social networking sites use (Felisoni & Godoi, 2018 ; Giunchiglia et al., 2018 ). The second variable was daily average minutes spent on smartphone, which was used to measure the amount of time students spent on their smartphones overall. This second variable was used as a control variable.

In order to construct daily average minutes spent on social networking sites, we first had to identify those applications that would qualify as social networking sites. For this purpose, we turned to the definition by Ellison and Boyd ( 2013 ) discussed in “Social networking sites: An overview”. We analyzed the usage history of each student and identified those mobile apps that offer social networking affordances as explicated in Ellison and Boyd’s definition. This process resulted in the identification of about 24 mobile apps, many of them household names around the world and their official variants, such as Facebook (Facebook Lite), Twitter, Instagram, YouTube (YouTube Go), Messenger (Messenger Lite, + Messenger), Telegram (Telegram+ , Telegram X), IMO, WhatsApp, Viber and Google+ . Some of the less-known names we came across include VidMate, Mobogram and Russogram. Our coding scheme is also consistent with previous categorization of social networking sites. For instance, Smith ( 2017 ) identified several social networking sites used in undergraduate learning, many of these sites are included in our coding.

Data on students’ academic performance were collected from the Office of the Vice-Dean, which is responsible for storing students’ academic records at the college level. We gathered semester and cumulative GPAs. Both GPAs were measured on a four-point scale (0.0 to 4.0). We also gathered information about participants’ departmental affiliation, academic year and gender from the same official source. Table 2 reports the descriptive statistics and pairwise correlation of the measured variables.

Analysis strategy

Traditionally, an inverted U-shaped relationship is empirically established by adding a squared term to the predictor variable of interest—in our case, social networking sites use—to a standard linear regression equation, as shown below:

where \({y}_{i}\) is the semester GPA for student i ; \({X}_{i}\) is the daily average minutes spent on social networking sites by student i ; \({x}_{i}^{2}\) is the squared term of daily average minutes spent on social networking sites by student i ; \({Z}_{ij}\) is the \(j^{\prime}s\) control variable for student i including daily average minutes spent on smartphone, gender, academic year and departmental affiliation; \({\beta }_{0}\) , \({\beta }_{1},\) …, \({\beta }_{j}\) are parameters to be estimated; and \({\varepsilon }_{i}\) is a normally distributed error term.

If β 2 from Eq.  1 is negative and statistically significant, an inverted U-shaped relationship can be claimed. However, this traditional approach has come under growing criticism for being simplistic and lacking in rigor (Haans et al., 2016 ; Simonsohn, 2018 ). Lind and Mehlum ( 2010 ) proposed a stricter approach that requires three necessary and sufficient conditions for establishing an inverted U-shaped relationship. The first condition is β 2 from Eq.  1 should be negative and statistically significant. The second condition is the turning point in Eq.  1 should fall within the data range (i.e., between the minimum and maximum values of the dependent variable). The turning point is arrived at by taking the first derivative of Eq.  1 and setting it to zero, which yields −  β 1 /2β 2 . The third and final condition is the slope at the lower half of the data should be positive and statistically significant and the slope at the upper half of the data should be negative and statistically significant. This condition can be tested by dividing the dataset into two parts, typically by using the turning point as a cutoff point, and estimating two separate linear regression equations for each part of the dataset (Simonsohn, 2018 ).

In addition to Lind and Mehlum’s ( 2010 ) three conditions, one also needs to establish that the quadratic regression model fits the data better than the linear model. If adding the squared term to the linear model leads to a significant improvement in model fit, as measured by a statistically significant R 2 change, for instance, the quadratic regression model should be retained (Weisberg, 2005 ). Otherwise, it has to be rejected in favor of the more parsimonious linear regression model. We analyzed the data according to the three conditions outlined above.

We started off our analysis by estimating the linear regression model. To correct for heteroscedasticity, we reported White’s heteroscedastic consistent standard errors (White, 1980 ). The linear regression model was statistically significant ( F  = 5.844; p  < 0.01), attaining R 2  = 0.401 and adjusted R 2  = 0.293. Likewise, the regression coefficient for daily average minutes spent on social networking sites was negative and statistically significant ( β 1  =  − 0.004; p  < 0.01). Table 3 reports the estimation results of the linear regression model.

Second, we estimated the quadratic regression model (Eq.  1 ). As in the linear model, we reported White’s heteroscedastic consistent standard errors. The quadratic regression model was also statistically significant ( F  = 12.75; p  < 0.01). It attained R 2  = 0.609 and adjusted R 2  = 0.524. The F-change from the linear model ( F lin  = 5.844; p  < 0.01) to the quadratic model ( F qdr  = 12.75; p  < 0.01) was statistically significant at p  < 0.01. We, therefore, retained the quadratic regression model as it offered a better fit to the data than the linear model (Weisberg, 2005 ). Table 4 reports the estimation results of the quadratic regression model.

Importantly, the squared term for the daily average minutes spent on social networking sites in the quadratic regression model was negative and statistically significant ( β 2  =  − 0.0000467; p  < 0.01). This result satisfied the first condition of Lind and Mehlum’s ( 2010 ) test, thereby offering initial evidence for an inverted U-shaped relationship between social networking sites use and academic performance.

The turning point (i.e., −  β 1 / − 2 β 2  =  − 0.0083 / − 2 × 0.0000934) occurred at 88.87 min, which is approximately one and half hours of daily average social networking sites use. This turning point lies well within the data range for daily average minutes spent on social networking sites (minimum daily average minutes spent on social networking sites = 5.62 min, maximum daily average minutes spent on social networking sites = 280.5 min), hence satisfying the second condition of Lind and Mehlum’s ( 2010 ) test.

To test the third condition, we grouped the students into two: low users (n = 25) and high users (n = 15). The turning point was used to create the two groups (i.e., students who spent a daily average of 88.87 min or less were categorized into the low user group; students who spent a daily average of 88.87 min or more were categorized into the high user group). Subsequently, we estimated two linear regression equations for each group. The estimation results are summarized in Tables 5 and 6 . The slope for the low user group was positive and statistically significant ( β 1  = 0.005; p  < 0.1), whereas the slope for the high user group was negative and statistically significant ( β 1  =  − 0.0097; p  < 0.01). Because of the limited observation in both the low and high user groups, we find it reasonable to reject the null hypothesis at p  < 0.1. The statistically significant and positive slope for the low user group and the statistically significant and negative slope for the high user group satisfied the third and final condition of Lind and Mehlum’s ( 2010 ) test.

To summarize, we find strong evidence for an inverted U-shaped relationship between college students’ social networking sites use and academic performance. We should further note that we conducted regression diagnostics (e.g., QQ plots, residual plots) for all estimated models and found that the models were well-behaved. Figure  1 visualizes the regression plots for the linear and quadratic regression models.

Regression plots

Robustness check

We implemented a robustness check to examine whether the inverted U-shaped relationship holds under different specifications of the dependent variable. Specifically, we replaced semester GPA with cumulative GPA as the dependent variable. While semester GPA captures academic performance in a single semester, cumulative GPA captures academic performance for several semesters. Therefore, cumulative GPA offers a more stable measure of academic performance. The results from the main model were fully replicated when cumulative GPA was used as the dependent variable. Specifically, the quadratic regression model fit the data better than the linear model ( R 2 lin  = 0.3 vs. R 2 qdr  = 0.39; F lin  = 3.57, p  < 0.01 vs. F qdr  = 5.34, p  < 0.01). The F-change was significant at p  < 0.05. As in the case of semester GPA, the squared term for daily average social networking sites use was negative and statistically significant ( β 2  =  − 1.21; p  < 0.05). Finally, the linear regression coefficient for the low user group was positive and statistically significant ( β 1  = 0.29; p  < 0.1), while it was negative and statistically significant for the high user group ( β 1  =  − 0.62; p  < 0.01). Overall, the results from the semester GPA model were fully replicated when cumulative GPA was employed as the dependent variable, suggesting that the inverted U-shaped relationship remained robust to a different measure of academic performance.

The pervasive adoption of social networking sites among college students has spurred a stream of research into the implications of social networking sites use for college students’ academic performance (Doleck & Lajoie, 2018 ; Koranteng et al., 2019 ; Masrom et al., 2021 ). Reported findings have been highly inconsistent, however, with some studies reporting negative relationships and others reporting positive relationships (Astatke et al., 2021 ; Masrom et al., 2021 ). Against this backdrop, we proposed and found support for an inverted U-shaped relationship. Following recent advances in the literature (Felisoni & Godoi, 2018 ; Giunchiglia et al., 2018 ), we measured social networking sites use with the help of a tracking app installed on students’ smartphones. Further, we measured students’ academic performance using semester and cumulative GPAs obtained from internal college records. By employing a combination of automatically tracked and institutional data, we avoided the measurement error common in self-reported data (Podsakoff et al., 2003 ).

Our main finding reveals that the inverted U-shaped relationship fits the data better than the linear relationship. The turning point on the inverted U-shaped regression curve occurred at 88.87 min, suggesting that spending up to 88.87 min daily on social networking sites (about an hour and a half) is positively associated with students’ academic performance, while spending more than 88.87 min daily on social networking sites is negatively associated with students’ academic performance. This finding was robust to an alternative specification of academic performance.

It thus appears that, when used modestly, social networking sites are positively associated with students’ academic performance. Modest use is less likely to interfere with students’ academic performance as they will be forced neither to reallocate time away for academic tasks nor to multi-task (Chawinga, 2017 ; Wang et al., 2015 ). In fact, modest use of social networking sites might boost students’ academic engagement (Al-Rahmi et al., 2020 ; Masrom et al., 2021 ). For instance, social networking sites have been shown to facilitate collaborative learning, where students engage in socially interactive learning by completing group work, receiving feedback, sharing course material and interacting with each other and their instructors (Al-Qaysi et al., 2020 ; Eid & Al-Jabri, 2016 ; Hoi et al., 2021 ; Lampe et al., 2015 ). Similarly, social networking sites offer students access to information and entertaining content that might contribute to improved academic performance (Alloway et al., 2013 ; Ansari & Kahn, 2020 ; Lepp et al., 2015 ; Masrom et al., 2021 ; Raza et al., 2020 ). This last point is particularly poignant in the national context of our study, where the media infrastructure is neither well developed nor widely accessible to satisfy college students’ demand for information and entertainment (Tafesse, 2020 ). Social networking sites thus double as a source of information and pastime for the college students in our sample (Alhabash & Ma, 2017 ; Chawinga, 2017 ).

In contrast, heavy use of social networking sites can interfere with students’ academic activities (Koranteng et al., 2019 ; Tafesse, 2020 ). With heavy use, students will be forced either to divert time away from crucial academic tasks or to multi-task, which will eventually hamper their academic performance (Kapriniski et al., 2013 ; Lepp et al., 2015 ). In fact, heavy social networking sites use can degenerate into compulsive behavior, such as excessive use and addiction, which can detriment not only students’ academic performance but also their overall well-being (Alt, 2015 ; Cao et al., 2018 ; Hsiao et al., 2017 ; Masrom et al., 2021 ).

Overall, our work contributes to a more nuanced understanding of the relationship between social networking sites use and academic performance among college students. The inverted U-shaped relationship that we proposed and validated serves to reconcile the empirical inconsistencies observed in the literature in terms of positive and negative effects of social networking sites use (Astatke et al., 2021 ; Masrom et al., 2021 ). As our findings demonstrate, social networking sites can produce both positive and negative academic outcomes depending on the intensity of their use. What is crucial to the relationship is the intensity of use, which can easily be captured by an inverted U-shaped model.

Finally, our study comes with a set of limitations that must be considered while interpreting the findings. First, the study relied on a small set of observations sampled using a snowball approach. As such, the sample may not offer an accurate representation of the total student population. The personal and highly sensitive nature of the data we gathered meant that we had to settle with the small number of participants we were able to recruit. However, the sample size we used is not unusual for studies of this nature. For instance, Felisoni and Godoi’s ( 2018 ) study, which tracked college students’ cellphone use in Brazil, was based on 42 observations. Second, we exclusively studied business and economics students. Since students from other disciplines were not included in our study, the findings may not extend to these other disciplines. Third, our sample is taken from a setting that has its own peculiarities. For instance, students at public universities in our setting live on campus for the entire academic year, have access to free WIFI connection and are connected to the internet almost exclusively by way of their smartphones. The students thus connect to social networking sites—and the internet more generally—at no personal cost to them, which might incentivize heavier use. Moreover, the mainstream media infrastructure in the country is rather underdeveloped, which amplifies the informational and entertainment value of social networking sites for college students. These combinations of factors must be considered when efforts are made to extend the findings to new settings.

With the pervasive adoption and use of social networking sites among college students, probing their relationship with college students’ academic performance has become an important research priority. Building on the extant literature, we proposed and validated an inverted U-shaped relationship between social networking sites use and college students’ academic performance. In so doing, we departed from the more traditional approach that casts the relationship between the two in linear terms.

As our findings suggest, moderate use of social networking sites is positively associated with academic performance, while heavy use is negatively associated with academic performance. These findings highlight the crucial role that the intensity of social networking sites use play in shaping the influence of social networking sites on college students’ academic performance.

To our knowledge, our study is the first to test and find support for an inverted U-shaped relationship between social networking sites use and college students’ academic performance. As such, the proposed model should be validated using fresh data, preferably from new contexts, to develop further confidence in the findings. With further validation, the findings can help in the continued effort to harness social networking sites for productive academic purposes in higher education settings (Masrom et al., 2021 ; Smith, 2017 ).

Availability of data and materials

No data will be publicly available for this study since the data was collected after assuring students that their data will not be publicly shared.

Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, twitter, Instagram, and Snapchat among college students? Social Media + Society, 3 (1), 1–13.

Google Scholar  

Alloway, T. P., Horton, J., Alloway, R. G., & Dawson, C. (2013). Social networking sites and cognitive abilities: Do they make you smarter? Computers & Education, 63 , 10–16.

Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). What leads to social learning? Students’ attitudes towards using social media applications in Omani higher education. Education and Information Technologies, 25 (3), 2157–2174.

Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2021). Developing a comprehensive theoretical model for adopting social media in higher education. Interactive Learning Environments . https://doi.org/10.1080/10494820.2021.1961809

Article   Google Scholar  

Al-Rahmi, W. M., Yahaya, N., Alturki, U., Alrobai, A., Aldraiweesh, A. A., Omar Alsayed, A., & Kamin, Y. B. (2020). Social media-based collaborative learning: the effect on learning success with the moderating role of cyberstalking and cyberbullying. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1728342

Alt, D. (2015). College students’ academic motivation, media engagement and fear of missing out. Computers in Human Behavior, 49 , 111–119.

Ansari, J. A. N., & Khan, N. A. (2020). Exploring the role of social media in collaborative learning the new domain of learning. Smart Learning Environments, 7 (1), 1–16.

Astatke, M., Weng, C., & Chen, S. (2021). A literature review of the effects of social networking sites on secondary school students’ academic achievement. Interactive Learning Environments . https://doi.org/10.1080/10494820.2021.1875002

Cao, X., Masood, A., Luqman, A., & Ali, A. (2018). Excessive use of mobile social networking sites and poor academic performance: Antecedents and consequences from stressor-strain outcome perspective. Computers in Human Behavior, 85 , 163–174.

Chawinga, W. D. (2017). Taking social media to a university classroom: Teaching and learning using Twitter and blogs. International Journal of Educational Technology in Higher Education., 14 (3), 2–19.

Doleck, T., Bazelais, P., & Lemay, D. J. (2018). Social networking and academic performance: A generalized structured component approach. Journal of Educational Computing Research, 56 , 1129–1148.

Doleck, T., & Lajoie, S. (2018). Social networking and academic performance: A review. Education and Information Technologies, 23 , 435–465.

Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14 (1), 1–16.

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing and student learning: The case of university students. Computers & Education, 99 , 14–27.

Ellison, N., & Boyd, D. (2013). Sociality through social network sites. In W. Dutton (Ed.), The Oxford handbook of internet studies (pp. 151–172). Oxford University Press.

Felisoni, D. D., & Godoi, A. S. (2018). Cell phone usage and academic performance: An experiment. Computers & Education, 117 , 175–187.

Giunchiglia, F., Zeni, M., Gobbi, E., Bignotti, E., & Bison, I. (2018). Mobile social media usage and academic performance. Computers in Human Behavior, 82 , 177–185.

Haans, R. F. J., Pieters, C., & He, Z. (2016). Thinking about U: Theorizing and testing U-and inverted U-shaped relationships in strategy research. Strategic Management Journal, 37 , 1177–1195.

Hoi, N. V. (2021). Augmenting student engagement through the use of social media: The role of knowledge sharing behaviour and knowledge sharing self-efficacy. Interactive Learning Environments . https://doi.org/10.1080/10494820.2021.1948871

Hsiao, K., Shu, Y., & Huang, T. (2017). Exploring the effect of compulsive social app usage on technostress and academic performance: Perspectives from personality traits. Telematics and Informatics, 34 , 679–690.

Huang, C. (2018). Social network site use and academic achievement: A meta-analysis. Computers & Education. , 119 , 76–83.

Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management , 36 (2), 192–206.

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58 (1), 162–171.

Junco, R. (2015). Student class standing, Facebook use, and academic performance. Journal of Applied Developmental Psychology, 36 , 18–29.

Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59 , 505–514.

Kane, G. C., Alavi, M., Labianca, G., & Borgatti, S. P. (2014). What’s different about social media networks? A framework and research agenda. MIS Quarterly, 38 (1), 274–304.

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior, 29 , 1182–1192.

Koranteng, F. N., Wiafe, I., & Kuada, E. (2019). An empirical study of the relationship between social networking sites and students’ engagement in higher education. Journal of Educational Computing Research, 57 (5), 1131–1159.

Lampe, C., Wohn, D. Y., Vitak, J., Ellison, N. B., & Wash, R. (2015). Student use of Facebook for organizing collaborative classroom activities. Computer Supported Collaborative Learning, 6 , 329–347.

Lau, W. W. F. (2017). Effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior , 68 , 286–291.

Lemay, D. J., Bazelais, P., & Doleck, T. (2020). Patterns of social networking use and academic performance: Examining the link between quality and frequency of social networking use and academic performance among college-level students. Education and Information Technologies . https://doi.org/10.1007/s10639-019-10065-7

Lepp, A., Barkley, J. E., & Karpinski, A. C. (2015). The relationship between cell phone use and academic performance in a sample of US college students. SAGE Open . https://doi.org/10.1177/2158244015573169

Lind, J. T., & Mehlum, H. (2010). With or without U? The appropriate test for a U-shaped relationship. Oxford Bulletin of Economics and Statistics, 72 , 109–118.

Liu, D., Kirschner, P. A., & Karpinski, A. C. (2017). A meta-analysis of the relationship of academic performance and social network site use among adolescents and young adults. Computers in Human Behavior, 77 , 148–157.

Manca, S., & Ranieri, M. (2016). Facebook and the others. Potentials and obstacles of social media for teaching in higher education. Computers & Education, 95 , 216–230.

Marker, C., Gnambs, T., & Appel, M. (2018). Active on Facebook and failing at school? Meta-analytic findings on the relationship between online social networking activities and academic achievement. Educational Psychology Review. https://doi.org/10.1007/s10648-017-9430-6

Masrom, M. B., Busalim, A. H., Abuhassna, H., & Mahmood, N. H. N. (2021). Understanding students’ behavior in online social networks: A systematic literature review. International Journal of Educational Technology in Higher Education, 18 (1), 1–27.

Nie, N. H. (2001). Sociability, interpersonal relations, and the Internet: Reconciling conflicting findings. American Behavioral Scientist, 45 , 420–435.

Park, E., Song, H., & Hong, A. J. (2018). The use of social networking services for classroom engagement? The effects of Facebook usage and the moderating role of user motivation. Active Learning in Higher Education. https://doi.org/10.1177/1469787418809227

Phua, J., Jin, S. V., & Kim, J. J. (2017). Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Computers in Human Behavior, 72 , 115–122.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879–903.

Raza, S. A., Qazi, W., Umer, B., & Khan, K. A. (2020). Influence of social networking sites on life satisfaction among university students: A mediating role of social benefit and social overload. Health Education, 120 (2), 141–164.

Samad, S., Nilashi, M., & Ibrahim, O. (2019). The impact of social networking sites on students’ social wellbeing and academic performance. Education and Information Technologies, 24 (3), 2081–2094.

Sarwar, B., Zulfiqar, S., Aziz, S., & Ejaz, C. K. (2019). Usage of social media tools for collaborative learning: The effect on learning success with the moderating role of cyberbullying. Journal of Educational Computing Research, 57 , 246–279.

Simonsohn, U. (2018). Two lines: A valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Advances in Methods and Practices in Psychology Science, 1 , 538–555.

Smith, E. E. (2017). Social media in undergraduate learning: Categories and characteristics. International Journal of Educational Technology in Higher Education, 14 (1), 1–24.

Tafesse, W. (2020). The effect of social networking site use on college students’ academic performance: The mediating role of student engagement. Education and Information Technologies, 25 , 4747–4763.

Tokunaga, R. S. (2016). An examination of functional difficulties from Internet use: Media habit and displacement theory explanations. Human Communication Research, 42 , 339–370.

Wang, Y., Niiya, M., Mark, G., Reich, S.M., & Warschauer, M. (2015). Coming of age (digitally): An ecological view of social media use among college students. Paper presented at the proceedings of the 18th ACM conference on computer supported cooperative work & social computing. 571–582.

Weisberg, S. (2005). Applied linear regression . Wiley.

MATH   Google Scholar  

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48 , 817–838.

MathSciNet   MATH   Google Scholar  

Zhang, Y., & Leung, L. (2015). A review of social networking service (SNS) research in communication journals from 2006 to 2011. New Media and Society, 17 (7), 1007–1024.

Download references

Acknowledgements

I would like to thank Zeleke Siraye and Elias Shitemam for their support in collecting the data for this study.

Funding Acknowledgement: This research is supported by United Arab Emirate University research grant. (Grant code: G00003359; Funding Number: 31B125).

Author information

Authors and affiliations.

United Arab Emirates University, Al Ain, United Arab Emirates

Wondwesen Tafesse

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Wondwesen Tafesse .

Ethics declarations

Competing interests.

The authors declare that they do not have any competing interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Illustration of custom reports produced by App Usage

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Tafesse, W. Social networking sites use and college students’ academic performance: testing for an inverted U-shaped relationship using automated mobile app usage data. Int J Educ Technol High Educ 19 , 16 (2022). https://doi.org/10.1186/s41239-022-00322-0

Download citation

Received : 11 November 2021

Accepted : 12 January 2022

Published : 28 March 2022

DOI : https://doi.org/10.1186/s41239-022-00322-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social media
• Mobile applications
• Academic performance
• Inverted U-shape
• Curvilinear relationship

• Open access
• Published: 16 March 2020

Exploring the role of social media in collaborative learning the new domain of learning

• Jamal Abdul Nasir Ansari 1 &
• Nawab Ali Khan 1  

Smart Learning Environments volume  7 , Article number:  9 ( 2020 ) Cite this article

379k Accesses

179 Citations

19 Altmetric

Metrics details

This study is an attempt to examine the application and usefulness of social media and mobile devices in transferring the resources and interaction with academicians in higher education institutions across the boundary wall, a hitherto unexplained area of research. This empirical study is based on the survey of 360 students of a university in eastern India, cognising students’ perception on social media and mobile devices through collaborative learning, interactivity with peers, teachers and its significant impact on students’ academic performance. A latent variance-based structural equation model approach was followed for measurement and instrument validation. The study revealed that online social media used for collaborative learning had a significant impact on interactivity with peers, teachers and online knowledge sharing behaviour.

Additionally, interactivity with teachers, peers, and online knowledge sharing behaviour has seen a significant impact on students’ engagement which consequently has a significant impact on students’ academic performance. Grounded to this finding, it would be valuable to mention that use of online social media for collaborative learning facilitate students to be more creative, dynamic and research-oriented. It is purely a domain of knowledge.

Introduction

The explosion of Information and Communication Technology (ICT) has led to an increase in the volume and smoothness in transferring course contents, which further stimulates the appeasement of Digital Learning Communities (DLCs). The millennium and naughtiness age bracket were Information Technology (IT) centric on web space where individual and geopolitical disperse learners accomplished their e-learning goals. The Educause Center for Applied Research [ECAR] ( 2012 ) surveyed students in higher education mentioned that students are pouring the acceptance of mobile computing devices (cellphones, smartphones, and tablet) in Higher Education Institutions (HEIs), roughly 67% surveyed students accepted that mobile devices and social media play a vital role in their academic performance and career enhancement. Mobile devices and social media provide excellent educational e-learning opportunities to the students for academic collaboration, accessing in course contents, and tutors despite the physical boundary (Gikas & Grant, 2013 ). Electronic communication technologies accelerate the pace of their encroachment of every aspect of life, the educational institutions incessantly long decades to struggle in seeing the role of such devices in sharing the contents, usefulness and interactivity style. Adoption and application of mobile devices and social media can provide ample futuristic learning opportunities to the students in accessing course contents as well as interaction with peers and experts (Cavus & Ibrahim, 2008 , 2009 ; Kukulska-Hulme & Shield, 2008 ; Nihalani & Mayrath, 2010 ; Richardson & Lenarcic, 2008 , Shih, 2007 ). Recently Pew Research Center reported that 55% American teenage age bracket of 15–17 years using online social networking sites, i.e. Myspace and Facebook (Reuben, 2008 ). Social media, the fast triggering the mean of virtual communication, internet-based technologies changed the life pattern of young youth.

Use of social media and mobile devices presents both advantages as well as challenges, mostly its benefits seen in terms of accessing course contents, video clip, transfer of the instructional notes etc. Overall students feel that social media and mobile devices are the cheap and convenient tools of obtaining relevant information. Studies in western countries have confronted that online social media use for collaborative learning has a significant contribution to students’ academic performance and satisfaction (Zhu, 2012 ). The purpose of this research project was to explore how learning and teaching activities in higher education institutions were affected by the integration and application of mobile devices in sharing the resource materials, interaction with colleagues and students’ academic performance. The broad goal of this research was to contemporise the in-depth perspectives of students’ perception of mobile devices and social media in learning and teaching activities. However, this research paper paid attention to only students’ experiences, and their understanding of mobile devices and social media fetched changes and its competency in academic performance. The fundamental research question of this research was, what are the opinions of students on social media and mobile devices when it is integrating into higher education for accessing, interacting with peers.

A researcher of the University of Central Florida reported that electronic devices and social media create an opportunity to the students for collaborative learning and also allowed the students in sharing the resource materials to the colleagues (Gikas & Grant, 2013 ). The result of the eight Egyptian universities confirmed that social media have the significant impact on higher education institutions especially in term of learning tools and teaching aids, faculty members’ use of social media seen at a minimum level due to several barriers (internet accessibility, mobile devices etc.).

Social media and mobile devices allow the students to create, edit and share the course contents in textual, video or audio forms. These technological innovations give birth to a new kind of learning cultures, learning based on the principles of collective exploration and interaction (Selwyn, 2012 ). Social media the phenomena originated in 2005 after the Web2.0 existence into the reality, defined more clearly as “a group of Internet-based applications that build on the ideological and technological foundation of web 2.0 and allow creation and exchange of user-generated contents (Kaplan & Haenlein, 2010 ). Mobile devices and social media provide opportunities to the students for accessing resources, materials, course contents, interaction with mentor and colleagues (Cavus & Ibrahim, 2008 , 2009 ; Richardson & Lenarcic, 2008 ).

Social media platform in academic institutions allows students to interact with their mentors, access their course contents, customisation and build students communities (Greenhow, 2011a , 2011b ). 90% school going students currently utilise the internet consistently, with more than 75% teenagers using online networking sites for e-learning (DeBell & Chapman, 2006 ; Lenhart, Arafeh, & Smith, 2008 ; Lenhart, Madden, & Hitlin, 2005 ). The result of the focus group interview of the students in 3 different universities in the United States confirmed that use of social media created opportunities to the learners for collaborative learning, creating and engaging the students in various extra curriculum activities (Gikas & Grant, 2013 ).

Research background and hypotheses

The technological innovation and increased use of the internet for e-learning by the students in higher education institutions has brought revolutionary changes in communication pattern. A report on 3000 college students in the United States revealed that 90% using Facebook while 37% using Twitter to share the resource materials as cited in (Elkaseh, Wong, & Fung, 2016 ). A study highlighted that the usage of social networking sites in educational institutions has a practical outcome on students’ learning outcomes (Jackson, 2011 ). The empirical investigation over 252 undergraduate students of business and management showed that time spent on twitter and involvement in managing social lives and sharing information, course-related influences their performance (Evans, 2014 ).

Social media for collaborative learning, interactivity with teachers, interactivity with peers

Many kinds of research confronted on the applicability of social media and mobile devices in higher education for interaction with colleagues.90% of faculty members use some social media in courses they were usually teaching or professional purposes out of the campus life. Facebook and YouTube are the most visited sites for the professional outcomes, around 2/3rd of the all-faculty use some medium fora class session, and 30% posted contents for students engagement in reading, view materials (Moran, Seaman, & Tinti-Kane, 2011 ). Use of social media and mobile devices in higher education is relatively new phenomena, completely hitherto area of research. Research on the students of faculty of Economics at University of Mortar, Bosnia, and Herzegovina reported that social media is already used for the sharing the materials and exchanges of information and students are ready for active use of social networking site (slide share etc.) for educational purposes mainly e-learning and communication (Mirela Mabić, 2014 ).

The report published by the U.S. higher education department stated that the majority of the faculty members engaged in different form of the social media for professional purposes, use of social media for teaching international business, sharing contents with the far way students, the use of social media and mobile devices for sharing and the interactive nature of online and mobile technologies build a better learning environment at international level. Responses on 308 graduate and postgraduate students in Saudi Arabia University exhibited that positive correlation between chatting, online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with students learning (Eid & Al-Jabri, 2016 ). The quantitative study on 168 faculty members using partial least square (PLS-SEM) at Carnegie classified Doctoral Research University in the USA confirmed that perceived usefulness, external pressure and compatibility of task-technology have positive effect on social media use, the higher the degree of the perceived risk of social media, the less likely to use the technological tools for classroom instruction, the study further revealed that use of social media for collaborative learning has a positive effect on students learning outcome and satisfaction (Cao, Ajjan, & Hong, 2013 ). Therefore, the authors have hypothesized:

H1: Use of social media for collaborative learning is positively associated with interactivity with teachers.

Additionally, Madden and Zickuhr ( 2011 ) concluded that 83% of internet user within the age bracket of 18–29 years adopting social media for interaction with colleagues. Kabilan, Ahmad, and Abidin ( 2010 ) made an empirical investigation on 300 students at University Sains Malaysia and concluded that 74% students found to be the same view that social media infuses constructive attitude towards learning English (Fig. 1 ).

Research Model

Reuben ( 2008 ) concluded in his study on social media usage among professional institutions revealed that Facebook and YouTube used over half of 148 higher education institutions. Nevertheless, a recent survey of 456 accredited United States institutions highlighted 100% using some form of social media, notably Facebook 98% and Twitter 84% for e-learning purposes, interaction with mentors (Barnes & Lescault, 2011 ).

Information and communication technology (ICT), such as web-based application and social networking sites enhances the collaboration and construction of knowledge byway of instruction with outside experts (Zhu, 2012 ). A positive statistically significant relationship was found between student’s use of a variety of social media tools and the colleague’s fellow as well as the overall quality of experiences (Rutherford, 2010 ). The potential use of social media leads to collaborative learning environments which allow students to share education-related materials and contents (Fisher & Baird, 2006 ). The report of 233 students in the United States higher educations confirmed that more recluse students interact through social media, which assist them in collaborative learning and boosting their self-confidence (Voorn & Kommers, 2013 ). Thus hypotheses as

H2: Use of social media for collaborative learning is positively associated with interactivity with peers.

Social media for collaborative learning, interactivity with peers, online knowledge sharing behaviour and students’ engagement

Students’ engagement in social media and its types represent their physical and mental involvement and time spent boost to the enhancement of educational Excellency, time spent on interaction with peers, teachers for collaborative learning (Kuh, 2007 ). Students’ engagement enhanced when interacting with peers and teacher was in the same direction, shares of ideas (Chickering & Gamson, 1987 ). Engagement is an active state that is influenced by interaction or lack thereof (Leece, 2011 ). With the advancement in information technology, the virtual world becomes the storehouse of the information. Liccardi et al. ( 2007 ) concluded that 30% students were noted to be active on social media for interaction with their colleagues, tutors, and friends while more than 52% used some social media forms for video sharing, blogs, chatting, and wiki during their class time. E-learning becomes now sharp and powerful tools in information technology and makes a substantial impact on the student’s academic performance. Sharing your knowledge will make you better. Social network ties were shown to be the best predictors of online knowledge sharing intention, which in turn associated with knowledge sharing behaviour (Chen, Chen, & Kinshuk, 2009 ). Social media provides the robust personalised, interactive learning environment and enhances in self-motivation as cited in (Al-Mukhaini, Al-Qayoudhi, & Al-Badi, 2014 ). Therefore, it was hypothesised that:

H3: Use of social media for collaborative learning is positively associated with online knowledge sharing behaviour.

Broadly Speaking social media/sites allow the students to interact, share the contents with colleagues, also assisting in building connections with others (Cain, 2008 ). In the present era, the majority of the college-going students are seen to be frequent users of these sophisticated devices to keep them informed and updated about the external affair. Facebook reported per day 1,00,000 new members join; Facebook is the most preferred social networking sites among the students of the United States as cited in (Cain, 2008 ). The researcher of the school of engineering, Swiss Federal Institute of Technology Lausanne, Switzerland, designed and developed Grasp, a social media platform for their students’ collaborative learning, sharing contents (Bogdanov et al., 2012 ). The utility and its usefulness could be seen in the University of Geneva and Tongji University at both two educational places students were satisfied and accept ‘ Grasp’ to collect, organised and share the contents. Students use of social media will interact ubiquity, heterogeneous and engaged in large groups (Wankel, 2009 ). So we hypotheses

H4: More interaction with teachers leads to higher students’ engagement.

However, a similar report published on 233 students revealed that social media assisted in their collaborative learning and self-confidence as they prefer communication technology than face to face communication. Although, the students have the willingness to communicate via social media platform than face to face (Voorn & Kommers, 2013 ). The potential use of social media tools facilitates in achieving higher-level learning through collaboration with colleagues and other renewed experts in their field (Junco, Heiberger, & Loken, 2011 ; Meyer, 2010 ; Novak, Razzouk, & Johnson, 2012 ; Redecker, Ala-Mutka, & Punie, 2010 ). Academic self-efficacy and optimism were found to be strongly related to performance, adjustment and consequently both directly impacted on student’s academic performance (Chemers, Hu, & Garcia, 2001 ). Data of 723 Malaysian researchers confirmed that both male and female students were satisfied with the use of social media for collaborative learning and engagement was found positively affected with learning performance (Al-Rahmi, Alias, Othman, Marin, & Tur, 2018 ). Social media were seen as a powerful driver for learning activities in terms of frankness, interactivity, and friendliness.

Junco et al. ( 2011 ) conducted research on the specific purpose of the social media; how Twitter impacted students’ engagement, found that it was extent discussion out of class, their participation in panel group (Rodriguez, 2011 ). A comparative study conducted by (Roblyer, McDaniel, Webb, Herman, & Witty, 2010 ) revealed that students were more techno-oriented than faculty members and more likely using Facebook and such similar communication technology to support their class-related task. Additionally, faculty members were more likely to use traditional techniques, i.e. email. Thus hypotheses framed is that:

H5: More interaction with peers ultimately leads to better students’ engagement.

Social networking sites and social media are closely similar, which provide a platform where students can interact, communicate, and share emotional intelligence and looking for people with other attitudes (Gikas & Grant, 2013 ). Facebook and YouTube channel use also increased in the skills/ability and knowledge and outcomes (Daniel, Isaac, & Janet, 2017 ). It was highlighted that 90% of faculty members were using some sort of social media in their courses/ teaching. Facebook was the most visited social media sites as per study, 40% of faculty members requested students to read and views content posted on social media; majority reports that videos, wiki, etc. the primary source of acquiring knowledge, social networking sites valuable tool/source of collaborative learning (Moran et al., 2011 ). However, more interestingly, in a study which was carried out on 658 faculty members in the eight different state university of Turkey, concluded that nearly half of the faculty member has some social media accounts.

Further reported that adopting social media for educational purposes, the primary motivational factor which stimulates them to use was effective and quick means of communication technology (Akçayır, 2017 ). Thus hypotheses formulated is:

H6: Online knowledge sharing behaviour is positively associated with the students’ engagement.

Using multiple treatment research design, following act-react to increase students’ academic performance and productivity, it was observed when self–monitoring record sheet was placed before students and seen that students engagement and educational productivity was increased (Rock & Thead, 2007 ). Student engagement in extra curriculum activities promotes academic achievement (Skinner & Belmont, 1993 ), increases grade rate (Connell, Spencer, & Aber, 1994 ), triggering student performance and positive expectations about academic abilities (Skinner & Belmont, 1993 ). They are spending time on online social networking sites linked to students engagement, which works as the motivator of academic performance (Fan & Williams, 2010 ). Moreover, it was noted in a survey of over 236 Malaysian students that weak association found between the online game and student’s academic performance (Eow, Ali, Mahmud, & Baki, 2009 ). In a survey of 671 students in Jordan, it was revealed that student’s engagement directly influences academic performance, also seen the indirect effect of parental involvement over academic performance (Al-Alwan, 2014 ). Engaged students are perceptive and highly active in classroom activities, ready to participate in different classroom extra activities and expose motivation to learn, which finally leads in academic achievement (Reyes, Brackett, Rivers, White, & Salovey, 2012 ). A mediated role of students engagement seen in 1399 students’ classroom emotional climate and grades (Reyes et al., 2012 ). A statistically significant relation was noticed between online lecture and exam performance.

Nonetheless, intelligence quotient, personality factors, students must be engaged in learning activities as cited in (Bertheussen & Myrland, 2016 ). The report of the 1906 students at 7 universities in Colombia confirmed that the weak correlation between collaborative learning, students faculty interaction with academic performance (Pineda-Báez et al., 2014 ) Thus, the hypothesis

H7: Student's Engagement is positively associated with the student's academic performance.

Methodology

To check the students’ perception on social media for collaborative learning in higher education institutions, Data were gathered both offline and online survey administered to students from one public university in Eastern India (BBAU, Lucknow). For the sake of this study, indicators of interactivity with peers and teachers, the items of students engagement, the statement of social media for collaborative learning, and the elements of students’ academic performance were adopted from (AL-Rahmi & Othman, 2013 ). The statement of online knowledge sharing behaviour was taken from (Ma & Yuen, 2011 ).

The indicators of all variables which were mentioned above are measured on the standardised seven-point Likert scale with the anchor (1-Strongly Disagree, to 7-Strongly Agree). Interactivity with peers was measured using four indicators; the sample items using social media in class facilitates interaction with peers ; interactivity with teachers was measured using four symbols, the sample item is using social media in class allows me to discuss with the teacher. ; engagement was measured using three indicators by using social media I felt that my opinions had been taken into account in this class ; social media for collaborative learning was measured using four indicators collaborative learning experience in social media environment is better than in a face-to-face learning environment ; students’ academic performance was measured using five signs using social media to build a student-lecturer relationship with my lecturers, and this improves my academic performance ; online knowledge sharing behaviour was assessed using five symbols the counsel was received from other colleague using social media has increased our experience .

Procedure and measurement

A sample of 360 undergraduate students was collected by convenience sampling method of a public university in Eastern India. The proposed model of study was measured and evaluated using variance based structured equation model (SEM)-a latent multi variance technique which provides the concurrent estimation of structural and measurement model that does not meet parametric assumption (Coelho & Duarte, 2016 ; Haryono & Wardoyo, 2012 ; Lee, 2007 ; Moqbel, Nevo, & Kock, 2013 ; Raykov & Marcoulides, 2000 ; Williams, Rana, & Dwivedi, 2015 ). The confirmatory factor analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminate and convergent validity met or not. The loading of all the indicators should be 0.50 or more (Field, 2011 ; Hair, Anderson, Tatham, & Black, 1992 ). And it should be statistically significant at least at the 0.05.

Demographic analysis (Table 1 )

The majority of the students in this study were females (50.8%) while male students were only 49.2% with age 15–20 years (71.7%). It could be pointed out at this juncture that the majority of the students (53.9%) in BBAU were joined at least 1–5 academic pages for their getting information, awareness and knowledge. 46.1% of students spent 1–5 h per week on social networking sites for collaborative learning, interaction with teachers at an international level. The different academic pages followed for accessing material, communication with the faculty members stood at 44.4%, there would be various forms of the social networking sites (LinkedIn, Slide Share, YouTube Channel, Researchgate) which provide the facility of online collaborative learning, a platform at which both faculty members and students engaged in learning activities.

As per report (Nasir, Khatoon, & Bharadwaj, 2018 ), most of the social media user in India are college-going students, 33% girls followed by 27% boys students, and this reports also forecasted that India is going to become the highest 370.77 million internet users in 2022. Additionally, the majority of the faculty members use smartphone 44% to connect with the students for sharing material content. Technological advantages were the pivotal motivational force which stimulates faculty members and students to exploits the opportunities of resource materials (Nasir & Khan, 2018 ) (Fig. 2 ).

Reasons for Using Social Media

When the students were asked for what reason did they use social media, it was seen that rarely using for self-promotion, very frequently using for self-education, often used for passing the time with friends, and so many fruitful information the image mentioned above depicting.

Instrument validation

The structural model was applied to scrutinize the potency and statistically significant relationship among unobserved variables. The present measurement model was evaluated using Confirmatory Factor Analysis (CFA), and allied procedures to examine the relationship among hypothetical latent variables has acceptable reliability and validity. This study used both SPSS 20.0 and AMOS to check measurement and structural model (Field, 2013 ; Hair, Anderson, et al., 1992 ; Mooi & Sarstedt, 2011 ; Norusis, 2011 ).

The Confirmatory Factor Analysis (CFA) was conducted to ensure whether the widely accepted criterion of discriminant and convergent validity met or not. The loading of all the indicators should be 0.70 or more it should be statistically significant at least at the 0.05 (Field, 2011 ; Hair, Anderson, et al., 1992 ).

CR or CA-based tests measured the reliability of the proposed measurement model. The CA provides an estimate of the indicators intercorrelation (Henseler & Sarstedt, 2013 . The benchmark limits of the CA is 0.7 or more (Nunnally & Bernstein, 1994 ). As per Table 2 , all latent variables in this study above the recommended threshold limit. Although, Average Variance Extracted (AVE) has also been demonstrated which exceed the benchmark limit 0.5. Thus all the above-specified values revealed that our instrument is valid and effective. (See Table 2 for the additional information) (Table 3 ).

In a nutshell, the measurement model clear numerous stringent tests of convergent validity, discriminant validity, reliability, and absence of multi-collinearity. The finding demonstrated that our model meets widely accepted data validation criteria. (Schumacker & Lomax, 2010 ).

The model fit was evaluated through the Chi-Square/degree of freedom (CMIN/DF), Root Mean Residual (RMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Goodness of fit index (GFI) and Tucker-Lewis Index (TLI). The benchmark limit of the CFI, TLI, and GFI 0.90or more (Hair et al., 2016 ; Kock, 2011 ). The model study demonstrated in the table, as mentioned above 4 that the minimum threshold limit was achieved (See Table 4 for additional diagnosis).

Path coefficient of several hypotheses has been demonstrated in Fig.  3 , which is a variable par relationship. β (beta) Coefficients, standardised partial regression coefficients signify the powers of the multivariate relationship among latent variables in the model. Remarkably, it was observed that seven out of the seven proposed hypotheses were accepted and 78% of the explained variance in students’ academic performance, 60% explained variance in interactivity with teachers, 48% variance in interactivity with peers, 43% variance in online knowledge sharing behaviour and 79% variance in students’ engagement. Social media collaborative learning has a significant association with teacher interactivity(β = .693, P  < 0.001), demonstrating that there is a direct effect on interaction with the teacher by social media when other variables are controlled. On the other hand, use of social media for collaborative learning has noticed statistically significant positive relationship with peers interactivity (β = .704, p  < 0.001) meaning thereby, collaborative learning on social media by university students, leads to the high degree of interaction with peers, colleagues. Implied 10% rise in social media use for learning purposes, expected 7.04% increase in interaction with peers.

Path Diagram

Use of social media for collaborating learning has a significant positive association with online knowledge sharing behaviour (β = .583, p  < 0.001), meaning thereby that the more intense use of social media for collaborative learning by university students, the more knowledge sharing between peers and colleagues. Also, interaction with the teacher seen the significant statistical positive association with students engagement (β = .450, p  < 0.001), telling that the more conversation with teachers, leads to a high level of students engagement. Similarly, the practical interpretation of this result is that there is an expected 4.5% increase in student’s participation for every 10% increase in interaction with teachers. Interaction with peers has a significant positive association with students engagement (β = .210, p  < 0.001). Practically, the finding revealed that 10% upturn in student’s involvement, there is a 2.1% increase in peer’s interaction. There is a significant positive association between online knowledge sharing behaviour and students engagement (β = 0.247, p  < 0.001), and finally students engagement has been a statistically significant positive relationship with students’ academic performance (β = .972, p  < 0.001), this is the clear indication that more engaged students in collaborative learning via social media leads to better students’ academic performance.

Discussion and implication

There is a continuing discussion in the academic literature that use of such social media and social networking sites would facilitate collaborative learning. It is human psychology generally that such communication media technology seems only for entertainment, but it should be noted here carefully that if such communication technology would be followed with due attention prove productive. It is essential to acknowledge that most university students nowadays adopting social media communication to interact with colleagues, teachers and also making the group be in touch with old friends and even a convenient source of transferring the resources. In the present era, the majority of the university students having diversified social media community groups like Whatsapp, Facebook pages following different academic web pages to upgrade their knowledge.

Practically for every 10% rise in students’ engagement, expected to be 2.1% increase in peer interaction. As the study suggested that students engage in different sites, they start discussing with colleagues. More engaged students in collaborative learning through social media lead better students’ academic performance. The present study revealed that for every 10% increase in student’s engagement, there would be an expected increase in student academic performance at a rate of 9.72. This extensive research finding revealed that the application of online social media would facilitate the students to become more creative, dynamics and connect to the worldwide instructor for collaborative learning.

Accordingly, the use of online social media for collaborative learning, interaction with mentors and colleagues leadbetter student’s engagement which consequently affects student’s academic performance. The higher education authority should provide such a platform which can nurture the student’s intellectual talents. Based on the empirical investigation, it would be said that students’ engagement, social media communication devices facilitate students to retrieve information and interact with others in real-time regarding sharing teaching materials contents. Additionally, such sophisticated communication devices would prove to be more useful to those students who feel too shy in front of peers; teachers may open up on the web for the collaborative learning and teaching in the global scenario and also beneficial for physically challenged students. It would also make sense that intensive use of such sophisticated technology in teaching pedagogical in higher education further facilitates the teachers and students to interact digitally, web-based learning, creating discussion group, etc. The result of this investigation confirmed that use of social media for collaborative learning purposes, interaction with peers, and teacher affect their academic performance positively, meaning at this moment that implementation of such sophisticated communication technology would bring revolutionary, drastic changes in higher education for international collaborative learning (Table 5 ).

Limitations and future direction

Like all the studies, this study is also not exempted from the pitfalls, lacunas, and drawbacks. The first and foremost research limitation is it ignores the addiction of social media; excess use may lead to destruction, deviation from the focal point. The study only confined to only one academic institution. Hence, the finding of the project cannot be generalised as a whole. The significant positive results were found in this study due to the fact that the social media and mobile devices are frequently used by the university going students not only as a means of gratification but also for educational purposes.

Secondly, this study was conducted on university students, ignoring the faculty members, it might be possible that the faculty members would not have been interested in interacting with the students. Thus, future research could be possible towards faculty members in different higher education institutions. To the authors’ best reliance, this is the first and prime study to check the usefulness and applicability of social media in the higher education system in the Indian context.

Concluding observations

Based on the empirical investigation, it could be noted that application and usefulness of the social media in transferring the resource materials, collaborative learning and interaction with the colleagues as well as teachers would facilitate students to be more enthusiastic and dynamic. This study provides guidelines to the corporate world in formulating strategies regarding the use of social media for collaborative learning.

Availability of data and materials

The corresponding author declared here all types of data used in this study available for any clarification. The author of this manuscript ready for any justification regarding the data set. To make publically available of the data used in this study, the seeker must mail to the mentioned email address. The profile of the respondents was completely confidential.

Akçayır, G. (2017). Why do faculty members use or not use social networking sites for education? Computers in Human Behavior, 71 , 378–385.

Article   Google Scholar  

Al-Alwan, A. F. (2014). Modeling the relations among parental involvement, school engagement and academic performance of high school students. International Education Studies, 7 (4), 47–56.

Al-Mukhaini, E. M., Al-Qayoudhi, W. S., & Al-Badi, A. H. (2014). Adoption of social networking in education: A study of the use of social networks by higher education students in Oman. Journal of International Education Research, 10 (2), 143–154.

Google Scholar  

Al-Rahmi, W. M., Alias, N., Othman, M. S., Marin, V. I., & Tur, G. (2018). A model of factors affecting learning performance through the use of social media in Malaysian higher education. Computers & Education, 121 , 59–72.

Al-Rahmi, W. M., & Othman, M. S. (2013). Evaluating student’s satisfaction of using social media through collaborative learning in higher education. International Journal of Advances in Engineering & Technology, 6 (4), 1541–1551.

Arbuckle, J. (2008). Amos 17.0 user's guide . Chicago: SPSS Inc..

Barnes, N. G., & Lescault, A. M. (2011). Social media adoption soars as higher-ed experiments and reevaluates its use of new communications tools . North Dartmouth: Center for Marketing Research. University of Massachusetts Dartmouth.

Bertheussen, B. A., & Myrland, Ø. (2016). Relation between academic performance and students’ engagement in digital learning activities. Journal of Education for Business, 91 (3), 125–131.

Bogdanov, E., Limpens, F., Li, N., El Helou, S., Salzmann, C., & Gillet, D. (2012). A social media platform in higher education. In Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–8). IEEE.

Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/windows: basic concepts, applications, and programming . Thousand Oaks: Sage.

Cain, J. (2008). Online social networking issues within academia and pharmacy education. American Journal of Pharmaceutical Education. https://doi.org/10.5688/aj720110 .

Cao, Y., Ajjan, H., & Hong, P. (2013). Using social media applications for educational outcomes in college teaching: a structural equation analysis. British Journal of Educational Technology, 44 (4), 581–593. https://doi.org/10.1111/bjet.12066 .

Cavus, N., & Ibrahim, D. (2008). A mobile tool for learning English words, Online Submission (pp. 6–9) Retrieved from http://libezproxy.open.ac.uk/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED504283&site=ehost-live&scope=site .

Cavus, N., & Ibrahim, D. (2009). M-learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology, 40 (1), 78–91.

Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first-year college student performance and adjustment. Journal of Educational Psychology, 93 (1), 55–64. https://doi.org/10.1037/0022-0663.93.1.55 .

Chen, I. Y. L., Chen, N.-S., & Kinshuk. (2009). International forum of Educational Technology & Society Examining the factors influencing participants’ knowledge sharing behavior in virtual learning communities published by : International forum of Educational Technology & Society Examining the factor. Educational Technology & Society, 12 (1), 134–148.

Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practise in undergraduate education. AAHE bulletin, 3 , 7.

Coelho, J., & Duarte, C. (2016). A literature survey on older adults' use of social network services and social applications. Computers in Human Behavior, 58 , 187–205.

Connell, J. P., Spencer, M. B., & Aber, J. L. (1994). Educational risk and resilience in African-American youth: Context, self, action, and outcomes in school. Child Development, 65 (2), 493–506.

Daniel, E. A., Isaac, E. N., & Janet, A. K. (2017). Influence of Facebook usage on employee productivity: A case of university of cape coast staff. African Journal of Business Management, 11 (6), 110–116. https://doi.org/10.5897/AJBM2017.8265 .

DeBell, M., & Chapman, C. (2006). Computer and internet use by students in 2003. Statistical analysis report. NCES 2006-065. National Center for education statistics.

Dziuban, C., & Walker, J. D. (2012). ECAR Study of Undergraduate Students and Information Technology, 2012 (Research Report) . Louisville: EDUCAUSE Centre for Applied Research.

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99 , 14–27. https://doi.org/10.1016/j.compedu.2016.04.007 .

Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6 (3), 192.

Eow, Y. L., Ali, W. Z. b. W., Mahmud, R. b., & Baki, R. (2009). Form one students’ engagement with computer games and its effect on their academic achievement in a Malaysian secondary school. Computers and Education, 53 (4), 1082–1091. https://doi.org/10.1016/j.compedu.2009.05.013 .

Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Wiley Online Library, 45 (5), 902–915. https://doi.org/10.1111/bjet.12099 .

Fan, W., & Williams, C. M. (2010). The effects of parental involvement on students’ academic self-efficacy, engagement and intrinsic motivation. Educational Psychology, 30 (1), 53–74. https://doi.org/10.1080/01443410903353302 .

Field, A. (2011). Discovering statistics using SPSS: (and sex and drugs and rock'n'roll) (Vol. 497). London: Sage.

Field, A. (2013). Factor analysis using SPSS. Scientific Research and Essays, 22 (June), 1–26. https://doi.org/10.1016/B978-0-444-52272-6.00519-5 .

Fisher, M., & Baird, D. E. (2006). Making mLearning work: Utilizing mobile technology for active exploration, collaboration, assessment, and reflection in higher education. Journal of Educational Technology Systems, 35 (1), 3–30.

Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones &amp; social media. Internet and Higher Education Mobile, 19 , 18–26. https://doi.org/10.1016/j.iheduc.2013.06.002 .

Greenhow, C. (2011a). Online social networks and learning. On the horizon, 19 (1), 4–12.

Greenhow, C. (2011b). Youth, learning, and social media. Journal of Educational Computing Research, 45 (2), 139–146. https://doi.org/10.2190/EC.45.2.a .

Hair Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis. International Journal of Pharmaceutics . https://doi.org/10.1016/j.ijpharm.2011.02.019 .

Hair Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European Business Review.

Harrington, D. (2009). Confirmatory factor analysis . Oxford university press.

Haryono, S., & Wardoyo, P. (2012). Structural Equation Modeling (Vol. 331).

Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28 (2), 565–580.

Jackson, C. (2011). Your students love social media… and so can you. Teaching Tolerance, 39 , 38–41.

Junco, R., Heiberger, G., & Loken, E. (2011). The effect of twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27 (2), 119–132.

Kabilan, M. K., Ahmad, N., & Abidin, M. J. Z. (2010). Facebook: An online environment for learning of English in institutions of higher education? The Internet and Higher Education, 13 (4), 179–187.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1), 59–68.

Kock, N. (2011). Using WarpPLS in e-collaboration studies: Mediating effects, control and second order variables, and algorithm choices. International Journal of e-Collaboration (IJeC), 7 (3), 1–13.

Kuh, G. D. (2007). What student engagement data tell us about college readiness. Peer Review, 9 (1), 4–8.

Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20 (3), 271–289.

Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach (Wiley series in probability and statistics). Ecotoxicology and Environmental Safety, 73 . https://doi.org/10.1016/j.ecoenv.2009.09.012 .

Leece, R. (2011). Engaging students through social media. Journal of the Australian and New Zealand Student Services Association, 38 , 10–14 Retrieved from https://www.researchgate.net/profile/Anthony_Jorm/publication/235003484_Introduction_to_guidelines_for_tertiary_education_institutions_to_assist_them_in_supporting_students_with_mental_health_problems/links/0c96052ba5314e1202000000.pdf#page=67 .

Lenhart, A., Arafeh, S., & Smith, A. (2008). Writing, technology and teens . Pew Internet & American Life Project.

Lenhart, A., Madden, M., & Hitlin, P. (2005). Teens and technology (p. 2008). Washington, DC: Pew Charitable Trusts Retrieved September 29.

Liccardi, I., Ounnas, A., Pau, R., Massey, E., Kinnunen, P., Lewthwaite, S., …, Sarkar, C. (2007). The role of social networks in students’ learning experiences. In ACM Sigcse Bulletin (39, 4, 224–237).

Ma, W. W. K., & Yuen, A. H. K. (2011). Understanding online knowledge sharing: An interpersonal relationship perspective. Computers & Education, 56 (1), 210–219.

Madden, M., & Zickuhr, K. (2011). 65% of online adults use social networking sites. Pew Internet & American Life Project, 1 , 14.

Meyer, K. A. (2010). A comparison of web 2.0 tools in a doctoral course. The Internet and Higher Education, 13 (4), 226–232.

Mirela Mabić, D. G. (2014). Facebook as a learning tool. Igarss, 2014 (1), 1–5. https://doi.org/10.1007/s13398-014-0173-7.2 .

Mooi, E., & Sarstedt, M. (2011). A concise guide to market research: The process, data, and methods using IBM SPSS statistics . Springeringer. https://doi.org/10.1007/978-3-642-12541-6 .

Moqbel, M., Nevo, S., & Kock, N. (2013). Organizational members’ use of social networking sites and job performance. Information Technology & People, 26 (3), 240–264. https://doi.org/10.1108/ITP-10-2012-0110 .

Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning, and sharing: How Today’s higher education faculty use social media (pp. 1–16). Babson survey research group, (April. https://doi.org/10.1016/j.chb.2013.06.015 .

Nasir, J. A., & Khan, N. A. (2018). Faculty member usage of social media and mobile devices in higher education institution. International Journal of Advance and Innovative Research, 6 (1), 17–25.

Nasir, J. A., Khatoon, A., & Bharadwaj, S. (2018). Social media users in India: A futuristic approach. International Journal of Research and Analytical Reviews, 5 (4), 762–765 Retrieved from http://ijrar.com/ .

Nihalani, P. K., & Mayrath, M. C. (2010). Statistics I. Findings from using an iPhone app in a higher education course. In White Paper .

Norusis, M. (2011). IBM SPSS statistics 20 brief guide (pp. 1–170). IBM Corporation Retrieved from http://www.ibm.com/support .

Novak, E., Razzouk, R., & Johnson, T. E. (2012). The educational use of social annotation tools in higher education: A literature review. The Internet and Higher Education, 15 (1), 39–49.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory .

Pineda-Báez, C., José-Javier, B. A., Rubiano-Bello, Á., Pava-García, N., Suárez-García, R., & Cruz-Becerra, F. (2014). Student engagement and academic performance in the Colombian University context. RELIEVE-Revista Electrónica de Investigación y Evaluación Educativa, 20 (2), 1–19.

Raykov, T., & Marcoulides, G. A. (2000). A First Course in Structural Equation Modeling .

Redecker, C., Ala-Mutka, K., & Punie, Y. (2010). Learning 2.0-the impact of social media on learning in Europe. Policy brief. JRC scientific and technical report. EUR JRC56958 EN, Available from http://bit.ly/cljlpq . Accessed 6 Feb 2011.

Reuben, B. R. (2008). The use of social Media in Higher Education for marketing and communications : A guide for professionals in higher education (Vol. 5) Retrieved from httpdoteduguru comwpcontentuploads200808socialmediainhighereducation pdf)). https://doi.org/10.1108/S2044-9968(2012)0000005018 .

Book   Google Scholar  

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104 (3), 700–712. https://doi.org/10.1037/a0027268 .

Richardson, J., & Lenarcic, J. (2008). Text Messaging as a Catalyst for Mobile Student Administration: The “Trigger” Experience. International Journal of Emerging Technologies & Society, 6 (2), 140–155.

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13 (3), 134–140.

Rock, M. L., & Thead, B. K. (2007). The effects of fading a strategic self-monitoring intervention on students’ academic engagement, accuracy, and productivity. Journal of Behavioral Education, 16 (4), 389–412. https://doi.org/10.1007/s10864-007-9049-7 .

Rodriguez, J. E. (2011). Social media use in higher education : Key areas to consider for educators. MERLOT Journal of Online Learning and Teaching, 7 (4), 539–550 https://doi.org/ISSN1558-9528 .

Rutherford, C. (2010). Using online social media to support Preservice student engagement. MERLOT Journal of Online Learning and Teaching, 6 (4), 703–711 Retrieved from http://jolt.merlot.org/vol6no4/rutherford_1210.pdf .

Schumacker, R. E., & Lomax, R. G. (2010). A Beginner’s Guide to structural equation modeling (3rd ed.). New York: Taylor & Francis Group.

Selwyn, N. (2012). Making sense of young people, education and digital technology: The role of sociological theory. Oxford Review of Education, 38 (1), 81–96.

Shih, Y. E. (2007). Setting the new standard with mobile computing in online learning. The International Review of Research in Open and Distributed Learning, 8 (2), 1–16.

Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of educational psychology, 85 (4), 571.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston: Pearson.

Voorn, R. J., & Kommers, P. A. (2013). Social media and higher education: Introversion and collaborative learning from the student’s perspective. International Journal of Social Media and Interactive Learning Environments, 1 (1), 59–73.

Wankel, C. (2009). Management education using social media. Organization Management Journal, 6 (4), 251–262.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28 (3), 443–488.

Zhu, C. (2012). Student satisfaction, performance, and knowledge construction in online collaborative learning. Journal of Educational Technology & Society, 15 (1), 127–136.

Download references

Acknowledgements

We want to express our special gratitude to the Almighty who has blessed us with such hidden talent to give the shape of this research paper.

The authors of this manuscript, solemnly declared that no funding agency was supported to execute this research project.

Author information

Authors and affiliations.

Department of Commerce, Aligarh Muslim University, Aligarh, 202002, India

Jamal Abdul Nasir Ansari & Nawab Ali Khan

You can also search for this author in PubMed   Google Scholar

Contributions

Jamal Abdul Nasir Ansari: The first author of this manuscript has performed all sorts of necessary works like the collection of data from respondents, administration of the questionnaire. Collection of information from the respondents was quite challenging. The author faced a lot of difficulties while collecting data. The main contribution of the author in this manuscript is that the entire work, like data analysis and its interpretation performed by him. Additionally, the author has tried to explore and usefulness of social media and its applicability in transferring the course contents. Nawab Ali Khan: The second author of this manuscript has checked all types of grammatical issues, and necessary corrections wherever required. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Jamal Abdul Nasir Ansari .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Ansari, J.A.N., Khan, N.A. Exploring the role of social media in collaborative learning the new domain of learning. Smart Learn. Environ. 7 , 9 (2020). https://doi.org/10.1186/s40561-020-00118-7

Download citation

Received : 27 November 2019

Accepted : 18 February 2020

Published : 16 March 2020

DOI : https://doi.org/10.1186/s40561-020-00118-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social media
• Higher education
• Faculty members

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

John a. naslund.

a Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA

Ameya Bondre

b CareNX Innovations, Mumbai, India

John Torous

c Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA

Kelly A. Aschbrenner

d Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH

Social media platforms are popular venues for sharing personal experiences, seeking information, and offering peer-to-peer support among individuals living with mental illness. With significant shortfalls in the availability, quality, and reach of evidence-based mental health services across the United States and globally, social media platforms may afford new opportunities to bridge this gap. However, caution is warranted, as numerous studies highlight risks of social media use for mental health. In this commentary, we consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services. Specifically, we summarize current research on the use of social media among mental health service users, and early efforts using social media for the delivery of evidence-based programs. We also review the risks, potential harms, and necessary safety precautions with using social media for mental health. To conclude, we explore opportunities using data science and machine learning, for example by leveraging social media for detecting mental disorders and developing predictive models aimed at characterizing the aetiology and progression of mental disorders. These various efforts using social media, as summarized in this commentary, hold promise for improving the lives of individuals living with mental disorders.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos ( Ahmed, Ahmad, Ahmad, & Zakaria, 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals, to upwards of 97% among younger individuals ( Aschbrenner, Naslund, Grinley, et al., 2018 ; M. L. Birnbaum, Rizvi, Correll, Kane, & Confino, 2017 ; Brunette et al., 2019 ; Naslund, Aschbrenner, & Bartels, 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges ( Bucci, Schwannauer, & Berry, 2019 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016b ).

Across the United States and globally, very few people living with mental illness have access to adequate mental health services ( Patel et al., 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health ( Orben & Przybylski, 2019 ), and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media”, and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population ( We Are Social, 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones ( Firth et al., 2015 ; Glick, Druss, Pina, Lally, & Conde, 2016 ; Torous, Chan, et al., 2014 ; Torous, Friedman, & Keshavan, 2014 ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals ( Trefflich, Kalckreuth, Mergl, & Rummel-Kluge, 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites ( Miller, Stewart, Schrimsher, Peeples, & Buckley, 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared to low-income groups from the general population ( Brunette et al., 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants ( Naslund, Aschbrenner, & Bartels, 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media ( Aschbrenner, Naslund, Grinley, et al., 2018 ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study ( Abdel-Baki, Lal, D.-Charron, Stip, & Kara, 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI), and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 hours each day ( Gay, Torous, Joseph, Pandya, & Duckworth, 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 hours per day ( M. L. Birnbaum et al., 2017 ). Similarly, in a sample of adolescents ages 13-18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat ( Aschbrenner et al., 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: 1) Facilitate social interaction; 2) Access to a peer support network; and 3) Promote engagement and retention in services.

Summary of potential benefits and challenges with social media for mental health

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals ( Torous & Keshavan, 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily ( Miller et al., 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions ( Berger, Wagner, & Baker, 2005 ), such as serious mental disorders ( Highton-Williamson, Priebe, & Giacco, 2015 ).

Studies have found that individuals with serious mental disorders ( Spinzy, Nitzan, Becker, Bloch, & Fennig, 2012 ) as well as young adults with mental illness ( Gowen, Deschaine, Gruttadara, & Markey, 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world, and also experience high rates of loneliness ( Badcock et al., 2015 ; Giacco, Palumbo, Strappelli, Catapano, & Priebe, 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone ( Brusilovskiy, Townley, Snethen, & Salzer, 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated ( Gowen et al., 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections ( Brusilovskiy et al., 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person ( Rideout & Fox, 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters ( Batterham & Calear, 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges, and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information ( Schrank, Sibitz, Unger, & Amering, 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations ( Docherty et al., 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction ( Kiesler, Siegel, & McGuire, 1984 ), with interactions being more fluid, and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction ( Indian & Grieve, 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect and attentional impairment, as well as active social avoidance due to hallucinations or other concerns ( Hansen, Torgalsbøen, Melle, & Bell, 2009 ); thus, potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support ( Bucci et al., 2019 ; Naslund, Aschbrenner, et al., 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges ( Davidson, Chinman, Sells, & Rowe, 2006 ; Mead, Hilton, & Curtis, 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure, and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication ( Haker, Lauber, & Rössler, 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness ( Vayreda & Antaki, 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al (2015) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience ( Highton-Williamson et al., 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness ( Naslund, Grande, Aschbrenner, & Elwyn, 2014 ). In another study, Chang (2009) delineated various communication patterns in an online psychosis peer-support group ( Chang, 2009 ). Specifically, different forms of support emerged, including ‘informational support’ about medication use or contacting mental health providers, ‘esteem support’ involving positive comments for encouragement, ‘network support’ for sharing similar experiences, and ‘emotional support’ to express understanding of a peer’s situation and offer hope or confidence ( Chang, 2009 ). Bauer et al. (2013) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group ( Bauer, Bauer, Spiessl, & Kagerbauer, 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. (2017) found that this served as an important opportunity to seek support and to hear about the experiences of others ( Berry et al., 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media ( Naslund et al., 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared ( Saha et al., 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information ( Lal, Nguyen, & Theriault, 2018 ), connecting with mental health providers ( M. L. Birnbaum et al., 2017 ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing ( Naslund et al., 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al (2018) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions, and may also improve perceived social support ( Biagianti, Quraishi, & Schlosser, 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis ( Alvarez-Jimenez et al., 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process ( Alvarez-Jimenez et al., 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services ( Alvarez-Jimenez et al., 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis ( Alvarez-Jimenez et al., 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools ( Schlosser et al., 2016 ). This unique approach to the design of the app is aimed at promoting engagement, and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia ( Schlosser et al., 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies ( Aschbrenner, Naslund, Shevenell, Kinney, & Bartels, 2016 ; Aschbrenner, Naslund, Shevenell, Mueser, & Bartels, 2016 ). The intervention holds tremendous promise as lack of support is one of the largest barriers toward exercise in patients with serious mental illness ( Firth et al., 2016 ) and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals ( Aschbrenner, Naslund, & Bartels, 2016 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016a ). To date, this program has demonstrate preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group ( Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016 ), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program ( Naslund, Aschbrenner, Marsch, McHugo, & Bartels, 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from a variety of real world community mental health services settings ( Aschbrenner, Naslund, Gorin, et al., 2018 ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway ( Alvarez-Jimenez et al., 2019 ; Aschbrenner, Naslund, Gorin, et al., 2018 ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services ( Gleeson et al., 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and well being, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem, and opportunities for self-disclosure ( Best, Manktelow, & Taylor, 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms and bullying ( Best et al., 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: 1) Impact on symptoms; 2) Facing hostile interactions; and 3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people ( Andreassen et al., 2016 ; Kross et al., 2013 ; Woods & Scott, 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented ( Stiglic & Viner, 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media ( Rideout & Fox, 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms ( Feinstein et al., 2013 ). Still, the cross sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences ( Orben & Przybylski, 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms ( Lin et al., 2016 ). More time spent using social media is also associated with greater symptoms of anxiety ( Vannucci, Flannery, & Ohannessian, 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health ( Primack et al., 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared to respondents using only 2 or fewer platforms, there was a 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms ( Primack et al., 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people ( Twenge & Campbell, 2018 ), and may contribute to greater loneliness ( Bucci et al., 2019 ), and negative effects on other aspects of health and wellbeing ( Woods & Scott, 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there was significantly greater depressive symptoms and increased risk of suicide when compared to adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities ( Twenge, Joiner, Rogers, & Martin, 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders ( Mittal, Tessner, & Walker, 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood ( Berry, Emsley, Lobban, & Bucci, 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies ( Orben & Przybylski, 2019 ), and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared to random hostile comments posted online ( Hamm et al., 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people ( Hamm et al., 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the United States, where females were twice as likely to be victims of cyberbullying compared to males ( Alhajji, Bass, & Dai, 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety ( Hamm et al., 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time ( Machmutow, Perren, Sticca, & Alsaker, 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there was over 3 times greater odds of facing online harassment in the last year compared to youth who reported mild or no depressive symptoms ( Ybarra, 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media, and in particular, were more likely to report having faced hostile comments, or being “trolled”, from others when compared to respondents without depressive symptoms (31% vs. 14%) ( Rideout & Fox, 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses ( Goodman et al., 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media ( Saha et al., 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr and other forums across 127 countries ( Sumner et al., 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online ( Torous & Keshavan, 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media, and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source ( Moorhead et al., 2013 ; Ventola, 2014 ). For persons living with mental illness there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media ( Naslund & Aschbrenner, 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt ( Naslund & Aschbrenner, 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion, or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while also highlighting that there could also be benefits. For individuals with mental illness who use social media, being aware of the risks is an essential first step, and then highlighting ways that use of these popular platforms could also contribute to some benefits, ranging from finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media, and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the United States found that female respondents were more likely to search online for information about depression or anxiety, and to try to connect with other people online who share similar mental health concerns, when compared to male respondents ( Rideout & Fox, 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information ( Rideout & Fox, 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males ( Booker, Kelly, & Sacker, 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual or transgender individuals frequently use social media for searching for health information and may be more likely compared to heterosexual individuals to share their own personal health experiences with others online ( Rideout & Fox, 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and more likely to experience online victimization when compared to heterosexual individuals ( Mereish, Sheskier, Hawthorne, & Goldbach, 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the United States ( Tynes, Willis, Stewart, & Hamilton, 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups ( Schueller, Hunter, Figueroa, & Aguilera, 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system ( Naslund et al., 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media-like features would have been omitted. Though it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature”, because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the United States, as well as from other higher income settings such as Australia or the United Kingdom. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide ( Naslund et al., 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as ‘digital phenotyping’ aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention ( Jain, Powers, Hawkins, & Brownstein, 2015 ; Onnela & Rauch, 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related ( Torous et al., 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms ( Shatte, Hutchinson, & Teague, 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health ( Manikonda & De Choudhury, 2017 ; Reece et al., 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression ( De Choudhury, Gamon, Counts, & Horvitz, 2013 ) as well as detecting users’ mood and affective states ( De Choudhury, Gamon, & Counts, 2012 ), while photos posted to Instagram can yield insights for predicting depression ( Reece & Danforth, 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared to a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns ( Michael L Birnbaum, Ernala, Rizvi, De Choudhury, & Kane, 2017 ), including more frequent discussion of tobacco use ( Hswen et al., 2017 ), symptoms of depression and anxiety ( Hswen, Naslund, Brownstein, & Hawkins, 2018b ), and suicide ( Hswen, Naslund, Brownstein, & Hawkins, 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala, Rizvi, Birnbaum, Kane, & De Choudhury, 2017). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive ‘digital phenotype’ to predict relapse and identify high-risk health behaviors among individuals living with mental illness ( Torous et al., 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary ( Chancellor, Birnbaum, Caine, Silenzio, & De Choudhury, 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users ( Bidargaddi et al., 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness ( Guntuku, Yaden, Kern, Ungar, & Eichstaedt, 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content, as this could place an individual at risk of harm or divulge sensitive health information ( Webb et al., 2017 ; Williams, Burnap, & Sloan, 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, as well as the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings ( Chancellor et al., 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media, and offer recommendations to promote safe use of these sites, while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients, while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers ( Hilty, Chan, Torous, Luo, & Boland, 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services, and coping with symptoms ( Bucci et al., 2019 ; Highton-Williamson et al., 2015 ; Naslund, Aschbrenner, et al., 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the United States and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Acknowledgements

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Conflict of Interest

The authors have nothing to disclose.

• Abdel-Baki A, Lai S, D.-Charron O, Stip E, & Kara N, (2017). Understanding access and use of technology among youth with first - episode psychosis to inform the development of technology - enabled therapeutic interventions . Early intervention in psychiatry , 77 ( 1 ), 72–76. [ PubMed ] [ Google Scholar ]
• Ahmed YA, Ahmad MN, Ahmad N, & Zakaria NH (2019). Social media for knowledge-sharing: A systematic literature review . Telematics and informatics , 37 , 72–112 . [ Google Scholar ]
• Alhajji M, Bass S, & Dai T (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 Youth Risk Behavior Survey . Global pediatric health , 6 , 2333794X19868887. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Bendall S, Koval P, Rice S, Cagliarini D, Valentine L, … Penn DL (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services . BMJ open , 9 ( 2 ), e024104. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Bendall S, Lederman R, Wadley G, Chinnery G, Vargas S, … Gleeson JF (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis . Schizophrenia research , 143 ( 1 ), 143–149. [ PubMed ] [ Google Scholar ]
• Alvarez-Jimenez M, Gleeson J, Bendall S, Penn D, Yung A, Ryan R, … Miles C (2018). Enhancing social functioning in young people at Ultra High Risk (UHR) for psychosis: A pilot study of a novel strengths and mindfulness-based online social therapy . Schizophrenia research , 202 , 369–377. [ PubMed ] [ Google Scholar ]
• Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, & Pallesen S (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study . Psychology of Addictive Behaviors , 30 ( 2 ), 252. [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, & Bartels SJ (2016). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness . Psychiatric rehabilitation journal , 39 ( 4 ), 328. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Gorin AA, Mueser KT, Scherer EA, Viron M, … Bartels SJ, (2018). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: Protocol for a randomized controlled trial . Contemporary clinical trials , 74 , 97–106. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Grinley T, Bienvenida JCM, Bartels SJ, & Brunette M (2018). A Survey of Online and Mobile Technology Use at Peer Support Agencies . Psychiatric Quarterly , 1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Shevenell M, Kinney E, & Bartels SJ (2016). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness . The Journal of nervous and mental disease , 204 ( 6 ), 483–486. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Shevenell M, Mueser KT, & Bartels SJ (2016). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness . Psychiatric Quarterly , 57 ( 3 ), 401–415. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Aschbrenner KA, Naslund JA, Tomlinson EF, Kinney A, Pratt SI, & Brunette MF (2019). Adolescents’ Use of Digital Technologies and Preferences for Mobile Health Coaching in Mental Health Settings . Frontiers in Public Health . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Badcock JC, Shah S, Mackinnon A, Stain HJ, Galletly C, Jablensky A, & Morgan VA (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile . Schizophrenia research , 169 ( 1-3 ), 268–273. [ PubMed ] [ Google Scholar ]
• Batterham PJ, & Calear AJ (2017). Preferences for internet-based mental health interventions in an adult online sample: Findings from ann online community survey . JMIR mental health , 4 ( 2 ), e26. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bauer R, Bauer M, Spiessl H, & Kagerbauer T (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder) . Nordic journal of psychiatry , 67 ( 3 ), 185–190. [ PubMed ] [ Google Scholar ]
• Berger M, Wagner TH, & Baker LC (2005). Internet use and stigmatized illness . Social science & medicine , 67 ( 8 ), 1821–1827. [ PubMed ] [ Google Scholar ]
• Berry N, Emsley R, Lobban F, & Bucci S (2018). Social media and its relationship with mood, self - esteem and paranoia in psychosis . Acta Psychiatrica Scandinavica , 138 , 558–570. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Berry N, Lobban F, Belousov M, Emsley R, Nenadic G, & Bucci S (2017). # Why We Tweet MH: understanding why people use Twitter to discuss mental health problems . Journal of medical Internet research , 19 ( 4 ), e107. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Best P, Manktelow R, & Taylor B (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review . Children and Youth Services Review , 41 , 27–36. [ Google Scholar ]
• Biagianti B, Quraishi SH, & Schlosser DA (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review . Psychiatric Services , 69 ( 4 ), 377–388. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bidargaddi N, Musiat P, Makinen V-P, Ermes M, Schrader G, & Licinio J (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies . Molecular psychiatry , 22 ( 2 ), 164. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Birnbaum ML, Emala SK, Rizvi AF, De Choudhury M, & Kane JM (2017). A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals . Journal of medical Internet research , 79 ( 8 ), e289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Birnbaum ML, Rizvi AF, Correll CU, Kane JM, & Confino J (2017). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non - psychotic mood disorders . Early intervention in psychiatry , 77 ( 4 ), 290–295. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Booker CL, Kelly YJ, & Sacker A (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK . BMC public health , 18 ( 1 ), 321. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brunette M, Achtyes E, Pratt S, Stilwell K, Opperman M, Guarino S, & Kay-Lambkin F (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention . Community mental health journal , 1–6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brusilovskiy E, Townley G, Snethen G, & Salzer MS (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses . Computers in Human Behavior , 65 , 232–240. [ Google Scholar ]
• Bucci S, Schwannauer M, & Berry N (2019). The digital revolution and its impact on mental health care . Psychology and Psychotherapy: Theory, Research and Practice , 1–21. [ PubMed ] [ Google Scholar ]
• Chancellor S, Birnbaum ML, Caine ED, Silenzio V, & De Choudhury M (2019). A taxonomy of ethical tensions in inferring mental health states from social media . Paper presented at the Proceedings of the Conference on Fairness, Accountability, and Transparency. [ Google Scholar ]
• Chang HJ (2009). Online supportive interactions: Using a network approach to examine communication patterns within a psychosis social support group in Taiwan . Journal of the American Society for Information Science and Technology , 60 ( 7 ), 1504–1517. [ Google Scholar ]
• Davidson L, Chinman M, Sells D, & Rowe M (2006). Peer support among adults with serious mental illness: a report from the field . Schizophrenia bulletin , 32 ( 3 ), 443–450. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• De Choudhury M, Gamon M, & Counts S (2012). Happy, nervous or surprised? classification of human affective states in social media . Paper presented at the Sixth International AAAI Conference on Weblogs and Social Media. [ Google Scholar ]
• De Choudhury M, Gamon M, Counts S, & Horvitz E (2013). Predicting Depression via Social Media . Paper presented at the Association for the Advancement of Artificial Intelligence. [ Google Scholar ]
• Docherty NM, Hawkins KA, Hoffman RE, Quinlan DM, Rakfeldt J, & Sledge WH (1996). Working memory, attention, and communication disturbances in schizophrenia . Journal of Abnormal Psychology , 105 ( 2 ), 212. [ PubMed ] [ Google Scholar ]
• Emala SK, Rizvi AF, Birnbaum ML, Kane JM, & De Choudhury M (2017). Linguistic Markers Indicating Therapeutic Outcomes of Social Media Disclosures of Schizophrenia . Proc. ACMHum.-Comput. Interact , 1 ( 1 ), 43. [ Google Scholar ]
• Feinstein BA, Hershenberg R, Bhatia V, Latack JA, Meuwly N, & Davila J (2013). Negative social comparison on Facebook and depressive symptoms: Rumination as a mechanism . Psychology of Popular Media Culture , 2 ( 3 ), 161. [ Google Scholar ]
• Firth J, Cotter J, Torous J, Bucci S, Firth JA, & Yung AR (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies . Schizophrenia bulletin , 42 ( 2 ), 448–455. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Firth J, Rosenbaum S, Stubbs B, Gorczynski P, Yung AR, & Vancampfort D (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis . Psychological medicine , 46 ( 14 ), 2869–2881. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gay K, Torous J, Joseph A, Pandya A, & Duckworth K (2016). Digital technology use among individuals with schizophrenia: results of an online survey . JMIR mental health , 3 ( 2 ), el5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Giacco D, Palumbo C, Strappelli N, Catapano F, & Priebe S (2016). Social contacts and loneliness in people with psychotic and mood disorders . Comprehensive Psychiatry , 66 , 59–66. [ PubMed ] [ Google Scholar ]
• Gleeson J, Lederman R, Herrman H, Koval P, Eleftheriadis D, Bendall S, … Alvarez-Jimenez M (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial . Trials , 75 ( 1 ), 27. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Glick G, Druss B, Pina J, Lally C, & Conde M (2016). Use of mobile technology in a community mental health setting . Journal of telemedicine and telecare , 22 ( 7 ), 430–435. [ PubMed ] [ Google Scholar ]
• Goodman LA, Thompson KM, Weinfurt K, Corl S, Acker P, Mueser KT, & Rosenberg SD (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness . Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies , 12 ( 4 ), 587–599. [ PubMed ] [ Google Scholar ]
• Gowen K, Deschaine M, Gruttadara D, & Markey D (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community . Psychiatric rehabilitation journal , 35 ( 3 ), 245–250. [ PubMed ] [ Google Scholar ]
• Guntuku SC, Yaden DB, Kern ML, Ungar LH, & Eichstaedt JC (2017). Detecting depression and mental illness on social media: an integrative review . Current Opinion in Behavioral Sciences , 18 , 43–49. [ Google Scholar ]
• Haker EL, Lauber C, & Rossler W (2005). Internet forums: a self - help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica , 112 ( 6 ), 474–477. [ PubMed ] [ Google Scholar ]
• Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P, … Hartling L (2015). Prevalence and effect of cyberbullying on children and young people: A scoping review of social media studies . JAMA pediatrics , 769 ( 8 ), 770–777. [ PubMed ] [ Google Scholar ]
• Hansen CF, Torgalsboen A-K, Melle I, & Bell MD (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes . The Journal of nervous and mental disease , 197 ( 4 ), 274–277. [ PubMed ] [ Google Scholar ]
• Highton-Williamson E, Priebe S, & Giacco D (2015). Online social networking in people with psychosis: a systematic review . International Journal of Social Psychiatry , 61 ( 1 ), 92–101. [ PubMed ] [ Google Scholar ]
• Hilty DM, Chan S, Torous J, Luo J, & Boland RJ (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues . Psychiatric Clinics , 42 ( 2 ), 513–534. [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Brownstein JS, & Hawkins JB (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study . JMIR mental health , 5 ( 4 ), e11483. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Brownstein JS, & Hawkins JB (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media . Psychiatric Quarterly , 89 ( 3 ), 569–580. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hswen Y, Naslund JA, Chandrashekar P, Siegel R, Brownstein JS, & Hawkins JB (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia . Psychiatry research , 257 , 479–484. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Indian M, & Grieve R (2014). When Facebook is easier than face-to-face: social support dervied from Facebook in socially anxious individuals . Personality and Individual Differences , 59 , 102–106. [ Google Scholar ]
• Jain SH, Powers BW, Hawkins JB, & Brownstein JS (2015). The digital phenotype . Nature Biotechnology , 33 ( 5 ), 462. [ PubMed ] [ Google Scholar ]
• Kiesler S, Siegel J, & McGuire TW (1984). Social psychological aspects of computer-mediated communication . American Psychologist , 39 , 1123–1134. [ Google Scholar ]
• Kross E, Verduyn P, Demiralp E, Park J, Lee DS, Lin N, … Ybarra O (2013). Facebook use predicts declines in subjective well-being in young adults . PloS one , 5 ( 8 ), e69841. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lai S, Nguyen V, & Theriault J (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first - episode psychosis . Early intervention in psychiatry , 72 ( 3 ), 324–330. [ PubMed ] [ Google Scholar ]
• Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, … Primack BA (2016). Association between social media use and depression among US young adults . Depression and anxiety , 22 ( 4 ), 323–331. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Machmutow K, Perren S, Sticca F, & Alsaker FD (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties , 17 ( 3-4 ), 403–420. [ Google Scholar ]
• Manikonda L, & De Choudhury M (2017). Modeling and understanding visual attributes of mental health disclosures in social media . Paper presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. [ Google Scholar ]
• Mead S, Hilton D, & Curtis L (2001). Peer support: A theoretical perspective . Psychiatric rehabilitation journal , 25 ( 2 ), 134. [ PubMed ] [ Google Scholar ]
• Mereish EH, Sheskier M, Hawthorne DJ, & Goldbach JT (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation . Culture, health & sexuality , 21 ( 9 ), 985–998. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miller BJ, Stewart A, Schrimsher J, Peeples D, & Buckley PF (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use . Psychiatry research , 225 ( 3 ), 458–463. [ PubMed ] [ Google Scholar ]
• Mittal VA, Tessner KD, & Walker EF (2007). Elevated social Internet use and schizotypal personality disorder in adolescents . Schizophrenia research , 94 ( 1-3 ), 50–57. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, & Hoving C (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication . Journal of medical Internet research , 15 ( 4 ), e85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, & Aschbrenner KA (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness . Psychiatric Services , appi. ps. 201800520. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, & Bartels SJ (2016). How people living with serious mental illness use smartphones, mobile apps, and social media . Psychiatric rehabilitation journal , 39 ( 4 ), 364–367. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, & Bartels SJ (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness . Digital health , 2 , 2055207616654822. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, & Bartels SJ (2016b). The future of mental health care: peer-to-peer support and social media . Epidemiology and Psychiatric Sciences , 25 ( 2 ), 113–122. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, Marsch LA, McHugo GJ, & Bartels SJ (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study . Psychiatric Quarterly , 59 ( 1 ), 81–94. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Aschbrenner KA, McHugo GJ, Unutzer J, Marsch LA, & Bartels SJ (2017). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness . Early intervention in psychiatry . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Gonsalves PP, Gruebner O, Pendse SR, Smith SL, Sharma A, & Raviola G (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention . Current Treatment Options in Psychiatry , 1–15. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Naslund JA, Grande SW, Aschbrenner KA, & Elwyn G (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube . PloS one , 9 ( 10 ), e110171. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Onnela J-P, & Rauch SL (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health . Neuropsychopharmacology , 41 ( 7 ), 1691. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Orben A, & Przybylski AK (2019). The association between adolescent well-being and digital technology use . Nature Human Behaviour , 3 ( 2 ), 173. [ PubMed ] [ Google Scholar ]
• Patel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, … Eaton J (2018). The Lancet Commission on global mental health and sustainable development . The Lancet . [ PubMed ] [ Google Scholar ]
• Primack BA, Shensa A, Escobar-Viera CG, Barrett EL, Sidani JE, Colditz JB, & James AE (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults . Computers in Human Behavior , 69 , 1–9. [ Google Scholar ]
• Reece AG, & Danforth CM (2017). Instagram photos reveal predictive markers of depression . EPJ Data Science , 6 ( 1 ), 15. [ Google Scholar ]
• Reece AG, Reagan AJ, Lix KL, Dodds PS, Danforth CM, & Langer EJ (2017). Forecasting the onset and course of mental illness with Twitter data . Scientific reports , 7 ( 1 ), 13006. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rideout V, & Fox S (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf [ Google Scholar ]
• Saha K, Torous J, Ernala SK, Rizuto C, Stafford A, & De Choudhury M (2019). A computational study of mental health awareness campaigns on social media . Translational behavioral medicine . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schlosser DA, Campellone T, Kim D, Truong B, Vergani S, Ward C, & Vinogradov S (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia . JMIR research protocols , 5 ( 2 ). [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schlosser DA, Campellone TR, Truong B, Etter K, Vergani S, Komaiko K, & Vinogradov S (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia . Schizophrenia bulletin , 44 ( 5 ), 1010–1020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schrank B, Sibitz I, Unger A, & Amering M (2010). How patients with schizophrenia use the internet: qualitative study . Journal of medical Internet research , 12 ( 5 ), e70. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schueller SM, Hunter JF, Figueroa C, & Aguilera A (2019). Use of digital mental health for marginalized and underserved populations . Current Treatment Options in Psychiatry , 6 ( 3 ), 243–255. [ Google Scholar ]
• Shatte AB, Hutchinson DM, & Teague SJ (2019). Machine learning in mental health: a scoping review of methods and applications . Psychological medicine , 49 ( 9 ), 1426–1448. [ PubMed ] [ Google Scholar ]
• Spinzy Y, Nitzan U, Becker G, Bloch Y, & Fennig S (2012). Does the Internet offer social opportunities for individuals with schizophrenia? a cross-sectional pilot study . Psychiatry research , 198 ( 2 ), 319–320. [ PubMed ] [ Google Scholar ]
• Stiglic N, & Viner RM (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews . BMJopen , 9 ( 1 ), e023191. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sumner SA, Galik S, Mathieu J, Ward M, Kiley T, Bartholow B, … Mork P (2019). Temporal and geographic patterns of social media posts about an emerging suicide game . Journal of Adolescent Health . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, Chan SR, Tan SY-M, Behrens J, Mathew I, Conrad EJ, … Keshavan M (2014). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics . JMIR mental health , 1 ( 1 ), e5. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, Friedman R, & Keshavan M (2014). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions . JMIR mHealth and uHealth , 2 ( 1 ), e2. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Torous J, & Keshavan M (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential . Current opinion in psychiatry , 29 ( 3 ), 190–195. [ PubMed ] [ Google Scholar ]
• Torous J, Wisniewski H, Bird B, Carpenter E, David G, Elejalde E, … Henson P (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach . Journal of Technology in Behavioral Science , 1–13. [ Google Scholar ]
• Trefflich F, Kalckreuth S, Mergl R, & Rummel-Kluge C (2015). Psychiatric patients’ internet use corresponds to the internet use of the general public . Psychiatry research , 226 , 136–141. [ PubMed ] [ Google Scholar ]
• Twenge JM, & Campbell WK (2018). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study . Preventive medicine reports , 12 , 271–283. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Twenge JM, Joiner TE, Rogers ML, & Martin GN (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time . Clinical Psychological Science , 6 ( 1 ), 3–17. [ Google Scholar ]
• Tynes BM, Willis HA, Stewart AM, & Hamilton MW (2019). Race-related traumatic events online and mental health among adolescents of color . Journal of Adolescent Health , 65 ( 3 ), 371–377. [ PubMed ] [ Google Scholar ]
• Vannucci A, Flannery KM, & Ohannessian CM (2017). Social media use and anxiety in emerging adults . Journal of affective disorders , 207 , 163–166. [ PubMed ] [ Google Scholar ]
• Vayreda A, & Antaki C (2009). Social support and unsolicited advice in a bipolar disorder online forum . Qualitative health research , 19 ( 7 ), 931–942. [ PubMed ] [ Google Scholar ]
• Ventola CL (2014). Social media and health care professionals: benefits, risks, and best practices . Pharmacy and Therapeutics , 39 ( 7 ), 491. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• We Are Social. (2020). Digital in 2020 . Retrieved from https://wearesocial.com/global-digital-report-2019
• Webb H, Jirotka M, Stahl BC, Housley W, Edwards A, Williams M, … Burnap P (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the Proceedings of the 2017 ACM on Web Science Conference. [ Google Scholar ]
• Williams ML, Burnap P, & Sloan L (2017). Towards an ethical framework for publishing Twitter data in social research: Taking into account users’ views, online context and algorithmic estimation . Sociology , 57 ( 6 ), 1149–1168. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Woods HC, & Scott H (2016). # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem . Journal of adolescence , 57 , 41–49. [ PubMed ] [ Google Scholar ]
• Ybarra ML (2004). Linkages between depressive symptomatology and Internet harassment among young regular Internet users . Cyber Psychology & Behavior , 7 ( 2 ), 247–257. [ PubMed ] [ Google Scholar ]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 22 May 2024

An empirical study on social network analysis for small residential communities in Gangwon State, South Korea

• Dae-hyun Jeong 1 ,
• Sang-Kyu Lee 2 ,
• Moo-Eob Ahn 2 ,
• Sang Mi Kim 3 ,
• Ohk-Hyun Ryu   ORCID: orcid.org/0000-0003-1118-6886 2 ,
• Kyung Suk Park 4 ,
• Se Gye Shin 4 &
• Jae Hyun Han 5  

Scientific Reports volume  14 , Article number:  11648 ( 2024 ) Cite this article

Metrics details

• Health care
• Medical research

Social Network Analysis (SNA) provides a dynamic framework for examining interactions and connections within networks, elucidating how these relationships impact behaviors and outcomes. This study targeted small residential communities in Gangwon State, South Korea, to explore network formation theories and derive strategies for enhancing health promotion services in rural communities. Conducted in 12 small residential areas, the survey led to a network categorization model distinguishing networks as formal, informal, or non-existent. Key findings demonstrated that demographic and socio-economic factors, specifically age, income, living environment, leisure activities, and education level, significantly influence network formation. Importantly, age, environmental conditions, satisfaction with public transportation, and walking frequency were closely associated with the evolution of formal networks. These results highlight the importance of early community network assessments, which must consider distinct network traits to develop effective health promotion models. Utilizing SNA early in the assessment process can improve understanding of network dynamics and optimize the effectiveness of health interventions.

Similar content being viewed by others

Revealing hazard-exposure heterophily as a latent characteristic of community resilience in social-spatial networks

Inequality is rising where social network segregation interacts with urban topology

The anatomy of a population-scale social network

Introduction.

Global trends show an increase in aging populations and urbanization 1 . In South Korea, particularly in rural areas, there is a notable presence of the elderly, aged 65 and over. These regions often exhibit lower socio-economic status, with reduced income and education levels, and a less healthy lifestyle characterized by lower rates of non-smoking, moderate drinking, and regular walking 2 , 3 . Rural communities face additional challenges such as sparse populations, vast geographical areas, inadequate public transportation, and limited health resources, deepening the health disparities with urban areas 4 , 5 . Furthermore, the Coronavirus Disease 2019 (COVID-19) pandemic has exacerbated social isolation and mental health issues like anxiety, depression, and stress due to reduced face-to-face interactions and weakened interpersonal bonds 6 , 7 .

Social Network Analysis (SNA) is a disciplined inquiry into the patterning of relations among social actors 8 . SNA enables the understanding of the structures and dynamics of relationships within social networks 9 . Additionally, SNA offers a robust framework for analyzing the interactions and connections within a network, revealing how these relationships influence behaviors and outcomes 10 , 11 . Effective network interventions highlight the strategic use of network insights from SNA to implement changes that foster stronger and more cohesive community ties 12 .

Collaborations with non-profit organizations and community networks are crucial for addressing diverse health and public service needs 13 , 14 , 15 . Such partnerships facilitate the sharing of resources and expertise, thus effectively meeting community demands, particularly in health promotion.

Previous studies on community networks have mainly focused on two aspects: the determinants of network formation and the dynamics of effective network structuring. Network formation is seen as crucial for addressing community needs, with ongoing research emphasizing the measurement of network centrality as a key factor in validating its importance. The primary aim of network formation is to enhance community problem-solving capabilities through collaborative efforts among members. Preliminary findings suggest that in larger networks, not all members are interconnected; typically, three to four subgroups are interlinked. While strong ties exist between these subgroups, intra-group connections tend to be weaker. This observation has led to the hypothesis that individuals with high centrality should build strong connections with leaders of other subgroups to ensure the efficient dissemination of critical information across the entire network, thereby enhancing service delivery throughout the community 10 , 12 , 14 , 16 , 17 .

The second research focus is on the sustainability of networks initially formed through personal ties. It has been proposed that these networks, while starting informally based on personal relationships, gradually become institutionalized over time. For such networks to remain sustainable, a balance between formal and informal structures is necessary 18 , 19 . However, empirical research on these dynamics is lacking in the South Korean regional context, which presents challenges in applying community-centered network theories and practices effectively.

To address these gaps, our study formulated and tested research questions derived from these two established perspectives. Firstly, we explored what factors influence community network formation in rural communities. Prior research suggests that community needs are met through network structuring; thus, we examined whether overall community satisfaction—including aspects like the living environment, public transportation, and leisure participation—affects network formation 13 , 14 , 18 , 20 , 21 , 22 . Secondly, we investigated the factors that contribute to the formalization of network structures. Following the insights of Goodman et al. 23 , who noted that younger networks based on personal relationships evolve into more institutionalized forms, we tested whether community satisfaction impacts this transition. Our findings aim to inform the development of a smart healthcare service that is tailored to the specific needs of the rural community.

Data collection

In this study, we implemented an empirical investigation of network formation in rural communities, focusing on 12 small residential communities in Gangwon State, South Korea. The research protocol was reviewed and approved by the Clinical Research Ethics Committee of Hallym University Chuncheon Sacred Heart Hospital (Approval Number: CHUNCHEON 2021-09-001-001) and was conducted in accordance with the Declaration of Helsinki (the Declaration’s principles ensure that research involving human subjects is conducted ethically, aligning with the values of respect for individuals, beneficence, and justice), which emphasizes ethical standards including informed consent and ethical treatment of research subjects.

The participants were adults aged 20 and above, residing in Gangwon State’s small rural residential communities. Eligibility criteria included an intent to use local public health facilities, willingness to engage with remote healthcare services, and the ability to comprehend and respond to the questionnaire effectively.

The survey ran from October to November 2021 and was completed by 206 participants. It covered various areas including health status self-assessment, medical history, socio-physical community environment, human network mapping, educational and economic activities, smoking and drinking habits, and assessments of physical and mental health. The questionnaire used in this study was based on the Korean Community Health Survey (KCHS) instrument, which is rigorously developed and widely used by government agencies to ensure comprehensive and reliable data collection. KCHS questionnaire is designed to collect detailed information on the health status, health behaviors, usage of health services, preventive health practices, environmental health, and healthcare utilization among the Korean population 24 .

To study human networks, we created and analyzed networks for individual communities based on initial survey data. We used a name generator method to identify members who recently interacted within each community, asking participants to list contacts from the last month to capture current and active connections 25 , 26 . We also had participants describe the nature of each interaction (familial, social, professional), helping to categorize and differentiate between formal and informal relationships. Our primary method was an egocentric network approach, using responses to map each participant's direct contacts and aggregating these to understand broader network dynamics. This method provides detailed insights into how individual interactions impact overall community structure. Based on this methodology, as illustrated in Fig.  1 , we created a co-occurrence matrix for the members and used it to implement a network for each community. One of our primary network measurement methods was calculating degree centrality values. Degree centrality, an indicator identifying key hubs within the network, shows that members with higher centrality values are crucial for facilitating information exchange and opinion sharing within the community.

Example of a network system. Based on a survey where individuals marked their acquaintances among neighbors, a co-occurrence matrix was created, which ultimately led to the construction of the network.

We constructed networks based on survey data to identify essential indicators and factors affecting network formation. For SNA, we utilized NetMiner 4.0 software (Cyram Inc., Seoul).

In this study, social networks were categorized as formal, informal, or non-existent based on specific criteria developed from both the literature and empirical observations in the field (Fig.  2 ). Formal networks are characterized by structured relationships often involving organizational roles, official memberships, or documented interactions. Informal networks, on the other hand, consist of spontaneous, less structured interactions based on personal relationships without formal roles. Networks were considered non-existent in communities where no significant interactions were observed among members beyond casual acquaintance. Specifically, communities where ordinary residents exhibit high centrality were identified as informal networks, whereas communities where individuals hold formal roles, such as village heads, were classified as formal networks. Areas lacking measurable network centrality were defined as having no formed network. Table 1 summarizes the network patterns and characteristics of each community. This classification is grounded in social network theory as discussed in works by authors such as Granovetter on the strength of weak ties 18 and Putnam 22 on the role of social capital (Supplementary Fig. 1 ).

Research framework. Based on the survey, basic statistical analysis was conducted and, as shown in Fig.  1 , network centrality was analyzed after the network implementation. Additionally, the forms of implemented networks (formal networks, non-formal networks, non-existing networks) were distinguished, and key indicators for each network, as well as major factors influencing the network, were analyzed.

Network analysis typically studies groups of actors, like individuals or organizations, across various domains, particularly in the social sciences. Multiple theories focus on network structures’ inherent characteristics. Identifying an area’s features based on its network type involves first establishing whether a ‘scale-free’ network exists. Introduced by Barabasi and Albert 27 , this model initially described internet web pages, suggesting that connections in random networks follow a power-law distribution, indicating hubs within the network, as shown in Fig.  3 .

Normal and scale-free networks. Scale-free networks possess a power law distribution form, and this form of power law distribution features network hubs, similar to those seen in aviation systems.

Scale-free networks are distinguished by their principles of growth and preferential attachment. The growth of such networks begins with a small number of nodes, expanding as new nodes are added. Preferential attachment refers to the tendency where new nodes are more likely to connect to nodes that are already well-connected. This phenomenon leads to the formation of network hubs, which are critical for determining the centrality within the network structure 27 .

Statistical analysis

The data were analyzed using IBM SPSS Statistics for Windows (Version 27; Armonk, NY, USA). All statistical results were derived from a two-sided test, and a P-value less than 0.05 was deemed statistically significant. Continuous variables are presented as mean and standard deviation, while categorical variables are expressed in terms of frequency and percentage. Multiple logistic regression analysis was utilized to explore the factors influencing network formation and formalization within the small living area communities of Gangwon State, Korea.

Table 2 outlines the demographics of the 201 study participants, who had an average age of 62.9 years. Approximately half were aged 65 or older, 75% were female, and 40% met the Asia–Pacific criteria for obesity based on their body mass index. Furthermore, 75% of the participants were married. Our basic statistical analysis showed that communities with established networks typically had older residents, lower income levels, reduced satisfaction with the local living environment and public transportation, and generally lower education levels. Interestingly, these communities displayed a higher engagement in recent weight management efforts (see Table 3 ).

In our logistic regression analysis, which aimed to identify factors influencing network formation, age, income, satisfaction with living and transportation conditions, leisure activity participation, and educational level were all found to be significant. Notably, dissatisfaction with community living conditions and public transportation significantly influenced the likelihood of network formation (Table 4 ).

Further statistical analysis, segmented by group, revealed that the formal network group had the highest average age and the lowest levels of satisfaction with their living environment and public transportation. While their engagement in leisure activities was comparatively lower than other groups, their commitment to health-promoting activities, such as frequent walking, was more pronounced (Table 5 ).

Our logistic regression analysis to determine factors influencing the formalization of networks within these communities found age, satisfaction with living environment and public transportation, and average walking days per week to be influential. Particularly, marked dissatisfaction with the community's living environment and public transportation significantly impacted the formalization of networks (Table 6 ).

In our study, we focused on 12 regions in Gangwon State to empirically investigate network formation within rural communities. We found that factors such as age, income, living environment, leisure activities, and education level influence network creation. Specifically, age, living environment, satisfaction with public transportation, and walking frequency were linked to the evolution of formal networks. Our findings indicate that areas with greater community needs, especially those less satisfied with basic community services, tend to form networks more actively. Notably, regions with limited capacities exhibited a stronger tendency to form networks, presumably to compensate for these deficiencies.

Our findings align with previous research, indicating that social networks play a critical role in health promotion and community resilience. As Fernández-Peña et al. highlighted in their systematic scoping review, SNA is invaluable for understanding social support dynamics within communities, particularly in how these networks provide care and facilitate health-related interventions 28 . This is particularly relevant to our study, as we explored the mechanisms through which network structure affects health services uptake in rural areas of Gangwon State.

Additionally, the systematic review and meta-analysis by Hunter et al. underscores the efficacy of leveraging network properties to enhance health behaviors and outcomes 29 . Our study contributes to this body of knowledge by demonstrating that network centrality and the formalization of networks can significantly impact community health practices, echoing Hunter et al.’s findings on the potential of structured network interventions.

Furthermore, Nickel and Knesebeck's research on community-based health interventions reveals the complexity of addressing health inequalities through networked community efforts 30 . Our research supports their conclusion that multi-faceted community interventions can effectively tackle health disparities, particularly when they leverage formal and informal networks to enhance service delivery and community engagement.

Lastly, Wolbring et al. provide a detailed analysis of community sports networks, which we found analogous to our study’s focus on health promotion networks. Their analysis of structural properties and cooperation conditions within networks offers valuable parallels to our findings, where the structural aspects of networks (e.g., centrality, tie formation) were crucial in mediating health outcomes 31 . By drawing on these parallels, we further substantiate our model of network-driven health promotion.

Previous research suggests that networks often formalize to ensure sustainability 20 , 21 . Our empirical data corroborates this, showing that areas with formal networks, as opposed to those formed through personal connections, generally reported lower satisfaction with critical community services. Additionally, these regions with formal networks often involved members who had limited individual capacities.

This study primarily involved participants aged 65 and above from rural community areas. Therefore, generalizing these findings to individuals under 65 or those residing in urban communities may be limited. Additionally, the selection of research areas and participants was not conducted through randomization. Instead, the study specifically targeted residents who showed an interest in non-face-to-face health management services and those who were connected to public health medical institutions within their communities. Therefore, it's important to acknowledge that the network analysis was predominantly conducted among residents with a heightened interest in health management and an inclination towards active participation in local public health initiatives. This targeted approach may influence the interpretation and applicability of the research results, as they may not reflect the perspectives or behaviors of a broader, more diverse population.

Our study revealed that networks in small living areas are formed to improve community living environment, especially in regions where members have limited individual capacities. We observed that influential community members, who play central roles in these networks, are vital for effectively disseminating healthcare information and services. Interestingly, areas with formal networks demonstrated better outcomes in recent health-promoting efforts compared to those without such networks. By integrating our findings with insights from related studies, we enhance the foundation of our network approach to improving health in rural communities. This discussion not only confirms the importance of our research but also highlights how using SNA can lead to innovative ways of delivering health services in rural area.

In our study, we conducted a community survey across 12 small living areas in Gangwon State, utilizing social network analysis (SNA) to identify and characterize the networks present within each rural community. Our findings indicate that factors such as age, income, satisfaction with living conditions and public transportation, leisure activity participation, and education level significantly influence the formation of these networks. Furthermore, aspects like age, satisfaction with living conditions and public transportation, and the frequency of walking per week were closely linked to the formalization of these networks. However, the acute shortage of community health and medical resources in Gangwon State underscores the urgent need for efficient healthcare service provision. SNA emerges as a pivotal tool in this context, providing insights into community member relationships and interactions.This analytical approach is instrumental in developing and evaluating health promotion programs that foster active participation from residents and in understanding the mechanisms and impacts of various interventions.

Looking ahead, it is imperative for public healthcare services targeting small residential communities to incorporate preliminary assessments of community networks. This approach necessitates the development of healthcare service models that duly consider the unique characteristics of these community networks. Additionally, employing SNA in post-intervention evaluations can significantly aid in elucidating the mechanisms and effects of these health initiatives, thereby enhancing their effectiveness and relevance.

Data availability

All data and materials will be provided upon individual requests to the corresponding author.

Leeson, G. W. The growth, ageing and urbanisation of our world. J. Popul. Ageing 11 , 107–115 (2018).

Article   Google Scholar  

Ha, K., Song, Y. J. & Kim, H. Regional disparities in the associations of cardiometabolic risk factors and healthy dietary factors in Korean adults. Nutr. Res. Pract. 14 , 519–531 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Azul, A. M. et al. Unhealthy lifestyles, environment, well-being and health capability in rural neighbourhoods: A community-based cross-sectional study. BMC Public Health 21 , 1–15 (2021).

Chung, S. et al. Urban and rural differences in the prevalence of gender and age specific obesity and related health behaviors in Korea. J. Korean Med. Sci. 20 , 713–720 (2005).

Article   PubMed   PubMed Central   Google Scholar  

Kim, H. S. et al. Comparison between urban and rural mortality in patients with acute myocardial infarction: A nationwide longitudinal cohort study in South Korea. BMJ Open 10 , e035501 (2020).

Clemente-Suárez, V. J. et al. Impact of COVID-19 pandemic in public mental health: An extensive narrative review. Sustainability 3 , 3221 (2021).

Di Fazio, N. et al. Mental health consequences of COVID-19 pandemic period in the European population: An institutional challenge. Int. J. Environ. Res. Public Health 19 , 9347 (2022).

Breiger, R. L. The duality of persons and groups. Soc. Forces 53 , 181–190 (1974).

Scott, J. Social Network Analysis (SAGE Publications Ltd, 2017).

Book   Google Scholar  

Borgatti, S. P., Everett, M. G. & Johnson, J. C. Analyzing Social Networks (SAGE Publications Ltd, 2018).

Google Scholar  

Prell, C. Social Network Analysis: History, Theory, and Methodology (SAGE Publications Ltd, 2012).

Valente, T. W. Network interventions. Science 337 , 49–53 (2012).

Article   ADS   CAS   PubMed   Google Scholar  

Baker, E. L. et al. Health reform and the health of the public: Forging community health partnerships. JAMA 272 , 1276–1282 (1994).

Article   CAS   PubMed   Google Scholar  

Israel, B. A. et al. Review of community-based research: Assessing partnership approaches to improve public health. Annu. Rev. Public Health 19 , 173–202 (1998).

Provan, K. G. & Milward, H. B. Do networks really work? A framework for evaluating public sector organizational networks. Public Admin. Rev. 61 (4), 400–409 (2001).

O’Toole, L. J. Jr. Treating networks seriously: Practical and research-based agendas in public administration. Public Admin. Rev. 57 , 45–52 (1997).

Chisholm, R. F. Developing Network Organizations: Learning from Practice and Theory (Addison-Wesley, 1997).

Granovetter, M. The strength of weak ties: A network theory revisited. Sociol. Theory 1 , 201–233 (1983).

Powell, W. W. et al. Neither market nor hierarchy: Network forms of organization. Res. Organ. Behav. 12 , 295–336 (1990).

Agranoff, R. Human services integration: Past and present challenges in public administration. Public Admin. Rev. 51 , 533–542 (1991).

Jennings, E. T. Jr. & Ewalt, J. A. G. Interorganizational coordination, administrative consolidation, and policy performance. Public Admin. Rev. 58 , 417–428 (1998).

Putnam, R. Making Democracy Work: Civic Traditions in Modern Italy (Princeton University Press, 1993).

Goodman, R. M. et al. Identifying and defining the dimensions of community capacity to provide a basis for measurement. Health Educ. Behav. 25 , 258–278 (1998).

Kang, Y. W. Korea community health survey data profiles. Osong Public Health Res. Perspect. 6 , 211–217 (2015).

Marsden, P. V. Network data and measurement. Annu. Rev. Sociol. 16 , 435–463 (1990).

McCarty, C., Bernard, H. R., Killworth, P. D., Shelley, G. A. & Johnsen, E. C. Eliciting representative samples of personal networks. Soc. Netw. 19 (4), 303–323 (1997).

Barabasi, A. L. & Albert, R. Emergence of scaling in random networks. Science 286 , 509–512 (1999).

Article   ADS   MathSciNet   CAS   PubMed   Google Scholar  

Fernández-Peña, R. et al. The use of social network analysis in social support and care: A systematic scoping review protocol. Syst. Rev. 11 , 9 (2022).

Hunter, R. F. Social network interventions for health behaviours and outcomes: A systematic review and meta-analysis. PLoS Med. 16 , e1002890 (2022).

Nickel, S. Do multiple community-based interventions on health promotion tackle health inequalities?. Int. J. Equity Health 19 , 157 (2020).

Wolbring, L. et al. Community networks of sport and physical activity promotion: An analysis of structural properties and conditions of cooperation. BMC Public Health 22 , 1966 (2022).

Download references

Acknowledgements

We would like to express our gratitude to the health officials of the Gangwon state public health clinics and the staffs at the telemedicine center of Hallym University for their dedication in making this research possible.

This research was supported by a grant of the Korea Health Promotion R&D Project, funded by the Ministry of Health & Welfare, Republic of Korea (grant number : HS21C0170).

Author information

Authors and affiliations.

Gangwon Institute, Chuncheon, Republic of Korea

Dae-hyun Jeong

Hallym University College of Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, Republic of Korea

Sang-Kyu Lee, Moo-Eob Ahn & Ohk-Hyun Ryu

Department of AI Health Information Management, Yonsei University, Wonju, Republic of Korea

Sang Mi Kim

Industry Academic Cooperation Foundation, Hallym University, Chuncheon, Republic of Korea

Kyung Suk Park & Se Gye Shin

Gangwon Technopark, Chuncheon, Gangwon-do, Republic of Korea

Jae Hyun Han

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, Dae-hyun Jeong and Ohk-Hyun Ryu; methodology, Dae-hyun Jeong and Ohk-Hyun Ryu; data acquisition and preprocessing, Dae-hyun Jeong, Kyung Suk Park and Se Gye Shin; statistical analysis and interpretation, Dae-hyun Jeong, Sang-Kyu Lee, Moo-Eob Ahn, Sang Mi Kim and Jae Hyun Han; drafting the work or revision Dae-hyun Jeong and Ohk-Hyun Ryu; final approval of the manuscript, Dae-jeong Jeong and Ohk-Hyun Ryu. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ohk-Hyun Ryu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary figure 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jeong, Dh., Lee, SK., Ahn, ME. et al. An empirical study on social network analysis for small residential communities in Gangwon State, South Korea. Sci Rep 14 , 11648 (2024). https://doi.org/10.1038/s41598-024-62371-x

Download citation

Received : 26 January 2024

Accepted : 16 May 2024

Published : 22 May 2024

DOI : https://doi.org/10.1038/s41598-024-62371-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Community network
• Health promotion
• Social network analysis

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

• Open access
• Published: 02 February 2021
• Volume 25 , pages 971–993, ( 2023 )

Cite this article

You have full access to this open access article

• Yogesh K. Dwivedi 1 ,
• Elvira Ismagilova 2 ,
• Nripendra P. Rana 2 &
• Ramakrishnan Raman 3  

100k Accesses

66 Citations

4 Altmetric

Explore all metrics

Social media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

Similar content being viewed by others

Use of Social Media by b2b Companies: Systematic Literature Review and Suggestions for Future Research

An exploratory investigation of social media adoption by small businesses.

Using Social Media for Business: Tools, Benefits and Pitfalls

Avoid common mistakes on your manuscript.

1 Introduction

The Internet has changed social communications and social behaviour, which lead to the development of new forms of communication channels and platforms (Ismagilova et al. 2017 ). Social media plays an important part in the digital transformation of businesses (Kunsman 2018 ). Digital transformation refers to the globally accelerated process of technical adaptation by companies and communities as a result of digitalisation (Sivarajah et al. 2019 ; Westerman et al. 2014 ). Web is developed from a tool used to provide passive information into the collaborative web, which allows and encourages active user engagement and contribution. If before social networks were used to provide the information about a company or brand, nowadays businesses use social media in their marketing aims and strategies to improve consumers’ involvement, relationship with customers and get useful consumers’ insights (Alalwan et al. 2017 ). Business-to-consumer (B2C) companies widely use social media as part of their digital transformation and enjoy its benefits such as an increase in sales, brand awareness, and customer engagement to name a few (Barreda et al. 2015 ; Chatterjee and Kar 2020 ; Harrigan et al. 2020 ; Kamboj et al. 2018 ; Kapoor et al. 2018 ).

From a marketing and sales research perspective, social media is defined as “the technological component of the communication, transaction and relationship building functions of a business which leverages the network of customers and prospects to promote value co-creation” (Andzulis et al. 2012 p.308). Industrial buyers use social media for their purchase as they compare products, research the market and build relationships with salesperson (Itani et al. 2017 ). Social media changed the way how buyers and sellers interact (Agnihotri et al. 2016 ) by enabling open and broad communications and cooperation between them (Rossmann and Stei 2015 ). Social media is an important facilitator of relationships between a company and customers (Agnihotri et al. 2012 ; Tedeschi 2006 ). Customers are more connected to companies, which make them more knowledgable about product selection and more powerful in buyer-seller relationships (Agnihotri et al. 2016 ). Social media also helps companies to increase business exposure, traffic and providing marketplace insight (Agnihotri et al. 2016 ; Stelzner 2011 ). As a result, the use of social media supports business decision processes and helps to improve companies’ performance (Rossmann and Stei 2015 ).

Due to digitalisation customers are becoming more informed and rely less on traditional selling initiatives (Ancillai et al. 2019 ). Buyers are relying more on digital resources and their buying process more often involves the use of social media. For example, in the research B2B buyer survey, 82% of buyers stated that social media content has a significant impact on the purchase decision (Ancillai et al. 2019 ; Minsky and Quesenberry 2016 ). As a result, these changes in consumer behaviour place high pressure on B2B salespeople and traditional sales companies (Ancillai et al. 2019 ). By using evidence from major B2B companies and consultancy report some studies claim that social media can be applied in sales to establish effective dialogues with buyers (Ancillai et al. 2019 ; Kovac 2016 ; McKinsey and Company 2015 ).

Now, business-to-business (B2B) companies started using social media as part of their digital transformation. 83% of B2B companies use social media, which makes it the most common marketing tactic (Pulizzi and Handley 2017 ; Sobal 2017 ). More than 70% of B2B companies use at least one of the “big 4” social media sites such as LinkedIn, Twitter, Facebook and YouTube. Additionally, 50% of the companies stated that social media has improved their marketing optimization and customer experience, while 25% stated that their revenue went up (Gregorio 2017 ; Sobal 2017 ). Even though B2B companies are benefitting from social media used by marketers, it is argued that research on that area is still in the embryonic stage and future research is needed (Salo 2017 ; Siamagka et al. 2015 ; Juntunen et al. 2020 ; Iannacci et al. 2020 ). There is a limited understanding of how B2B companies need to change to embrace recent technological innovations and how it can lead to business and societal transformation (Chen et al. 2012 ; Loebbecke and Picot 2015 ; Pappas et al. 2018 ).

The topic of social media in the context of B2B companies has started attracting attention from both academics and practitioners. This is evidenced by the growing number of research output within academic journals and conference proceedings. Some studies provided a comprehensive literature review on social media use by B2B companies (Pascucci et al. 2018 ; Salo 2017 ), but focused only on adoption of social media by B2B or social media influence, without providing the whole picture of the use of social media by B2B companies. Thus, this study aims to close this gap in the literature by conducting a comprehensive analysis of the use of social media by B2B companies and discuss its role in the digital transformation of B2B companies. The findings of this study can provide an informative framework for research on social media in the context of B2B companies for academics and practitioners.

The remaining sections of the study are organised as follows. Section 2 offers a brief overview of the methods used to identify relevant studies to be included in this review. Section 3 synthesises the studies identified in the previous section and provides a detailed overview. Section 4 presents weight analysis and its findings. Next section discusses the key aspects of the research, highlights any limitations within existing studies and explores the potential directions for future research. Finally, the paper is concluded in Section 6 .

2 Literature Search Method

The approach utilised in this study aligns with the recommendations in Webster and Watson ( 2002 ). This study used a keyword search-based approach for identifying relevant articles (Dwivedi et al. 2019b ; Ismagilova et al. 2020a ; Ismagilova et al. 2019 ; Jeyaraj and Dwivedi 2020 ; Williams et al. 2015 ). Keywords such as “Advertising” OR “Marketing” OR “Sales” AND TITLE (“Social Media” OR “Web 2.0” OR “Facebook” OR “LinkedIn” OR “Instagram” OR “Twitter” OR “Snapchat” OR “Pinterest” OR “WhatsApp” OR “Social Networking Sites”) AND TITLE-ABS-KEY (“B2B” OR “B to B” OR “Business to Business” OR “Business 2 Business”) were searched via the Scopus database. Scopus database was chosen to ensure the inclusion of only high quality studies. Use of online databases for conducting a systematic literature review became an emerging culture used by a number of information systems research studies (Dwivedi et al. 2019a ; Gupta et al. 2019 ; Ismagilova et al. 2020b ; Muhammad et al. 2018 ; Rana et al. 2019 ). The search resulted in 80 articles. All studies were processed by the authors in order to ensure relevance and that the research offered a contribution to the social media in the context B2B discussion. The search and review resulted in 70 articles and conference papers that formed the literature review for this study. The selected studies appeared in 33 separate journals and conference proceedings, including journals such as Industrial Marketing Management, Journal of Business and Industrial Marketing and Journal of Business Research.

3 Literature Synthesis

The studies on social media research in the context of B2B companies were divided into the following themes: effect of social media, adoption of social media, social media strategies, social media use, measuring the effectiveness of use of social media, and social media tools (see Table 1 ). The following subsections provide an overview of each theme.

3.1 Effect of Social Media

Some studies focus on the effect of social media for B2B companies, which include customer satisfaction, value creation, intention to buy and sales, building relationships with customers, brand awareness, knowledge creation, perceived corporate credibility, acquiring of new customers, salesperson performance, employee brand engagement, and sustainability (Table 2 ).

3.1.1 Customer Satisfaction

Some studies investigated how the use of social media affected customer satisfaction (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ). For example, Agnihotri et al. ( 2016 ) investigated how the implementation of social media by B2B salesperson affects consumer satisfaction. Salesperson’s social media use is defined as a “salesperson’s utilization and integration of social media technology to perform his or her job” (Agnihotri et al. 2016 , p.2). The study used data from 111 sales professionals involved in B2B industrial selling to test the proposed hypotheses. It was found that a salesperson’s use of social media will have a positive effect on information communication, which will, in turn, lead to improved customer satisfaction with the salesperson. Also, it was investigated that information communication will be positively related to responsiveness, which impacts customer satisfaction.

Another study by Rossmann and Stei ( 2015 ) looked at the antecedents of social media use, social media use by B2B companies and their effect on customers. By using data from 362 chief information officers of B2B companies the study found the following. Social media usage of sales representative has a positive impact on customer satisfaction. Age has a negative effect on content generation. It seems that older salespeople use social media in passive ways or interacting with the customer rather than creating their own content. It was found that the quality of corporate social media strategy has a positive impact on social media usage in terms of the consumption of information, content generation, and active interaction with customers. Also, the expertise of a salesperson in the area of social media has a positive impact on social media usage.

3.1.2 Value Creation

Research in B2B found that social media can create value for customers and salesperson (Agnihotri et al. 2012 ; Agnihotri et al. 2017 ). Agnihotri et al. ( 2012 ) proposed a theoretical framework to explain the mechanisms through which salespeople’s use of social media operates to create value and propose a strategic approach to social media use to achieve competitive goals. The study draws on the existing literature on relationship marketing, task–technology fit theory, and sales service behavior to sketch a social media strategy for business-to-business sales organizations with relational selling objectives. The proposed framework describes how social media tools can help salespeople perform service behaviors (information sharing, customer service, and trust-building) leading to value creation.

Some researchers investigated the role of the salesperson in the value creation process after closing the sale. By employing salesperson-customer data within a business-to-business context, Agnihotri et al. ( 2017 ) analysed the direct effects of sales-based CRM technology on the post-sale service behaviors: diligence, information communication, inducements, empathy, and sportsmanship. Additionally, the study examines the interactive effects of sales-based CRM technology and social media on these behaviors. The results indicate that sales-based CRM technology has a positive influence on salesperson service behaviors and that salespeople using CRM technology in conjunction with social media are more likely to exhibit higher levels of SSBs than their counterparts with low social media technology use. Data were collected from 162 salespeople from India. SmartPLS was used to analyse the data.

3.1.3 Intention to Buy and Sales

Another group of studies investigated the effect of social media on the level of sales and consumer purchase intention (Ancillai et al. 2019 ; Itani et al. 2017 ; Salo 2017 ; Hsiao et al. 2020 ; Mahrous 2013 ). For example, Itani et al. ( 2017 ) used the theory of reasoned actions to develop a model that tests the factors affecting the use of social media by salesperson and its impact. By collecting data from 120 salespersons from different industries and using SmartPLS to analyse the data, it was found that attitude towards social media usefulness did not affect the use of social media. It was found that social media use positively affects competitive intelligence collection, adaptive selling behaviour, which in turn influenced sales performance. Another study by Ancillai et al. ( 2019 ) used in-depth interviews with social selling professionals. The findings suggest that the use of social media improves not only the level of sales but also affects relationship and customer performance (trust, customer satisfaction, customer referrals); and organisational performance (organisational selling performance and brand performance).

It was investigated that social media has a positive effect on the intention to purchase (Hsiao et al. 2020 ; Mahrous 2013 ). For instance, Mahrous ( 2013 ) by reviewing the literature on B2B and B2C companies concluded that social media has a significant influence on consumer buying behaviour.

3.1.4 Customer Relationships

Another group of studies focused on the effect of social media on customer relationships (Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ; Iankova et al. 2018 ; Jussila et al. 2011 ; Kho 2008 ; Niedermeier et al. 2016 ; Ogilvie et al. 2018 ). For example, Bhattacharjya and Ellison ( 2015 ) investigated the way companies build relationships with customers by using responsive customer relationship management. The study analysed customer relationship management activities from Twitter account of a Canadian company Shopify (B2B service provider). The company uses Twitter to engage with small business customers, develops and consumers. Jussila et al. ( 2011 ), by reviewing the literature, found that social media leads to increased customer focus and understanding, increased level of customer service and decreased time-to-market.

Gáti et al. ( 2018 ) focused their research efforts on social media use in customer relationship performance, particularly in customer relations. The study investigated the adoption and impact of social media by salespeople of B2B companies. By using data of 112 salespeople from several industries the study found that the intensity of technology use positively affects attitude towards social media, which positively affects social media use. Intensive technology use in turn positively affects customer relationship performance (customer retention). PLS-SEM was applied for analysis.

Another study by Gruner and Power ( 2018 ) investigated the effectiveness of the use of multiple social media platforms in communications with customers. By using data from 208 large Australian organisations, the paper explores how companies’ investment in one form of social media impacts activity on another form of social media. A regression analysis was performed to analyse the data. It was found that widespread activities on LinkedIn, Twitter and YouTube have a negative effect on a company’s marketing activity on Facebook. Thus, having it is more effective for the company to focus on a specific social media platform in forming successful inter-organisational relationships with customers.

Hollebeek ( 2019 ) proposed an integrative S-D logic/resource-based view (RBV) model of customer engagement. The proposed model considers business customer actors and resources in driving business customer resource integration, business customer resource integration effectiveness and business customer resource integration efficiency, which are antecedents of business customer engagement. Business customer engagement, in turn, results in business customer co-creation and relationship productivity.

Niedermeier et al. ( 2016 ) investigated the use of social media among salespeople in the pharmaceutical industry in China. Also, the study investigated the impact of social media on building culturally specific Guanxi relationships-it involves the exchange of factors to build trust and connection for business purpose. By using in-depth interviews with 3 sales managers and a survey of 42 pharmaceutical sales representatives that study found that WeChat is the most common social media platform used by businesses. Also, it was found to be an important tool in building Guanxi. Future studies should focus on other industries and other types of cultural features in doing business.

Ogilvie et al. ( 2018 ) investigated the effect of social media technologies on customer relationship performance and objective sales performance by using two empirical studies conducted in the United States. The first study used 375 salespeople from 1200 B2B companies. The second study used 181 respondents from the energy solution company. It was found that social media significantly affects salesperson product information communication, diligence, product knowledge and adaptability, which in turn affect customer relationship performance. It was also found that the use of social media technologies without training on technology will not lead to good results. Thus, the results propose that companies should allocate the resources required for the proper implementation of social media strategies. Future research should examine how the personality traits of a salesperson can moderate the implementation of social media technologies.

While most of the studies focused on a single country, Iankova et al. ( 2018 ) investigated the perceived effectiveness of social media by different types of businesses in two countries. By using 449 respondents from the US and the UK businesses, it was found that social media is potentially less important, at the present time, for managing ongoing relationships in B2B organizations than for B2C, Mixed or B2B2C organizations. All types of businesses ascribe similar importance to social media for acquisition-related activities. Also it was found that B2B organizations see social media as a less effective communication channel, and to have less potential as a channel for the business.

3.1.5 Brand Awareness

Some researchers argued that social media can influence brand awareness (Ancillai et al. 2019 ; Hsiao et al. 2020 ). For instance, Hsiao et al. ( 2020 ) investigated the effect of social media in the fashion industry. By collecting 1395 posts from lookbook.nu and employing regression analysis it was found that the inclusion of national brand and private fashion brands in the post increased the level of popularity which leads to purchasing interest and brand awareness.

3.1.6 Knowledge Creation

Multiple types of collaborative web tools can help and significantly increase the collaboration and the use of the distributed knowledge inside and outside of the company (McAfee 2006 ). Kärkkäinen et al. ( 2011 ) by analysing previous literature on social media proposed that social media use has a positive effect on sharing and creation of customer information and knowledge in the case of B2B companies.

3.1.7 Corporate Credibility

Another study by Kho ( 2008 ) states the advantages of using social media by B2B companies, which include faster and more personalised communications between customer and vendor, which can improve corporate credibility and strengthen the relationships. Thanks to social media companies can provide more detailed information about their products and services. Kho ( 2008 ) also mentions that customer forums and blog comments in the B2B environment should be carefully monitored in order to make sure that inappropriate discussions are taken offline and negative eWOM communications should be addressed in a timely manner.

3.1.8 Acquiring New Customers

Meire et al. ( 2017 ) investigated the impact of social media on acquiring B2B customers. By using commercially purchased prospecting data, website data and Facebook data from beverage companies the study conducted an experiment and found that social media us an effective tool in acquiring B2B customers. Future work might assess the added value of social media pages for profitability prediction instead of prospect conversion. When a longer timeframe becomes available (e.g., after one year), the profitability of the converted prospects can be assessed.

3.1.9 Salesperson Performance

Moncrief et al. ( 2015 ) investigated the impact of social media technologies on the role of salesperson position. It was found that social media affects sales management functions (supervision, selection, training, compensation, and deployment) and salesperson performance (role, skill, and motivation). Another study by Rodriguez et al. ( 2012 ) examines the effect of social media on B2B sales performance by using social capital theory and collecting data from 1699 B2B salespeople from over 25 different industries. By employing SEM AMOS, the study found that social media usage has a positive significant relationship with selling companies’ ability to create opportunities and manage relationships. The study also found that social media usage has a positive and significant relationship with sales performance (based on relational measurers of sales that focus on behaviours that strengthen the relationship between buyers and sellers), but not with outcome-based sales performance (reflected by quota achievement, growth in average billing size, and overall revenue gain).

3.1.10 Employee Brand Management

The study by Pitt et al. ( 2018 ) focuses on employee engagement with B2B companies on social media. By using results from Glassdoor (2315 five-star and 1983 one-star reviews for the highest-ranked firms, and 1013 five star and 1025 one-star reviews for lowest ranked firms) on employee brand engagement on social media, two key drivers of employee brand engagement by using the content analysis tool DICTION were identified-optimism and commonality. Individuals working in top-ranked companies expressed a higher level of optimism and commonality in comparison with individuals working in low-ranked companies. As a result, a 2 × 2 matrix was constructed which can help managers to choose strategies in order to increase and improve employee brand engagement. Another study by Pitt et al. ( 2017 ) focused on employee engagement of B2B companies on social media. By using a conceptual framework based on a theory of word choice and verbal tone and 6300 reviews collected from Glassdoor and analysed using DICTION. The study found that employees of highly ranked B2B companies are more positive about their employer brand and talk more optimistically about these brands. For low ranked B2B companies it was found that employees express a greater level of activity, certainty, and realism. Also, it was found that they used more aggressive language.

3.1.11 Sustainability

Sustainability refers to the strategy that helps a business “to meet its current requirements without compromising its ability to meet future needs” (World Commission Report on Environment and Development 1987 , p 41). Two studies out of 70 focused on the role of social media for B2B sustainability (Sivarajah et al. 2019 ; Kasper et al. 2015 ). For example, Sivarajah et al. ( 2019 ) argued that big data and social media within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities.

Another study by Kasper et al. ( 2015 ) proposed the Social Media Matrix which helps companies to decide which social media activities to execute based on their corporate and communication goals. The matrix includes three parts. The first part is focusing on social media goals and task areas, which were identified and matched. The second part consists of five types of social media activities (content, interaction/dialog, listening and analysing, application and networking). The third part provides a structure to assess the suitability of each activity type on each social media platform for each goal. The matrix was successfully tested by assessing the German B2B sector by using expert interviews with practitioners.

Based on the reviewed studies, it can be seen that if used appropriately social media have positive effect on B2B companies before and after sales, such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salesperson performance, employee brand management, and sustainability. However, limited research is done on the negative effect of social media on b2b companies.

3.2 Adoption of Social Media

Some scholars investigated factors affecting the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ; Lacka and Chong 2016 ). For instance, Lacka and Chong ( 2016 ) investigated factors affecting the adoption of social media by B2B companies from different industries in China. The study collected the data from 181 respondents and used the technology acceptance model with Nielsen’s model of attributes of system acceptability as a theoretical framework. By using SEM AMOS for analysis the study found that perceived usability, perceived usefulness, and perceived utility positively affect adoption and use of social media by B2B marketing professionals. The usefulness is subject to the assessment of whether social media sites are suitable means through which marketing activities can be conducted. The ability to use social media sites for B2B marketing purposes, in turn, is due to those sites learnability and memorability attributes.

Another study by Müller et al. ( 2018 ) investigated factors affecting the usage of social media. By using survey data from 100 Polish and 39 German sensor suppliers, it was found that buying frequency, the function of a buyer, the industry sector and the country does not affect the usage of social media in the context of sensor technology from Poland and Germany. The study used correlation analysis and ANOVA.

Lashgari et al. ( 2018 ) studied the adoption and use of social media by using face-to-face interviews with key managers of four multinational corporations and observations from companies’ websites and social media platforms. It was found that that the elements essential in forming the B2B firm’s social media adoption strategies are content (depth and diversity), corresponding social media platform, the structure of social media channels, the role of moderators, information accessibility approaches (public vs. gated-content), and online communities. These elements are customized to the goals and target group the firm sets to pursue. Similarly, integration of social media into other promotional channels can fall under an ad-hoc or continuous approach depending on the scope and the breadth of the communication plan, derived from the goal.

Similar to Lashgari et al. ( 2018 ), Shaltoni ( 2017 ) used data from managers. The study applied technology organisational environmental framework and diffusion of innovations to investigate factors affecting the adoption of social media by B2B companies. By using data from marketing managers or business owners of 480 SMEs, the study found that perceived relative advance, perceive compatibility, organizational innovativeness, competitor pressure, and customer pressure influence the adoption of social media by B2B companies. The findings also suggest that many decision-makers in B2B companies think that Internet marketing is not beneficial, as it is not compatible with the nature of B2B markets.

Buratti et al. ( 2018 ) investigated the adoption of social media by tanker shipping companies and ocean carriers. By using data from 60 companies the following was found. LinkedIn is the most used tool, with a 93.3% adoption rate. Firm size emerges as a predictor of Twitter’s adoption: big companies unveil a higher attitude to use it. Finally, the country of origin is not a strong influential factor in the adoption rate. Nonetheless, Asian firms clearly show a lower attitude to join SM tools such as Facebook (70%) and LinkedIn (86.7%), probably also due to governmental web restrictions imposed in China. External dimensions such as the core business, the firm size, the geographic area of origin, etc., seem to affect network wideness. Firm size, also, discriminates the capacity of firms to build relational networks. Bigger firms create networks larger than small firms do. Looking at geographical dimensions, Asian firms confirm to be far less active on SM respect to European and North American firms. Finally, the study analyzed the format of the contents disclosed by sample firms, observing quite limited use of photos and videos: in the sample industries, informational contents seem more appropriate for activating a dialogue with stakeholders and communication still appears formulated in a very traditional manner. Preliminary findings suggest that companies operating in conservative B2B services pursue different strategic approaches toward SMM and develop ad hoc communication tactics. Nonetheless, to be successful in managing SM tools, a high degree of commitment and a clear vision concerning the role of SM within communication and marketing strategy is necessary.

Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies and performing regression analysis, the following results were received. Years in business, new sales revenue, product type, amount of available information on a company website, perceived importance of e-commerce and perceived ease of use of social media significantly affected social media use. Also, it was found that companies’ strategies and internal resources and capabilities and influence a company’s decision to adopt social media. Also, it was found that 94 of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers. Facebook was found to be the most effective social media platform reported by the US forest industry.

The study by Kumar and Möller ( 2018 ) investigated the role of social media for B2B companies in their recruitment practices. By using data from international B2B company with headquarter in Helsinki, Finland comprised of 139 respondents it was found that brand familiarity encourages them to adopt social media platforms for a job search; however, the effect of the persuasiveness of recruitment messages on users’ adoption of social media platforms for their job search behavior is negative. The study used correlation analysis and descriptive analysis to analyse the data.

Nunan et al. ( 2018 ) identified areas for future research such as patterns of social media adoption, the role of social media platforms within the sales process, B2B consumer engagement and social media, modeling the ROI of social media, and the risks of social media within B2B sales relationships.

The study by Pascucci et al. ( 2018 ) conducted a systematic literature review on antecedents affecting the adoption and use of social media by B2B companies. By reviewing 29 studies published in academic journal and conferences from 2001 to 2017, the study identified external (pressure from customers, competitors, availability of external information about social media) and internal factors (personal characteristics -managers age, individual commitment, perceptions of social media-perceived ease of use, perceived usefulness, perceived utility), which can affect adoption of social media.

The study by Siamagka et al. ( 2015 ) aims to investigate factors affecting the adoption of social media by B2B organisations. The conceptual model was based on the technology acceptance model and the resource-based theory. AMOS software and Structural equation modelling were employed to test the proposed hypotheses. By using a sample of 105 UK companies, the study found that perceived usefulness of social media is influenced by image, perceived ease of use and perceived barriers. Also, it was found that social media adoption is significantly determined by organisational innovativeness and perceived usefulness. Additionally, the study tested the moderating role of organisational innovativeness and found that it does not affect the adoption of social media by B2B organisations. The study also identified that perceived barriers to SNS (uncertainty about how to use SNS to achieve objectives, employee’s lack of knowledge about SNS, high cost of investment needed to adopt the technology) have a negative impact on perceived usefulness of social media by B2B organisations. The study also used nine in-depth interviews with B2B senior managers and social media specialists about adoption of social media by B2B. It was found that perceived pressure from stakeholders influences B2B organisations’ adoption intention of social media. Future research should test it by using quantitative methods.

While most of the studies focused on the antecedents of social media adoption by B2B companies, Michaelidou et al. ( 2011 ) investigated the usage, perceived barriers and measuring the effectiveness of social media. By using data from 92 SMEs the study found that over a quarter of B2B SMEs in the UK are currently using SNS to achieve brand objectives, the most popular of which is to attract new customers. The barriers that prevent SMEs from using social media to support their brands were lack of staff familiarity and technical skills. Innovativeness of a company determined the adoption of social media. It was found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made. The findings showed that the size of the company does not influence the usage of social media for small and medium-sized companies. Future research should investigate the usage of social media in large companies and determine if the size can have and influence on the use. The benefits of using social media include increasing awareness and communicating the brand online. B2B companies can employ social media to create customer value in the form of interacting with customers, as well as building and fostering customer relationships. Future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Future research should also investigate how the attitude towards technology can influence the adoption of social media.

Based on the reviewed studies it can be seen that the main factors affecting the adoption of social media by B2B companies are perceived usability, technical skills of employees, pressure from stakeholders, perceived usefulness and innovativeness.

3.3 Social Media Strategies

Another group of studies investigated types of strategies B2B companies apply (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ; Swani et al. 2013 ; Swani et al. 2014 ; Swani et al. 2017 ; Watt 2010 ). For example, Cawsey and Rowley ( 2016 ) focused on the social media strategies of B2B companies. By conducting semi-structured interviews with marketing professionals from France, Ireland, the UK and the USA it was found that enhancing brand image, extending brand awareness and facilitating customer engagement were considered the most common social media objective. The study proposed the B2B social media strategy framework, which includes six components of a social media strategy: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media.

Chirumalla et al. ( 2018 ) focused on the social media engagement strategies of manufacturing companies. By using semi-structured interviews (36), observations (4), focus group meetings (6), and documentation, the study developed the process of social media adoption through a three-phase engagement strategy which includes coordination, cooperation, and co-production.

McShane et al. ( 2019 ) proposed social media strategies to influence online users’ engagement with B2B companies. Taking into consideration fluency lens the study analysed Twitter feeds of top 50 social B2B brands to examine the influence of hashtags, text difficulty embedded media and message timing on user engagement, which was evaluated in terms of likes and retweets. It was found that hashtags and text difficulty are connected to lower levels of engagement while embedded media such as images and videos improve the level of engagement.

Swani et al. ( 2014 ) investigate the use of Twitter by B2B and B2C companies and predict factors that influence message strategies. The study conducted a longitudinal content analysis by collecting 7000 tweets from Fortune 500 companies. It was found that B2B and B2C companies used different message appeals, cues, links and hashtags. B2B companies tend to use more emotional than functional appeals. It was found that B2B and B2C companies do not use hard-sell message strategies.

Another study by Swani et al. ( 2013 ) aimed to investigate message strategies that can help in promoting eWOM activity for B2B companies. By applying content analysis and hierarchical linear modeling the study analysed 1143 wall post messages from 193 fortune 500 Facebook accounts. The study found that B2B account posts will be more effective if they include corporate brand names and avoid hard sell or explicitly commercial statement. Also, companies should use emotional sentiment in Facebook posts.

Huotari et al. ( 2015 ) aimed to investigate how B2B marketers can influence content creation in social media. By conducting four face-to-face interviews with B2B marketers, it was found that a B2B company can influence content creation in social media directly by adding new content, participating in a discussion and removing content through corporate user accounts and controlling employees social media behaviour. Also, it can influence it indirectly by training employees to create desired content and perfuming marketing activities that influence other users to create content that is favorable for the company.

Most of the studies investigated the strategies and content of social media communications of B2B companies. However, the limited number of studies investigated the importance of CEO engagement on social media in the company’s strategies. Mudambi et al. ( 2019 ) emphasise the importance of the CEO of B2B companies to be present and active on social media. The study discusses the advantages of social media presence for the CEO and how it will benefit the company. For example, one of the benefits for the CEO can be perceived as being more trustworthy and effective than non-social CEOs, which will benefit the company in increased customer trust. Mudambi et al. ( 2019 ) also discussed the platforms the CEO should use and posting frequencies depending on the content of the post.

From the above review of the studies, it can be seen that B2B companies social media strategies include enhancing brand image, extending brand awareness and facilitating customer engagement. Companies use various message strategies, such as using emotional appeal, use of brand names, and use of hashtags. Majority of the companies avoid hard sell or explicitly commercial statement.

3.4 Social Media Use

Studies investigated the way how companies used social media and factors affecting the use of social media by B2B (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). For example, Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the posts had a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities.

Andersson and Wikström ( 2017 ) used case studies of three B2B companies to investigate reasons for using social media. It was found that companies use social media to enhance customer relationships, support sales and build their brands. Also, social media is used as a recruiting tool, a seeking tool, and a product information and service tool.

Bell and Shirzad ( 2013 ) aimed to conduct social media use analysis in the context of pharmaceutical companies. The study analysed 54,365 tweets from the top five pharmaceutical companies. The study analysed the popular time slots, the average number of positive and negative tweets and its content by using Nvivo9.

Bernard ( 2016 ) aims to examine how chief marketing officers use social media. By using case studies from IBM experience with social media it was found that B2B CMO’s are not ready to make use of social media. It was proposed that social media can be used for after-sales service, getting sales leads, engaging with key influencers, building the company’s reputation and enhancing the industry status of key individuals. B2B firms need to exploit the capabilities of processing massive amounts of data to get the most from social media.

Bolat et al. ( 2016 ) explore how companies apply mobile social media. By employing a grounded theory approach to analyse interviews from 26 B2B company representatives from UK advertising and marketing sector companies. It was found that companies use social media for branding, sensing market, managing relationships, and developing content.

Denktaş-Şakar and Sürücü ( 2018 ) investigated how social media usage influence stakeholder engagement focusing on the corporate Facebook page of 30 3PLs companies. In total 1532 Facebook posts were analysed. It was found that the number of followers, post sharing frequency, negatively affect stakeholder engagement. It was found that content including photos facilitates more stakeholder engagement (likes, comment, share) in comparison with other forms. Vivid posts and special day celebration posts strengthen relationships with stakeholders.

Dyck ( 2010 ) discussed the advantages of using social media for the device industry. Social media can be used for product innovation and development, to build a team and collaborate globally. Also, there is an opportunity to connect with all of the stakeholders needed in order to deliver the device to the market. Additionally, it provides to receive feedback from customers (doctors, hospitals) in real-time.

The study by Guesalaga ( 2016 ) draws on interactional psychology theory to propose and test a model of usage of social media in sales, analysing individual, organizational, and customer-related factors. It was found that organizational competence and commitment to social media are key determinants of social media usage in sales, as well as individual commitment. Customer engagement with social media also predicts social media usage in sales, both directly and (mostly) through the individual and organizational factors analysed, especially organizational competence and commitment. Finally, the study found evidence of synergistic effects between individual competence and commitment, which is not found at the organizational level. The data obtained by surveying 220 sales executives in the United States were analysed using regression analysis.

Habibi et al. ( 2015 ) proposed a conceptual model for the implementation of social media by B2B companies. Based on existing B2B marketing, social media and organisational orientational literature the study proposed that four components of electronic market orientation (philosophical, initiation, implementation and adoption) address different implementation issues faced in implementing social media.

Katona and Sarvary ( 2014 ) presented a case of using social media by Maersk-the largest container shipping company in the world. The case provided details on the program launch and the integration strategy which focused on integrating the largest independent social media operation into the company’s broader marketing efforts.

Moore et al. ( 2013 ) provided insights into the understanding of the use of social media by salespersons. 395 salespeople in B2B and B2C markets, utilization of relationship-oriented social media applications are presented and examined. Overall, findings show that B2B practitioners tend to use media targeted at professionals whereas their B2C counterparts tend to utilize more sites targeted to the general public for engaging in one-on-one dialogue with their customers. Moreover, B2B professionals tend to use relationship-oriented social media technologies more than B2C professionals for the purpose of prospecting, handling objections, and after-sale follow-up.

Moore et al. ( 2015 ) investigated the use of social media between B2B and B2C salespeople. By using survey data from 395 sales professionals from different industries they found that B2B sales managers use social selling tools significantly more frequently than B2C managers and B2C sales representatives while conducting sales presentations. Also, it was found that B2B managers used social selling tools significantly more frequently than all sales representatives while closing sales.

Müller et al. ( 2013 ) investigated social media use in the German automotive market. By using online analysis of 10 most popular car manufacturers online social networks and surveys of six manufacturers, 42 car dealers, 199 buyers the study found that social media communication relations are widely established between manufacturers and (prospective) buyers and only partially established between car dealers and prospective buyers. In contrast to that, on the B2B side, social media communication is rarely used. Social Online Networks (SONs) are the most popular social media channels employed by businesses. Manufacturers and car dealers focus their social media engagement, especially on Facebook. From the perspective of prospective buyers, however, forums are the most important source of information.

Sułkowski and Kaczorowska-Spychalska ( 2016 ) investigated the adoption of social media by companies in the Polish textile-clothing industry. By interviewing seven companies representatives of small and medium-sized enterprises the study found that companies started implementing social media activities in their marketing activities.

Vukanovic ( 2013 ) by reviewing previous literature on social media outlined advantages of using social media for B2B companies, which include: increase customer loyalty and trust, building and improving corporate reputation, facilitating open communications, improvement in customer engagement to name a few.

Keinänen and Kuivalainen ( 2015 ) investigated factors affecting the use of social media by B2B customers by conducting an online survey among 82 key customer accounts of an information technology service company. Partial least squares path modelling was used to analysed the proposed hypotheses. It was found that social media private use, colleague support for using SM, age, job position affected the use of social media by B2B customers. The study also found that corporate culture, gender, easiness to use, and perception of usability did not affect the use of social media by B2B customers.

By using interviews and survey social media research found that mostly B2B companies use social media to enhance customer relationships, support sales, build their brands, sense market, manage relationships, and develop content. Additionally, some companies use it social media as a recruitment tool. The main difference between B2B and B2C was that B2B sales managers use social selling tools significantly more frequently than B2C managers.

3.5 Measuring the Effectiveness of Social Media

It is important for a business to be able to measure the effectiveness of social media by calculating return on investment (ROI). ROI is the relationship between profit and the investment that generate that profit. Some studies focused on the ways B2B companies can measure ROI and the challenges they face (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). For example, Gazal et al. ( 2016 ) investigated the adoption and measuring of the effectiveness of social media in the context of the US forest industry by using organisational-level adoption framework and TAM. By using data from 166 companies it was found that 94% of respondents do not measure the ROI from social media use. The reason is that the use of social media in marketing is relatively new and companies do not possess the knowledge of measuring ROI from the use of social media. Companies mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with the key audience, audience participation, moving from monologue to dialogue with consumers).

Another study by Michaelidou et al. ( 2011 ) found that most of the companies do not evaluate the effectiveness of their SNS in supporting their brand. The most popular measures were the number of users joining the groups/discussion and the number of comments made.

Vasudevan and Kumar ( 2018 ) investigated how B2B companies use social media and measure ROI from social media by analysing 325 brand posts of Canon India, Epson India, and HP India on Linkedin, Facebook, and Twitter. By employing content analysis the study found that most of the post has a combination of text and message. More than 50% of the posts were about product or brand-centric. The study argued that likes proved to be an unreliable measure of engagement, while shares were considered a more reliable metric. The reason was that likes had high spikes when brand posts were boosted during promotional activities. Future research should conduct longitudinal studies.

By reviewing the above studies, it can be concluded that companies still struggle to find ways of measuring ROI and applying correct metrics. By gaining knowledge in how to measure ROI from social media activities, B2B companies will be able to produce valuable insights leading to better marketing strategies (Lal et al. 2020 ).

3.6 Social Media Tools

Some studies proposed tools that could be employed by companies to advance their use of social media. For example, Mehmet and Clarke ( 2016 ) proposed a social semiotic multimodal (SSMM) framework that improved the analysis of social media communications. This framework employs multimodal extensions to systemic functional linguistics enabling it to be applying to analysing non-language as well as language constituents of social media messages. Furthermore, the framework also utilises expansion theory to identify, categorise and analyse various marketing communication resources associated with marketing messages and also to reveal how conversations are chained together to form extended online marketing conversations. This semantic approach is exemplified using a Fairtrade Australia B2B case study demonstrating how marketing conversations can be mapped and analysed. The framework emphasises the importance of acknowledging the impact of all stakeholders, particularly messages that may distract or confuse the original purpose of the conversation.

Yang et al. ( 2012 ) proposed the temporal analysis technique to identify user relationships on social media platforms. The experiment was conducted by using data from Digg.com . The results showed that the proposed techniques achieved substantially higher recall but not very good at precision. This technique will help companies to identify their future consumers based on their user relationships.

Based on the literature review, it can be seen that B2B companies can benefit by using the discussed tools. However, it is important to consider that employee should have some technical skills and knowledge to use these tools successfully. As a result, companies will need to invest some resources in staff training.

4 Weight Analysis

Weight analysis enables scrutiny of the predictive power of independent variables in studied relationships and the degree of effectiveness of the relationships (Jeyaraj et al. 2006 ; Rana et al. 2015 ; Ismagilova et al. 2020a ). The results of weight analysis are depicted in Table 3 providing information about an independent variable, dependent variable, number of significant relationships, number of non-significant relationships, the total number of relationships and weight. To perform weight analysis, the number of significant relationships was divided by the total number of analysed relationships between the independent variable and the dependent variable (Jeyaraj et al. 2006 ; Rana et al. 2015 ). For example, the weight for the relationship between attitude towards social media and social media is calculated by dividing ‘1’ (the number of significant relationships) by ‘2’ (the total number of relationships) which equals 0.5.

A predictor is defined as well-utilised if it was examined five or more times, otherwise, it is defined as experimental. It can be seen from Table 3 that all relationships were examined less than five times. Thus all studied predictors are experimental. The predictor is defined as promising when it has been examined less than five times by existing studies but has a weight equal to ‘1’ (Jeyaraj et al. 2006 ). From the predictors affecting the adoption of social media, it can be seen that two are promising, technical skills of employees and pressure from stakeholders. Social media usage is a promising predictor for acquiring new customers, sales, stakeholder engagement and customer satisfaction. Perceived ease of use and age of salesperson are promising predictors of social media usage. Even though this relationship was found to be significant every time it was examined, it is suggested that this variable, which can also be referred to as experimental, will need to be further tested in order to qualify as the best predictor. Another predictor, average rating of product/service, was examined less than five times with a weight equal to 0.75, thus it is considered as an experimental predictor.

Figure 1 shows the diagrammatic representation of the factors affecting different relationships in B2B social media with their corresponding weights, based on the results of weight analysis. The findings suggest that promising predictors should be included in further empirical studies to determine their overall performance.

Diagrammatic representation of results of weight analysis. Note: experimental predictors

It can be seen from Fig. 1 that social media usage is affected by internal (e.g. attitude towards social media, technical skills of employees) and external factors (e.g. pressure from stakeholders) of the company. Also, the figure depicts the effect of social media on the business (e.g. sales) and society (e.g. customer satisfaction).

5 Discussion

In reviewing the publications gathered for this paper, the following themes were identified. Some studies investigated the effect of social media use by B2B companies. By using mostly survey to collect the data from salespeople and managers, the studies found that social media has a positive effect on number of outcomes important for the business such as customer satisfaction, value creation, intention to buy and sales, customer relationships, brand awareness, knowledge creation, corporate credibility, acquiring new customers, salespersons performance, employee brand management, and sustainability. Most of the outcomes are similar to the research on social media in the context of B2C companies. However, some of the outcomes are unique for B2B context (e.g. employee brand management, company credibility). Just recently, studies started investigating the impact of the use of social media on sustainability.

Another group of studies looked at the adoption of social media by B2B companies (Buratti et al. 2018 ; Gáti et al. 2018 ; Gazal et al. 2016 ; Itani et al. 2017 ; Kumar and Möller 2018 ). The studies investigated it mostly from the perspectives of salespersons and identify some of the key factors which affect the adoption, such as innovativeness, technical skills of employees, pressure from stakeholders, perceived usefulness, and perceived usability. As these factors are derived mostly from surveys conducted with salespersons findings can be different for other individuals working in the organisation. This it is important to conduct studies that will examine factors affecting the adoption of social media across the entire organisation, in different departments. Using social media as part of the digital transformation is much bigger than sales and marketing, it encompasses the entire company. Additionally, most of the studies were cross-sectional, which limits the understanding of the adoption of social media by B2B over time depending on the outcomes and environment (e.g. competitors using social media).

Some studies looked at social media strategies of B2B companies (Cawsey and Rowley 2016 ; Huotari et al. 2015 ; Kasper et al. 2015 ; McShane et al. 2019 ; Mudambi et al. 2019 ). By employing interviews with companies’ managers and analysing its social media platforms (e.g. Twitter) it was found that most of the companies follow the following strategies: 1) monitoring and listening 2) empowering and engaging employees 3) creating compelling content 4) stimulating eWOM 5) evaluating and selecting channels 6) enhancing brand presence through integrating social media (Cawsey and Rowley 2016 ). Some studies investigated the difference between social media strategies of B2B and B2C companies. For example, a study by Swani et al. ( 2017 ) focused on effective social media strategies. By applying psychological motivation theory the study examined the key differences in B2B and B2C social media message strategies in terms of branding, message appeals, selling, and information search. The study used Facebook posts on brand pages of 280 Fortune companies. In total, 1467 posts were analysed. By using Bayesian models, the results showed that the inclusion of corporate brand names, functional and emotional appeals and information search cues increases the popularity of B2B messages in comparison with B2C messages. Also, it was found that readers of B2B content show a higher message liking rate and lower message commenting rate in comparison with readers of B2C messages.

The next group of studies looked at social media use by B2B companies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ; Habibi et al. 2015 ). B2B companies use social media for enhancing and managing customer relationships (Andersson and Wikström 2017 ; Bolat et al. ( 2016 ); branding (Andersson and Wikström 2017 ; Bolat et al. 2016 ), sensing market (Bolat et al. 2016 ) and co-production (Chirumalla et al. 2018 ). Additionally, it was mentioned that some of the B2B companies use social media as a recruiting tool, and tool which helps to collaborate globally (Andersson and Wikström 2017 ; Dyck 2010 ).

It is important for companies to not only use social media to achieve positive business outcomes but also it is important to measure their achievements. As a result, some of the studies focused on the measuring effectiveness of social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ; Vasudevan and Kumar 2018 ). Surprisingly, it was found that not so many companies measure ROI from social media (Gazal et al. 2016 ; Michaelidou et al. 2011 ). The ones who do it mostly use quantitative metrics (number of site visits, number of social network friends, number of comments and profile views) and qualitative metrics (growth of relationships with key audience, audience participation, moving from monologue to dialogue with consumers) (Gazal et al. 2016 ). Some future studies should investigate how ROI influences the strategy of B2B companies over period of time.

The last group of studies focused on social media tools used by B2B companies (Keinänen and Kuivalainen 2015 ; Mehmet and Clarke 2016 ; Yang et al. 2012 ). By using number of social media tools (Social Semiotic Multimodal) companies are able to improve their analysis of social media communications and identify their future consumers based on their user relationships. Studies investigating barriers and factors adoption of various social media tools by B2B companies are needed.

After reviewing studies on b2B social media, weight analysis was performed. Based on the results of weight analysis the conceptual model for future studies was proposed (Fig.  2 ). It is important to note that a limited number of studies focused and empirically tested factors affecting the adoption, use, and effect of social media. As a result, identified factors were considered as experimental (examined less than five times). It is too early to label these experimental predictors as worst or best, thus their further investigation is encouraged.

Social media impact on digital transformation and sustainable societies

Additionally, our review of the literature on B2B social media identified dominant research methods used by scholars. Qualitative and quantitative techniques were used by most of these studies. Closer analysis of 70 publications reviewed in this study revealed the multiple techniques applied for gathering data. Quantitative methods used in the studies mostly used surveys (see Table 4 ).

The data was mostly gathered from salespersons, managers and data from social media platforms (e.g. Twitter, Facebook). Just a limited number of studies employed consumer reported data (see Table 5 ).

On the other hand, publications using qualitative methods mainly used interviews and web scraping for the collection of the required data. To analyse the data studies used a variety of techniques including SEM, regression analysis and content analysis being one of the most used (see Table 6 ).

5.1 Digital Transformation and Sustainability Model

Based on the conducted literature review and adapting the model by Pappas et al. ( 2018 ) Fig. 2 presents the digital transformation and sustainability model in the context of B2B companies, which conceptualise the social media ecosystems, and the factors that need to collaborate to enable the use of social media towards the achievement of digital transformation and the creation of sustainable societies. The model comprises of social media stakeholders, the use of social media by B2B companies, and effect of social media on business and society.

5.1.1 Social Media Stakeholders

Building on the discussion and model provided by Pappas et al. ( 2018 ), this paper posits that the social media ecosystem comprises of the data stakeholders (company, society), who engage on social media (posting, reading, using information from social media). The use of social media by different stakeholders will lead to different effects affecting companies, customers and society. This is an iterative process based on which the stakeholders use their experience to constantly improve and evolve their use of social media, which has impacts on both, business and society. The successful implementation of this process is key to digital transformation and the creation of sustainable societies. Most of the current studies (Andersson et al. 2013 ; Bernard 2016 ; Bolat et al. 2016 ; Denktaş-Şakar and Sürücü 2018 ; Dyck 2010 ; Guesalaga 2016 ) focus mostly on the company as a stakeholder. However, more research is needed on other types of stakeholders (e.g. society).

5.1.2 Use of Social Media by B2B Companies

Social media affects not only ways how companies connect with their clients, but it is also changing their business models, the way how the value is delivered and profit is made. To successfully implement and use social media, B2B companies need to consider various social media tools, antecedents/barriers of its adoption, identify suitable social media strategies which are in line with the company’s overall strategy, and measure effectiveness of the use of social media. There are various factors that affect the use of social media by B2B companies. The study found that social media usage is influenced by perceived ease of use, adoption of social media, attitude towards social media and age of salesperson.

The majority of the studies focus on the management of the marketing department. However, digital transformation is much bigger than just marketing as it encompasses the entire organisation. As a result, future studies should look like the entire organisation and investigate barriers and factors affecting the use of social media.

It is crucial for companies to design content which will be noticed on social media by their potential, actual and former customers. Social media content should be interesting and offer some beneficial information, rather than just focus on services the company provides. Companies could use fresh views on relevant industry news, provide information how they are contributing to society and environment, include humour in their posts, share information about the team, make it more personal. It is also useful to use images, infographics, and video content.

It is also important for companies to measure digital marketing actions. More studies are needed on how to isolate the impact of specific media marketing actions to demonstrate their impact on the desired business outcomes (Salo 2017 ). Thus, future studies can consider how particular social media channels (e.g. Facebook, LinkedIn) in a campaign of a new product/ service influence brand awareness and sales level. Also, a limited number of studies discussed the way B2B companies can measure ROI. Future research should investigate how companies can measure intangible ROI, such as eWOM, brand awareness, and customer engagement (Kumar and Mirchandani 2012 ). Also, future research should investigate the reasons why most of the users do not assess the effectiveness of their SNS. Furthermore, most of the studies focused on likes, shares, and comments to evaluate social media engagement. Future research should focus on other types of measures. More research needs considering the impact of legislation on the use of social media by companies. Recent B2B studies did not consider recent legislation (General Data Protection Regulation 2018 ) in the context B2B (Sivarajah et al. 2019 ).

5.1.3 Effect of Social Media on Business and Society

Social media plays an important part in the company’s decision-making process. Social media can bring positive changes into company, which will result in improving customer satisfaction, value creation, increase in sales, building relationships with customers, knowledge creation, improve the perception of corporate credibility, acquisition of new customers, and improve employment brand engagement. Using information collected from social media can help companies to have a set of reliable attributes that comprise social, economic and environmental aspects in their decision-making process (Tseng 2017 ). Additionally, by using social media B2B companies can provide information to other stakeholders on their sustainability activities. By using data from social media companies will be able to provide products and services which are demanded by society. It will improve the quality of life and result in less waste. Additionally, social media can be considered as a tool that helps managers to integrate business practices with sustainability (Sivarajah et al. 2019 ). As a result, social media use by B2B companies can lead to business and societal changes.

A limited number of studies investigated the effect of social media on word of mouth communications in the B2B context. Future research should investigate the differences and similarities between B2C and B2B eWOM communications. Also, studies should investigate how these types of communications can be improved and ways to deal with negative eWOM. It is important for companies to respond to comments on social media. Additionally, future research should investigate its perceived helpfulness by customers.

Majority of studies (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Rossmann and Stei 2015 ; Agnihotri et al. 2012 ; Agnihotri et al. 2017 ; Itani et al. 2017 ; Salo 2017 ; Bhattacharjya and Ellison 2015 ; Gáti et al. 2018 ; Gruner and Power 2018 ; Hollebeek 2019 ) investigated positive effect of social media such consumer satisfaction, consumer engagement, and brand awareness. However, it will be interesting to consider the dark side of social media use such as an excessive number of requests on social media to salespeople (Agnihotri et al. 2016 ), which can result in the reduction of the responsiveness; spread of misinformation which can damage the reputation of the company.

Studies were performed in China (Lacka and Chong 2016 ; Niedermeier et al. 2016 ), the USA (Guesalaga 2016 ; Iankova et al. 2018 ; Ogilvie et al. 2018 ), India (Agnihotri et al. 2017 ; Vasudevan and Kumar 2018 ), the UK (Bolat et al. 2016 ; Iankova et al. 2018 ; Michaelidou et al. 2011 ). It is strongly advised that future studies conduct research in other countries as findings can be different due to the culture and social media adoption rates. Future studies should pay particular attention to other emerging markets (such as Russia, Brazil, and South Africa) as they suffer from the slow adoption rate of social media marketing. Some companies in these countries still rely more on traditional media for advertising of their products and services, as they are more trusted in comparison with social media channels (Olotewo 2016 ). The majority of studies investigate the effect of social media in B2B or B2C context. Future studies should pay attention to other contexts (e.g. B2B2B, B2B2C). Another limitation of the current research on B2B companies is that most of the studies on social media in the context of B2B focus on the effect of social media use only on business outcomes. It is important for future research to focus on societal outcomes.

Lastly, most of the studies on social media in the context of B2B companies use a cross-sectional approach to collect the data. Future research can use the longitudinal approach in order to advance understanding of social media use and its impact over time.

5.2 Research Propositions

Based on the social media research in the context of B2B companies and the discussion above the following is proposed, which could serve as a foundation for future empirical work.

Social media is a powerful tool to deliver information to customers. However, social media can be used to get consumer and market insights (Kazienko et al. 2013 ). A number of studies highlighted how information obtained from a number of social media platforms could be used for various marketing purposes, such as understanding the needs and preferences of consumers, marketing potential for new products/services, and current market trends (Agnihotri et al. 2016 ; Constantinides et al. 2008 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding customers. Therefore, the following proposition can be formulated:

Proposition 1

Social media usage of B2B companies has a positive influence on understanding its customers.

By using social media companies can examiner valuable information on competitors. It can help to understand competitors’ habits and strategies, which can lead to the competitive advantage and help strategic planning (Dey et al. 2011 ; Eid et al. 2019 ; Teo and Choo 2001 ). It is advised that future research employs a longitudinal approach to study the impact of social media use on understanding its competitors. As a result, using social media to understand customers and competitors can create business value (Mikalef et al. 2020a ) for key stakeholders and lead to positive changes in the business and societies. The above discussion leads to the following proposition:

Proposition 2

Social media usage of B2B companies has a positive influence on understanding its competitors.

Proposition 3

Culture influences the adoption and use of social media by B2B companies.

Usage of social media can result in some positive marketing outcomes such as building new customer relationships, increasing brand awareness, and level of sales to name a few (Agnihotri et al. 2016 ; Ancillai et al. 2019 ; Dwivedi et al. 2020 ; Rossmann and Stei 2015 ). However, when social media is not used appropriately it can lead to negative consequences. If a company does not have enough resources to implement social media tools the burden usually comes on a salesperson. A high number of customer inquiries, the pressure to engage with customers on social media, and monitor communications happening on various social media platforms can result in the increased workload of a salesperson putting extra pressure (Agnihotri et al. 2016 ). As a result, a salesperson might not have enough time to engage with all the customers online promptly or engage in reactive and proactive web care. As a result, customer satisfaction can be affected as well as company reputation. To investigate the negative impact of social media research could apply novel methods for data collection and analysis such as fsQCA (Pappas et al. 2020 ), or implying eye-tracking (Mikalef et al. 2020b ). This leads to the following proposition:

Proposition 4

Inappropriate use of social media by B2B companies has a negative effect on a) customer satisfaction and b) company reputation.

According to Technology-Organisation-Environment (TOE) framework environmental context significantly affects a company’s use of innovations (Abed 2020 ; Oliveira and Martins 2011 ). Environment refers to the factors which affect companies from outside, including competitors and customers. Adopting innovation can help companies to change the rules of the competition and reach a competitive advantage (Porter and Millar 1985 ). In a competitive environment, companies have a tendency to adopt an innovation. AlSharji et al. ( 2018 ) argued that the adoption of innovation can be extended to social media use by companies. A study by AlSharji et al. ( 2018 ) by using data from 1700 SMEs operating in the United Arab Emirates found that competitive pressure significantly affects the use of social media by SMEs. It can be explained by the fact that companies could feel pressure when other companies in the industry start adopting a particular technology and as a result adopt it to remain competitive (Kuan and Chau 2001 ). Based on the above discussion, the following proposition can be formulated:

Proposition 5

Competitive pressure positively affects the adoption of social media by B2B companies.

Companies might feel that they are forced to adopt and use IT innovations because their customers would expect them to do so. Meeting customers’ expectations could result in adoption of new technologies by B2B companies. Some research studies investigated the impact of customer pressure on companies (AlSharji et al. 2018 ; Maduku et al. 2016 ). For example, a study by Maduku et al. ( 2016 ) found that customer pressure has a positive effect on SMEs adoption of mobile marketing in the context of South Africa. Future research could implement longitudinal approach to investigate how environment affects adoption of social media by B2B companies. This leads to the formulation of the following proposition:

Proposition 6

Customer pressure positively affects the adoption of social media by B2B companies.

6 Conclusion

The aim of this research was to provide a comprehensive systematic review of the literature on social media in the context of B2B companies and propose the framework outlining the role of social media in the digital transformation of B2B companies. It was found that B2B companies use social media, but not all companies consider it as part of their marketing strategies. The studies on social media in the B2B context focused on the effect of social media, antecedents, and barriers of adoption of social media, social media strategies, social media use, and measuring the effectiveness of social media. Academics and practitioners can employ the current study as an informative framework for research on the use of social media by B2B companies. The summary of the key observations provided from this literature review is the following: [i] Facebook, Twitter, and LinkedIn are the most famous social media platforms used by B2B companies, [ii] Social media has a positive effect on customer satisfaction, acquisition of new customers, sales, stakeholder engagement, and customer relationships, [iii] In systematically reviewing 70 publications on social media in the context of B2B companies it was observed that most of the studies use online surveys and online content analysis, [iv] Companies still look for ways to evaluate the effectiveness of social media, [v] Innovativeness, pressure from stakeholders, perceived usefulness, and perceived usability have a significant positive effect on companies’ adoption to use social media, [vi] Lack of staff familiarity and technical skills are the main barriers that affect the adoption of social media by B2B, [vii] Social media has an impact not only on business but also on society, [viii] There is a dark side of social media: fake online reviews, an excessive number of requests on social media to salespeople, distribution of misinformation, negative eWOM, [ix] Use of social media by companies has a positive effect on sustainability, and [x] For successful digital transformation social media should change not only the way how companies integrate it into their marketing strategies but the way how companies deliver values to their customers and conduct their business. This research has a number of limitations. First, only publications from the Scopus database were included in literature analysis and synthesis. Second, this research did not use meta-analysis. To provide a broader picture of the research on social media in the B2B context and reconcile conflicting findings of the existing studies future research should conduct a meta-analysis (Ismagilova et al. 2020c ). It will advance knowledge of the social media domain.

Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53 , 102118.

Article   Google Scholar  

Agnihotri, R., Kothandaraman, P., Kashyap, R., & Singh, R. (2012). Bringing “social” into sales: The impact of salespeople’s social media use on service behaviors and value creation. Journal of Personal Selling and Sales Management, 32 (3), 333–348. https://doi.org/10.2753/PSS0885-3134320304 .

Agnihotri, R., Dingus, R., Hu, M. Y., & Krush, M. T. (2016). Social media: Influencing customer satisfaction in B2B sales. Industrial Marketing Management, 53 , 172–180. https://doi.org/10.1016/j.indmarman.2015.09.003 .

Agnihotri, R., Trainor, K. J., Itani, O. S., & Rodriguez, M. (2017). Examining the role of sales-based CRM technology and social media use on post-sale service behaviors in India. Journal of Business Research, 81 , 144–154. https://doi.org/10.1016/j.jbusres.2017.08.021 .

Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34 (7), 1177–1190.

AlSharji, A., Ahmad, S. Z., & Bakar, A. R. A. (2018). Understanding social media adoption in SMEs. Journal of Entrepreneurship in Emerging Economies, 10 (2), 302–328.

Ancillai, C., Terho, H., Cardinali, S., & Pascucci, F. (2019). Advancing social media driven sales research: Establishing conceptual foundations for B-to-B social selling. Industrial Marketing Management, 82 , 293–308. https://doi.org/10.1016/j.indmarman.2019.01.002 .

Andersson, S., & Wikström, N. (2017). Why and how are social media used in a B2B context, and which stakeholders are involved? Journal of Business and Industrial Marketing, 32 (8), 1098–1108. https://doi.org/10.1108/JBIM-07-2016-0148 .

Andersson, S., Evers, N., & Griot, C. (2013). Local and international networks in small firm internationalization: Cases from the Rhône-Alpes medical technology regional cluster. Entrepreneurship & Regional Development, 25 (9–10), 867–888.

Andzulis, J. M., Panagopoulos, N. G., & Rapp, A. (2012). A review of social media and implications for the sales process. Journal of Personal Selling & Sales Management, 32 (3), 305–316.

Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2015). Generating brand awareness in online social networks. Computers in Human Behavior, 50 , 600–609.

Bell, D., & Shirzad, S, R. (2013). Social media domain analysis (SoMeDoA): A pharmaceutical study. 9th international conference on web information systems and technologies, 561–570.  https://doi.org/10.5220/0004499105610570 .

Bernard, M. (2016). The impact of social media on the B2B CMO. Journal of Business and Industrial Marketing, 31 (8), 955–960. https://doi.org/10.1108/JBIM-10-2016-268 .

Bhattacharjya, J., & Ellison, A, B. (2015). Building business resilience with social media in B2B environments: The emergence of responsive customer relationship management processes on twitter. Working Conference on Virtual Enterprises doi: https://doi.org/10.1007/978-3-319-24141-8_15 .

Bolat, E., Kooli, K., & Wright, L. T. (2016). Businesses and mobile social media capability. Journal of Business and Industrial Marketing, 31 (8), 971–981. https://doi.org/10.1108/JBIM-10-2016-270 .

Buratti, N., Parola, F., & Satta, G. (2018). Insights on the adoption of social media marketing in B2B services. TQM Journal, 30 (5), 490–529. https://doi.org/10.1108/TQM-11-2017-0136 .

Cawsey, T., & Rowley, J. (2016). Social media brand building strategies in B2B companies. Marketing Intelligence and Planning, 34 (6), 754–776. https://doi.org/10.1108/MIP-04-2015-0079 .

Chatterjee, S., & Kar, A. K. (2020). Why do small and medium enterprises use social media marketing and what is the impact: Empirical insights from India. International Journal of Information Management, 53 , 102103.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36 , 1165–1188.

Chirumalla, K., Oghazi, P., & Parida, V. (2018). Social media engagement strategy: Investigation of marketing and R&D interfaces in manufacturing industry. Industrial Marketing Management, 74 , 138–149. https://doi.org/10.1016/j.indmarman.2017.10.001 .

Constantinides, E., Romero, C. L., & Boria, M. A. G. (2008). Social media: A new frontier for retailers?. In European retail research (pp. 1–28) . Wiesbaden: Gabler Verlag.

Google Scholar  

Denktaş-Şakar, G., & Sürücü, E. (2018). Stakeholder engagement via social media: An analysis of third-party logistics companies. Service Industries Journal, 40 , 866–889. https://doi.org/10.1080/02642069.2018.1561874 .

Dey, L., Haque, S, M., Khurdiya, A., & Shroff, G. (2011). Acquiring competitive intelligence from social media. In proceedings of the 2011 joint workshop on multilingual OCR and analytics for noisy unstructured text data (pp. 1-9) . https://doi.org/10.1145/2034617.2034621 .

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019a). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21 (3), 719–734.

Dwivedi, Y, K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Galanos, V. (2019b). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management , 101994, doi: https://doi.org/10.1016/j.ijinfomgt.2019.08.002 .

Dwivedi, Y, K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Kumar, V. (2020). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management , 102168, doi: https://doi.org/10.1016/j.ijinfomgt.2020.102168 .

Dyck, P. V. (2010). As social media evolves, the device industry must also. Medical Device and Diagnostic Industry, 32 (8).

Eid, R., Abdelmoety, Z., & Agag, G. (2019). Antecedents and consequences of social media marketing use: An empirical study of the UK exporting B2B SMEs. Journal of Business & Industrial Marketing., 35 , 284–305.

Gáti, M., Mitev, A., & Bauer, A. (2018). Investigating the impact of salespersons’ use of technology and social media on their customer relationship performance in B2B settings. Trziste, 30 (2), 165–176. https://doi.org/10.22598/mt/2018.30.2.165 .

Gazal, K., Montague, I., Poudel, R., & Wiedenbeck, J. (2016). Forest products industry in a digital age: Factors affecting social media adoption. Forest Products Journal, 66 (5-6), 343–353. https://doi.org/10.13073/FPJ-D-15-00007 .

General Data Protection Regulation (2018). Guide to the General Data Protection Regulation. Available at https://www.gov.uk/government/publications/guide-to-the-general-data-protection-regulation . Accessed 28 Jan 2021.

Gregorio, J. (2017). 10 reasons to diversify your digital marketing efforts. Digital marketing Philippines. Available at https://digitalmarketingphilippines.com/10-reasons-to-diversify-your-digital-marketing-efforts// Accessed on April , 27 , 2019.

Gruner, R. L., & Power, D. (2018). To integrate or not to integrate? Understanding B2B social media communications. Online Information Review, 42 (1), 73–92. https://doi.org/10.1108/OIR-04-2016-0116 .

Guesalaga, R. (2016). The use of social media in sales: Individual and organizational antecedents, and the role of customer engagement in social media. Industrial Marketing Management, 54 , 71–79. https://doi.org/10.1016/j.indmarman.2015.12.002 .

Gupta, P., Chauhan, S., & Jaiswal, M. P. (2019). Classification of smart city research-a descriptive literature review and future research agenda. Information Systems Frontiers, 21 (3), 661–685.

Habibi, F., Hamilton, C. A., Valos, M. J., & Callaghan, M. (2015). E-marketing orientation and social media implementation in B2B marketing. European Business Review, 27 (6), 638–655. https://doi.org/10.1108/EBR-03-2015-0026 .

Harrigan, P., Miles, M. P., Fang, Y., & Roy, S. K. (2020). The role of social media in the engagement and information processes of social CRM. International Journal of Information Management, 54 , 102151.

Hollebeek, L. D. (2019). Developing business customer engagement through social media engagement-platforms: An integrative SD logic/RBV-informed model. Industrial Marketing Management, 81 , 89–98.

Hsiao, S. H., Wang, Y. Y., Wang, T., & Kao, T. W. (2020). How social media shapes the fashion industry: The spillover effects between private labels and national brands. Industrial Marketing Management, 86 , 40–51.

Huotari, L., Ulkuniemi, P., Saraniemi, S., & Mäläskä, M. (2015). Analysis of content creation in social media by B2B companies. Journal of Business and Industrial Marketing, 30 (6), 761–770. https://doi.org/10.1108/JBIM-05-2013-0118 .

Iankova, S., Davies, I., Archer-Brown, C., Marder, B., & Yau, A. (2018). A comparison of social media marketing between B2B, B2C and mixed business models. Industrial Marketing Management, 81 , 169–179. https://doi.org/10.1016/j.indmarman.2018.01.001 .

Iannacci, F., Fearon, C., & Pole, K. (2020). From acceptance to adaptive acceptance of social media policy change: A set-theoretic analysis of b2b SMEs. Information Systems Frontiers , 1-18. https://doi.org/10.1007/s10796-020-09988-1 .

Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic word of mouth (eWOM) in the marketing context: A state of the art analysis and future directions . Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-52459-7 .

Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47 , 88–100.

Ismagilova, E., Slade, E. L., Rana, N. P., & Dwivedi, Y. K. (2020a). The effect of electronic word of mouth communications on intention to buy: A meta-analysis. Information Systems Frontiers, 22 , 1203–1226. https://doi.org/10.1007/s10796-019-09924-y .

Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020b). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53 , 101736.

Ismagilova, E., Rana, N, P., Slade, E., & Dwivedi, Y, K. (2020c). A meta-analysis of the factors affecting eWOM providing behaviour. European Journal of Marketing. doi: https://doi.org/10.1108/EJM-07-2018-0472 , ahead-of-print.

Itani, O. S., Agnihotri, R., & Dingus, R. (2017). Social media use in B2b sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66 , 64–79. https://doi.org/10.1016/j.indmarman.2017.06.012 .

Jeyaraj, A., & Dwivedi, Y. K. (2020). Meta-analysis in information systems research: Review and recommendations. International Journal of Information Management, 55 , 102226.

Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21 (1), 1–23.

Juntunen, M., Ismagilova, E., & Oikarinen, E. L. (2020). B2B brands on twitter: Engaging users with a varying combination of social media content objectives, strategies, and tactics. Industrial Marketing Management, 89 , 630–641.

Jussila, J. J., Kärkkäinen, H., & Leino, M. (2011). Benefits of social media in business-to-business customer interface in innovation. 15th International Academic MindTrek Conference: Envisioning Future Media Environments . MindTrek, 2011 , 167–174. https://doi.org/10.1145/2181037.2181065 .

Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of stimulus-organism-response. International Journal of Information Management, 39 , 169–185.

Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20 (3), 531–558.

Kärkkäinen, H., Jussila, J., & Janhonen, J. (2011). Managing customer information and knowledge with social media in business-to-business companies. ACM International Conference Proceeding Series, doi: https://doi.org/10.1145/2024288.2024309 .

Kasper, H., Koleva, I., & Kett, H. (2015). Social media matrix matching corporate goals with external social media activities doi: https://doi.org/10.1007/978-3-662-46641-4_17 .

Katona, Z., & Sarvary, M. (2014). Maersk line: B2B social media-“it’s communication, not marketing. California Management Review , 56(3), 142–156. doi: https://doi.org/10.1525/cmr.2014.56.3.142 .

Kazienko, P., Szozda, N., Filipowski, T., & Blysz, W. (2013). New business client acquisition using social networking sites. Electronic Markets, 23 (2), 93–103. https://doi.org/10.1007/s12525-013-0123-9 .

Keinänen, H., & Kuivalainen, O. (2015). Antecedents of social media B2B use in industrial marketing context: Customers’ view. Journal of Business and Industrial Marketing, 30 (6), 711–722. https://doi.org/10.1108/JBIM-04-2013-0095 .

Kho, N. D. (2008). B2B gets social media. EContent, 31 (3), 26–30.

Kovac, M. (2016). Social media works for B2B sales, too. Harvard Business Review. Retrieved from https://hbr.org/2016/01/social-media-worksfor-b2b-sales-too . Accessed on April , 27 , 2020 .

Kuan, K. K., & Chau, P. Y. (2001). A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Information & Management, 38 (8), 507–521.

Kumar, V., & Mirchandani, R. (2012). Increasing the ROI of social media marketing. MIT Sloan Management Review, 54 (1), 55–61.

Kumar, A., & Möller, K. (2018). Extending the boundaries of corporate branding: An exploratory study of the influence of brand familiarity in recruitment practices through social media by B2B firms. Corporate Reputation Review, 21 (3), 101–114. https://doi.org/10.1057/s41299-018-0046-7 .

Kunsman, T. (2018). Internal marketing: Why your company should prioritize it. https://everyonesocial.com/blog/internal-marketing/ . Accessed on September, 25, 2019.

Lacka, E., & Chong, A. (2016). Usability perspective on social media sites' adoption in the B2B context. Industrial Marketing Management, 54 , 80–91. https://doi.org/10.1016/j.indmarman.2016.01.001 .

Lal, B., Ismagilova, E., Dwivedi, Y.,. K., & Kwayu, S. (2020). Return on Investment in Social Media Marketing: Literature review and suggestions for future research. In Digital and Social Media Marketing (pp. 3–17) . Cham: Springer.

Lashgari, M., Sutton-Brady, C., Solberg Søilen, K., & Ulfvengren, P. (2018). Adoption strategies of social media in B2B firms: A multiple case study approach. Journal of Business and Industrial Marketing, 33 (5), 730–743. https://doi.org/10.1108/JBIM-10-2016-0242 .

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24 (3), 149–157.

Maduku, D. K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by south African SMEs: A multi-perspective framework. International Journal of Information Management, 36 (5), 711–723.

Mahrous, A, A. (2013). Social media marketing: Prospects for marketing theory and practice on the social web. E-marketing in developed and developing countries: Emerging practices (pp. 56-68) doi: https://doi.org/10.4018/978-1-4666-3954-6.ch004 Retrieved from www.scopus.com .

McAfee, A. P. (2006). Enterprise 2.0: The dawn of emergent collaboration. Enterprise, 2 , 15–26.

McKinsey & Company (2015). Transforming the business through social tools. McKinsey.com , ( http://www.mckinsey.com/industries/high-tech/our-insights/transformingthe-business-through-social-tools ).

McShane, L., Pancer, E., & Poole, M. (2019). The influence of B to B social media message features on brand engagement: A fluency perspective. Journal of Business-to-Business Marketing, 26 (1), 1–18. https://doi.org/10.1080/1051712X.2019.1565132 .

Mehmet, M. I., & Clarke, R. J. (2016). B2B social media semantics: Analysing multimodal online meanings in marketing conversations. Industrial Marketing Management, 54 , 92–106. https://doi.org/10.1016/j.indmarman.2015.12.006 .

Meire, M., Ballings, M., & Van den Poel, D. (2017). The added value of social media data in B2B customer acquisition systems: A real-life experiment. Decision Support Systems, 104 , 26–37. https://doi.org/10.1016/j.dss.2017.09.010 .

Michaelidou, N., Siamagka, N. T., & Christodoulides, G. (2011). Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands. Industrial Marketing Management, 40 (7), 1153–1159. https://doi.org/10.1016/j.indmarman.2011.09.009 .

Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020a). Big data and business analytics: A research agenda for realizing business value. Information & Management, 57 (1), 103237.

Mikalef, P., Sharma, K., Pappas, I, O., & Giannakos, M. (2020b). Seeking information on social commerce: An examination of the impact of user-and marketer-generated content through an eye-tracking study. Information Systems Frontiers , 1-14, doi: https://doi.org/10.1007/s10796-020-10034-3 .

Minsky, L., & Quesenberry, K, A. (2016). How B2B sales can benefit from social selling . Harvard Business Review. Available at https://hbr.org/2016/11/84-of-b2b-sales-start-with-a-referral-not-a-salesperson . Accessed 28 Jan 2021.

Moncrief, W. C., Marshall, G. W., & Rudd, J. M. (2015). Social media and related technology: Drivers of change in managing the contemporary sales force. Business Horizons, 58 (1), 45–55. https://doi.org/10.1016/j.bushor.2014.09.009 .

Moore, J. N., Hopkins, C. D., & Raymond, M. A. (2013). Utilization of relationship-oriented social media in the selling process: A comparison of consumer (B2C) and industrial (B2B) salespeople. Journal of Internet Commerce, 12 (1), 48–75. https://doi.org/10.1080/15332861.2013.763694 .

Moore, J. N., Raymond, M. A., & Hopkins, C. D. (2015). Social selling: A comparison of social media usage across process stage, markets, and sales job functions. Journal of Marketing Theory and Practice, 23 (1), 1–20. https://doi.org/10.1080/10696679.2015.980163 .

Mudambi, S. M., Sinha, J. I., & Taylor, D. S. (2019). Why B-to-B CEOs should be more social on social media. Journal of Business-to-Business Marketing, 26 (1), 103–105. https://doi.org/10.1080/1051712X.2019.1565144 .

Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20 (3), 559–576.

Müller, L., Griesbaum, J., & Mandl, T. (2013). Social media relations in the german automotive market. Paper presented at the proceedings of the IADIS international conference ICT, society and human beings 2013, Proceedings of the IADIS International Conference e-Commerce 2013, 19–26.

Müller, J. M., Pommeranz, B., Weisser, J., & Voigt, K. I. (2018). Digital, social media, and Mobile marketing in industrial buying: Still in need of customer segmentation? Empirical evidence from Poland and Germany. Industrial Marketing Management, 73 , 70–83. https://doi.org/10.1016/j.indmarman.2018.01.033 .

Niedermeier, K. E., Wang, E., & Zhang, X. (2016). The use of social media among business-to-business sales professionals in China: How social media helps create and solidify guanxi relationships between sales professionals and customers. Journal of Research in Interactive Marketing, 10 (1), 33–49. https://doi.org/10.1108/JRIM-08-2015-0054 .

Nunan, D., Sibai, O., Schivinski, B., & Christodoulides, G. (2018). Reflections on “social media: Influencing customer satisfaction in B2B sales” and a research agenda. Industrial Marketing Management, 75 , 31–36. https://doi.org/10.1016/j.indmarman.2018.03.009 .

Ogilvie, J., Agnihotri, R., Rapp, A., & Trainor, K. (2018). Social media technology use and salesperson performance: A two study examination of the role of salesperson behaviors, characteristics, and training. Industrial Marketing Management, 75 , 55–65. https://doi.org/10.1016/j.indmarman.2018.03.007 .

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14 (1), 110.

Olotewo, J. (2016). Social media marketing in emerging markets. International Journal of Online Marketing Research, 2 (2), 10–18.

Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16 , 479–491.

Pappas, I. O., Papavlasopoulou, S., Mikalef, P., & Giannakos, M. N. (2020). Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach. International Journal of Information Management, 53 , 102128.

Pascucci, F., Ancillai, C., & Cardinali, S. (2018). Exploring antecedents of social media usage in B2B: A systematic review. Management Research Review, 41 (6), 629–656. https://doi.org/10.1108/MRR-07-2017-0212 .

Pitt, C. S., Plangger, K. A., Botha, E., Kietzmann, J., & Pitt, L. (2017). How employees engage with B2B brands on social media: Word choice and verbal tone. Industrial Marketing Management, 81 , 130–137. https://doi.org/10.1016/j.indmarman.2017.09.012 .

Pitt, C. S., Botha, E., Ferreira, J. J., & Kietzmann, J. (2018). Employee brand engagement on social media: Managing optimism and commonality. Business Horizons, 61 (4), 635–642. https://doi.org/10.1016/j.bushor.2018.04.001 .

Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard Business Review, 63 (4), 149–160.

Pulizzi, J., & Handley, A. (2017). B2C content marketing-2018 benchmarks, budgets, and trends—North America. Available at https://contentmarketinginstitute.com/wp-content/uploads/2016/09/2017_B2B_Research_FINAL.pdf Accessed on April , 27 , 2020.

Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2015). A meta-analysis of existing research on citizen adoption of e-government. Information Systems Frontiers, 17 (3), 547–563.

Rana, N. P., Luthra, S., Mangla, S. K., Islam, R., Roderick, S., & Dwivedi, Y. K. (2019). Barriers to the development of smart cities in Indian context. Information Systems Frontiers, 21 (3), 503–525.

Rodriguez, M., Peterson, R. M., & Krishnan, V. (2012). Social media's influence on business-to-business sales performance. Journal of Personal Selling and Sales Management, 32 (3), 365–378. https://doi.org/10.2753/PSS0885-3134320306 .

Rossmann, A., & Stei, G. (2015). Sales 2.0 in business-to-business (B2B) networks: Conceptualization and impact of social media in B2B sales relationships . Paper presented at the Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI), 244 67–78. Available at https://subs.emis.de/LNI/Proceedings/Proceedings244/67.pdf . Accessed 28 Jan 2021.

Salo, J. (2017). Social media research in the industrial marketing field: Review of literature and future research directions. Industrial Marketing Management, 66 , 115–129. https://doi.org/10.1016/j.indmarman.2017.07.013 .

Shaltoni, A. M. (2017). From websites to social media: Exploring the adoption of internet marketing in emerging industrial markets. Journal of Business and Industrial Marketing, 32 (7), 1009–1019. https://doi.org/10.1108/JBIM-06-2016-0122 .

Siamagka, N. T., Christodoulides, G., Michaelidou, N., & Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management, 51 , 89–99. https://doi.org/10.1016/j.indmarman.2015.05.005 .

Sivarajah, U., Irani, Z., Gupta, S., & Mahroof, K. (2019). Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management . https://doi.org/10.1016/j.indmarman.2019.04.005 .

Sobal, A. (2017). 30 statistics about B2B social media usage. Available at https://www.weidert.com/blog/statistics-about-b2b-social-media-usage Accessed on April , 27 , 2020.

Stelzner, M. (2011). 2012 social media marketing industry report. Social media examiner . Available at https://www.socialmediaexaminer.com/socialmedia-marketing-industry-report-2012/ . Accessed 28 Jan 2021.

Sułkowski, Ł., & Kaczorowska-Spychalska, D. (2016). Social media in the process of marketing evolution in polish textile-clothing industry. Fibres and Textiles in Eastern Europe, 24 (5), 15–20. https://doi.org/10.5604/12303666.1215521 .

Swani, K., Milne, G., & Brown, B. P. (2013). Spreading the word through likes on facebook: Evaluating the message strategy effectiveness of fortune 500 companies. Journal of Research in Interactive Marketing, 7 (4), 269–294. https://doi.org/10.1108/JRIM-05-2013-0026 .

Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of fortune 500 companies' twitter communications. Industrial Marketing Management, 43 (5), 873–881. https://doi.org/10.1016/j.indmarman.2014.04.012 .

Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2017). What messages to post? Evaluating the popularity of social media communications in business versus consumer markets. Industrial Marketing Management, 62 , 77–87. https://doi.org/10.1016/j.indmarman.2016.07.006 .

Tedeschi, B. (2006). Like shopping? Social networking? Try social shopping. New York Times, 11 , 09.

Teo, T. S., & Choo, W. Y. (2001). Assessing the impact of using the internet for competitive intelligence. Information & Management, 39 (1), 67–83.

Tseng, M. L. (2017). Using social media and qualitative and quantitative information scales to benchmark corporate sustainability. Journal of Cleaner Production, 142 , 727–738.

Vasudevan, S., & Kumar, F. J. P. (2018). Social media and B2B brands: An Indian perspective. International Journal of Mechanical Engineering and Technology, 9 (9), 767–775.

Vukanovic, Z. (2013). Managing social media value networks: From publisher (broadcast) to user-centric (broadband-narrowcast) business models. Handbook of social media management: Value chain and business models in changing media markets (pp. 269-288) doi: https://doi.org/10.1007/978-3-642-28897-5_16 .

Wang, Y., Rod, M., Ji, S., & Deng, Q. (2017). Social media capability in B2B marketing: Toward a definition and a research model. Journal of Business and Industrial Marketing, 32 (8), 1125–1135. https://doi.org/10.1108/JBIM-10-2016-0250 .

Watt, I. (2010). Changing visions of parliamentary libraries: From the enlightenment to Facebook. IFLA Journal, 36 (1), 47–60. https://doi.org/10.1177/0340035209359574 .

Webster, J., & Watson, R.,. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, 26 (2), xiii–xxiii.

Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation . Boston, MA: Harvard Business Press.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28 (3), 443–488.

World Commission Report on Environment and Development. (1987). Our Common Future . Oxford: Oxford University Press.

Yang, C. C., Yang, H., Tang, X., & Jiang, L. (2012). Identifying implicit relationships between social media users to support social commerce. In ACM international conference proceeding series (pp. 41–47). https://doi.org/10.1145/2346536.2346544 .

Chapter   Google Scholar  

Download references

Author information

Authors and affiliations.

Emerging Markets Research Centre (EMaRC), School of Management, Swansea University, Bay Campus, Swansea, UK

Yogesh K. Dwivedi

School of Management, University of Bradford, Bradford, UK

Elvira Ismagilova & Nripendra P. Rana

Symbiosis Institute of Business Management, Pune & Symbiosis International, Deemed University, Pune, India

Ramakrishnan Raman

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yogesh K. Dwivedi .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Dwivedi, Y.K., Ismagilova, E., Rana, N.P. et al. Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review. Inf Syst Front 25 , 971–993 (2023). https://doi.org/10.1007/s10796-021-10106-y

Download citation

Accepted : 07 January 2021

Published : 02 February 2021

Issue Date : June 2023

DOI : https://doi.org/10.1007/s10796-021-10106-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Business-to-business
• Digital transformation
• Information systems
• Literature review
• Social media

Advertisement

• Find a journal
• Publish with us
• Track your research

Search form

Yale awards nine honorary degrees.

Front row, from left, Risa Lavizzo-Mourey, Judith Rodin, Peter Salovey, Mahzarin Banaji, and Hortense Spillers.  Second row, from left, László Lovász,  Kehinde Wiley,  Mario Capecchi, and Stephen Breyer. (Photo by Michael Marsland)

During its 323rd graduation ceremony on Monday, Yale conferred honorary degrees on nine individuals who have achieved distinction in their fields.

This year’s honorary degree recipients included the eminent social psychologist Mahzarin Banaji; retired U.S. Supreme Court Associate Justice Stephen Breyer; Nobel Prize-winning molecular geneticist Mario Capecchi; health policy leader Risa Lavizzo-Mourey; mathematician and computer scientist László Lovász; research psychologist and global thought leader Judith Rodin; literary critic Hortense Spillers; and visual artist Kehinde Wiley ’01 M.F.A.

And also receiving an honorary degree was Yale President Peter Salovey, who presided over his final Commencement as Yale’s leader before his planned return to the faculty this summer.

The awarding of honorary degrees, which has been a Yale tradition since 1702, recognizes pioneering achievement or exemplary contribution to the common good.

The honorary degree recipients and their citations follow:

Mahzarin Banaji Social psychologist Doctor of Social Science

“ Groundbreaking scholar whose pioneering work has helped establish the role that unconscious processes play in governing human social action, you have educated us to appreciate how our judgment of others may spring, not from conscious dislike or animosity, but from implicit biases we do not recognize or understand. These ‘mind bugs’ occur outside of our awareness or control and give rise to prejudices based on race, gender, age, and other characteristics. Intrepid investigator whose work has opened minds and hearts by illuminating what leads us to categorize others, we are pleased to admit explicit bias in your favor as we honor a beloved former Yale faculty member with the degree of Doctor of Social Science. ”

Stephen Breyer Jurist, retired associate justice of the U.S. Supreme Court Doctor of Laws

“ Supreme court justice for over a quarter century, you are known for your pragmatic philosophy, a belief that the judiciary must adapt to changing society and consider real-world consequences for human beings when deciding cases. Your fact and data-driven decisions in matters involving school integration, the rights of criminal suspects, a woman’s prerogative to control her own body, and many more, mark you as someone who shares Justice Holmes’ belief that the important thing is ‘not where we stand, but in what direction we are moving.’ Quintessential enlightenment man, Yale celebrates a justice who reminds us that judges must hew to principle, not politics, as we honor you with the degree of Doctor of Laws. ”

Mario Capecchi Molecular geneticist Doctor of Science

“ Born in Verona to a mother who was taken to Dachau, you lived alone on the streets during the Holocaust from age four, scrounging for food, until, through a set of miraculous circumstances, you were brought to the United States. Without any formal schooling until you were nine, you rose to share the Nobel Prize in medicine for the development of gene targeting in mouse embryo stem cells, a discovery that has led to major advancements in human disease, drug development, and more. Inspiring scientist, whose life lessons teach us all and whose story exemplifies the triumph of the human spirit, we award you the degree of Doctor of Science. ”

Risa Lavizzo-Mourey Health policy leader Doctor of Medical Sciences

“ Trailblazing physician, geriatrician, and first woman and first African American to be president and chief executive of the Robert Wood Johnson Foundation, you have devoted your career to empowering communities and corporations to making equitable health care a shared value. Your persuasiveness has prevailed on big corporations to heed your cry of ‘Less sugar! Less sugar!’ and to help create a healthier America. Yale salutes a visionary who is insistent that all Americans — from every zip code in our nation — can live longer, healthier, better lives, as, with a big glass of delicious water, we toast and award you the degree of Doctor of Medical Sciences. ”

László Lovász Mathematician and computer scientist Doctor of Engineering and Technology

“ Brilliant mathematician and theoretical computer scientist, your pathbreaking contributions in combinatorics, a branch of pure mathematics, have led to real-life applications in computer science, engineering and technology, statistics, and science that serve and advance humankind.  Over time you have received nearly every award a mathematician can earn, including the Abel Prize, the highest award in mathematics. We are honored that you have agreed to receive one more, from the university where you taught and conducted research for over a half decade, and which itself is honored to present you with the degree of Doctor of Engineering and Technology. ”

Judith Rodin Global thought leader Doctor of Humane Letters

“ Pioneering leader who served as the first woman president of both the University of Pennsylvania and the Rockefeller Foundation, you have helped reshape two great institutions to face the needs of modern times. In both, your creative and forward-looking ideas — from health psychology to resilient cities — galvanized initiatives that emphasized change amidst challenge. Yale celebrates as well your twenty-two years in New Haven as a Yale faculty member, educator, dean of the graduate school, and university provost. A resilient and transformational leader wherever you go, Yale salutes an innovator we still think of as ‘one of our own,’ as we proudly confer on you the degree of Doctor of Humane Letters. ”

Peter Salovey P resident of Yale University Doctor of Humane Letters

“ When you step down in June as Yale’s 23rd president, you will enter history as the Yale professor who has held more senior leadership positions at the university than anyone in its 322-year history. Beginning with your presidency of the  Graduate and Professional Student Senate  when you were a Ph.D. student, you have been, serially, chair of the Psychology Department, dean of the Graduate School of Arts and Sciences, dean of Yale College, provost, and president — a cornucopia of senior positions held by no other Yale historical personage, ever.

“ When you were appointed, you said you hoped to help a great university create a more accessible, a more innovative, and a more excellent Yale. You have done all three. Yale now has a dramatically wider array of socioeconomic and geographic diversity across its student body, departments, and schools. New buildings have brought together scattered faculty who now work with and learn from each other. New Haven’s economy is strengthened because of your partnership with its mayor. And the new faculty and academic collaborations in schools and programs that you have prioritized have made Yale more innovative and forward looking in developing ways to address society’s greatest challenges.

“ From the start of your presidency your stated aim has been inspiring Yale as a community where students, staff, and faculty collaborate with one another to make a whole that is more than the sum of its parts. As you return now to the faculty after a suitable rest, no doubt to galvanize students as the excellent teacher you always have been, Yale offers its thanks, as we gratefully confer on you your fourth Yale degree, doctor of Humane Letters. ”

Hortense Spillers Literary critic Doctor of Humanities

“ Inspiring Black feminist theorist and critic, your foundational work, embedded in your deep historical and literary knowledge, challenges received thought and provides us a profound understanding of how race and gender shape the modern world. In three books and dozens of essays, you rewrite the American grammar book, claiming the insurgent ground as you revolutionize how we consider and write about our nation’s history and culture. Pioneering thinker, we celebrate the marvels of your inventiveness, and your enduring contributions to letters, as we proudly confer on you the degree of Doctor of Humanities. ”

Kehinde Wiley ’01 M.F.A. Visual artist Doctor of Fine Arts

“ Internationally renowned painter and sculptor, whose portrait of President Obama hangs in the Smithsonian’s National Portrait Gallery, your arresting portraits, like all pioneering art, break the category, depicting ‘common’ people in traditional styles that raise questions about privilege, power, authority, and representation. Artist recognized around the world for your vibrant and imaginative work, and an awardee of the W.E.B. Du Bois medal for ‘contributions to African and African American culture, and advocacy for intercultural understanding and human rights,’ Yale honors you with a second Yale degree, Doctor of Fine Arts. ”

• Commencement 2024: A celebration of community
• Yale College 2024: Meet some of the graduates
• ‘Until we meet again’ — Baccalaureate and Class Day at Yale

Campus & Community

Peter Raymond appointed Oastler Professor of Biogeochemistry

YPEI graduation marks first B.A. presentation in a Connecticut prison

First U.S. trial of drug for e-cigarette cessation shows positive results

YSPH graduates urged to adopt a ‘healthy disregard for the impossible’

• Show More Articles