research about online selling in pandemic

The pandemic has changed consumer behaviour forever - and online shopping looks set to stay

More and more consumers are ordering goods online. Image:  REUTERS/Danish Siddiqui

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Stay up to date:.

• Consumer shift to digital channels will remain after the pandemic -PwC report.
• Customer loyalty has plummeted, with buyers switching brands at unprecedented rates.
• The use of smartphones for online shopping has more than doubled since 2018.

Billions of people affected by the COVID-19 pandemic are driving a “historic and dramatic shift in consumer behaviour” – according to the latest research from PwC.

The consulting and accounting firm's June 2021 Global Consumer Insights Pulse Survey reports a strong shift to online shopping as people were first confined by lockdowns, and then many continued to work from home. Other trends in this shift towards digital consumption include online shoppers being keen to find the best price, choosing more healthy options and being more eco-friendly by shopping locally where possible.

Another significant finding from the report is that consumers do not think they’ll go back to their old ways of shopping once the pandemic is over.

A consumer pivot to digital and devices

More than 8,600 people across 22 territories took part in PwC’s survey. They were asked how often, in the past 12 months, they had bought clothes, books and electronics using a range of shopping channels.

Have you read?

Covid-19 pandemic accelerated shift to e-commerce by 5 years, new report says, these charts show how covid-19 has changed consumer spending around the world.

The chart below illustrates their answers, and shows a shift to digital and a growing trend for shopping using connected devices such as smartphones, tablets and smart voice assistants such as Amazon Echo, Google Home and Samsung SmartThings.

More than 50% of the global consumers responding to the June 2021 survey said they had used digital devices more frequently than they had six months earlier, when they had taken part in a prior PwC survey. The report also finds the use of smartphones for shopping has more than doubled since 2018.

COVID-19 has exposed digital inequities globally and exacerbated the digital divide. Most of the world lives in areas covered by a mobile broadband network, yet more than one-third (2.9 billion people) are still offline. Cost, not coverage, is the barrier to connectivity.

At The Davos Agenda 2021 , the World Economic Forum launched the EDISON Alliance , the first cross-sector alliance to accelerate digital inclusion and connect critical sectors of the economy.

Through the 1 Billion Lives Challenge , the EDISON Alliance aims to improve 1 billion lives globally through affordable and accessible digital solutions across healthcare, financial services and education by 2025.

Read more about the EDISON Alliance’s work in our Impact Story.

Medicines and groceries on demand

A survey of US consumers by McKinsey & Company gives a more detailed breakdown of the shift to digital shopping channels and the kinds of purchases consumers are making.

The survey found a 15-30% overall growth in consumers who made purchases online across a broad range of product categories. Many of the categories see a double-digit percentage growth in online shopping intent, led by over-the-counter medicines, groceries, household supplies and personal care products.

And McKinsey noted that “consumer intent to shop online [post-pandemic] continues to increase, especially in essentials and home-entertainment categories”.

A decline in brand loyalty

With consumers shopping from their sofas and home offices, another trend flagged up by McKinsey is a marked decline in brand loyalty.

In total, 75% of US consumers have tried a new shopping behaviour and over a third of them (36%) have tried a new product brand. In part, this trend has been driven by popular items being out of stock as supply chains became strained at the height of the pandemic. However, 73% of consumers who had tried a different brand said they would continue to seek out new brands in the future.

What is the World Economic Forum doing to manage emerging risks from COVID-19?

The first global pandemic in more than 100 years, COVID-19 has spread throughout the world at an unprecedented speed. At the time of writing, 4.5 million cases have been confirmed and more than 300,000 people have died due to the virus.

As countries seek to recover, some of the more long-term economic, business, environmental, societal and technological challenges and opportunities are just beginning to become visible.

To help all stakeholders – communities, governments, businesses and individuals understand the emerging risks and follow-on effects generated by the impact of the coronavirus pandemic, the World Economic Forum, in collaboration with Marsh and McLennan and Zurich Insurance Group, has launched its COVID-19 Risks Outlook: A Preliminary Mapping and its Implications - a companion for decision-makers, building on the Forum’s annual Global Risks Report.

Companies are invited to join the Forum’s work to help manage the identified emerging risks of COVID-19 across industries to shape a better future. Read the full COVID-19 Risks Outlook: A Preliminary Mapping and its Implications report here , and our impact story with further information.

Healthy, hygienic and sustainable

The trend towards online shopping has also seen consumers focus on staying healthy during long periods in lockdown. McKinsey notes a desire to reduce touchpoints to ensure greater hygiene with the shopping experience.

One enterprise in the US has tapped into these trends to provide a service for shopping online at a range of farm shops local to the buyer. To qualify for the FarmMatch scheme, farmers must grow their food using sustainable methods.

As the world navigates its way out of the pandemic, the way we all act as consumers has been changed fundamentally by COVID-19. The research points to this change becoming permanent, leaving retailers and manufacturers with the challenge of attracting and retaining consumers in an 'omnichannel' world, where customer loyalty is hard-won.

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Open Access

Peer-reviewed

Research Article

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis

Roles Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Zhengzhou University of Light Industry, High-tech District, Zhengzhou City, Henan Province, China

Roles Conceptualization, Funding acquisition, Project administration, Supervision

* E-mail: [email protected]

Affiliation School of Politics and Public Administration, Soochow University, Gusu District, Suzhou City, Jiangsu Province, China

Roles Data curation, Funding acquisition, Project administration

Roles Formal analysis, Funding acquisition, Project administration

• Qiwei Wang, 
• Xiaoya Zhu, 
• Manman Wang, 
• Fuli Zhou, 
• Shuang Cheng

• Published: May 18, 2023
• https://doi.org/10.1371/journal.pone.0286034
• Peer Review
• Reader Comments

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com . Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Citation: Wang Q, Zhu X, Wang M, Zhou F, Cheng S (2023) A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis. PLoS ONE 18(5): e0286034. https://doi.org/10.1371/journal.pone.0286034

Editor: Ahmad Samed Al-Adwan, Al-Ahliyya Amman University, JORDAN

Received: April 19, 2023; Accepted: May 5, 2023; Published: May 18, 2023

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Henan Province Philosophy and Social Science Planning Project (grant number. 2020CZH012), the Henan Key Research and Development and Promotion Special (Soft Science Research) (grant number. 222400410126), the Jiangsu Province Social Science Foundation Youth Project (grant number. 21GLC012) and the Doctor Fund of Zhengzhou University of Light Industry (grant number. 2020BSJJ022, 2019BSJJ017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

A prolonged quarantine and lockdown imposed by the coronavirus disease 2019 (COVID-19) pandemic has changed the human lifestyle worldwide. The COVID-19 pandemic has negatively impacted various sectors such as manufacturing, import and export trade, tourism, catering, transportation, entertainment, especially retail and hence the global economy. Consumer behavior has gradually shifted toward contactless services and e-commerce activities owing to the COVID-19 [ 1 ].

Consumers are relying on e-commerce more than ever to protect their health. Recent advances in information technology, digital transformation, and the Internet helped consumers to encounter the COVID-19 to meet the needs of the daily lives, which led to an increase in the importance of e-commerce and changes in consumers’ online purchasing patterns [ 2 ]. When consumers shop online, their behavior is considered non-traditional, and is illustrated by a new trend and current environment. To analyze the influencing factors of online consumer purchasing behavior (OCPB), it is necessary to consider several factors, such as the price and quality of a product, consumers’ preferences, website design, function, security, search, and electronic word-of-mouth (e-WOM) [ 3 ]. As the current website design and payment security have become a user-friendly and guaranteed system compared with a decade ago, some factors are no longer considered as essential. By contrast, greater diversity and complexity have become the main characteristics of the influencing factors. Furthermore, under the traditional sales model, consumers’ purchase decisions were simple, while online consumers have more options in terms of shopping channels and decision choices. Meanwhile, in recent years, consumers’ preferences have gradually shifted from standardized products to customized and personalized. In line with these changes, information technology and data science, such as big data analytics, data mining from e-WOM, and machine learning (ML), adaptively analyze data regarding online consumers’ needs to obtain more accurate data.

Since the concept of big data was proposed in 2008, it has been applied and developed lasting 14 years, emerging as a valuable tool for global e-commerce recently. However, most enterprises have failed to seize the benefits generated from big data. In the context of big data, a huge number of comments were posted regarding e-malls (Amazon, Taobao, etc.) and online social media (blogs, Bulletin Board System, etc.). For instance, Amazon was the first e-commerce company to establish an e-WOM system in 1995, which provided the company with valuable suggestions from online consumers. E-WOM has greater credibility and persuasiveness, compared with traditional word of mouth (WOM), which is limited by various subjective factors. Moreover, e-WOM has the advantage of containing not only structured data (e.g., ratings) but also unstructured data (e.g., the specific content of consumer reviews). However, e-WOM provides product-related information that cannot be directly transformed to a research objective. Thus, an innovative method of big data analytics needs to be utilized to explore the influencing factors of OCPB, which shows the advantage of interdisciplinary applications.

The research problems are to explore the factors influencing OCPB through e-WOM data mining and analysis and explain the most important influencing factors for online consumers that are likely to exist in the future within the context of the COVID-19. The study fulfills the literature gaps on exploring influencing factors of OCPB from the perspective of e-WOM. The study makes a significant contribution to the consumer study because its findings can adequately identify the influencing factors of OCPB. It also provides the theoretical and managerial implications of its findings including how e-commerce platforms can use such data to adapt their platforms and marketing strategies to diverse situations.

The remainder of this is organized as follows. Section 1 presents the introduction. Section 2 discusses the literature review and hypotheses. Section 3 provides the methodology, including data mining and analysis. Section 4 describes the results, including K-means results, performance metrics, hypotheses results, and a theoretical model. Sections 5 and 6 provide discussion and conclusion, respectively.

2. Literature review and hypotheses

2.1 influencing factors of ocpb.

Online shopping has an increasing sales volume each year, which has become huge challenges for offline retailers. Venkatesh et al. [ 4 ] found that culture, demographics, economics, technology, and personal psychology were the main antecedents of online shopping, and the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment by a comprehensive model of consumers online purchasing behavior. Within the context of COVID-19, OCPB is positively impacted by attitude toward online shopping [ 5 ]. Melović et al. [ 6 ] focused on millennials’ online shopping behavior and noted that the demographic characteristics, the affirmative characteristics, risks and barriers of online shopping were the key influencing factors. Based on the stimulus-organism-response (SOR) theory model, consumers’ actual impulsive shopping behavior is impacted by arousal and pleasure [ 7 ]. Furthermore, the influencing factors of consumers’ purchase behavior toward green brands are green perceived quality, green perceived value, green perceived risk, information costs saved, and purchase intentions by perceived risk theory [ 8 ]. The positive and negative effects of corporate social responsibility practices on consumers’ pro-social behavior are moderated by consumer-brand social distance, although it also impacts consumer behavior beyond the consumer-brand dyadic relationship [ 9 ]. Green perceived value, functional value, conditional value, social value, and emotional value may impact green energy consumers’ purchase behavior [ 10 ]. Recipients’ behavior and WOM predict distant consumers’ behavior [ 11 ]. Moreover, consumer behavior is significantly impacted by financial rewards, perceived intrusiveness, attitudes toward e-mail advertising, and intentions toward the senders [ 12 ]. Store brand consumer purchase behavior is positively impacted by store image perceptions, store brand price-image, value consciousness, and store brand attitude [ 13 ]. A meta-analysis summarizes the influencing factors of consumer behavior, household size, store brands, store loyalty, innovativeness, familiarity with store brands, brand loyalty to national brands, price consciousness, value consciousness, perceived quality of store brands, perceived value for money of store brands, and search versus experience positively impact consumer behavior, whereas price–quality consciousness, quality consciousness, price of store brands, and the consequences of making a mistake in a purchase negatively impact consumer behavior [ 14 ].

Based on protection motivation theory and theory of planned behavior (TPB), consumers are more likely to use online shopping channels than offline channels during the COVID-19 pandemic [ 15 ]. The TPB is also adapted to explain the influencing factors of consumers’ behavior in different areas. For instance, the attitude, perceived behavioral control, policy information campaigns, and past-purchase experiences significantly impact consumers’ purchase intention, whereas subjective and moral norms show no significant relationship based on the extended TPB [ 16 ]. Although green purchase behavior has different antecedents, only personal norms and value for money have fully significant relationships with green purchase behavior, environmental concern, materialism, creativity, and green practices. Functional value positively influences purchase satisfaction, physical unavailability, materialism, creativity, and green practices, and negatively influences the frequency of green product purchase by extending the TPB [ 17 ]. Meanwhile, Nimri et al. [ 18 ] utilized the TPB in green hotels and showed that knowledge and attitudes, as well as subjective injunctive norms, positively impacted consumers’ purchase intention. Yi [ 19 ] observed that attitude, social norm, and perceived behavioral control positively impacted consumers’ purchase intention based on the TPB. The factors of supportive behaviors for environmental organizations, subjective norms, consumer attitude toward sustainable purchasing, perceived marketplace influence, consumers’ knowledge regarding sustainability-related issues, and environmental concern are the influencing factors of consumers sustainable purchase behavior [ 20 ]. Consumers’ green purchase behavior is impacted by the intention through support of the TPB [ 21 ].

2.2 Influencing factors of emergency context attribute

Consumers exhibited panic purchase behavior during the COVID-19, which might have been caused by psychological factors such as uncertainty, perceptions of severity, perceptions of scarcity, and anxiety [ 22 ]. In the reacting phase, consumers responded to the perceived unexpected threat of the COVID-19 and intended to regain control of lost freedoms; in the coping phase, they addressed this issue by adopting new behaviors and exerting control in other areas, and in the adapting phase, they became less reactive and accommodated their consumption habits to the new normal [ 23 ]. The positive and negative e-WOMs may have significant influence on online consumers’ psychology. Specifically, e-WOM that conveys positive emotions (pride, surprise) tends to have a greater impact on male readers’ perception of the reviewer’s cognitive effort than female readers, whereas e-WOM that conveys negative emotions (anger, fear) has a greater impact on cognitive effort of female readers than male readers [ 24 ]. When online consumers believe their behavioral effect is feasible and positive, while their behavioral decision is related to the behavioral outcome [ 25 ]. Traditionally, there are five stages of consumer behavior that include demand identification, information search, evaluation of selection, purchase, and post-purchase evaluation. In addition, online purchase behavior involved in the various stages can be categorized into: attitude formation, intention, adoption, and continuation. Most of the important factors that influence online purchasing behavior are attitude, motivation, trust, risk, demographics, website, etc. “Internet Adoption” is widely used as a basic framework for studying “online buying adoption”. Psychological and economic structures associated with the IT adoption model can be used as the online consumer’s behavior models for innovative marketers. The adoption of online purchasing behavior is explained by different classic models of attitude behavior [ 26 ]. Consumer behaviors represented by customer trust and customer satisfaction, influence repurchase and positive WOM intentions [ 27 ]. Return policy leniency, cash on delivery, and social commerce constructs were significant facilitators of customer trust [ 28 ]. Meanwhile, seller uncertainty was negatively influenced by return policy leniency, information quality, number of positive comments, seller reputation, and seller popularity [ 29 ]. Social commerce components were a necessity in complementing the quality dimensions of e-service in the environment of e-commerce [ 30 ]. Perceived security, perceived privacy and perceived information quality were all significant facilitators of online customer trust and satisfaction [ 31 ].

E-service quality, consumer social responsibility, green trust and green perceived value have a significant positive impact on green purchase intention, whereas greenwashing has a significant negative impact on green purchase intention. In addition, consumer social responsibility, green WOM, green trust and green perceived value positively moderated the relationship between e-service quality and green purchase intention, while greenwashing and green participation negatively moderated the relationships [ 32 ]. Large-scale online promotions provide mobile users with a new shopping environment in which contextual variables simultaneously influence consumer behavior. There is ample evidence suggesting that mobile phone users are more impulsive during large-scale online promotion campaigns, which are the important contextual drivers that lead to the occurrence of mobile users’ impulse buying behavior in the “Double 11” promotion. The results show that promotion, impulse buying tendency, social environment, aesthetics, and interactivity of mobile platforms, and available time are the key influencing factors of impulse buying by mobile users [ 33 ]. Environmental responsibility, spirituality, and perceived consumer effectiveness are the key psychological influencing factors of consumers’ sustainable purchase decisions, whereas commercial campaigns encourage young consumers to make sustainable purchases [ 34 ]. The main psychological factors affecting consumers’ green housing purchase intention include the attitude, perceived moral obligation, perceived environmental concern, perceived value, perceived self-identity, and financial risk. Subjective norms, perceived behavioral control, performance risk, and psychological risk are not included. Meanwhile, the purchase intention is an important predictor of consumers’ willingness to buy [ 35 ]. The perceived control of flow and focus will positively affect the utilitarian value of consumers, while focus and cognitive enjoyment will positively impact the hedonic value. Moreover, utilitarian value has a greater impact on satisfaction than hedonic value. Finally, hedonic value positively impacts unplanned purchasing behavior [ 36 ]. Utilitarian and hedonic features achieve high purchase and WOM intentions through social media platforms and also depend on gender and consumption history [ 37 ].

Therefore, we present the following hypothesis:

• Hypothesis 1 (H1): Perceived emergency context attribute is the influencing factor of OCPB.

2.3 Influencing factors of perceived product attribute

Product quality and preferential prices are the major factors considered by online consumers, especially within the context of the COVID-19. Specifically, online shopping offers lower price, more choices for better quality products, and comparison between them [ 1 ]. Under the circumstance of online reviews, an original equipment manufacturer (OEM) selling a new product carefully decides whether to adopt the first phase remanufacturing entry strategy or to adopt the phase 2 remanufacturing entry strategy under certain conditions. Meanwhile, the OEM adopts penetration pricing for new and remanufactured products, when the actual quality of the product is high. Otherwise, it adopts a skimming pricing strategy, which is different from uniform pricing when there are no online reviews. Online reviews significantly impact OEM’s product profits and consumer surplus. Especially when the actual quality of the product is high enough, the OEM and the consumer will be also reciprocal [ 38 ]. Online reviews reduce consumers’ product uncertainty and improve the effect of consumer purchase decisions [ 39 , 40 ]. Uzir et al. [ 41 ] utilized the expectancy disconfirmation theory to prove that product quality positively impacts customer satisfaction, while product quality and customer satisfaction are mediated by customer’s perceived value. Product quality and customer’s perceived value will have greater influence with higher frequency of social media use. Nguyen et al. [ 42 ] studies consumer behavior from a cognitive perspective, and theoretically develops and tests two key moderators that influence the relationship between green consumption intention and behavior, namely the availability of green products and perceived consumer effectiveness.

Both sustainability-related and product-related texts positively influence consumer behavior on social media [ 43 ]. Online environment, price, and quality of the products are significantly impacted by OCPB. Godey et al. [ 44 ] explained the connections between social media marketing efforts and brand preference, price premium, and loyalty. Brand love positively impacts brand loyalty, and both positively impact WOM and purchase intention [ 45 ]. Brand names have a systematic influence on consumer’s product choice, which is moderated by consumer’s cognitive needs, availability of product attribute information, and classification of brand names. In the same choice set, the share of product choices with a higher brand name will increase and be preferred even if it is objectively inferior to other choices. Consumers with low cognitive needs use the heuristic of “higher is better” to select options labeled with brand names and choose brands with higher numerical proportions [ 46 ].

• Hypothesis 2 (H2): Perceived product attribute is the influencing factor of OCPB.

2.4 Influencing factors of perceived innovation attribute

Product innovation increases company’s competitive advantage by attracting consumers, whereas the enhancement of innovative design according to consumer behavior accelerates the development of sustainable product [ 47 , 48 ]. The innovation, WOM intentions and product evaluation can be improved positively by emotional brand attachment and decreased by perceived risk [ 49 ]. Based on the perspective of evolutionary, certain consumer characteristics, such as buyer sophistication, creativity, global identity, and local identity, influence firms’ product innovation performance, which can increase the success rate of product innovation, and enhance firms’ research and development performance [ 50 ]. However, technological innovation faces greater risk as it depends on market acceptance [ 51 ]. Moreover, electronic products rely more on technological innovation compared with other products, which maintain the profit and market [ 52 ]. The technological innovation needs to apply logical plans and profitable marketing strategies to reduce consumer resistance to innovation. Thus, Sun [ 53 ] explains the relationship between consumer resistance to innovation and customer churn based on configurational perspective, whereas the results show that response and functioning effect are significant but cognitive evaluation is not.

Based on the perspective of incremental product innovation, aesthetic and functional dimensions positively impact perceived quality, purchase intention, and WOM, whereas symbolic dimension only positively impacts purchase intention and WOM. By contrast, aesthetic and functional dimensions only positively impact perceived quality, whereas symbolic dimension positively impacts purchase intention and WOM. Furthermore, perceived quality partially mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by incremental product innovation, whereas perceived quality fully mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by radical product innovation [ 54 ]. Contextual factors, such as size of organizations and engagement in research and development activity, moderate the relationship between design and product innovation outcomes [ 55 ]. For radical innovations, low level of product innovation leads to more positive reviews and less inference of learning costs. As the functional attribute of radical innovations is not consistent with existing products, it is difficult for consumers to access relevant product category patterns and thus transfer knowledge to new products. The product innovation of aesthetics, functionality, and symbolism positively impact willingness to pay, purchase intention, and WOM through brand attitude [ 56 ]. This poor knowledge transfer results in consumers feeling incapable of effectively utilizing radical innovations, resulting in greater learning costs. In this case, product designs with low design novelty can provide a frame of reference for consumers to understand radical innovations. However, incremental product innovation shows no significant difference between a low and high level of design newness [ 57 ].

• Hypothesis 3 (H3): Perceived innovation attribute is the influencing factor of OCPB.

2.5 Influencing factors of perceived motivation attribute

The research has proven that almost all consumers’ purchases are motivated by emotion. Under this circumstance, an increase in online consumers’ positive emotions increases, their purchase frequency, whereas an increase in online consumers’ negative emotions reduces their purchase frequency. Additionally, user interface quality, product information quality, service information quality, site awareness, safety perception, information satisfaction, relationship benefits and related benefit factors have negative impacts on consumers’ online shopping emotionally. Nevertheless, only product information quality, user interface quality, and safety perception factors have positive effects on online consumer sentiment [ 58 ]. E-WOM carries emotional expressions, which can help consumers express the emotions timely. Pappas et al. [ 59 ] divides consumers’ motivation into four factors, namely entertainment, information, social-psychological, and convenience, while emotions into two factors, namely positive and negative. Specifically, according to complexity and configuration theories, a conceptual model by a fuzzy-set qualitative comparative analysis examines the relationship between a combination of motivations, emotions, and satisfaction, while results indicate that both positive and negative emotions can lead to high satisfaction when combing motivations.

From the perspective of SOR theory, consumers’ motivation is greatly influenced by self-consciousness, while conscious cognition plays the role of intermediary. First, after being stimulated by the external environment, online consumers will form “cognitive structure” depending on their subjectivity. Instead of taking direct action, they deliberately and actively obtain valid information from the stimulus process, considering whether to choose the product, and then react. Second, the stimulation stage in the retail environment can often attract the attention of consumers and cause the change of their psychological feelings. This stimulation is usually through external environmental factors, including marketing strategies and other objective influences. Third, organism stage is the internal process of an individual. It is a consumers’ cognitive process about themselves, their money, and risks after receiving the information they have seen or heard. Reaction includes psychological response and behavioral response, which is the decision made by the consumer after processing the information [ 60 ]. Based on literature review, 10 utilitarian motivation factors, such as desire for control, autonomy, convenience, assortment, economy, availability of information, adaptability/customization, payment services, absence of social interaction, and anonymity and 11 hedonic motivation factors, such as visual appeal, sensation seeking/entertainment, exploration/curiosity, escape, intrinsic enjoyment, relaxation, pass time, socialize, self-expression, role shopping, and enduring involvement with a product or service, are refined [ 61 ]. Consumers’ incidental moods can improve online shopping decisions impulsivity, while decision making process can be divided into orientation and evaluation [ 62 ]. Sarabia‐Sanchez et al. [ 63 ] combine K-means cluster and ANOVA analyses to explore the 11 motivational types of consumer values, which are achievement, tradition, inner space, universalism, hedonism, ecology, self-direction (reinforcement, creativity, harmony, and independence), and conformity.

• Hypothesis 4 (H4): Perceived motivation attribute is the influencing factor of OCPB.

3. Materials and methods

3.1 research design.

Given the present study’s objective to identify the influencing factors of OCPB, we analyzed e-WOM using big data analysis. To obtain accurate data of the influencing factors on OCPB, smartphones were the main object of data crawling. The rationale behind this choice is as follows. First, the time people spend using their smartphones is gradually increasing. Nowadays, smart phones are not only used for telephone calls or text messages, but also for taking photographs, recording video, surfing the web, online chatting, online shopping, and other such uses [ 64 ]. Second, smartphones have become a symbol of personal identification, as users’ using fingerprint or facial scans are frequently used to unlock devices, conduct online transactions, and make reservations, etc. Finally, smartphones’ software and hardware are updated frequently, so they may be considered high-tech products. Therefore, smartphones were chosen as the research object to determine which influencing factors affect OCPB.

Fig 1 shows the e-WOM data mining process and methods used. A dataset obtained from Taobao.com and Jingdong.com was collected by utilizing a Python crawling code, additional details of which are provided in Section 2.3. Section 2.4 addresses issues regarding language complexity. Moreover, Section 2.5 refers to the clustering of the influencing factors of OCPB through the K-means method of ML.

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https://doi.org/10.1371/journal.pone.0286034.g001

3.2 Data collection

The data were crawled from the e-commerce platforms Jingdong.com and Taobao.com by utilizing Python software. Jingdong and Taobao are the most powerful and popular platforms in China having professional e-WOM and user-friendly review systems. Specifically, the smartphone brands selected for analysis were Apple, Samsung, and Huawei because these three smartphone companies occupy the largest percentage of the smartphone market.

The authors determined that the analysis of the influencing factors of OCPB would be more persuasive and realistic by choosing smartphone models with high usage rate and liquidity. Thus, products reviews were crawled for the purchase of newly launched smartphones from Apple, Samsung, and Huawei in 2022. Specifically, to guarantee high-quality data, reviews from Taobao flagship stores and Jingdong directly operated stores were selected. However, we only collected reviews’ text content instead of images, videos, ratings, or rankings, the rationale was to ensure the reliability of data and meet research objectives. For instance, some e-commerce sellers attempt to increase their sales volume through deceitful methods, such as by faking ratings, rankings, and positive comments. Furthermore, online sellers and e-commerce companies (rather than consumers) often decide which smartphones are highest-rated and highest-selling. Finally, nowadays, the content of online reviews is not limited to text, as they also involve pictures, videos, and ratings, which have limited contribution in analyzing influencing factors of OCPB. Thus, the analyzed data regarding e-WOM in reviews was limited to text content.

In addition, to accurately reflect the real characteristics of OCPB during the COVID-19 pandemic, the study period ranged between February and May, 2022 (4 months). During that 4-month period, consumers exhibited a preference for buying products from e-commerce platforms. Specifically, the number of text reviews for the aforementioned types of smartphones was 51,2613 and 44,3678 in Taobao and Jingdong, respectively, for a total of 956,291 reviews.

3.3 Textual review processing method

As the crawled data exhibited noise, several data cleaning methods were adopted to filter noise and transform unstructured data of complex contextual review into structured data. Fig 1 shows the main procedures of the reviews’ pre-processing and the details are as follows.

First, to identify the range of sentences and for further data processing, sentences were apportioned using Python’s tokenizer package.

Second, this study employed Python’s Jieba package to perform word segmentation. The Jieba package is the Python’s best Chinese word segmentation module, comprising three modes. The exact mode was used to segment the sentences as accurately as possible, so they may be suitable for textual context analysis. The full mode was used to scan and process all words in each sentence, although it had a relatively high speed, it had a low capacity to resolve ambiguity. Additionally, the search engine mode segmented long words a second time, which allowed for the improvement of the recall rate, and was suitable for engine segmentation based on Jieba’s exact mode.

Third, stop words were deleted by referring to a stop words list. These included conjunctions, interjections, determiners, and meaningless words, among others. Finally, Python’s Word-to-vector (Word2vec) package was imported in the next step. Word2vec is an efficient training word vector model proposed by Mikolov [ 65 , 66 ]. The basic starting point was to match pairs of similar words. For instance, when “like” and “satisfy” appeared in a same context, they showed a similar vector, as both words had a similar meaning. Kim et al. [ 67 ] stated that a word could be considered a single vector and real numbers in the Word2vec model. In fact, most supervised ML models could be summarized as f ( x )−> y . Moreover, x could be considered a word in a sentence, while y could be considered this word in the context. Word2vec aimed to decide whether the sample of ( x , y ) could match the laws of natural language. Namely, after the process of Word2vec, the combination of word x and word y could be reasonable and logical or not. Table 1 shows the results of text processing.

https://doi.org/10.1371/journal.pone.0286034.t001

3.4 Influencing factors analysis by K-means

ML styles are divided into supervised and unsupervised algorithms. This study mainly utilized unsupervised algorithms to analyze the clusters of influencing factors of OCPB. Unsupervised algorithms consist in the clustering of unknown or unmarked objects without a trained sample [ 68 ]. This study utilized K-means to cluster the influencing factors.

For a given sample set, the K-means algorithm divides the sample set into k clusters according to the distance between samples. The main algorithm’s logic is to make the points in the cluster as close as possible, and to make the distance between the clusters as large as possible. Assuming that clusters can be divided into ( C 1 , C 2 ,…, C k ), the Euclidean distance of E is shown in Eq 1 .

The main procedures of K-means were the following.

Step 1 consisted of inputting the samples D = { x 1 , x 2 ,… x m }, K is the number of clusters, and appears as C = { C 1 , C 2 ,… C k }.

In Step 2, K samples were randomly selected from data set D as the initial K centroid vectors: { μ 1 , μ 2 ,… μ k }.

For Step5, it was necessary to repeat Steps 3 and 4, until all the centers μ remained steady. The final clustering result can be shown as C = { C 1 , C 2 ,… C k }.

The main procedures of K-means, according to Jain [ 69 ], are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0286034.t002

4.1 K-means results

Based on the main procedures of K-means ( Table 2 ), the results are presented in Figs 2 – 4 .

https://doi.org/10.1371/journal.pone.0286034.g002

https://doi.org/10.1371/journal.pone.0286034.g003

https://doi.org/10.1371/journal.pone.0286034.g004

Four clusters of influencing factors of OCPB can be clearly identified in the analyses of the Jingdong dataset, Taobao dataset, and combined Jingdong and Taobao dataset. After checking the context of four clusters, even though small differences were found, their influence was found to be negligible for our analyses. Thus, Fig 4 was chosen as the benchmark of influencing factors of OCPB. In Section 4.3, the explanation and analysis of influencing factors of OCPB will be presented.

4.2 Performance metrics

First, performance metrics of sum of the square errors (SSE) and silhouette coefficient were adapted to verify the clustering results of K-means.

When the number of clusters does not reach the optimal numbers K, SSE decreases rapidly with the increase of the number of clusters, while SSE decreases slowly after reaching the optimal numbers, and the maximum slope is the optimal numbers K.

Where C i is the i th cluster, p is the sample point in C i (the mean value of all samples in C i ), and SSE is the clustering error of all samples, which represents the quality of clustering effect.

Fig 5 indicates that the SSE decreases rapidly when K equals the number of four.

https://doi.org/10.1371/journal.pone.0286034.g005

The range of sc i is between -1 and 1, the clustering effect is bad when sc i is below zero, whereas the clustering effect is good when sc i is near 1 conversely.

Based on Fig 6 , it is obviously to show that the silhouette coefficient reaches highest when K equals the number of four. Therefore, the results of the SSE and the silhouette coefficient jointly prove the number of K is four.

https://doi.org/10.1371/journal.pone.0286034.g006

4.3 Hypotheses results

Based on the K-means analysis, this section presents the influencing factors identified in the data from Jingdong and Taobao, which indicate the influencing factors influencing OCPB.

The first cluster comprises the perceived emergency context attribute, such as logistics, expressage, delivery, customer service, promotion, and reputation.

The second cluster comprises the perceived product attribute, such as appearance, brand, hand feeling, color, cost-performance ratio, price, design, and usability.

The third cluster comprises the perceived innovation attribute, such as photograph, quality and effects, screen quality, audio and video quality, pixel density, image resolution, earphone capabilities, and camera specifications.

The fourth cluster comprises the influencing factors, such as processing speed, operation, standby time, battery, system, internal storage, chip, performance, and fingerprint and face recognition, which cannot represent the perceived motivation attribute.

The results match the findings of Zhang et al. [ 70 ] to some extent, who identified 11 smartphone attributes based on online reviews: performance, appearance, battery, system, screen, user experience, photograph, price, quality, audio and video, and after-sale service. In addition, other scholars have explained the relationship between feature preferences and customer satisfaction [ 71 , 72 ], usage behavior and purchase [ 73 , 74 ], importance and costs of smartphones’ features and services [ 75 ], brand effects [ 76 ], and purchase behavior of people of different ages and gender groups [ 77 – 79 ]. Thus, H1, H2 and H3 are supported, while H4 is not supported according to the results of the K-means analysis.

4.4 Theoretical framework and validity of OCPB influencing factors

Kotler’s five product level model states that consumers have five levels of need comprising the core level, generic level, expected level, augmented level, and potential level. First, the core benefit is the fundamental need or want that consumers satisfy by consuming a product or service. Second, the generic level is a basic version of a product made up of only those features necessary for it to function. Third, the expected level includes additional features that the consumer might expect. Fourth, the augmented level refers to any product variations or extra features that might help differentiate a product from its competitors and make the brand a preferred choice amongst its competitors. Finally, a potential product includes all augmentations and improvements that a product might experience in the future [ 80 ].

In contrast with these levels, this study proposed the four influencing factors of OCPB. Based on Table 3 , first, the perceived emergency context H1 is not included in Kotler’s five products level, while the influencing factor expresses the significant characteristics of OCPB compared with Kotler’s model. Second, the perceived product attribute H2 could be considered the core and generic level. Third, the perceived innovation attribute H3 could be considered the potential level. Fourth, the results of H4 mainly reflects additional or special function of product, which meets the definition of the expected and augmented level. To refine the theoretical framework, H4 changes to the perceived functionality attribute by combing the explanation of the expected and augmented level, instead of the perceived motivation attribute. The details are shown in Fig 7 .

https://doi.org/10.1371/journal.pone.0286034.g007

https://doi.org/10.1371/journal.pone.0286034.t003

Fig 7 shows the four influencing factors of the theoretical framework of OCPB. Specifically, according to Kotler’s five products level, the perceived product attribute is the necessary influencing factor of OCPB, which meets the core drive and basic requirement. For instance, the core drive of purchasing of a smartphone is the core function of communication, and then the appearance, brand, color, etc. The perceived functionality attribute is the additional influencing factor of OCPB, which meets the expected and augmented requirement. For instance, when smartphones are in the same price range, consumers prefer to choose a smartphone belonging to better quality, smarter design, or better functionality. Moreover, the perceived innovation attribute is the attractive influencing factor of OCPB, which reflects the potential level. For instance, most consumers are the Apple fans mostly because the Apple products offer innovative usage experience and different technology elements yearly. Finally, the perceived emergency context attribute is the adaptive influencing factor of OCPB, which shows the main distinction with Kotler’s five products level. Further, because of the COVID-19, consumers only have online channel to purchase product under a prolonged quarantine and lockdown. Thus, in the emergency context, consumers primarily consider whether the product can be purchased in the e-commerce platform, whether the product can be delivered normally, or whether the packaged has been disinfected fully.

5. Discussion

Traditional consumer behavior is mainly affected by psychological, social, cultural, economic, and personal factors [ 81 , 82 ]. Park and Kim [ 83 ] conducted an empirical study to identify the key influencing factors that impact OCPB, which include service information quality, user interface quality, security perception, information satisfaction, and relational benefit. Further, Sata [ 84 ] conducted an empirical study and found that price, social group, product features, brand name, durability and after-sales services were important to consumers’ buying behavior when choosing a smartphone for purchase. Simultaneously, some studies have utilized big data technology to explore OCPB, exploring online consumers’ attitude toward products in different countries, and identified product features. However, these studies do not identify the influencing factors of OCPB and ignore e-WOM. To better explain OCPB influencing factors, e-WOM should be integrated into the theoretical framework and used in practical applications. Thus, this study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM.

5.1 Theoretical implications

First, perceived emergency context attribute is the influencing factor of OCPB. Because of the COVID-19, e-commerce is the priority choice for consumers under circumstances of prolonged quarantine and lockdown, and then considering logistics and delivery. Furthermore, customer service, packaging, promotion, and reputation are critical to online consumers.

Second, perceived product attribute is the influencing factors of OCPB. The basic features of product, such as appearance, brand, hand feeling, price, and design, positively attract online consumers. Elegant appearance, famous brand, better hand feeling, lower price, and better design would be more impactful to OCPB.

Third, perceived innovation attribute is the influencing factor of OCPB. For smartphone, online consumers would show more interest in the innovation of speed, operation, standby time, chip, etc. Scientific and technological innovation for most products could improve the level of OCPB. Thus, the guarantee and improvement of functionality of a product could create more opportunities for online consumers to make purchasing decisions.

Fourth, according to Kotler’s five products level, perceived product attribute satisfies the characteristics of core drive and basic, while the perceived innovation attribute satisfies the characteristics of the potential level. Because hypothesis of perceived motivation attribute is not supported. Based on the analyzing results, the perceived functionality attribute is refined instead of the perceived motivation functionality attribute, which satisfies the expected and augmented. Meanwhile, the perceived emergency context attribute is not included, which shows the main difference with Kotler’s five products level.

5.2 Managerial implications

The influencing factors of OCPB were clustered into four categories: perceived emergency context, product, innovation, and function attributes. The definition and explanation of these categories may have important managerial implications for both OCPB and e-commerce. First, the findings of this study suggest that e-commerce enterprises should pay more attention to improving the quality, user experience, and additional design features of their products to arouse the interest of OCPB. However, this may be difficult for e-commerce enterprises because achieving these goals requires updating the software and hardware constantly, which involves significant investment. For most scientific and technical corporations, making heavy investments is not particularly difficult, however, service-type enterprises and small and medium enterprises may have insufficient funds to afford such heavy investments. This is the main reason that most online consumers buy products from famous brands instead of small and medium enterprises. Therefore, to improve their situation, both types of companies could jointly develop products or services, for instance, small and medium enterprises may purchase patents from large enterprises, jointly researching and developing products, or large enterprises could share their achievements at a price.

Second, the pandemic has accelerated the spread of e-commerce considerably, changing consumers’ shopping style in the process. Accordingly, e-commerce enterprises should adapt their marketing strategies, especially as the COVID-19 pandemic is still ongoing, due to the rapid development of the economy and its dynamic environment. For instance, e-commerce platforms should realize that changes in OCPB will continue to contribute to the growth of the e-commerce market. Moreover, e-commerce enterprises should combine their online presence with brick-and-mortar stores. Even more importantly, e-commerce enterprises should successfully operate their supply chain to adapt to the implementation of lockdown measures and the closing of manufacturing factories. Consumers should exercise caution when facing e-commerce enterprises’ adaptive financial policy, such as interest-free rates, which may cause financial burden.

Third, e-commerce enterprises should offer a simple and smooth shopping experience, clearly display practical information, increase the value of goods (by improving the quality, design, and performance of products or services) and improve their brand image for online consumers. However, e-commerce enterprises sometimes rely on certain fraudulent methods to increase their sales volume, such as falsifying positive e-WOM and deleting negative feedback, as was identified during the data processing stage. Therefore, online consumers should select online stores cautiously to avoid buying products of poor quality or performance.

Fourth, nowadays, technology is constantly evolving at an accelerated rate, particularly in the smartphone industry, as companies launch new products with innovative functions each year. Thus, e-commerce enterprises should strive to innovate to secure their position in the market. In addition, consumers should reconsider the need to experience the state-of-the-art products because these may have high prices.

6. Conclusion and limitations

In conclusion, during the COVID-19 pandemic, consumers highly preferred to buy products online, because most brick-and-mortar stores were closed due to lockdowns and social distancing measures. Additionally, with the rapid development of e-commerce, online shopping has become the most popular shopping style because it allows consumers to not only save time and money, but also review e-WOM before purchasing a product. Moreover, e-WOM is much more reliable compared with traditional WOM. Thus, this study proposed a theoretical framework to explore and define the influencing factors of OCPB based on e-WOM data mining and analyzing. The data were crawled from Jingdong and Taobao, while the data process was also fully demonstrated. Comparing the results, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. Moreover, perceived emergency context attribute is the main difference compared with Kotler’s five products level, while perceived product attribute meets the core and generic level, perceived functionality attribute meets the expected and augmented level, and perceived innovation attribute meets the potential level.

However, this study still has certain limitations. First, the data were crawled from Chinese e-commerce websites, hence, they may not be generalized in contexts where the influencing factors and dimensions may vary compared with other countries or regions. Second, this study only explored and defined the antecedents of OCPB. Data should be added from Western e-commerce websites. Moreover, the present study’s results should be compared with Western studies to generate a more comprehensive view of the antecedents of OCPB. Future studies should explore the underlying mechanisms influencing OCPB.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0286034.s001

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• Published: 09 October 2023

Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea

• Jiam Song   ORCID: orcid.org/0000-0002-7975-0909 1 ,
• Kwangmin Jung   ORCID: orcid.org/0000-0002-5615-8865 2 &
• Jonghun Kam   ORCID: orcid.org/0000-0002-7967-7705 1  

Humanities and Social Sciences Communications volume  10 , Article number:  669 ( 2023 ) Cite this article

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• Environmental studies

A Correction to this article was published on 30 October 2023

This article has been updated

The COVID-19 pandemic has changed the level of the received risk of the public and their social behavior patterns since 2020. This study aims to investigate temporal changes of online search activities of the public about shopping products, harnessing the NAVER DataLab Shopping Insight (NDLSI) data (weekly online search activity volumes about +1,800 shopping products) over 2017–2021. This study conducts the singular value decomposition (SVD) analysis of the NDLSI data to extract the major principal components of online search activity volumes about shopping products. Before the pandemic, the NDLSI data shows that the first principal mode (15% of variance explained) is strongly associated with an increasing trend of search activity volumes relating to shopping products. The second principal mode (10%) is strongly associated with the seasonality of monthly temperature, but in advance of four weeks. After removing the increasing trend and seasonality in the NDLSI data, the first major mode (27%) is related to the multiple waves of the new confirm cases of corona virus variants. Generally, life/health, digital/home appliance, food, childbirth/childcare shopping products are associated with the waves of the COVID-19 pandemic. While search activities for 241 shopping products are associated with the new confirmed cases of corona virus variants after the first wave, 124 and 190 shopping products are associated after the second and third waves. These changes of the public interest in online shopping products are strongly associated with changes in the COVID-19 prevention policies and risk of being exposed to the corona virus variants. This study highlights the need to better understand changes in social behavior patterns, including but not limited to e-commerce activities, for the next pandemic preparation.

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Introduction

The COVID-19 pandemic has spread out since early 2020. The corona virus is contagious and has transformed to variants. The global community has been suffered with multiple waves of new confirmed cases of the corona virus variants. The herd community of the corona virus consists of the natural infected groups and vaccinated groups. However, the herd immunity for COVID-19 is required to prevent multiple variants from Alpha through Omicron (Moghnieh et al. 2022 ). Particularly, the absence of vaccines for corona virus and its variants has exacerbated the pandemic over the world. The variants have decreased the probability to form the herd immunity.

The spread of corona virus variants has significantly increased public risk perception, thereby leading people to avoid in-person activities (Dryhurst et al. 2020 ). Markets have responded to such changes in socioeconomic landscape by rapidly adapting digital transformations, which consequently boosted online platforms relating to shopping. The public have become preferred to online shopping, rather than in-person shopping, particularly when the number of infected people increases (Grashuis et al. 2020 ; Li et al. 2020 ; Mouratidis and Papagiannakis, 2021 ; Pham et al. 2020 ). This shift of the public’s lifestyle provides an opportunity to understand the impact of the COVID-19 pandemic on socioeconomic change via big social monitoring data relating to online information seeking activities.

The impact of the COVID-19 pandemic can be examined by comparing socioeconomic activities before and after COVID-19 pandemic. However, the long-lasting pandemic crisis makes it difficult to investigate the time-varying impact of the COVID-19 pandemic. Few literature has considered temporal changes of the impact of COVID-19 through its multiple waves due to the cost of collecting relevant data and the time-consuming data preprocessing. Online social monitoring data enables us to investigate the impact of the multiple waves of the corona virus variants and relevant prevention policies on online socioeconomic activities, which are costly-efficient and real-time monitoring. Recent studies have investigated changes in online activity patterns during the COVID-19 pandemic (Gu et al. 2021 ; Lampos et al. 2021 ; Nasser et al. 2021 ). However, the socioeconomic impact of the multiple waves of the corona virus variants remains unknown.

During the COVID-19 pandemic, online shopping patterns has been investigated in various ways. A previous study discussed a chance to die or modify old purchasing habits from in-person activities and to create new habits (Sheth, 2020 ). The new habits are likely to be influenced by socioeconomic constraints, such as public policy, technology and changing demographics. Another study proposed this behavior pattern change during the COVID-19 pandemic introducing the “react”, “cope”, and “adapt” phases of the Reacting Coping Adapt (RCA) framework (Kirk and Rifkin, 2020 ). At the “react” phase, the public change their purchasing behavior based on pandemic risk perception as a social response to dynamic social distancing policies. At the “coping” phase, they start adopting new purchasing pattern based on the public policy level. At “adapt” phase, they establish/stabilize new purchasing pattern and less reactive to the pandemic situation (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ). The RCA framework has been validated by the online shopping patterns in France before, during, and after the COVID-19 pandemic (Guthrie et al. 2021 ). The application of this RCA framework to other countries and social behaviors is still lacking.

Nowadays, the Internet service providers monitor and record online search activities through data logging and analyze these online search activity data to detect changes in the user’s interest and optimize the search algorithm for most relevant information to their interest in a timely manner. For example, increased online search activities about a specific shopping product hint an emerging demand of the shopping product, which is a practical information for inventory and supply chain management.

Online social network data, such as Twitter, have been already used to predict stock market price change (Almehmadi, 2021 ). Online information search activity data, such as Google Trends, have been used to forecast the near-term values of economic indicators (Carrière‐Swallow and Labbé, 2013 ; Choi and Varian, 2012 ), private consumption (Vosen and Schmidt, 2011 ), and epidemics (Carneiro and Mylonakis, 2009 ; Teng et al. 2017 ). Recently, the utility of these data has been examined in investigating spatiotemporal changes of social response to natural disasters, such as earthquakes and droughts (Gizzi et al. 2020 ; Kam et al. 2021 ; Kam et al. 2019 ; Kim et al. 2019 ). However, These social monitoring big data have been underutilized to investigate the changes of socioeconomic activities during the multiple waves of the corona virus variants.

The NAVER Shopping website is the most popular online shopping platform among the citizens of the Republic of Korea with online sales valued at about 2.7 billion KRW in the third quarter of 2021 (2.3 million USD) ( https://www.wiseapp.co.kr/insight/detail/89 ). Online shopping activities via the NAVER shopping website can capture major modes of online shopping activities of the Koreans. For example, increased online search activities relating to a specific shopping product hint an emerging demand of the NAVER’s users relating to the shopping product (Woo and Owen, 2019 ). Rumors about an emerging topic can affect the public’s social behavior patterns via social media (Alkhodair et al. 2020 ). However, the quality of social monitoring data determines an appropriate analysis spatial scale, and a careful design of data preprocesses is necessary for quality control (Wilcoxson et al. 2020 ). Recently, it has been found that the public interest in nationwide natural disasters and global pandemics can reduce the impact of rumors on social media and online seeking activities because the rumors can be verified by the direct and indirect experience of the public from the disaster or pandemic (Park et al. 2022 ; Kam et al. 2021 ).

Recent studies found a relationship between decision making and consumer behavior patterns at the individual level during the COVID-19 pandemic (Birtus and Lăzăroiu, 2021 ; Smith and Machova, 2021 ; Vătămănescu et al. 2021 ). Statewise sentimental alterations have been also found from the public’s complaints about water pollution during the COVID-19 pandemic (Liu et al. 2023 ). However, the impact of the COVID-19 pandemic and associated prevention policies on national-level social behavior pattern remains unknown. Online social monitoring data provides a unique opportunity to examine the relationship between decision making and consumer behaviors as response to changes of the COVID-19 pandemic prevention policies.

This study aims to investigate the impact of multi-year COVID-19 pandemic, using the NAVER DataLab Shopping Insight (NDLSI) data that provided by the NAVER Corporation. The data provides online search activity volumes relating to +1,800 shopping products at the nation level, which can detect an emerging change of online purchasing activities of the Koreans. The NAVER Corporation has operated the online search engine since 1999 and is the most popular internet search engine platform in South Korea. It had 1.2 billion visits from August through October 2022, and 94% of these visits solely from the Republic of Korea ( https://www.similarweb.com/website/naver.com/#traffic ). The NAVER Coporation provides weekly online search activity volume data of 1,800 shopping times since 2017 via the NDLSI platform. Such big social monitoring data provide a unique research opportunity to examine the COVID-19 impact on online shopping activities of the Koreans within the RCA framework by answering the following questions:

What are the major components of the dynamic patterns of online search activities before and after COVID-19?

How have the social behavior patterns related to online shopping search activities changed along multiple waves of corona virus variants?

Which prevention policies are key factors of the temporal changes of online shopping search activities during the COVID-19 pandemic?

To answer these questions, this study extracts the major modes of information seeking behavior patterns relating to shopping products from the NDLSI data (2017–2021) via the singular value decomposition algorithm-based Principal Component Analysis (PCA). Furthermore, the RCA framework is validated by the major modes of the NDLSI data during the multiple waves of the COVID-19 pandemic. The PCA analysis of the NDLSI data will advances the current understanding about changes in e-commerce before and after the two-year long COVID-19 pandemic.

Data and methods

Naver datalab shopping insight (ndlsi) data.

The NDLSI data includes the number of clicks on 1,837 shopping products from the NAVER Shopping platform. This study uses the NDLSI data that provide 214-week online search activities relating to 1,837 shopping products (July 31, 2017 through August 30, 2021). Weekly relative search activity volumes of the NDLSI data range from 0 to 100 (normalized by the maximum number of clicks during the search period and multiplied by 100). The NDLSI data is classified at the three levels: 11 categories for the first level, 204 categories for the second level, and 1,837 items for the third level (see Table S1 . in Supplementary Material). These categories of shopping products are provided from NAVER shopping platform, which are based on the merchant category codes (MCCs) that a credit card issuer to uses to categorize the transactions consumers complete using a particular card. The MCCs is used to classify merchants and businesses by the type of goods or services provided in order to keep a track of transactions. Recently, changes in credit/debit card spending in the MCCs have been analyzed during the COVID-19 pandemic (Darougheh, 2021 ; Dunphy et al. 2022 ). The first level categories include Fashion clothing, Fashion Miscellaneous Goods, Cosmetics/Beauty, Digital/Home Appliance, Furniture/Interior, Childbirth/Childcare, Food, Sports/Leisure, Life/Health, Leisure/Life convenience, and Duty-free shops. The category and product names are provided in Korean. In this study, the category and product names are translated in English via the Google Translator.

Six COVID-19 metrics

This study uses the six COVID-19 metrics from the Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE) COVID-19 dataset (Dong et al. 2020 ). The six COVID-19 metrics include new confirmed cases, stringency index, residential index, vaccination index, new death cases, and fatality. New confirmed/death cases are the number of the corresponding case of the Koreans over the study period. The stringency index is estimated based on the nine metrics: school closures, workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movements, and international travel controls. The stringency index shows the strictness of the government prevention policies in quantitative method (Dong et al. 2020 ). The value ranges from 0 (lowest stringency) to 100 (highest stringency). Higher stringency values represent more strict prevention policies. The residential index shows the number of people who spend more time at home after the COVID-19 pandemic than before. The vaccination index is a partial vaccinated index that represents the percent of who have vaccinated at least once. The fatality index is the ratio of the number of the number of new death cases to the number of new confirmed cases. While these daily six metrics are available, this study computes and uses the weekly sums of new confirmed cases and the weekly averages of the other five COVID-19 metrics, which is a consistent temporal scale with the NDLSI data analysis. The Korea Meteorological Administration (KMA) provides the historical meteorological data of the Republic of Korea through the Open MET Data Portal platform ( https://data.kma.go.kr/cmmn/main.do ). In this study, weekly temperature averages of the 95 stations in the Republic of Korea are computed to extract the seasonality of the regional climate system.

Singular value decomposition (SVD)-based principal component analysis

In the machine learning field, the principal component analysis (PCA) is a popular unsupervised learning method. The PCA technique is known as a data compressing technique to extract key features of the high dimension data. Singular value decomposition (SVD) algorithm can be used to extract the PCA major modes (Vosen and Schmidt, 2011 ; Wilks, 2011 ). The SVD algorithm-based PCA decomposes a covariance matrix into three matrixes if the A matrix has m x n dimension (n < m; Eq. 1 ). These matrixes include the U matrix (an m by m matrix), the Σ matrix (a m diagonal matrix) and the V transpose matrix (an n by n matrix). The Σ matrix is a diagonal matrix which have one to one correspondence with the U matrix. The U matrix shows the orthogonal eigenvectors, which are known as the principal components (PCs).

In this study, the SVD algorithm is employed to the covariance matrix of the NDLSI data over the five different periods. The five periods include the period before the COVID-19 pandemic (July 31, 2017–December 31, 2019), Wave 1 (July 31, 2017–May 25, 2020), Wave 2 (July 31, 2017–October 19, 2020), Wave 3 (July 31, 2017–March 1, 2021), and Wave 4 (July 31, 2017–August 31, 2021) of the corona virus variants. Here, the waves are defined based on the surges of the new confirm cases. To explore shopping products with an increasing/decreasing interest of the public during each wave of the COVID-19 pandemic, the SVD analysis period for the wave of interest covers before the emergence of the next wave, which includes the overlapped analysis period of the previous wave. It enables us to investigate the impact of the wave of interest on the public interest relating to shopping products compared to that of the previous wave.

Two major modes are found before the COVID-19 pandemic: the increasing line trend and the seasonality pattern of the online search activities. Before employing the SVD-based PCA analysis to the NDLSI data, the linear trend and the seasonality are removed from the NDLSI matrixes over the period of Wave 1, 2, 3 and 4. The detrended NDLSI data over the different periods enable us to investigate changes of online search activities relating to shopping products over the multiple waves of COVID-19. Not available values in the NDLSI data were replaced with zeros. The U and V matrixes are the same eigenvectors of the covariance matrix and the Σ matrix includes the eigenvalues. The Σ matrix’s diagonal values show the quantitative contribution of the corresponding vector to the total variance of the covariance matrix.

Spearman’s rank correlation

Spearman’s rank correlation is a non-parametric metric to find a relationship between two variables based on their ranks (Spearman, 1904 ). This study uses Spearman’s rank correlation because online search activities of most items in the NDLSI data do not have normal distribution. Spearman’s rank correlation efficiency ranges from −1 (negative perfect relation between two variables) to +1 (positive perfect). In this study, Spearman’s rank correlation is used to trace the user’s interest in shopping products that are associated with the wave of corona virus variants. Furthermore, Spearman’s rank correlation is computed between the COVID-related metrics and NDLSI data to examine which socio-economic factors associated on e-commerce search activities. First, Spearman’s rank correlation coefficients are computed between the first principal component (PC1; one time series) and the search activities of +1,800 shopping products (>1,800 time series) during the periods of Wave 1 through 4. Furthermore, the distribution of Spearman’s rank correlation coefficient is constructed by the kernel density estimate (KDE) method from the Joyplot python package ( https://github.com/leotac/joypy ). Shopping products with a high coefficient have increased from Wave 1 through 4 (Fig. S 1 ). In this study, 0.45 of Spearman’s coefficient is a threshold value to detect up to 20% of associated item with the PC1 mode with the COVID-19 pandemic.

Quantile-Quantile plot (QQ plot)

The number of the PC-associated shopping products affect the construction of the reliable correlation distributions with the COVID-19 metrics. A Quantile-Quantile (QQ) plot is a common visualization method to determine whether two data sets are came from same distributions or not. Despite different numbers of the COVID-19 associated shopping products during each wave period, the QQ plot can detect the stability of the correlation distribution shape. The QQ plot is based on the ranks of each data, which gives an advantage that the two dataset still can be compared in the QQ plot even though the sample sizes of the two datasets are different. The one-to-one line is a reference line of the QQ plot. When the quantile line of the two data is close to the reference line, the two sample data are from the same distribution (Nist, 2006 ). In this study, the QQ plots are constructed for the sensitive analysis of the stability of the correlation distribution shape to shopping products numbers. This analysis can determine how many shopping products are needed to generate the reliable distributions of its correlation with the waves of corona virus variants (Figs. S 2 and S 3 ).

Principal components of NDLSI data

Before the COVID-19 pandemic (hereafter, Wave 0), the first and second Principal Component (PC) modes (PC1 and PC2, respectively) explained around 15% and 10% of the total variance, respectively. PC1 was a monotonic increasing trend of online search activities for shopping products. PC2 was strongly associated with the seasonality of weekly mean temperature, however the seasonal cycle of online search activities relating to shopping products was four weeks ahead of the seasonality of the temperature (Fig. 1A, B ). Based on Spearman’s rank correlation coefficients with the PC1 and PC2, the top 10 shopping products showed that these two major modes captured well an increasing trend of shopping product-specific e-commerce and the seasonality of online search relating to shopping items during Wave 0 (Fig. 1C–F ).

Weekly time series of Principal Component 1 (PC1) of NDLSI data before COVID-19 ( A ) and PC2 ( B ) with heatmap of correlation coefficients of top 10 correlated items. Associated online search activities of top 10 shopping products with the PC1 time series ( A ): Positive ( C ) and negative correlation ( D ). Associated online search activities of top 10 shopping products with seasonality ( B ): Summer- and winter-related shopping products in ( C ) and ( D ), respectively.

Results showed that the top 10 PC1-related shopping products included toothpaste, table tennis shoes, and cleaning tissue, packed lunch, and hair spray. Shopping products with a negative correlation coefficient with the PC1 mode included (car) hands free, sea fishing, Random Access Memory (RAM), and Network Attached Storage (NAS). Shopping products with a positive (negative) correlation coefficient with the PC2 mode (the seasonality of temperature in advance of four weeks) were summer (winter) season shopping products. Based on the correlation coefficients with PC2, summer season shopping products included fan, parasol, yeolmu kimchi (a type of kimchi for summer), and tarp. Winter season shopping products included brooch, beanie, and neck cape. These PC2-based items were the well-known popular shopping products for summer and winter, respectively, confirming that the PCA technique is useful to extract and interpret key features in the NDLSI data when the principal major mode is associated with a certain temporal pattern (herein, the seasonality of temperature).

Flow of PC1 related items during the COVID-19 pandemic

Results from the PCA analysis of the detrended NDLSI data showed that PC1 resembled the new confirm cases of COVID-19 over the four waves of the corona virus variants (Fig. 2 ). The percent of explained variance by the PC1 mode increased from the first wave (20%) through the fourth wave (27%), which means that associated shopping products with the corona virus variants increased during the COVID-19 pandemic. The first-level category shopping products associated with the PC1 mode showed temporal changes from Wave 1 through 4 (Fig. 3 ). For visualization, the Sankey diagram was constructed, which has been often used as an efficient visualization for changes of the flow/volume of the data (Lupton and Allwood, 2017 ).

Weekly time series of the PC1 mode of the detrended NDLSI data up to Wave 1 through Wave 4 (gray dash lines) along South Korea’s COVID-19 new confirmed cases (a sky line).

Sankey diagram of COVID-19 associated shopping products during the four waves.

Based on the result of the explained variance by the PC1 mode (around 20% of the total variance), changes in online search activities relating to shopping products with the correlation coefficient, 0.45, or higher (close to 20% of total items) were analyzed. Overall, life/health, digital/home appliance items showed a large percentage during the study periods). Outdoor activity-related category items, including cosmetics/beauty, fashion clothing and fashion miscellaneous goods, account for small portions than other category items. Associated items with the corona virus variants have increased from Wave 1 through Wave 4 by more than twice (from 327 to 714). After the first wave, new 241 shopping products showed the correlation coefficient, 0.45 or above. This inflow of online search activities were associated with shopping items in the categories of life/health (25%), digital/home appliances (15%), and food (15%) (Fig. 4 ).

Percentages of the first-level shopping product categories of inflow after Wave 2, 3, and 4.

After Wave 2, the inflow of the 125 items included life/health (29%), digital/home appliance (19%), and childbirth/childcare (12%) items with decreased item numbers (125 items). After Wave 3, the inflow of 190 items included life/health (22%), digital/home appliance (17%), and childbirth/childcare (19%) items. Interestingly, duty-free shopping products and leisure/life convenience items first appeared after the Wave 2 and 4, respectively. The leisure/life convenience category items included work out class (fitness/personal training and Pilates) abroad travel items (abroad travel package, airline ticket, Wi-Fi/ Universal Subscriber Identity Module (USIM)). Increasing online search activities relating to work out class may be come from a concern about health due to a restrict quarantine policy. Increased interest in abroad travel cases after Wave 4 suggests that the public in South Korea might have a low perceived risk of the COVID-19 pandemic and begin to consider that the pandemic is over.

To investigate the temporal change of the third-level (product-specific) category shopping products associated with the waves of the corona virus variants, changes in the correlation coefficients of the top 10 items were selected for each waves (Fig. 5 ). The results showed that 31 shopping products were associated with the PC1 component throughout the four waves. More than 32% shopping products were in the category of life/health shopping products. These 31 items can be classified into two groups: the items with a higher and lower correlation coefficient over time. The first group items included minidisc player monitor arms, webcam, interphone box, fabric, handicraft supplies/subsidiary materials, character card/ticket, processed snacks, cooking oil/oil, bread, tuning supplies, craft, feed, seeds/seedlings, water aperture, gravel/sands/soil, landscape tree/sapling. These first group shopping products showed a persistent increase in the correlation coefficient through the multiple waves. The second group items included gas range, microwave, toothbrush, hula hoop. These second group shopping products showed a decrease in the correlation coefficient (Fig. 5 ).

The numbers of the Wave 1 through 4 heatmaps are Spearman’s rank correlation coefficients of the shopping products with the PC1 mode. The Wave 2 to 4 heatmap depict the percent changes of Spearman’s rank correlation coefficients compared with the correlation coefficients after Wave 1 (( Corr X – Corr 1 )/ Corr 1 ) * 100, where X depicts the wave occurrence order (X = 2, 3, and 4).

Weekly time series of the COVID-19 new confirmed cases ( A ), the stringency ( B ), residential index ( C ), vaccinated rate ( D ), new deaths by the corona virus ( E ), and fatality ( F ).

These two shopping product groups might originate from the different social response to the strictness of prevention policies. During the first wave, the government forced the public to stay at home to minimize the risk of being exposed to the corona virus. However, the prevention policies became less strict at Wave 4 to account for the fatigue of the public from the multi-year pandemic and revive local business and industry sectors. While the first group items have become more associated with the waves of the corona virus variants, the second group items no longer showed a high correlation coefficient with the corona virus variants.

Association with the six COVID-19 metrics

A surge of new confirmed cases of corona virus variants can influence social behavior patterns relating to e-commerce in a different way due to a different level of the COVID-19 prevention policy and the easy access of online shopping activities. In this study, Spearman’s rank correlation coefficients between the six COVID-19 metrics and the NDLSI data are computed to investigate potential causes of changes in online search activity volumes of shopping products (Fig. 6 ).

The six COVID-19 metrics showed different correlation distributions with the six COVID-19 metrics (Fig. 7 ). As the sensitivity test of the correlation distribution shape to the number of shopping products, the Quantile-Quantile (QQ) plots have been made along the different shopping times (see Figs. S 2 and S 3 ). According to the QQ plots, the top 50 items were chosen to construct the correlation distributions of the top 50 shopping products with the vaccination index. The correlation coefficients were widely distributed, indicating a relatively weak association with online search activities relating to the shopping products (Fig. 7A ). The correlation distributions with the stringency and fatality indices showed a low variance with high correlation coefficients above 0.8. The correlation distribution with the residential index showed a relatively low correlation coefficients than those with the stringency and fatality indices. New confirmed and death cases showed a relatively high variance than the correlation distributions with the fatality and stringency data. The categories of the top 50 shopping products included life/health (20%), digital/home appliance (16%) and food (16%), shopping products (Fig. 7B ).

Distributions of Spearman’s rank correlation coefficient of top 50 items related to the COVID-19 pandemic with six COVID-19 metrics ( A ), and the pie chart of first category percentage of items of top 50 items ( B ).

To investigate associations of online search activities relating shopping products with the six COVID-19 metrics, the Spearman’s rank correlation coefficients with the 31 PC1-associated items associated with the COVID-19 pandemic were computed (Fig. 8 ). New confirmed cases, stringency, residential index, new death cases and fatality showed a high correlation coefficient with the most of top 10 shopping products. The vaccination index showed no significant correlation coefficient with the top 10 shopping products. Gas range, baby walker and toothbrush items showed a relatively low correlation with the COVID-19 metrics than other shopping products. Online search activities relating to these shopping items showed a decreasing correlation during COVID-19 pandemic (see Fig. 5 ), that is, these items no longer show a significant effect of the COVID-19 pandemic after the Wave 4.

Heatmap of Spearman’s rank correlation coefficient between COVID-19 metrics and the 31 shopping products.

Overall, the stringency and fatality metrics generally have high association with the changes in online search activity patterns for shopping product. Stringency can be regarded as how government control public strictly. Fatality shows seriousness of pandemic. The results indicate that consumer behavior response sensitively to extent of restriction policies and seriousness of pandemic.

This study used the NDLSI data about the online search activity volumes for shopping products, not real purchasing data. Using the data of online search activities can provide an evidence on emerging purchasing patterns of the public in the next regime, implying that the public might tend to purchase items that have been most searched in the previous timeframe (Chen et al. 2017 ). Lately, credit card data Footnote 1 and bar cord data Footnote 2 include the records of actual purchase activities. Integrating the actual purchase data and online search activity data can provide more practical guidelines and plans for socio-economic changes not only during the COVID-19 pandemic, but also the post pandemic period.

This study revealed that the public interest in online shopping products had been changed not only after the first wave of the COVID-19 pandemic but also during the following three waves. These dynamic patterns of the public interest in online shopping products were possibly explained by the RCA framework (Kirk and Rifkin, 2020 ). The RCA framework consists of reacting, coping, and adapting phases, and significant changes in social behavior patterns are expected during a transition period from one to another phase. The first wave was a typical ‘react’ phase because people responded to the pandemic situation. A large inflow volume after Wave 2 (241 items) indicated a coping phase. The new confirmed cases were relatively low during Wave 2 (see a line colored in sky in Fig. 2 ) compared with those during other waves. Inflow of online search activities relating shopping products was the minimum after Wave 2. This finding suggests that a transition from a ‘react’ to ‘coping’ phase might occur between Wave 2 and Wave 3. After Wave 3, the public coped with the long-term pandemic. During Wave 4, the categories related with outdoor activities show a low percentage, indicating a low level of the public interest in outdoor activities due to the COVID-19 quarantine policy. The result that the inflow of online search activities relating to leisure/life convenience items (workout class, abroad travel) at Wave 4 indicates that the public became less reactive to the wave of the corona virus variants, which hints an emerging signal of a low perceived risk of the COVID-19 pandemic after Wave 4. Therefore, the ‘adapt’ phase transition is expected after Wave 4.

Understanding the public’s purchasing patterns amid a global crisis via big social monitoring data is critical from the risk management perspective. Risk control (e.g., self-protection) and financing (insurance) strategies can be improved for the next global crisis by understanding and predicting changes in social behaviors. This study found that the shopping products with an increased interest of the public have been changed during the two year-long COVID-19 pandemic, which can be explained by different stages of the RCA framework. The social behavior patterns found by this study had been also reported from the observed reacting and coping consumer behaviors in mass media and online and reacting public behavior to social distance during the COVID-19 pandemic (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ; Tintori et al. 2020 ). Specifically, better understanding and predicting of which products can help markets manage inventory of shopping products that are in an emerging high/low demand throughout different regimes of the crisis. This study found that associations of these products were more clear when they were used for self-protection measures (e.g., facial masks in the COVID-19 pandemic).

Governments and authorities can accordingly implement changes in the public’s actions to prevent potential market failures that, for example, self-protection measures may not be sufficiently supplied, or big market players use their power to dominate necessity markets (Stiglitz, 2021 ). These responses from the public and private sectors can be optimized with prevention plans in a timely manner of different waves of the crisis by analyzing big social monitoring data. This study found changes in the interest and demand of the shopping products related to self-protection measures during the COVID-19 pandemic, which hints how to facilitate big social monitoring data to mitigate the adverse effects of daily infections. Furthermore, this information can help insurance industries manage systematic risks that cannot be fully controlled by individuals or other industry sectors, which can offer risk transfer measures (Alonso et al. 2020 ; Harris et al. 2021 ; Peiffer-Smadja et al. 2020 ; Rita et al. 2019 ). This study also found a strong association between changes in online search activities of the public relating to shopping items and perceived risk, which was previously found in the travel insurance purchasing patterns (Al Mamun et al. 2022 ; Tan and Caponecchia, 2021 ). This information can give an insight for how to increase the public’s willingness to prepare for the next pandemic.

Search engine optimization (SEO) algorithms for searching items have been developed, particularly in the e-commerce sector to increase the customer’s satisfaction and loyalty (Husain et al. 2020 ; Liu et al. 2008 ; Pratminingsih et al. 2013 ). Some online search engine platforms collect the data of the user’s online activities and optimize the customized recommendation algorithm that could give more relevant result of searching. Especially, e-commerce sites, such as Amazon, have developed this customized SEO algorithm to increase a chance to purchase the products (Heng et al. 2018 ; Linden et al. 2003 ). In this study, the observational evidence of the COVID-19 impact on online search activities about shopping products was reported, which was also found in online shopping pattern for apparel (Watanabe et al. 2021 ). The SEO algorithms developed by the data before the COVID-19 pandemic increased the user’s complaint by three times (Dahiya et al. 2021 ), implying that the COVID-19 pandemic was an unprecedented event since the advent of Internet that supposedly cause a drastic context difference. Therefore, the SEO algorithms are needed to update until the data after the pandemic is sufficient. Furthermore, the expected continued growth of online commerce industries requires the coping strategies to adapt an increasing trend of not only pandemics but also other disasters such as climatic extremes, pandemic, war, and terror.

This study provides an insight about how social big monitoring data can help authorities to better understand the social response to COVID-19 via near real-time social monitoring data. In this study, the NDLSI data about the online search activity volumes relating to shopping products, not real purchasing data, were used. The NDLSI data analysis provided a possible evidence on an emerging change in the public’s purchasing patterns at the shopping product level. Previously, it was found that the public tended to purchase shopping products that have been most searched in the previous timeframe (Chen et al. 2017 ). Associations between the public interest in shopping products and purchase records can be explored using credit card data Footnote 3 and barcode data Footnote 4 . These data have been used to investigate changes in spending associated with stringent nonpharmaceutical interventions during the COVID-19 pandemic (Horvath et al. 2023 ). Integrating the actual purchase data and online search activity data can give more practical guidelines and plans for socio-economic changes during not only the COVID-19 pandemic, but also the post pandemic period. Furthermore, the e-commerce sector can harness social big monitoring data to develop their strategic plans for supply chain management for the next pandemic.

This study also explored associations of changes of online search activity patterns with the COVID-19 metrics. The results showed that the COVID-19 metrics, except for vaccination, were strongly associated with changes in online search activity patterns relating to shopping products. The stringency index was a reliable indicator of the strictness of the government’s response to the COVID-19 pandemic and had a significant impact on social behavior patterns, which is in line with the findings of Makki et al. ( 2020 ) that the timing and duration of the stringency implementation are key factors to prevent the spread of the corona virus variants. Furthermore, a recent study found that policy perceptions affect the practice of volunteered prevention behaviors, such as mask waring and social distancing (Lee et al. 2021 ). They found that the perceived policy stringency was associated with actual risk and political ideology, causing noncompliance in communities during the COVID-19 pandemic.

The proposed methods in this study have some limitations. For example, the results based on the correlation analysis provide potential, not actual, triggers of changes in the social behavior patterns during the COVID-19 pandemic, which have previously known as the caveat of the correlation analysis (Haley and Drazen, 1998 ; Stigler, 2005 ). The findings of this study about potential triggers however can help design more effective and efficient interview and survey questionnaires to investigate true triggers of changes in the public interest in shopping products. Combined information from big social monitoring and survey/interview data will create new knowledge about the dynamics of social behavior patterns and help develop a reliable social behavior prediction modeling.

Conclusions

This study succeeded to extract the major modes of the public’s interest in shopping products and investigate changes in online search activities relating to associated shopping products with the COVID-19 pandemic. The SVD algorithm-based PCA analysis of the NDLSI data showed the dynamic patterns of online search activities relating to shopping products during the two year-long COVID-19 pandemic. Before the COVID-19 pandemic, an increasing trend and seasonality of online search activity volumes about shopping products are the major mode of the NDLSI data. After the COVID-19 pandemic, the impact of COVID-19 on online search activities relating to shopping products were various during the four waves of the corona virus variants, particularly when the objective risk was dramatically increased. Changes of the online search activity patterns were associated with the change of the COVID-19 prevention policy and objective risk of being exposed to the corona virus variants. This study attempted to explain the changes of these online search activity patterns within the RCA framework by identifying the react, coping, and adapt phases.

This study highlights the utility of online social monitoring data in developing strategic plans for preparation, mitigation, and recovery policies for the next pandemic. Furthermore, the findings of this study can guide how to design interview and survey questionnaires to investigate actual drivers of social behavior changes during the COVID-19 pandemic. Integrated studies using online social monitoring data and survey and interview data will advance the current knowledge and prediction skill of social behavior changes, which can provide actionable information to mitigate its adverse effects for the sustainable development of our communities Kim et al. ( 2019 ), Spearman ( 1904 ).

Data availability

The data used in this study are available at Harvard Dataverse: https://doi.org/10.7910/DVN/JT8RCK .

Change history

30 october 2023.

A Correction to this paper has been published: https://doi.org/10.1057/s41599-023-02297-3

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Acknowledgements

We thank the NAVER DataLab for making available the NAVER DataLab Shopping Insight (NDLSI) data. This study was supported by a grant from the National Research Foundation of Korea (NRF-2021R1A2C1093866).

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Jiam Song & Jonghun Kam

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JS: conceptualization, data curation, investigation, formal analysis, methodology, software, validation, visualization, writing—original draft, writing—review and editing. JK: conceptualization, methodology, funding acquisition, project administration, resources, supervision, writing—review and editing. KJ: analysis, validation, writing—review and editing.

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Song, J., Jung, K. & Kam, J. Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea. Humanit Soc Sci Commun 10 , 669 (2023). https://doi.org/10.1057/s41599-023-02183-y

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DOI : https://doi.org/10.1057/s41599-023-02183-y

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Impact of COVID Pandemic on eCommerce

The COVID pandemic crisis has forced many small businesses to reassess the decades-old traditional business models or face closing permanently. New and existing technologies are thrust to the forefront of every business toolkit, and forward-looking businesses are addressing talent questions that arise from these new digital business skillsets.

A Global Post-Crisis Bounce in eCommerce Sales?

Risk of further business closures from COVID-related disruptions, in addition to the inherent financial fragility of business, paints a grim forecast for many businesses still open. Or is this just an opinion based on a lack of data?

In the chart below we see a distinct upward jog in total global retail sales from 2019-2020, giving a strong boost to a steady 8% growth in retail ecommerce sales worldwide forecast through 2024 .This shows us an increase in online retail sales as a result of the paradigm shift that COVID disruptions have brought to business.

Pandemic Impact to Worldwide Consumer Behavior

As various pandemic-related business restrictions that prevented in-person activities crept across the world’s regions, business turned to the pandemic-proof ecommerce sales channels for basic survival. Online, global consumers could not stop purchasing through their favorite websites (44% of global digital purchases) and online marketplaces (47% of global digital purchases). In response to this consumer migration to digital, Brazil , Spain , Japan saw the largest increase in number of businesses selling online as a reaction to the pandemic.

  In the chart below we see a forecast increase of 19% n worldwide ecommerce revenue between pre-and-post COVID-19 timeframes in 2020. Food & Personal Care products show the most growth with a forecast increase of 26% of revenue as a result of consumer transition to online sales channels.

Pandemic Impact to Global Small B2B

The COVID pandemic has impacted business countries around the globe differently, creating opportunities for some where business was once lost. Small B2B companies in the United Kingdom and Brazil for example had significant increases in online revenue from their pre-COVID online sales figures.

Boosted by Pandemic, Cross-Border eCommerce Continues to Grow

The data tells us that COVID pandemic-related business restrictions have forced a global business paradigm shift towards the digital economy, which has negatively impacted traditional business models while also creating opportunity through sales diversification online.

This chart shows us clearly the impact to global ecommerce revenues the pandemic has had, adding an additional 19% sales growth for 2020, and additional 22% sales growth to the existing 9% and 12% regular forecast sales growth rates, respectively.

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Americans Keep Clicking to Buy, Minting New Online Shopping Winners

By Nathaniel Popper May 13, 2020

Change in consumer spending

Online sales in the United States have surged in recent weeks, after shelter-in-place measures enacted in March shuttered brick-and-mortar stores throughout the country.

While the shutdowns immediately altered how people spent their money , the patterns have continued to shift as the weeks have gone on, new data shows, shaped by waves of panic buying and even payouts of government aid. The latest bump in online spending came after the government sent out stimulus payments to tens of millions of American households beginning on April 11 .

Beyond what might be temporary shifts, consumer habits appear to be changing in ways that may well endure beyond the pandemic and determine who will become the most important online players.

Change in sales for major e-commerce categories

Big and sometimes unexpected winners have emerged in several of the industries that have come to define the coronavirus economy, according to data from Earnest Research, which tracks millions of credit and debit card transactions in the United States. Many e-commerce companies are now in a much stronger position than before. But even within the areas of rapid growth, the recent gains have not been spread evenly.

In grocery delivery, there is Instacart, and then everyone else

Change in online sales for grocery delivery companies.

Relative share of online sales in January

Note: Year-over-year change in sales through April 29   ·   Source: Earnest Research

There are few activities that have been upended more than grocery shopping, which had long been analog and resisted the world of online commerce . All that changed in a few short weeks, as people were told to stay home, without their need for food diminishing. According to several surveys, more than a third of all Americans have ordered groceries online for the first time over the last month, and people have spent more ordering groceries online each succeeding week of the crisis.

The clear winner so far has been Instacart. It was not the biggest going into the crisis, but it has the advantage of working with several grocery chains rather than directly selling products on its own, unlike most of its competitors.

The popularity of online grocery shopping has meant that some services have strained to meet the demand — and this could diminish the long-term appeal of the services.

FreshDirect and Peapod have been relatively flat, according to Earnest’s data, despite being some of the most established names in the industry. FreshDirect, which is largely focused on New York, talked publicly about its difficulty finding healthy employees. Peapod made ill-timed cutbacks right before the virus hit.

The central battle now is most likely between Instacart and the biggest forces in online retailing, Amazon, Walmart and Target, all of which have been investing more heavily in grocery sales. Walmart had the biggest established presence, but it has grown more slowly than Amazon and Target.

In overall e-commerce, Target and Walmart have been gaining on the behemoth, Amazon

Change in sales for e-commerce giants.

This grocery battle is part of a much bigger push by Target and Walmart to take on the behemoth of online shopping, Amazon. Both companies have recently expanded their online sales much faster than Amazon.

Amazon’s slower growth is largely explained by the fact that it started with such an enormous lead, and had already attracted many of the Americans willing to shop online.

For Target and Walmart, many of their new sales came from people who had never shopped with them before, while Amazon has relied more heavily on existing customers, according to data from Facteus, a firm that analyzes consumer transactions. Those elevated spending levels may go down at Amazon, but new customers are more likely to stick around.

Target was particularly well positioned going into this crisis because of its purchase of Shipt, a company that specializes in fast delivery of things like groceries. That has helped Target pull even with Amazon recently in terms of delivery time, data from Rakuten Intelligence shows, even as Target’s sales have grown.

Average shipping times

The companies delivering meals are now chasing DoorDash

Change in online sales for food delivery companies.

The crisis has also given a shot in the arm to online restaurant and meal delivery services, which were broadly experiencing slower growth earlier this year.

Grubhub, one of the bigger players in this industry, is now the subject of acquisition talks with Uber, which is a bigger company over all, but is smaller when it comes to delivery, through its Uber Eats service.

Grubhub has recently grown more slowly in large part, analysts say, because the company was long focused on independent restaurants, which have been more likely to close during the quarantines, and on New York, where the crisis hit the hardest.

DoorDash, the market leader, has focused on chain restaurants, and areas outside the big cities, where the sense of crisis was less acute. That has allowed it to expand its dominant position in the industry.

Video games, video games, video games

Change in online sales for electronics retailers.

The electronics industry has long developed its online delivery capabilities, but that has not helped everyone equally. Apple has seen the supply of its biggest seller, the iPhone, crimped by problems with factories in China. GameStop, on the other hand, was a much smaller player going in — but the one thing people can’t seem to get enough of during these days at home is video games.

It’s hard to sell clothes, other than leggings and sweatpants

Change in online sales for apparel companies.

The growth of online sales has not been enough to save all e-commerce players. The start-ups that were set up to deliver people their clothing at home have almost all struggled as people have stopped needing nice clothes to go to work. One of the few apparel companies that have been doing well, at least online, is Lululemon, thanks to its generous selection of the sweatpants and leggings that serve as particularly good work clothes when your office is in the basement.

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Transformation of personal selling during and after the COVID-19 pandemic

• Malaysia Sch of Business

Research output : Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

The personal selling process (PSP) has experienced dramatic changes due to the COVID-19 pandemic, including the transition from face-to-face presentations to online presentations and from paperwork to e-processes, and from the delivery of printed product materials to the provision of softcopies. Sudden changes to the PSP have affected salespeople differently and how salespeople have transformed themselves to cope with the challenges is themain concern of this chapter.After analyzing changes to the PSP due to changes in Malaysia’s external environment, we propose a personal selling transformation framework to assist salespeople to cope with the “new normal” way of conducting business. The proposed framework aims to help salespeople overcome sales challenges posed by the COVID-19 pandemic, and it suggests that salespeople should develop a long-term strategy to achieve better work performance after the end of the pandemic. The framework consists of four interconnected components: psychological capital, learning orientation, work process, and the use of technology. The effectiveness in enacting one component may influence the effectiveness of the others, thus, an integrated approach to effectuate these components may produce an overall positive impact on the performance of salespeople during and after the pandemic. The conceptual framework contributes to the extant literature by extending the socio-technical model of sales force change from an organizational level to an individual level. The framework also focuses on the B2C rather than B2B context. In particular, the framework highlights the importance of hope, efficacy, resilience, and optimism (HERO) in psychological capital, learning orientation, a hybrid work process, and the use of appropriate technology to enhance job performance. The implications of the framework are also discussed in this chapter.

• Financial services
• Learning orientation
• Personal selling
• Psychological capital
• Socio-technical model

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T1 - Transformation of personal selling during and after the COVID-19 pandemic

AU - Ewe, Soo Yeong

AU - Ho, Helen Hui Ping

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

N2 - The personal selling process (PSP) has experienced dramatic changes due to the COVID-19 pandemic, including the transition from face-to-face presentations to online presentations and from paperwork to e-processes, and from the delivery of printed product materials to the provision of softcopies. Sudden changes to the PSP have affected salespeople differently and how salespeople have transformed themselves to cope with the challenges is themain concern of this chapter.After analyzing changes to the PSP due to changes in Malaysia’s external environment, we propose a personal selling transformation framework to assist salespeople to cope with the “new normal” way of conducting business. The proposed framework aims to help salespeople overcome sales challenges posed by the COVID-19 pandemic, and it suggests that salespeople should develop a long-term strategy to achieve better work performance after the end of the pandemic. The framework consists of four interconnected components: psychological capital, learning orientation, work process, and the use of technology. The effectiveness in enacting one component may influence the effectiveness of the others, thus, an integrated approach to effectuate these components may produce an overall positive impact on the performance of salespeople during and after the pandemic. The conceptual framework contributes to the extant literature by extending the socio-technical model of sales force change from an organizational level to an individual level. The framework also focuses on the B2C rather than B2B context. In particular, the framework highlights the importance of hope, efficacy, resilience, and optimism (HERO) in psychological capital, learning orientation, a hybrid work process, and the use of appropriate technology to enhance job performance. The implications of the framework are also discussed in this chapter.

AB - The personal selling process (PSP) has experienced dramatic changes due to the COVID-19 pandemic, including the transition from face-to-face presentations to online presentations and from paperwork to e-processes, and from the delivery of printed product materials to the provision of softcopies. Sudden changes to the PSP have affected salespeople differently and how salespeople have transformed themselves to cope with the challenges is themain concern of this chapter.After analyzing changes to the PSP due to changes in Malaysia’s external environment, we propose a personal selling transformation framework to assist salespeople to cope with the “new normal” way of conducting business. The proposed framework aims to help salespeople overcome sales challenges posed by the COVID-19 pandemic, and it suggests that salespeople should develop a long-term strategy to achieve better work performance after the end of the pandemic. The framework consists of four interconnected components: psychological capital, learning orientation, work process, and the use of technology. The effectiveness in enacting one component may influence the effectiveness of the others, thus, an integrated approach to effectuate these components may produce an overall positive impact on the performance of salespeople during and after the pandemic. The conceptual framework contributes to the extant literature by extending the socio-technical model of sales force change from an organizational level to an individual level. The framework also focuses on the B2C rather than B2B context. In particular, the framework highlights the importance of hope, efficacy, resilience, and optimism (HERO) in psychological capital, learning orientation, a hybrid work process, and the use of appropriate technology to enhance job performance. The implications of the framework are also discussed in this chapter.

KW - COVID-19

KW - Financial services

KW - Learning orientation

KW - Personal selling

KW - Psychological capital

KW - Socio-technical model

UR - http://www.scopus.com/inward/record.url?scp=85146827685&partnerID=8YFLogxK

U2 - 10.1007/978-981-19-2749-2_13

DO - 10.1007/978-981-19-2749-2_13

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AN - SCOPUS:85146827685

SN - 9789811927485

BT - COVID-19 and the Evolving Business Environment in Asia

A2 - Kwok, Andrei O. J.

A2 - Watabe, Motoki

A2 - Koh, Sharon G. M.

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ORIGINAL RESEARCH article

From screens to carts: the role of emotional advertising appeals in shaping consumer intention to repurchase in the era of online shopping in post-pandemic provisionally accepted.

• 1 College of Business Administration, University of Business and Technology, Saudi Arabia

The final, formatted version of the article will be published soon.

This study examines the evolving dynamics of online shopping behavior in the post-COVID-19 era, focusing on the intricate relationship between perceived usefulness, ease of use, pleasure, arousal, dominance emotional state, and intention to repurchase by integrating and employing the technology acceptance model and pleasure, arousal, and dominance emotional model. These emotional states, identified as pivotal drivers of online shopping behavior, contribute to recognizing a brand's function and aesthetic features. 509 male and female respondents from Saudi Arabia participated in the present study. The statistical tools unveil significant indirect relationships and mediation effects, offering insights into the nuanced pathways through which perceived usefulness and ease of use impact consumer intentions to repurchase. Demographic variables, particularly age, and gender, are explored to understand variations in emotional responses, guiding businesses in tailoring marketing strategies to diverse consumer segments. Practical implications highlight the importance of strategic considerations for brand retailers, emphasizing enhancements to elements related to emotional branding, product presentations, interface design, and interactive services on websites. The research advocates for a dynamic and personalized approach to online experiences, positioning brands favorably in the competitive digital landscape. Overall, the findings contribute valuable insights for businesses seeking to navigate the dynamic terrain of post-COVID-19 online shopping and foster enduring connections with their digital consumer base.

Keywords: Emotional appeals, advertising, online shopping, Consumer intention, Post-Pandemic 1

Received: 14 Jan 2024; Accepted: 18 Apr 2024.

Copyright: © 2024 Alshohaib. 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) or licensor 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: Dr. Khalid Ali Alshohaib, College of Business Administration, University of Business and Technology, Jeddah, Saudi Arabia

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How online selling is thriving in the new normal

By Hannah Mallorca , Features Writer, The Philippine STAR

The growth of many e-commerce platforms is the effect of transition from traditional shopping channels to online platforms during quarantine

The coronavirus disease 2019 (COVID-19) pandemic has caused the rise and fall of several industries worldwide. Even though e-commerce platforms have long been on the rise, the process was fast-tracked when quarantine guidelines were put into place.

With the absence of traditional shopping channels, customers have flocked to e-commerce platforms to secure purchases and transactions. Many stores and restaurants have also transitioned online to serve their target market.

To discuss the current state of online selling in the new normal, The Philippine STAR’s Career Guide shared insights on the progress of e-commerce platforms and other online means during this time.

The online discussion featured Entrego retail director Xervin Maulanin, PurpleBug Inc. president and CEO Marlon Gonzales, La Carnita Modern Mexican Cantina co-founder Lenlen Mesina, Lazada Philippines head of business development Petrus Carbonell, and Seven Days of Greens co-founder Roel Uy Chan.

Growth of e-commerce platforms

Even though various e-commerce platforms were established pre-pandemic, its identity strengthened since the start of quarantine. Mr. Carbonell shared that Lazada has witnessed significant growth.

“So far, we see a lot of demand. We’re also seeing people who are more interested in starting their businesses online,” he added. “I think this will continue even after quarantine and if you think about it, these trends have always been present. Ang nangyari lang ngayong quarantine , na-accelerate siya .”

According to Mr. Carbonell, some of the most popular products in Lazada’s platform are groceries, medical items, and ready-to-eat products.

Mr. Maulanin noted that the transition from traditional shopping methods into online has pushed e-commerce platforms to sell more essential goods and daily needs. He added that delivery personnel are also considered as frontliners due to their service during the quarantine.

“ Lumalaki ang volume natin compared to before. We’re still a long way to go before online shopping becomes the predominant channel for us, but I think we’re going to see a lot of acceleration there. We’re very excited to see ano’ng magiging trend nito,” he said.

Transitioning from traditional shopping methods to online

The pandemic has caused many businesses to transition to online to cater to its customers. Many restaurants have also moved towards delivery services to serve their target market.

According to Ms. Mesina, proper research and development are needed to ensure the quality of Cantina’s products even in the new normal.

“At the moment, what we’re trying to do, instead of dispensing or distributing the product, we had to come up with product lines that can be experienced by the customers in the comfort of their homes, that’s why we really value the support and the service of third-party suppliers when it comes to delivery,” she shared.

Ms. Mesina also noted that the quarantine is an opportunity for many businesses to understand how to navigate into e-commerce and to incorporate online payments.

“You need to make sure that you’re able to deliver what you’re promising to your online market and that the product is available from you. It’s also not just being available, the products must be consistent and of very good quality kasi ‘yun ‘yung magiging labanan when it comes to online selling,” she added.

On the other hand, Mr. Gonzales said that quarantine has challenged e-commerce platforms, restaurants and other online sellers to develop its services since it will reflect on customers.

“People will always buy if maganda ‘yung feedback na makikita nila . What we’ve noticed din karamihan ng returning customers namin are referrals so very important ‘ yung feedback na nakikita nila online,” he said.

Mr. Uy Chan stated that the new normal has also urged online sellers, e-commerce platforms and restaurants to refine collaboration methods with its partner channels to ensure quality service.

“The principle behind online selling is still intact and similar to traditional selling wherever you go. It’s still just a channel,” he added.

In addition, Mr. Carbonell noted that e-commerce platforms and online sellers would continue to grow, even in a post-pandemic society.

“In terms of the potential of people reaching success, I would say that the sky is the limit because we see new millionaire sellers every time that we run a campaign. I’m not saying that everyone who goes online will be successful, but we see many cases that the potential is huge,” he said.

Online selling platforms have witnessed significant growth in customer behavior during the pandemic. With this, it’s up to business sectors to improve its services to ensure loyalty among its target market.

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Mental health and the pandemic: What U.S. surveys have found

The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.

Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .

Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.

The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.

At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.

Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.

Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.

In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.

The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.

While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.

More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).

In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.

On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.

Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.

While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)

Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)

Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.

White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.

While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.

• Coronavirus (COVID-19)
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Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, economy remains the public’s top policy priority; covid-19 concerns decline again, most popular.

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‘An epidemic of loneliness’: How the pandemic changed life for aging adults

Stock image of a sign at a park in 2020, calling for social distancing. Four years later, a new study shows many are still keeping to themselves more than they did pre-pandemic.  

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Years after the U.S. began to slowly emerge from mandatory COVID-19 lockdowns, more than half of older adults still spend more time at home and less time socializing in public spaces than they did pre-pandemic, according to new CU Boulder research. 

Participants cited fear of infection and “more uncomfortable and hostile” social dynamics as key reasons for their retreat from civic life.

“The pandemic is not over for a lot of folks,” said Jessica Finlay, an assistant professor of geography whose findings are revealed in a series of new papers . “Some people feel left behind.”

The study comes amid what the U.S. Surgeon General recently called an “ epidemic of loneliness ” in which older adults—especially those who are immune compromised or have disabilities—are particularly vulnerable.

“We found that the pandemic fundamentally altered neighborhoods, communities and everyday routines among aging Americans, and these changes have long-term consequences for their physical, mental, social and cognitive health,” said Finlay.

‘I just can’t go back’

As a health geographer and environmental gerontologist, Finlay studies how social and built environments impact health as we age.

In March 2020 as restaurants, gyms, grocery stores and other gathering places shuttered amid shelter-in-place orders, she immediately wondered what the lasting impacts would be. Shortly thereafter, she launched the COVID-19 Coping Study with University of Michigan epidemiologist Lindsay Kobayashi. They began their research with a baseline and monthly survey. Since then, nearly 7,000 people over age 55 from all 50 states have participated.

The researchers check in annually, asking open-ended questions about how neighborhoods and relationships have changed, how people spend their time, opinions and experiences of the COVID-19 pandemic, and their physical and mental health.

By the numbers

How aging adults spend their time

• 59% spend more time at home than before pandemic
• 41% go to the grocery less often
• 75% eat out less often 
• 57% exercise indoors less often
• 62% visit an arts or cultural site less often
• 53% attend religious services less often
• 10% exercise outdoors more often

Source: Data from COVID-19 Coping Study survey results from May 2022. A more recent survey found that more than half still had not returned to pre-pandemic social routines.

“We’ve been in the field for some incredibly pivotal moments,” said Finlay, noting that surveys went out shortly after George Floyd was murdered in May 2020 and again after the attack on the U.S. Capitol on Jan. 6, 2021.

Collectively, the results paint a troubling picture in which a substantial portion of the older population remains isolated even after others have moved on. 

In one paper published in February in the journal Wellbeing, Space and Society , 60% of respondents said they spend more time in their home while 75% said they dine out less. Some 62% said they visit cultural and arts venues less, and more than half said they attend church or the gym less than before the pandemic.

The most recent survey, taken in spring 2023, showed similar trends, with more than half of respondents still reporting that their socialization and entertainment routines were different than they were pre-pandemic. 

In another paper titled “ I just can’t go back ,” 80% of respondents reported there are some places they are reluctant to visit in person anymore.

“The thought of going inside a gym with lots of people breathing heavily and sweating is not something I can see myself ever doing again,” said one 72-year-old male.

Those who said they still go to public places like grocery stores reported that they ducked in and out quickly and skipped casual chitchat. 

“It’s been tough,” said one 68-year-old female. “You don’t stop and talk to people anymore.”

Many respondents reported they were afraid of getting infected with a virus or infecting young or immune-compromised loved ones, and said they felt “irresponsible” for being around a lot of people.

Some reported getting dirty looks or rude comments when wearing masks or asking others to keep their distance—interpersonal exchanges that reinforced their inclination to stay home.

Revitalizing human connection

Jessica Finlay, a health geographer and environmental gerontologist, studies how built environments impact aging.

The news is not all bad, stresses Finlay.

At least 10% of older adults report exercising outdoors more frequently since the pandemic. And a small but vocal minority said that their worlds had actually opened up, as more meetings, concerts and classes became available online.

Still, Finlay worries that the loss of spontaneous interactions in what sociologists call “third places” could have serious health consequences.

Previous research shows that a lack of social connection can increase risk of premature death as much as smoking 15 cigarettes a day and exacerbate mental illness and dementia.

“For some older adults who live alone, that brief, unplanned exchange with the butcher or the cashier may be the only friendly smile they see in the day, and they have lost that,” Finlay said.

Societal health is also at risk.

“It is increasingly rare for Americans with differing sociopolitical perspectives to collectively hang out and respectfully converse,” she writes. 

Finlay hopes that her work can encourage policymakers to create spaces more amenable to people of all ages who are now more cautious about getting sick—things like outdoor dining spaces, ventilated concert halls or masked or hybrid events.

She also hopes that people will give those still wearing masks or keeping distance some grace.

“It is a privilege to be able to ‘just get over’ the pandemic and many people, for a multitude of reasons, just don’t have that privilege. The world looks different to them now,” she said. “How can we make it easier for them to re-engage?”

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Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers’ perspectives ☆

Md rukon miah.

a Department of Marketing, Comilla University, Cumilla, Bangladesh

Afzal Hossain

b Department of Business Administration, Trust University, Barishal, Bangladesh

Rony Shikder

Meher neger, associated data.

Data will be made available on request.

Background of the study

Nowadays, the business pattern is changing globally. The business organization is influenced customers to purchase their necessary goods and services from online businesses. The online-based business takes promotional activities through social media platforms like Facebook, Twitter, Instagram, and Pinterest.

The aim of the research was to investigate the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

Research methods

Quantitative type research was applied and the study used descriptive research design. A standardized questionnaire was used to collect 350 data points from Bangladeshi consumers using an online purposive sampling method. A partial least square structural equation modeling (PLS-SEM) approach was used to evaluate the data and test the hypotheses.

PLS-SEM analysis method demonstrated that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior during the COVID-19 pandemic in the perspective of Bangladesh.

The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. The customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

Online shopping; Social media, Bangladeshi consumers, COVID-19 pandemic, PLS-SEM.

1. Introduction

With the expansion and spread of the 2019 novel coronavirus (2019-nCoV), also known as the severe acute respiratory syndrome coronavirus 2, a new public health crisis is threatening the world (SARS-CoV-2). In December 2019, the virus was revealed in bats and conveyed to humans via anonymous intermediary species in Wuhan, Hubei Province, China. To date (05/03/2020), there have been roughly 96,000 recorded cases of coronavirus disease 2019 (COVID-2019) and 3300 recognized deaths. The disease is spread through inhalation or contact with polluted droplets, with a 2 to 14-day incubation period. Fever, cough, sore throat, dyspnea, weariness, and malaise are common symptoms. Most people have a minor case of the common symptoms. Most people have a minor case of the condition. However, certain people (typically the elderly and those with comorbidities) may develop complications ( Singhal, 2020 ). The global proliferation of coronavirus has had a number of negative effects on human health ( Jajodia et al., 2020 ; Rajendran et al., 2020 ). Most enterprises have been adversely impacted by COVID-19, and as a consequence, they have been compelled to implement multiple measures to limit the proliferation of the coronavirus while also harming their organizational performance and effectiveness ( Bartik et al., 2020 ; Donthu and Gustafsson, 2020 ; Sohrabi et al., 2020 ). To contain the spread, people should exercise social detachment, self-isolation, and reduce travel, which also led to a significant decrease in institutional and business output ( Nicola et al., 2020 ). The global COVID-19 epidemic has severely affected societies and economies around the world and has hit various sectors of society in various ways. This unprecedented situation has far-reaching consequences for consumers’ daily lives and has dramatically changed how businesses operate and how consumers behave ( Donthu and Gustafsson, 2020 ; Yuen et al., 2020 ). The current situation, after the first wave and the beginning of the second wave of the COVID-19 epidemic in Europe, has forced many consumers to reconsider their established shopping and shopping habits or even learn new ones ( Sheth, 2020 ). Nowadays, social media is playing a significant role in the online marketing environment for buying products from online stores rather than traditional themed stores with the help of an internet connection. In the current situation, social media is a relatively new trend. The most popular social networking sites like Facebook, Twitter, LinkedIn, Pinterest, and Google contribute to the majority of activities such as messaging, chatting, gambling, and blogging. Consumers typically participate actively on social media and spend long hours on Facebook and Twitter, creating content and sharing it. Companies that are aware of these issues are moving towards various activities to attract customers, increase their level of awareness and make the most of the opportunities offered through social media. Accordingly, firms conduct strategic campaigns that overlap with customer structures and brand values to increase the level of social brand recognition. Digital and social media marketing allows companies to accomplish their marketing aims at relatively low cost ( Ajina, 2019 ; Yadav, 2016 ).

Individuals and families who buy a company's goods for personal consumption are denoted as consumers ( Kotler, 2004 ). Consumer behavior refers to the actions that consumers participate in when buying, consuming, and disposing of products and services. Consumer behavior is the study of how people shop, what they shop for, when they shop, and why they shop. When a customer needs to make a purchase, they will go through the steps of acknowledgement, information search, evaluation, purchase, and feedback ( Blackwell et al., 2006 ). Finally, the customer will select a product or brand to consume from a variety of options available in the market. These factors, on the other hand, have an impact on consumer purchasing behavior. When it comes to consumer buying choice behavior, it's critical to identify the many sorts of consumers who have different buying decision behaviors based on their level of involvement and capacity to discern significant differences between brands. The term “buying participation” is defined by Hawkins and Mothersbaugh (2010) as the level of interest a buyer has in purchasing a product or service. Retail managers and marketers must keep records of shifts in consumer buying behavior and attitudes in order to identify which strategies they should implement ( Verma and Gustafsson, 2020 ). Pantano et al. (2020) argue that customers have re-examined their buying habits even while recognizing advantages from previously unknown services. On the one hand, social media is a rich source of information about a company's consumer views; on the other hand, it promotes social interaction among consumers, which results in increased trust and, thus, changes in customer preferences' purchasing behavior ( Hajli, 2014 ).

Online shopping behavior involves the process of purchasing goods and services through the internet ( Sun et al., 2019 ). After collecting product information, the consumer selects an item according to its requirements and transaction criteria for the selected product, evaluates the product along with other available options, and gains post-press experience ( Kotler, 2000 ). Online shopping behavior is related to the psychological state of the customer buying online ( Li and Zhang, 2002 ). Social networking sites have been widely used by people for their professional and personal use in the era of global communication. According to E-marketer (2013) , companies for various marketing activities such as marketing research, branding, customer relationship management, sales promotion, and service and service delivery have gradually adopted various studies as well as social networking sites that ensure the positive effects of social development in marketing strategy media.

The World Wide Web has persuaded people around the world to make small changes in their behavior and attitudes. Because of these blessings, online shopping has emerged, which affects the lives of ordinary citizens. Online shopping has started in Bangladesh, but consumers are still not very accustomed to shopping online. Customers are becoming familiar with the internet and its benefits. Online shopping is becoming popular and a priority among a group of customers to get better quality offers related to information, benefits, and cost choice. Like other young Asians, Bangladeshi youth are experimenting with new ways of shopping that have led to the rise and popularity of online shopping in Bangladesh.

Nowadays, customers' purchasing patterns are changing globally, and they are purchasing goods and services through online shopping. Customers were heavily influenced by social media to shop online. During COVID-19, customers didn't go to shopping malls frequently because of lockdown, isolation, and fear of being affected by the coronavirus ( Eger et al., 2021 ). Business organizations can motivate customers to purchase through online shopping via social media platforms like Facebook, Twitter, Instagram, and Pinterest. Marketers have a great advantage on social media because they can influence or create awareness about goods and services and motivate them to purchase via online shopping. Business organizations can use social media platforms to influence their existing and potential customers to purchase their necessary goods and services through online shopping or online business platforms ( Chaturvedi and Gupta, 2014 ). Customers have been influenced by organizations via live streaming, celebrity endorsements, online reviews of customers, and promotional tools like target advertising ( Geng et al., 2020 ; Schouten et al., 2020 ). During the corona pandemic, the marketers took home delivery services to the customers ( Wang et al., 2021 ). Good online reviews have influenced potential customers to purchase through online shopping ( Mo et al., 2015 ). Online shopping behavior will benefit both customers and marketers ( Berman, 2012 ). Nowadays, in our society, some customers are so busy that they don't have the available time to purchase their necessary products or services. That's why they are not able to go to the market practically within a short time. They prefer to order any kind of commodity or service via online shopping. At present, customers want a relaxed environment on social media for shopping. Marketers provide target advertising via social media like Facebook, Twitter, and so on ( Luo et al., 2019 ). Thus, social media marketing tools are more useful than other marketing communication mixes. Word of mouth from celebrities and positive customer reviews encourages other customers to shop online.

This study was conducted on social media due to several factors that influence buying behavior. Purchasing online remittances has become an interesting and new topic for researchers around the world. People's buying patterns are changing. Online social media is a tool that has only recently developed and developed rapidly in the last few years, and it might have the problem of a lack of studies in all countries since it is at an early stage in the field of social commerce ( Huang and Benyoucef, 2015 ; Hossain et al., 2019 ). There are a lot of social media users in Bangladesh and they prefer to shop online, but there is still a lack of research on the trend of social media impact when buying a product online. Thus, by doing this research, marketers can focus on the areas that have the most impact on their online buying behavior. The purpose of the study is to understand the buying behavior of online shoppers.

After reviewing most of the related literature on social media that influences online shopping, it is clear that most researchers tried to assess the influence of social media (live streaming, celebrity endorsements, promotional tools, and online reviews) on buying behavior, purchase intention, purchase decision, customer satisfaction, and online shopping behavior from the perspectives of customers all over the world, but this research has been tried to focus on investigating the influence of social media on online shopping behavior during the COVID-19 pandemic from the perspectives of Bangladesh, which remained an unexplored field. This research provides an insight on the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on online shopping behavior during the COVID-19 pandemic of citizenship customers' level in eminent Bangladeshi purchasers' and sellers' experiences, which will help policy makers and stakeholders formulate better digital marketing strategies in Bangladesh, as well as the research field in the perspectives of the COVID-19 pandemic.

The broad objective of the research was to investigate the influence of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers. Specific objectives are: to assess the behavior pattern of consumers towards online platforms; to explore the impacts of the COVID-19 pandemic on buying behavior; and to study the effect of live streaming, celebrity endorsements, promotional tools, and online reviews on the online shopping behavior of consumers during the coronavirus pandemic in the context of Bangladesh.

The theory behind the study and the terminology and propositions that will be used to achieve the research objective will be explained. Furthermore, the interrelated association of dependent and independent variables will also be deliberated upon following past studies. The key research questions of the study are stated as follows: Is there any significant relationship between live streaming and online shopping behavior?; How is celebrity endorsement relevant to online shopping behavior?; How are promotional tools relevant to online shopping behavior?; and what are the relationships between online reviews and online shopping behavior?

The research paper is allocated into several sections. Initially, the literature review is provided based on a past study. Secondly, the conceptual model and hypotheses developed have been demonstrated. Thirdly, research methodologies that are applied to the current research are described. Fourthly, the paper is presented with the results and interpretations. Fifthly, the discussions, conclusion, and implications sections incorporate the consequences of the present research and its linkups with the previous studies. At the end of the segment, the shortcomings and potential directions of the research are stated.

2. Literature review

2.1. theoretical background, 2.1.1. social influential theory.

According to Kelman (1958) , SIT (Social Influential Theory) is defined as individuals' beliefs, attitudes, and consequent activities or manners that are impacted on other people over three procedures: compliance, identification, and internalization. Persuasion is expected to happen when people receive influence and accept the persuaded conduct to increase rewards and evade punishments. Hence, satisfaction resulting from compliance is because of the social effect of acquiescent influence. Identification might be said to occur when individuals embrace persuasion with the purpose of making or sustaining a preferred and useful connection to other people or a group. Internalization is expected to happen when individuals receive influence and later observe that the gratified of the persuaded performance is pleasing in which the content designates the attitudes as well as actions of others. Influencers perform their functions as a third party who can meaningfully form the company's purchasers' opinions, choices, and actions. Any person can be an influencer by influencing customers to purchase goods and services within a community ( Gillin, 2007 ). Information transferred from one person to another person influences customers through word of mouth. Celebrity people's behavior influences customers through talking about the company ( Sernovitz et al., 2012 ).

2.1.2. Information processing theory

How people collect, illustrate, and use information to make decisions is the main concept of Human Information Processing Theory ( Newell and Simon, 1958 ; Norman, 1968 ; Reitman, 1965 ). Information process theory conceptualizes how individuals take care of ecological occasions, encode data to be learned, relate it to what they know, store new information in their memory, and retrieve it depending on the situation ( Shuell, 1986 ), cited in Schunk (2012) . Researchers have shown that buyers' decisions are formed by the manner in which humans' process information ( Huber and Seiser, 2001 ). In this study, online shopping behavior also depends on the buyer's decision. Information is one of the most important things that influences the consumer's purchasing pattern. When consumers gather or collect information from online reviews and celebrity endorsements, they will be motivated to purchase the products or services.

2.1.3. Social exchange theory

SET was developed initially to investigate human behavior ( Homans, 1958 ) and was later applied to comprehend hierarchical behavior ( Blau, 1964 ; Emerson, 1962 ). The Social Exchange Theory states that individuals and organizations are assisted to maximize their rewards and limit their expenses ( Salam et al., 1998 ). Individuals regularly anticipate proportional advantages, like individual warmth, trust, appreciation, and monetary return, at the point when they act as indicated by social norms. Accordingly, relational cooperation from a money-saving perspective is an exchange where actors obtain benefits. From a cost-benefit perspective, they communicate individually, which aids in exchange where the actor gains an opportunity ( Blau, 1964 ). In the present day, SET has been adopted in social networking research. So, this theory is suitable for this study because it depends on online shopping behavior. Based on psychology, SET accepts the fundamental ideas of modern economics as a foundation for analyzing human behavior and connections in order to determine the complexity of social structures. At the time of promoting, companies require a cost to get a customer's attractions in order to retain the customer. Hence, if the research is used promotional tools more, such as advertising, personal selling, and sales promotion, as a result, it's possible to get customer attention whenever they are motivated or influenced, at which time they will purchase goods and services online. Promotional tools and live streaming are both related to human behavior and easily affect online shopping behavior.

2.2. Live streaming

The coronavirus pandemic calamity knocked out the world and affected all sides of our lives, including customers' preferences, habits, and shopping behaviors. During the corona pandemic times, e-shops were stimulated on social media ( Ali et al., 2021 ). Day by day, live streaming has been popular. Numerous merchants on social commerce display places have embraced it because of its ability to increase their company's sales performance. Live streaming shopping is a new form of social commerce that has already been developed and implemented by social commerce merchants ( Adoeng et al., 2019 ; Taobangdan and Taobao, 2019 ). The live presentation helps a businessman influence the online customer to purchase products. Live streaming has transformed the out-of-date social business model in different ways. In outdated online shopping, customers can only know about goods and services via text and pictures. Otherwise, live streaming allows online sellers to show real-time videos of the products and also let customers know about the product's overall features and quality ( Wongkitrungrueng and Assarut, 2018 ). In traditional social commerce, shoppers could only ask about product-related topics, but in modern times, consumers can ask the question via screen and streamers can give the answer in real-time ( Wongkitrungrueng and Assarut, 2018 ). Live streaming shopping creates a real-time stream between sellers and buyers. Online shoppers can watch the live presentations of products that influence customers to purchase that product. Customers' any confusion about products can be reduced through visual presentations of products ( Chen et al., 2017 ; Kim and Park, 2013 ; Zhou et al., 2018 ). The increasing popularity of visual presentations highly influences customers to buy the products ( Yu et al., 2018 ). While customers' engagement with live presentations of products is positively impacted on customer minds about products, it is also a stimulus to shop for those products ( Wongkitrungrueng and Assarut, 2018 ). Despite the fact that buyer commitment has been identified as a significant antecedent persuading purchaser buying in online spending ( Prentice et al., 2019 ), only a few studies have measured the previous circumstances and outcomes of purchaser assignation according to live streaming shop. Live streaming broadcasting makes use of one or more pieces of equipment that can instantly show images and sounds to other locations, allowing users to observe their existence ( Chen and Lin, 2018 ). Live streaming shopping is a new social media form with a high HCI that raises customer awareness of products. Preceding live-streaming lessons have chiefly concentrated on video games and e-sports ( Cheung and Huang, 2011 ; Sjoblom and Hamari, 2017 ). Many customers increase their capacity to buy through live streaming shopping by gaining new perspectives and asking pertinent questions ( Lu et al., 2018 ). Live streaming can show images as well as sounds from one place to a different place instantly ( Chen and Lin, 2018 ). Live streaming purchasing is an extremely noticeable form of merchandise demonstration through online videos. When customers make purchase decisions, they need clear information about products and also want to see the products visibly through the live presentation. It gives the clients an intellect of engagement. Besides, the richness of live streaming spending makes it stress-free to fascinate buyers. Consequently, consumers observe immersion ( Yim et al., 2017 ). Besides, live presentations can communicate complete videos to consumers, as well as the sellers can show how to use the merchandise through live streaming, which permits the product to be visualized ( Li, 2019 ; Javadi et al., 2012 ). In live presentations, sellers and customers interact with each other through live streaming, and customers watch the seller's voice, movement, and product features. So, customers know that the sellers are real people because of the live presentation via social media. Live streaming allows companies to broadcast their products' different items via live presentations. Furthermore, live presentations can prompt captivation, which can lead to a logic of immersion ( Shin, 2017 ). Online shopping and e-commerce have developed an innovative and lucrative business model. Here, buyers and sellers are both connected with live presentations, with buyers asking product-related questions to sellers and also watching the product and product features ( Attfield et al., 2011 ). Visual presentation shopping is being subjected to extraordinary growth. On the other hand, interest in the live-stream market is in its embryonic stage. Different celebrities talk about products and motivate them through live presentations ( Ma, 2021 ). Day by day, with the increase of online shopping, many companies provide live help or visual presentations through test chatting, instant messaging, and live product presentations. Businesses and customers can conduct real-time human-to-human communications for e-commerce Web sites ( Qiu and Benbasat, 2005 ). E-retailers are taking on innovative arithmetic advertising tactics to deliver more accurate information to their consumers. In real-time business, live video streaming allows sellers and consumers to interact ( Zhang et al., 2019 ). Nowadays, consumers have become familiar with visual presentations and product features online and have finally purchased those products that they like. Consumers are motivated to purchase products through live presentations ( Yin, 2020 ).

2.3. Celebrity endorsement

There are many social media platforms, for instance, Facebook, Twitter, Snapchat, and Instagram. Day by day, social media continues to rise speedily in popularity. Celebrity people are using different social media platforms and distributing different information about products to customers. The celebrity of Instagram is influencing consumers' online purchasing behaviors ( Gupta et al., 2020 ). Through social media, online information sharing in the communal sphere has not only promoted the customers' buying choices. Celebrity people provide information about goods and services to actual and potential customers ( Lee et al., 2008 ; Ashfaq and Ali, 2017 ). Along with the diverse investigators, the practice of celebrity endorsements supports in structure the products' identification as well as generates optimistic insolence ( Petty et al., 1983 ), improves the prospect of buying ( Friedman and Friedman, 1979 ), nurtures trademark trustworthiness, and completely influences positive word of mouth ( Bush et al., 2004 ). Celebrity endorsements have a significant impact on consumers' purchase decisions ( Ohanian, 1990 ). In the same way, Instagram celebrity has a momentous impact on consumers' online shopping behaviors ( Kutthakaphan and Chokesamritpol, 2013 ). Most celebrities have a more positive impact on consumers' minds about the products than less credible celebrities. Credible celebrity people influence consumers' online shopping behavior ( Aziz et al., 2013 ). Celebrity people created a brand different from another one because consumers can easily select their preferred products. Through social media advertisements ( Meng et al., 2020 ). celebrity endorsements have an effect on customers' buying behavior. Celebrity images might have an effect on positive and negative consumer attitudes. A celebrity's usefulness depends on their trustworthiness and credibility in an online advertisement. A celebrity's good image can have a positive effect on product acceptance ( Ibok, 2013 ). A celebrity can easily motivate consumers towards purchasing products because people believe infamous people. Through social media, a famous personality created awareness about products with customers. They can positively influence customers' opinions of the brand ( Rai and Sharma, 2013 ). Celebrity endorsement is one kind of promotional activity that attracts customers to specific products. Different companies use different celebrities to promote the awareness of their products to customers, and customers might be motivated to purchase those products. Customers purchased products based on the credibility of celebrities ( Khatri, 2006 ). The influence of superstars' post-legitimacy, observational learning, sentimentality polarization, and impulse purchasing propensity reins in the dormant state-trait theory. Security is influencing consumers' online shopping behavior through social media ( Zafar et al., 2021a ). Normally, followers consider that celebrity posts are authentic; that's why they easily influence consumers to make online purchases ( Wilcox and Stephen, 2013 ). On social media, celebrities share their opinions and advertisements that highly stimulate potential buyers to purchase products ( Chung and Cho, 2017 ; Xiang et al., 2016 ). Celebrity advertisements have so many advantages and disadvantages. Celebrity advertisements can be used to achieve a company's competitive advantage ( Han and Yazdanifard, 2015 ). With regard to a celebrity's values, occupation, ethnicity, and other characteristics, the customer ought to never be curious about why this star is certifying the merchandise ( Meng et al., 2021 ; Gan and Wang, 2015 ). Generally, the research should be focused on celebrities' groups or pages where customers are replaying or commenting on celebrities' posts as well as their peers' social communication. Some celebrities have a large number of followers; they maintain an online community. Business organizations give priority to social media celebrities in their marketing strategy to motivate online shopping behavior ( Pemberton, 2017 ). Consumers follow the celebrity's posts and pursue their lifestyle, with clothing, makeup, fashion, the destination of holidays, even restaurant choice. Organizations try to use such celebrities for effective social media marketing promotions ( Hennig-Thurau et al., 2013 ; Kumar and Mirchandani, 2013 ). Celebrity followers always enquire for recommendations from business organizations. Celebrities' any business-related posts that stimulate consumers' online purchasing behavior ( Wilcox and Stephen, 2013 ).

2.4. Promotional tools

Technological changes are occurring in eye flashes and values are changing over time. Customers' buying habits change rapidly, and the fortunes of different companies vary. Online marketing has been seen as a new form of marketing and has given companies new opportunities to do business. According to Dehkordi et al. (2012) , e-commerce and e-marketing show that internet marketing is easier than conventional marketing ( Dehkordi et al., 2012 ). Leena Jeenefa noted that there are several notable relationships between purchasing behavior and the effects of media advertising ( Jenefa, 2017 ). Reza Jalilvand and Samiei (2012) evaluates how advertisers use social media to make their products popular. The reason for the promotional price promotion is that the consumer does not have the rational mindset to think about whether it is worth buying more at that moment, and this also increases online purchasing behavior ( Agyeman-Darbu, 2017 ). Some social media stated that if consumers buy two, they will get one free, and this also leads to the consumer having a strong positive feeling. Ibok (2013) found that young people feel more comfortable when choosing and buying products online than in physical shopping options. Social media helps them save time and effort examining product information. Privacy, trust, and protection play an important role in social media networking sites. Online advertising businesses use electronic marketing tools to create marketing strategies, advertising theories, and customer buying behavior due to potential market segmentation. According to Eyre et al. (2020) , online advertising includes contextual ads on examining banner ads, rich media ads, social network advertising, online classified advertising, and marketing email like spam. Advertising is defined as the definition of any personal meaning related to product ideas and information in the media to create a brand image ( Kotler and Amstrong, 2010 ). For many years, television, radio, newspapers, and magazines were the only means and channels of advertising, but nowadays, online advertising is becoming the main driving force in many advertising initiatives and efforts ( Kotler and Amstrong, 2010 ). Content is one of the most important features of e-advertisement. It delivers written information regarding particular products or services to online users. Customers are rapidly adopting online shopping day by day due to a busy lifestyle. Undoubtedly, as a developing country, Bangladesh has a lot of potential customers for online businesses. Bangladesh is one of the countries that uses social media the most. It is important to know what causes online buying behavior on social media.

2.5. Online reviews

Purchase intention can be used to measure the possibility of a consumer buying a certain product. When deciding to buy a product, most customers are influenced by comments and ratings from online reviews, and they take a positive or negative view of the product. Social media enabled through mobile devices can be accessed everywhere, instead of not only increasing access to information but also allowing people to create content and strengthen their voices around the world ( Labrecque et al., 2013 ). Social media is playing a crucial role in sharing opinions and product knowledge with consumers and, as a result, having an impact on other consumers ( Lim et al., 2016 ). According to Zhang et al. (2019) , the availability of online reviews plays an important role in online shopping behavior compared to other things. The availability of online reviews refers to the large number of online reviews that are sufficiently available online for the consumer's decision-making process ( Zhang and Zhu, 2010 ). Social media users have realized that a good number of online reviews point to online shopping behavior among customers. Good online shops create an opportunity to search for any product ( Zhang and Zhu, 2010 ). Furthermore, the availability of online reviews makes online shopping appreciate the quality and motivates the customer to try it for the first time ( Cui et al., 2010 ). A good number of customer reviews will have a positive impact on other users on social media, and it can be effective for the online shopping industry to increase sales volume through social media reviews ( Geetha et al., 2018 ). In addition, many researchers have found that a large number of online reviews can influence a potential customer when they choose a product through social media. Significantly, if consumers respond positively to a good number on social media sites, they are more likely to choose their favorite product than cheap ones ( Geng et al., 2020 ). For example, the availability of online reviews on social media should create an opportunity to try a new product, and potential customers may be the priority in their selection criteria ( Geetha et al., 2018 ). Numerous empirical studies across different industries have already investigated the influence of the number of review attributes from a variety of perspectives. For example, the number of reviews ( Dellarocas et al., 2007 ; Ghose and Ipeirotis, 2010 ), the response to negative reviews for online product management ( Kim et al., 2015 ), the positive online product reviews ( Ye et al., 2009 ), and the overall valence of a set of reviews of a product ( Spark & Browning, 2011 ). Consumers consider the internet as a tool to obtain information as a part of the decision-making process before purchasing products. The number of online reviews needs to have a positive impact on potential customers of unfamiliar products ( Zhang and Zhu, 2010 ). As a result, the brand availability of online-spread products increases because customers share their experiences on social media pages. A product review site assesses consumers on their own and how they feel about product quality, service systems, and their overall environment. For this reason, the behavioral motive of the customer should change when they decide to choose a product from the review site ( Gan and Wang, 2015 ). An online review is similar to a traditional face-to-face communication messenger. It is considered a new form of recommendation ( Helm et al., 2010 ). Zhang and Zhu (2010) indicate that the reviews' perceptual information and reasoning power are an important determinant of customer behavioral will, although the source is not credible. So online review materials still play an important role in consumer decision-making because good reviews from one customer can lead to another customer purchasing the product. Additionally, many prior studies have examined whether the availability of online reviews has a significant influence on consumers' product selection when they search for other reviews on social media ( Zhang et al., 2019 ; Cui et al., 2010 ). It has also been noted that the availability of online reviews has been verified as an effective tool for conducting research questions on consumer product selection ( Zhang et al., 2019 ).

2.6. Online shopping behavior

Businesses turned to alternatives and took up online marketing because of COVID-19 pandemic. Online marketing is a significant method for streamlining business processes, reducing managerial costs and turnaround time, maintaining social distance, staying at home, protecting against viruses, and illuminating associations with customers and business partners ( Hossain, et al., 2022 ; Hossain and Khan, 2018 ). At present, online shopping is becoming more popular all over the world, especially for retailers and customers. Online shopping creates opportunities for both online retailers and customers ( Kuester and Sabine, 2012 ; Hossain et al., 2018b ). Customer research has shown that customer assessments dispatched online and the allotment of information or particular views have become enormously influential means of communication. Online reviews have taken over business organizations through social media (Facebook, Snapchat, Twitter, and Instagram) ( Doh and Hwang, 2009 ; Lee et al., 2011a ; Jalilvand and Samiei, 2012 ; Huete-Alcocer, 2017 ). Different types of online reviews have improved the customers' internet shopping performance. Satisfied customers are giving online reviews through social media that influence other consumers' online shopping ( Fu et al., 2020 ). Nowadays, several customers are purchasing social media. Many business organizations have opted to take advantage of opportunities obtainable through social media networks to gain more consumers ( Kaplan and Haenlein, 2014 ). Live streaming stimuli motivate consumer cognitive states that influence consumer online shopping behavior ( Xu et al., 2020 ). The business organization has promoted social media advertising to attract online shoppers to purchase products online ( Mumtaz et al., 2011 ). Targeted advertising by businesses on social media (Facebook, Instagram, and so on). Business organizations know about customers' choices, preferences, and information through social media. They are doing e-advertising based on customers' preferable products and are influencing customers to purchase those products. An organization is able to run different advertising for different categories of customers, and an organization can set their target price ( Iyer et al., 2005 ). Companies can transfer information about products through online advertising. Consumers can visually watch their preferred products via advertising. Entrepreneurs use celebrity endorsements to promote their company's products, and it is increasing consumer purchase intentions. Consumers purchase products online and the created appeal of a statement by a celebrity might influence a customer's product image ( Wang et al., 2013 ).

This research has been prepared during COVID-19. In the research has applied three types of theories, such as social influence theory, information processing theory, and social exchange theory. In previous research, researchers have used online reviews as well as celebrity endorsements as factors under both social influence theory and information processing theory. For the first time at COVID-19, the research has applied these factors under the social influence theory and information processing theory, which have never been used before. The research paper has used social exchange theory. This theory identifies that promotional tools influence customers to buy their necessary goods and services through online shopping. The previous researchers didn't show social media impacts on online shopping behavior during COVID-19. The research has applied those factors during the COVID-19 time period, which made research paper unique from previous research. During COVID-19, The research was used technical tools that had never been applied to that type of theory before. The research paper has analyzed by SmartPLS version 3.0 and used a structural equation model..

3. Conceptual model and hypotheses development

According to Zhang et al. (2019) , by reducing psychological distance and perceived uncertainty, a live streaming strategy can improve a customer's online purchase intention. Chandrruangphen et al. (2022) find out vendors to concentrate on significant live streaming attributes to develop trust with their clients and increase their customers' intentions to watch and buy. The literature and researcher findings suggest that offering live presentations enables sellers to introduce items in a novel way, which might improve customers' moods and sentiments towards the product. So, customers should feel more confidence in the seller and his/her items because of live streaming. Thus, it is expected that:

Hypothesis 1 (H1) : Live streaming has a significant impact on online shopping behavior.

Park and Lin (2020) develop and test an integrative model of online celebrity endorsement by exploring compatibility impacts on customers. Meng et al. (2021) find that the feelings of audiences towards online celebrities can influence a buyer's willingness to buy products suggested by the online superstar. The literature and researcher findings suggest that celebrity endorsements represent attractiveness, believability, and celebrity-product compatibility, which have positive effects on a buyer's attitude towards products and brands as well as purchase intention. As a result, celebrity endorsement may increase users' desire to purchase any product. Therefore, it is expected that:

Hypothesis 2 (H2) : Celebrity endorsement has a positive influence on online shopping behavior.

Ashraf et al. (2014) found that sales promotion played a more significant role in the development of consumer buying behavior. Yahya et al. (2019) and Shamout (2016) revealed in their study that coupons, discounts, free delivery, and other promotional tools have a positive impact on consumer buying decisions. The literature and researcher findings suggest that sales promotion has a huge impact on consumer buying behavior, such as purchase time, product brand, product quantity, brand switching, and so on. Again, sales promotion can be used by marketers to create a long-term customer relationship, which can help them increase their sales. Based on the previous discussion, it is expected that promotional tools will have a positive relationship with purchase intention ( Siddique and Hossain, 2018 ). Thus, it is expected that:

Hypothesis 3 (H3) : Promotional tools have a positive influence on online shopping behavior.

According to Nuseir (2019) and Ventre and Kolbe (2020) , organizations should seek to increase customers' sharing of their positive online opinions in order to improve attachment and encourage online shopping. When the reviews contain detailed information about the product, consumers deem online reviews to be more credible ( Jimenez and Mendoza, 2013 ). The literature and researcher findings suggest that consumer opinion and peer reviews are among the top factors to consider for online shopping behavior. Thus, online sentimental reviews grab more attention from consumers and affect them positively when purchasing products. Therefore, it is expected that:

: Online reviews have a significant impact on online shopping behavior.

In this study, four independent variables (live streaming celebrity endorsements, promotional tools, and online reviews) and one dependent variable (online shopping behavior) have been recognized. Based on the previous literature and discussions, the conceptual framework ( Figure 1 ).

Research model.

4. Research methods

4.1. research design.

The research design was applied when the collection of data and analysis of data processed by combining them were used in the research ( Jahoda et al., 1951 ). This study is based on the quantitative survey method, with data collected using a structural questionnaire. To test the hypothesis, the study was conducted based on an online convenience and judgmental sampling survey. This study applied a descriptive study and collected respondents' attitudes and behaviors about social media's impact on online shopping behavior.

4.2. Methods of research data collection

The study collected data from respondents in written form. The study confirmed that informed consent was obtained from all participants for our research paper. The research paper applied primary and secondary data to prepare the study and make it more presentable. Primary data was collected via a survey and developed questionnaire. Business market research might use a questionnaire technique to collect consumer and customer opinions ( Wang and Feng, 2012 ). Online surveys are used to learn about the impact of social media on internet shopping behavior. Primary data was collected from respondents by developing a Google form and sharing that form with other respondents via Facebook, WhatsApp, e-mail, and so on. In particular, the questionnaire was developed for those people who connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on.

This research paper also used secondary data that was collected from different articles, books, and newspapers. The research was collected secondary data by penetrating electronic databases, including Research Gate, Google Scholars, and Emerald Insight. The research was collected secondary data by searching top journals like the Journal of Marketing Analytics, the Journal of Business Research, the Journal of Consumer Research, and so on.

4.3. Method of sampling

4.3.1. sampling unit.

People who have the equivalent attitudes and behavior in the direction of an entire group of people ( Sekaran and Bougie, 2016 ). These people use social media and their age is above 15 years old. They are considered the population of this study. So, the population is unfamiliar with this research paper. For this research paper, there is no earmarked sampling unit among the total population. In this study, the population is considered students, managerial-level people, businessmen, and teachers.

4.3.2. Sampling technique

Respondents for this study were chosen using an online purposive sampling technique and non-probability sampling methods. This research data was collected during the corona pandemic. The researcher collected data by distributing the questionnaire through Google Form Link and sharing this link with different convenient people. Non-probability sampling has been used because it is less time-consuming and less costly to prepare a sampling frame. Among the numerous ways of non-probability sampling, purposive sampling technique has been used because they are cheerfully available, generate a relatively low cost, and are convenient.

4.3.3. Sample size

The purposive sampling method is applied to collect (N = 350) respondents' opinions through a developed questionnaire. The sample (N = 350) was collected from the Dhaka, Sylhet, Khulna, and Chattogram divisions among eight divisions of Bangladesh.

4.4. Measurement scale of dependent and independent variable

The study used the Likert Scale (5 ratings). The Likert Scale is used for individual responses and measures the dependent variable and independent variable about the impact of social media on online shopping behavior during the coronavirus pandemic. The Likert Scale has five stages, and each statement in the form was measured by 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree.

4.4.1. Measurement instruments

As illustrated in Table 1 , the study used four constructs of social media to examine online shopping behavior during the COVID-19 pandemic. Live streaming factors include social sharing, hedonic consumption, cognitive assimilation, and impulsive consumption. The celebrity endorsement factor includes the number of shares, authenticity, positive sentiments, and recognizable celebrity. Promotional tools factor includes price discount, sales promotion, buy one get one, surroundings influence. Online review factors include the reviewer's reputation, the review's reliability, good customer rating, and argument quality.

Table 1

Origin of constructs and measured variables.

4.5. Data analysis

The smartPLS software version 3.0 was applied to examine the data collected via questionnaire. The conceptual model of the study was verified using structural equation modeling (SEM). For sample distribution, percentile measures and frequency distribution were primarily used in this study. The study's descriptive statistics were tested using mean as well as standard deviation measures. Collinearity statistics were used to test for multicollinearity among the independent variables. Besides, the reliability of the data or scale items was ascertained using Cronbach's alpha coefficients and composite reliability (CR). Discriminant validity was also used to test the Fornell-Larcker Criterion and the Heterotrait-Monotrait ratio (HTMT) among the independent and dependent variables.

4.6. Quality of data assurance

Enumerators and overseers were knowledgeable about this research objective, scale, data collection technique, and questionnaire. On a daily basis, the data collected is appropriately administered by superintendents and the data comprehensiveness and reliability are tested before the data is input to SmartPLS version 3.0 for more treatment as well as analysis.

5. Results and interpretations

5.1. descriptive analysis.

The study used mean and standard deviation scores to explore all of the aspects. The constructs were ranked in accordance with their enumerated mean standards. As shown in Table 2 , online reviews had the highest mean score (M = 4.1164), while celebrity endorsements had the lowest mean score (M = 3.4829). Most of the factors produced medium mean scores. Therefore, the factor mean scores recommend that among all perspectives, there be no higher variation.

Table 2

Descriptive statistics analysis.

5.2. Multicollinearity test

The study used a multicollinearity test to measure the independent variables that were highly correlated among themselves. The estimated path coefficients were affected by the predictor constructs of collinearity. Tolerance values below 0.10 and variance inflation facet values above 5 specify the existence of inter predictor constructs' collinearity ( Hair et al., 2019 ). As illustrated in Table 3 , all tolerance and VIF values have an acceptable range in collinearity statistics. So, it was recommended that multicollinearity wouldn't affect the independent variable's capability to take to mean the outcome variable.

Table 3

Multicollinearity test.

5.3. Measurement model analysis (outer model)

Hair et al. (2019) define "the measurement model as a constituent of a theoretical path model that holds the pointers and their associations with the factors; also called the outer model in PLS-SEM." In this study, confirmatory factor analysis (CFA) is applied to square in the event the variables are loaded on their relevant constructs ( Hair et al., 2019 ). In this study, SmartPLS software version 3.0 was applied to conduct structural equation modelling ( Ringle et al., 2015 ).

5.3.1. Unidimensionality

In the present constructs, the unidimensionality component designates that every measurement item has a satisfactory equal factor loading according to the corresponding latent construct. Hair et al. (2019) claim that each factor has a measurement variable with a least factor loading of 0.70. According to Table 4 , online reviews (OR1) and online shopping behavior (OSB6) have factor loadings of 0.674 and 0.663, respectively. However, OR1 and OSB6 factor loading values are close to 0.70. So, the research can be recommended that the unidimensionality measurement model has been recognized.

Table 4

Measurement model summary.

5.3.2. Construct reliability tests

The researcher used Cronbach's alpha and composite reliability (CR) to test the internal consistency. The recommended values of composite reliability (CR) and Cronbach's alpha are equal to or greater than 0.70, which is considered satisfactory to good for research ( Hair et al., 2019 ). As illustrated in Table 4 , all of the CR and Cronbach's alpha values have a satisfactory level. So, the researcher recommended that the constructs be reliable for further research.

5.3.3. Convergent validity tests

The average variance extracted (AVE) is 0.50 or greater than 0.50, assuring the convergent validity of the latent constructs ( Hair et al., 2019 ). As illustrated in Table 4 , all the average variance extracted (AVE) values are greater than 0.50 in this study because of the appropriateness of the constructs for further research.

5.3.4. Discriminant validity tests

Discriminant validity implies that each construct is empirically distinct from the other cross-loading that exists among the latent constructs. The correlation coefficients and square root of average variance extracted (AVE) among the constructs are associated to create discriminant validity ( Hair et al., 2019 ). According to Table 5 , the diagonal numbers are higher than the inter-construct resemblances presented off-diagonally. However, the discriminant's legitimacy is gained for the research constructs.

Table 5

Discriminant validity: Fornell-Larcker Criterion.

5.4. Measurement model analysis (Inner model)

The study measurement model recommended that all the measurement models be valid, then analyze the structural model relationship ( Hair et al., 2019 ). The researcher makes an assessment which one accepts and rejects via significant and insignificant relationships that can be identified by structural model analysis. Besides, the researchers used a bootstrapping procedure with a subsample of 500 to assess the size of the path coefficients ( Ringle et al., 2015 ).

Figure 2. Structural model.

The structural model analysis includes the paths, path coefficients, t values, p values, and path coefficient results. A two-tailed t-test with a level of significance of 5% was used to test the hypotheses that had been developed. The coefficients are statistically significant if the measured t-value is greater than the critical value of 1.96. According to Table 6 and Figure 2 , the path coefficients of three latent constructs, including celebrity endorsement, promotional tools, and online reviews, had a positive and significant association with online shopping behavior at p < 0.05. Here, the researchers mention that hypotheses H2, H3, and H4 are accepted. However, hypothesis H1 has no significant and positive relationship with online shopping behavior. Accordingly, H1 live streaming was rejected. According to Table 6 and Figure 2 , the celebrity endorsement perspective's highest path coefficient (β2 = 0.452) specifies that if celebrity endorsement were to grow by one standard deviation unit, online shopping behavior could increase by 0.452 standard deviation units if all other independent perspectives continued constant.

Table 6

Structural model estimates.

Note: p∗< 0.05, based on the two-tailed test; t = 1.96.

6. Discussions

In the Bangladeshi setting, the research aimed at understanding the impact of social media on online shopping behavior during the COVID-19 pandemic. It has been found that most researchers explored the influence of social media on purchase intention, behavioral intention, satisfaction, purchase decision, and loyalty ( Hossain et al., 2020 ; Gupta et al., 2020 ; Fu et al., 2020 ; Zhou et al., 2018 ; Jenefa, 2017 ). However, there was less focus and thus fewer studies into the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

According to the findings of the above analysis, three social media factors out of four had a significant and positive impact on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Besides, the rest of the factors of social media have no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic in the country. The celebrity endorsement factor (β2 = 0.452, t = 10.233), promotional tools factor (β3 = 0.215, t = 3.809), and online reviews factor (β4 = 0.207, t = 4.901) are significantly and positively related to the online shopping behavior of Bangladeshi consumers during the COVID-19 pandemic at p < 0.05.

From the above findings, the study found that those three independent variables, like celebrity endorsements, promotional tools, and online reviews, have a significant positive relationship with the dependent variable, online shopping behavior. Based on the analysis, the researcher found that the independent variable live streaming has no significant positive relationship with the dependent variable online shopping behavior. Here, the live streaming was not supported at a significant value of 0.380, which is higher than the p value of 0.05. The study recommended that live streaming has no significant positive relationship with online shopping behavior. Based on the research, celebrity endorsement's significant value was notated at 0.000, which is lower than the p-value of 0.05. This indicates that celebrity endorsement has a significant positive relationship with consumers' online shopping behavior. Xiang et al. (2016) ; Zafar et al., 2021a ; and Ahmed et al. (2015) , also supported that celebrity endorsement has a positive impact on consumers' online shopping behavior. Based on the analysis, the researchers found that promotional tools have a positive connection with consumers' online shopping behavior. Here, the significant value of 0.00 is lower than the p-value of 0.05. Based on the study, online reviews were significant at a significant value of 0.00, which is smaller than the p-value of 0.05. This suggests that online reviews have a significant positive relationship with customers' online shopping behavior. According to Zhang and Zhu (2010) ; Fu et al. (2020) , also supported that online reviews have a strong relationship with customers' online shopping behavior.

7. Conclusion and implications

During the COVID-19 pandemic, customers are purchasing their necessary products through an online platform. Customers are learning about new products being launched in the market through social media. Customers are safely purchasing their products through online shopping behavior during the corona pandemic. The study has been conducted with the objective of exploring the impact of social media on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Different aspects of social media are important tools to guide consumers' online shopping behavior during the coronavirus pandemic in Bangladesh. This research studies the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on consumers’ online shopping behavior during the coronavirus pandemic in the context of Bangladesh. The results of the research has revealed that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior in the perspectives of Bangladesh. In contrast, live streaming had no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic. The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. Customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

7.1. Theoretical implications

Day by day, people are becoming more accustomed to online shopping during the corona pandemic. Most people have connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on. Social media has a positive impact on online shopping behavior. Customers are watching different advertisements via social media, and they are motivating consumers to shop online. The study has proven that celebrity endorsements, promotional tools, and online shopping have a significant positive impact on online shopping behavior. In the meantime, with the development of social media, the influences on online shopping are increasing. During the coronavirus pandemic, social media-based marketing has also attracted the attention of enterprises. However, there has recently been little research studying the relationship between social media and online shopping behavior. To compensate for the gap, this research has been based on the impact of social media on online shopping behavior. Live streaming has no significant relationship with online shopping during the COVID-19 pandemic. On the other hand, celebrity endorsement has a significant positive connection with online shopping behavior. Besides, promotional tools and online reviews have a positive impact on online shopping behavior during the corona pandemic. Business organizations are highly focused on social media-based promotional activities. Consumers have adjusted their online shopping behavior during the COVID-19 pandemic.

7.2. Practical implications

Introducing celebrity endorsements, promotional tools, and online reviews of social media constructs have a positive connection with online shopping behavior during a COVID-19 pandemic. The research paper yields several practical suggestions for social commerce sellers and e-commerce-based organizations. First, the research results illustrated that celebrity endorsements have a positive relationship with customers' online shopping behavior, which includes attractive celebrities, celebrities, and recognizable celebrities. Hence, social commerce sellers who have not until now accepted celebrity endorsements for promotion should adopt celebrity endorsements that help increase the consumer's online shopping behavior. When famous or attractive celebrities talk about products and live streaming products, then customers are stimulated to purchase those products through the online market. Celebrity endorsers should have clear knowledge about product features before motivating them to purchase those products via online shopping.

Second, the research results showed that promotional tools constructed by social media have a significant positive connection with online shopping behavior. E-commerce sellers should promote promotional activities to increase the sales volume of online shopping. Besides, they should have used re-targeting advertising via social media to enhance online shopping behavior.

Third, the study also found that online reviews have a significant positive relationship with online shopping behavior during the corona pandemic. Potential customers' positive reviews or good ratings influence potential customers’ online shopping behavior. To connect with current and potential customers, e-commerce business sellers should have Facebook pages, Twitter accounts, Instagram accounts, and so on. The social media seller requests that customers give reviews about their product features, price, and quality via social media. Actual customers' positive reviews are highly motivated by other actual and potential customers' purchases through an online business.

8. Limitations and future research

In the study, the main objective was to investigate the major influencing factors that impact consumers' online shopping behavior during COVID-19 outbreaks. The research paper has several limitations. For instance, in the literature, there are several antecedents of the impact of social media on online shopping behavior, but in this study, the researchers only used four antecedents, like live streaming, celebrity endorsement, promotional tools, and online reviews. Future research should add more antecedents in their research paper with four antecedents. Second, this study used an online purposive sampling technique to investigate the impact of social media on consumers' online shopping behavior. The research will be recommended that for future research, they should use experimental methods to measure customers’ online buying behavior through social media. Third, due to the COVID-19 pandemic outbreaks, data was collected from respondents through an online survey using a self-administered questionnaire. For that reason, in some cases, it was not possible to know more properly about the respondents. Field-level surveys and face-to-face interview methods should be used to collect data for further research to address the problem of false information and data. Fourth, current research is based on quantitative information but may differ in results when applying qualitative information. Future research should apply a combination of quantitative and qualitative data analysis.

Declarations

Author contribution statement.

Md Rukon Miah: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Afzal Hossain: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper; and Corrected proof.

Rony Shikder: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Tama Saha: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Meher Neger, PhD: Conceived and designed the experiments; Analyzed and interpreted the data; Overall Supervision of the Study.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interest’s statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

☆ This article is a part of the "Business and Economics COVID-19 Special Issue.

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