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Including full dissertations, proposals, individual dissertation chapters, and study guides for students working on their undergraduate or masters dissertation.
Example Literature Reviews
Tesco is an organization which believes in contributing for the social welfare of the people. Let us discuss how it relates itself to the CSR....
Last modified: 22nd Nov 2023
Dissertation Examples
The H&M cluster is one of the world’s top fashion companies, with brands being H&M, Weekday, Monki amongst others. H&M hopes to inspire fashion globally to dress their individual style....
The iPod is perhaps the most talked about technological product in recent times. It has set sales records that have lave literally destroyed the predictions of all analysts by superseding everyone’s...
Last modified: 6th Nov 2023
A comparison of the globalization strategies of Ford and Toyota including joint ventures, manufacturing and marketing....
Last modified: 2nd Mar 2022
This study will investigate how people interact with the concept of innovations through crowdfunding platforms (Kickstarter, Indiegogo, GoFundMe etc )....
This dissertation has confirmed, through the literature and primary research findings, that consumers are reacting favourably to luxury fashion brands that have embraced product fuzzification....
The aims of this study are in two folds; first to examine if any EPL club could maintain efficiency over the period (2005 – 2015) and second, to identify the most efficient club(s) within the same period using Data Envelopment Analysis (DEA)....
The issue of social media usage in the workplace has become critical, and corporations have increasingly sought to minimize the losses in productivity which represent billions of dollars in lost revenue....
Last modified: 1st Mar 2022
This paper gives an overview on cloud security and seeks to identify the major security issues and their solutions in cloud computing security as well as identifying areas for future research....
Last modified: 23rd Feb 2022
The central focus of this study is to understand the shopping preferences of Indian millennials to identify the reasons that are stopping them from shopping online....
Last modified: 22nd Feb 2022
Dissertation Proposals
The motivation behind the proposed research study is to examine if negative product review deters customers from purchasing a specific product....
The focus of this research is on why and how FMCG companies can increase their penetration online....
In this paper, I study the effects of common ownership on executive compensation, specifically on the use of relative performance evaluation (RPE)....
Last modified: 18th Feb 2022
This research project aims to investigate the antecedents and consequences of negative aspects of innovative networks, in an attempt to bridge the gap that exists in current literature on business networks....
Last modified: 28th Jan 2022
This research methodology investigates the issue of employment within the company Uber Technologies from the point of view of its drivers, focussing specifically on issues regarding work and employment in a collaborative economy....
This research aims to explore the impact of social media on customer purchases. To achieve this, the study should understand more on the consumer buying behaviour and factors affecting it....
At present, the vast majority of the clubs are still using private ownership (i.e. capital investment) model, investors basically are from China, with both of state-owned enterprises and private enterprises sponsors soccer clubs....
Last modified: 25th Jan 2022
Research Project on Adequacy of Human Resource Planning and Recruitment processes for the smooth implementation of Enterprise Resource Planning (ERP) system in Field Services, Brisbane City Council....
This paper aims to review the literature covered on Big Data analytics considering; what it is, its characteristics and its implication in decision making....
Last modified: 21st Jan 2022
Communication is a process of exchanging verbal and nonverbal messages. It is a constant process. It is essential for message to be conveyed through some medium to the recipient....
Last modified: 20th Jan 2022
Dissertation Methodologies
An exploration of improving the quality of customer service provided in the cellular industry of Pakistan. A case study of Mobilink Telecom Limited....
Our research explores whether High Performance Work Systems (HPWS) can enhance firm performance in organizations facing skills shortages....
Last modified: 19th Jan 2022
This project report explains through different chapters the process of credit appraisal followed by the banks and its importance with respect to current economic scenario....
Last modified: 17th Jan 2022
Dissertation Introductions
The research project proves the decision for a merger rather than an alliance and the synergies gathered due to this tool of development. ...
Airbnb is an online peer-to-peer (P2P) corporation which is framed in the lodging industry. Its aim is to create a platform where (hosts) people who rent a bed in excess in their own property or offer the whole living space are meeting with the guest....
Last modified: 12th Jan 2022
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Methodology
Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.
What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .
There are five key steps to writing a literature review:
A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.
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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.
When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:
Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.
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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.
You can also check out our templates with literature review examples and sample outlines at the links below.
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Before you begin searching for literature, you need a clearly defined topic .
If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .
Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.
Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:
You can also use boolean operators to help narrow down your search.
Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.
You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.
For each publication, ask yourself:
Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.
You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.
As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.
It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.
To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.
There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.
Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.
For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.
If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:
A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.
You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.
The introduction should clearly establish the focus and purpose of the literature review.
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.
As you write, you can follow these tips:
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.
When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !
This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.
Scribbr slides are free to use, customize, and distribute for educational purposes.
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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
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Research bias
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
There are several reasons to conduct a literature review at the beginning of a research project:
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved June 10, 2024, from https://www.scribbr.com/dissertation/literature-review/
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Program overview.
The Doctor of Business Administration (DBA) program is the most advanced business degree program at GGU that is designed for professionals who wish to further their careers. The DBA program addresses the learning needs and objectives of senior business managers, consultants, and university professors. Its primary objective is to produce graduates who can contribute to the advancement of their professions and to the expansion of knowledge and awareness of contemporary strategic issues and practices. This is a STEM-designated degree program.
The curriculum includes a three-tiered focus. Students examine current theories, practices, and issues in business; train in research methods; and study the relationships between business, social and global issues. It is critical for doctoral students to be adept in these areas and to contribute to the expansion of knowledge and improvement of business practices. For the dissertation, students conduct original research on a topic of current importance and personal interest. They also have an option to file a patent and gain provisional status only in countries who are World Intellectual Property Organization (WIPO) signatories or submit and get preliminary approval of their scholarly manuscript to produce and be published by an academic publisher into a book. These should impact and help illuminate the strategic issues they face in their professions.
The program encourages students to accept the added responsibility of a shared commitment to the advancement of their professions and to upholding the highest ethical standards in the private or public sector.
The DBA program has been designed with a focus on the “practitioner educational model,” which distinguishes Golden Gate University from other institutions. This focus is consistent with the position adopted by the Association of Business Schools, which can be summarized as follows:
Our students are one of the program’s greatest strengths. Typical doctoral students at GGU attend part time. Without exception, they come from successful careers in top positions in the private, nonprofit and government sectors. They bring their experiences and knowledge to the classroom and, in turn, demand incisive instruction and intelligent, well-developed classroom discussions.
Faculty members who teach in our DBA program have doctoral degrees from leading universities in their fields and possess extensive practical experience. They bring a theoretical as well as a real-world view to their teaching and a commitment to dynamic, progressive education.
This program is delivered via the online-synchronous, online-asynchronous, and in-person instruction modes and offers a state-of-the-art curriculum delivered by experienced, highly qualified professors.
Graduates of the DBA program will achieve the program’s primary objectives through the development of:
GGU seeks doctoral candidates with strong intellect, proper educational preparation, breadth and depth of managerial or professional experience, and the capacity for disciplined scholarly investigation. While most applicants have a master’s degree in a business-related field, applicants with academic preparation in other fields are most welcome to apply.
Doctoral candidates must be fluent in English and are expected to write at a level that meets the standards of scholarly publications. They are expected to understand contemporary practices in business and the economic, social, and political context in which they are conducted.
The admission decision is made by a faculty committee and is based on the applicant’s total accomplishments and skills. Specifically, admission to the program requires:
The Doctor of Business Administration in Emerging Technologies with Concentration in Generative AI requires completion of 20 units of major courses, 8 units of dissertation foundation courses, and 28 units of dissertation work, for a total of 56 units. Students must earn a “B-” or better in each course and a cumulative grade-point average of 3.00 or better.
Although research papers, reports and examinations may be required in doctoral seminars, the major assessment points in the DBA program are the qualifying examination, taken after the foundation curriculum is completed, and the dissertation research. Students must receive a passing score on the qualifying examination and successfully complete all required courses before they are allowed to present a dissertation proposal and officially advance to candidacy.
Students must complete and successfully defend their dissertations within five years of beginning the program.
BUS 240 Data Analysis for Managers (Waived with documentation of student’s having completed equivalent course covering statistics and regression analysis with grade of “B” or better.)
This program includes three, week-long global immersion sessions to enable learners to network, interact with thought leaders, and exchange ideas among their peer groups. The immersion sessions will occur at the end of year one in Mumbai/Bangalore; end of year two in Singapore (tentative); and end of year three in San Francisco.
Foundations of Machine Learning and AI – 4 unit(s)
Deep Learning and its Variants – 4 unit(s)
Generative AI Using Pre-Trained Models – 4 unit(s)
AI Project Design and Execution – 4 unit(s)
Responsible AI – 4 unit(s)
The integrative examination will be offered to students prior to the start of dissertation research courses. The exam will test the student’s mastery of AI foundational skills.
Qualifying Exam – 0 unit(s)
After successfully passing the qualifying examination, students may begin the dissertation coursework.
Doctoral Research Methods and Analysis – 4 unit(s)
Applied AI Innovation – 4 unit(s)
Students may register for DBA 890 Dissertation Topic Proposal only after having first completed all required doctoral foundation coursework, having passed the qualifying examination, and having completed the concentration coursework.
Dissertation Topic Proposal – 8 unit(s)
Dissertation Proposal Defense – 8 unit(s)
Dissertation Completion and Approval by Committee – 12–16 unit(s)
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It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
The following are examples of new skills needed for the successful deployment of generative AI tools:
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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Gamestop shares continue to plunge as frenzy over roaring kitty’s return fizzles.
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Shares of GameStop tumbled for a second consecutive session on Monday, extending deep losses after stock influencer Keith Gill’s return to YouTube last week failed to spark fresh investor enthusiasm for the struggling shopping mall retailer.
Gill, known on YouTube as “Roaring Kitty,” held his first livestream in three years on Friday, the day GameStop unveiled its second share sale in days .
A key figure behind an eye-popping rally in GameStop in 2021, Gill joked about memes and interspersed his discussion of GameStop with several disclaimers in a livestream that by Monday had over 2.4 million views on YouTube.
On Monday, GameStop shares sank about 12% to close at $24.83, following a dive of nearly 40% on Friday after the company reported a drop in quarterly sales.
Also on Friday, GameStop said it would sell up to 75 million shares, days after it made $933 million by selling 45 million shares.
Gill acquired 5 million shares of GameStop at an average price of $21.274, according to details he shared on social media. In addition, he bought 120,000 GameStop June 21 call options at a strike price of $20 at $5.6754 per contract. Reuters was unable to verify the size and value of his holdings.
On Monday afternoon, the options contracts were changing hands at $6.40 a contract, according to LSEG data.
Other so-called meme stocks also gave back recent gains on Monday, with AMC Entertainment losing nearly 4% and headphone seller Koss down 5.9%.
Shares of GameStop nearly tripled in value over two days through May 14 after an account associated with Gill returned to X.com, formerly called Twitter.
Since then, GameStop shares have given up most of those gains, and the stock remains up about 42% so far in 2024.
The videogame retailer has been losing money for years as customers shift to online purchases, and its latest quarter was no different.
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Top Business Dissertation Topics. Topic 1: Assessing how the regional differences between countries influence the business strategies of multinational companies. Topic 2: How corporate social responsibility (CSR) affects customer loyalty: A case study of the UK petroleum industry.
Theses and dissertations published by graduate students in the Business Administration program, College of Business, Old Dominion University, since Fall 2016 are available in this collection. Backfiles of all dissertations (and some theses) have also been added. ... Dissertation: Two Essays on Industry Tournament Incentives, Sarah Almisher. PDF.
Craft a convincing dissertation or thesis research proposal. Write a clear, compelling introduction chapter. Undertake a thorough review of the existing research and write up a literature review. Undertake your own research. Present and interpret your findings. Draw a conclusion and discuss the implications.
Leadership and Innovation Business Dissertation Topics. Innovation has become a primary force driving the growth, performance, and valuation of companies. However, sometimes there is a wide gap between the aspirations of executives to innovate and their ability to execute. Many companies make the mistake of trying to spur innovation by turning ...
ScholarWorks at Georgia State University includes Doctoral Dissertations contributed by students of the J. Mack Robinson College of Business, Department of Business Administration at Georgia State University. The institutional repository is administered by the Georgia State University Library in cooperation with individual departments and academic units of the University.
Theses/Dissertations from 2023. For Love or Money: Investor Motivations in Equity-Based Crowdfunding, Jason C. Cherubini. The Great Resignation: An Exploration of Strategies to Combat School Bus Driver Shortages in the Post-COVID-19 Era, James E. Cole Jr. The Great Resignation: An Exploration of Strategies to Combat School Bus Driver Shortages ...
Business administration [6] Business Administration, Accounting [5] Business Administration, General [10] Business Administration, Management [11] Business Administration, Marketing [6] CEO [1] Climate change [1] close relationships [1]
Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you've landed on this post, chances are you're looking for a business/management-related research topic, but aren't sure where to start.Here, we'll explore a variety of research ideas and topic thought-starters for management ...
Theses/Dissertations from 2024 PDF. Two Essays on Asset Management, Quan Qi. Theses/Dissertations from 2016 PDF. SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA, Shih-Hui Hsiao. PDF
Importantly, the book recognizes that writing up a research project is rarely organized in the form in which the dissertation is finally presented. Readers are given guidelines to help them assess the kind of researcher they are and the all important question of how to chose a research project is answered. ... Doing your dissertation in ...
The College of Business Administration Dissertations Series is comprised of dissertations and theses authored by Marquette University's College of Business Administration doctoral and master's students. ... Theses/Dissertations from 1923 PDF. Common Law trusts as substitutes for private corporations, Emory L. Grady . Enter search terms:
The dissertation is the final requirement for the PhD degree. The research required for the dissertation must be of publishable quality and a significant contribution in a scholarly field. The dissertation is evidence of the candidate's proficiency and future potential in research. Students work closely with faculty throughout the program ...
Theses/Dissertations from 2023. PDF. Analyzing the Effect of Sponsorship Disclosure on Social Media Influencer Contribution to Engagement in the Test and Measurement Industry, Todd B. Baker. PDF. Moral Virtues: A Quantitative Study on the Impact of National Culture on Integrity, Andrew I. Ellestad. PDF.
A dissertation is a long-form piece of academic writing based on original research conducted by you. It is usually submitted as the final step in order to finish a PhD program. Your dissertation is probably the longest piece of writing you've ever completed. It requires solid research, writing, and analysis skills, and it can be intimidating ...
Prize-Winning Thesis and Dissertation Examples. Published on September 9, 2022 by Tegan George.Revised on July 18, 2023. It can be difficult to know where to start when writing your thesis or dissertation.One way to come up with some ideas or maybe even combat writer's block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.
Study of pre-professionalisation processes: the case of corporate social responsibility in the UK . Pan, Yinuo (The University of Edinburgh, 2024-04-26) This thesis examines the complex processes of pre-professionalisation, taking corporate social responsibility (CSR) in the UK as its empirical setting. Drawing on insights from distinguished ...
A Multiple Case Study: Male Correctional Officers' Experiences and Attitudes Regarding "Gender Quota" Human Resource Management Strategies in Corrections, Rebecca Jo Patterson. PDF. Quantifying the Value of Renewable Energy as a Hedge Against the Volatility of Natural Gas Prices in Wisconsin, Miodrag Petrovic. PDF.
Unique Business Management Dissertation Topics. Coordinating communications and teamwork among remote workers. How business attract their customers. Artificial intelligence investment and its effect on customer satisfaction. Impact of globalisation on corporate management. Customer viewpoint on how they use their data when using mobile banking.
Dissertation: Business Is War: An Investigation Into Metaphor Use in Internet and Non-Internet IPOs; Shigeo Kagami, DM Dissertation: Theoretical Aspects of the Japanese Institutional Relations Model and Its Effectiveness for Corporate Governance in the Context of Globalization;
Here, course leaders identify five of the most in-demand areas of business research. 1. Managing technology & innovation. "Management of innovation and technology is of particular importance right now," says Sabatier. "Questions about R&D, strategy and business models, and innovation are very important both from a theoretical and ...
Dissertation examples. Listed below are some of the best examples of research projects and dissertations from undergraduate and taught postgraduate students at the University of Leeds We have not been able to gather examples from all schools. The module requirements for research projects may have changed since these examples were written.
International Business Management Name of thesis IMPACT OF TECHNOLOGY ON BUSINESS Centre supervisor Dr. Weimu You Pages 52+2 Performance, productivity, management, policy, manpower, human resources, marketing, and sales are some of the areas where the effects of technology on businesses have been listed in the literature review section.
Dissertations on Business. The term Business relates to commercial or industrial activities undertaken to realise a profit including producing or trading in products (goods or services). A general business studies degree could cover subjects such as accounting, finance, management and increasingly, entrepreneurship.
What is the purpose of a literature review? Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
The Doctor of Business Administration in Emerging Technologies with Concentration in Generative AI requires completion of 20 units of major courses, 8 units of dissertation foundation courses, and 28 units of dissertation work, for a total of 56 units. Students must earn a "B-" or better in each course and a cumulative grade-point average of 3.00 or better.
Written by Coursera Staff • Updated on Apr 19, 2024. Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock ...
Mission. The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives.
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI: Be targeted in ramping up your data quality and data augmentation efforts.
Roaring Kitty/YouTube. On Monday, GameStop shares sank about 15% to $24.06, following a dive of nearly 40% on Friday after the company reported a drop in quarterly sales. Also on Friday, GameStop ...
June 10, 2024 • Reading Time: 2 minutes. Nicholas Ros, a master's student at the University of Florida Warrington College of Business explains his practicum project working with Bank of America. As a proud double-gator at the University of Florida Warrington College of Business, I have embraced both my undergraduate and graduate studies ...