(Mark 72)
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This dissertation achieved a mark of 84:
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The following outstanding dissertation example PDFs have their marks denoted in brackets. (Mark 70) (Mark 78) |
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Published by Owen Ingram at January 4th, 2023 , Revised On June 6, 2024
A degree in business administration is intended for those wishing to start their own business or expand an existing one. When you choose business management as your field of study, you are not a typical student because you want to learn about all possible aspects of managing a business.
However, if you are struggling to develop a trending and meaningful business management dissertation topic and need a helping hand, there’s no need to worry! Our unique business management dissertation topic ideas have been developed specifically to ensure you have the best idea to investigate as part of your project.
ResearchProspect writers can send several custom topic ideas to your email address. Once you have chosen a topic that suits your needs and interests, you can order for our dissertation outline service which will include a brief introduction to the topic, research questions , literature review , methodology , expected results , and conclusion . The dissertation outline will enable you to review the quality of our work before placing the order for our full dissertation writing service!
Choosing a business management dissertation topic can be extremely stressful for anyone. You can research your topics online and find topics on any subject, for example, nursing dissertation topics or even those related to business, such as marketing dissertation topics.
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To ensure that your dissertation captures the reader’s attention, choose a dissertation topic that is currently popular. Once you have selected a topic, you can take help from proposal writing services before you start working on the actual thesis paper.
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The law of contracts is considered to be one of the most integral parts of business. Employees and companies exchange financial information through this system. The task of writing a top-notch dissertation.
It is often said that commercial law covers a broad study area since it cannot be studied exclusively in one legal jurisdiction. However, England and Wales are preferred as commercial centers.
Find unique and interesting remote-working dissertation topics for your thesis, mentioning positive and negative aspects of remote work.
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The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.
Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.
Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).
Other highlights include:
If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.
How to access materials at the Harvard University Archives
How to find and request dissertations, in person or virtually
How to find and request undergraduate honors theses
How to find and request Thomas Temple Hoopes Prize papers
How to find and request Bowdoin Prize papers
Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.
UKnowledge > Gatton College of Business and Economics > Business Administration > Theses & Dissertations
Theses/dissertations from 2024 2024.
Two Essays on Asset Management , Quan Qi
SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA , Shih-Hui Hsiao
WHY SUPPLIER INTEGRATION FAILS: A SALESPERSON’S PERSPECTIVE , Jae-Young Oh
WHY SUPPLIER DEVELOPMENT WORKS? A KNOWLEDGE-MANAGEMENT PERSPECTIVE , Liang Chen
MODELING LARGE-SCALE CROSS EFFECT IN CO-PURCHASE INCIDENCE: COMPARING ARTIFICIAL NEURAL NETWORK TECHNIQUES AND MULTIVARIATE PROBIT MODELING , Zhiguo Yang
ENTREPRENEURIAL ORIENTATION, COLLABORATIVE NETWORKS, AND NONPROFIT PERFORMANCE , Brandon Ofem
HOW DO CONSUMERS USE SOCIAL SHOPPING WEBSITES? THE IMPACT OF SOCIAL ENDORSEMENTS , Pei Xu
AN INVESTIGATION INTO LONG-RUN ABNORMAL RETURNS USING PROPENSITY SCORE MATCHING , Sunayan Acharya
USE OF VISUALIZATION IN DIGITAL FINANCIAL REPORTING: THE EFFECT OF SPARKLINE , Priyanka Meharia
NETWORK DRIVERS OF INTERCUSTOMER SOCIAL SUPPORT , Hulda G. Black
AN EMPIRICAL ANALYSIS OF REPUTATION EFFECTS AND NETWORK CENTRALITY IN A MULTI-AGENCY CONTEXT , Emily Jane Plant
AN INVESTIGATION INTO THE UNINTENDED CONSEQUENCES OF DOWNSTREAM CHANNEL ALLOWANCES , William Jason Rowe
COMPETITIVE STRATEGY, ALLIANCE NETWORKS, AND FIRM PERFORMANCE , Goce Andrevski
LINKING SERVICE ENCOUNTERS TO FINANCIAL PERFORMANCE: AN EXTENDED APPROACH TO VALUATION , Carla Yvonne Childers
INVESTORS REACTIONS TO COMPETITIVE ACTIONS AMONG RIVALS: A STEP TOWARD STRATEGIC ASSET PRICING THEORY , Margaret Vardell Hughes
THE INTERACTION OF HAPTIC IMAGERY WITH HAPTIC PERCEPTION FOR SIGHTED AND VISUALLY IMPAIRED CONSUMERS , Shannon Bridgmon Rinaldo
TWO ESSAYS ON BORROWING FROM BANKS AND LENDING SYNDICATES , Pankaj Kumar Maskara
THE DEVELOPMENT AND TEST OF AN EXCHANGE-BASED MODEL OF INTERPERSONAL WORKPLACE EXCLUSION , Kristin Damato Scott
INFORMATION SYSTEM CONTEXTUAL DATA QUALITY: A CASE STUDY , Daniel Lee Davenport
INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES , David Alexander Gaines
THE GAP BETWEEN WHAT TAXPAYERS WANT AND WHAT TAX PROFESSIONALS THINK THEY WANT: A REEXAMINATION OF CLIENT EXPECTATIONS AND TAX PROFESSIONAL AGGRESSIVENESS , Teresa Stephenson
COMPETITIVE DYNAMICS IN ELECTRONIC NETWORKS - ACHIEVING COMPETITIVENESS THROUGH INTERORGANIZATIONAL SYSTEMS , Lei Chi
SIMULATION AND OPTIMIZATION OF A CROSSDOCKING OPERATION IN A JUST-IN-TIME ENVIRONMENT , Karina Hauser
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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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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.
Download Word doc Download Google doc
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.
Statistics
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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In M&A, clarity on business rationale and investment thesis is critical.
So how do you build it?
Often the lack of specificity in M&A investment thesis leads to poor decision-making. It is crucial to determine at a detailed level “how” and “where” a deal will create value. This involves moving beyond generic assumptions to a detailed vision, rigorous due diligence and a robust implementation plan.
Common questions relating to the integration of operational platforms and the impacts of divestments need clear, granular answers.
The M&A investment thesis should guide deal objectives and execution performance tracking while ensuring alignment across all organisational levels. Initial stakeholder alignment on success metrics is key and while agility in approach is necessary, the core M&A investment thesis and goals must remain.
Ultimately a detailed understanding of value creation sharpens focus and drives success in M&A.
Access our free guide to build a robust M&A investment thesis for your business.
We help clients to address the inherently complex risk of M&A by uplifting capability and overcoming any capacity challenges. Our integrated team accompanies you through the deal lifecycle with a single point of accountability from the pre-deal strategy and preparation, deal execution all the way through to close and integration/separation. We also provide specialised advice in M&A ESG, ventures and joint ventures.
For more information reach out to Guy Fisher .
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June 11, 2024
Dissertation Title: “Leadership – Managing People Amidst Paradoxes” by Shoaib Amjad Hussain, Candidate PhD Management.
Date: June 11, 2024 - Tuesday Time: 10:00 a.m. Venue: Faculty Lounge (SDSB building, 4th floor) Zoom link: https://lums-edu-pk.zoom.us/j/91346414299?pwd=YlZPZ1FEWDFtK0U3V0xaRTZkcFk2UT09
Meeting ID: 913 4641 4299
Passcode: 842143
Dissertation Defence Committee Dr Muhammad Abdur Rahman Malik - Supervisor & Chair Dr Ghufran Ahmad – Member SDSB Dr Ayesha Masood – Member SDSB Dr Muhammad Hamad Alizai ‐ Member (LUMS) Dr Muhammad Athar Siddiqui - External Examiner (UCP)
Leadership significantly shapes the vision and success of individuals and organisations. However, research often oversimplifies leadership complexities, creating a gap between theory and practice. This thesis aims to address this shortcoming using the paradox theory to better understand the complexity in leadership studies. Focusing on leader behaviors, it considers leaders' cognitive processes of behavioral decision making, contextual factors such as people management paradoxes, cognitive aspects like the leader paradox mindset, and behavioral elements including paradoxical leader behavior.
The first paper introduces a 2 x 2 typology of leader behaviors, revealing gaps in existing research. The second paper develops a framework within Leader-Member Exchange (LMX) and Conservation of Resources (COR) theories to explain how leaders strategically use positive or negative behaviours to achieve desired outcomes. The third paper examines how a leader's paradox mindset and role ambiguity influence leader authenticity and use of different influence tactics. The fourth paper investigates the impact of people management paradoxes on leader behaviours and subordinate performance, highlighting the roles of leader political skill and subordinate work grit.
With these four papers this thesis provides nuanced insights into leadership and offers valuable theoretical and practical implications for leaders and HR practitioners.
Recent news.
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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Most dissertations run a minimum of 100-200 pages, with some hitting 300 pages or more. When editing your dissertation, break it down chapter by chapter. Go beyond grammar and spelling to make sure you communicate clearly and efficiently. Identify repetitive areas and shore up weaknesses in your argument.
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.
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Doing Your Dissertation in Business and Mangement by Reva Berman Brown; Mark N. K. Saunders This is a research book with a difference. It tells the truth about the research process. Each phase of a research project is addressed in the simultaneous order in which researchers often undertake them. Importantly, the book recognizes that writing up ...
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 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.
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.
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 ...
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 ...
Essays in Intellectual Property Bargaining and Trade . Ahn, Pyoungchan Joseph (2015-09-16) In this dissertation, I present three essays on the dynamics of intellectual property bargaining and trade, particularly of patents. The first essay presents a game theoretic model examining the sale of intellectual ...
Printed copies of HBS Doctoral theses are available for use in Baker Library Special Collections and Archives. Electronic versions are available through the ProQuest Dissertations & Theses database (HarvardKey required). Since 2015, electronic copies are also available through DASH, Harvard's open access repository.
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 ...
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 ...
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.
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;
The dissertation committee is composed of at least three members who must be approved by the Director of the Ph.D. program. All committee members must be tenured or tenure-track Rice faculty members. At least two committee members must be Jones Graduate School of Business faculty. At least one committee member must be a non-Jones Graduate ...
dissertation. Reason The introduction sets the stage for the study and directs readers to the purpose and context of the dissertation. Quality Markers A quality introduction situates the context and scope of the study and informs the reader, providing a clear and valid representation of what will be found in the remainder of the dissertation.
Business and Management thesis and dissertation collection. Browse By. By Issue Date Authors Titles Subjects Publication Type Sponsor Supervisors. Search within this Collection: Go This is a collection of some recent PhD theses from Business and Management. Please note that this is not a comprehensive list of all doctorate degrees from this School.
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.
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.
The Harvard University Archives' collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University's history.. Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research ...
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
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.
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: Search for relevant literature; Evaluate sources; Identify themes, debates, and gaps
Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance.
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Suleman Dawood School of Business (SDSB) is organising a PhD Management Dissertation Defense. June 11, 2024. ... Dissertation Title: "Leadership - Managing People Amidst Paradoxes" by Shoaib Amjad Hussain, Candidate PhD Management. Date: June 11, 2024 - Tuesday Time: 10:00 a.m. Venue: Faculty Lounge (SDSB building, 4th floor)
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.