Exploring Global Potential through Innovation and Entrepreneurship:
Emerging Trends and the Use of AI in Research
by
Eugene Fregetto, PhD
University of Illinois Chicago (Retired)
Editorial note
This document is a carefully edited, reorganized, and rewritten version of the original spoken transcript. The purpose of the edits is clarity: removing verbal fillers, correcting sentence structure, grouping ideas into coherent sections, and preserving the speaker’s meaning and voice. It is not a verbatim transcription.
About this booklet
This booklet transforms a conference presentation into a readable reference for students, faculty, and emerging researchers. It focuses on (1) how entrepreneurship research is evolving, and (2) how artificial intelligence (AI) can be used to strengthen the research process, while protecting rigor, transparency, and scholarly integrity.
About SLFE and the Annual Research Conference
The Sri Lanka Forum of Entrepreneurship (SLFE) connects universities across Sri Lanka to advance entrepreneurship education and training. Established at the University of Sri Jayewardenepura in April 2021, the forum fosters networking among academic staff and students, and provides a platform for knowledge exchange and collaboration. (Department of Entrepreneurship, University of Sri Jayewardenepura, n.d.)
SLFE emphasizes the integration of artificial and human intelligence in entrepreneurship. Its objectives include expanding professional networks, strengthening industry-government relationships, encouraging collaborative research, showcasing student innovations, and promoting an entrepreneurial culture in Sri Lanka. The annual research conference is a flagship event hosted by various universities since 2022; in 2025, it was sponsored by the Sabaragamuwa University of Sri Lanka. (Department of Entrepreneurship, University of Sri Jayewardenepura, n.d.; Sabaragamuwa University of Sri Lanka, 2025)
Contents
1. Webinar opening and introduction
2. Opening remarks
3. Why entrepreneurship research feels different in the 2020s
4. How research opportunities emerge: a dynamic system
5. A dynamic system of knowledge creation
6. The research process—and where opportunities hide
7. Finding research opportunities: six levels of analysis
8. Using one dataset well: six different analyses
9. One topic, three audiences
10. Research directions and “new trends” in entrepreneurship scholarship
11. Revisiting classic works and building integrative frameworks
12. An open resource for research skills: the ICSB video library
13. AI in research: principles, limits, and ethics
14. Using AI in the research workflow (responsibly)
15. Practical workflow: how I use AI while writing
16. Rigor, transparency, and replication
17. Closing discussion and encouragement
18. Closing: building Sri Lanka’s global research potential
19. Q&A (lightly edited for clarity)
20. Acknowledgements
21. References
1. Webinar opening and introduction
Moderator: Ruvini Perera, Assistant Lecturer at University of Sri Jayewardenepura
Good evening, everyone, and welcome to our first webinar in the SLFE conference webinar series on new trends and the use of AI in research. This session is part of the Fourth Annual Research Conference of the Sri Lanka Forum of Entrepreneurship. This year’s conference theme is “Exploring the global potential through innovation and entrepreneurship.”
The Sri Lanka Forum of Entrepreneurship (SLFE) is a national platform that connects universities across Sri Lanka to strengthen entrepreneurship education and training. The Forum supports networking among academic staff and students, encourages knowledge exchange and collaboration, and helps build relationships with industry and government. The annual research conference is a key event, hosted by different universities since 2022, with Sabaragamuwa University of Sri Lanka hosting in 2025.
One of the key objectives of this year’s conference is to strengthen the network among emerging researchers and young scholars—locally and internationally—through mentorship and academic growth. With this objective in mind, we launched this webinar series to provide practical research guidance.
Tonight’s session explores how artificial intelligence is reshaping the research landscape—from early-stage topic selection to writing and dissemination. Whether you are an undergraduate, an academic, or an industry practitioner, we hope you will take away practical insights about how AI can enhance the quality, speed, and impact of research across disciplines.
It is my pleasure to welcome our guest speaker, Professor Eugene Fregetto. Professor Fregetto has taught entrepreneurship and marketing for many years, served on editorial boards of scholarly journals, and has extensive professional experience beyond academia. He has also worked collaboratively with Sri Lankan universities, contributing his expertise to support the development of entrepreneurship education in Sri Lanka.
Please join me in welcoming Professor Eugene Fregetto, whose insights will guide us through the evolving intersection of research and AI.
2. Opening remarks
Guest Speaker: Eugene Fregetto, PhD
Thank you for the kind introduction. I’m honored to be with you. I have visited Sri Lanka multiple times in recent years, and I’ve tried to build bridges between Sri Lankan and U.S. universities. I’m grateful to see continued progress and collaboration, and I hope this session stimulates your curiosity—more than it simply gives you answers.
I want to begin with a mindset: research is hard, and it should humble us. A quote often attributed to Albert Einstein captures this well: if we knew exactly what we were doing, we would not call it research. Research involves searching—asking meaningful questions, following evidence, and accepting uncertainty (Stedman & Beckley, 2007).
I also want to highlight a forward-looking idea from Alvin Toffler’s Future Shock (1970): every new technique changes existing techniques by allowing new combinations. That is a helpful way to think about AI. AI is not “one more tool.” It is a tool that changes how other tools combine—and it shifts what becomes possible (Toffler, 1970).
Finally, consider the idea of ‘unknown unknowns’: not only do we have things we know and things we know we don’t know, but also things we don’t realize we don’t know. Research is partly about discovering those hidden questions (Rumsfeld, 2002).
3. Why entrepreneurship research feels different in the 2020s
Entrepreneurship is an old human activity, but the conditions shaping entrepreneurial behavior have changed quickly. Digital platforms, global supply chains, mobile connectivity, and new forms of work have expanded what counts as a venture and who can participate. At the same time, public concerns around inclusion, sustainability, and resilience are pushing entrepreneurship research to address outcomes beyond firm growth.
Several shifts are especially relevant for emerging scholars:
• Entrepreneurship is increasingly digital (platforms, marketplaces, app-based ventures, and creator-led micro-enterprises).
• Entrepreneurship is increasingly embedded in ecosystems (universities, accelerators, policy, finance, culture, and networks).
• Entrepreneurship is increasingly mission-oriented (social, environmental, health, education, and community outcomes).
• Entrepreneurship is increasingly data-rich (digital traces, online transactions, sensor data, and remote field methods).
• Entrepreneurship research is increasingly interdisciplinary (drawing on strategy, sociology, psychology, geography, IS, and public policy).
These shifts do not eliminate classic questions about opportunity, resources, uncertainty, and judgment. They do create new research opportunities: new units of analysis, new boundary conditions, and new theoretical bridges.
4. How research opportunities emerge: a dynamic system
I developed a simple framework that I call the Dynamic System of Knowledge Creation. It illustrates how research opportunities emerge from the interaction of four domains—reading, experience, imagination, and conversation—with writing at the center.
The key point is that scholarship is not a linear pipeline. It is recursive. What we read shapes what we imagine; what we experience changes how we interpret what we read; conversations refine our thinking; and writing forces clarity.
Writing is not the final step. Writing is generative. Drafting and revising help you organize ideas, reveal gaps, and form better questions.
The four domains surrounding writing contribute in distinct ways:
• Reading exposes you to theories, findings, debates, and contradictions—helping you see what has been over-studied and what remains under-explored.
• Experience grounds scholarship in reality—through fieldwork, professional practice, and everyday observation.
• Imagination allows you to connect ideas, form hypotheses, and envision possibilities beyond current evidence.
• Conversation (with peers, mentors, and practitioners) challenges and improves your thinking. Knowledge construction is inherently dialogic.
A brief editorial comment: our era often defaults to ‘text, not talk.’ I understand the convenience of messaging and email, but meaningful conversation usually carries nuance and insight that text can miss. If you remove conversation from the system, you weaken the entire ecology of discovery.
5. A dynamic system of knowledge creation
Good research is not a single step. It is a dynamic system: questions lead to theory, theory guides methods, methods generate data, data produce findings, findings influence practice and policy, and real-world change creates new questions. (Van de Ven, 2007)
A practical alignment check uses six elements:
1. Phenomenon and puzzle: What is happening, and why is it interesting or surprising?
2. Concepts and constructs: What exactly are you trying to explain or predict?
3. Theory and mechanisms: What causal story connects your concepts?
4. Method and evidence: What data and design can credibly test or illuminate that story?
5. Interpretation: What do the findings mean for theory, context, and boundary conditions?
6. Contribution and impact: Who can use this, and what changes as a result?
When alignment is strong, readers can see your logic from start to finish-and they trust your conclusions.
6. The research process—and where opportunities hide
Let’s revisit the research process briefly, not as a checklist, but as a map of where research opportunities can be found.
Research begins with a world of phenomena. From that broad world, you select a specific phenomenon and craft a problem statement. You observe symptoms and propose initial assumptions. You then search existing knowledge, your literature review, to understand what is known and where gaps remain.
Next, you define constructs and develop hypotheses about their relationships. You operationalize constructs into measurable variables, create or select valid scales, identify the population, and design sampling procedures. You then collect data, code it carefully, analyze it, report findings, interpret what the findings mean, and discuss implications, then answer the ‘so what?’ question.
Many papers compress or blur these steps. Substantial scholarship clearly separates them: findings are not the same as interpretation, and interpretation is not the same as implications.
Where do research opportunities exist? Almost everywhere in this process. For example:
• Choosing a different angle on the same phenomenon (different assumptions; different framing).
• Improving a study design by tightening measurement, refining constructs, or sampling a more appropriate population.
• Replicating and extending work with better controls or stronger comparison groups.
• Using different methods (qualitative, quantitative, mixed methods, longitudinal designs, experiments where feasible) (Open Science Collaboration, 2015; Nosek et al., 2018).
7. Finding research opportunities: six levels of analysis
Research opportunities are often missed because scholars look at only one level at a time. Entrepreneurship is multi-level by nature: individuals act, teams coordinate, ventures grow, ecosystems shape constraints, and institutions define rules.
Six Levels of Analysis
Level Typical research focus (examples)
Individual Cognition, identity, judgment under uncertainty, entrepreneurial learning, ethics.
Team Founding teams, complementary skills, conflict, governance, distributed leadership.
Venture Business models, innovation, scaling, pivots, performance measurement.
Ecosystem/Industry Networks, clusters, intermediaries, financing, spillovers, competitive dynamics.
Institutional and policy regulations, IP, education systems, inclusion policies, and public procurement.
Global/Development Internationalization, diaspora networks, cross-border platforms, global value chains.
Use levels as a lens for generating questions. If your topic feels too familiar, shift levels: ask how the same phenomenon changes when you move from individual to team, from venture to ecosystem, or from local to global.
8. Using one dataset well: six different analyses
If you design a study carefully, one dataset can yield multiple meaningful papers—not by slicing data to inflate publication counts, but by asking distinct questions at distinct analytical levels.
Think of six different analyses:
1) Variability (how much does a measure vary across cases?)
2) Central tendency and frequency (what is typical; how common is something?)
3) Hypothesis testing (underlying phenomenon or random chance)
4) Differences between groups (e.g., t-tests, ANOVA)
5) Associations among variables (correlation, contingency)
6) Causal modeling (regression and related approaches)
Too often, a study rushes to causal claims without first mapping variability, frequency, and group differences. If you plan the study with all six levels in mind, you will ask better questions and extract richer meaning from your data.
9. One topic, three audiences
When you think about implications, remember this: the same research can, and often should, speak to three distinct audiences.
· Academic scholars seek explanatory and predictive clarity—strong theory, careful testing, and well-specified mechanisms.
· Policymakers focus on public outcomes such as jobs, productivity, innovation, and regional development.
· Practitioners and the public want actionable guidance—insights that help solve real problems and improve decisions and results.
If you keep these audiences in mind early, as you design the study, you can shape a project that yields multiple meaningful contributions rather than a single narrow publication.
Designing for three audiences: scholarship, policy, practice
A proper discipline is to design your research with three audiences in mind from the outset: scholars, policymakers, and practitioners. This does not mean one paper must serve everyone equally. It means you should be explicit about who benefits from your contribution—and how.
Audience Questions: Your study should answer
Scholars, what theoretical gap do you address? What mechanism do you clarify? What boundary condition do you establish?
Policymakers, what public problem is at stake? What policy levers exist? What evidence supports action—and what tradeoffs follow?
Practitioners, what decisions must be made? What patterns, tools, or heuristics improve action under uncertainty?
Practical test
If you cannot state your contribution in one sentence for each audience, your framing is probably too broad.
10. Research directions and “new trends” in entrepreneurship scholarship
Across entrepreneurship, and similarly in management and marketing, several research directions remain promising:
• Studying under-represented groups and contexts (including minority and ethical entrepreneurship).
• Using novel and rigorous designs: longitudinal studies, mixed methods, and experiments where feasible.
• Investigating the social role of entrepreneurship and innovation.
• Revisiting classic works to see what remains valid, what was neglected, and what should be reframed.
• Strengthening conceptual clarity and legitimacy in a field whose popularity often exceeds its empirical foundations.
A recurring challenge in entrepreneurship research is conceptual fuzziness, especially regarding the relationship between entrepreneurship and small business. Small businesses are the overwhelming majority of enterprises, yet the language of entrepreneurship often dominates because it is seen as more glamorous. This is not merely a semantic issue; it affects what gets studied, funded, taught, and legitimized.
11. Revisiting classic works and building integrative frameworks
A helpful strategy is to revisit foundational publications, not for nostalgia, but for clarity: early works often state problems and assumptions more plainly. They can help you see which questions remain unanswered.
Another strategy is integrative framework-building. Fields grow when scholars offer conceptual maps that organize theories and findings, connect micro- and macro-level dynamics, and propose coherent pathways for future research.
As an illustration, I’ve been developing two integrative models (still in progress): one aimed at clarifying the ‘core’ of entrepreneurship education beyond the business-plan era; and another, which I call Entrepreneurial Thinking, that links deep beliefs and cognitive structures to competencies and entrepreneurial intention.
12. An open resource for research skills: The ICSB video library
A practical resource worth exploring is the International Council for Small Business (ICSB) at www.icsb.org, which released an open video library designed to strengthen research skills and support practitioners. The library includes videos with subtitles and transcripts across many languages and can be shared freely as an educational resource.
One issue highlighted in these materials is the importance of replication and transparency. If a paper does not report enough methodological detail, it is difficult—sometimes impossible—to replicate. Replication is essential to credibility, yet it is often undervalued in publication incentives that reward novelty over verification.
13. AI in research: Principles, limits, and ethics
Now to AI. My position is simple: education drives AI—not the other way around. AI can support learning and research, but it does not replace the human elements of judgment, ethics, and interpretation.
AI is not only a technology story; it is a business and organizational story. It changes workflows, decision processes, and the value of data. A key question becomes: where, specifically, will AI unlock value? That single question produces many research opportunities.
Several issues follow:
• Diffusion: AI adoption is a journey; everyone must come along.
• AI literacy: it should become as common as proficiency with word processing or spreadsheets.
• Reskilling and upskilling: labor markets will shift, and institutions must respond.
• Humanizing AI: In an era of isolation and ‘text not talk,’ we must protect the human voice and human relationships.
• Ethical and political implications: many of our most complex problems are not technical; they are ethical and political (Rogers, 2003).
AI has apparent limitations. It is often mimetic rather than truly innovative: it synthesizes patterns in existing data but lacks agency, ownership, and real-world experience. It can also ‘hallucinate’—confidently completing a thought that is not grounded in truth. That means you must verify outputs, especially when accuracy matters (Bender et al., 2021; Ji et al., 2023).
Finally, consider publication ethics. Current journal policies commonly require that AI not be listed as an author and that authors disclose relevant use of AI tools. Using AI may be appropriate for brainstorming, drafting, and search assistance, but transparency and accountability remain the human author’s responsibility (COPE Council, 2023; ICMJE, 2025).
14. Using AI in the research workflow (responsibly)
AI is changing research practice by lowering the cost of routine cognitive labor: searching, summarizing, classifying, drafting, and translating. Used well, AI can increase research productivity and help scholars explore more ideas before committing to a design. (Scherbakov et al., 2025; Zhang et al., 2025)
Framing: AI works best as a copilot, not a replacement: it can accelerate steps, but you remain accountable for truth, ethics, and interpretation.
High-value uses of AI for entrepreneurship research include:
• Literature discovery: expanding keyword sets, mapping adjacent concepts, and identifying debate clusters.
• Concept clarification: generating candidate definitions and boundary conditions to verify against sources.
• Interview support: refining protocols, generating probes, and building coding dictionaries (with human review).
• Data preparation: cleaning text, tagging documents, and drafting reproducible scripts (validated by the researcher).
• Writing support: restructuring drafts for clarity, tightening claims, and aligning sections to a target journal.
• Teaching and dissemination: converting findings into policy briefs, practitioner tools, or student learning modules.
Risks and controls (what responsible use looks like): (Hosseini et al., 2023; Nature Portfolio, n.d.; Nature, 2023)
• Fabricated or incorrect citations and quotations → verify every reference in an authoritative database (e.g., Scopus/Web of Science/PubMed) before submission. (Nature, 2023)
• Hallucinated facts or overconfident synthesis → triangulate with primary sources and report uncertainty where it exists. (Zhang et al., 2025)
• Loss of nuance in summaries (especially theory and methods) → treat AI outputs as drafts; re-check the original texts for meaning, scope, and limitations. (Scherbakov et al., 2025)
• Confidentiality or data leakage → do not upload proprietary, sensitive, or identifiable data to external systems unless your institution and data owners explicitly permit it. (Nature Portfolio, n.d.)
• Bias and uneven representation of the literature → audit whose work is being surfaced and intentionally broaden search strategies and inclusion criteria. (Hosseini et al., 2023)
• Lack of transparency in AI use → disclose material use of AI tools (and how they were used) consistent with journal and institutional policies. (Nature Portfolio, n.d.; Nature, 2023; Hosseini et al., 2023)
Risk Practical control
Hallucination: Verify every factual claim and every citation against primary sources.
Bias Check outputs for omitted perspectives; diversify datasets and sources.
Confidentiality: Do not paste sensitive data or identifiable participant information into public tools.
Attribution Disclose AI assistance where required; never present AI-generated text as evidence.
Overreliance: Use AI to generate options, then choose with theory and judgment.
15. Practice workflow: How I use AI while writing
Here is a practical workflow that reflects how I use AI as a support tool:
• For reading: I use AI to clarify concepts, deepen understanding, refresh memory, and explore possible connections among ideas.
• For writing: when I have a paragraph that is overly long or unclear, I may ask an AI tool to rewrite it. Then I revise the output to restore my intent and voice.
• For editing: I use grammar tools to improve readability.
• For integrity: I run plagiarism and AI-detection checks where appropriate, and I always verify factual claims and citations.
A key limitation is access: AI tools often retrieve only what is publicly available, potentially missing relevant work behind paywalls. Also, AI tends to provide a single polished answer; it does not naturally engage in the kind of skeptical dialogue that a human colleague would. You often have to reframe questions to elicit alternative perspectives deliberately.
16. Rigor, transparency, and replication
Across many disciplines, scholars have debated the reliability of published findings. The solution is better practice: clearer reporting, stronger designs, and transparent evidence. (Ioannidis, 2005; Open Science Collaboration, 2015)
Simple practices that raise trust in your work:
• State your identification strategy (why the design supports the inference you make).
• Define constructs precisely and report measurement choices and alternatives.
• Provide enough detail for others to reproduce key steps (sampling, coding, model specification).
• Separate results from interpretation; be explicit about uncertainty and limitations.
• Use robustness checks where possible and report null results honestly. (Rosenthal, 1979)
17. Closing discussion and encouragement
Let me end with encouragement. You are doing research at a moment of rapid change. The tools will keep shifting. But the fundamentals remain: curiosity, rigor, transparency, and meaningful conversation. If you keep asking good questions—and if you use AI as a tool rather than as a substitute for thinking, you will contribute work that matters.
18. Closing: building Sri Lanka’s global research potential
Sri Lanka’s entrepreneurship scholarship can have a global impact by combining local relevance with international theoretical conversations. Networks such as SLFE make that combination easier: shared methods, co-authorship, student showcases, and relationships with industry and government.
Final encouragement: Aim for research that is meaningful, credible, and usable. AI can help you move faster, but quality comes from careful design and scholarly judgment.
Possible next steps for an SLFE research agenda:
• Identify two or three national-level research themes (e.g., digital micro-entrepreneurship, inclusive ecosystem building, venture resilience).
• Create multi-university datasets or shared case libraries to reduce duplicated effort.
• Run methods workshops focused on strong research designs and transparent reporting.
• Develop a student innovation showcase that connects prototypes to research questions and publication pathways.
19. Q&A
The session concluded with a short Q&A focused on institutional constraints, research funding, and how universities can build AI readiness despite tight budgets. The discussion emphasized that AI adoption is not a small add-on; it requires changes in how knowledge is taught, managed, and shared, and it is costly in time and coordination. Even so, participants noted the value of curated resources and the importance of sustained learning communities.
Acknowledgements
With thanks to the Sri Lanka Forum of Entrepreneurship (SLFE) and the organizers of the 4th Annual Research Conference for hosting the webinar and supporting emerging scholars.
References:
Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43(7), 1097–1108. https://doi.org/10.1016/j.respol.2014.01.015
Bartunek, J. M., & Rynes, S. L. (2014). Academics and practitioners are alike and unlike: The paradoxes of academic–practitioner relationships. Journal of Management, 40(5), 1181–1201. https://doi.org/10.1177/0149206314529160
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21) (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
COPE Council. (2023, February 13). Authorship and AI tools (COPE position statement). Committee on Publication Ethics. https://doi.org/10.24318/cCVRZBms
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Davidsson, P., & Wiklund, J. (2001). Levels of analysis in entrepreneurship research: Current research practice and suggestions for the future. Entrepreneurship Theory and Practice, 25(4), 81–100. https://doi.org/10.1177/104225870102500406
Department of Entrepreneurship, University of Sri Jayewardenepura. (n.d.). Sri Lanka Forum of Entrepreneurship (SLFE). University of Sri Jayewardenepura. (Web page).
Elia, G., Margherita, A., & Passiante, G. (2020). Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process. Technological Forecasting and Social Change, 150, 119791. https://doi.org/10.1016/j.techfore.2019.119791
Hosseini, M., Resnik, D. B., & Holmes, K. (2023). The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts. Research Ethics, 19(4), 449–465. https://doi.org/10.1177/17470161231180449
ICMJE. (2025). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals (updated April 2025)—International Committee of Medical Journal Editors.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Chen, D., Dai, W., Chan,
H. S., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55, Article 248. https://doi.org/10.1145/3571730
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepreneurship Theory and Practice, 41(6), 1029–1055. https://doi.org/10.1111/etap.12254
Nature. (2023). Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, 613(7945), 612. https://doi.org/10.1038/d41586-023-00191-1
Nature Portfolio. (n.d.). Artificial intelligence (AI): Editorial policies. (Web page).
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638
Rumsfeld, D. H. (2002, February 12). Defense Department briefing (remarks on “known knowns,” “known unknowns,” and “unknown unknowns”). U.S. Department of Defense. https://usinfo.org/wf-archive/2002/020212/epf202.htm
Sabaragamuwa University of Sri Lanka. (2025). 4th Annual Research Conference of the Sri Lanka Forum of Entrepreneurship (ARCSLFE 2025). (Web page).
Santos, F. M. (2012). A positive theory of social entrepreneurship. Journal of Business Ethics, 111(3), 335–351. https://doi.org/10.1007/s10551-012-1413-4
Scherbakov, M., Hubig, N., Jansari, M. M., Bakumenko, A., & Lenert, P. (2025). The emergence of large language models as tools in literature reviews: A large language model-assisted systematic review—Journal of the American Medical Informatics Association. Advance online publication. https://doi.org/10.1093/jamia/ocaf100
Shepherd, D. A., & Patzelt, H. (2011). The new field of sustainable entrepreneurship: Studying entrepreneurial action linking “what is to be sustained” with “what is to be developed”. Entrepreneurship Theory and Practice, 35(1), 137–163. https://doi.org/10.1111/j.1540-6520.2010.00426.x
Stam, E. (2015). Entrepreneurial ecosystems and regional policy: A sympathetic critique. European Planning Studies, 23(9), 1759–1769. https://doi.org/10.1080/09654313.2015.1061484
Stedman, R. C., & Beckley, T. M. (2007). “If We Knew What it Was We Were Doing, it Would Not be Called Research, Would it?” Society & Natural Resources, 20(10), 939–943. https://doi.org/10.1080/08941920701561031
Toffler, A. (1970). Future shock. Random House.
Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.
Zhang, Y., Khan, S. A., Mahmud, A., Yang, H., Lavin, A., Levin, M., Frey, J., Dunnmon, J.,
Evans, J., Bundy, A., Džeroski, S., Tegnér, J., & Zenil, H. (2025). Exploring the role of large language models in the scientific method: From hypothesis to discovery. npj Artificial Intelligence, 1, Article 14. https://doi.org/10.1038/s44387-025-000
