Beyond Assistance: A Role-Based Conceptual Framework for Generative and Agentic Artificial Intelligence in Case-Based Management and Governance Research

Titolo Rivista CORPORATE GOVERNANCE AND RESEARCH & DEVELOPMENT STUDIES
Autori/Curatori Rosario Faraci, Rosaria Ferlito
Anno di pubblicazione 2026 Fascicolo 2026/1
Lingua Inglese Numero pagine 28 P. 57-84 Dimensione file 0 KB
DOI 10.3280/cgrds1-2026oa23327
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The use of artificial intelligence (AI) in qualitative management research has so far been framed predominantly as assistance: a researcher interacting with generative AI during the design or analysis of a case study. Yet methodological discussions still offer limited understanding of how qualitatively different forms of AI involvement reconfigure the production of theoretical insight in case-based research. The existing differentiation remains stage-bound and tool-centred; what is missing is a systematic coupling of AI roles to forms of theorising and to specific validity threats. Drawing on a review of 139 documents, the authors develop a four-role conceptual framework, AI as Discoverer, Selector, Co-Constructor and Generator. Each role is examined through affordance theory, theorising from cases and sociomaterial perspectives in order to analyse how different forms of AI involvement influence theorising, interpretation and engagement with empirical material. The study’s contribution lies less in naming four roles than in coupling them to forms of theorising and to role-specific validity threats. AI involvement cannot be treated as a uniform methodological condition: different roles raise different implications for contextual understanding, methodological rigor, researcher oversight and the empirical status of research material. We advance six propositions linking each AI role to conditions of legitimacy, characteristic validity threats and disclosure requirements, and we illustrate the framework through a corporate-governance research scenario. The paper closes with role-sensitive guidance for authors, reviewers and editors of governance and R&D case research.

Parole chiave:case study; qualitative research; artificial intelligence; affordance theory; sociomateriality; theorising

  1. Acharya D.B., Kuppan K., & Divya B. (2025). Agentic AI: Autonomous intelligence for complex goals. A comprehensive survey. IEEE Access, 13: 18912-18936. DOI: 10.1109/ACCESS.2025.3532853
  2. Argyle L.P., Busby E.C., Fulda N., Gubler J.R., Rytting C., & Wingate D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3): 337-351. DOI: 10.1017/pan.2023.2
  3. Cornelissen J. P. (2017). Preserving theoretical divergence in management research: Why the explanatory potential of qualitative research should be harnessed rather than suppressed. Journal of Management Studies, 54(3): 368-383. DOI: 10.1111/joms.12210
  4. Eisenhardt K.M. (1989). Building theories from case study research. Academy of management review, 14(4): 532-550. DOI: 10.5465/amr.1989.4308385
  5. Eisenhardt K.M., & Graebner M.E. (2007). Theory building from cases: Opportunities and challenges. Academy of management journal, 50(1): 25-32. DOI: 10.5465/amj.2007.24160888
  6. Faraj S., & Azad B. (2012). The materiality of technology: An affordance perspective. Materiality and organizing: Social interaction in a technological world, 237(1): 237-258. DOI: 10.1093/acprof:oso/9780199664054.003.0012
  7. Faraj S., Pachidi S., & Sayegh K. (2018). Working and organizing in the age of the learning algorithm. Information and organization, 8(1): 62-70. DOI: 10.1016/j.infoandorg.2018.02.005
  8. Gioia D.A., Corley K.G., & Hamilton A.L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1): 15-31. DOI: 10.1177/1094428112452151
  9. Glaser V. L., Sloan J., & Gehman J. (2024). Organizations as algorithms: A new metaphor for advancing management theory. Journal of Management Studies, 61(6): 2748-2769. DOI: 10.1111/joms.13033
  10. Gummesson, E. (2017). Case theory in business and management. SAGE Publications Ltd, DOI: 10.4135/9781473920811
  11. Hämäläinen P., Tavast M., & Kunnari A. (2023). Evaluating large language models in generating synthetic hci research data: a case study. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1-19).
  12. Hoorani B.H., Nair L.B., & Gibbert M. (2019). Designing for impact: The effect of rigor and case study design on citations of qualitative case studies in management. Scientometrics, 121(1): 285-306. DOI: 10.1007/s11192-019-03178-w
  13. Hullman J., Brosk D., Sun, H, & Shaw A. (2026). This human study did not involve human subjects: Validating LLM simulations as behavioral evidence. arXiv preprint arXiv:2602.15785.
  14. Kellogg K.C., Valentine M.A., & Christin A. (2020). Algorithms at work: The new contested terrain of control. Academy of management annals, 14(1): 366-410. DOI: 10.5465/annals.2018.0174
  15. Langley A. (1999). Strategies for theorizing from process data. Academy of Management review, 24(4): 691-710. DOI: 10.5465/amr.1999.2553248
  16. Leonardi P.M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS quarterly, 35(1): 147-167. DOI: 10.2307/23043493
  17. Li C. Y., Fang Y.H., & Chiang Y.H. (2023). Can AI chatbots help retain customers? An integrative perspective using affordance theory and service-domain logic. Technological Forecasting and Social Change, 197, 122921. DOI: 10.1016/j.techfore.2023.122921
  18. Lin X., Wang T., & Sheng F. (2025). The relationship between AI-adoption intensity and firm innovation performance: The role of AI affordances. IEEE Transactions on Engineering Management.
  19. Lindebaum D., & Ashraf M. (2024). The ghost in the machine, or the ghost in organizational theory? A complementary view on the use of machine learning. Academy of Management Review, 49(2): 445-448. DOI: 10.5465/amr.2021.0036
  20. Lindebaum D., Moser C., Ashraf M., & Glaser V.L. (2023). Reading the technological society to understand the mechanization of values and its ontological consequences. Academy of management review, 48(3): 575-592. DOI: 10.5465/amr.2021.0159
  21. Lindgreen A., Di Benedetto C.A., & Beverland M.B. (2021). How to write up casestudy methodology sections. Industrial Marketing Management, 96, A7-A10. DOI: 10.1016/j.indmarman.2020.04.012
  22. Naeem M., & Thomas L. (2025). Case study research and artificial intelligence: A step-by-step process to using ChatGPT in the development of case studies in qualitative research. International Journal of Qualitative Methods, 24, 16094069251371478. DOI: 10.1177/16094069251371478
  23. Naeem M., Ozuem W., Howell K., & Ranfagni S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International journal of qualitative methods, 22, 16094069231205789. DOI: 10.1177/16094069231205789
  24. Naeem M., Smith T., & Thomas L. (2025). Thematic analysis and artificial intelligence: A step-by-step process for using ChatGPT in thematic analysis. International Journal of Qualitative Methods, 24, 16094069251333886. DOI: 10.1177/16094069251333886
  25. Odacioglu E.C., Zhan L. & Allmendinger R. (2022). Combining topic modeling with grounded theory: Case studies of project collaboration. arXiv preprint arXiv:2207.02212.
  26. O’Grady C. (2025). AI-generated’ participants’ can lead social science experiments astray. Science, 390(6769). DOI: 10.1126/science.z232cbo
  27. Orlikowski W.J., & Scott S.V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. Academy of Management annals, 2(1): 433-474. DOI: 10.1080/19416520802211644
  28. Pachidi S., Berends H., Faraj S., & Huysman M. (2021). Make way for the algorithms: Symbolic actions and change in a regime of knowing. Organization Science, 32(1): 18-41. DOI: 10.1287/orsc.2020.1377
  29. Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., ... & Moher D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372. DOI: 10.1136/bmj.n71
  30. Pillai, S.D., Goldfarb, B., & Kirsch, D. (2024). Lovely and likely: Using historical methods to improve inference to the best explanation in strategy. Strategic Management Journal, 45(8): 1539-1566. DOI: 10.1002/smj.3593
  31. Raisch S., & Krakowski S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of management review, 46(1): 192-210. DOI: 10.5465/amr.2018.0072
  32. Sapkota R., Roumeliotis K.I., & Karkee M. (2025). Ai agents vs. agentic ai: A conceptual taxonomy, applications and challenges. Information Fusion, 103599. DOI: 10.1016/j.inffus.2025.103599
  33. Snyder H. (2019). “Literature review as a research methodology: An overview and guidelines”, Journal of Business Research, 104: 333-339. DOI: 10.1016/j.jbusres.2019.07.039
  34. Stake R.E. (1995). The art of case study research. Sage.
  35. Welch C., Piekkari R., Plakoyiannaki E., & Paavilainen-Mäntymäki E. (2019). Theorising from case studies: Towards a pluralist future for international business research. In Research methods in international business (pp. 171-220). Cham: Springer International Publishing.
  36. Welch C., Paavilainen-Mäntymäki E., Piekkari R., & Plakoyiannaki E. (2022). Reconciling theory and context: How the case study can set a new agenda for international business research. Journal of International Business Studies, 53(1): 4-26. DOI: 10.1057/s41267-021-00484-5
  37. Yang J., Amrollahi A., & Marrone M. (2024). Harnessing the potential of artificial intelligence: affordances, constraints, and strategic implications for professional services. The Journal of Strategic Information Systems, 33(4): 101864. DOI: 10.1016/j.jsis.2024.101864
  38. Yin R.K. (2018). Case study research and applications (Vol. 6). Sage.

Rosario Faraci, Rosaria Ferlito, Beyond Assistance: A Role-Based Conceptual Framework for Generative and Agentic Artificial Intelligence in Case-Based Management and Governance Research in "CORPORATE GOVERNANCE AND RESEARCH & DEVELOPMENT STUDIES" 1/2026, pp 57-84, DOI: 10.3280/cgrds1-2026oa23327