Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.
Get full access to this article
View all access options for this article.
References
1.
Adler, M. J., & van Doren, C. (1940/1972). How to read a book - the classic guide to intelligent reading. Touchstone.
AguinisH.CummingsC.RamaniR. S.CummingsT. G. (2020). “An A is an A”: The new bottom line for valuing academic research. Academy of Management Perspectives, 34(1), 135–154. https://doi.org/10.5465/amp.2017.0193
4.
AlkaissiH.McFarlaneS. I. (2023). Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus. https://doi.org/10.7759/cureus.35179
5.
AmabileT. (2020). GUIDEPOST: Creativity, artificial intelligence, and a world of surprises guidepost letter for academy of management discoveries. Academy of Management Discoveries, 6(3), 351–354. https://doi.org/10.5465/amd.2019.0075
6.
AmabileT. M.HallD. T. (2021). The undervalued power of self-relevant research: The case of researching retirement while retiring. Academy of Management Perspectives, 35(3), 347–366. https://doi.org/10.5465/amp.2018.0083
7.
AndersonP.TushmanM. L. (1990). Technological discontinuities and dominant designs: A cyclical model of technological change. Administrative Science Quarterly, 35(4), 604–633. https://doi.org/10.2307/2393511
8.
AnthonyC. (2018). To question or accept? How Status differences influence responses to new epistemic technologies in knowledge work. Academy of Management Review, 43(4), 661–679. https://doi.org/10.5465/amr.2016.0334
AustinJ. L. (1975). How to do things with words (Vol. 88). Oxford University Press.
11.
BabayiğitS.StainthorpR. (2011). Modeling the relationships between cognitive–linguistic skills and literacy skills: New insights from a transparent orthography. Journal of Educational Psychology, 103(1), 169–189. https://doi.org/10.1037/a0021671
12.
BalmerA. (2023). A sociological conversation with ChatGPT about AI ethics, affect and reflexivity. Sociology, 57(5), 1249–1258. https://doi.org/10.1177/00380385231169676
13.
BaradK. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. https://doi.org/10.1086/345321
14.
BarleyS. R. (1986). Technology as an occasion for structuring: Evidence from observations of CT scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78–108. https://doi.org/10.2307/2392767
15.
BarthesR. (1977). The Death of the Author. In Image, Music, Text (pp. 142–148). Fontana Press.
16.
BenderE. M.GebruT.McMillan-MajorA.ShmitchellS. (2021, March). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
BergM.SeeberB. K. (2016). The slow professor: Challenging the culture of speed in the academy. University of Toronto Press.
19.
BouchardF. (2016). The roles of institutional trust and distrust in grounding rational deference to scientific expertise. Perspectives on Science, 24(5), 582–608. https://doi.org/10.1162/POSC_a_00224
BubeckS.ChandrasekaranV.EldanR.GehrkeJ.HorvitzE.KamarE.LeeP.LeeY. T., LiY.LundbergS.NoriH.PalangiH.RibeiroM. T.ZhangY. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv,2303.12712. https://doi.org/10.48550/arXiv.2303.12712
22.
Buchholz, R. A., & Rosenthal, S. B. (2008). The unholy alliance of business and science. Journal of Business Ethics, 78, 199–206.
23.
CabantousL.GondJ. P. (2011). Rational decision making as performative praxis: Explaining rationality's Éternel retour. Organization Science, 22(3), 573–586. https://doi.org/10.1287/orsc.1100.0534
24.
CameronL. D. (2022). “Making out” while driving: Relational and efficiency games in the gig economy. Organization Science, 33(1), 231–252. https://doi.org/10.1287/orsc.2021.1547
ChristianoP.LeikeJ.BrownT. B.MarticM.LeggS.AmodeiD. (2023). Deep reinforcement learning from human preferences. http://arxiv.org/abs/1706.03741
30.
ChristiansonM. (2011). Using surprise as a guide:” Aha” moments of confirmation and disconfirmation. In CarlsenA.DuttonJ. E. (Eds.), Research alive: Exploring generative moments in doing qualitative research (Vol. 27, pp. 38–42). Copenhagen Business School Press.
31.
CookS. D.BrownJ. S. (1999). Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science, 10(4), 381–400. https://doi.org/10.1287/orsc.10.4.381
CreedW. E. D.DeJordyR.LokJ. (2010). Being the change: Resolving institutional contradiction through identity work. Academy of Management Journal, 53(6), 1336–1364. https://doi.org/10.5465/amj.2010.57318357
34.
CzarniawskaB. (1999). Writing management: Organization theory as a literary genre. Oxford University Press.
35.
DavenportT. H. (2005). Thinking for a living: How to get better performances and results from knowledge workers. Harvard Business Press.
36.
DomingosP. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
DwivediY. K.KshetriN.HughesL.SladeE. L.JeyarajA.KarA. K.BaabdullahA. M.KoohangA.RaghavanV.AhujaM.AlbannaH.AlbashrawiM. A.Al-BusaidiA. S.BalakrishnanJ.BarletteY.BasuS.BoseI.BrooksL.BuhalisD., …, WrightR. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
EisenhardtK. M. (1989). Building theories from case study research. The Academy of Management Review, 14(4), 532–550. https://doi.org/10.2307/258557
42.
EloundouT.ManningS.MishkinP.RockD. (2023). GPTs are GPTs: An early Look at the labor market impact potential of large language models. arXiv, 2303, 10130. arXiv:2303.10130v5.
43.
EspelandW. N.SauderM. (2007). Rankings and reactivity: How public measures recreate social worlds. The American Journal of Sociology, 113(1), 1–40. https://doi.org/10.1086/517897
44.
FeldbergA. C. (2022). The task bind: Explaining gender differences in managerial tasks and performance. Administrative Science Quarterly, 67(4), 1049–1092. https://doi.org/10.1177/00018392221124607
Fleming, P. (2020). Dark academia: Despair in the neoliberal business school. Journal of Management Studies, 57(6), 1305–1311.
47.
FoucaultM. (1969). What is an author? In MarshJ.CaputoJ. D., & WestphalM. (Eds.), Modernity and its discontents (pp. 299–314). Fordham University Press.
48.
Foucault, M. (1980). Power/knowledge: Selected interviews and other writings, 1972-1977 (Colin Gordon et al., Trans.). Pantheon.
49.
FreyC. (2019). The technology trap: Capital, labor, and power in the age of automation. Princeton University Press. https://doi.org/10.1515/9780691191959
50.
FriedlandJ. (2019). Activating moral agency by design: A model for ethical AI development. MIT Sloan Management Review, 60(4), 2019. 1–2017.
51.
GabrielY. (2016). The essay as an endangered species: Should we care. Journal of Management Studies, 53(2), 244–249. https://doi.org/10.1111/joms.12176
GlaserB. G.StraussA. L. (2017). Discovery of grounded theory: Strategies for qualitative research. Routledge.
54.
GlaserV. L.FissP. C.KennedyM. T. (2016). Making snowflakes like stocks: Stretching, bending, and positioning to make financial market analogies work in online advertising. Organization Science, 27(4), 1029–1048. https://doi.org/10.1287/ORSC.2016.1069
55.
GlaserV. L.GehmanJ.SloanJ. (2023). Organizations as algorithms: A new management metaphor. Working paper.
56.
GlaserV. L.PollockN.D'AdderioL. (2021). The biography of an algorithm: Performing algorithmic technologies in organizations. Organization Theory, 2, 2. https://doi.org/10.1177/26317877211004609
57.
GondJ. P.CabantousL.HardingN.LearmonthM. (2016). What do we mean by performativity in organizational and management theory? The uses and abuses of performativity. International Journal of Management Reviews, 18(4), 440–463. https://doi.org/10.1111/ijmr.12074
58.
GreenwoodM.Wolfram CoxJ. (2023). Seduced by technology? How moral agency is mediated by the invisibility of everyday technologies. Organization Studies, 44(4), 523–543. https://doi.org/10.1177/01708406221107455
59.
GrodalS.AntebyM.HolmA. L. (2021). Achieving rigor in qualitative analysis: The role of active categorization in theory building. Academy of Management Review, 46(3), 591–612. https://doi.org/10.5465/amr.2018.0482
60.
HabermasJ. (1971). Knowledge and Human Interests. Beacon Press.
61.
HanniganT. R.HaansR. F.VakiliK.TchalianH.GlaserV. L.WangM. S.KaplanS.JenningsP. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586–632. https://doi.org/10.5465/annals.2017.0099
Howard-GrenvilleJ. (2021). Caring, courage and curiosity: Reflections on our roles as scholars in organizing for a sustainable future. Organization Theory, 2(1), 1–19. https://doi.org/10.1177/2631787721991143
JensenT.MahmudY. (2023). Decentering sensemaking: The Mann Gulch disaster revisited. Scandinavian Journal of Management, 39(3), 101–279. https://doi.org/10.1016/j.scaman.2023.101279
67.
KelleU. (1997). Theory building in qualitative research and computer programs for the management of textual data. Sociological Research Online, 2(2), 10–22. https://doi.org/10.5153/sro.86
68.
KelleU. (2005). ‘Emergence’ vs. ‘Forcing’ of Empirical Data? A Crucial Problem of ‘Grounded Theory’ Reconsidered. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 6, 133–156.
69.
KelloggK. C. (2009). Operating room: Relational spaces and microinstitutional change in surgery. American Journal of Sociology, 115(3), 657–711. https://doi.org/10.1086/603535
70.
KennedyM. T.PhillipsN. (2023). The participation game. arXiv preprint arXiv,2304.12700.
71.
KetokiviM. J.MantereS.CornelissenJ. (2017). Reasoning by analogy and the progress of theory. Academy of Management Review, 42(4), 637–658. https://doi.org/10.5465/amr.2015.0322
72.
KorinekA. (2023). Language models and cognitive automation for economic research. National Bureau of Economic Research. https://doi.org/10.3386/w30957
73.
KrakowskiS.LugerJ.RaischS. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/smj.3387
74.
LatourB. (2005). Reassembling the social. Oxford University Press.
75.
LebovitzS.LevinaN.Lifshitz-AssafH. (2021). Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quarterly, 45(3), 1501–1525. https://doi.org/10.25300/MISQ/2021/16564
76.
LebovitzS.Lifshitz-AssafH.LevinaN. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126–148. https://doi.org/10.1287/orsc.2021.1549
77.
LeoneP. V.MantereS.FarajS. (2021). Open theorizing in management and organization studies. Academy of Management Review, 46(4), 725–749. https://doi.org/10.5465/amr.2019.0279
LiG.HammoudH. A. A. K.ItaniH.KhizbullinD.GhanemB. (2023). CAMEL: Communicative agents for “mind” exploration of large-scale language model society. http://arxiv.org/abs/2303.17760.
LindebaumD. (2023a). Management learning and education as ‘big picture’ social science. Academy of Management Learning Education.https://doi.org/10.5465/amle.2023.0173
Lindebaum, D., & Fleming, P. (forthcoming). We need to talk about ChatGPT! On why the management educators must resist that siren song. British Journal of Management.
84.
LindebaumD.MoserC.AshrafM.GlaserV. L. (2023). Reading the technological society to understand the mechanization of values and its ontological consequences. Academy of Management Review, 48(3), 575–592. https://doi.org/10.5465/amr.2021.0159
85.
LindebaumD.RamirezM. F. (2023). “Negative” resource review: On the essay-writing algorithm Essay Genius at https://essaygenius.ai/. Academy of Management Learning & Education. https://doi.org/10.5465/amle.2022.0474
86.
LindebaumD.VesaM.Den HondF. (2020). Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review, 45(1), 247–263. https://doi.org/10.5465/amr.2018.0181
87.
LockeK.FeldmanM. S.Golden-BiddleK. (2015). Discovery, validation, and live coding. In Handbook of qualitative organizational research: Innovative pathways and methods (pp. 371–380). Routledge.
88.
Locke, K., Feldman, M., & Golden-Biddle, K. (2022). Coding practices and iterativity: Beyond templates for analyzing qualitative data. Organizational Research Methods, 25(2), 262-284.
89.
LockeK.Golden-BiddleK. (1997). Constructing opportunities for contribution: Structuring intertextual coherence and “problematizing” in organizational studies. Academy of Management Journal, 40(5), 1023–1062. https://doi.org/10.5465/256926
90.
LongJ. (2023). Large language model guided tree-of-thought. arXiv:2305.08291.
91.
McLuhanM. (1964). Understanding media: The extensions of man. MIT Press.
92.
MertonR. K. (1973). The sociology of science: Theoretical and empirical investigations. The University of Chicago Press.
MoserC.den HondF.LindebaumD. (2022). Morality in the age of artificially intelligent algorithms. Academy of Management Learning & Education, 21(1), 139–155. https://doi.org/10.5465/amle.2020.0287
96.
MurrayA.RhymerJ. E. N.SirmonD. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552–571. https://doi.org/10.5465/amr.2019.0186
97.
NelsonA.AnthonyC.TripsasM. (2023). “If I could turn back time”: Occupational dynamics, technology trajectories, and the reemergence of the analog music synthesizer. Administrative Science Quarterly, 68(2), 551–599. https://doi.org/10.1177/00018392231163178
OrlikowskiW. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427. https://doi.org/10.1287/orsc.3.3.398
102.
OrlikowskiW. J. (2002). Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249–273. https://doi.org/10.1287/orsc.13.3.249.2776
103.
OrlikowskiW. J.ScottS. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433–474. https://doi.org/10.5465/19416520802211644
104.
OrlikowskiW. J.ScottS. V. (2014). What happens when evaluation goes online? Exploring apparatuses of valuation in the travel sector. Organization Science, 25(3), 868–891. https://doi.org/10.1287/orsc.2013.0877
105.
OuyangL.WuJ.JiangX.AlmeidaD.WainwrightC.MishkinP.ZhangC.AgarwalS.SlamaK.RayA.SchulmanJ.HiltonJ.KeltonF.MillerL.SimensM.AskellA.WelinderP.ChristianoP.LeikeJ.LoweR. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744. https://arxiv.org/abs/2203.02155
106.
PachidiS.BerendsH.FarajS.HuysmanM. (2021). Make way for the algorithms: Symbolic actions and change in a regime of knowing. Organization Science, 32(1), 18–41. https://doi.org/10.1287/orsc.2020.1377
107.
PachidiS.HuysmanM. (2017). Organizational intelligence in the digital age: Analytics and the cycle of choice. In The Routledge companion to management information systems (pp. 391–402). Routledge.
108.
PakarinenP.HuisingR. (2023). Relational expertise: What machines can't know. Journal of Management Studies. https://doi.org/10.1111/joms.12915
109.
ParkJ. S.O'BrienJ. C.CaiC. J.MorrisM. R.LiangP.BernsteinM. S. (2023a). Generative agents: Interactive simulacra of human behavior. UIST‘23. http://arxiv.org/abs/2304.03442
PetriglieriG.AshfordS. J. (2023). Theorizing gets personal: Management academia in the mirror of independent work. Organization Theory, 4(1), 1–24. https://doi.org/10.1177/26317877231153188
114.
PetriglieriG.PetriglieriJ. L. (2015). Can business schools humanize leadership?Academy of Management Learning & Education, 14(4), 625–647. https://doi.org/10.5465/amle.2014.0201
115.
PetriglieriG.PetriglieriJ. L. (2022). The work is alive! Systems psychodynamics and the pursuit of pluralism without polarization in human relations. Human Relations, 75(8), 1431–1460. https://doi.org/10.1177/00187267221089208
116.
PetriglieriG.WoodJ. D.PetriglieriJ. L. (2011). Up close and personal: Building foundations for leaders’ development through the personalization of management learning. Academy of Management Learning & Education, 10(3), 430–450. https://doi.org/10.5465/amle.2010.0032
117.
PierreA. S.AdamsE. (2015). Writing as method. In The Blackwell encyclopaedia of sociology (pp. 5304–5306). John Wiley & Sons.
RahmanH. A. (2019). Invisible Cages: Algorithmic Evaluations in Online Labor Markets Dissertation (Publication No. 28113148) [Doctoral dissertation, Stanford University]. ProQuest Dissertations and Theses Global.
121.
RahmanH. A.WeissT.KarunakaranA. (2023). The experimental hand: How platform-based experimentation reconfigures worker autonomy. Academy of Management Journal, 1–62. https://doi.org/10.5465/amj.2022.0638
122.
RindermannH.BeckerD. (2023). The future of intelligence: A prediction of the FLynn effect based on past student assessment studies until the year 2100. Personality and Individual Differences, 206, 1–10. https://doi.org/10.1016/j.paid.2023.112110
StraussA. C.CorbinJ. M. (1998). Basics of Qualitative Research: Second Edition: Techniques and Procedures for Developing Grounded Theory. Sage Publications.
132.
SusarlaA.GopalR.ThatcherJ. B.SarkerS. (2023). The Janus effect of generative AI: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, 34(2), 399–408. https://doi.org/10.1287/isre.2023.ed.v34.n2
133.
SuttonR. L. (1988). Untangling the relationship between displayed emotions and organizational sales: The case of convenience stores. Academy of Management Journal, 31(3), 461–487. https://doi.org/10.2307/256456
134.
SuzmanJ. (2020). Work: A deep history from the stone age to the age of robots. Penguin Press.
Tilcsik, A., Anteby, M., & Knight, C. R. (2015). Concealable stigma and occupational segregation: Toward a theory of gay and lesbian occupations. Administrative Science Quarterly, 60(3), 446–481.
137.
Tourish, D. (2020). The triumph of nonsense in management studies. Academy of Management Learning & Education, 19(1), 99–109.
138.
TrinhM. P.KirschR.CastilloE. A.BatesD. E. (2022). Forging paths to interdisciplinary research for early career academics. Academy of Management Learning & Education, 21(2), 318–335. https://doi.org/10.5465/amle.2019.0386
139.
VaaraE.SonensheinS.BojeD. (2016). Narratives as sources of stability and change in organizations: Approaches and directions for future research. Academy of Management Annals, 10(1), 495–560. https://doi.org/10.5465/19416520.2016.1120963
140.
Van den BroekE.SergeevaA.HuysmanM. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580. https://doi.org/10.25300/MISQ/2021/16559
141.
van DisE. A. M.BollenJ.ZuidemaW.van RooijR.BocktingC. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226. https://doi.org/10.1038/d41586-023-00288-7
VerbeekP. P. (2011). Moralizing technology: Understanding and designing the morality of things. University of Chicago Press.
144.
von KroghG.RobersonQ.GruberM. (2023). Recognizing and utilizing novel research opportunities with artificial intelligence. Academy of Management Journal, 66(2), 367–373. https://doi.org/10.5465/amj.2023.4002
145.
WaardenburgL.HuysmanM. (2022). From coexistence to co-creation: Blurring boundaries in the age of AI. Information and Organization, 32(4), 100432. https://doi.org/10.1016/j.infoandorg.2022.100432
146.
WatsonG.FreemanR. E.ParmarB. (2008). Connected moral agency in organizational ethics. Journal of Business Ethics, 81(2), 323–341. https://doi.org/10.1007/s10551-007-9497-y
147.
WeiJ.WangX.SchuurmansD.BosmaM.IchterB.XiaF.ChiE.LeQ.ZhouD. (2023). Chain-of-thought prompting elicits reasoning in large language models. arXiv:2201.11903
148.
WeickK. E. (1995). Sensemaking in organizations. Sage Publications.
149.
WeickK. E. (1998). The attitude of wisdom: Ambivalence as the optimal compromise. In Organizational wisdom and executive courage (pp. 40–64). New Lexington Press.
150.
WeissR. S. (1995). Learning from strangers: The art and method of qualitative interview studies. Simon and Schuster.
WoodmanseeM. (1992). On the author effect: Recovering collectivity. <Case Legal Studies Research Paper, (1992), 10. https://ssrn.com/abstract=3730199
153.
WoodmanseeM. (1994). The author, art, and the market: Rereading the history of aesthetics. Columbia University Press.
154.
XiaoZ.YuanX.LiaoQ. V.AbdelghaniR.OudeyerP. Y. (2023, March). Supporting qualitative analysis with large language models: Combining codebook with GPT-3 for deductive coding. In Companion proceedings of the 28th international conference on intelligent user interfaces (pp. 75–78).
155.
XieS. M.MinS. (2022, August 1). How does in-context learning work? A framework for understanding the differences from traditional supervised learning. The Stanford AI Lab Blog. http://ai.stanford.edu/blog/understanding-incontext/
156.
YoreL. D.HandB. M.FlorenceM. K. (2004). Scientists’ views of science, models of writing, and science writing practices. Journal of Research in Science Teaching, 41(4), 338–369. https://doi.org/10.1002/tea.20008
157.
ZhangC.ZhangC.LiC.QiaoY.ZhengS.DamS. K.ZhangM.KimJ. U.KimS. T.ChoiJ.ParkG.-M.BaeS.-H.LeeL.-H.HuiP.KweonI. S.HongC. S. (2023). One small step for generative AI, one giant leap for AGI: A complete survey on ChatGPT in AIGC Era. http://arxiv.org/abs/2304.06488
158.
ZhaoW. X.ZhouK.LiJ.TangT.WangX.HouY.MinY.ZhangB.ZhangJ.DongZ.DuY.YangC.ChenY.ChenZ.JiangJ.RenR.LiY.TangX.LiuZ.LiuP.NieJ.-Y.WenJ.-R. (2023). A survey of large language models. http://arxiv.org/abs/2303.18223
159.
ZimanJ. (2001). Real science: What it is, and what it means. Cambridge University Press.
