The Role of Artificial Intelligence in Business and Management Decision-making: A Bibliometric Analysis to Map the Scientific Discourse
DOI:
https://doi.org/10.33032/acr.7005Kulcsszavak:
artificial intelligence, business decision-making, bibliometric analysis, scientific networksAbsztrakt
The role of artificial intelligence (AI) in business and management decision-making has become one of the most intensively studied areas in recent years. The present study conducts a bibliometric analysis based on 4,165 publications in the Web of Science database in order to map the main structural and thematic characteristics of the scientific discourse. It is possible to identify three dominant scientific clusters based on the co-occurrence of keywords, co-citation networks, and scientific collaborations between countries. The following three approaches have been identified: firstly, technology- and algorithm-focused approaches; secondly, organisational and human-centred interpretations; and thirdly, research based on business decision support and information management. The objective of the present study is to emphasise that AI is not merely a technological innovation, but rather an interdisciplinary phenomenon that fundamentally transforms the organisational, ethical and strategic frameworks of decision-making. The results obtained contribute to a more accurate understanding of research trends in the field and provide a basis for further analysis.
Hivatkozások
LeCun, Y. – Bengio, Y. – Hinton, G. (2015): Deep learning. Nature 521, 436–444 https://doi.org/10.1038/nature14539
Sutton, R. S. – Barto, A. G. (2018): Reinforcement Learning: An Introduction (2nd ed.). The MIT Press.
Esteva, A. – Kuprel, B. – Novoa, R. et al. (2017): Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118. https://doi.org/10.1038/nature21056
Lundberg, S. M. – Lee, S-I. (2017): A unified approach to interpreting model predictions, In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768–4777. https://doi.org/10.48550/arXiv.1705.07874
Moher, D. – Liberati, A. – Tetzlaff, J – Altman, D. G. – The PRISMA Group (2009): Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Russell, S. (2019): Human Compatible: Artificial Intelligence and the Problem of Control. Lon-don: Viking.
Gandomi, A. – Haider, M. (2015): Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Mnih, V. – Kavukcuoglu, K. – Silver, D. et al. (2015): Human-level control through deep rein-forcement learning. Nature, 518, 529–533. https://doi.org/10.1038/nature14236
Kellogg, K. C. – Valentine, M. A. – Christin, A. (2020): Algorithms at Work: The New Con-tested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Raisch, S. – Krakowski, S. (2021): Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Barocas, S. – Selbst, A. D. (2016): Big Data's Disparate Impact (2016). 104 California Law Re-view, 671. http://dx.doi.org/10.2139/ssrn.2477899
Davenport, T. H. (2018): The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press. https://doi.org/10.7551/mitpress/11781.001.0001
Kaplan, A. – Haenlein, M. (2019): Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
Jarrahi, M. H. (2018): Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Duan, Y. – Edwards, J. S. – Dwivedi, Y. K. (2019): Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Infor-mation Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Tranfield, D. – Denyer, D. – Smart, P. (2003): Towards a methodology for developing evi-dence‐informed management knowledge by means of systematic review. British Journal of Man-agement, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
Podsakoff, P. M. – MacKenzie, S. B. – Lee, J.-Y., – Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Fornell, C. – Larcker, D. F. (1981): Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Ransbotham, S. – Kiron, D. – Gerbert, P. – Reeves, M. (2017): Reshaping Business with Artifi-cial Intelligence. MIT Sloan Management Review. https://web-assets.bcg.com/img-src/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm9-177882.pdf
Dwivedi, Y. K. – Hughes, L. – Ismagilova, E. et al. (2021): Artificial Intelligence (AI): Multi-disciplinary perspectives on emerging challenges, opportunities, and agenda for research, prac-tice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Wolfert, S. – Ge, L. – Verdouw, C. – Bogaardt, M. (2017): Big Data in Smart Farming – A re-view, Agricultural Systems, 153, 2017, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
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