Parole chiave
Decision Making
Small and Medium Firms
Consultancy
Come citare
##plugins.generic.dates.accepted## 2024-09-27
##plugins.generic.dates.published## 2024-12-23
Abstract
Scopo. Questo studio esamina come le PMI nel settore della consulenza che non hanno ancora adottato l'IA ne giudicano l'adozione, l'utilizzo, i vantaggi e gli svantaggi. Disegno/metodologia/approccio. Abbiamo condotto quattro studi di caso e interviste semi-strutturate con quattro PMI nel settore della consulenza che non hanno ancora adottato l'IA. Risultati. L'introduzione dell'IA è vista positivamente, nonostante debbano essere affrontate alcune questioni relative all'etica, alla privacy e alla responsabilità delle decisioni. Implicazioni pratiche e sociali. L'adozione dell'IA nel processo decisionale può portare numerosi vantaggi alle PMI nel settore della consulenza, che devono essere consapevoli dei potenziali svantaggi. I responsabili politici dovrebbero progettare interventi efficaci che supportino e guidino queste aziende nell'adozione dell'IA. Originalità dello studio. Questo studio si è concentrato sulle PMI nel settore della consulenza, che possono incontrare difficoltà nell'introduzione dell'IA a causa di risorse e conoscenze insufficienti, ma che allo stesso tempo sono spinte dal settore della consulenza a incorporare urgentemente l'IA.Riferimenti bibliografici
Anthony, R. N. (1965). Planting Relational Mode of Thinking in Strategy as Practice: Carry with Context into Field as Social Space. Harvard Business Review Press
Bhalerao, K., Kumar, A., Kumar, A., & Pujari, P. (2022). A study of barriers and benefits of artificial intelligence adoption in small and medium enterprise. Academy of Marketing Studies Journal, 26, 1–6
Bingham, C. B., & Eisenhardt, K. M. (2011). Rational heuristics: The ‘simple rules’ that strategists learn from process experience. Strategic Management Journal, 32(13), 1437–1464. https://doi.org/10.1002/smj.965
Booyse, D., & Scheepers, C. B. (2024). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, 47(1), 64–85. https://doi.org/10.1108/MRR-09-2021-0701
Bunte, A., Richter, F., & Diovisalvi, R. (2021). Why it is hard to find AI in SMEs: A survey from the practice and how to promote it. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) (pp. 614-620). SCITEPRESS Science and Technology Publications, Lda
Corbin, J. M., & Strauss, A. L. (2015). Basics of qualitative research: Techniques and procedures for developing grounded theory (Fourth edition). SAGE
Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. https://doi.org/10.1016/j.techsoc.2020.101257
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 Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Edwards, J. S., Duan, Y., & Robins, P. C. (2000). An analysis of expert systems for business decision making at different levels and in different roles. European Journal of Information Systems, 9(1), 36–46. https://doi.org/10.1057/palgrave.ejis.3000344
European Commission (2023). COM(2023) 535 - Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - SME Relief Package. Strasbourg
Feuerriegel, S., Shrestha, Y.R., von Krogh, G. & Zhang, C. (2022) Bringing artificial intelligence to business management. Nature Machine Intelligence, 4(7), 611–613. https://doi.org/10.1038/s42256-022-00512-5
Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362–372. https://doi.org/10.1016/j.jmsy.2020.08.009
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
Kshetri, N. (2021). Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: Some preliminary evidence. Management Research Review, 44(7), 970–990. https://doi.org/10.1108/MRR-03-2020-0168
Kuusi, O., & Heinonen, S. (2022). Scenarios from artificial narrow intelligence to artificial general intelligence—Reviewing the results of the international work/technology 2050 study. World Futures Review, 14(1), 65–79. https://doi.org/10.1177/19467567221101637
Langer, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878. https://doi.org/10.1016/j.chb.2021.106878
Lee, S.-G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. https://doi.org/10.1016/j.jwb.2012.06.003
Leyer, M., & Schneider, S. (2021). Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers? Business Horizons, 64(5), 711–724. https://doi.org/10.1016/j.bushor.2021.02.026
Mantri, A., & Mishra, R. (2023). Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management. The Journal of High Technology Management Research, 34, 100476. https://doi.org/10.1016/j.hitech.2023.100476
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment and productivity. McKinsey Global Institute
McCarthy, J. (2007). What Is Artificial Intelligence? Computer Science Department Stanford University Stanford, CA 94305. http://jmc.stanford.edu/articles/whatisai/whatisai.pdf
Mellon, C. A. (1990). Naturalistic inquiry for library science: Methods and applications for research, evaluation, and teaching. Greenwood Press
Metcalf, L., Askay, D. A., & Rosenberg, L. B. (2019). Keeping humans in the loop: Pooling knowledge through artificial swarm intelligence to Improve business decision making. California Management Review, 61(4), 84–109. https://doi.org/10.1177/0008125619862256
Moser, C., Den Hond, F., & Lindebaum, D. (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
Nilsson, N. J. (2009). The Quest for Artificial Intelligence. Cambridge University Press
Rowley, J. (2012). Conducting research interviews. Management Research Review, 35(3/4), 260–271. https://doi.org/10.1108/01409171211210154
Samokhvalov, K. (2024). The transformative impact of artificial intelligence on the management consultancy sector. Management Consulting Journal, 7(1), 59–68. https://doi.org/10.2478/mcj-2024-0006
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Mission AI: The New System Technology. Springer International Publishing. https://doi.org/10.1007/978-3-031-21448-6
Simon, H. A. (1987). Making Management Decisions: The Role of Intuition and Emotion. Academy of Management Perspectives, 1(1), 57–64. https://doi.org/10.5465/ame.1987.4275905
Tamò-Larrieux, A. (2021). Decision-making by machines: Is the ‘Law of Everything’ enough? Computer Law & Security Review, 41, 105541. https://doi.org/10.1016/j.clsr.2021.105541
Yin, R. K. (2003). Case study research: Design and methods (3rd ed). Sage Publications
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Copyright (c) 2024 Emilia Filippi, Mariasole Bannò, Isabella Elisa Nencini