MAPPING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MANAGEMENT AND COMMERCE
A BIBLIOMETRIC ANALYSIS
DOI:
https://doi.org/10.33736/ijbs.9289.2025Keywords:
Artificial Intelligence, Management and Commerce.Abstract
This study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) in commerce and management, focusing on scholarly publications indexed in the Web of Science (WOS) database between 2019 and 2023. AI has emerged as a pivotal research area; however, few studies have addressed its bibliometric dimensions. This study aims to bridge this gap by evaluating the academic landscape and comparing AI research to assess its impact on decision-making in business management. Data for the analysis was collected from WOS, and performance analysis and science mapping were conducted using R-Studio and MS Excel. The findings highlight AI’s growing role in enhancing business functions, including decision-making, process optimization, and innovation. Key themes include "performance" (9%), "innovation" (6%), and "integration" (3%), reflecting AI’s potential in improving organizational efficiency and competitiveness. The research reveals an annual growth rate of 9.05%, with a peak in 2022, and underscores the significant influence of international collaboration, particularly in smaller countries such as Canada and Australia. The study identifies the USA and China as leading contributors and highlights the concentrated influence of a few prolific scholars and journals. The analysis concludes that AI is an indispensable tool for enhancing agility and adaptability in business management, positioning it as a strategic asset in a rapidly evolving global marketplace.
References
Agrawal, A., Gans, J. S., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
Ahmi, A. (2022). Optimizing the utilization of the Web of Science core collection for comprehensive research. Journal of Scholarly Research, 45(2), 123-137.
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
https://doi.org/10.1016/j.joi.2017.08.007
Bessen, J., & Righi, C. (2020). Artificial intelligence and jobs: Evidence from online job postings. National Bureau of Economic Research. https://doi.org/10.3386/w27420
https://doi.org/10.3386/w27420
Binns, R. (2018). Algorithmic accountability and public reason. Philosophy & Technology, 31(4), 543-556. https://doi.org/10.1007/s13347-018-0336-7
https://doi.org/10.1007/s13347-017-0263-5
Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215-2222. https://doi.org/10.1002/asi.23329
https://doi.org/10.1002/asi.23329
Brynjolfsson, E., & McAfee, A. (2014).The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
Bughin, J., McCarthy, B., & Chui, M. (2017). Ten red flags signaling your analytics program will fail. McKinsey Quarterly, 57(1), 6-15.
Choudhury, P., Larson, B. Z., & Foroughi, C. (2021). Work from anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655-683. https://doi.org/10.1002/smj.3251
https://doi.org/10.1002/smj.3251
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
https://doi.org/10.1016/j.jbusres.2021.04.070
Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62-73.
Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90-93. https://doi.org/10.1001/jama.295.1.90
https://doi.org/10.1001/jama.295.1.90
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1-8. https://doi.org/10.1007/s11747-019-00711-4
https://doi.org/10.1007/s11747-019-00711-4
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
https://doi.org/10.1177/0008125619864925
Jadil, Y., Ladhari, R., & Moutinho, L. (2021). Meta-analyses using bibliometrics: Enhancing visual representations in systematic literature reviews. Journal of Business Research, 130, 170-183. https://doi.org/10.1016/j.jbusres.2021.02.035
https://doi.org/10.1016/j.jbusres.2021.02.035
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
https://doi.org/10.1016/j.bushor.2018.03.007
Kaur, H., Sharma, S., & Singh, A. (2021). Bibliometric analysis as a methodological approach for reviewing scientific knowledge. Scientometrics, 126, 1213-1228. https://doi.org/10.1007/s11192-020-03798-6
https://doi.org/10.1007/s11192-021-03884-4
Leydesdorff, L., & Vaughan, L. (2006). Co-occurrence and visualization in bibliometrics: Techniques for knowledge mapping. Journal of the American Society for Information Science and Technology, 57(12), 1470-1486. https://doi.org/10.1002/asi.20454
https://doi.org/10.1002/asi.20454
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
https://doi.org/10.1016/j.futures.2017.03.006
Mariani, M., & Nambisan, S. (2021). Innovation analytics and digital innovation experimentation: The rise of research in their role in AI development. Information & Management, 58(2), 103439. https://doi.org/10.1016/j.im.2020.103439
Martin, S., & Turner, K. (2015). Grounded theory: Methodology and method. Grounded Theory Review, 14(1), 32-43.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Morales, J., Palacios-González, C., & García-Alonso, J. (2021). Exploring the ethical implications of AI and big data: A bibliometric analysis. Technology in Society, 64, 101477. https://doi.org/10.1016/j.techsoc.2020.101477
https://doi.org/10.1016/j.techsoc.2020.101477
Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.
https://doi.org/10.1017/CBO9780511819346
Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know? International Business Review, 29(4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717
https://doi.org/10.1016/j.ibusrev.2020.101717
Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348-349.
https://doi.org/10.1108/eb026482
Rai, A., Chen, H., & Pye, J. (2019). Explaining and predicting information systems implementation success. MIS Quarterly, 43(2), 509-520.
https://doi.org/10.25300/MISQ/2019/432E0
Rahman, M. A., Khan, A., & Rahman, M. (2022). Science-mapping analysis of bibliometric data: Application for literature reviews in AI and management. Journal of Big Data Research, 9, 57-70. https://doi.org/10.1016/j.jbdr.2021.100152
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review, 59(1), 5-9.
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson.
Sharma, G., Shah, P., & Kulkarni, D. (2021). The role of artificial intelligence in customer experience management. Journal of Business Research, 123, 241-253. https://doi.org/10.1016/j.jbusres.2020.09.019
https://doi.org/10.1016/j.jbusres.2020.09.019
Siau, K., & Yang, Y. L. (2018). Impact of artificial intelligence, robotics, and automation on the accounting profession. Journal of Strategic Innovation and Sustainability, 13(3), 78-85.
Ullah, F., Ahmad, S., & Waheed, S. (2022). Scholarly collaboration and bibliometric coupling: An overview of co-authorship and inter-institutional partnerships. Research Evaluation, 31(2), 147-159. https://doi.org/10.1093/reseval/rva020
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
https://doi.org/10.1007/s11192-009-0146-3
Verma, S., & Gustafsson, A. (2020). AI in marketing: A systematic review of past research and future directions. Journal of the Academy of Marketing Science, 48(3), 364-389. https://doi.org/10.1007/s11747-019-00676-7
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2016). The role of big data analytics in supply chain management. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014
https://doi.org/10.1016/j.ijpe.2016.03.014
Zhang, Y., Wang, L., & Li, X. (2021). A bibliometric approach for analyzing the diffusion of AI knowledge in management studies. 'Journal of Artificial Intelligence Research, 35', 42-59. https://doi.org/10.1016/j.jair.2021.09.007
Zou, Z., Liu, X., Wang, M., & Yang, X. (2023). Insight into digital finance and fintech: A bibliometric and content analysis. 'Technology in Society, 73', 102221. https://doi.org/10.1016/j.techsoc.2023.102221
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