Revisiting the Interlink between Artificial Intelligence, Market Competition, and Consumer Behaviour
Abstract views: 22
,
PDF downloads: 8
Abstract
Backgrounds. Artificial Intelligence (AI) has emerged as a transformative force in modern business, influencing competitive dynamics and reshaping consumer behavior. As AI applications expand across industries, understanding their strategic impact on market competition and consumer engagement becomes increasingly vital for business sustainability and innovation.
Methods. This study employs a mixed-method approach combining bibliometric analysis and systematic literature review to examine the interrelationship between AI, market competition, and consumer behaviour. Bibliometric analysis was conducted using the Scopus database for the period 2015–2025, with VOSviewer utilized to map keyword co-occurrences and thematic clusters. Subsequently, a qualitative literature review was performed on thematically relevant and highly cited articles to extract insights on AI’s practical implementations, competitive implications, consumer analytics, and ethical concerns.
Results. The findings reveal a marked increase in scholarly attention to AI-driven business strategies, particularly between 2023 and 2024. AI is shown to influence market competition by enhancing operational efficiency, fostering innovation, supporting data-driven decision-making, and improving strategic adaptability. In terms of consumer behaviour, AI enables pattern recognition, real-time responsiveness, personalized marketing, and demand forecasting, contributing to customer satisfaction and loyalty. Additionally, AI-powered business operations—such as dynamic pricing and product recommendation systems—further optimize performance. However, ethical challenges, including data privacy, algorithmic bias, and regulatory gaps, underscore the need for responsible AI adoption.
Conclusions. AI serves as both a technological enabler and strategic asset in contemporary business ecosystems. Its influence extends beyond automation, offering firms competitive advantage through improved agility and consumer-centered strategies. To fully leverage AI’s potential, businesses must balance innovation with ethical considerations, ensuring transparent governance and human oversight in AI integration.
Downloads
References
Ahmed, A. A. A., Agarwal, S., Kurniawan, I. G. A., Anantadjaya, S. P. D., & Krishnan, C. (2022). Business boosting through sentiment analysis using Artificial Intelligence approach. International Journal of System Assurance Engineering and Management, 13(S1), 699–709. https://doi.org/10.1007/s13198-021-01594-x
Attard-Frost, B., De los Ríos, A., & Walters, D. R. (2023). The ethics of AI business practices: a review of 47 AI ethics guidelines. AI and Ethics, 3(2), 389–406. https://doi.org/10.1007/s43681-022-00156-6
Baker, M., & Hart, S. (2008). The marketing book. Routledge. https://doi.org/10.4324/9780080942544
Baker-Brunnbauer, J. (2021). Management perspective of ethics in artificial intelligence. AI and Ethics, 1(2), 173–181. https://doi.org/10.1007/s43681-020-00022-3
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-023-05339-7
Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), 1974. https://doi.org/10.3390/su13041974
Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(7). https://doi.org/10.1111/exsy.13406
Chan, E. Y. (2024). Consumer behavior in practice (2024th ed.). Springer International Publishing. https://doi.org/10.1007/978-3-031-50947-6
Chowdhury, R. H. (2024). The evolution of business operations: unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain. World Journal of Advanced Research and Reviews, 22(3), 2135–2147. https://doi.org/10.30574/wjarr.2024.22.3.1992
De Cremer, D., & Kasparov, G. (2021). The ethical AI—paradox: why better technology needs more and not less human responsibility. AI and Ethics. https://doi.org/10.1007/s43681-021-00075-y
Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974. https://doi.org/10.1016/j.jbusres.2020.08.024
Efendioğlu, İ. H. (2024). Digital consumer behavior: A systematic literature review. Prizren Social Science Journal, 8(1). https://doi.org/10.32936/pssj.v8i1.479
Eitel-Porter, R. (2021). Beyond the promise: implementing ethical AI. AI and Ethics, 1(1), 73–80. https://doi.org/10.1007/s43681-020-00011-6
Gołąb-Andrzejak, E. (2022). Enhancing Customer Engagement in Social Media with AI – a Higher Education case study. Procedia Computer Science, 207, 3028–3037. https://doi.org/10.1016/j.procs.2022.09.361
Gregoire, E. (2024, October 24). AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value. Boston Consulting Group. https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 308(1–2), 177–213. https://doi.org/10.1007/s10479-020-03683-9
Gunawan, W. B. (2025). MSME Sustainability as the Key to Sustainability: Role and Contribution of MSMEs to Sustainability with a Bibliometric Approach and Literature Review. Equator Journal of Management and Entrepreneur, 13(1), 1–17. https://doi.org/10.26418/ejme.v13i1.89630
Hermann, E. (2022). Leveraging artificial intelligence in marketing for social good-an ethical perspective. Journal of Business Ethics, 179(1), 43–61. https://doi.org/10.1007/s10551-021-04843-y
Hidayat, S., Setiawan, M., Rohman, F., & Hussein, A. S. (2022). Development of quality digital innovation by optimally utilizing company resources to increase competitive advantage and business performance. Administrative Sciences, 12(4), 157. https://doi.org/10.3390/admsci12040157
Himeur, Y., Elnour, M., Fadli, F., Meskin, N., Petri, I., Rezgui, Y., Bensaali, F., & Amira, A. (2023). AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artificial Intelligence Review, 56(6), 4929–5021. https://doi.org/10.1007/s10462-022-10286-2
Hossain, F., Ahmed, G. M. S., Shuvo, S. P. P., Kona, A. N., Raina, M. U. H., & Shikder, F. (2024). Unlocking artificial intelligence for strategic market development and business growth: innovations, opportunities, and future directions. Edelweiss Applied Science and Technology, 8(6), 5825–5846. https://doi.org/10.55214/25768484.v8i6.3263
Hossain, M. A., Agnihotri, R., Rushan, M. R. I., Rahman, M. S., & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption, and firms’ competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240–255. https://doi.org/10.1016/j.indmarman.2022.08.017
Hussein Al-shami, S. A., Mamun, A. A., Ahmed, E. M., & Rashid, N. (2022). Artificial intelligent towards hotels’ competitive advantage. An exploratory study from the UAE. Foresight, 24(5), 625–636. https://doi.org/10.1108/fs-01-2021-0014
Khamaj, A., & Ali, A. M. (2024). Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time. Alexandria Engineering Journal, 95, 164–173. https://doi.org/10.1016/j.aej.2024.03.045
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising. Journal of Advertising Research, 58(3), 263–267. https://doi.org/10.2501/jar-2018-035
Konar, A. (2018). Artificial intelligence and soft computing: Behavioral and cognitive modeling of the human brain. CRC Press. https://doi.org/10.1201/9781315219738
Kordestani, G. R., & Mohammadi, M. R. (2016). A study of the relationship between product market competition and earnings management. Procedia Economics and Finance, 36, 266–273. https://doi.org/10.1016/s2212-5671(16)30037-5
Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/smj.3387
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information Communication and Ethics in Society, 22(1), 124–144. https://doi.org/10.1108/jices-05-2023-0068
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77(102783), 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Kumari, A., & Kumar, M. (2024). Dynamic pricing: Trends, challenges and new frontiers. 48, 1–7. https://doi.org/10.1109/inc460750.2024.10649341
Madanchian, M. (2024). Generative AI for consumer behavior prediction: Techniques and applications. Sustainability, 16(22), 9963. https://doi.org/10.3390/su16229963
Malega, P., Rudy, V., Kovac, J., & Kovac, J. (2019). The competitive market map as the basis for an evaluation of the competitiveness of the Slovak republic on an international scale. Journal of Competitiveness, 11(4), 113–119. https://doi.org/10.7441/joc.2019.04.07
Messaoudi, F., & Loukili, M. (2024). E-commerce personalized recommendations: A deep neural collaborative filtering approach. Operations Research Forum, 5(1). https://doi.org/10.1007/s43069-023-00286-5
Rai, A., Indu, Smita, N., Deepshikha, G., Gaurav, K., Dhanesh, K., Suresh, G., Sasikala, C., & Ramana, C. V. (2019). Emerging concepts in bacterial taxonomy. In Microbial Diversity in Ecosystem Sustainability and Biotechnological Applications (pp. 3–22). Springer Singapore. https://doi.org/10.1007/978-981-13-8315-1_1
Rajagopal. (2012). Understanding market competition. In Darwinian Fitness in the Global Marketplace (pp. 34–65). Palgrave Macmillan UK. https://doi.org/10.1057/9781137268334_2
Rana, J., Gaur, L., Singh, G., Awan, U., & Rasheed, M. I. (2022). Reinforcing customer journey through artificial intelligence: a review and research agenda. International Journal of Emerging Markets, 17(7), 1738–1758. https://doi.org/10.1108/ijoem-08-2021-1214
Rehman, A. U., Mahmood, A., & Iqbal, M. (2024). Big data in marketing: a new paradigm on trends, developments and insights. International Journal of Technology Marketing, 18(4), 454–483. https://doi.org/10.1504/ijtmkt.2024.141882
Sanil, H. S., Singh, D., Raj, K. B., Choubey, S., Bhasin, N. K. K., Yadav, R., & Gulati, K. (2022). Role of machine learning in changing social and business eco-system – a qualitative study to explore the factors contributing to competitive advantage during COVID pandemic. World Journal of Engineering, 19(2), 238–243. https://doi.org/10.1108/wje-06-2021-0357
Sarker, I. H. (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158. https://doi.org/10.1007/s42979-022-01043-x
Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies. International Journal of Scholarly Research in Engineering and Technology, 4(1), 001–018. https://doi.org/10.56781/ijsret.2024.4.1.0018
Sestino, A., & De Mauro, A. (2022). Leveraging artificial intelligence in business: Implications, applications and methods. Technology Analysis and Strategic Management, 34(1), 16–29. https://doi.org/10.1080/09537325.2021.1883583
Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial intelligence: Definition and background. In Research for Policy (pp. 15–41). Springer International Publishing. https://doi.org/10.1007/978-3-031-21448-6_2
Shokran, Islam, M. S., & Ferdousi, J. (2025). Harnessing AI Adoption in the Workforce: A Pathway to Sustainable Competitive Advantage through Intelligent Decision-Making and Skill Transformation. American Journal of Economics and Business Management, 8(3), 954–976.
Straka, M. (2016). Simulation of the process for production of plastics films as a way to increase the competitiveness of the company. Przemysł Chemiczny, 1(1), 39–43. https://doi.org/10.15199/62.2016.1.3
Sun, Y., Xu, X., Yu, H., & Wang, H. (2022). Impact of value co‐creation in the artificial intelligence innovation ecosystem on competitive advantage and innovation intelligibility. Systems Research and Behavioral Science, 39(3), 474–488. https://doi.org/10.1002/sres.2860
Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3(100026), 100026. https://doi.org/10.1016/j.sca.2023.100026
Theodorakopoulos, L., & Theodoropoulou, A. (2024). Leveraging big data analytics for understanding consumer behavior in digital marketing: A systematic review. Human Behavior and Emerging Technologies, 2024(1). https://doi.org/10.1155/2024/3641502
Wang, Z., Lin, S., Chen, Y., Lyulyov, O., & Pimonenko, T. (2023). Digitalization effect on business performance: Role of business model innovation. Sustainability, 15(11), 9020. https://doi.org/10.3390/su15119020
Wu, C.-W., & Monfort, A. (2023). Role of artificial intelligence in marketing strategies and performance. Psychology & Marketing, 40(3), 484–496. https://doi.org/10.1002/mar.21737
Yılmaz Benk, G., Badur, B., & Mardikyan, S. (2022). A new 360° framework to predict customer lifetime value for multi-category E-commerce companies using a multi-output deep neural network and explainable artificial intelligence. Information (Basel), 13(8), 373. https://doi.org/10.3390/info13080373
Zoting, S. (2025, February 10). Artificial Intelligence (AI) Market Size, Share, and Trends 2025 to 2034. Precedence Research. https://www.precedenceresearch.com/artificial-intelligence-market
Copyright (c) 2025 William Ben Gunawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
