ORGANIZATIONAL AND ECONOMIC MECHANISMS FOR ENHANCING THE EFFICIENCY OF RETAIL SERVICES THROUGH ARTIFICIAL INTELLIGENCE TECHNOLOGIES
Keywords:
artificial intelligence, retail trade, service efficiency, digital transformation, machine learning, customer experience, organizational mechanisms, economic mechanisms, personalization, UzbekistanAbstract
This article examines the organizational and economic mechanisms through which artificial intelligence (AI) enhances service efficiency in retail trade enterprises. The relevance of the topic is driven by intensifying competition, changing consumer expectations, and the rapid development of the digital economy in Uzbekistan under the national Strategy for the Development of Artificial Intelligence Technologies until 2030. The aim of the study is to assess the impact of AI adoption on retail service efficiency and to substantiate the mechanisms that convert this adoption into measurable performance gains. A mixed-methods design was applied: a survey of 128 retail enterprises operating in different regions of Uzbekistan, semi-structured interviews with 17 industry experts, and analysis of official statistical data. Service efficiency was measured through an integral coefficient combining speed, accuracy, availability, cost, and customer satisfaction. The results show that enterprises applying AI solutions in an integrated manner reduced average customer service time by 41 per cent, increased order fulfilment accuracy by 17 per cent, raised the customer satisfaction index by 24 per cent, and lowered operating costs by 21 per cent. Correlation analysis confirmed a strong positive relationship between the intensity of AI adoption and service efficiency (r = 0.58– 0.74). The study also identifies key barriers such as digital-skills shortages, high integration costs, and data-security concerns. On this basis, a phased implementation model and a set of organizational and economic mechanisms are proposed. The findings are useful for retail managers, sector regulators, and researchers of the digital economy.
References
1. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a New Dominant Logic for Marketing. Journal of Marketing, 68(1), 1–17.
2. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64(1), 12–40.
3. Rust, R. T., & Huang, M.-H. (2014). The Service Revolution and the Transformation of Marketing Science. Marketing Science, 33(2), 206–221.
4. Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172.
5. Huang, M.-H., & Rust, R. T. (2021). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30–41.
6. Shankar, V. (2018). How Artificial Intelligence (AI) Is Reshaping Retailing. Journal of Retailing, 94(4), vi–xi.
7. Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6.
8. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.
9. Vial, G. (2019). Understanding Digital Transformation: A Review and a Research Agenda. The Journal of Strategic Information Systems, 28(2), 118–144.
10. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital Transformation: A Multidisciplinary Reflection and Research Agenda. Journal of Business Research, 122, 889–901.
11. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482.
12. Brynjolfsson, E., & McElheran, K. (2016). The Rapid Adoption of Data-Driven Decision Making. American Economic Review, 106(5), 133–139.
13. Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69–96.
14. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to OmniChannel Retailing. Journal of Retailing, 91(2), 174–181.
15. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer Experiences in the Age of Artificial Intelligence. Computers in Human Behavior, 114, 106548.
16. Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial Intelligence in Advertising: How Marketers Can Leverage Artificial Intelligence Along the Consumer Journey. Journal of Advertising Research, 58(3), 263–267.
17. 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.
18. Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J., Bull, J. S., & Waddoups, R. (2021). How Technology Is Changing Retail. Journal of Retailing, 97(1), 13–27.
19. Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135–155.
20. Wirth, N. (2018). Hello Marketing, What Can Artificial Intelligence Help You With? International Journal of Market Research, 60(5), 435–438.
21. Resolution of the President of the Republic of Uzbekistan No. RP-358 of 14 October 2024 “On the Approval of the Strategy for the Development of Artificial Intelligence Technologies until 2030”. Tashkent.
22. Decree of the President of the Republic of Uzbekistan No. PF-6079 of 5 October 2020 “On the Approval of the Strategy ‘Digital Uzbekistan – 2030’ and Measures for Its Effective Implementation”. Tashkent.
23. OECD (2024). Government AI Readiness Index and National AI Strategies: Country Overview – Uzbekistan. OECD.AI Policy Observatory.
24. Chui, M., Manyika, J., & Miremadi, M. (2018). Notes from the AI Frontier: Applications and Value of Deep Learning. McKinsey Global Institute
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Urinov Yigitali Muratovich (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
