Dushyant Bodkhey,
Shriram Dawkhar,
- Research Scholar, Sinhgad Business, School Research Center, Maharashtra, India
- Director, Sinhgad Business, School Research Center, Maharashtra, India
Abstract
The rapid deployment of artificial intelligence (AI) in e-retailing has revolutionized the way companies interact with customers in real time. Conversational agents and recommendation engines are two of the most popular AI techniques used to influence customer engagement, while empirical knowledge on the relative effectiveness of these approaches is rare. To bridge this gap, the current research examines and compares the impact of AI-driven conversational agents to that of AI-oriented recommendation Systems on real-time customer engagement in e-retailing, considering perceived personalization as a mediating factor and trust in AI systems as a moderating factor. Grounded in customer engagement theory, the technology acceptance model, and service-dominant logic, a conceptual framework is proposed and tested with empirical data from 200 active customers of e-retail based on survey responses. Direct, mediating, and moderating relationships are examined using structural equation modelling. The findings suggest that conversational agents and recommendation systems increase real-time customer engagement, but promotional campaigns discourage engagement. Perceived personalization partially mediates the influence of AI tools on engagement, and trust in AI systems has a significant positive moderating impact. Contributions to literature: This study contributes to existing literature by providing a comparative, engagement-directed assessment of AI tools and identifying the core psychological mechanism that underpins the efficacy of AI in retail. From a managerial point of view, these contributions enable managers to answer where to invest in AI, how to craft personalization strategies, and how to build trust in AI systems for retail services.
Keywords: Artificial intelligence, conversational agents, recommendation systems, real-time customer engagement, perceived personalization, trust in AI, e-retailing
[This article belongs to E-Commerce for Future & Trends ]
References
- Grewal D, Levy M, Kumar V. Customer experience management in retailing: An organizing framework. J Retailing. 2009;85:1-14. doi:10.1016/j.jretai.2009.01.001.
- Brodie RJ, Hollebeek LD, Jurić B, Ilić A. Customer engagement: Conceptual domain, fundamental propositions, and implications for research. J Serv Res. 2011;14:252-71. doi:10.1177/1094670511411703.
- Hollebeek LD, Glynn MS, Brodie RJ. Consumer brand engagement in social media: Conceptualization, scale development and validation. J Interact Mark. 2014;28:149-65. doi:10.1016/j.intmar.2013.12.002.
- Ng SC, Sweeney JC, Plewa C. Customer engagement: A systematic review and future research priorities. Australas Mark J. 2020;28:235-52. doi:10.1016/j.ausmj.2020.05.004.
- Ho FN, Wong J, Brodowsky G. Does masstige offer the prestige of luxury without the social costs? Status and warmth perceptions from masstige and luxury signals. J Bus Res. 2023;155:113382. doi:10.1016/j.jbusres.2022.113382.
- Huang MH, Rust RT. Artificial intelligence in service. J Serv Res. 2018;21:155-72. doi:10.1177/1094670517752459.
- Park J, Han S. Investigating older consumers’ acceptance factors of autonomous vehicles. J Retail Consum Serv. 2023;72:103241. doi:10.1016/j.jretconser.2022.103241.
- Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;17:734-49. doi:10.1109/TKDE.2005.99.
- Xiao B, Benbasat I. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Q. 2007;31:137-209. doi:10.2307/25148784.
- Cavusgil E, Yayla S, Cem Kutlubay OC, Yeniyurt S. The impact of demographic similarity on customers in a service setting. J Bus Res. 2022;139:145-60. doi:10.1016/j.jbusres.2021.09.030.
- Dessart L, Veloutsou C, Morgan-Thomas A. Capturing consumer engagement: Duality, dimensionality and measurement. J Mark Manag. 2016;32:399-426. doi:10.1080/0267257X.2015.1130738.
- Zhang D, Cheng Z, Mohammad HAQH, Nault BR. Research commentary—Information technology substitution revisited. Inf Syst Res. 2015;26:480-95. doi:10.1287/isre.2015.0570.
- Eastin MS, Kahlor LA, Liang MC, Abi Ghannam N. Information-seeking as a precaution behavior: Exploring the role of decision-making stages. Hum Commun Res. 2015;41:603-21. doi:10.1111/hcre.12062.
- Wedel M, Kannan PK. Marketing analytics for data-rich environments. J Mark. 2016;80:97-121. doi:10.1509/jm.15.0413.
- Qiu L, Benbasat I. Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. J Manag Inf Syst. 2009;25:145-82. doi:10.2753/MIS0742-1222250405.
- Ashfaq M, Yun J, Yu S, Loureiro SMC. I, chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telemat Inform. 2020;54:101473. doi:10.1016/j.tele.2020.101473.
- Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13:319-40. doi:10.2307/249008.
- Vargo SL, Lusch RF. Service-dominant logic: Continuing the evolution. J Acad Mark Sci. 2008;36:1-10. doi:10.1007/s11747-007-0069-6.
E-Commerce for Future & Trends
| Volume | 13 | |
| Issue | 02 | |
| Received | 13/05/2026 | |
| Accepted | 24/07/2026 | |
| Published | 02/09/2026 | |
| Publication Time | 112 Days |