Purpose : The study aimed to understand the relationship between artificial intelligence anthropomorphism, algorithm aversion, and platform loyalty among college students of Navi Mumbai who had used artificial intelligence-powered shopping chatbots.

Design/Methodology/Approach : Questionnaire responses were obtained from 229 eligible college students in Navi Mumbai. As part of the assessment of the measurement model, convergent and discriminant validity were analyzed using confirmatory factor analysis. The hypotheses were tested by means of regression analysis. Mediation analysis was conducted with bootstrapping (5,000 resamples).

Findings : Perceived artificial intelligence anthropomorphism was significantly negatively associated with algorithm aversion and positively associated with platform loyalty. Algorithm aversion was negatively associated with platform loyalty, and the findings supported partial statistical mediation of the relationship between perceived artificial intelligence anthropomorphism and platform loyalty.

Practical Implications : The findings suggested that e-tailers could consider consumers’ perceptions of the human-like qualities of chatbots when reviewing chatbot experiences. In the current study, perceiving chatbots as having human-like characteristics was associated with lower levels of algorithm aversion and greater platform loyalty; these findings offered useful insights for e-tailers evaluating artificial intelligence shopping chatbot experiences.

Originality/Value : The study provided empirical evidence on the mediating role of algorithm aversion in the relationship between perceived artificial intelligence anthropomorphism and platform loyalty among college students in Navi Mumbai with experience using artificial intelligence shopping chatbots.

Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology & Marketing, 20(2), 123–138. https://doi.org/10.1002/mar.10063

Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051

Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y

Burton, J. W., Stein, M. K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155

Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788

Chi, O. H., Chi, C. G., & Gursoy, D. (2025). Seeing personhood in machines: Conceptualizing anthropomorphism of social robots. Journal of Service Research, 28(1), 78–92. https://doi.org/10.1177/10946705241297196

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033

Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020

Guru, S., Bhatt, N., & Agrawal, N. (2021). Prioritization of dimensions of online trust using analytical hierarchy approach. Indian Journal of Marketing, 51(5–7), 81–92. https://doi.org/10.17010/ijom/2021/v51/i5-7/163886

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.

Hart, P. M., & Jha, S. (2015). The variation of consumer anthropomorphism across cultures. Indian Journal of Marketing, 45(11), 7–16. https://doi.org/10.17010/ijom/2015/v45/i11/81873

Haupt, M., Freidank, J., & Haas, A. (2025). Consumer responses to human-AI collaboration at organizational frontlines: Strategies to escape algorithm aversion in content creation. Review of Managerial Science, 19(2), 377–413. https://doi.org/10.1007/s11846-024-00748-y

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Inc42. (2023, October 4). Ahead of festive season, Flipkart launches ChatGPT-powered shopping assistant 'Flippi'. https://inc42.com/buzz/flipkart-launches-chatgpt-powered-shopping-assistant-flippi/

India Brand Equity Foundation. (2025). E-commerce industry in India. Retrieved August 19, 2026, from https://www.ibef.org/industry/ecommerce

Kagan, E., Dada, M., & Hathaway, B. (2026). Why are customers averse to service chatbots? Manufacturing & Service Operations Management. Advance online publication. https://doi.org/10.1287/msom.2024.1141

Kumar, N., & Singh, V. (2022). Shopping with voice assistant: Understanding consumer intention and the mediating role of trust. Indian Journal of Marketing, 52(8), 51–65. https://doi.org/10.17010/ijom/2022/v52/i8/171224

Kurup, A. J., & Jain, P. (2018). Effect of e-loyalty cues on repurchase behavioural intentions among online shoppers. Indian Journal of Marketing, 48(11), 7–22. https://doi.org/10.17010/ijom/2018/v48/i11/137982

Lee, K.-W., & Li, C.-Y. (2023). It is not merely a chat: Transforming chatbot affordances into dual identification and loyalty. Journal of Retailing and Consumer Services, 74, Article 103447. https://doi.org/10.1016/j.jretconser.2023.103447

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013

Mahadevan, K., & Joshi, S. (2022). Impact of e-service quality dimensions on customer satisfaction and loyalty in online apparel shopping in India. Indian Journal of Marketing, 52(12), 20–38. https://doi.org/10.17010/ijom/2022/v52/i12/172560

Mahajan, S., Gupta, M., & Chauhan, V. (2025). How may “AI” help you? Analyzing the role of AI-backed chatbot interactions in customer services. Indian Journal of Marketing, 55(4), 69–88. https://doi.org/10.17010/ijom/2025/v55/i4/174929

Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159–168. https://doi.org/10.1207/s15327574ijt0502_4

Myntra unveils AI-powered interactive chatbot for better shopping experience. (2023, September 27). Times of India. https://timesofindia.indiatimes.com/gadgets-news/myntra-unveils-ai-powered-interactive-chatbot-for-better-shopping-experience/articleshow/103985830.cms

Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(4_suppl1), 33–44. https://doi.org/10.1177/00222429990634s105

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

Rao, A. A., & Kothari, R. (2017). Determinants of customer loyalty towards e-tailers in India: An empirical study. Indian Journal of Marketing, 47(11), 48–60. https://doi.org/10.17010/ijom/2017/v47/i11/119296

Ribbink, D., van Riel, A. C. R., Liljander, V., & Streukens, S. (2004). Comfort your online customer: Quality, trust and loyalty on the internet. Managing Service Quality, 14(6), 446–456. https://doi.org/10.1108/09604520410569784

Sharma, R., Datt, S., & Sachdeva, K. (2025). Impact of website design and product image quality on efficient online shopping experiences. Indian Journal of Marketing, 55(6), 29–47. https://doi.org/10.17010/ijom/2025/v55/i6/175109

Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14–24. https://doi.org/10.1016/j.jbusres.2020.04.030

Shivam, H. (2024, August 27). Amazon's Rufus now available in India: Know all about AI shopping assistant. Business Standard. https://www.business-standard.com/technology/tech-news/amazon-s-rufus-now-available-in-india-know-all-about-ai-shopping-assistant-124082700420_1.html

Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41–50. https://doi.org/10.1016/S0022-4359(01)00065-3

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. https://doi.org/10.2307/1251929