Impact Of Artificial Intelligence on Online Shopping Behaviour: A Simplified Study
Keywords:
Chatbots, Personalization, Online Shopping Behaviour, Recommender Systems, Artificial IntelligenceAbstract
Artificial Intelligence (AI) has become a central force in shaping online shopping behaviour worldwide. This paper offers a simple, practitioner-friendly study of how AI influences consumer decision-making, purchase intentions, and post-purchase behaviour in e-commerce contexts. The study synthesizes secondary evidence from academic literature, industry reports, and market analyses to describe core AI applications—recommender systems, personalization engines, chatbots, visual search, dynamic pricing, and immersive technologies—and their behavioral effects. Recommender systems and personalization engines increase product discoverability and perceived relevance, driving higher conversion rates and average order values; industry research shows personalization expectations are high among consumers and that getting personalization right materially affects loyalty and revenue.
AI-powered chatbots and virtual assistants improve response times and provide 24/7 support, but quality varies: poorly designed chatbots can frustrate users, damaging trust and causing cart abandonment. Regulatory attention and consumer reports have highlighted cases where chatbot failures lead to negative customer outcomes, underlining the need for hybrid human-AI models. Recommender systems demonstrably increase product exposures and purchase likelihood in many contexts, though effects differ by product type and recommendation design; recent empirical studies find measurable uplifts in sessions and purchases but note trade-offs like exploration reduction.
Privacy and trust are recurring moderating factors: consumers often accept personalization but express concern about data use and transparency. Effective consent management and first-party data strategies are now recommended practices for retailers seeking personalization at scale.
The paper draws practical implications for commerce practitioners (prioritise customer-centric personalization, invest in chatbot UX and escalation flows, apply transparency and consent-first data practices) and policy implications (consumer protections, AI explainability, and oversight). Limitations of this study and directions for future empirical research (primary surveys, A/B tests) are discussed. The conclusion argues that AI is reshaping online shopping behaviour by increasing convenience, personalization, and operational efficiency — but retail success depends on responsible, consumer-sensitive implementation.
References
Arner, D. W., Barberis, J., & Buckley, R. (2017). FinTech and regtech: Impact on regulators and banks. Journal of Banking Regulation, 19(3), 1–14. (Used foundational background on financial AI; see broader references above.)
Bhagat, P. H., & Shaikh, S. A. (2025). Managing health care in the digital world: A comparative analysis on customers using health care services in Mumbai suburbs and Pune city. IJCRT. Registration ID: IJCRT_216557.
Chougle, Z. S., & Shaikh, S. (2022). To understand the impact of Ayurvedic health-care business & its importance during COVID-19 with special reference to “Patanjali Products”. In Proceedings of the National Conference on Sustainability of Business during COVID-19, IJCRT, 10(1),
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Deloitte. (2024). Personalizing brand experiences; accelerating retail personalization at scale. Deloitte Digital insights.
Feng, L. (2024). Exploring the impacts of a recommendation system on an e-commerce platform. Journal of Retailing and Consumer Services
Grewal, D., Roggeveen, A., & Nordfält, J. (2020). The future of retailing. Journal of Retailing, 96(1), 1–6.
Investopedia / CFPB reporting on chatbot issues (2023). (Used to illustrate regulatory attention and real consumer harms).
Kumar, V., Dixit, A., Javalgi, R. G., Dass, M., & Garza, M. (2021). Digital transformation and business model innovation. Journal of Business Research, 128, 688–698.
Lee, J. (2020). Effects of recommender systems in e-commerce vary by use-case. Carnegie Mellon University (Tepper) research summary.
McKinsey & Company. (2021). The value of getting personalization right—or wrong—is multiplying.
McKinsey & Company. (2023). What is personalization? McKinsey Explainers. Retrieved from McKinsey website.
Misischia, C. V. (2022). Chatbots in customer service: Their relevance and impact. Procedia Computer Science.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
PwC. (2025). AI in retail: Industry perspectives and transformation. PwC insights.
ResearchGate / Empirical analyses on recommender systems and personalization (various authors, 2024–2025). (Representative studies used to synthesize recommender findings).
Shaikh, S. A. (2024). Empowering Gen Z and Gen Alpha: A comprehensive approach to cultivating future leaders. In Futuristic Trends in Management (IIP Series, Vol. 3, Book 9, Part 2, Chapter 2). IIP Series. https://doi.org/10.58532/V3BHMA9P2CH2
Shaikh, S. A., & Jagirdar, A. H. (2026). Beyond AI dependence: Pedagogical approaches to strengthen student reasoning and analytical skills. In S. Khan & P. Pringuet (Eds.), Empowering learners with AI: Strategies, ethics, and frameworks (Chapter 8, pp. 1–16). IGI Global. https://doi.org/10.4018/979-8-3373-7386-7.ch008
Springer. (2025). The impact of artificial intelligence on consumer behavior towards brands: A systematic review. Electronic Markets / Journal.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management. Academy of Management Annals, 13(1), 23–59.
Vogue Business & Google (2023). Unfolding AI: New worlds of fashion — AI’s impact on consumer perceptions.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
