IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

CONSUMER PERCEPTION AND PREFERENCES TOWARDS AI CHATBOTS: AN EMPIRICAL STUDY OF E-COMMERCE USERS IN LUDHIANA

Amit Sethi Ishita Gulati

Department of Business Management, Punjab College of Technical Education, Ludhiana – 142021, Punjab, India
DOI:https://doi.org/10.55041/ijcope.v2i6.411

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

Artificial Intelligence (AI) chatbots have become a defining feature of the online shopping experience, reshaping the way e-commerce platforms communicate with and serve their customers. This study examines consumer perception, trust, and attitude toward AI chatbots in the context of online retail, and identifies the key factors that drive their preference and adoption among shoppers. An analytical research design was adopted, with primary data gathered through a structured questionnaire administered to 145 respondents from Ludhiana who had prior experience using AI chatbots while shopping online. Respondents were selected through random sampling, and the resulting data were examined using mean analysis and exploratory factor analysis. The findings indicate that consumers hold a broadly favourable view of AI chatbots, rating them positively for their helpfulness, ease of use, and convenience during the purchase process. Attitudes toward chatbot usage were equally encouraging, pointing to a growing willingness among consumers to rely on AI-driven assistance while shopping online. Trust, while moderately high overall, was tempered by lingering concerns over data privacy, security, and the reliability of chatbot responses. Exploratory factor analysis further revealed three underlying dimensions shaping chatbot adoption: User Acceptance, Perceived Convenience, and Behavioural Intention. Taken together, the results suggest that while AI chatbots are increasingly seen as valuable shopping companions, e-commerce businesses must continue to invest in stronger data security, deeper personalization, and more responsive design if they are to fully earn consumer trust and sustain long-term adoption.

Keywords: Consumer Perception, Trust, Attitude, AI Chatbots, Consumer Preferences, Customer Satisfaction, E-commerce

How to Cite this Paper

Sethi, A. & Gulati, I. (2026). Consumer Perception and Preferences towards AI Chatbots: An Empirical Study of E-Commerce Users in Ludhiana. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.411

Sethi, Amit, and Ishita Gulati. "Consumer Perception and Preferences towards AI Chatbots: An Empirical Study of E-Commerce Users in Ludhiana." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.411.

Sethi, Amit, and Ishita Gulati. "Consumer Perception and Preferences towards AI Chatbots: An Empirical Study of E-Commerce Users in Ludhiana." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.411.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  1. Chen, S., Wang, P., & Wood, J. (2025). Exploring the varying effects of chatbot service quality dimensions on customer intentions to switch service agents. Scientific Reports, 15(1), 22559.

  2. Kim, S., & Priluck, R. (2025). Consumer responses to generative AI chatbots versus search engines for product evaluation. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 93.

  3. Dewanti, R., & Ikhsan, R. B. (2025). Do chatbots support consumer performance? Investigating the role of e-lifestyle and anthropomorphism in the service industry. International Journal of Information Management Data Insights, 5(1), 100339.

  4. Marjerison, R. K., Zhang, Y., & Zheng, H. (2022). AI in e-commerce: Application of the use and gratification model to the acceptance of chatbots. Sustainability, 14(21), 14270.

  5. Gu, C., Zhang, Y., & Zeng, L. (2024). Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: A perspective based on attribution and CASA theories. Humanities and Social Sciences Communications, 11(1), 1–12.

  6. Ranieri, A., Di Bernardo, I., & Mele, C. (2024). Serving customers through chatbots: Positive and negative effects on customer experience. Journal of Service Theory and Practice, 34(2), 191–215.

  7. Timothy, O. T., Fisayo, A. A., Thiru, R., Raji, M. A., & Olodo, H. B. (2024). Impact of AI chatbots on youth consumer behavior in e-commerce: Evidence from southwest, Nigeria. International Journal of Multidisciplinary Research Updates, 7(2), 043–051.

  8. Huseynov, F. (2023). Chatbots in digital marketing: Enhanced customer experience and reduced customer service costs. In Contemporary approaches of digital marketing and the role of machine intelligence (pp. 46–72). IGI Global Scientific Publishing.

  9. Morsi, S. (2023). Artificial intelligence in electronic commerce: Investigating the customers' acceptance of using chatbots. Journal of Systems Management Science, 13(3), 156–176.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jul 01 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 6View All Issues Next Article

← Previous Article

Consequences of Climate Change

Next Article →

Cost-Aware Energy Management Strategy for Microgrid Systems using Hybrid Optimization Techniques
Scroll to Top