Unlocking the precursors of customer service experience using artificial intelligence-driven chatbot: calibration of gratification theory and expectation confirmation model Samar Rahi Hailey College of Banking and Finance, University of the Punjab, Lahore, Pakistan and Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu (UMT), Kuala Nerus, Malaysia Abdul Hafaz Ngah Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu (UMT), Kuala Nerus, Malaysia and Applied Science Research Center, Applied Science Private University, Amman, Jordan Firas Alnasr Department of Communication and Digital Marketing, An-Najah National University, Nablus, Palestinian Authority Mahmoud Alghizzawi Department of Marketing, Applied Science Private University, Amman, Jordan Rizwana Rasheed Lucille and Jay Chazanoff School of Business, College of Staten Island, City University of New York, Staten Island, New York, USA and Department of Business Administration, IQRA University, Karachi, Pakistan, and Aamir Rashid Department of Business and Economics, York College, City University of New York, Jamaica, New York, USA Abstract Purpose – Artificial intelligence (AI)-enabled chatbots have the potential to revolutionize the way businesses interact with customers. Nevertheless, there is a lack of empirical research that discloses factors that enhance chatbot user satisfaction and experience. The current study fills a research gap and develops a research framework that combines technology gratification theory, expectation confirmation model and technology competency to investigate artificial intelligence (AI)-driven chatbot user satisfaction and chatbot user experience. Design/methodology/approach – The research design of this study is based on a quantitative research approach. The research framework has outlined multiple latent factors that are verified with empirical data. Overall, 373 respondents had participated in the chatbot research survey. Data are analyzed with structural equation modeling. Findings – Findings of the structural equation modeling have unveiled that utilitarian gratification, hedonic gratification, social gratification, emotional competency, cognitive competency, relational competency and expectation confirmation explained substantial variance R^2 53.4% in measuring chatbot user satisfaction. Likewise, user satisfaction and perceived anthropomorphism had explained large variance R^2 55.5% in measuring chatbot user experience. Nevertheless, effect size f^2 analysis has revealed a large effect size of cognitive competency in measuring chatbot user satisfaction. Practical implications – This study sheds light on crucial factors influencing chatbot user satisfaction and gleans numerous theoretical and practical implications. For instance, the research model is grounded in Journal of Enterprise Information Management The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1741-0398.htm Received 3 October 2024 Revised 9 February 2025 11 April 2025 3 June 2025 Accepted 30 June 2025 Journal of Enterprise Information Management © Emerald Publishing Limited e-ISSN: 1758-7409 p-ISSN: 1741-0398 DOI 10.1108/JEIM-10-2024-0551 Downloaded from http://www.emerald.com/jeim/article-pdf/doi/10.1108/JEIM-10-2024-0551/10057777/jeim-10-2024-0551en.pdf by Mr Rahi on 13 August 2025