Does Artificial Intelligence Satisfy You? A Meta-Analysis of User Gratification and User Satisfaction with AI-Powered Chatbots

Abstract

While artificial intelligence (AI) has been increasingly employed in communication technologies, limited research has explored the user experience of AI-powered chatbots. Based on Uses & Gratification (U&G) theory, this study conducted a quantitative meta-analysis of 12 studies to examine the relationship between four dimensions of user gratification (utilitarian, technology, hedonic, and social) and user satisfaction with AI-powered chatbots. The results indicate that these four categories of gratifications are strongly associated with user satisfaction to different extents, with utilitarian gratification having the strongest factor influence. The findings suggest that utility is the core aspect of consideration for the designers of AI-powered chatbots. The results further extend the existing U&G literature in the context of AI.

Acknowledgments

We would like to thank Dr. Matthew Bennion, Dr. Muhammad Ashfaq, and Chun Shao for providing detailed information about their data to help us with the data collection process. We also thank the anonymous reviewers for providing valuable and insightful comments on our manuscript.

Disclosure statement

The authors have no conflict of interest.

Notes

1 The degree of correlation was assessed using Cohen’s (1988) classification.

2 Belanche et al.’s (2019) work was not included since the participants in this research were from both Europe and North America. Shao & Kwon’s (2021) and Zarouali et al.’s (2018) studies were not included because the participants from these two studies were not specified.

Additional information

Notes on contributors

Chenxing Xie

Chenxing Xie is a Ph.D. student in the Communication, Rhetoric, and Digital Media program at North Carolina State University. Her research interests include technical communication, intercultural communication, and user-centered design. Her work has been published in the Proceedings of the ACM International Conference on the Design of Communication.

Yanding Wang

Yanding Wang is a master’s student at School of Public Health at China Medical University. Her research interests include public health, epidemiology and statistics, and spatio-temporal analysis and modeling of disease. Her work has been published in the Chinese Journal of Vector Biology and Control.

Yang Cheng

Yang Cheng is an associate professor in the department of communication at North Carolina State University. She has conducted extensive research in global public relations management, social media and artificial intelligence, and crisis communication. Her publications have appeared in top journals such as the New Media & Society and Public Relations Review.