“I Am Here to Assist Your Tourism”: Predicting Continuance Intention to Use AI-based Chatbots for Tourism. Does Gender Really Matter?

Abstract: AI-based chatbot, a typical product of human–computer interaction (HCI), is widely employed by tourism service providers. However, there is a lack of research on the determinants that explain why customers continuously use chatbots for tourism. Based on the Unified Theory of Adoption and Use of Technology 2 (UTAUT2), the Theory of Perceived Risk (TPR), anthropomorphism, and personalization, this research developed an integrated model to investigate the determinants behind customers’ continuance intention to use chatbots for tourism. In addition, the moderating role of gender differences in the relationships between determinants and continuance intention was tested. The analysis based on a sample of 613 users highlighted the positive effects of performance expectancy, social influence, habit, anthropomorphism, and personalization. However, the findings showed that time risk and privacy risk have negative influences. Although the moderating test did find two differences due to gender, many other relationships showed no differences between male and female.

  AI-based chatbots become game-changer for tourism industry. These AI-driven agents are expected to be integrated into many aspects of tourism in the near future (Bowen & Morosan, 2018; Pillai & Sivathanu, 2020; Tussyadiah, 2020), an increasing number of tourism service providers begin to massively employ them to offer services. The purpose of this research is to understand the determinants that influence the continuance intention of chatbots for tourism based on UTAUT2, TPR, and supplementary antecedents. The findings highlighted the significances of performance expectancy, social influence, habit, time risk, privacy risk, anthropomorphism, and personalization. Moreover, this research also attempts to examine whether customers’ perceptions of chatbots are homogeneous across genders. According to the findings, performance expectancy and habit had stronger influences on male. This research devotes to strengthening theoretical framework regarding the use of chatbots for tourism. In addition, it reconsiders the generalizability of SHT and GMT in the context of AI-based chatbot use.

  Meanwhile, future studies should address some limitations related to this research. First, this study has geographic limitations: only Chinese tourism market was investigated. Thus, the findings in this research cannot be generalized to other countries. Future studies should examine the research model in this study across a variety of countries to improve the generalization. Second, the cultural differences with regard to users’ perceptions on HCI are not fully taken into account. Thus, it might be problematic to generalize the findings in this research over the context of Chinese users. Third, there were biases of age (mostly young users) and educational level (mostly bachelor degree or above) in this research. Fourth, this study only tested the moderating effect of gender differences on nine factors. Some other factors, such as technological anxiety, may show different impact between male and female. Therefore, future studies should be extended to include more antecedents and understand the moderating effect of gender differences on them.

About the authors

• Banghui Zhang is a full Professor in School of Marxism, Chongqing University. He is also the Dean of School of Marxism, Chongqing University and the Executive Director of Chinese Public Administration Society. His research interests include public policy and public service.

• Yonghan Zhu is a doctoral candidate in School of Public Policy and Administration, Chongqing University. He received Master degree in University College London (UCL). His research focuses on topics of digital government and society, including user satisfaction with e-government services, behavioral intention of AI-based services, and experience with online games.

• Jie Deng is a Master student in School of Public Policy and Administration, Chongqing University. She focuses on data security and social media.

• Weiwei Zheng is a doctoral candidate in Antai College of Economics and Management, Shanghai Jiao Tong University. His research includes the areas of public policy, big data, and macroeconomics.

• Yang Liu is a doctoral candidate in School of Public Policy and Administration, Chongqing University. His research includes the areas of social media and public service.

• Chunshun Wang is a doctoral candidate in School of Social Sciences, Tsinghua University. He focuses on public policy and AIbased sports.

• Rongcan Zeng is a doctoral candidate in School of Marxism, Chongqing University. Her research interests include mental health, behavioral psychology, and gender studies.

Disclosure statement

No potential conflict of interest was reported by the author(s).