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Double-sided messages improve the acceptance of chatbots

Author

Listed:
  • Meng, Lu (Monroe)
  • Li, Tongmao
  • Shi, Xiaolin (Crystal)
  • Huang, Xin

Abstract

Customers may not wish to use an artificial intelligence (AI) chatbot after knowing its nonhuman identity. Based on the inoculation theory, this study explores how to increase customers' willingness to communicate with AI chatbots by adopting a double-sided message strategy after AI's nonhuman identity is disclosed through one field experiment in a hotel and three online vignette experiments. Results indicate that a double-sided message strategy enhances customers' willingness to interact with AI chatbots via the mediating role of perceived authenticity. Furthermore, the study highlights that customers' attitudes toward interacting with AI chatbots are moderated by customer demand types—complaints and inquiries. This study proposes an effective solution to customers' rejection of AI chatbots.

Suggested Citation

  • Meng, Lu (Monroe) & Li, Tongmao & Shi, Xiaolin (Crystal) & Huang, Xin, 2023. "Double-sided messages improve the acceptance of chatbots," Annals of Tourism Research, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:anture:v:102:y:2023:i:c:s0160738323001172
    DOI: 10.1016/j.annals.2023.103644
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    References listed on IDEAS

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    1. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    2. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
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    Cited by:

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    2. Lu, Zhenzhen & Min, Qingfei, 2025. "Self-recovery or human Intervention? understanding the role of task type and failure frequency in chatbot failure recovery," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    3. Peng, Zixi (Lavi) & Mattila, Anna, 2026. "How AI modality shapes tourists' prosocial behaviors," Annals of Tourism Research, Elsevier, vol. 116(C).
    4. Chan, Hau-Ling & Choi, Tsan-Ming, 2025. "Using generative artificial intelligence (GenAI) in marketing: Development and practices," Journal of Business Research, Elsevier, vol. 191(C).
    5. Wang, Xueying & Zhang, Yuexian, 2025. "Can imagining the future repair trust? The impact of chatbots' trust repair on the continuous interaction intention in service failure," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    6. Lv, Linxiang & Liang, Yongheng & Chen, Siyun & Liu, Gus Guanrong & Liao, Jiancai, 2025. "Good deeds deserve good outcomes: Leveraging generative artificial intelligence to reduce tourists' avoidance of ethical brands embracing stigmatized groups," Annals of Tourism Research, Elsevier, vol. 110(C).
    7. Jia, Guangmei & Luo, Xiaoyan & Wan, Lisa C., 2025. "How the elderly tackle age discrimination from human or AI servers," Annals of Tourism Research, Elsevier, vol. 113(C).
    8. S. Jerrin Issac Sam & K. Mohamed Jasim, 2025. "Diving into the technology: a systematic literature review on strategic use of chatbots in hospitality service encounters," Management Review Quarterly, Springer, vol. 75(1), pages 527-555, February.

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