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Trust me, if you can: a study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments

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  • Chiahui Yen
  • Ming-Chang Chiang

Abstract

Nowadays, chatbots is one of the fast rising artificial intelligence (AI) trend relates to the utilisation of applications that interact with users in a conversational format and mimic human conversation. Chatbots allow business to enhance customer experiences and fulfil expectations through real-time interactions in e-commerce environment. Therefore, factors influence consumer’s trust in chatbots is critical. This study demonstrates a chatbots trust model to empirically investigate consumer’s perception by questionnaire from self-reported approach and by electroencephalography (EEG) from neuroscience approach. This study starts from integrating three key elements of chatbots, in terms of machine communication quality aspect, human-computer interaction (HCI) aspect, and human use and gratification (U&G) aspects. Moreover, this study chooses EEG instrument to explore the relationship between trust and purchase intention in chatbots condition. We collect 204 questionnaires and invite 30 respondents to participate the survey. The results indicated that credibility, competence, anthropomorphism, social presence, and informativness have influence on consumer’s trust in chatbots, in turn, have effect on purchase intention. Moreover, the findings show that the dorsolateral prefrontal cortex and the superior temporal gyrus are significantly associated with building a trust relationship by inferring chatbots to influence subsequent behaviour.

Suggested Citation

  • Chiahui Yen & Ming-Chang Chiang, 2021. "Trust me, if you can: a study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments," Behaviour and Information Technology, Taylor & Francis Journals, vol. 40(11), pages 1177-1194, August.
  • Handle: RePEc:taf:tbitxx:v:40:y:2021:i:11:p:1177-1194
    DOI: 10.1080/0144929X.2020.1743362
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    Cited by:

    1. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.

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