Investigating the influence of AI chatbot interactions on attitudes and purchase intentions: extending the UTAUT framework from brands perspective

Eya Kbaier et al.

Journal of Business Strategy2025https://doi.org/10.1108/jbs-05-2024-0086article
AJG 1ABDC B
Weight
0.41

Abstract

Purpose This study suggests an extended UTAUT framework that examines the effect of performance expectancy, effort expectancy, social influence and facilitating conditions of chatbots on attitudes and in turn on purchase intentions toward brands. In addition, it investigates the moderator role of user resistance to change in the relationship between effort expectancy and attitude. The purpose of this study is to examine how factors of human-computer AI chatbots influence user attitudes and purchase intentions, considering the moderating role of user resistance to change. Design/methodology/approach An online survey (n = 397) was used to collect data from a diverse sample of participants to empirically test the conceptual model proposed in this study. Findings The findings underscored the significant contribution of performance expectancy, effort expectancy, social influence and facilitating conditions of chatbots in shaping positive brand attitudes and its impact on purchase intention. In addition, the authors highlighted that users with higher resistance to change tend to have a negative interaction with effort expectancy further affecting their attitudes toward the brand. Originality/value While previous studies have predominantly investigated the impact of interactions with chatbots on intention and actual use of this technology within the UTAUT framework, our study adopts a new and broader perspective by investigating the impact of these interactions on brand attitude.

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https://doi.org/https://doi.org/10.1108/jbs-05-2024-0086

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@article{eya2025,
  title        = {{Investigating the influence of AI chatbot interactions on attitudes and purchase intentions: extending the UTAUT framework from brands perspective}},
  author       = {Eya Kbaier et al.},
  journal      = {Journal of Business Strategy},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/jbs-05-2024-0086},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.