Determinants of consumers’ emotions and willingness to use artificial intelligence in Indonesia

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This research examines the key factors influencing Indonesian consumer’ willingness to use AI chatbots, focusing on technological characteristics, hedonic motivations, anthropomorphism, AI performance and user experience, using the extended Artificially Intelligent Device Usage Acceptance (AIDUA) model. This is quantitative research where a survey technique was adopted, and two hundred and eight participants’ responses were obtained. The participants were consumers in Indonesia who had prior experience using AI chatbot. The study reveals that anthropomorphism, technological competence, and consumer hedonic motivation while using a chatbot affects the consumer’s perception about the perceived performance of a chatbot and the user experience. These perceived performance and experiences influence feelings, and then influence the willingness to use the AI chatbot. Mediation analysis indicated that perceived performance mediated the relationship between anthropomorphism and willingness to use AI, while user experience did not. That hedonic motivation affects willingness to adopt AI through the mediations of user experience, emotions, and perceived performance. Further, technological factors influence willingness to use AI mediated by perceived performance, in which case, user experience is not a mediator. The results indicate that the factors influencing the willingness to use AI include technological readiness, anthropomorphism, and hedonic motivation, which are mediated by perceived performance and emotions, whereas user experience does not significantly mediate the relationship.

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    • Figure 1. Research model – determinants of consumers’ willingness to use AI
    • Figure 2. Path coefficient and p-value
    • Table 1. Respondents
    • Table 2. Convergent validity
    • Table 3. Several criteria for the outer model and inner model
    • Table 4. Direct effect
    • Table 5. Indirect effect
    • Conceptualization
      Dwinita Laksmidewi, Efendi
    • Formal Analysis
      Dwinita Laksmidewi, Efendi
    • Funding acquisition
      Dwinita Laksmidewi, Wong Chee Hoo
    • Data curation
      Dwinita Laksmidewi, Efendi
    • Methodology
      Dwinita Laksmidewi, Efendi
    • Software
      Dwinita Laksmidewi, Efendi
    • Supervision
      Dwinita Laksmidewi
    • Writing – original draft
      Dwinita Laksmidewi, Efendi, Wong Chee Hoo
    • Writing – review & editing
      Dwinita Laksmidewi, Efendi, Wong Chee Hoo
    • Project administration
      Efendi
    • Resources
      Efendi, Wong Chee Hoo
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    Acknowledgment
    This work was funded by a research grant from the Universitas Padjadjaran, Indonesia.

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