Explaining purchase intention in AI-powered e-commerce chatbots: An integrative model of functional, experiential, and credibility drivers
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DOIhttp://dx.doi.org/10.21511/im.22(3).2026.01
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Article InfoVolume 22 2026, Issue #3, pp. 1–20
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Type of the article: Research Article
Abstract
Artificial intelligence-based chatbots are increasingly used on e-commerce platforms to improve customer interactions and service efficiency, yet their effectiveness in influencing consumer purchase intentions in emerging markets remains insuffi-ciently understood. This study aims to examine how interaction, entertainment, perceived enjoyment, service quality, per-ceived ease of use, trendiness, communication competence, and credibility influence purchase intention, mediated by cus-tomer satisfaction, in the context of Tokopedia’s AI chatbot in Indonesia. The study employed a quantitative approach using a structured online survey of 240 Tokopedia users in Yogyakarta and Central Java conducted between March and July 2024, and the collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indi-cate that communication competence (β = 0.287, p < 0.001), credibility (β = 0.254, p < 0.001), and trendiness (β = 0.231, p < 0.01) are the strongest predictors of customer satisfaction and indirectly influence purchase intention. Service quality (β = 0.214, p < 0.001) and perceived ease of use (β = 0.198, p < 0.01) also significantly contribute to satisfaction, while entertainment (β = 0.176, p < 0.05) and perceived enjoyment (β = 0.169, p < 0.05) enhance the experiential aspect of chatbot interaction. Satisfaction demonstrates a strong positive effect on purchase intention (β = 0.412, p < 0.001), confirming its mediating role in translating chatbot service attributes into behavioral outcomes. These findings suggest that effective AI chatbot design should integrate functional service quality with relational and experiential communication capabilities to strengthen consumer engagement and purchasing behavior on e-commerce platforms.
Acknowledgment
The authors gratefully acknowledge the financial support provided by the Ministry of Higher Education, Science, and Tech-nology (KEMENDIKTISAINTEK) of the Republic of Indonesia through the regular fundamental research grant scheme (PFR). This research was funded under the contract number 126/C3/DT.05.00/PL/2025; 0498.02/LL5-INT/AL.04/2025; 012/DPPM.LLDIKTI/PFR/UST/LP2M/K/VI/2025. The findings and opinions expressed in this article are solely the responsibility of the authors and do not necessarily reflect the views of the funding institution.
- Keywords
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JEL Classification (Paper profile tab)M31, L81, L86, O33
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References61
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Tables8
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Figures1
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- Figure 1. Research model
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- Table 1. Outer loading
- Table 2. Measurement model reliability and validity
- Table 3. Discriminant validity
- Table 4. Fornell-Larcker criterion
- Table 5. HTMT ratios
- Table 6. R-square
- Table 7. Q-square
- Table 8. Hypotheses testing
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