Technology acceptance model, trust, and financial behavior in shaping consumer well-being: Insights from fintech adoption in urban Indonesia

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Consumer well-being reflects subjective physical, emotional, and psychological satisfaction derived from services. This study investigates how the Technology Acceptance Model (TAM), trust, and intention to use fintech services influence consumer well-being in urban Indonesia, with financial behavior as a moderating variable. The research surveyed 390 active fintech users in the Jabodetabek metropolitan area in Indonesia, which includes Jakarta, Bogor, Depok Tangerang, and Bekasi selected due to their high engagement with digital financial services, with the survey being conducted over a three-month period from May to July 2024. The findings reveal that TAM and trust significantly influence consumers’ intention to use fintech services, which mediates their impact on consumer well-being. Notably, intention to use has the strongest direct effect on well-being (path coefficient = 0.548, p < 0.001). However, financial behavior does not significantly moderate the relationship between intention to use and well-being (p = 0.441). These results highlight the pivotal role of trust and ease of access in enhancing consumer satisfaction. From a practical perspective, the findings suggest that fintech providers and policymakers should focus on financial literacy to mitigate risks associated with unregulated fintech use. This study extends theoretical insights into the intersection of technology acceptance and consumer behavior, emphasizing the importance of user-centered approaches. Future research should explore these dynamics in rural contexts to compare community-specific impacts.

Acknowledgment
This research was funded by Directorate of Research, Technology and Community Service (DRTPM) of the Indonesian Ministry of Education and Culture in 2024 with the National Competitive Basic Research Grant scheme with contract number; 0667/E5/AL.04/2024. 

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    • Figure 1. Research model
    • Figure 2. Structural model
    • Table 1. Validity, AVE, and reliability test
    • Table 2. Discriminant validity of the HTMT
    • Table 3. Model fit and predictive power multicollinearity
    • Table 4. Multicolinierity test value
    • Table 5. Demographic overview and characteristics of respondents
    • Table 6. Hypothesis test
    • Conceptualization
      Arief Budiyanto
    • Formal Analysis
      Arief Budiyanto
    • Funding acquisition
      Arief Budiyanto
    • Methodology
      Arief Budiyanto
    • Supervision
      Arief Budiyanto, Iman Lubis, Ibrahim Bali Pamungkas, Asep Erlan Maulana
    • Validation
      Arief Budiyanto, Iman Lubis, Ibrahim Bali Pamungkas, Asep Erlan Maulana
    • Writing – original draft
      Arief Budiyanto
    • Writing – review & editing
      Arief Budiyanto, Iman Lubis, Ibrahim Bali Pamungkas, Asep Erlan Maulana
    • Data curation
      Iman Lubis
    • Resources
      Iman Lubis
    • Visualization
      Iman Lubis, Asep Erlan Maulana
    • Investigation
      Ibrahim Bali Pamungkas
    • Project administration
      Ibrahim Bali Pamungkas, Asep Erlan Maulana
    • Software
      Asep Erlan Maulana
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