Behavioral Biases and FinTech Adoption Intention Among Young Adults: A Structural Equation Modeling Study in the Indian Context
DOI:
https://doi.org/10.68337/cpsm.v1.i1.2026-020Keywords:
FinTech adoption, behavioral biases, structural equation modeling, confirmatory factor analysis, behavioral finance, young adults, India, prospect theoryAbstract
India has developed one of the fastest-growing FinTech markets in the world through the expansion of digital financial services delivered via smartphones. Although the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) dominate the adoption literature, few studies have examined the psychological drivers of FinTech adoption. This study investigated the impact of four behavioral biases, namely overconfidence (OC), herd behavior (HB), present bias (PB), and loss aversion (LA), on FinTech adoption intention (FAI) among university students in India. Drawing on Kahneman's dual-process theory and prospect theory, a five-point Likert-type survey was administered to 148 respondents at Anurag University, Hyderabad. Confirmatory factor analysis (CFA) was first used to test construct reliability and validity, followed by path analysis with structural equation modeling (SEM). The measurement model showed good fit (comparative fit index [CFI] = 0.940, Tucker-Lewis index [TLI] = 0.928, root mean square error of approximation [RMSEA] = 0.062), good internal reliability (Cronbach's α = 0.766 to 0.850; composite reliability [CR] = 0.765 to 0.852), and average variance extracted (AVE) values of 0.486 to 0.655; the AVE of herd behavior (0.486) fell below the 0.50 threshold for convergent validity. All heterotrait-monotrait (HTMT) ratios were below 0.85, but discriminant validity for loss aversion was marginal under the Fornell-Larcker criterion. In the structural model, present bias (β = 0.370, p = 0.004) and loss aversion (β = 0.499, p = 0.001) were significant predictors of adoption intention, and overconfidence (β = 0.216, p = 0.038) also had a significant influence, whereas herd behavior (β = −0.204, p = 0.054) was not a significant predictor. The model explained 74.1% of the variance in FinTech adoption intention (R2 = 0.741). The results challenge purely utilitarian adoption models and offer implications for FinTech product designers, financial educators, and regulators.
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