A mixed methods analysis of palm payment adoption based on UTAUT2 and perceived trust

The emergence of palm payment systems signals a transformative shift in digital transaction. While widely adopted in developed countries, their presence in developing economies remains limited and under-researched. This study examines factors influencing the adoption intention of palm payment through the UTAUT2 framework, integrating perceived trust as a key determinant. Data from 413 respondents were analyzed mix methods (PLS-SEM, ANN, and fsQCA). Firstly, this study employs the PLS-SEM approach due to its suitability for small sample sizes and its robustness against violations of multivariate normality assumptions. However, a major limitation in prior research using single-method approaches, particularly PLS-SEM, is the inability to fully address measurement errors. This study overcomes this issue by integrating ANN and fsQCA to validate and complement PLS-SEM results. ANN helps mitigate measurement error concerns by identifying patterns beyond linear relationships, reducing reliance on strict parametric assumptions. Meanwhile, fsQCA provides a configurational perspective, testing how different combinations of factors lead to adoption, thereby compensating for potential biases in individual variable estimates. The combined use of methods strengthens result reliability by cross-verifying findings through distinct analytical lenses, reducing the risk of over-or underestimating effects due to measurement inconsistencies. Findings reveal that hedonic motivation, price value, habit, perceived trust, social influence, facilitating conditions, performance expectancy, and effort expectancy significantly drive adoption intention. Theoretically, this study extends UTAUT2 by revealing the role of biometric trust in contactless payments. Methodologically, it confirms the value of a mixed-methods approach. Practically, it offers insights for enhancing biometric payment adoption in emerging markets.