The mediating roles of trust on intentions toward mobile wallet adoption in social health insurance: a deep learning–based SEM-ANN analysis

Abstract

Purpose

This study aims to examine the factors influencing Filipinos’ intention to adopt mobile wallets to pay social health insurance premiums.

Design/methodology/approach

The authors used an integrative model framework using the key indicators from the lenses of the technology acceptance model, the unified theory of acceptance and usage of technology model 2 and the theory of planned behavior with trust serving as a mediator. The sample size was calculated using an inverse square root ratio composed of 624 survey participants purposively identified across selected cities in the Philippines. The formulated hypotheses were examined using partial least squares structural equation modeling and deep learning–based artificial neural networks.

Findings

The results substantiate this study’s integrative model explaining the positive influence and relative importance of perceived usefulness, habit and subjective norms in developing trust in mobile wallet applications. Moreover, health insurance literacy, subjective norms and trust positively and significantly drive individuals’ intentions to adopt mobile wallets as a payment platform for social health insurance premiums. The mediation analysis also exemplified that trust positively mediates the influences of technology acceptance factors such as perceived usefulness, habit and subjective norms in the intention of individuals to adopt mobile wallet applications in social health insurance payment of premiums.

Originality/value

This study is a pioneering study in the Philippine context that used an integrative model to predict and explain the relative importance of predictors of Filipino intentions to adopt mobile wallets as a payment platform for social health insurance premiums.

Citation

Catacutan, Z.M. and Mabesa, J.J.O. (2024), "The mediating roles of trust on intentions toward mobile wallet adoption in social health insurance: a deep learning–based SEM-ANN analysis", Journal of Science and Technology Policy Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JSTPM-05-2024-0175