Predicting the intention and adoption of wearable payment devices using hybrid SEM-neural network analysis

Abstract

AbstractThis study aims to examine the mediating effect of the intention to use wearable payment devices (WPD) between perceived ease of use (PE), perceived usefulness (PU), social influence (SI), perceived trust (TR), and lifestyle compatibility (CM) on the adoption of WPD. Examination was made on the moderating effect of age and gender to improve the understanding of the adoption of WPD as a new payment system. Empirical data was collected through an online survey from 1094 respondents in Malaysia. Furthermore, this study employed dual-stage data analysis through partial least squares structural equation modelling (PLS-SEM) to test the causal and moderating effects, including artificial neural network (ANN) to examine the predictive power of the selected model. As a result, it was found that PE, PU, TR, and CM had a significant positive influence on the intention to use WPD. Furthermore, facilitating conditions and the intention to use WPD exhibited strong positive impacts on the adoption of WPD among Malaysian youth. The intention to use WPD positively and significantly mediated all predictors of adoption of WPD. Following that, ANN analysis confirmed high prediction accuracy of the data fitness. Overall, the findings for ANN highlighted the importance of PE, CM, and TR on the intention to adopt WPD and the impact of facilitating conditions on the adoption of WPD among Malaysian youth. Theoretically, the study extended UTAUT with two additional determinants (e.g., perceived trust and lifestyle compatibility), which were found to have significant influences on the intention to use WPD. The study results would be able to help payment service providers and the smart wearable device industry offer an innovative spectrum of products and present effective marketing tactics to encourage the prospective consumers of Wearable Payment Devices in Malaysia.