Modeling the intention and usage of medicine vending machine: Using partial least squares-structural equation modelling and necessary condition analysis

Abstract

Objective This study aimed to investigate the factors influencing the intention to use and actual usage of medicine vending machines (MVMs) in China and to close the existing literature gap by examining the relationship between perceived convenience (PC), perceived trust, performance expectancy, effort expectancy, and social influence, on the intention to use MVM in a comprehensive manner. The impact of facilitating conditions on MVM adoption was also examined. Finally, customer age was tested as a moderator. Methods This was a cross-sectional study that used data collected through a self-administered questionnaire. A combination of partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) technique was used to analyze and discuss the 308 valid questionnaires, test the hypotheses, and conduct an in-depth analysis. Results The results showed that PC, perceived trust, and performance expectancy were significantly related to the intention to use MVM. Effort expectancy was a non-significant predictor of intention to use MVM. Social influence was a significant negative predictor of the intention to use MVM. More importantly, performance expectancy was found to be a necessary factor for MVM intention, providing new marketing ideas for MVM owners. Age had a significant moderating effect on the facilitating conditions and intention to use vending machines. The relatively young population is more conscious of the facilitating conditions. Conclusions The findings of this study are of considerable importance as a guide for the main user group of vending machines. The combined analysis and discussion of PLS-SEM and NCA provide a sound theoretical basis for the practical implications of this study. In the future, we will attempt to use this technique in other areas of study. In terms of theoretical implications, this study provides technical references for future research.