Understanding the determinants of online pharmacy adoption: a two-staged SEM-neural network analysis approach

Abstract

Purpose

This study aims to understand the determinants of online pharmacy or epharmacy adoption among young consumers in Bangladesh using an extended unified theory of acceptance and use of technology (UTAUT) model.

Design/methodology/approach

A structured Google Docs questionnaire was sent out to 420 respondents using messenger service; 285 useable responses were finally extracted. Data were empirically validated using the two-staged structural equation model (SEM)-neural network analysis approach.

Findings

The robustness of the classical UTAUT model remains intact in the context of online pharmacy adoption. Among the integrated variables, while perceived trust and health literacy were found significant, perceived risk and personal innovativeness were found insignificant in determining consumers’ intention to adopt online pharmacy. The neural network analysis provided further verification of these findings derived from the SEM.

Practical implications

The findings of this study would facilitate in devising better strategies for entering or expanding online pharmacy business in developing countries such as Bangladesh.

Originality/value

The originality of the current study relates to the two-fold contributions of this study. First, while this study extended the classical UTAUT model by incorporating perceived risk, perceived trust, personal innovativeness and health literacy, the inclusion of the following two variables is fresh within the extant online pharmacy literature. Second, by using a two-staged SEM-neural network analysis approach, this study advances the past studies on e-commerce adoption in pharmaceutical settings and provides a general understanding of the customers of developing countries.

Citation

Sabbir, M.M., Islam, M. and Das, S. (2021), "Understanding the determinants of online pharmacy adoption: a two-staged SEM-neural network analysis approach", Journal of Science and Technology Policy Management, Vol. 12 No. 4, pp. 666-687. https://doi.org/10.1108/JSTPM-07-2020-0108