Consumers’ receptivity to mHealth technologies: a hybrid PLS–ANN approach

Abstract

Purpose

Mobile health (mHealth) technologies, in particular, have been sought after and advocated as a means of dealing with the pandemic situation. Despite the obvious advantages of mHealth, which include monitoring and exchanging health information via mobile applications, mHealth adoption has yet to take off exponentially. Expanding on the unified theory of acceptance and use of technology (UTAUT) model, this study aims to better comprehend consumers’ receptivity to mHealth even after the pandemic has subsided.

Design/methodology/approach

Through purposive sampling, data were collected from a sample of 345 mobile phone users and analysed using partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) capture both linear and nonlinear relationships.

Findings

Effort expectancy, performance expectancy, social influence, pandemic fear and trustworthiness positively influenced mHealth adoption intention, with the model demonstrating high predictive power from both the PLSpredict and ANN assessments.

Research limitations/implications

The importance–performance map analysis (IPMA) results showed that social influence had great importance for mHealth uptake, but demonstrated low performance.

Practical implications

Referrals are an alternative that policymakers and mHealth service providers should think about to increase uptake. Overall, this study provides theoretical and practical insights that contribute to the advancement of digital healthcare, aligning with the pursuit of Sustainable Development Goal 3 (SDG 3) (good health and well-being).

Originality/value

This study has clarified both linear and nonlinear relationships among the factors influencing intentions to adopt mHealth. The findings from both PLS and ANN were juxtaposed, demonstrating consistent findings.

Citation

Ooi, S.K., Yeap, J.A.L., Lam, S.L. and Gim, G.C.W. (2024), "Consumers’ receptivity to mHealth technologies: a hybrid PLS–ANN approach", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-10-2023-2029