Acceptance and usage of mHealth technologies amid COVID-19 pandemic in a developing country: the UTAUT combined with situational constraint and health consciousness

Abstract

Purpose The COVID-19 pandemic has created a devastating effect on public health. As “social distance” is a highly effective preventive measure of this pandemic, close contact between a patient and physician is strongly discouraged where possible. Consequently, mobile health (mHealth) technology is taking its momentum to fulfil this gap. The purpose of this study is, therefore, to empirically investigate the moderating as well as the direct role of situational constraint and health consciousness in the unified theory of acceptance and use of technology (UTAUT) constructs to understand the adoption and use behavior of mHealth care services amid pandemic. Design/methodology/approach Data were collected from existing mHealth users using an online survey questionnaire in Bangladesh. SmartPLS 3.0 and SPSS 23.0 were used for partial least squares-structural equation modeling. Findings Situational constraint and health consciousness both have strong direct positive effects on both behavioral intention (in all models) and use behavior (in Models 2 and 3). Further, this study revealed that effort expectancy remains insignificant in both direct and interaction effects whereas social influence becomes insignificant in interaction effects from direct significant effect (Models 1 and 2). Besides, the study reported that the relationship between behavioral intention and use behavior is moderated by situational constraint. Originality/value To the best of the authors’ knowledge, this study is the first in terms of mHealth empirical investigation considering the current pandemic situation. The incorporation of the situational constraint and health consciousness into the UTAUT model provides a holistic framework to understand the influence of the adoption and use behavior of mHealth amid pandemic.

The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy

“…Other studies, however, have corroborated our findings of the non-significant impact of performance expectancy and effort expectancy on the intention to adopt (51, 92). In addition, other previous findings have also illustrated that performance (93)(94)(95) and effort expectancy (96)(97)(98)(99) does not determine the intention to use. The reasons accounting for the nonsignificant performance expectancy and effort expectancy on the behavioral intention to adopt mobile health services could be due to first, the advanced technology savvy of citizens along with its long usage and hence they have become skilled at the use of technology related applications.…”

Demand for Mobile Health in Developing Countries During COVID-19: Vietnamese’s Perspectives from Different Age Groups and Health Conditions

“…25 Moreover, another study also identified a strong direct positive effect of both situational constraint and health consciousness on the behavioral intention to use mHealth technologies. 26 Second, there have been several papers showing empirical evidence to prove a rising demand for mHealth apps due to the pandemic situation. In the United States, patients with chronic health conditions such as cardiometabolic diseases, respiratory conditions, immune-related conditions, mental health, and obesity are more likely to use COVID-19 mHealth applications.…”

Re-Conceptualizing the Drivers Toward mHealth Adoption in a Least Developing Country: A Qualitative Exploration

“…A cross-cultural (Bangladesh and China) study on mHealth investigated the relative importance of several factors influencing the intention and actual use of mHealth (Alam, Hu, Hoque, et al, 2020). However, while the COVID-19 outbreak was in full swing, Barua and Barua (2021) investigated the acceptance and usage behavior of mHealth care services. Beyond the behavioral study, Miah et al (2017) and Rahman et al (2021) have designed the mHealth prototype and evaluated it using a mixed-method approach.…”