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Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology.

ABSTRACT: Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers' intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and collected quantitative data from conveniently selected respondents through an online survey, which involved 2068 Telegram users in Indonesia. All data were subjected to the analysis of partial least square- structural equation modeling (PLS-SEM). The obtained results demonstrated the moderating effect of perceived product value on the relationship between intention to use m-Health application (m-health app) and actual usage of m-Health app and the mediating effects of intention to use m-Health app on the relationships of perceived critical mass, perceived usefulness, perceived convenience, perceived technology accuracy, and perceived privacy protection on actual usage of m-Health app. However, the intention to use m-Health app did not mediate the influence of health consciousness and health motivation on the actual usage of m-Health app. Overall, this study's findings on the significance of intention to use m-Health app and perceived product value based on the UTAUT framework serve as insightful guideline to expand the usage of m-Health app among consumers.

SUBMITTER: Yang M

PROVIDER: S-EPMC10764358 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology.

Yang Marvello M Al Mamun Abdullah A Gao Jingzu J Rahman Muhammad Khalilur MK Salameh Anas A AA Alam Syed Shah SS

Scientific reports 20240103 1

Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers' intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and ...[more]

PMID: 38172184

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Project description:BackgroundArtificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited.ObjectiveThis meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence.MethodsThe literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs.ResultsThe meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy.ConclusionsThis meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.

Project description:BACKGROUND:Technology-based physical activity suggests new opportunities for public health initiatives. Yet only 45% of technology interventions are theoretically based, and the acceptability mechanisms have been insufficiently studied. Acceptability and acceptance theories have provided interesting insights, particularly the unified theory of acceptance and use of technology 2 (UTAUT2). In several studies, the psychometric qualities of acceptability scales have not been well demonstrated. OBJECTIVE:The aim of this study was to adapt the UTAUT2 to the electronic health (eHealth) context and provide a preliminary validation of the eHealth acceptability scale in a French sample. METHODS:In line with the reference validation methodologies, we carried out the following stages of validating the scale with a total of 576 volunteers: translation and adaptation, dimensionality tests, reliability tests, and construct validity tests. We used confirmatory factor analysis to validate a 22-item instrument with 7 subscales: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit. RESULTS:The dimensionality tests showed that the bifactor confirmatory model presented the best fit indexes: χ2173=434.86 (P<.001), χ2/df=2.51, comparative fit index=.97, Tucker-Lewis index=.95, and root mean square error of approximation=.053 (90% CI .047-.059). The invariance tests of the eHealth acceptability factor structure by sex demonstrated no significant differences between models, except for the strict model. The partial strict model demonstrated no difference from the strong model. Cronbach alphas ranged from .77 to .95 for the 7 factors. We measured the internal reliability with a 4-week interval. The intraclass correlation coefficients for each subscale ranged from .62 to .88, and there were no significant differences in the t tests from time 1 to time 2. Assessments for convergent validity demonstrated that the eHealth acceptability constructs were significantly and positively related to behavioral intention, usage, and constructs from the technology acceptance model and the theory of planned behavior. CONCLUSIONS:The 22-item French-language eHealth acceptability scale, divided into 7 subscales, showed good psychometric qualities. This scale is thus a valid and reliable tool to assess the acceptability of eHealth technology in French-speaking samples and offers promising avenues in research, clinical practice, and marketing.

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Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology.

Publications

Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology.

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