A meta-analysis of a comprehensive m-health technology acceptance

Calegari, Luiz Philipi; R.D., Barkokebas; Fettermann, Diego de Castro

doi:10.1108/ijlss-01-2023-0012

Cited by 1 publication

(1 citation statement)

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“…Indeed, a great number of studies have made efforts to explain and promote the user acceptance of m-health within the framework of varied theories [2,3,5,6,[11][12][13][14][15][16][17][18][19][20][21], including some primitive social psychological theories (i.e., the Theory of Reasoned Action (TRA) [12], Theory of Planned Behavior (TPB) [11], and the Technology Acceptance Model (TAM) [3]), The Unified Theory of Acceptance and Use of Technology (UTAUT) [22], and their derivative models [23]. Among these, TAM and its extensions [19,24,25] are the most widely used due to their parsimony and effectiveness in explaining acceptance behavior.…”

Section: Introductionmentioning

confidence: 99%

Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task–Technology Fit, and the Technology Acceptance Model

et al. 2023

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Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers’ acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task–Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task–technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task–technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

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