A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices

Imdadullah Hidayat-ur Rehman, Arshad Ahmad, Fahim Akhter, Amer Aljarallah

Source Title: Journal of Global Information Management (JGIM)29(6)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799872627|DOI: 10.4018/JGIM.294123

MLA

Rehman, Imdadullah Hidayat-ur, et al. "A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices." JGIM vol.29, no.6 2021: pp.1-30. http://doi.org/10.4018/JGIM.294123

APA

Rehman, I. H., Ahmad, A., Akhter, F., & Aljarallah, A. (2021). A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices. Journal of Global Information Management (JGIM), 29(6), 1-30. http://doi.org/10.4018/JGIM.294123

Chicago

Rehman, Imdadullah Hidayat-ur, et al. "A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices," Journal of Global Information Management (JGIM) 29, no.6: 1-30. http://doi.org/10.4018/JGIM.294123

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Abstract

Advances in information technology have included the development of smart wearable healthcare (SWH) devices that have potential benefits for consumer health. The adoption of SWH devices is limited, however, compared with other established digital technologies. This study examines the determinants of consumers’ adoption of SWH devices. A conceptual model is proposed that incorporates health (health beliefs and health information accuracy), and technology (compatibility and functional congruence) attributes into the technology acceptance model framework. The proposed model was tested in two steps. Structural equation modelling (SEM) was performed with 473 usable responses to test the hypothesized relationships. The artificial neural network (ANN) approach was then applied to validate the outcomes of Step 1. The SEM analysis indicates that all the hypothesized relationships are supported. The ANN analysis further validates the outcomes of the SEM. The findings of this study and the dual-stage SEM-ANN methodology will have a strong impact on the existing literature regarding SWH devices.