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

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.

Factors influencing consumer use of a sport-branded app: the technology acceptance model integrating app quality and perceived enjoyment

“…A series of meta-analyses for IS usage have identified the predictive roles of PEOU and PU in behavioral intentions (Chiu et al, 2021;Yousafzai et al, 2007aYousafzai et al, , 2007b. In the context of mobile apps, users' perceptions of PEOU and PU are two important antecedents across different types of apps, such as mobile payment (Bailey et al, 2017), transportation services (Wang et al, 2021), health monitoring (Cho et al, 2020;Rehman et al, 2021), and social network games (Park et al, 2014). In a similar vein, previous studies found that PEN is a critical antecedent of individuals' intention to use mobile apps with gamified features (Byun et al, 2018;Chiu and Cho, 2021a, b;Kondrateva et al, 2020).…”

Towards Perceived Playfulness and Adoption of Hearables in Smart Cities of China

“…Extensive efforts in the technology acceptance models (Davis, 1985(Davis, , 1989Davis et al, 1989), as well as the unified theory of acceptance and use of technology (Venkatesh et al 2003;Venkatesh, 2015;Venkatesh et al 2016;Abdou & Jasimuddin, 2020), have enriched knowledge on acceptance of technologies. For example, Rehman et al (2021) used the framework of the technology acceptance model, and a concept model is proposed that integrates health and technology attributes to confirm that smart wearable healthcare (SWH) devices have potential benefits for customer health. De Moya et al ( 2021) investigated the perceived risks and benefits related to self-tracking technologies (SST) and evaluated the impacts of social, technological, and health factors on STT adoption through the use of the extended valence framework.…”

Does Workplace Bullying Responsible for Low Job Performance? A Twofold SEM-ANN Approach

“…The ANN technique works like the human brain (Rehman et al, 2021), the ANN contains neurons, synopses, and axons and it can be trained to improve performance (Hew, Badaruddin, & Moorthy, 2017). The ANN links the input data to output data through arti cial neurons and their interrelationships are kept in the hidden layers to increase the prediction capacity (Leong et al, 2019).…”