Adoption of fitness wearables

Abstract

Purpose This paper aims to analyse the adoption of fitness wearables by using the unified theory of acceptance and use of technology (UTAUT). The study analyses the relative weights and causal combinations of antecedent variables on use and intention to use fitness wearables. Design/methodology/approach The study design involves two stages: first, from the perspective of variable-oriented analysis, a structural equation model is tested using partial least squares (PLS) technique on a sample of 176 adopters and a second sample of 187 non-adopters. Second, from the perspective of case-oriented analysis, a fuzzy set qualitative comparative analysis (fsQCA) identifies causal combinations of variables that lead to use of wearables by adopters and intention to use by non-adopters. Findings PLS results show that performance expectancy and effort expectancy have high net effects on use and intention to use for adopters. FsQCA analysis shows that current users follow a streamlined path to adoption. High beliefs on performance expectancy and effort expectancy are the main influences of intention to use a fitness wearable for non-adopters. In contrast to adopters, non-adopters may follow a number of paths to intention to use through performance expectancy, effort expectancy or facilitating conditions. This insight was apparent only after analysing the data sets by using fsQCA. Research limitations/implications For sake of parsimony, this paper tested UTAUT model instead of the more complex unified theory of acceptance and use of technology 2. Practical implications Marketers in the fitness category can enhance use and intention to use by utilising not one but a combination of causal factors such as performance expectancy, effort expectancy and facilitating conditions. Wide societal deployment of wearables depends on performance and expectations. Social implications The widespread use of mobile devices depends on performance expectancy and effort expectancy. To transit to a real knowledge economy, co-creation should occur at early stages of product development so that these expectations are shared and better products be developed. Originality/value This paper offers a nuanced understanding of fitness wearable adoption by analysing adopters and non-adopters through variable- and case-oriented techniques. It complements the one-linear-path perspective with a number of alternative causal combinations of variables that lead to use and intention to use fitness wearables. While the causal path for adopters is unique, there are a number of causal combinations of antecedents that lead to high intention to use in potential adopters.

How robust is the UTAUT theory in explaining the usage intention of e-government services in an unstable security context?: A study in Iraq

“…This finding is consistent with previous studies that have provided empirical evidence confirming the significant, positive effect of EE on UB (Wu and Tao, 2007). Previous literature confirms that EE is an important factor in the adoption of IT in different countries (Abdul-Rahman et al, 2011;AlAwadhi and Morris, 2008;Weerakkody, 2009, 2010;Al-Sobhi et al, 2011;Foon and Fah, 2011;Reyes-Mercado, 2018;Venkatesh et al, 2003Venkatesh et al, , 2011. Importantly, there is a "need for skilled human resources for e-government success" [Khan et al, (2012), p.451].…”

“…They have also tested 32 of the constructs taken from eight of the theoretical models in order to identify the most influential construct to explain the use of IT (Venkatesh et al, 2003). It was also confirmed by UTAUT theory and other recent studies (Alshehri et al, 2012;Reyes-Mercado, 2018) that the intention to use IT system is a major direct determinant of system use (Wu and Tao, 2007).…”

Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

“…The easier the individuals believe the fitness apps are to use, the higher is their intention to use them. Prior studies have revealed a positive relationship between effort expectancy and behavioral intention to use fitness apps [15,19] and fitness wearables [18,34]. As people should be interested in intuitive and easy app usage, we expect the following: Hypothesis 2: effort expectancy is positively related to behavioral intentions of individuals to use fitness apps.…”

“…Performance expectancy is defined as the "degree to which using a technology will provide benefits to consumers in performing certain activities" [9]. It was the strongest predictor of behavioral intentions in the original UTAUT study [8] and is a pivotal determinant of new technology acceptance in health care [32,33] and fitness wearables [21,34]. In the context of this study, performance expectancy refers to the degree to which a user believes that using a particular fitness app would help improve their fitness.…”

Research on Tuning Fork Dimension Optimization and Density Calculation Model Based on Viscosity Compensation for Tuning Fork Density Sensor

“…Considering that there are errors in the measurement process of tuning fork frequency, which makes the measurement result nonrandom deviation, the partial least square model is selected for fitting [27]. e Figure 13 describes the frequency-density curve and its standard residual based on the least square model fitting.…”