Research on Intention to Adopt Smart Wear: Based on Extended UTAUT Model

Abstract

The objective of this study is to investigate the intention to adopt smart wear, based on extended UTAUT model. We examined the effects of performance expectancy (PE), effort expectancy (EE), hedonic motivation (HE), social influence (SI), facilitating conditions (FC), and price value (PV) on the intended adoption of smart watch and smart shoes, respectively. In addition, moderating effects of gender, age, and innovation resistance were examined. An online survey was conducted, comprised of 2030 consumers who were aware of smart watch or smart shoes. In total, 393 responses were analyzed. About 50.4% were male, and 44.8% were in their 20's. An exploratory factor analysis generated five factors PE & HM, EE, SI, FC, and PV-which were employed as independent variables in -the multiple regression models. PE & HM, PV, and SI influenced on the intention to use both smart devices. FC showed the significant effect only on the intention to adopt the smart watch. In terms of gender differences, SI and PV were the important predictors of the intention to adopt the smart watch in the female group only. With respect to age difference, SI was very effective in explaining the intention of individuals in their 30's to adopt smart wear. Among the low innovation resistance group, SI was significant predictor,

Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia

“…PV was found to elicit a significant impact on BI. The findings of Nair et al (2015), Sung and Sung (2015), and Xu (2014) agreed with the outcome of this research, and it can be concluded that the cost and charges of mobile learning affect the intention of students in applying mobile learning system. Learners are prone to embrace the technology when it harvests greater benefits compared to the cost (Isa & Wong, 2015).…”

“…In contrast with the organisational use setting, individual consumers generally endure the monetary cost of using a technology (Yang, 2013). The findings of Nair, Ali, and Leong 2015, Sung and Sung (2015), and Xu 2014showed that BI is significantly influenced by PV. This leads to the next hypothesis:…”