Abstract
Users of wearable services are different in age, occupation, income, education, personality, values and lifestyle, which also determine their different consumption patterns. Therefore, for the trust of wearable services, the influencing factors or strength may not be the same for different users. This article starts with the resource and motivation dimensions of VALSTM model, and the clustering model and questionnaire scale for consumers of wearable services were constructed. And then the users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization. Through clustering analysis of 535 valid questionnaires, users are grouped into three types of consumers with different lifestyles, respectively named: trend-following users, fashion-leading users and economic-rational users. Finally, this paper analyzes and compares the trust subgroup models of three clusters, and draws some conclusions.
Highlights
Wearable devices are hardware, and a comprehensive service combining hardware and software
The users and potential users of wearable service are clustered by an improved clustering algorithm based on adaptive chaotic particle swarm optimization
Through the author’s previous research(Gu,2020; Gu,2017; Gu,2016) and a large number of literature studies(Sarah,2012; Huang,2014; Gefen,2000; Junglas,2006; McKnight,2002) by combining the specific factors influencing the initial trust of wearable services with the model of UTAUT2 theory, we explored the antecedent variables of wearable devices’ trust and constructed an overall integrated conceptual model of initial trust of wearable services
Summary
Wearable devices are hardware, and a comprehensive service combining hardware and software. Different consumers consider different factors before making a decision about whether to use it, so there is a great deal of uncertainty, but it is often accompanied by trust issues. For the trust of wearable services, the influencing factors or strength may not be the same for different users. The structural equation is used to conduct empirical analysis on the overall model and different subgroup models of wearable service consumers trust based on user clustering, so as to find out the key factors. Affecting wearable service trust, the particularity of wearable service consumer behavior and the difference of influencing factors of different subgroup clusters of trust, and analyze the reasons and propose countermeasures
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
