Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61423
Title: Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study
Contributor(s): Talukder, Md Shamim (author); Chiong, Raymond  (author)orcid ; Bao, Yukun (author); Hayat Malik, Babur (author)
Publication Date: 2019
DOI: 10.1108/IMDS-01-2018-0009
Handle Link: https://hdl.handle.net/1959.11/61423
Abstract: 

Purpose – The purpose of this paper is to identify the key facilitators and inhibitors of fitness wearable technology (FWT) adoption and the intention to recommend this technology.

Design/methodology/approach – An innovative and integrated research model was developed by combining constructs from two well-established theoretical models, the extended unified theory of acceptance and use of technology (UTAUT2) and diffusion of innovation (DOI). The proposed research model was empirically validated using data collected from 392 respondents in China. The data was analyzed using the partial least squares method, a statistical analysis technique based on structural equation modeling.

Findings – The results indicate that performance expectancy, effort expectancy, social influence, habit, compatibility and innovativeness have significant direct and indirect effects on FWT adoption and the intention to recommend it. The significance of people's intention to recommend FWT to others in social networking sites (e.g. Facebook, Weibo, and WeChat) is also confirmed.

Practical implications – The findings may facilitate the design and implementation of FWT products, applications and functionalities that can achieve high consumer acceptance and positive recommendations in social networks.

Originality/value – This study is among the first to investigate FWT adoption from behavioral, social and environmental perspectives. It also highlights the importance of social marketing campaigns and suggests directions of future wearable technology adoption research.

Publication Type: Journal Article
Source of Publication: Industrial Management and Data Systems, 119(1), p. 170-188
Publisher: Emerald Publishing Limited
Place of Publication: United Kingdom
ISSN: 1758-5783
0263-5577
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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