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Sustaining the Well-Being of Wearable Technology Users: Leveraging SEM-Based IPMA and VIKOR Analyses to Gain Deeper Insights

Author

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  • Huan-Ming Chuang

    (Department of Information Management, National Yunlin University of Science and Technology, 123, University Rd., Section 3, Douliou 64002, Yunlin, Taiwan
    These authors contributed equally to this work.)

  • Chien-I Chen

    (Department of Information Management, National Yunlin University of Science and Technology, 123, University Rd., Section 3, Douliou 64002, Yunlin, Taiwan
    These authors contributed equally to this work.)

Abstract

Wearable technology is a self-contained computer system that can record muscular activity data. Wearable technologies are rapidly evolving that have the potential to enhance the well-being of healthier lives. However, wearable technologies are finding slow adoption rates relative to mainstream technologies such as smartphones. Consequently, both designers and manufacturers are increasingly interested in key decision factors that influence the acceptance of these technologies. As discussions relating to wearable technologies are often approached from different perspectives, a general framework featuring not only a synthesis of general acceptance issues but also with consideration of contingent factors would be a useful research undertaking. Furthermore, wearable technology acceptance studies are insufficient to supplement practical implementation and promotion issues. In this regard, methods for further analysis of results from structural equation modeling (SEM), such as importance-performance map analysis (IPMA) and VIKOR for multi-criteria optimization and compromise solution, can be used to derive greater insights. The primary research findings are extensively discussed, and practical promotion strategies for wearable technologies for health care are suggested.

Suggested Citation

  • Huan-Ming Chuang & Chien-I Chen, 2022. "Sustaining the Well-Being of Wearable Technology Users: Leveraging SEM-Based IPMA and VIKOR Analyses to Gain Deeper Insights," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7799-:d:848645
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    References listed on IDEAS

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    1. Mahdokht Kalantari & Philipp Rauschnabel, 2018. "Exploring the Early Adopters of Augmented Reality Smart Glasses: The Case of Microsoft HoloLens," Progress in IS, in: Timothy Jung & M. Claudia tom Dieck (ed.), Augmented Reality and Virtual Reality, pages 229-245, Springer.
    2. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    5. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2007. "Extended VIKOR method in comparison with outranking methods," European Journal of Operational Research, Elsevier, vol. 178(2), pages 514-529, April.
    6. Marko Sarstedt & Erik Mooi, 2019. "A Concise Guide to Market Research," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-56707-4, October.
    7. Julian K. Ayeh & Norman Au & Rob Law, 2013. "Towards an Understanding of Online Travellers’ Acceptance of Consumer-Generated Media for Travel Planning: Integrating Technology Acceptance and Source Credibility Factors," Springer Books, in: Lorenzo Cantoni & Zheng (Phil) Xiang (ed.), Information and Communication Technologies in Tourism 2013, edition 127, pages 254-267, Springer.
    8. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
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    2. Zhigang Wang & Dan Fang & Xintao Liu & Lei Zhang & Hongyan Duan & Chao Wang & Kai Guo, 2023. "Consumer Acceptance of Sports Wearables: The Role of Products Attributes," SAGE Open, , vol. 13(3), pages 21582440231, July.

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