Study on wearable device users’ willingness to continue using—ECM-IS based on the expansion model

Yansheng Zhao, Zhongjie Wang

Article ID: 1638
Vol 3, Issue 1, 2022

VIEWS - 302 (Abstract)

Abstract

Based on the Expectation Confirmation Model of Information System(ECM-IS), three personal characteristic factors of self-efficacy, privacy concerns, and innovation as well as two external environmental factors of subjective reference and switching costs were introduced to construct a model of factors affecting users’ continuance intention of wearable devices from the perspective of “technology-individual-environment”. 356 valid samples were collected through the questionnaire for empirical analysis. The results of the study show that self-efficacy, switching costs, and perceived usefulness in the ECM-IS model have a significant effect on users’ continuance intention at p<0.001 level while innovativeness and subjective references affect users’ continuance intention at p<0.05, but privacy concerns have no effect on continuance intention.


Keywords

wearable devices; continuance intention; expectation confirmation model of information system (ECM-IS)

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References

1. Yang H, Yu J, Zo H, et al. User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics 2016; 33(2): 256–269.

2. Gu Z, Xu F, Wei J. An empirical study on the influencing factors of initial trust of wearable business consumers. Management Review 2015; 27(7): 168–176.

3. Wu J, Zeng M, Liu F, et al. Research on wearable device user adoption behavior based on meta-analysis method. Journal of Information Resource Management 2017; 7(2): 5–13.

4. Bhattacherjee A. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly 2001; 25(3): 351–370.

5. Larsen T, Sreb A, Sreb Y. The role of task-technology fit as user’s motivation to continue information system use. Computers in Human Behavior 2009; 25: 778–784.

6. Cho J. The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics 2016; 87: 75–83.

7. Yin M, Li Q. Research on the willingness of mobile app to continue to use integrating ECT and IS success theory-Taking health app as an example. Journal of Dalian University of Technology (Social Science Edition) 2017; 38(1): 81–87.

8. Reddy S, Soror A. Why do you keep doing that? The biasing effects of mental states on it continued usage intentions. Computers in Human Behavior 2017; 33: 209–223.

9. Liu Q, Zuo M, Liu M. Empirical analysis on the continuous use of internet applications by the elderly based on expectation confirmation theory. Management Review 2012; 24(5): 89–101.

10. Hsu MH, Chiu CM. Predicting electronic service continuance with a decomposed theory of planned behavior. Behavior & Information Technology 2004; 23(5): 359–373.

11. Cao Z, Zhao X, Dai Q. Research on influencing factors of customer self-service technology. Nankai Management Review 2010; (3): 90–100.

12. Zhou J. User stickiness in the context of social commerce: Indirect influence and regulation of user interaction. Management Review 2015; 27(7): 127–136.

13. Dong T, Cheng N, Wu Y, et al. A study of the social net working website service in digital content industries: The Facebook case in Taiwan. Computers in Human Behavior 2014; 30: 708–714.

14. Chen H, Li W, Ke Y. Research on sustainable use of social media: Mediated by emotional response. Management Review 2016; 28(9): 61–71.

15. Lassar W, Manolis C, Lassar S. The relationship between consumer innovativeness, personal characteristics, and online banking adoption. International Journal of Bank Marketing 2005; 23(2): 176–199.

16. Lu J. Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research 2014; 24(2): 134–159.

17. Lin Z, Filieri R. Airline passengers continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge. Transportation Research Part E: Logistics and Transportation Review 2015; 81: 158–168.

18. Lee M. Explaining and predicting user’s continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education 2010; 54(2): 506–516.

19. Chen S, Yen D, Hwang M. Factors influencing the continuance intention to the usage of web 2.0: An empirical study. Computers in Human Behavior 2012; 28(3): 933–941.

20. Oyeniyi O, Abiodun A. Switching cost and customers loyalty in the mobile phone market: The Nigerian experience. Business Intelligence Journal 2010; 3(1): 111–121.

21. Deng A, Tao B, Ma Y. An empirical study on the influencing factors of online shopping customer loyalty. China Management Science 2014; 22(6): 94–102.

22. Son J, Kim S. Internet users’ information privacy-protective responses: A taxonomy and a nomological model. Mis Quarterly 2008; 32(3): 503–529.

23. Jones M. Mothersbaugh D, Beatty S. Switching barriers and repurchase intentions in services. Journal of Retailing 2000; 76(2): 259–274.


DOI: https://doi.org/10.54517/wt.v3i1.1638
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