The Moderating Role of Privacy Concerns on Intention to Use Smart Wearable Technologies: An Integrated Model Combining UTAUT2 Theoretical Framework and Privacy Dimensions

References

• Adapa, A., Nah, F., Hall, R., Siau, K., & Smith, S. (2018). Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), 399–409. https://doi.org/10.1080/10447318.2017.1357902
   Web of Science ®Google Scholar
• Beh, P., Ganesan, Y., Iranmanesh, M., & Foroughi, B. (2021). Using smartwatches for fitness and health monitoring: The UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology, 40(3), 282–299. https://doi.org/10.1080/0144929X.2019.1685597
   Web of Science ®Google Scholar
• Brown, S., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. https://doi.org/10.2307/25148690
   Web of Science ®Google Scholar
• Canhoto, A., & Arp, S. (2017). Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management, 33(1-2), 32–60. https://doi.org/10.1080/0267257X.2016.1234505
   Web of Science ®Google Scholar
• Cecchinato, M., Cox, A., & Bird, J. (2015). Smartwatches: The good, the bad and the ugly? [Paper presentation]. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2133–2138).
   Google Scholar
• Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777–786. https://doi.org/10.1016/j.chb.2016.06.007
   Web of Science ®Google Scholar
• Chuah, S., Rauschnabel, P., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276–284. https://doi.org/10.1016/j.chb.2016.07.047
   Web of Science ®Google Scholar
• Debnath, A., Kobra, K., Rawshan, P., Paramita, M., & Islam, M. (2018). An explication of acceptability of wearable devices in context of Bangladesh: A user study. IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). https://doi.org/10.1109/ficloud.2018.00027
   Google Scholar
• Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behaviour & Information Technology, 37(2), 145–158. https://doi.org/10.1080/0144929X.2018.1424246
   Web of Science ®Google Scholar
• Dehghani, M., & Kim, K. J. (2019). The effects of design, size, and uniqueness of smartwatches: Perspectives from current versus potential users. Behaviour & Information Technology, 38(11), 1143–1153. https://doi.org/10.1080/0144929X.2019.1571111
   Web of Science ®Google Scholar
• Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2019). Consumer adoption of smartphone fitness apps: An extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363–388. https://doi.org/10.1108/JIBR-05-2018-0158
   Web of Science ®Google Scholar
• Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
   Web of Science ®Google Scholar
• Dutot, V., Bhatiasevi, V., & Bellallahom, N. (2019). Applying the technology acceptance model in a three-country study of smartwatch adoption. The Journal of High Technology Management Research, 30(1), 1–14. https://doi.org/10.1016/j.hitech.2019.02.001
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800313
   Web of Science ®Google Scholar
• Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704–1723. https://doi.org/10.1108/IMDS-03-2015-0087
   Web of Science ®Google Scholar
• Garland, R. (1991). The mid-point on a rating scale: Is it desirable. Marketing Bulletin, 2(1), 66–70. http://marketing-bulletin.massey.ac.nz/.
   Google Scholar
• Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. http://doi.org/10.1093/biomet/61.1.101
   Web of Science ®Google Scholar
• Giovanis, A., Assimakopoulos, C., & Sarmaniotis, C. (2018). Adoption of mobile self-service retail banking technologies. International Journal of Retail & Distribution Management, 47(9), 894–914. https://doi.org/10.1108/ijrdm-05-2018-0089
   Google Scholar
• Grand View Research. (2020). Wearable technology market size, share & trends analysis report by product (Wrist-wear, eye-wear & head-wear, foot-wear, neck-wear, body-wear), by application, by region, and segment forecasts, 2020–2027 [978-1-68038-165-8]. https://www.grandviewresearch.com/industry-analysis/wearable-technology-market
   Google Scholar
• Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2021). Social comparison and continuance intention of smart fitness wearables: An extended expectation confirmation theory perspective. Behaviour & Information Technology, 40(13), 1341–1354. https://doi.org/10.1080/0144929X.2020.1748715
   Web of Science ®Google Scholar
• Gupta, S., Yun, H., Xu, H., & Kim, H. W. (2017). An exploratory study on mobile banking adoption in Indian metropolitan and urban areas: A scenario-based experiment. Information Technology for Development, 23(1), 127–152. https://doi.org/10.1080/02681102.2016.1233855
   Web of Science ®Google Scholar
• Hair, J. F., Anderson, R., Tatham, R., & Black, W. (2006). Multivariate data analysis (6th ed.). Prentice Hall.
   Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017a). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x
   Web of Science ®Google Scholar
• Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017b). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.
   Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://link.springer.com/content/pdf/10.1007%2Fs11747-014-0403-8.pdf. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121. https://doi.org/10.1177/109442819800100106
   Web of Science ®Google Scholar
• Hong, J.-C., Lin, P.-H., & Hsieh, P.-C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264–272. https://doi.org/10.1016/j.chb.2016.11.001
   Web of Science ®Google Scholar
• Hsiao, K.-L. (2017). What drives smartwatch adoption intention? Comparing Apple and non-Apple watches. Library Hi Tech, 35(1), 186–206. https://doi.org/10.1108/LHT-09-2016-0105
   Web of Science ®Google Scholar
• Industry Research. (2020). Global wearable technology market 2020-2024 [TNV-15166028]. https://www.industryresearch.co/global-wearable-technology-market-15166028
   Google Scholar
• Karahanoğlu, A., & Erbuğ, C. (2011). Perceived qualities of smart wearables [Paper presentation]. Proceedings of the 2011 Conference on Designing Pleasurable Products and Interfaces – DPPI ‘11. https://doi.org/10.1145/2347504.2347533
   Google Scholar
• Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541. https://doi.org/10.1108/IntR-05-2014-0126
   Web of Science ®Google Scholar
• Kranthi, A. K., & Asraar Ahmed, K. A. (2018). Determinants of smartwatch adoption among IT professionals – An extended UTAUT2 model for smartwatch enterprise. International Journal of Enterprise Network Management, 9(3/4), 294. https://doi.org/10.1504/IJENM.2018.10015843
   Google Scholar
• Krey, N., Chuah, S. H.-W., Ramayah, T., & Rauschnabel, P. A. (2019). How functional and emotional ads drive smartwatch adoption. Internet Research, 29(3), 578–602. https://doi.org/10.1108/IntR-12-2017-0534
   Web of Science ®Google Scholar
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
   Web of Science ®Google Scholar
• Lin, F., & Windasari, N. A. (2019). Continued use of wearables for wellbeing with a cultural probe. The Service Industries Journal, 39(15-16), 1140–1166. https://doi.org/10.1080/02642069.2018.1504924
   Web of Science ®Google Scholar
• Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users’ Information Privacy Concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032
   Web of Science ®Google Scholar
• Mani, Z., & Chouk, I. (2017). Drivers of consumers’ resistance to smart products. Journal of Marketing Management, 33(1-2), 76–97. https://doi.org/10.1080/0267257X.2016.1245212
   Web of Science ®Google Scholar
• Mani, Z., & Chouk, I. (2019). Impact of privacy concerns on resistance to smart services: Does the ‘Big Brother effect’ matter? Journal of Marketing Management, 35(15-16), 1460–1479. https://doi.org/10.1080/0267257X.2019.1667856
   Web of Science ®Google Scholar
• Mishra, A., Baker-Eveleth, L., Gala, P., & Stachofsky, J. (2021). Factors influencing actual usage of fitness tracking devices: Empirical evidence from the UTAUT model. Health Marketing Quarterly, 1–20. https://doi.org/10.1080/07359683.2021.1994170
   Google Scholar
• Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879.
   PubMed Web of Science ®Google Scholar
• Raptis, D., Papachristos, E., Bruun, A., & Kjeldskov, J. (2022). Why did you pick that? A study on smartwatch design qualities and people’s preferences. Behaviour & Information Technology, 41(4), 827–844. https://doi.org/10.1080/0144929X.2020.1836259
   Web of Science ®Google Scholar
• Reyes-Mercado, P. (2018). Adoption of fitness wearables: Insights from partial least squares and qualitative comparative analysis. Journal of Systems and Information Technology, 20(1), 103–127. https://doi.org/10.1108/JSIT-04-2017-0025
   Google Scholar
• Ringle, C., Sarstedt, M., & Straub, D. (2012). Editor’s comments: A critical look at the use of PLS-SEM in “MIS Quarterly”. MIS Quarterly, 36(1), Iii–Xiv. https://doi.org/10.2307/41410402
   Web of Science ®Google Scholar
• Ringle, C., Wende, S., & Becker, J. (2015). SmartPLS 3 (Version 3.2. 3). SmartPLS GmbH.
   Google Scholar
• Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
   Web of Science ®Google Scholar
• Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989. https://doi.org/10.2307/41409970
   Web of Science ®Google Scholar
• Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167. https://doi.org/10.2307/249477
   Web of Science ®Google Scholar
• Solove, D. J. (2006). A taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477–560. https://doi.org/10.2307/40041279
   Web of Science ®Google Scholar
• Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
   Web of Science ®Google Scholar
• Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2019). Acceptance and use predictors of fitness wearable technology and intention to recommend. Industrial Management & Data Systems, 119(1), 170–188. https://doi.org/10.1108/IMDS-01-2018-0009
   Web of Science ®Google Scholar
• Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wu, L.-H., Wu, L.-C., & Chang, S.-C. (2016). Exploring consumers’ intention to accept smartwatch. Computers in Human Behavior, 64, 383–392. https://doi.org/10.1016/j.chb.2016.07.005
   Web of Science ®Google Scholar
• Xu, H., Gupta, S., Rosson, M. B., & Carroll, J. M. (2012). Measuring mobile users’ concerns for information privacy. In: Thirty Third International Conference on Information Systems, Orlando.
   Google Scholar
• Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256–269. https://doi.org/10.1016/j.tele.2015.08.007
   Web of Science ®Google Scholar
• Yuan, S., Ma, W., Kanthawala, S., & Peng, W. (2015). Keep using my health apps: Discover users’ perception of health and fitness apps with the UTAUT2 model. Telemedicine Journal and e-Health, 21(9), 735–741. https://doi.org/10.1089/tmj.2014.0148
   PubMed Web of Science ®Google Scholar
• Zimmer, M., Kumar, P., Vitak, J., Liao, Y., & Chamberlain Kritikos, K. (2020). ‘There’s nothing really they can do with this information’: Unpacking how users manage privacy boundaries for personal fitness information. Information, Communication & Society, 23(7), 1020–1037. https://doi.org/10.1080/1369118X.2018.1543442
   Web of Science ®Google Scholar