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Research Report

Understanding Determinants of End-User’s Continuance Intention on Fitness Wearable Technology

ORCID Icon &
Pages 537-557 | Received 03 Jun 2022, Accepted 08 Sep 2022, Published online: 22 Sep 2022

References

  • Agarwal, B. L. (2006). Basic statistics. New Age International. https://books.google.co.in/books?id=qT2srNLLxB0C
  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. INFORMS.
  • Anderson, N., Lankshear, C., Timms, C., & Courtney, L. (2008). ‘Because it’s boring, irrelevant and I don’t like computers’: Why high school girls avoid professionally-oriented ICT subjects. Computers & Education, 50(4), 1304–1318. https://doi.org/10.1016/j.compedu.2006.12.003
  • Ariff, M. S. M., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The effects of computer self-efficacy and technology acceptance model on behavioral intention in internet banking systems. Procedia - Social and Behavioral Sciences, 57, 448–452. https://doi.org/10.1016/j.sbspro.2012.09.1210
  • Attig, C., & Franke, T. (2019). I track, therefore I walk – Exploring the motivational costs of wearing activity trackers in actual users. International Journal of Human-Computer Studies, 127, 211–224. https://doi.org/10.1016/j.ijhcs.2018.04.007
  • Baker, B. J., Zhou, X., Pizzo, A. D., Du, J., & Funk, D. C. (2017). Collaborative self-study: Lessons from a study of wearable fitness technology and physical activity. Sport Management Review, 20(1), 114–127. https://doi.org/10.1016/j.smr.2016.10.008
  • Beh, P. K., 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
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Booth, F. W., Roberts, C. K., & Laye, M. J. (2012). Lack of exercise is a major cause of chronic diseases. Comprehensive Physiology, 2(2), 1143–1211. https://doi.org/10.1002/cphy.c110025
  • Canhoto, A. I., & 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
  • Cho, J., Park, D., & Lee, H. E. (2014). Cognitive factors of using health apps: Systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. Journal of Medical Internet Research, 16(5), e125. https://doi.org/10.2196/jmir.3283
  • Chuah, S. H.-W. (2019). You inspire me and make my life better: Investigating a multiple sequential mediation model of smartwatch continuance intention. Telematics and Informatics, 43, 101245. https://doi.org/10.1016/j.tele.2019.101245
  • Chuah, S. H.-W., Rauschnabel, P. A., 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
  • Cotte, J., & Wood, S. L. (2004). Families and innovative consumer behavior: A triadic analysis of sibling and parental influence. Journal of Consumer Research, 31(1), 78–86. https://doi.org/10.1086/383425
  • Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218. https://doi.org/10.1016/S0022-4359(00)00028-2
  • Currys. (2019). Fitness tracker or smartwatch – What’s the difference? https://techtalk.currys.co.uk/gadgets/fitness-smart-watches/fitness-tracker-or-smartwatch-what-s-the-difference/
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  • DeMarco, J. (2022). Nearly 70% of Americans would wear a fitness tracker/Smartwatch for discounted health insurance. Value Penguin. Retrieved July 20, 2022, from https://www.valuepenguin.com/fitness-tracker-smartwatch-health-survey
  • Diviani, N., van den Putte, B., Giani, S., & van Weert, J. C. (2015). Low health literacy and evaluation of online health information: A systematic review of the literature. Journal of Medical Internet Research, 17(5), e112. https://doi.org/10.2196/jmir.4018
  • Dobre, C., & Milovan-Ciuta, A.-M. (2015). Personality influences on online stores customers behavior. Ecoforum Journal, 4, 69–76.
  • Eng, T. R. (2001). The eHealth landscape: A terrain map of emerging information and communication technologies in health and health care. Robert Wood Johnson Foundation.
  • Finstad, K. (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Study, 5(3), 104–110.
  • Fritz, T., Huang, E. M., Murphy, G., & Zimmermann, T. (2014). Persuasive technology in the real world: A study of long-term use of activity sensing devices for fitness [Paper presentation]. The SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2556288.2557383
  • Futuresource Consulting. (2022). Futuresource wearables. https://www.futuresource-consulting.com/reports/futuresource-wearables/
  • 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
  • Gatzoulis, L., & Iakovidis, I. (2007). Wearable and portable eHealth systems. IEEE Engineering in Medicine and Biology Magazine, 26(5), 51–56. https://doi.org/10.1109/EMB.2007.901787
  • Gefen, D., & Straub, D. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400. https://doi.org/10.2307/249720
  • Glaros, C., & Fotiadis, D. I. (2005). Wearable devices in healthcare. In B. G. Silverman, A. Jain, A. Ichalkaranje, & L. C. Jain (Eds.), Intelligent paradigms for healthcare enterprises (pp. 237–264). Springer. https://doi.org/10.1007/11311966_8
  • Gliem, J. A., & Gliem, R. R. (2003, October 8–10). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for likert-type scales. Midwest Research to Practice Conference in Adult, Continuing, and Community Education, The Ohio State University. https://scholarworks.iupui.edu/bitstream/handle/1805/344/gliem+&+gliem.pdf?sequence=1
  • Goldsmith, R. E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209–221. https://doi.org/10.1007/BF02726497
  • Grand View Research. (2021). Fitness tracker market size, share & trends analysis report by type (smartwatches, smart bands), by application (glucose monitoring, sports), by distribution channel (online, offline), and segment forecasts, 2021 - 2028. Retrieved December 16, 2021, from https://www.grandviewresearch.com/industry-analysis/fitness-tracker-market
  • 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.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Prentice-Hall.
  • Hair, J. F., Black, W. C., Balin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Maxwell Macmillan International Editions.
  • Harman, H. H. (1960). Modern factor analysis. University of Chicago Press.
  • Hattie, J. (1985). Methodology review: Assessing unidimensionality of tests and ltenls. Applied Psychological Measurement, 9(2), 139–164. https://doi.org/10.1177/014662168500900204
  • He, Z. L., Kim, S. H., & Gong, D. H. (2017). The influence of consumer and product characteristics on intention to repurchase of smart band: Focus on Chinese consumers. International Journal of Asia Digital Art and Design Association, 21(1), 13–18. https://doi.org/10.20668/adada.21.1_13
  • Henriksen, A., Mikalsen, M. H., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018). Using fitness trackers and smartwatches to measure physical activity in research: Analysis of consumer wrist-worn wearables. Journal of Medical Internet Research, 20(3), e110. https://doi.org/10.2196/jmir.9157
  • 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://doi.org/10.1007/s11747-014-0403-8
  • Hirunyawipada, T., & Paswan, A. K. (2006). Consumer innovativeness and perceived risk: Implications for high technology product adoption. Journal of Consumer Marketing, 23(4), 182–198. https://doi.org/10.1108/07363760610674310
  • Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576
  • Hollis, J. F., Gullion, C. M., Stevens, V. J., Brantley, P. J., Appel, L. J., Ard, J. D., Champagne, C. M., Dalcin, A., Erlinger, T. P., Funk, K., Laferriere, D., Lin, P. H., Loria, C. M., Samuel-Hodge, C., Vollmer, W. M., & Svetkey, L. P. (2008). Weight loss during the intensive intervention phase of the weight-loss maintenance trial. American Journal of Preventive Medicine, 35(2), 118–126. https://doi.org/10.1016/j.amepre.2008.04.013
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60. https://doi.org/10.21427/D7CF7R
  • International Organization for Standardization. (1998). ISO 9241-11:1998(en): Ergonomic requirements for office work with visual display terminals (VDTs) - Part 11: Guidance on usability. https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-1:v1:en
  • Irvine, A. B., Billow, M. B., Bourgeois, M., & Seeley, J. R. (2012). Mental illness training for long term care staff. Journal of the American Medical Directors Association, 13(1), 81.e87–13. https://doi.org/10.1016/j.jamda.2011.01.015
  • Jain, K., Sharma, I., & Singh, G. (2018). An empirical study of factors determining wearable fitness tracker continuance among actual users. International Journal of Technology Marketing, 13(1), 83. https://doi.org/10.1504/IJTMKT.2018.099877
  • Jeong, S. C., Kim, S., Park, J. Y., & Choi, B. (2017). Domain-specific innovativeness and new product adoption: A case of wearable devices. Telematics and Informatics, 34(5), 399–412. https://doi.org/10.1016/j.tele.2016.09.001
  • Kaewkannate, K., & Kim, S. (2016). A comparison of wearable fitness devices. BMC Public Health, 16(1), 433. https://doi.org/10.1186/s12889-016-3059-0
  • Kalantari, M. (2017). Consumers’ adoption of wearable technologies: Literature review, synthesis, and future research agenda. International Journal of Technology Marketing, 12(3), 274–307. https://doi.org/10.1504/IJTMKT.2017.10008634
  • Kayser, L., Kushniruk, A., Osborne, R. H., Norgaard, O., & Turner, P. (2015). Enhancing the effectiveness of consumer-focused health information technology systems through eHealth literacy: A framework for understanding users’ needs. JMIR Human Factors, 2(1), e9. https://doi.org/10.2196/humanfactors.3696
  • Khalifa, M., & Liu, V. (2004). The state of research on information system satisfaction. Journal of Information Technology Theory and Application (JITTA), 5(4), 37–49.
  • Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smart watches. Internet Research, 25(4), 527–541. https://doi.org/10.1108/IntR-05-2014-0126
  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Publications. https://books.google.co.uk/books?id=EkMVZUxZrgIC
  • Knitza, J., Simon, D., Lambrecht, A., Raab, C., Tascilar, K., Hagen, M., Kleyer, A., Bayat, S., Derungs, A., Amft, O., Schett, G., & Hueber, A. J. (2020). Mobile Health usage, preferences, barriers, and eHealth literacy in rheumatology: Patient survey study. JMIR mHealth and uHealth, 8(8), e19661. https://doi.org/10.2196/19661
  • 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
  • Ledger, D., & McCaffrey, D. (2014). Inside wearables: How the science of human behavior change offers the secret to long-term engagement. https://endeavour.partners/assets/Wearables-and-the-Science-of-Human-Behavior-Change-EP4.pdf
  • Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Li, Z., & Bai, X. (2010). Influences of perceived risk and system usability on the adoption of mobile banking service. International Symposium on Computer Science and Computational Technology (ISCSCT). https://citeseerx.ist.psu.edu/viewdoc/download?doi=10<?sch-permit JATS-0034-007?>.1.1.403.7748&rep=rep1&type=pdf
  • Liang, J., Xian, D., Liu, X., Fu, J., Zhang, X., Tang, B., & Lei, J. (2018). Usability study of mainstream wearable fitness devices: Feature analysis and system usability scale evaluation. JMIR mHealth and uHealth, 6(11), e11066. https://doi.org/10.2196/11066
  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683–693. https://doi.org/10.1016/j.im.2004.04.003
  • Lymberis, A. (2003). Smart wearables for remote health monitoring, from prevention to rehabilitation: Current R&D, future challenges [Paper presentation]. 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003. https://ieeexplore.ieee.org/document/1222530
  • Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., & Pounders, K. (2016). Health literacy and health information technology adoption: The potential for a new digital divide. Journal of Medical Internet Research, 18(10), e264. https://doi.org/10.2196/jmir.6349
  • Maican, C. I., Cazan, A.-M., Lixandroiu, R. C., & Dovleac, L. (2019). A study on academic staff personality and technology acceptance: The case of communication and collaboration applications. Computers & Education, 128, 113–131. https://doi.org/10.1016/j.compedu.2018.09.010
  • MarketWatch. (2021). Fitness tracker market size 2021 regions will have the highest revenue, top countries data, which will emerge in importance in the market 2026. Retrieved May 10, 2021, from https://www.marketwatch.com/press-release/fitness-tracker-market-size-2021-regions-will-have-the-highest-revenue-top-countries-data-which-will-emerge-in-importance-in-the-market-2026-2021-03-17
  • Mencarini, E., Rapp, A., Tirabeni, L., & Zancanaro, M. (2019). Designing wearable systems for sports: A review of trends and opportunities in human–computer interaction. IEEE Transactions on Human-Machine Systems, 49(4), 314–325. https://doi.org/10.1109/THMS.2019.2919702
  • Michaelis, J. R., Rupp, M. A., Kozachuk, J., Ho, B., Zapata-Ocampo, D., McConnell, D. S., Smither, J. A. (2016). Describing the user experience of wearable fitness technology through online product reviews. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 1073–1077. https://doi.org/10.1177/1541931213601248
  • Mouakket, S. (2018). The role of personality traits in motivating users’ continuance intention towards Facebook: Gender differences. The Journal of High Technology Management Research, 29(1), 124–140. https://doi.org/10.1016/j.hitech.2016.10.003
  • Muller, C., & de Klerk, N. (2020). Influence of design aesthetics and brand name on generation Y students’ intention to use wearable activity-tracking devices. International Journal of eBusiness and eGovernment Studies, 12(2), 107–121. https://doi.org/10.34111/ijebeg.202012202
  • Najam, N., & Ashfaq, H. (2012). Gender differences in physical fitness, body shape satisfaction, and body figure preferences. Pakistan Journal of Psychological Research, 27(2), 187–200. https://www.pjprnip.edu.pk/index.php/pjpr/article/view/272
  • Nascimento, B. (2016). Determinants of continuance intention in wearables: The case of smartwatches. https://run.unl.pt/bitstream/10362/19342/1/TGI0055.pdf
  • Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and Consumer Services, 43, 157–169. https://doi.org/10.1016/j.jretconser.2018.03.017
  • Nelson, B., & Allen, N. (2019). Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: Intraindividual validation study. JMIR Mhealth Uhealth, 7(3), e10828. https://doi.org/10.2196/10828
  • Noblin, A. M., Wan, T. T. H., & Fottler, M. (2012). The impact of health literacy on a patient’s decision to adopt a personal health record. Perspectives in Health Information Management, 9(Fall), 1–13. https://pubmed.ncbi.nlm.nih.gov/23209454 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510648/
  • Norman, C. D., & Skinner, H. A. (2006a). eHEALS: The eHealth Literacy Scale. Journal of Medical Internet Research, 8(4), e27. https://doi.org/10.2196/jmir.8.4.e27
  • Norman, C. D., & Skinner, H. A. (2006b). eHealth literacy: Essential skills for consumer health in a networked world. Journal of Medical Internet Research, 8(2), e9. https://doi.org/10.2196/jmir.8.2.e9
  • Nov, O., & Ye, C. (2008). Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change. Hawaii International Conference on System Sciences. https://ieeexplore.ieee.org/document/4439153
  • Ogbanufe, O., & Gerhart, N. (2018). Watch it! Factors driving continued feature use of the smartwatch. International Journal of Human–Computer Interaction, 34(11), 999–1014. https://doi.org/10.1080/10447318.2017.1404779
  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
  • Olson, J. C., & Dover, P. A. (1979). Disconfirmation of consumer expectations through product trial. Journal of Applied Psychology, 64(2), 179–189. https://doi.org/10.1037/0021-9010.64.2.179
  • Osborne, J., Costello, A., & Kellow, J. (2008). Best practices in quantitative methods. SAGE Publications, Inc.
  • Özen, H. (2021). Gender difference in eHealth literacy: Empirical evidence from Turkey. International Journal of Academic Research in Business and Social Sciences, 11(4), 1058–1068. https://doi.org/10.6007/IJARBSS/v11-i4/9769
  • Paek, H. J., & Hove, T. (2012). Social cognitive factors and perceived social influences that improve adolescent eHealth literacy. Health Communication, 27(8), 727–737. https://doi.org/10.1080/10410236.2011.616627
  • Paige, S. R., Krieger, J. L., & Stellefson, M. L. (2017). The influence of eHealth literacy on perceived trust in online health communication channels and sources. Journal of Health Communication, 22(1), 53–65. https://doi.org/10.1080/10810730.2016.1250846
  • Pal, D., Funilkul, S., & Vanijja, V. (2020). The future of smartwatches: Assessing the end-users’ continuous usage using an extended expectation-confirmation model. Universal Access in the Information Society, 19(2), 261–281. https://doi.org/10.1007/s10209-018-0639-z
  • Pandey, S., Chawla, D., Puri, S., & Jeong, L. S. (2022). Acceptance of wearable fitness devices in developing countries: Exploring the country and gender-specific differences. Journal of Asia Business Studies. 16(4), 676–692. https://doi.org/10.1108/JABS-11-2020-0456
  • Papadakis, S. (2018). Evaluating pre-service teachers’ acceptance of mobile devices with regards to their age and gender: A case study in Greece. International Journal of Mobile Learning and Organisation, 12(4), 336. https://doi.org/10.1504/IJMLO.2018.095130
  • Parasuraman, A. (2000). Technology Readiness Index (Tri): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
  • Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318. https://doi.org/10.1016/j.tele.2019.101318
  • Rapp, A., & Tirabeni, L. (2018). Personal informatics for sport: Meaning, body, and social relations in amateur and elite athletes. ACM Transactions on Computer-Human Interaction, 25(3), 1–30. https://doi.org/10.1145/3196829
  • Rauschnabel, P. A., & Ro, Y. K. (2016). Augmented reality smart glasses: An investigation of technology acceptance drivers. International Journal of Technology Marketing, 11(2), 123–148. https://doi.org/10.1504/IJTMKT.2016.075690
  • Reyes-Mercado, P. (2018). Adoption of fitness wearables. Journal of Systems and Information Technology, 20(1), 103–127. https://doi.org/10.1108/JSIT-04-2017-0025
  • Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A. (2016). The impact of technological trust and self-determined motivation on intentions to use wearable fitness technology. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 1434–1438. https://doi.org/10.1177/1541931213601329
  • Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A. (2018). The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Applied Ergonomics, 70, 77–87. https://doi.org/10.1016/j.apergo.2018.02.005
  • Santos, J. R. A. (1999). Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of Extension, 37(2), 1–5. https://archives.joe.org/joe/1999april/tt3.php
  • Schmuck, P., Kasser, T., & Ryan, R. M. (2000). Intrinsic and extrinsic goals: Their structure and relationship to well-being in German and U.S. college students. Social Indicators Research, 50(2), 225–241. https://doi.org/10.1023/A:1007084005278
  • Schneegass, S., Mayer, S., Olsson, T., Van Laerhoven, K. (2015). From mobile to wearable: Using wearable devices to enrich mobile interaction [Paper presentation]. Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct. https://www.researchgate.net/publication/301376025
  • Seiler, R., & Huettermann, M. (2015). E-health, fitness trackers and wearables: Use among Swiss students. Advances in Business-Related Scientific Research Conference (ABSRC).
  • Song, J., Kim, J., & Cho, K. (2018). Understanding users’ continuance intentions to use smart-connected sports products. Sport Management Review, 21(5), 477–490. https://doi.org/10.1016/j.smr.2017.10.004
  • Steinmetz, H. (2015). Re: What is the acceptable range for factor loading in SEM? https://www.researchgate.net/post/What-is-the-acceptable-range-for-factor-loading-in-SEM/559cbd005f7f71021b8b45de/citation/download
  • Streichan, C. (2020). Continuous usage of fitness tracker systems: Expanding the UTAUT2 model with perceived privacy risk, health valuation, and satisfaction. https://essay.utwente.nl/81439/
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson Education. https://books.google.com.hk/books?id=ucj1ygAACAAJ
  • Tagiev, R., Bulatovych, D. (2020). Latest technologies for fitness app development in 2020. Retrieved February 01, 2020, from https://yalantis.com/blog/what-activity-tracking-technology-is-used-in-fitness-app-development/
  • Takpor, T., & Atayero, A. A. (2015, July 1–3). Integrating internet of things and eHealth solutions for students’ healthcare. World Congress on Engineering and Computer Science, London, UK. http://eprints.covenantuniversity.edu.ng/7009/
  • 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
  • The Hartman Group. (2015). Consumer trends in health and wellness. https://www.forbes.com/sites/thehartmangroup/2015/11/19/consumer-trends-in-health-and-wellness/#1b81943313e0
  • Thompson, A. E., Anisimowicz, Y., Miedema, B., Hogg, W., Wodchis, W. P., & Aubrey-Bassler, K. (2016). The influence of gender and other patient characteristics on health care-seeking behaviour: A QUALICOPC study. BMC Family Practice, 17(1), 38. https://doi.org/10.1186/s12875-016-0440-0
  • Tsukahara, S., Yamaguchi, S., Igarashi, F., Uruma, R., Ikuina, N., Iwakura, K., Koizumi, K., & Sato, Y. (2020). Association of eHealth literacy with lifestyle behaviors in university students: questionnaire-based cross-sectional study. Journal of Medical Internet Research, 22(6), e18155. https://doi.org/10.2196/18155
  • Turhan, G. (2013). An assessment towards the acceptance of wearable technology to consumers in Turkey: The application to smart bra and t-shirt products. Journal of the Textile Institute, 104(4), 375–395. https://doi.org/10.1080/00405000.2012.736191
  • U.S. Department of Health and Human Services. (2019). Physical activity guidelines for Americans. https://www.hhs.gov/fitness/be-active/physical-activity-guidelines-for-americans/index.html#:∼:text=For%20substantial%20health%20benefits%2C%20adults,or%20an%20equivalent%20combination%20of
  • Van Laerhoven, K., Lo, B., Ng, J., Thiemjarus, S., King, R., Kwan, S., Gellersen, H., Sloman, M., Wells, O., Needham, P., Peters, N., Darzi, A., Toumazou, C., Yang, G.-Z. (2004). Medical healthcare monitoring with wearable and implantable sensors. International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications (UbiHealth). https://www.researchgate.net/publication/216439761
  • Vandecasteele, B., & Geuens, M. (2010). Motivated consumer innovativeness: Concept, measurement, and validation. International Journal of Research in Marketing, 27(4), 308–318. https://doi.org/10.1016/j.ijresmar.2010.08.004
  • Venkatraman, M. P., & Price, L. L. (1990). Differentiating between cognitive and sensory innovativeness: Concepts, measurement, and implications. Journal of Business Research, 20(4), 293–315. https://doi.org/10.1016/0148-2963(90)90008-2
  • Vidal, L. T., Zhu, H., Waern, A., Segura, E. M. (2021). The design space of wearables for sports and fitness practices [Paper presentation]. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445700
  • Wagner, I., He, Y., Rosenberg, D., Janicke, H. (2016, January 9–12). User interface design for privacy awareness in eHealth technologies [Paper presentation]. 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). https://www.researchgate.net/publication/28425597
  • Ware, J. E., Jr., & Gandek, B. (1998). Methods for testing data quality, scaling assumptions, and reliability: The IQOLA Project approach. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51(11), 945–952. https://doi.org/10.1016/s0895-4356(98)00085-7
  • Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3), 1. https://doi.org/10.33151/ajp.8.3.93
  • World Health Organization. (2007). Steps to health: A European framework to promote physical activity for health. https://www.euro.who.int/__data/assets/pdf_file/0020/101684/E90191.pdf
  • Xie, B. (2011a). Effects of an eHealth literacy intervention for older adults. Journal of Medical Internet Research, 13(4), e90. https://doi.org/10.2196/jmir.1880
  • Xie, B. (2011b). Older adults, e‐health literacy, and collaborative learning: An experimental study. Journal of the American Society for Information Science and Technology, 62(5), 933–946. https://doi.org/10.1002/asi.21507
  • Yul, K. J. (2014). Determinants of users intention to adopt mobile fitness applications: An extended technology acceptance model approach. University of New Mexico. https://digitalrepository.unm.edu/educ_hess_etds/16
  • Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1990). Delivering quality service: Balancing customer perceptions and expectations. Free Press.
  • Zhang, M., Luo, M., Nie, R., & Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, 97–109. https://doi.org/10.1016/j.ijmedinf.2017.09.016
  • Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health & Social Care, 42(2), 194–206. https://doi.org/10.1080/17538157.2016.1200053

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