Mobile fitness application quality analysis: KANO model satisfaction or dissatisfaction

Ave Adriana Pinem; Dana Indra Sensuse; Ryan Randy Suryono; Kautsarina Kautsarina; Achmad Nizar Hidayanto

doi:10.24294/jipd.v8i8.5960

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Mobile fitness application quality analysis: KANO model satisfaction or dissatisfaction

Ave Adriana Pinem

Dana Indra Sensuse

Ryan Randy Suryono

Kautsarina Kautsarina

Achmad Nizar Hidayanto

Journal of Infrastructure Policy and Development 2024, 8(8), 5960; https://doi.org/10.24294/jipd.v8i8.5960

Submitted：22 Apr 2024

Accepted：05 Jun 2024

Published：07 Aug 2024

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Abstract

The mobile health market is expected to continue to grow that will make it harder for mobile application developer to compete. One of the most popular types of mobile health application is health and fitness applications. This application aims to modify user behavior; therefore, it requires user to use the system continuously in relatively longer period of time to effectively change user behavior. Thus, user satisfaction is essential and must be maintained to reach this goal. This study aims to define the mobile health application qualities that would influence user satisfaction level. Developer can priorities the most influential qualities when building their application. Quality dimensions would be explored by literature review and Google Play Store review and categorised using DeLone McLean IS Success Model. We identified 12 quality dimension that will furthered analysed using Kano Model. The data collecting was conducted with online form with 12 pairs of Kano two-dimensional questionnaires (n = 115). The results show that the important qualities of mobile health application are Privacy, Availability, Reliability, Ease of Use, Accuracy and Responsiveness, lack of these qualities would cause dissatisfaction from user. The developer might also consider to improve user interface and usefulness of the application to increase user satisfaction even though these qualities would not cause much of dissatisfaction

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References

Alzahrani, A. I., Al-Samarraie, H., Eldenfria, A., et al. (2022). Users’ intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic. Technology in Society, 68, 101862. https://doi.org/10.1016/j.techsoc.2022.101862

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Birkmeyer, S., Wirtz, B. W., & Langer, P. F. (2021). Determinants of mHealth success: An empirical investigation of the user perspective. International Journal of Information Management, 59, 102351. https://doi.org/10.1016/j.ijinfomgt.2021.102351

Charlie, C., & Cilliers, L. (2024). Introducing the Chronic Disease mHealth App Quality Evaluation Framework (CHRONIQ). In: Proceedings of the 2024 Conference on Information Communications Technology and Society (ICTAS). https://doi.org/10.1109/ictas59620

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage Publications.

DeLone, W., & McLean, E. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 9–30.

Edney, S., Plotnikoff, R., Vandelanotte, C., et al. (2017). “Active Team” a social and gamified app-based physical activity intervention: randomised controlled trial study protocol. BMC Public Health, 17(1). https://doi.org/10.1186/s12889-017-4882-7

Google Playstore. (2022). Google Play Store Home. Available online: https://play.google.com/store/apps (accessed on 10 March 2024).

Handayani, P. W., Meigasari, D. A., Pinem, A. A., et al. (2018). Critical success factors for mobile health implementation in Indonesia. Heliyon, 4(11), e00981. https://doi.org/10.1016/j.heliyon.2018.e00981

Hashemi, P., Reychav, I., Arora, A., et al. (2024). How does information technology affect individuals’ health behavior in a pandemic? Insights from mobile health application use during COVID-19. Computers in Human Behavior, 151, 108005. https://doi.org/1

Herzberg, F., Mausner, B., & Snyderman, B. B. (2017). The Motivation to Work. Routledge. https://doi.org/10.4324/9781315124827

Huang, E. Y., Lin, S. W., & Fan, Y. C. (2015). M-S-QUAL: Mobile service quality measurement. Electronic Commerce Research and Applications, 14(2), 126–142. https://doi.org/10.1016/j.elerap.2015.01.003

Jen, W. Y., & Chao, C. C. (2008). Measuring mobile patient safety information system success: An empirical study. International Journal of Medical Informatics, 77(10), 689–697. https://doi.org/10.1016/j.ijmedinf.2008.03.003

Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive Quality and Must-Be Quality. The Journal of the Japanese Society for Quality Control, 39–48.

Keikhosrokiani, P., Mustaffa, N., & Zakaria, N. (2018). Success factors in developing iHeart as a patient-centric healthcare system: A multi-group analysis. Telematics and Informatics, 35(4), 753–775. https://doi.org/10.1016/j.tele.2017.11.006

Khan, M. I., & Saleh, M. A. (2022). Facebook Users’ Satisfaction and Intention to Continue Using It: Applying the Expectation Confirmation Model. Social Science Computer Review, 41(3), 983–1000. https://doi.org/10.1177/08944393211066584

Kim, K. H., Kim, K. J., Lee, D. H., et al. (2019). Identification of critical quality dimensions for continuance intention in mHealth services: Case study of onecare service. International Journal of Information Management, 46, 187–197. https://doi.org/10.1016/j.ijinfomgt.2018.12.008

Lim, M. H., Gleeson, J. F. M., Rodebaugh, T. L., et al. (2019). A pilot digital intervention targeting loneliness in young people with psychosis. Social Psychiatry and Psychiatric Epidemiology, 55(7), 877–889. https://doi.org/10.1007/s00127-019-01681-2

Malinka, C., von Jan, U., & Albrecht, U. V. (2022). Prioritization of Quality Principles for Health Apps Using the Kano Model: Survey Study. JMIR MHealth and UHealth, 10(1), e26563. https://doi.org/10.2196/26563

Nguyen, G. D., & Dao, T. H. T. (2024). Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-02778-z

Nie, L., Oldenburg, B., Cao, Y., et al. (2023). Continuous usage intention of mobile health services: model construction and validation. BMC Health Services Research, 23(1). https://doi.org/10.1186/s12913-023-09393-9

Pyky, R., Koivumaa-Honkanen, H., Leinonen, A. M., et al. (2017). Effect of tailored, gamified, mobile physical activity intervention on life satisfaction and self-rated health in young adolescent men: A population-based, randomized controlled trial (MOPO study). Computers in Human Behavior, 72, 13–22. https://doi.org/10.1016/j.chb.2017.02.032

Seo, Y. J., & Um, K. H. (2019). The asymmetric effect of fairness and quality dimensions on satisfaction and dissatisfaction: An application of the Kano model to the interdisciplinary college program evaluation. Studies in Educational Evaluation, 61, 183–195. https://doi.org/10.1016/j.stueduc.2019.04.007

Statista. (2019). Percentage of the global population that used a mobile app or fitness tracking device to track their health as of 2016, by age. Available online: https://www.statista.com/statistics/742448/global-fitness-tracking-and-technology-by-age/ (accessed on 8 March 2024).

Statista. (2022). Health and fitness apps-Statistics & Facts. Available online: https://www.statista.com/topics/9204/health-and-fitness-apps/#dossierKeyfigures (accessed on 8 March 2024).

Suh, Y., Woo, C., Koh, J., et al. (2019). Analysing the satisfaction of university–industry cooperation efforts based on the Kano model: A Korean case. Technological Forecasting and Social Change, 148, 119740. https://doi.org/10.1016/j.techfore.2019.119740

Tempo. (2021). 6 Personal Data Leak Cases in Indonesia (Indonesian). Available online: https://nasional.tempo.co/read/1501790/6-kasus-kebocoran-data-pribadi-di-indonesia (accessed on 8 March 2024).

Yan, M., Filieri, R., Raguseo, E., et al. (2021). Mobile apps for healthy living: Factors influencing continuance intention for health apps. Technological Forecasting and Social Change, 166, 120644. https://doi.org/10.1016/j.techfore.2021.120644

Yang, M., Al Mamun, A., Gao, J., et al. (2024). Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-023-50436-2

Yao, M. L., Chuang, M. C., & Hsu, C. C. (2018). The Kano model analysis of features for mobile security applications. Computers & Security, 78, 336–346. https://doi.org/10.1016/j.cose.2018.07.008

Yin, S., Cai, X., Wang, Z., et al. (2022). Impact of gamification elements on user satisfaction in health and fitness applications: A comprehensive approach based on the Kano model. Computers in Human Behavior, 128, 107106. https://doi.org/10.1016/j.chb.2021.107106

Yu, W. J., Hung, S. Y., Yu, A. P. I., et al. (2024). Understanding consumers’ continuance intention of social shopping and social media participation: The perspective of friends on social media. Information & Management, 61(4), 103808. https://doi.org/10.1016/j.im.2023.103808

Zhou, C., Guo, J., Zhang, Y., et al. (2024). A home health management app designed in the post-epidemic era using empirical evidence based on the demand collection of elderly users. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03042-0

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