Determinants of health-conscious consumers’ intention to adopt fitness apps
-
DOIhttp://dx.doi.org/10.21511/im.19(3).2023.01
-
Article InfoVolume 19 2023, Issue #3, pp. 1-10
- Cited by
- 528 Views
-
257 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study aims to investigate the factors influencing consumers’ intention to adopt health fitness apps. The target population for this study were current users of health fitness apps. The data collection process was completed using a Google e-mail form with a cover letter for the convenience of customers, wherein 308 respondents were the final sample size. Data were collected from current members of health clubs and fitness centers in Dhaka, Bangladesh. Most of the respondents were males (56%, n = 308), whereas females were 44%, n = 134. Five-point Likert scale was used, where ‘1’ means ‘Strongly Disagree’ and ‘5’ ‘Strongly Agree’, to clarify the item-wise questionnaire. SPSS data analysis software for research purposes was used to evaluate the hypotheses. Cronbach Alpha (α) value was used to justify the reliability of the variables. Four items measure price value; perceived performance, health consciousness, facilitating condition, hedonic motivation are measured by two items; self-efficacy – by three. The results show that the selected six determinants positively and significantly affect consumers’ intention to use health fitness apps. Overall, these variables can explain 55.50% of the variance in predicting behavioral intentions to adopt health fitness apps. Furthermore, this results could provide significant clues to health fitness app developers that can severely influence users to adopt health fitness apps for their wellbeing.
- Keywords
-
JEL Classification (Paper profile tab)M31, M21, M30
-
References43
-
Tables4
-
Figures2
-
- Figure 1. Study framework
- Figure 2. Regression results
-
- Table 1. Reliability and validity analysis
- Table 2. Demographic information
- Table 3. Regression coefficient analysis
- Table 4. Hypothesis testing summary
-
- Ahmed, S., Asheq, A. A., Ahmed, E., Chowdhury, U. Y., Sufi, T., & Mostofa, M. G. (2023). The intricate relationships of consumers’ loyalty and their perceptions of service quality, price and satisfaction in restaurant service. The TQM Journal, 35(2), 519-539.
- Akhter, A., Islam, K. M. A., Karim, M. M., & Latif, W. B. (2022). Examining Determinants of Digital Entrepreneurial Intention: A Case of Graduate Students. Problems and Perspectives in Management, 20(3), 153-163.
- Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110.
- Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., Lal, B., & Williams, M. D. (2015). Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. Journal of Financial Services Marketing, 20(1), 145-157.
- Ali, M. J., Karim, M. M., Hitoishi, B. I., Wafiq, H. A., & Islam, K. M. A. (2022). Determinants of consumers’ purchase intention to buy smartphones online. Innovative Marketing, 18(2), 109-119.
- Al-Somali, S. A., Gholami, R., & Clegg, B. (2009). An investigation into the acceptance of online banking in Saudi Arabia. Technovation, 29(2), 130-141.
- Asheq, A. A., Tanchi, K. R., Akhter, S., Kamruzzaman, M., & Islam, K. M. A. (2022). Examining university students’ behaviors towards online shopping: an empirical investigation in an emerging market. Innovative Marketing, 18(1), 94-103.
- Baer, N. R., Vietzke, J., & Schenk, L. (2022). Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: A systematic mixed studies review. Plos One, 17(12), e0278879.
- Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373.
- Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882-893.
- Brindal, E., Hendrie, G., Freyne, J., Coombe, M., Berkovsky, S., & Noakes, M. (2013). Design and pilot results of a mobile phone weight-loss application for women starting a meal replacement programme. Journal of Telemedicine and Telecare, 19(3), 166-174.
- Chen, M. F., & Lin, N. P. (2018). Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 28(2), 351-373.
- Chen, X., Miraz, M. H., Gazi, M. A. I., Rahaman, M. A., Habib, M. M., & Hossain, A. I. (2022). Factors affecting cryptocurrency adoption in digital business transactions: The mediating role of customer satisfaction. Technology in Society, 70, 102059.
- Cho, H., Chi, C., & Chiu, W. (2020). Understanding sustained usage of health and fitness apps: Incorporating the technology acceptance model with the investment model. Technology in Society, 63, 101429.
- Cimperman, M., Brencic, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior – applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22-31.
- Cowan, L. T., Van Wagenen, S. A., Brown, B. A., Hedin, R. J., Seino-Stephan, Y., Hall, P. C., & West, J. H. (2013). Apps of steel: are exercise apps providing consumers with realistic expectations? A content analysis of exercise apps for presence of behavior change theory. Health Education & Behavior, 40(2), 133-139.
- Dahl, A. J., Milne, G. R., & Peltier, J. W. (2021). Digital health information seeking in an omni-channel environment: A shared decision-making and service-dominant logic perspective. Journal of Business Research, 125, 840-850.
- Damberg, S. (2022). Predicting future use intention of fitness apps among fitness app users in the United Kingdom: the role of health consciousness. International Journal of Sports Marketing and Sponsorship, 23(2), 369-384.
- De Veer, A. J., Peeters, J. M., Brabers, A. E., Schellevis, F. G., Rademakers, J. J., & Francke, A. L. (2015). Determinants of the intention to use e-Health by community-dwelling older people. BMC Health Services Research, 15(1), 1-9.
- Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2020). Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363-388.
- Duarte, P., & Pinho, J. C. (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption. Journal of Business Research, 102, 140-150.
- Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84.
- Hsia, J. W., Chang, C. C., & Tseng, A. H. (2014). Effects of individuals’ locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 51-64.
- Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015, 1-23.
- Im, I., Hong, S., & Kang, M.S. (2011). An international comparison of technology adoption: testing the UTAUT model. Information and Management, 48(1), 1-8.
- Lupton, D. (2013). Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23(4), 393-403.
- Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
- Peng, W., Kanthawala, S., Yuan, S., & Hussain, S. A. (2016). A qualitative study of user perceptions of mobile health apps. BMC Public Health, 16(1), 1-11.
- Quaosar, G. A. A., Hoque, M. R., & Bao, Y. (2018). Investigating factors affecting elderly’s intention to use m-health services: an empirical study. Telemedicine and e-Health, 24(4), 309-314.
- Rahaman, M. A., Hassan, H. K., Asheq, A. A., & Islam, K. A. (2022). The interplay between eWOM information and purchase intention on social media: Through the lens of IAM and TAM theory. Plos One, 17(9), e0272926.
- Schoeppe, S., Alley, S., Van Lippevelde, W., Bray, N. A., Williams, S. L., Duncan, M. J., & Vandelanotte, C. (2016). Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. International Journal of Behavioral Nutrition and Physical Activity, 13(1), 1-26.
- Tavares, J., & Oliveira, T. (2016). Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. Journal of Medical Internet Research, 18(3), e5069.
- Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.
- 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.
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
- Wang, Y., & Collins, W. B. (2021). Systematic evaluation of mobile fitness apps: Apps as the Tutor, Recorder, Game Companion, and Cheerleader. Telematics and Informatics, 59, 101552.
- Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118.
- Wei, J., Vinnikova, A., Lu, L., & Xu, J. (2021). Understanding and predicting the adoption of fitness mobile apps: evidence from China. Health Communication, 36(8), 950-961.
- Yuan, D., Rahman, M. K., Issa Gazi, M. A., Rahaman, M. A., Hossain, M. M., & Akter, S. (2021). Analyzing of user attitudes toward intention to use social media for learning. Sage Open, 11(4), 21582440211060784.
- 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 and e-Health, 21(9), 735-741.
- Zhang, Y., & Jin, S. (2016). The impact of social support on postpartum depression: The mediator role of self-efficacy. Journal of Health Psychology, 21(5), 720-726.
- Zhao, X., Mattila, A. S., & Eva Tao, L. S. (2008). The role of post-training self-efficacy in customers’ use of self-service technologies. International Journal of Service Industry Management, 19(4), 492-505.
- Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.