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An Empirical Study on the Influencing Factors of the Continued Usage of Fitness Apps

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Book cover Smart Health (ICSH 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11924))

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Abstract

In order to improve users’ fitness frequency and to promote the sustainable development of fitness apps, it is necessary to study factors that influence the continued usage of fitness apps to provide some suggestions for providers. Based on the task-technology fit theory, a research model of the continued usage behavior of fitness apps was constructed. Through a questionnaire survey, 331 valid samples were collected, and then an empirical test was carried out on the model. The results showed that the task characteristics and technology characteristics significantly and positively influenced the task-technology fit; the task-technology fit positively affected the performance impacts, continued usage attitude and continued usage behavior; performance impacts was positively related to continued usage attitude and continued usage behavior; continued usage attitude had a significantly positive impact on continued usage behavior; the mediating roles of continued usage attitude with respect to the relationship between the task-technology fit and performance impacts, as well as continued usage behavior, were supported by empirical results; and the performance impacts played a significant intermediary role between task-technology fit and continued usage behavior. Therefore, according to the research results, providers can improve the matching degree between fitness apps’ functions and the users’ needs, enhance users’ perception of fitness apps’ using effects and prompt users to maintain a positive attitude during the continuous use process to promote the continued usage of apps and to improve users’ fitness frequency.

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References

  1. Chuan-hai, L., Qing-mei, W., Jun-wei, Q.: The influence of sports apps on the promotion of physical exercise behavior and the formation of exercise habits. J. Nanjing Sport Inst. (Soc. Sci. Edition) 29(03), 109–115 (2015)

    Google Scholar 

  2. Litman, L., et al.: Mobile exercise apps and increased leisure time exercise activity: a moderated mediation analysis of the role of self-efficacy and barriers. J. Med. Internet Res. 17(8), 15 (2015)

    Article  Google Scholar 

  3. Sama, P.R., et al.: An evaluation of mobile health application tools. Jmir Mhealth Uhealth 2(2), 6 (2014)

    Article  Google Scholar 

  4. Bida consultancy: 3rd quarter 2017 sports fitness apps product market research report in China. in Beijing: Bida consultancy (2017)

    Google Scholar 

  5. Institute: Fitness app market report for the first half of 2017. Internet World 09, 32–34 (2017)

    Google Scholar 

  6. Zhang, H., Liu, X.: Resources and use of fitness instruction APP. J. Wuhan Inst. Phys. Educ. 51(10), 37–42 (2017)

    Google Scholar 

  7. Bhattacherjee, A.: Understanding information systems continuance: an expectation-confirmation model. MIS Q. 25(3), 351–370 (2001)

    Article  Google Scholar 

  8. Limayem, M., Cheung, C.M.K.: Understanding information systems continuance: the case of Internet-based learning technologies. Inf. Manag. 45(4), 227–232 (2008)

    Article  Google Scholar 

  9. Yang, H., Yuan, W., Wang, W.: A review of domestic and foreign researches on users continuance of social apps. Inf. Sci. 35(03), 164–170 + 176 (2017)

    Google Scholar 

  10. Hui-ping, C., Ling, J.: Analysis on the research status of domestic IT/IS user continuance usage. Inf. Sci. 36(04), 171–176 (2018)

    Google Scholar 

  11. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. Sloan School of Management, Massachusetts Institute of Technology (1986)

    Google Scholar 

  12. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  13. Rogers, E.M.: Diffusion of Innovations, 4th edn., pp. 1–20. Clarendon Press, New York (1995)

    Google Scholar 

  14. Blumler, J.G., Katz, E.: The uses of mass communications: current perspectives on gratifications research. Am. J. Sociol. 3(6), 318 (1974)

    Google Scholar 

  15. Hong, S.J., et al.: Understanding the behavior of mobile data services consumers. Inf. Syst. Front. 10(4), 431–445 (2008)

    Article  Google Scholar 

  16. Kim, M.J., et al.: Dual-route of persuasive communications in mobile tourism shopping. Telematics Inform. 33(2), 293–308 (2016)

    Article  Google Scholar 

  17. Gan, C.M., Li, H.X.: Understanding the effects of gratifications on the continuance intention to use WeChat in China: a perspective on uses and gratifications. Comput. Hum. Behav. 78, 306–315 (2018)

    Article  Google Scholar 

  18. Lu, Y., Papagiannidis, S., Alamanos, E.: Exploring the emotional antecedents and outcomes of technology acceptance. Comput. Hum. Behav. 90, 153–169 (2019)

    Article  Google Scholar 

  19. Joo, Y.J., Park, S., Shin, E.K.: Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Comput. Hum. Behav. 69, 83–90 (2017)

    Article  Google Scholar 

  20. Yuan, S.P., et al.: Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model. Telemed. E-Health 21(9), 735–774 (2015)

    Article  Google Scholar 

  21. Gowin, M., et al.: Health and fitness app use in college students: a qualitative study. Am. J. Health Educ. 46(4), 223–230 (2015)

    Article  Google Scholar 

  22. Lee, H.E., Cho, J.: What motivates users to continue using diet and fitness apps? application of the uses and gratifications approach. Health Commun. 32(12), 1 (2016)

    Google Scholar 

  23. Lee, S., Kim, S., Wang, S.: Motivation factors influencing intention of mobile sports apps use by applying the unified theory of acceptance and use of technology (UTAUT). IJASS (Int. J. Appl. Sport. Sci.) 29(2), 115–127 (2017)

    Google Scholar 

  24. Beldad, A.D., Hegner, S.M.: 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. Int. J. Hum.-Comput. Interact. 34(9), 882–893 (2018)

    Article  Google Scholar 

  25. Park, M., et al.: Why do young people use fitness apps? cognitive characteristics and app quality. Electron. Commer. Res. 6, 1–7 (2018)

    Google Scholar 

  26. Li, J., et al.: Users’ intention to continue using social fitness-tracking apps: expectation confirmation theory and social comparison theory perspective. Inform. Health Soc. Care, 1–15 (2018)

    Google Scholar 

  27. Alter, S.: 18 Reasons why IT-reliant work systems should replace the IT artifact as the core subject matter of the IS field. Commun. Assoc. Inform. Syst. 12(23), 365–394 (2003)

    Google Scholar 

  28. Alter, S.: Are the fundamental concepts of information systems mostly about work systems? Equine Vet. J. 44(5), 621–625 (2001)

    Google Scholar 

  29. Alter, S.: Which life cycle–work system, information system, or software? Commun. Assoc. Inf. Syst. 7 (2001)

    Google Scholar 

  30. Alter, S.: Architecture of Sysperanto: a model-based ontology of the IS field (2005)

    Google Scholar 

  31. Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995)

    Article  Google Scholar 

  32. Ferratt, T.W., Vlahos, G.E.: An investigation of task-technology fit for managers in Greece and the US. Eur. J. Inf. Syst. 7(2), 123–136 (1998)

    Article  Google Scholar 

  33. Larsen, T.J., Sorebo, A.M., Sorebo, O.: The role of task-technology fit as users’ motivation to continue information system use. Comput. Hum. Behav. 25(3), 778–784 (2009)

    Article  Google Scholar 

  34. Lin, W.S.: Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. Int. J. Hum Comput Stud. 70(7), 498–507 (2012)

    Article  Google Scholar 

  35. Lin, W.S., Wang, C.H.: Antecedences to continued intentions of adopting e-learning system in blended learning instruction: a contingency framework based on models of information system success and task-technology fit. Comput. Educ. 58(1), 88–99 (2012)

    Article  Google Scholar 

  36. Yang, S.Q., et al.: Understanding consumers’ mobile channel continuance: an empirical investigation of two fitness mechanisms. Behav. Inf. Technol. 34(12), 1135–1146 (2015)

    Article  Google Scholar 

  37. Ajayi, I.H., et al.: A conceptual model for flipped classroom: Influence on continuance use intention. In: 2017 5th International Conference on Research and Innovation in Information Systems (2017)

    Google Scholar 

  38. Lin, K.Y.: User communication behavior in mobile communication software. Online Inf. Rev. 40(7), 1071–1089 (2016)

    Article  Google Scholar 

  39. Lin, K.M., Chen, N.S., Fang, K.T.: Understanding e-learning continuance intention: a negative critical incidents perspective. Behav. Inf. Technol. 30(1), 77–89 (2011)

    Article  Google Scholar 

  40. Hong, S., Kim, J., Lee, H.: Antecedents of use-continuance in information systems: toward an inegrative view. J. Comput. Inf. Syst. 48(3), 61–73 (2008)

    Google Scholar 

  41. Liang, T.P., et al.: Contextual factors and continuance intention of mobile services. Int. J. Mobile Commun. 11(4), 313–329 (2013)

    Article  Google Scholar 

  42. Cheng, Y.M.: What drives nurses’ blended e-learning continuance intention? J. Educ. Technol. Soc. 17(4), 203–215 (2014)

    Google Scholar 

  43. Chang, I.C., et al.: Assessing the performance of long-term care information systems and the continued use intention of users. Telematics Inform. 32(2), 273–281 (2015)

    Article  Google Scholar 

  44. Yuan, S.B., et al.: An investigation of users’ continuance intention towards mobile banking in China. Inf. Dev. 32(1), 20–34 (2016)

    Article  Google Scholar 

  45. Wu, B., Chen, X.H.: Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput. Hum. Behav. 67, 221–232 (2017)

    Article  Google Scholar 

  46. Hong, T., Xu, F.: Research review of task-technology fit model. R&D Manage. 24(04), 24–31 (2012)

    Google Scholar 

  47. Ip, R.K.F., Wagner, C.: Weblogging: a study of social computing and its impact on organizations. Decis. Support Syst. 45(2), 242–250 (2008)

    Article  Google Scholar 

  48. Lu, H.P., Yang, Y.W.: Toward an understanding of the behavioral intention to use a social networking site: an extension of task-technology fit to social-technology fit. Comput. Hum. Behav. 34(5), 323–332 (2014)

    Article  Google Scholar 

  49. Zhou, T., Lu, Y.B., Wang, B.: Integrating TTF and UTAUT to explain mobile banking user adoption. Comput. Hum. Behav. 26(4), 760–767 (2010)

    Article  Google Scholar 

  50. Wang, S., Zhang, J., Liu, Y.: The effective factor study of sports app promote public exercise adherence. J. Fujian Norm. Univ. (Philos. Soc. Sci. Ed.) (06), 88–99 + 170–171 (2018)

    Google Scholar 

  51. Bida consultancy: July 2016 sports fitness apps users monitoring report. in Beijing: Bida consultancy (2016)

    Google Scholar 

  52. iResearch consultancy: Case studies: user insights and case studies on the big data sports apps, in iResearch consultancy (2018)

    Google Scholar 

  53. Wells, J.D., et al.: Studying customer evaluations of electronic commerce applications: a review and adaptation of the task-technology fit perspective. In: Hawaii International Conference on System Sciences (2003)

    Google Scholar 

  54. Gebauer, J., Shaw, M.J.: Success factors and impacts of mobile business applications: results from a mobile e-procurement study. Int. J. Electron. Commer. 8(3), 19–41 (2004)

    Article  Google Scholar 

  55. Ruoxi, W., Qingjun, W.: Fitness app: development status, problems and countermeasures. J. Shandong Sport. Univ. 31(04), 18–22 (2015)

    Google Scholar 

  56. D’Ambra, J., Wilson, C.S.: Use of the world wide web for international travel: integrating the construct of uncertainty in information seeking and the task-technology fit (TTF) model. J. Am. Soc. Inform. Sci. Technol. 55(8), 731–742 (2004)

    Article  Google Scholar 

  57. Yen, D.C., et al.: Determinants of users’ intention to adopt wireless technology: an empirical study by integrating TTF with TAM. Comput. Hum. Behav. 26(5), 906–915 (2010)

    Article  Google Scholar 

  58. Yang, G.: Research on continuous use and performance influencing factors of network teaching platform under mixed learning mode. E-Educ. Res. 36(07), 42–48 (2015)

    Google Scholar 

  59. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Article  Google Scholar 

  60. Westbrook, R.A.: Product/Consumption-based affective responses and post purchase processes. J. Mark. Res. 24(3), 258–270 (1987)

    Article  Google Scholar 

  61. Zhang, X.: The model construction and empirical research of consumer’s continuance intention on using e-commerce web sites. Zhejiang Gongshang University (2013)

    Google Scholar 

  62. Chunmei, G.A.N.: Empirical analysis on continuance intention to use mobile library services. Libr. Trib. 36(01), 79–84 (2016)

    Google Scholar 

  63. McGill, T.J., Klobas, J.E.: A task-technology fit view of learning management system impact. Comput. Educ. 52(2), 496–508 (2009)

    Article  Google Scholar 

  64. Yu, T.K., Yu, T.Y.: Modelling the factors that affect individuals’ utilisation of online learning systems: an empirical study combining the task technology fit model with the theory of planned behaviour. Br. J. Edu. Technol. 41(6), 1003–1017 (2010)

    Article  Google Scholar 

  65. Staples, D.S., Seddon, P.B.: Testing the technology-to-performance chain model. J. Organ. End User Comput. 16(4), 17–36 (2004)

    Article  Google Scholar 

  66. Harrati, N., Bouchrika, I., Mahfouf, Z.: Investigating the uptake of educational systems by academics using the technology to performance chain model. Libr. Hi Tech 35(4), 629–648 (2017)

    Article  Google Scholar 

  67. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  68. Hwang, C., Chung, T.L., Sanders, E.A.: Attitudes and purchase intentions for smart clothing: examining U.S. consumers’ functional, expressive, and aesthetic needs for solar-powered clothing. Cloth. Text. Res. J. 34(3) (2016)

    Article  Google Scholar 

  69. Xin, G.: The model construction and empirical research of user’s continuance intention towards LBS applications. Wuhan University of Technology (2015)

    Google Scholar 

  70. Ajzen, I.: From Intentions to Actions: A Theory of Planned Behavior. Springer, Heidelberg (1985)

    Chapter  Google Scholar 

  71. Rai, A., Lang, S.S., Welker, R.B.: Assessing the validity of IS success models: an empirical test and theoretical analysis. Inf. Syst. Res. 13(1), 50–69 (2002)

    Article  Google Scholar 

  72. Pavlou, P.A., Fygenson, M.: Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q. 30(1), 115–143 (2006)

    Article  Google Scholar 

  73. Luarn, P., Lin, H.H.: Toward an understanding of the behavioral intention to use mobile banking. Comput. Hum. Behav. 21(6), 873–891 (2005)

    Article  Google Scholar 

  74. Jian, L., Ning, Z.: A study on intention of taking high-speed rail based on theory of planned behavior. Chin. J. Manag. 11(09), 1403–1410 (2014)

    Google Scholar 

  75. Wang, D., Yao, F., Zheng, Y.: A study on credit cards usage intention and its marketing strategies based on theory of planned behavior. Chin. J. Manag. 8(11), 1682–1689 + 1713 (2011)

    Google Scholar 

  76. Lu, H.P., Yang, Y.W.: Toward an understanding of the behavioral intention to use a social networking site: an extension of task-technology fit to social-technology fit. Comput. Hum. Behav. 34, 323–332 (2014)

    Article  Google Scholar 

  77. Cheng, T.C.E., Lam, D.Y.C., Yeung, A.C.L.: Adoption of internet banking: an empirical study in Hong Kong. Decis. Support Syst. 42(3), 1558–1572 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Commercial College, Central South University, Changsha, 410083, Hunan, China

    Yu Yu & Qing Chen

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  1. Yu Yu
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  2. Qing Chen
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Correspondence to Yu Yu .

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Editors and Affiliations

  1. The University of Arizona, Tucson, AZ, USA

    Hsinchun Chen

  2. College of Management, The University of Arizona, Tucson, AZ, USA

    Daniel Zeng

  3. University of Science and Technology Beijing, Beijing, China

    Xiangbin Yan

  4. Tsinghua University, Beijing, China

    Chunxiao Xing

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Yu, Y., Chen, Q. (2019). An Empirical Study on the Influencing Factors of the Continued Usage of Fitness Apps. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-34482-5_11

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