IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14453-d963146.html
   My bibliography  Save this article

How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model

Author

Listed:
  • Ji-Hyoung Chin

    (College of Education, Hankuk University of Foreign Studies, Seoul 02450, Korea)

  • Chanwook Do

    (Department of Kinesiology & Sport Management, College of Education & Human Development, Texas A&M University, College Station, TX 77843, USA)

  • Minjung Kim

    (Department of Kinesiology & Sport Management, College of Education & Human Development, Texas A&M University, College Station, TX 77843, USA)

Abstract

Artificial intelligence (AI) has recently been introduced as a new way of analyzing and predicting sport consumer behavior. The goal of this study was to investigate the relationships among the perceived usefulness, perceived ease of use, the importance of exercise, attitudes towards use, and the behavioral intention to use AI services based on the technology adoption model. The authors recruited 408 participants who participated in an experiment designed to provide a deeper understanding of AI fitness services. After screening, the collected data were screened through assumption tests, and we conducted a confirmatory factor analysis and structural equation modeling to analyze research hypotheses. The results indicated that three types of consumer evaluations (i.e., perceived usefulness, perceived ease of use, and importance of exercise) positively influence their attitudes toward AI fitness services. In addition, the positive attitudes regarding AI services positively influenced the intention to use AI services. The results of this research contribute to our knowledge of the consumers’ attitudes and behaviors toward AI services in the sport industry based on the technology acceptance model. Furthermore, this study provided the empirical evidence critically needed to increase our understanding of AI in the sport industry and offered new insights into how sport facility managers can predict their consumers’ intention to use AI services.

Suggested Citation

  • Ji-Hyoung Chin & Chanwook Do & Minjung Kim, 2022. "How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model," IJERPH, MDPI, vol. 19(21), pages 1-12, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14453-:d:963146
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14453/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14453/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Belanche & Luis V. Casaló & Carlos Flavián, 2021. "Frontline robots in tourism and hospitality: service enhancement or cost reduction?," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 477-492, September.
    2. Arts, Joep W.C. & Frambach, Ruud T. & Bijmolt, Tammo H.A., 2011. "Generalizations on consumer innovation adoption: A meta-analysis on drivers of intention and behavior," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 134-144.
    3. Bernadette Szajna, 1996. "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, INFORMS, vol. 42(1), pages 85-92, January.
    4. Sebastian Uhrich, 2022. "Sport spectator adoption of technological innovations: a behavioral reasoning analysis of fan experience apps," Sport Management Review, Taylor & Francis Journals, vol. 25(2), pages 275-299, March.
    5. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    7. Albert Satorra & Peter Bentler, 2001. "A scaled difference chi-square test statistic for moment structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 507-514, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Hui & Sun, Qi, 2024. "The transformation mechanism of fitness clubs: Pricing of joint fitness courses by online platforms and well-known coaches," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    2. Minjun Kim, 2023. "Connecting artificial intelligence to value creation in services: mechanism and implications," Service Business, Springer;Pan-Pacific Business Association, vol. 17(4), pages 851-878, December.
    3. Chaoyu Yin & Yihan Huang & Daehwan Kim & Kyungun Kim, 2023. "The Effect of Esports Content Attributes on Viewing Flow and Well-Being: A Focus on the Moderating Effect of Esports Involvement," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sarv Devaraj & Robert F. Easley & J. Michael Crant, 2008. "Research Note ---How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use," Information Systems Research, INFORMS, vol. 19(1), pages 93-105, March.
    2. Sohn, Stefanie, 2017. "A contextual perspective on consumers' perceived usefulness: The case of mobile online shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 22-33.
    3. Wei Wang & Shoujian Zhang & Yikun Su & Xinyang Deng, 2019. "An Empirical Analysis of the Factors Affecting the Adoption and Diffusion of GBTS in the Construction Market," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
    4. Raphael Warren Jankeeparsad & Dev Tewari, 2018. "End-User Adoption of Bitcoin in South Africa," Journal of Economics and Behavioral Studies, AMH International, vol. 10(5), pages 230-243.
    5. Xin Xu & Viswanath Venkatesh & Kar Yan Tam & Se-Joon Hong, 2010. "Model of Migration and Use of Platforms: Role of Hierarchy, Current Generation, and Complementarities in Consumer Settings," Management Science, INFORMS, vol. 56(8), pages 1304-1323, August.
    6. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    7. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    8. Johannes Putzke & Detlef Schoder & Kai Fischbach, 2010. "Adoption of Mass-Customized Newspapers: An Augmented Technology Acceptance Perspective," Journal of Media Economics, Taylor & Francis Journals, vol. 23(3), pages 143-164.
    9. Irfan Bashir & C. Madhavaiah, 2014. "Determinants of Young Consumers’ Intention to Use Internet Banking Services in India," Vision, , vol. 18(3), pages 153-163, September.
    10. Wong Lai Soon & Bobby Chai Boon Hui & Wong Kee Luen, 2013. "Joining the New Band: Factors Triggering the Intentions of Malaysian College and University Students to Adopt 4G Broadband," Information Management and Business Review, AMH International, vol. 5(2), pages 58-65.
    11. Hasnan Baber & N M Baki Billah, 2022. "Fintech and Islamic Banks - an integrative model approach to predict the intentions," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 24(2), pages 24-45, December.
    12. Naresh K. Malhotra & Sung S. Kim & Ashutosh Patil, 2006. "Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research," Management Science, INFORMS, vol. 52(12), pages 1865-1883, December.
    13. Dawei Liu & Xiaohong Guo, 2017. "Exploring gender differences in acceptance of mobile computing devices among college students," Information Systems and e-Business Management, Springer, vol. 15(1), pages 197-223, February.
    14. Euehun Lee & Semi Han, 2017. "Mobile service consumption values: an exploratory mixed-method study," Information Technology and Management, Springer, vol. 18(4), pages 253-264, December.
    15. Devaki Rau & Thorvald Haerem, 2010. "Applying an organizational learning perspective to new technology deployment by technological gatekeepers: A theoretical model and key issues for future research," Information Systems Frontiers, Springer, vol. 12(3), pages 287-297, July.
    16. Hsiao, Chun Hua & Yang, Chyan, 2011. "The intellectual development of the technology acceptance model: A co-citation analysis," International Journal of Information Management, Elsevier, vol. 31(2), pages 128-136.
    17. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Thrassou, Alkis & Ghosh, Soumya Kanti, 2021. "Adoption of artificial intelligence-integrated CRM systems in agile organizations in India," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    18. Christopher R. Plouffe & John S. Hulland & Mark Vandenbosch, 2001. "Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions—Understanding Merchant Adoption of a Smart Card-Based Payment System," Information Systems Research, INFORMS, vol. 12(2), pages 208-222, June.
    19. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    20. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14453-:d:963146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.