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

Author

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  • 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
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    References listed on IDEAS

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    1. Bernadette Szajna, 1996. "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, INFORMS, vol. 42(1), pages 85-92, January.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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