Korean Society of Leisure, Recreation & Park
[ Article ]
Korean Journal of Leisure, Recreation & Park - Vol. 50, No. 2, pp.95-109
ISSN: 1598-0413 (Print)
Print publication date 30 Jun 2026
Received 30 Apr 2026 Revised 02 Jun 2026 Accepted 15 Jun 2026
DOI: https://doi.org/10.26446/kjlrp.2026.6.50.2.95

달리기 참여자의 지각된 이득에 따른 스마트워치 기반 운동 수용의도 분석

사혜지1 ; 김형훈2 ; 장성호3
1동국대학교 학술연구교수
2호남대학교 부교수
3용인대학교 부교수
Runners’ Intention to Adopt Smartwatches: Focusing on the Role of Perceived Benefits
Sa, Hye Ji1 ; Kim, Hyung-hoon2 ; Jang, Sung-ho3
1Dongguk University
2Honam University
3Yongin University

Correspondence to: Kim, Hyung-hoon E-mail: kh1181@naver.com

Abstract

This study investigates the technology acceptance process of smartwatch-based physical activity among runners by extending the Technology Acceptance Model (TAM) with perceived benefit. A survey was conducted with 249 adults experienced in using smartwatches for running, and the proposed model was validated using structural equation modeling (SEM). The results indicate that perceived benefit significantly augments both perceived usefulness (PU) and perceived ease of use (PEOU). Furthermore, PEOU was found to influence PU and behavioral intention, with PU emerging as the strongest predictor of the intention to adopt the technology. These findings suggest that smartwatch acceptance among runners is driven not only by functional attributes but also by the outcome-related benefits derived from the technology. This study contributes to the literature by incorporating a result-oriented perspective into the TAM framework and provides practical implications for the development of wearable fitness technologies.

Keywords:

Technology Acceptance Model, Perceived Benefit, Smartwatch, Running, Wearable Devices

키워드:

달리기, 지각된 이득, 스마트워치, 기술수용모델

Acknowledgments

이 논문은 2025년 대한민국 교육부와 한국연구재단의 인문사회분야 중견연구자지원사업의 지원을 받아 수행된 연구임(NRF-2025S1A5A2A01005566)

References

  • 김성수, 정철호(2017). 신규 IT 서비스의 사전 기대와 지각된 성과 간의 일치도가 서비스 만족도에 미치는 영향. 예술인문사회 융합멀티미디어 논문지, 845-852.
  • 김태윤, 홍명보, 이진경(2009). 인터넷 스포츠 사이트 이용에 따른 만족도 분석 연구. 스포츠문화 과학연구지, 15, 41-50.
  • 김현석, 권만우, 이상호(2020). 도시관광시설 이용객의 이성, 감성 요인이 만족과 구전, 재방문에 미치는 영향 요인 연구. 한국융합학회논문지, 11(7), 113-123.
  • 박동진, 최정화, 김도진(2015). 헬스 앱의 효능감과 만족도, 지속적 사용의도가 웨어러블 기기의 수용에 미치는 효과: 융복합적 관점. 디지털융복합연구, 13(7). 137-145.
  • 박성준, 조준석, 윤유진(2021). 러닝 앱의 서비스 품질이 사용자의 앱 만족도 및 운동 만족도에 미치는 영향. 한국웰니스학회 학술발표회, 20-20.
  • 서금란, 윤정희(2024). 운동하는 시니어의 디지털 헬스케어: 스마트워치 사용 경험 중심으로. 한국스포츠사회학회지, 37(2), 103-118.
  • 이주희, 고경아, 하대권(2018). 1 인 미디어 이용자들의 라이브 스트리밍 방송 시청 동기 및 사용자 반응에 관한 연구: 후기 수용 모델(PAM) 을 중심으로. 한국광고홍보학보, 20(2), 178-215.
  • 이민석, 이평원, 서광봉(2021). 베이비붐 세대의 진지한여가 신체활동 참여가 레저스마트기기 사용의도에 미치는 영향: 확장된 TAM모형을 적용하여. 한국여가레크리에이션학회지, 45(4), 51-63.
  • 이현주, 신혜리, 한여정, 조민슬(2022). 중·고령자의 온라인 여가활동 유형에 따라 여가자원이 여가만족도, 행복감에 미치는 영향 연구. 여가학연구, 20(2), 39-65.
  • 정유진, 이철원, 한지훈. (2019). VR 여가스포츠 콘텐츠 이용자의 소비자혁신성이 수용의도에 미치는 영향: 기술수용모델 (TAM)을 중심으로. 한국여가레크리에이션학회지, 43(4), 77-89.
  • 정유진, 사혜지. (2025). 문화 여가 기반 메타버스 이용자의 소비자 혁신특성이 수용의도에 미치는 영향: TAM 모델을 중심으로. 한국여가레크리에이션학회지, 49(3), 97-109.
  • 한수정(2020). 유튜브 관광콘텐츠 특성이 이용만족, 지속이용의도, 정보공유의도에 미치는 영향. 기업과혁신연구, 43(3), 155-175.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238. [https://doi.org/10.1037/0033-2909.107.2.238]
  • Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision support systems, 32(2), 201-214. [https://doi.org/10.1016/S0167-9236(01)00111-7]
  • Chiu, C. M., Wang, E. T., Fang, Y. H., & Huang, H. Y. (2014). Understanding customers' repeat purchase intentions in B2C e‐commerce: the roles of utilitarian value, hedonic value and perceived risk. Information systems journal, 24(1), 85-114. [https://doi.org/10.1111/j.1365-2575.2012.00407.x]
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. [https://doi.org/10.2307/249008]
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. [https://doi.org/10.1177/002224378101800104]
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. [https://doi.org/10.1080/10705519909540118]
  • Karahanoğlu, A., Gouveia, R., Reenalda, J., & Ludden, G. (2021). How are sports-trackers used by runners? Running-related data, personal goals, and self-tracking in running. Sensors, 21(11), 3687. [https://doi.org/10.3390/s21113687]
  • Kim, T., & Chiu, W. (2019). Consumer acceptance of sports wearable technology: the role of technology readiness. International Journal of Sports Marketing and Sponsorship, 20(1), 109-126. [https://doi.org/10.1108/IJSMS-06-2017-0050]
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. [https://doi.org/10.1016/j.compedu.2012.10.001]
  • Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114-120. [https://doi.org/10.1016/j.chb.2016.08.007]
  • Magni, D., Scuotto, V., Pezzi, A., & Del Giudice, M. (2021). Employees’ acceptance of wearable devices: Towards a predictive model. Technological forecasting and social change, 172, 121022. [https://doi.org/10.1016/j.techfore.2021.121022]
  • Misra, S., Adtani, R., Singh, Y., Singh, S., & Thakkar, D. (2023). Exploring the factors affecting behavioral intention to adopt wearable devices. Clinical Epidemiology and Global Health, 24, 101428. [https://doi.org/10.1016/j.cegh.2023.101428]
  • Rapp, A., & Tirabeni, L. (2020). Self-tracking while doing sport: Comfort, motivation, attention and lifestyle of athletes using personal informatics tools. International Journal of Human-Computer Studies, 140, 102434. [https://doi.org/10.1016/j.ijhcs.2020.102434]
  • Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1-10. [https://doi.org/10.1007/BF02291170]
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. [https://doi.org/10.1287/mnsc.46.2.186.11926]
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS quarterly, 27(3), 425-478. [https://doi.org/10.2307/30036540]
  • Wang, Z., Fang, D., Liu, X., Zhang, L., Duan, H., Wang, C., & Guo, K. (2023). Consumer acceptance of sports wearables: The role of products attributes. Sage Open, 13(3), 21582440231182653. [https://doi.org/10.1177/21582440231182653]
  • Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and informatics, 33(2), 256-269. [https://doi.org/10.1016/j.tele.2015.08.007]