IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1279-d1332183.html
   My bibliography  Save this article

A Hybrid Model for Fitness Influencer Competency Evaluation Framework

Author

Listed:
  • Chin-Cheng Yang

    (Department of Leisure Services Management, Chaoyang University of Technology, Taichung 413310, Taiwan
    Graduate School of Technological and Vocational Education, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan)

  • Wan-Chi Jackie Hsu

    (Department of Marketing Management, Central Taiwan University of Sciences and Technology, Taichung 406053, Taiwan)

  • Chung-Shu Yeh

    (Department of Leisure Services Management, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Yu-Sheng Lin

    (General Education Center, Chaoyang University of Technology, Taichung 413310, Taiwan
    Department of Industrial Education and Technology, National Changhua University of Education, Changhua 50007, Taiwan)

Abstract

Fitness influencers are an emerging profession in recent years. At present, the main research on fitness influencers focuses on their personal traits, professional knowledge and skills, and course content, while there is still a large research gap on the social media marketing strategies of fitness influencers, how they interact with fans, and the reasons for their influence on fans. There is a lack of a comprehensive evaluation framework for fitness influencer research, and there is no clear research on what competencies are required to become a qualified fitness influencer. Therefore, it has become an important issue to establish a comprehensive fitness influencer competency evaluation. In this study, a hybrid model of fitness influencer competency evaluation framework was developed based on government competency standards and expert knowledge using the Multiple Criteria Decision-Making (MCDM) model perspective. This evaluation should expand to include the principles of sustainable development, emphasizing the influencers’ role in advocating for environmental responsibility, social equity, and economic viability within the fitness industry. First, the study developed 21 criteria in six dimensions of fitness influencer competencies through a literature survey and interviews with several experts. The 21 criteria resonate with many of the Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), SDG 5 (Gender Equality), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities). The Bayesian Best-Worst Method (Bayesian BWM) was used to generate the best group weights for fitness influencer competencies. Then, a modified Technique for Order Preference by Similarity to the Ideal Solution Based on Aspiration Level (modified TOPSIS-AL) was applied to evaluate the performance ranking of major fitness influencers in Taiwan by integrating the concept of the aspiration level. The results of the study revealed that behavioral standards were the most important dimension, emphasizing the need for fitness influencers to establish a comprehensive set of norms for their own behavioral standards. The top five criteria for fitness influencers’ competencies were self-review, punctuality and prudence, creativity, rapport and motivation, and the need to conform to one’s body image. The performance ranking was used to compare the evaluated subjects to the desired level to obtain a basis for improvement. This study effectively identifies key fitness industry competency indicators and refines business performance through the management implications proposed in this study to facilitate the development of the fitness industry.

Suggested Citation

  • Chin-Cheng Yang & Wan-Chi Jackie Hsu & Chung-Shu Yeh & Yu-Sheng Lin, 2024. "A Hybrid Model for Fitness Influencer Competency Evaluation Framework," Sustainability, MDPI, vol. 16(3), pages 1-23, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1279-:d:1332183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1279/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/3/1279/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen Dobson & Pete McLuskie, 2020. "Performative entrepreneurship: identity, behaviour and place in adventure sports Enterprise," International Entrepreneurship and Management Journal, Springer, vol. 16(3), pages 879-895, September.
    2. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    3. Jacqueline Ahrens & Fiona Brennan & Sarah Eaglesham & Audrey Buelo & Yvonne Laird & Jillian Manner & Emily Newman & Helen Sharpe, 2022. "A Longitudinal and Comparative Content Analysis of Instagram Fitness Posts," IJERPH, MDPI, vol. 19(11), pages 1-13, June.
    4. Kuo, Ting, 2017. "A modified TOPSIS with a different ranking index," European Journal of Operational Research, Elsevier, vol. 260(1), pages 152-160.
    5. Paul Jones & Vanessa Ratten & Ted Hayduk, 2020. "Sport, fitness, and lifestyle entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 16(3), pages 783-793, September.
    6. Casaló, Luis V. & Flavián, Carlos & Ibáñez-Sánchez, Sergio, 2020. "Influencers on Instagram: Antecedents and consequences of opinion leadership," Journal of Business Research, Elsevier, vol. 117(C), pages 510-519.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Le & Luo, Xin (Robert) & Li, Han, 2022. "Envy or conformity? An empirical investigation of peer influence on the purchase of non-functional items in mobile free-to-play games," Journal of Business Research, Elsevier, vol. 147(C), pages 308-324.
    2. Sarfaraz Hashemkhani Zolfani & Ramin Bazrafshan & Fatih Ecer & Çağlar Karamaşa, 2022. "The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    3. Zhang, Sheng & Lin, Zhang & Ai, Zhengtao & Huan, Chao & Cheng, Yong & Wang, Fenghao, 2019. "Multi-criteria performance optimization for operation of stratum ventilation under heating mode," Applied Energy, Elsevier, vol. 239(C), pages 969-980.
    4. Xiaodong Yuan & Weiling Song, 2022. "Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies," Information Technology and Management, Springer, vol. 23(2), pages 65-76, June.
    5. Rodriguez, Virginie & Sangle-Ferriere, Marion, 2023. "Do supermarkets’ emails have any value for their customers? The effect of emails’ content and interestingness on customers’ attitude and engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    6. Yeo, Sook Fern & Tan, Cheng Ling & Kumar, Ajay & Tan, Kim Hua & Wong, Jee Kit, 2022. "Investigating the impact of AI-powered technologies on Instagrammers’ purchase decisions in digitalization era–A study of the fashion and apparel industry," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    7. Raza, Ali & Ishaq, Muhammad Ishtiaq & Khan, Ayesha & Ahmad, Rehan & Haj Salem, Narjes, 2023. "How fashion cewebrity influences customer engagement behavior in emerging economy? Social network influence as moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    8. Yanni Ping & Chelsey Hill & Yun Zhu & Jorge Fresneda, 2023. "Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach," Electronic Commerce Research, Springer, vol. 23(3), pages 1459-1484, September.
    9. Shilei Lu & Minchao Fan & Yiqun Zhao, 2018. "A System to Pre-Evaluate the Suitability of Energy-Saving Technology for Green Buildings," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    10. Francesco Ciardiello & Andrea Genovese, 2023. "A comparison between TOPSIS and SAW methods," Annals of Operations Research, Springer, vol. 325(2), pages 967-994, June.
    11. Haojianxiong Yu & Jianjian Shen & Chuntian Cheng & Jia Lu & Huaxiang Cai, 2023. "Multi-Objective Optimal Long-Term Operation of Cascade Hydropower for Multi-Market Portfolio and Energy Stored at End of Year," Energies, MDPI, vol. 16(2), pages 1-21, January.
    12. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    13. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching, 2019. "A novel failure mode and effect analysis model for machine tool risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 173-183.
    14. Ozum Egilmez & Gozde Koca, 2021. "Drivers, Challenges, and Integration of Health 4.0 Societal Engagement: Evidence from Turkey," Istanbul Business Research, Istanbul University Business School, vol. 50(1), pages 127-148, May.
    15. Mu-Hsin Chang & James J. H. Liou & Huai-Wei Lo, 2019. "A Hybrid MCDM Model for Evaluating Strategic Alliance Partners in the Green Biopharmaceutical Industry," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    16. Byun, Kate Jeonghee & Park, Jimi & Yoo, Shijin & Cho, Minhee, 2023. "Has the COVID-19 pandemic changed the influence of word-of-mouth on purchasing decisions?," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    17. Susmaga, Robert & Szczȩch, Izabela & Zielniewicz, Piotr & Brzezinski, Dariusz, 2023. "MSD-space: Visualizing the inner-workings of TOPSIS aggregations," European Journal of Operational Research, Elsevier, vol. 308(1), pages 229-242.
    18. Soheil Azizi & Milad Mohammadi, 2023. "Strategy selection for multi-objective redundancy allocation problem in a k-out-of-n system considering the mean time to failure," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 1021-1044, June.
    19. Huai-Wei Lo & Chao-Che Hsu & Chun-Nen Huang & James J. H. Liou, 2021. "An ITARA-TOPSIS Based Integrated Assessment Model to Identify Potential Product and System Risks," Mathematics, MDPI, vol. 9(3), pages 1-17, January.
    20. Sergio Barta & Raquel Gurrea & Carlos Flavián, 2023. "Telepresence in live-stream shopping: An experimental study comparing Instagram and the metaverse," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.

    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:jsusta:v:16:y:2024:i:3:p:1279-:d:1332183. 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.