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Open AccessOpen Access JUSTC Management 06 September 2022

Promoting exercise behavior with monetary and social incentives: An empirical study based on an online fitness program

  • 1.

    Department of Management Science, School of Management, University of Science and Technology of China, Hefei 230026, China

  • 2.

    Business School, Central South University, Changsha 410083, China

  • 3.

    Division of Business Management, Beijing Normal University – Hong Kong Baptist University United International College, Zhuhai 519087, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0062
More Information
  • Author Bio:

    Zhiguo Zhang is a postgraduate student at the School of Management, University of Science and Technology of China. His research interests include social media, e-health, and information privacy

    Jun Zhang is currently an Associate Professor with the School of Information Management, Wuhan University. He received his Ph.D. degree in Information Systems from the City University of Hong Kong in 2016. His research interests include online deviant behaviors, information privacy and security, e-health, and human-computer interaction. His research has been published in many academic journals and conferences, such as ISR, JMIS, CHB, I&M, IT&P, ICIS, and PACIS. He currently serves as an associate editor for CAIS, and has served as a guest associate editor for EJIS, JGIM, ICIS, PACIS, ECIS, etc

  • Corresponding author: E-mail: jzhang90@ustc.edu.cn
  • Received Date: 06 April 2022
  • Accepted Date: 23 May 2022
    • Available Online: 06 September 2022
    Abstract

  • Abstract

    Due to the importance of employees’ physical well-being, organizations have long been conducting wellness programs to motivate their employees to exercise. The wide use of wearable devices (e.g., smart bands and smartphones) and fitness applications (e.g., fitness mobile applications) enable organizations to shift from offline to online fitness programs where participants use physical activity records tracked by wearable devices to complete fitness tasks and challenges. To better motivate employees’ exercise behavior, online fitness programs widely offer monetary or social incentives strategies. However, little is known about the interaction effects of the two types of incentives when they are jointly offered. Besides, organizers lack knowledge of how to set an optimal fitness challenge for the incentives in online fitness programs. In this study, we obtained a rich panel dataset from a university-wide online fitness program, which includes the daily exercise records of 2578 participants during a 100-day period, to empirically investigate the joint effects of monetary and social incentives on individuals’ exercise behavior. Most interestingly, we found that there is a crowd-out effect between monetary and social incentives—the influences of social incentives (i.e., social support and social contagion) are relatively weaker when there exists an unachieved monetary goal; once the monetary goal has been achieved, the influences of social incentives become stronger. In addition, we found that participants’ exercise behavior can be maximized when the dynamic goal is set at an optimal level. Our findings can help practitioners better design the online fitness programs and the associated fitness technologies.

    Graphical abstract

    In online fitness programs, the exercise behavior of participants changes along with the dynamic goal distance and the goal-achieving status.

    Abstract

    Due to the importance of employees’ physical well-being, organizations have long been conducting wellness programs to motivate their employees to exercise. The wide use of wearable devices (e.g., smart bands and smartphones) and fitness applications (e.g., fitness mobile applications) enable organizations to shift from offline to online fitness programs where participants use physical activity records tracked by wearable devices to complete fitness tasks and challenges. To better motivate employees’ exercise behavior, online fitness programs widely offer monetary or social incentives strategies. However, little is known about the interaction effects of the two types of incentives when they are jointly offered. Besides, organizers lack knowledge of how to set an optimal fitness challenge for the incentives in online fitness programs. In this study, we obtained a rich panel dataset from a university-wide online fitness program, which includes the daily exercise records of 2578 participants during a 100-day period, to empirically investigate the joint effects of monetary and social incentives on individuals’ exercise behavior. Most interestingly, we found that there is a crowd-out effect between monetary and social incentives—the influences of social incentives (i.e., social support and social contagion) are relatively weaker when there exists an unachieved monetary goal; once the monetary goal has been achieved, the influences of social incentives become stronger. In addition, we found that participants’ exercise behavior can be maximized when the dynamic goal is set at an optimal level. Our findings can help practitioners better design the online fitness programs and the associated fitness technologies.

    • Public Summary

    • In online fitness programs, offering monetary incentives will motivate participants to do more exercise. However, participants’ exercise behavior will dramatically drop after they achieve the monetary goal.
    • When the dynamic goal is at an optimal level, participants’ daily steps will be maximized.
    • There is a crowd-out effect between the monetary and social incentives in motivating people to do more exercise. The influence of social incentives will become stronger after individuals achieve their monetary goals.

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    • References(63)
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    Figure  1.  User interfaces of the WeChat mini-program for the USTC-BWP.

    [1]
    Joo S Y, Lee C B, Joo N Y, et al. Feasibility and effectiveness of a motion tracking-based online fitness program for office workers. Healthcare, 2021, 9 (5): 584. doi: 10.3390/healthcare9050584
    [2]
    Walsh J C, Corbett T, Hogan M, et al. An mHealth intervention using a smartphone app to increase walking behavior in young adults: A pilot study. JMIR mHealth and uHealth, 2016, 4 (3): 1–8. doi: 10.2196/mhealth.5227
    [3]
    Sullivan A N, Lachman M E. Behavior change with fitness technology in sedentary adults: A review of the evidence for increasing physical activity. Frontiers in Public Health, 2017, 4: 1–16. doi: 10.3389/fpubh.2016.00289
    [4]
    Wilson C, Boe B, Sala A, et al. User interactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Computer Systems. New York: ACM, 2009.
    [5]
    Woldaregay A Z, Issom D Z, Henriksen A, et al. Motivational factors for user engagement with mHealth apps. In: pHealth 2018. Amsterdam: IOS Press, 2018.
    [6]
    Park K, Weber I, Cha M, et al. Persistent sharing of fitness app status on Twitter. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. New York: ACM, 2016.
    [7]
    National Institutes of Health. Behavior Change and Maintenance. [2022-01-01]. https://commonfund.nih.gov/behaviorchange.
    [8]
    Schwarzer R. Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology, 2008, 57 (1): 1–29. doi: 10.1111/j.1464-0597.2007.00325.x
    [9]
    Hosseinpour M, Terlutter R. Your personal motivator is with you: A systematic review of mobile phone applications aiming at increasing physical activity. Sports Medicine, 2019, 49 (9): 1425–1447. doi: 10.1007/s40279-019-01128-3
    [10]
    Dewhurst M, Guthridge M, Mohr E. Motivating people: Getting beyond money. McKinsey Quarterly, 2009, 1 (4): 12–15.
    [11]
    Harkins K A, Kullgren J T, Bellamy S L, et al. A trial of financial and social incentives to increase older adults’ walking. American Journal of Preventive Medicine, 2017, 52 (5): e123–e130. doi: 10.1016/j.amepre.2016.11.011
    [12]
    Loewenstein G, Asch D A, Volpp K G. Behavioral economics holds potential to deliver better results for patients, insurers, and employers. Health Affair, 2013, 32 (7): 1244–1250. doi: 10.1377/hlthaff.2012.1163
    [13]
    Adams M A, Hurley J C, Todd M, et al. Adaptive goal setting and financial incentives: A 2×2 factorial randomized controlled trial to increase adults’ physical activity. BMC Public Health, 2017, 17 (1): 286. doi: 10.1186/s12889-017-4197-8
    [14]
    Adjerid I, Loewenstein G, Purta R, et al. Gain-loss incentives and physical activity: The role of choice and wearable health tools. Management Science, 2021, 68 (4): 2642–2667. doi: 10.1287/mnsc.2021.4004
    [15]
    Atkin C K. Theory and principles of media health campaigns. In: Public Communication Campaigns. Thousand Oaks, CA: SAGE Publications, 2001, 3: 49–67.
    [16]
    Soontornwat A, Funilkul S, Supasitthimethee U. Essential social attributes and Habit in fitness mobile applications usage to motivate a physical activity. In: 2016 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand. IEEE, 2016.
    [17]
    Aral S, Nicolaides C. Exercise contagion in a global social network. Nature Communications, 2017, 8: 14753. doi: 10.1038/ncomms14753
    [18]
    Yoganathan D, Kajanan S. Persuasive technology for smartphone fitness apps. In: PACIS 2013 Proceedings. The Association for Information Systems, 2013.
    [19]
    McEwan D, Harden S M, Zumbo B D, et al. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: A systematic review and meta-analysis. Health Psychology Review, 2016, 10 (1): 67–88. doi: 10.1080/17437199.2015.1104258
    [20]
    Honary M, Bell B T, Clinch S, et al. Understanding the role of healthy eating and fitness mobile apps in the formation of maladaptive eating and exercise behaviors in young people. JMIR mHealth and uHealth, 2019, 7 (6): e14239. doi: 10.2196/14239
    [21]
    Curşeu P L, Janssen S E, Meeus M T. Shining lights and bad apples: The effect of goal-setting on group performance. Management Learning, 2014, 45 (3): 332–348. doi: 10.1177/1350507613483425
    [22]
    Bonner S E, Sprinkle G B. The effects of monetary incentives on effort and task performance: Theories, evidence, and a framework for research. Accounting, Organizations and Society, 2002, 27: 303–345. doi: 10.1016/s0361-3682(01)00052-6
    [23]
    Lourenço S M. Monetary incentives, feedback, and recognition: Complements or substitutes? Evidence from a field experiment in a retail services company. The Accounting Review, 2016, 91 (1): 279–297. doi: 10.2308/accr-51148
    [24]
    Pearson E, Prapavessis H, Higgins C, et al. Adding team-based financial incentives to the Carrot Rewards physical activity app increases daily step count on a population scale: A 24-week matched case control study. International Journal of Behavioral Nutrition and Physical Activity, 2020, 17 (1): 1–10. doi: 10.1186/s12966-020-01043-1
    [25]
    Mitchell M S, Orstad S L, Biswas A, et al. Financial incentives for physical activity in adults: Systematic review and meta-analysis. British Journal of Sports Medicine, 2020, 54 (21): 1259–1268. doi: 10.1136/bjsports-2019-100633
    [26]
    Taylor D G, Davis D F, Jillapalli R. Privacy concern and online personalization: The moderating effects of information control and compensation. Electronic Commerce Research, 2009, 9 (3): 203–223. doi: 10.1007/s10660-009-9036-2
    [27]
    Paul-Ebhohimhen V, Avenell A. Systematic review of the use of financial incentives in treatments for obesity and overweight. Obesity Reviews, 2008, 9 (4): 355–367. doi: 10.1111/j.1467-789x.2007.00409.x
    [28]
    Burns R J, Donovan A S, Ackermann R T, et al. A theoretically grounded systematic review of material incentives for weight loss: Implications for interventions. Annals of Behavioral Medicine, 2012, 44 (3): 375–388. doi: 10.1007/s12160-012-9403-4
    [29]
    Cahill K, Perera R. Competitions and incentives for smoking cessation. Cochrane Database of Systematic Reviews, 2011, 4 (4): CD004307. doi: 10.1002/14651858.cd004307.pub4
    [30]
    Wang L, Guo X, Wu T, et al. Short-term effects of social encouragement on exercise behavior: Insights from China’s Wanbu network. Public Health, 2017, 148: 25–29. doi: 10.1016/j.puhe.2017.03.004
    [31]
    Duncan S C, Duncan T E, Strycker L A. Sources and types of social support in youth physical activity. Health Psychology, 2005, 24 (1): 3–10. doi: 10.1037/0278-6133.24.1.3
    [32]
    Schwarzer R, Knoll N. Functional roles of social support within the stress and coping process: A theoretical and empirical overview. International Journal of Psychology, 2007, 42 (4): 243–252. doi: 10.1080/00207590701396641
    [33]
    Kiefer S M, Alley K M, Ellerbrock C R. Teacher and peer support for young adolescents’ motivation, engagement, and school belonging. RMLE Online, 2015, 38 (8): 1–18. doi: 10.1080/19404476.2015.11641184
    [34]
    Ballantine P W, Stephenson R. Help me, I’m fat! Social support in online weight loss networks. Journal of Consumer Behaviour, 2011, 10 (6): 332–337. doi: 10.1002/cb.374
    [35]
    Courneya K S, Plotnikoff R C, Hotz S B, et al. Social support and the theory of planned behavior in the exercise domain. American Journal of Health Behavior, 2000, 24 (4): 300–308. doi: 10.5993/ajhb.24.4.6
    [36]
    Trost S, Owen N. Correlates of adults’ participation in physical activity: Review and update. Medicine & Science in Sports & Exercise, 2002, 34 (12): 1996–2001. doi: 10.1097/00005768-200212000-00020
    [37]
    Giles-Corti B, Donovan R J. The relative influence of individual, social and physical environment determinants of physical activity. Social Science & Medicine, 2002, 54 (12): 1793–1812. doi: 10.1016/s0277-9536(01)00150-2
    [38]
    Gellert P, Ziegelmann J P, Warner L M, et al. Physical activity intervention in older adults: Does a participating partner make a difference? European Journal of Ageing, 2011, 8 (3): 211–219. doi: 10.1007/s10433-011-0193-5
    [39]
    McAuley E, Jerome G J, Elavsky S, et al. Predicting long-term maintenance of physical activity in older adults. Preventive Medicine, 2003, 37 (2): 110–118. doi: 10.1016/s0091-7435(03)00089-6
    [40]
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