Impact of Public Risk Perception in China on the Intention to Use Sports APPs during COVID-19 Pandemic
Abstract
:1. Introduction
2. Literature Review
2.1. Pandemic Risk Perception and Health-Related Behaviour
2.2. Self-Efficacy (SE) and Sports Apps
2.3. Social Norms (SNs) and Sports Apps
3. Materials and Methods
3.1. Participants and Data Collection
3.2. Measurement
3.3. Reliability and Validity Analysis
4. Data Analysis and Results
4.1. Descriptive Statistics
4.2. HLR Analysis
5. Discussion
6. Limitations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PA | physical activity |
RP | risk perception |
SE | self-efficacy |
SNs | social norms |
U | usage intention |
HLR | hierarchical linear regression |
References
- Johns Hopkins University. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Available online: https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6 (accessed on 12 April 2022).
- Yang, Y.; Koenigstorfer, J. Determinants of physical activity maintenance during the COVID-19 pandemic: A focus on fitness apps. Transl. Behav. Med. 2020, 10, 835–842. [Google Scholar] [CrossRef] [PubMed]
- Wilke, J.; Mohr, L.; Tenforde, A.S.; Edouard, P.; Fossati, C.; González-Gross, M.; Sánchez Ramírez, C.; Laiño, F.; Tan, B.; Pillay, J.D. A pandemic within the pandemic? Physical activity levels substantially decreased in countries affected by COVID-19. Int. J. Environ. Res. Public Health 2021, 18, 2235. [Google Scholar] [CrossRef] [PubMed]
- Unifying Communications. Annual Report. 2015. Available online: http://www.annualreports.com/HostedData/AnnualReportArchive/v/ (accessed on 22 March 2022).
- Statista. Forecast Number of Mobile Users Worldwide from 2020 to 2025. Available online: https://www.statista.com/statistics/218984/number-of-global-mobile-users-since-2010/ (accessed on 12 July 2021).
- Mobile Marketing Statistics Compilation. Available online. Available online: https://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/ (accessed on 30 March 2021).
- Mobile App Download and Usage Statistics. 2022. Available online: https://buildfire.com/app-statistics/ (accessed on 19 March 2022).
- Number of Mobile App Downloads Worldwide in 2017, 2018 and 2022 (in Billions). Available online: https://www.statista.com/statistics/271644/worldwide-free-and-paid-mobile-app-store-downloads/ (accessed on 20 March 2022).
- How Often Do You Currently Make Use of Sports and Fitness Apps? Available online: https://www.statista.com/statistics/639567/sports-and-fitness-app-usage-frequency-in-us/ (accessed on 21 March 2022).
- QuestMobile 2021 Sports Health Population Insights Report. Available online: https://www.questmobile.com.cn/research/report-new/149 (accessed on 23 May 2022).
- Romeo, A.; Edney, S.; Plotnikoff, R.; Curtis, R.; Ryan, J.; Sanders, I.; Crozier, A.; Maher, C. Can smartphone apps increase physical activity? Systematic review and meta-analysis. J. Med. Internet Res. 2019, 21, e12053. [Google Scholar] [CrossRef] [PubMed]
- Slovic, P. Perception of risk. Science 1987, 236, 280–285. [Google Scholar] [CrossRef] [PubMed]
- Chua, B.-L.; Al-Ansi, A.; Lee, M.J.; Han, H. Impact of health risk perception on avoidance of international travel in the wake of a pandemic. Curr. Issues Tour. 2021, 24, 985–1002. [Google Scholar] [CrossRef]
- Duan, T.; Jiang, H.; Deng, X.; Zhang, Q.; Wang, F. Government intervention, risk perception, and the adoption of protective action recommendations: Evidence from the COVID-19 prevention and control experience of China. Int. J. Environ. Res. Public Health 2020, 17, 3387. [Google Scholar] [CrossRef] [PubMed]
- Wei, J.; Vinnikova, A.; Lu, L.; Xu, J. Understanding and predicting the adoption of fitness mobile apps: Evidence from China. Health Commun. 2021, 36, 950–961. [Google Scholar] [CrossRef]
- Schnall, R.; Higgins, T.; Brown, W.; Carballo-Dieguez, A.; Bakken, S. Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Stud. Health Technol. Inform. 2015, 216, 467. [Google Scholar]
- Huynh, T.L.D. Does culture matter social distancing under the COVID-19 pandemic? Saf. Sci. 2020, 130, 104872. [Google Scholar] [CrossRef]
- Gelfand, M.J.; Raver, J.L.; Nishii, L.; Leslie, L.M.; Lun, J.; Lim, B.C.; Duan, L.; Almaliach, A.; Ang, S.; Arnadottir, J. Differences between tight and loose cultures: A 33-nation study. Science 2011, 332, 1100–1104. [Google Scholar] [CrossRef]
- Zhang, X.A. Understanding the cultural orientations of fear appeal variables: A cross-cultural comparison of pandemic risk perceptions, efficacy perceptions, and behaviors. J. Risk Res. 2021, 24, 432–448. [Google Scholar] [CrossRef]
- 2021 China Sports and Fitness App Industry Analysis Report–Market Operation Situation and Development Potential Evaluation. Available online: https://baogao.chinabaogao.com/wentibangong/487552487552.html (accessed on 23 May 2022).
- Starr, C. Social benefit versus technological risk. In Readings in Environmental Impact; MSS Information Corp: New York, NY, USA, 1974; pp. 78–92. [Google Scholar]
- Risk Perception. Available online: https://en.wikipedia.org/wiki/Risk_perception (accessed on 12 April 2022).
- Langford, I.H.; Marris, C.; McDonald, A.-L.; Goldstein, H.; Rasbash, J.; O’riordan, T. Simultaneous analysis of individual and aggregate responses in psychometric data using multilevel modeling. Risk Anal. 1999, 19, 675–683. [Google Scholar] [CrossRef] [PubMed]
- Aven, T.; Kristensen, V. Perspectives on risk: Review and discussion of the basis for establishing a unified and holistic approach. Reliab. Eng. Syst. Saf. 2005, 90, 1–14. [Google Scholar] [CrossRef]
- Aven, T. The risk concept—Historical and recent development trends. Reliab. Eng. Syst. Saf. 2012, 99, 33–44. [Google Scholar] [CrossRef]
- Carman, K.G.; Kooreman, P. Probability perceptions and preventive health care. J. Risk Uncertain. 2014, 49, 43–71. [Google Scholar] [CrossRef]
- Vieira, K.M.; Potrich, A.C.G.; Bressan, A.A.; Klein, L.L.; Pereira, B.A.D.; Pinto, N.G.M. A pandemic risk perception scale. Risk Anal. 2022, 42, 69–84. [Google Scholar] [CrossRef]
- Kiecolt-Glaser, J.K.; McGuire, L.; Robles, T.F.; Glaser, R. Emotions, morbidity, and mortality: New perspectives from psychoneuroimmunology. Annu. Rev. Psychol. 2002, 53, 83–107. [Google Scholar] [CrossRef]
- Dionne, G.; Desjardins, D.; Lebeau, M.; Messier, S.; Dascal, A. Health care workers’ risk perceptions and willingness to report for work during an influenza pandemic. Risks 2018, 6, 8. [Google Scholar] [CrossRef]
- Beldad, A.D.; Hegner, S.M. Running frequently with an app to be fantastic! Determinants of Runtastic usage continuation intention among German users. Int. J. Mob. Commun. 2022, 20, 174–195. [Google Scholar] [CrossRef]
- Kinateder, M.T.; Kuligowski, E.D.; Reneke, P.A.; Peacock, R.D. Risk perception in fire evacuation behavior revisited: Definitions, related concepts, and empirical evidence. Fire Sci. Rev. 2015, 4, 1–26. [Google Scholar] [CrossRef]
- Claar, C.L. The Adoption of Computer Security: An Analysis of Home Personal Computer User Behavior Using the Health Belief Model; Utah State University: Logan, UT, USA, 2011. [Google Scholar]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
- Ceasar, J.N.; Claudel, S.E.; Andrews, M.R.; Tamura, K.; Mitchell, V.; Brooks, A.T.; Dodge, T.; El-Toukhy, S.; Farmer, N.; Middleton, K. Community engagement in the development of an mHealth-enabled physical activity and cardiovascular health intervention (Step It Up): Pilot focus group study. JMIR Form. Res. 2019, 3, e10944. [Google Scholar] [CrossRef] [PubMed]
- Garrin, J.M. Self-efficacy, self-determination, and self-regulation: The role of the fitness professional in social change agency. J. Soc. Chang. 2014, 6, 4. [Google Scholar]
- Carpenter, C.J. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 2010, 25, 661–669. [Google Scholar] [CrossRef] [PubMed]
- McArthur, L.H.; Riggs, A.; Uribe, F.; Spaulding, T.J. Health belief model offers opportunities for designing weight management interventions for college students. J. Nutr. Educ. Behav. 2018, 50, 485–493. [Google Scholar] [CrossRef] [PubMed]
- Cialdini, R.B.; Goldstein, N.J. Social influence: Compliance and conformity. Annu. Rev. Psychol. 2004, 55, 591–621. [Google Scholar] [CrossRef]
- Yang, S.; Lu, Y.; Gupta, S.; Cao, Y.; Zhang, R. Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Comput. Hum. Behav. 2012, 28, 129–142. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Processes 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Yun, D.; Silk, K.J. Social norms, self-identity, and attention to social comparison information in the context of exercise and healthy diet behavior. Health Commun. 2011, 26, 275–285. [Google Scholar] [CrossRef]
- Report Iresearch. Available online: https://report.iresearch.cn/report_pdf.aspx?id=3345 (accessed on 10 May 2022).
- Percentage of the Global Population that Used a Mobile App or Fitness Tracking Device to Track Their Health as of 2016, by Age. Available online: https://www.statista.com/statistics/742448/global-fitness-tracking-and-technology-by-age/ (accessed on 10 March 2022).
- Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Green Version]
- Raykov, T.; Marcoulides, G.A. Introduction to Psychometric Theory; Routledge: London, UK, 2011. [Google Scholar]
Step | Variable Entered | β | β | β |
---|---|---|---|---|
1 | Gender (male = 1) | −0.008 | −0.014 | 0.011 |
Age | −0.017 | −0.019 | −0.000 | |
Education | 0.008 | 0.019 | −0.007 | |
2 | RP | 0.111 *** | 0.022 *** | |
3 | SN | 0.214 *** | ||
SE | 0.230 *** | |||
N | 1366 | 1366 | 1366 | |
R²/adjusted R2 | 0.01/0.00 | 0.07/0.07 | 0.48/0.48 | |
ΔR2 | 0.00 | 0.07 | 0.41 | |
ΔF | 0.703 | 95.862 *** | 541.533 *** | |
Model F | 0.703 | 24.530 *** | 209.863 *** |
Mode | Variables | Standardized Coefficients | R² | Change Statistics | Collinearity Statistics | ||
---|---|---|---|---|---|---|---|
∆R2 | ∆F | Tolerance | VIF | ||||
1 | RP | 0.026 | 0.458 | 0.458 ** | 575.869 | 0.913 | 1.096 |
SN | 0.422 | 0.913 | 1.096 | ||||
2 | RP | 0.120 | 0.460 | 0.002 ** | 4.439 | 0.880 | 1.137 |
SN | 0.487 | 0.779 | 1.283 | ||||
RP × SN | −0.021 | 0.850 | 1.177 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gu, P.; Zhang, H.; Liang, Z.; Zhang, D. Impact of Public Risk Perception in China on the Intention to Use Sports APPs during COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 11915. https://doi.org/10.3390/ijerph191911915
Gu P, Zhang H, Liang Z, Zhang D. Impact of Public Risk Perception in China on the Intention to Use Sports APPs during COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2022; 19(19):11915. https://doi.org/10.3390/ijerph191911915
Chicago/Turabian StyleGu, Peng, Hao Zhang, Zeheng Liang, and Dazhi Zhang. 2022. "Impact of Public Risk Perception in China on the Intention to Use Sports APPs during COVID-19 Pandemic" International Journal of Environmental Research and Public Health 19, no. 19: 11915. https://doi.org/10.3390/ijerph191911915