Open Access
Issue
SHS Web Conf.
Volume 155, 2023
2022 2nd International Conference on Social Development and Media Communication (SDMC 2022)
Article Number 01005
Number of page(s) 7
Section Research on Social Development and Humanities Education
DOI https://doi.org/10.1051/shsconf/202315501005
Published online 12 January 2023
  1. Nieman, D. C., & Pedersen, B. K. (1999). Exercise and immune function. Sports Medicine, 27(2), 73–80. [CrossRef] [Google Scholar]
  2. Martin, S. A., Pence, B. D., & Woods, J. A. (2009). Exercise and respiratory tract viral infections. Exercise and Sport Sciences Reviews, 37(4), 157. DOI: 10.1097/JES.0b013e3181b7b57b [CrossRef] [Google Scholar]
  3. Yang, Y., & Koenigstorfer, J. (2020). Determinants of physical activity maintenance during the Covid-19 pandemic: a focus on fitness apps. Translational behavioral medicine, 10(4), 835–842. DOI: https://doi.org/10.1093/tbm/ibaa086 [CrossRef] [Google Scholar]
  4. Mohiyeddini, C., Pauli, R., & Bauer, S. (2009). The role of emotion in bridging the intention - behaviour gap: The case of sports participation. Psychology of Sport and Exercise, 10(2), 226–234. DOI: https://doi.org/10.1016/j.psychsport.2008.08.005 [CrossRef] [Google Scholar]
  5. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human decision processes, 50(2), 179–211. [CrossRef] [Google Scholar]
  6. Godin, G., & Kok, G. (1996). The theory of planned behavior: a review of its applications to health-related behaviors. American journal of health promotion, 11(2), 87–98. [CrossRef] [Google Scholar]
  7. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982–1003. [CrossRef] [Google Scholar]
  8. George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet research. [Google Scholar]
  9. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261. [CrossRef] [Google Scholar]
  10. De Leeuw, A., Valois, P., Ajzen, I., & Schmidt, P. (2015). Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: Implications for educational interventions. Journal of Environmental Psychology, 42, 128–138. DOI: https://doi.org/10.1016/jjenvp.2015.03.005 [CrossRef] [Google Scholar]
  11. Cheng, O. Y., Yam, C. L. Y., Cheung, N. S., Lee, P. L. P., Ngai, M. C., & Lin, C. Y. (2019). Extended theory of planned behavior on eating and physical activity. American Journal of Health Behavior, 43(3), 569–581. DOI: https://doi.org/10.5993/AJHB.43.3.11 [CrossRef] [Google Scholar]
  12. Liao, C., Chen, J. L., & Yen, D. C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23(6), 2804–2822. DOI: https://doi.org/10.1016/j.chb.2006.05.006 [CrossRef] [Google Scholar]
  13. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human decision processes, 50(2), 179–211. [CrossRef] [Google Scholar]
  14. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2), 137–155. DOI: https://doi.org/10.1016/0167-8116(94)00019-K [CrossRef] [Google Scholar]
  15. Chiou, J. S. (1998). The effects of attitude, subjective norm, and perceived behavioral control on consumers’ purchase intentions: The moderating effects of product knowledge and attention to social comparison information. Proc. Natl. Sci. Counc. ROC (C), 9(2), 298–308. [Google Scholar]
  16. Bandura, A. (1982). The assessment and predictive generality of self-percepts of efficacy. Journal of behavior Therapy and Experimental Psychiatry, 13(3), 195–199. [CrossRef] [Google Scholar]
  17. Finlay, K. A., Trafimow, D., & Jones, D. (1997). Predicting health behaviors from attitudes and subjective norms: Between-subjects and within-subjects analyses. Journal of Applied Social Psychology, 27(22), 2015–2031. [CrossRef] [Google Scholar]
  18. Finlay, K. A., Trafimow, D., & Moroi, E. (1999). The importance of subjective norms on intentions to perform health behaviors. Journal of Applied Social Psychology, 29(11), 2381–2393. [CrossRef] [Google Scholar]
  19. Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In Gilbert, D. T., Fiske, S. T., & Lindzey, G. (Eds.), The Handbook of Social Psychology, 151–192. New York, NY, US: McGraw-Hill. [Google Scholar]
  20. Smith, J. R., Louis, W. R., Terry, D. J., Greenaway, K. H., Clarke, M. R., & Cheng, X. (2012). Congruent or conflicted? The impact of injunctive and descriptive norms on environmental intentions. Journal of Environmental Psychology, 32(4), 353–361. DOI: https://doi.org/10.1016/j.jenvp.2012.06.001 [CrossRef] [Google Scholar]
  21. Gelfand, M. J., & Harrington, J. R. (2015). The motivational force of descriptive norms: For whom and when are descriptive norms most predictive of behavior?. Journal of Cross-Cultural Psychology, 46(10), 1273–1278. [CrossRef] [Google Scholar]
  22. Varshney, D., & Vishwakarma, D. K. (2021). A review on rumour prediction and veracity assessment in online social network. Expert Systems with Applications, 168, 114208. DOI: https://doi.org/10.1016/j.eswa.2020.114208 [CrossRef] [Google Scholar]
  23. Wong, A., Ho, S., Olusanya, O., Antonini, M. V., & Lyness, D. (2021). The use of social media and online communications in times of pandemic COVID-19. Journal of the Intensive Care Society, 22(3), 255–260. DOI: https://doi.org/10.1177/1751143720966280 [CrossRef] [Google Scholar]
  24. Friedman, D. B., Gibson, A., Torres, W., Irizarry, J., Rodriguez, J., Tang, W., & Kannaley, K. (2016). Increasing community awareness about Alzheimer’ s disease in Puerto Rico through coffee shop education and social media. Journal of Community Health, 41(5), 1006–1012. [CrossRef] [Google Scholar]
  25. Bicen, H., & Arnavut, A. (2015). Determining the effects of technological tool use habits on social lives. Computers in Human Behavior, 48, 457–462. DOI: https://doi.org/10.1016/j.chb.2015.02.012 [CrossRef] [Google Scholar]
  26. McGuire, W. J. (1989). Theoretical foundations of campaigns. In R. E. Rice, & C. K. Atkin (Eds.), Public communication campaigns (2nd ed., pp. 43–65). Newbury Park, CA: Sage. [Google Scholar]
  27. Bandura, A. (2002). Social cognitive theory of mass communication. In J. Bryant & M. B. Oliver (Eds.), Media effects: Advances in theory and research (pp. 94–124). New York, NY: Routledge. [Google Scholar]
  28. Chen, L., & Fu, L. (2022). Let’s fight the infodemic: the third-person effect process of misinformation during public health emergencies. Internet Research. DOI: https://doi.org/10.1108/INTR-03-2021-0194 [Google Scholar]
  29. Dunne, A., Lawlor, M. A., & Rowley, J. (2010). Young people’s use of online social networking sites - a uses and gratifications perspective. Journal of Research in interactive Marketing. DOI: https://doi.org/10.1108/17505931011033551 [Google Scholar]
  30. Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. Cyberpsychology & Behavior, 12(6), 729–733. DOI: https://doi.org/10.1089/cpb.2009.0003 [CrossRef] [Google Scholar]
  31. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2020). A 2020 perspective on “ How live streaming influences purchase intentions in social commerce: An IT affordance perspective”. Electronic Commerce Research and Applications, 40, 100958. DOI: https://doi.Org/10.1016/j.elerap.2020.100958 [CrossRef] [Google Scholar]
  32. Skoric, M. M., & Poor, N. (2013). Youth engagement in Singapore: The interplay of social and traditional media. Journal of Broadcasting & Electronic Media, 57(2), 187–204. DOI: https://doi.org/10.1080/08838151.2013.787076 [CrossRef] [Google Scholar]
  33. Kaplanidou, K., & Gibson, H. J. (2012). Event image and traveling parents’ intentions to attend youth sport events: a test of the reasoned action model. European Sport Management Quarterly, 12(1), 3–18. DOI: https://doi.org/10.1080/16184742.2011.637173 [CrossRef] [Google Scholar]
  34. Ho, S. S., Chen, L., & Ng, A. P. (2017). Comparing cyberbullying perpetration on social media between primary and secondary school students. Computers & Education, 109, 74–84. DOI: https://doi.org/10.1016/j.compedu.2017.02.004 [CrossRef] [Google Scholar]
  35. Kim, Y. J., & Kim, E. S. (2021). Analysis of Korean Fencing Club Members’ Participation Intention Using the TPB Model. International Journal of Environmental Research and Public Health, 18(6), 2813. DOI: https://doi.org/10.3390/ijerph18062813 [CrossRef] [Google Scholar]
  36. Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. (2017). Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 136–144). [Google Scholar]
  37. Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services, 51, 72–82. DOI: https://doi.org/10.1016/j.jretconser.2019.05.026 [CrossRef] [Google Scholar]
  38. Jette, A. M., Harris, B. A., Sleeper, L., Lachman, M. E., Heislein, D., Giorgetti, M., & Levenson, C. (1996). A home -based exercise program for nondisabled older adults. Journal of the American Geriatrics society, 44(6), 644–649. [CrossRef] [Google Scholar]
  39. Ho, S. S., & Chuah, A. S. (2022). Thinking, not talking, predicts knowledge level: Effects of media attention and reflective integration on public knowledge of nuclear energy. Public Understanding of Science, 31(5), 572–589. DOI: https://doi.org/10.1177/09636625211070786 [CrossRef] [Google Scholar]

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