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Articles

The impact of social influence on perceived usefulness and behavioral intentions in the usage of non-pharmaceutical interventions (NPIs)

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Pages 145-156 | Received 03 Mar 2022, Accepted 20 May 2022, Published online: 31 May 2022

References

  • Calvo F, Lucie YZ. Racing to immunity: journey to a COVID-19 vaccine and lessons for the future. Br J Pharmacol Soc. 2020;87:3408–33424.
  • Center of Disease Control and Prevention. Community mitigation guidelines to prevent Pandemic influenza – United States. [Online] April 21, 2017. [cited 2022 Apr 4]. Available from: https://www.cdc.gov/mmwr/volumes/66/rr/pdfs/rr6601.pdf.
  • Benjamin JC, et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. The Lancet. 2020;5:279–288.
  • Christophe F, et al. Factors that make an infectious disease outbreak controllable. PNAS. 2004 Apr 20;101:6146–6151.
  • Niels GB, et al. Controlling emerging infectious diseases like SARS. Math Biosci. 2005;193:205–221.
  • Holly S, et al. Improving the impact of non-pharmaceutical interventions during COVID-19: examining the factors that influence engagement and the impact on individuals. BMC Infect Dis. 2020 Aug 17;20:1–11.
  • Fred DD. A Technology Acceptance Model for empirically testing New End-user information systems: theory and results. Cambridge: Massachusetts Institute of Technology; 1986.
  • Mohammad C. Overview of the Technology Acceptance Model: origins, developments and future directions. [Online] September 9, 2009. [cited 2022 Apr 4]. Available from: https://aisel.aisnet.org/sprouts_all/290.
  • Nuray OG, Ozlem SB. Technology acceptance in health care: an integrative review of predictive factors and intervention programs. The Lancet. Jul 2015;195:1698–1704.
  • Fishbein MA, Icek A. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading: Addison-Wesley; 1975.
  • Richard PB, Paul RW. An examination of the etiology of the attitude-behavior relation for goal-directed behaviors. Multivariate Behav Res. 1992;27:601–634.
  • Younghwa L, Jintae L, Zoonky L. Social influence on technology acceptance behavior: self-identity theory perspective. Adv Inf Syst. 2006;37:60–75.
  • Fred DD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13:319–340.
  • Viswanath V, et al. User acceptance of information technology: toward a unified view. MIS Q. 2003; 27:425–478.
  • Natsuko I, et al. Adoption and impact of non-pharmaceutical interventions for COVID-19. Wellcome Open Res. 2020;5:59.
  • Bella NK, Jonathan K. Non-pharmaceutical interventions for pandemic COVID-19: a cross-sectional investigation of US general public beliefs, attitudes, and actions. Front Med (Lausanne). 2020 Jul 3.
  • Rine CR, et al. Knowledge, attitudes and practices towards COVID–19: an epidemiological survey in North–Central Nigeria. J Community Health. 2020;46:457–470.
  • Merriam-Webster. Attitude. Merriam-Webster. [Online] February 8, 2022. Available from: https://www.merriam-webster.com/dictionary/attitude.
  • Hee-dong Y, Youngjin Y. It’s all about attitude: revisiting the technology acceptance model. Decis Support Syst. 2004;38:19–31.
  • Young CJ, Choong-Ki L, Yae-Na P. Trust in social non-pharmaceutical interventions and travel intention during a pandemic. J Vacat Mark. 2021;27:437–448.
  • Moses P, Wong SL, Bakar KA. Perceived usefulness and perceived ease of use: antecedents of attitude towards laptop use among science and mathematics teachers in Malaysia. Asia-Pac Educ Res. 2013;22:293–299.
  • Goo KT, Hyoung LJ, Rob L. An empirical examination of the acceptance behaviour of hotel front office systems: an extended technology acceptance model. Tour Manag. 2008;29:500–513.
  • Janell R, Gerard JPF. Determinants of perceived usefulness and perceived ease of use in the Technology Acceptance Model: Senior consumers' adoption of self-service banking technologies. 2nd Biennial Conference of the Academy of World Business, Marketing and Management Development: Business Across Borders in the 21st Century; 2006.
  • Na JM, Jaebin C. Consumer attitudes and behavioral intentions on delivery application quality: focusing on Technology Acceptance Model (TAM). J Tour Sci. 2017;41:171–184.
  • John RC. Applying a modified technology Acceptance Model to determine factors affecting behavioral intentions to adopt electronic shopping on the World Wide Web: a structural equation modeling approach. Philadelphia: Drexel University; 2000.
  • Hsieh LY, Lu YJ, Lee YH. Using the Technology Acceptance Model to explore the behavioral intentions toward blended learning. [book auth.] L Uden, J Sinclair and YH, Liberona, DT. Learning Technology for Education in Cloud. MOOC and Big Data. Cham: Springer; 2014.
  • White BE, Al-Gahtani Said S, Hubona Geoffrey S. The effects of gender and age on new technology implementation in a developing country: testing the theory of planned behavior (TPB). Inf Technol People. 2007;20:352–375.
  • Simone C, Terry L, Hsu CH. Negative word-of-mouth communication intention: an application of the theory of planned behavior. J Hosp Tour Res. 2006;30:95–116.
  • LaMorte WW. The theory of planned behavior. Boston University School of Public Health. [Online] September 9, 2019. [cited 2022 Apr 4]. Available from: https://sphweb.bumc.bu.edu/otlt/mph-modules/sb/behavioralchangetheories/BehavioralChangeTheories3.html.
  • Icek A. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.
  • Luigi L, Marco P, Anna P E. Studying, practicing, and mastering: a test of the model of goal-directed behavior (MGB) in the software learning domain. J Appl Soc Psychol. 2004;34:1945–1973.
  • Marco P, Richard B. The role of desires and anticipated emotions in goal-directed behaviors: broadening and deepening the theory of planned behavior. Br J Soc Psychol. 2001;40:79–98.
  • Andrew P, Marco P, Robert H. Goal desires moderate intention - behaviour relations. Br J Soc Psychol. 2011;47:49–71.
  • Richard LO. Satisfaction: a behavioral perspective on the consumer. New York: Routledge; 2014.
  • Nowak A, Szamrej J, Latan B. From private attitude to public opinion: a dynamic theory of social impact. Psychol Rev. 1990;97:362–376.
  • Latané B. The psychology of social impact. Am Psychol. 1981;36:343–356.
  • Turner JC. Social influence. Mikton Keynes: Open University Press; 1991.
  • Izuma K. The neural bases of social influence on valuation and behavior. Decis Neurosci. 2017: 199–209.
  • Jung-Hua C, Wang Y-QZ, Huei S, et al. Would you change your mind? An empirical study of social impact theory on Facebook. Telemat Inform. 2018;35:282–292.
  • Brennen JS, et al. Types, sources, and claims of COVID-19 misinformation. Reuters Institute. [Online] April 2020. [cited 2022 Apr 4]. Available from: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-04/Brennen%20-%20COVID%2019%20Misinformation%20FINAL%20(3).pdf.
  • Eva HT, et al. Social influence on positive youth development: a developmental neuroscience perspective. Adv Child Dev Behav. 2018;54:215–258.
  • Yogesh M, Dennis FG. Extending the Technology Acceptance Model to account for social influence: theoretical bases and empirical validation. IEEE. Proceedings of the 32nd Hawaii International Conference on System Sciences; 1999.
  • Brian RK, Michael L. C, Jerry L K. The impact of social presence on technology based self-service use: the role of familiarity. Ser Mark Q. 2009;30:303–314.
  • Mark AB, Gon KW, Meehee C. Purchasing wine online: the effects of social influence, perceived usefulness, perceived ease of use, and wine involvement. J Hosp Mark Manag. 2016;25:851–869.
  • Richard JH, Ben TK. The Technology Acceptance Model: its past and its future in health care. J Biomed Inform. 2010 Feb;43:159–172.
  • Venkatesh V, Davis FD. A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Manage Sci. 2000;46:186–204.
  • Hartwick J, Barki H. Explaining the role of user participation in information system use. Manage Sci. 1994;40:440–465.
  • National Science Foundation. Frequently asked questions and vignettes. National Science Foundation. [Online] 2022. [cited 2022 Apr 4]. Available from: https://www.nsf.gov/bfa/dias/policy/hsfaqs.jsp#snow.
  • Cochran WG. Sampling techniques. New York: John Wiley & Sons; 1977.
  • Joe H, et al. A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage; 2021.
  • Sadat AA, Pahlevan SS, Sim OF. Online health information seeking among women: the moderating role of health consciousness. Online Inf Rev. 2018;42:58–72.
  • Davis F, Bagozzi RP. User acceptance on computer technology: a comparison of two theoretical models. Manage Sci. 1989;35:982–1003.
  • Xitong G, et al. The dark side of elderly acceptance of preventive mobile health services in China. Electron Mark. December 2012;23:49–61.
  • Ming-Chien H, Wen-Yuan J. The adoption of mobile health management services: an empirical study. J Med Syst. 2012;36:1381–1388.
  • Melinda W, Ronald G. Factors influencing intention to use personal health records. Int J Pharm Healthc Mark. 2009;3:8–25.
  • Jen-Her W, Shu-Ching W, Li-Min L. Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int J Med Inf. 2007;76:66–77.
  • Yiwen G, He L, Yan L. An empirical study of wearable technology acceptance in healthcare. Ind Manag Data Syst. 2015;9:1704–1723.
  • Javier R-CF, Jorge A-G, Esteban R-CP. A comparison of the different versions of popular technology acceptance models a non-linear perspective. Kybernetes. 2015;44:788–805.
  • Joe H, et al. Advanced issues in Partial Least Squares structural equation modeling. Thousand Oaks: Sage; 2018.
  • Jana R, Rosnita II, Marc RC. The agony of choice for medical tourists: a patient satisfaction index model. J Hosp Tour Technol. 2018;9:267–279.
  • Jörg H, Christian M. R, Marko S. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115–135.
  • Seymour G. The predictive sample reuse method with applications. J Am Stat Assoc. 1974;70:320–328.
  • Mervyn S. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc. 1974;36:111–147.
  • Hair JF, Howard MC, Nitzl C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J Bus Res. 2020;109:101–110.
  • Jacob C. Things I have Learned (so far). [book auth.] AE, Kazdin. Methodological Issues and Strategies in Clinical Research. Washington : American Psychological Association, 1992, Vol. 45, pp. 1304–1312.
  • Kline RB. Beyond significance testing: reforming data analysis methods in behavioral research. Washington, DC: American Psychological Association; 2004.
  • Gail MS, Richard F. Using effect size—or why the p value is not enough. J Grad Med Educ. 2012;4:279–282.
  • Christian R, et al. Partial least squares structural equation modeling in HRM research. Int J Hum Resour Manag. 2018;19:1617–1643.
  • Jürgen M, Julia B, Silvia S. Adherence to behavioral COVID-19 mitigation measures strongly predicts mortality. PLOSONE. 2021;16:e0249392.

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