A national survey to evaluate the effectiveness of public communication in COVID-19 management

Journal title MECOSAN
Author/s Marco Benvenuto, Francesco Sambati, Carmine Viola
Publishing Year 2022 Issue 2022/121
Language Italian Pages 26 P. 31-56 File size 0 KB
DOI 10.3280/mesa2022-121oa13858
DOI is like a bar code for intellectual property: to have more infomation click here

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

Institutional communication is a central lever of government actions, it facilitates the relationship between institutions and citizens. In this context, the tools of institutional communication acquire strategic value. With the COVID-19 epoch, the level of attention on institutionalcommunication dynamics highlights a change in public sector and its opening towards a new managerial approach,which involves organizational, cultural, communicational and IT modernization processes. In this context this scientific contribution intends to evaluate the relationsbetween institutions and citizens in the Institutional Communication and COVID-19 report.

Keywords: ; Covid-19; service management; institutional communication; risk communication; Structural Equation Model; effectiveness

  1. Corvi E. (2007). La comunicazione aziendale: obiettivi, tecniche, strumenti. Milano: Egea.
  2. Abraham T. (2009). Risk and outbreak communication: lessons from alternative paradigms. Bulletin of the World Health Organization, 87(8): 604-607. http://www.who.int/bulletin/volumes/87/8/08-058149.pdf.
  3. Anselmi L. (2013). Percorsi aziendali per le pubbliche amministrazioni. Torino: Giappichelli.
  4. Aylward R., Clements J., Olive J. (1997). The impact of immunization control activities on measles outbreaks in middle and low income countries. International Journal of Epidemiology, 26(3): 662-669. DOI: 10.1093/ije/26.3.662
  5. Benton A., Dionne K. (2015). International Political Economy and the 2014 West African Ebola Outbreak. African Studies Review, 58(1): 223-236. DOI: 10.1017/asr.2015.11
  6. Benvenuto M., Avram A., Sambati F. V., Avram M., Viola C. (2020). The Impact of Internet Usage and Knowledge-Intensive Activities on Households’ Healthcare Expenditures. International Journal of Environmental Research and Public Health, 17(12): 4470. DOI: 10.3390/ijerph17124470
  7. Benvenuto M., Rosa A., Viola C. (2020). Analisi prospettica per il design di un nuovo dominio di pianificazione, programmazione e controllo sociotecnico nel settore della salute. Mecosan, 113: 259-269. DOI: 10.3280/MESA2020-113030
  8. Benvenuto M., Sambati F.V., Viola C. (2019). The impact of internet usage on health-care expenditures and sustainability. 5th International Conference – ERAZ 2019 – Knowledge Based Sustainable Development, Budapest – Hungary, May 23, 2019, selected papers,pp. 95-107. DOI: 10.31410/ERAZ.S.P.2019.95
  9. Bethlehem J. (2010). Selection Bias in Web Surveys. International Statistical Review, 78(2): 161-188. DOI: 10.1111/J.1751-5823.2010.00112.X
  10. Carney M.T., Buchman T., Neville S., Thengampallil A., Silverman R. (2015). A community partnership to respond to an outbreak: A model that can be replicated for future events. Progress in Community Health Partnerships: Research, Education, and Action, 8(4): 531-540. DOI: 10.1353/cpr.2014.0065
  11. Choi D.-H., Yoo W., Noh G.-Y., Park K. (2017). The impact of social media on risk perceptions during the MERS outbreak in South Korea. Computers in Human Behavior, 72: 422-431. DOI: 10.1016/j.chb.2017.03.004
  12. Chou C.-P., Bentler P.M. (1995). Estimates and tests in structural equation modeling. In: Hoyle R.H. (Ed.). Structural equation modeling: Concepts, issues, and applications. London: Sage Publications, Inc., pp. 37-55.
  13. Costantino C., Restivo V., Ventura G., D’Angelo C., Randazzo M., Casuccio N., Palermo M., Casuccio A., Vitale F. (2018). Increased Vaccination Coverage among Adolescents and Young Adults in the District of Palermo as a Result of a Public Health Strategy to Counteract an ‘Epidemic Panic.’ International Journal of Environmental Research and Public Health, 15(5): 1014. DOI: 10.3390/ijerph15051014
  14. Couper M.P., Antoun C., Mavletova A. (2017). Mobile Web Surveys. In: Total Survey Error in Practice (pp. 133-154). New York: John Wiley & Sons, Ltd. DOI: 10.1002/9781119041702.CH7
  15. Cowper A. (2020). Covid-19: Are we getting the communications right?. The BMJ, 368: 1-3. BMJ Publishing Group. DOI: 10.1136/bmj.m919
  16. Crouse Quinn S. (2008). Crisis and emergency risk communication in a pandemic: a model for building capacity and resilience of minority communities. Health Promotion Practice, 9(4 Suppl): 18-25. DOI: 10.1177/1524839908324022
  17. Cucciniello M., Fattore G., Longo F., Ricciuti E., Turrini A. (2018). Management pubblico (Vol. 1). Milano: Egea S.p.A., pp. 133-142.
  18. Ding H. (2009). Rhetorics of Alternative Media in an Emerging Epidemic: SARS, Censorship, and Extra-Institutional Risk Communication. Technical Communication Quarterly, 18(4): 327-350. DOI: 10.1080/10572250903149548
  19. Dunleavy P., Hood C. (1994). From old public administration to new public management. Public Money and Management, 14(3): 9-16. DOI: 10.1080/09540969409387823
  20. Gorla N., Somers T.M., Wong B. (2010). Organizational impact of system quality, information quality, and service quality. Journal of Strategic Information Systems, 19(3): 207-228. DOI: 10.1016/j.jsis.2010.05.001
  21. Gravili G., Benvenuto M., Avram A., Viola C. (2018). The influence of the Digital Divide on Big Data generation within supply chain management. International Journal of Logistics Management, 29(2): 592-628. DOI: 10.1108/IJLM-06-2017-0175
  22. Hall K., Wolf M. (2019). Whose crisis? Pandemic flu, ‘communication disasters’ and the struggle for hegemony. Health: An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine, 1363459319886112. DOI: 10.1177/1363459319886112
  23. Heckman J. (1990a). Selection Bias and Self-selection. Econometrics, 201-224. DOI: 10.1007/978-1-349-20570-7_29
  24. Heckman J. (1990b). Varieties Of Selection Bias. The American Economic Review, 80(2): 313-318. -- https://www.jstor.org/stable/2006591%0A.
  25. Helms J.E., Henze K.T., Sass T.L., Mifsud V.A. (2006). Treating Cronbach’s Alpha Reliability Coefficients as Data in Counseling Research. The Counseling Psychologist, 34(5): 630-660. DOI: 10.1177/0011000006288308
  26. Holland K., Blood R.W., Imison M., Chapman S., Fogarty A. (2012). Risk, expert uncertainty, and Australian news media: public and private faces of expert opinion during the 2009 swine flu pandemic. Journal of Risk Research, 15(6): 657-671. DOI: 10.1080/13669877.2011.652651
  27. Jones S.C., Waters L., Holland O., Bevins J., Iverson D. (2010). Developing pandemic communication strategies: Preparation without panic. Journal of Business Research, 63(2): 126-132. DOI: 10.1016/j.jbusres.2009.02.009
  28. Kendall M.G. (1960). Studies in the History of Probability and Statistics. Where Shall the History of Statistics Begin?. Biometrika, 47(3/4): 447. DOI: 10.2307/2333315
  29. Kosec K., Wantchekon L. (2020). Can information improve rural governance and service delivery?. World Development, 125: 104376. DOI: 10.1016/j.worlddev.2018.07.017
  30. Lammers J.C. (2011). How Institutions Communicate: Institutional Messages, Institutional Logics, and Organizational Communication. Management Communication Quarterly, 25(1): 154-182. DOI: 10.1177/0893318910389280
  31. Lillrank P. (2003). The quality of information. International Journal of Quality and Reliability Management, 20(6): 691-703. DOI: 10.1108/02656710310482131
  32. Lin L., McCloud R.F., Bigman C.A., Viswanath K. (2017). Tuning in and catching on? Examining the relationship between pandemic communication and awareness and knowledge of MERS in the USA. Journal of Public Health (United Kingdom), 39(2): 282-289. DOI: 10.1093/pubmed/fdw028
  33. Lohmoller J.-B. (1988). The PLS Program System: Latent Variables Path Analysis with Partial Least Squares Estimation. Multivariate Behavioral Research, 23(1): 125-127. DOI: 10.1207/s15327906mbr2301_7
  34. Nieto A. (2006). Economia della comunicazione istituzionale. Milano: FrancoAngeli.
  35. Nutbeam D. (2000). Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promotion International, 15(3): 259-267. DOI: 10.1093/heapro/15.3.259
  36. Oh S.-H., Lee S.Y., Han C. (2020). The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception. Health Communication, 1-10. DOI: 10.1080/10410236.2020.1724639
  37. Pierantoni P., Rovinetti A. (2002). La comunicazione istituzionale: dieci anni di riforme nella pubblica amministrazione. Pisa: ETS.
  38. Reynolds B., Quinn Crouse S. (2008). Effective communication during an influenza pandemic: the value of using a crisis and emergency risk communication framework. Health Promotion Practice, 9(4 Suppl): 13-17. DOI: 10.1177/1524839908325267
  39. Rice R.E., Atkin C. (2013). Public Communication Campaigns. London: SAGE Publications, Inc.
  40. Rosa A., Marolla G., Benvenuto M. (2020). Il modello Value-Based Health Care: una possibile risposta alla gestione Covid-19. Mecosan, 113: 243-257. DOI: 10.3280/MESA2020-113029
  41. Schaurer I., Weiß B. (2020). Investigating selection bias of online surveys on coronavirus-related behavioral outcomes. Survey Research Methods, 14(2): 103-108. DOI: 10.18148/SRM/2020.V14I2.7751
  42. Suárez-Gonzalo S. (2018). Your likes, your vote? Big personal data exploitation and media manipulation in the US presidential election campaign of Donald Trump in 2016. Quaderns Del CAC, 44(21) (November): 25-33. -- https://www.researchgate.net/publication/337030800_Your_likes_your_vote_Big_personal_data_exploitation_and_media_manipulation_in_the_US_presidential_election_campaign_of_Donald_Trump_in_2016.
  43. Texier G., Farouh M., Pellegrin L., Jackson M.L., Meynard J.-B., Deparis X., Chaudet H. (2016). Outbreak definition by change point analysis: a tool for public health decision?. BMC Medical Informatics and Decision Making, 16(1): 33. DOI: 10.1186/s12911-016-0271-x
  44. van Nijnatten C. (2006). Meta-communication in Institutional Talks. Qualitative Social Work: Research and Practice, 5(3): 333-349. DOI: 10.1177/1473325006067364
  45. White L.A., Forester J.D., Craft M.E. (2018). Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology. Proceedings of the National Academy of Sciences, 115(28): 7374-7379. DOI: 10.1073/pnas.1801383115
  46. Wong C.M.L., Jensen O. (2020). The paradox of trust: perceived risk and public compliance during the COVID-19 pandemic in Singapore. Journal of Risk Research, 1-10. DOI: 10.1080/13669877.2020.1756386
  47. World Health Organization (2020). Risk communication and community engagement readiness and initial response for novel coronaviruses (nCoV). Who, 1( January): 1-3. WHO/2019-n-CoV/RCCE/2020.2.

Marco Benvenuto, Francesco Sambati, Carmine Viola, Una survey nazionale per valutare l’efficacia della comunicazione istituzionale nella gestione del Covid-19 in "MECOSAN" 121/2022, pp 31-56, DOI: 10.3280/mesa2022-121oa13858