Current issue

The Korean Society for Journalism & Communication Studies - Vol. 68 , No. 1
[Paper List]

[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 5, pp. 402-436
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 31 Oct 2021
Received 11 Jun 2021 Accepted 28 Sep 2021 Revised 07 Oct 2021
https://doi.org/10.20879/kjjcs.2021.65.5.011

코로나19(COVID-19) 팬데믹 정보 이용채널과 위험인식, 그리고 행동반응 간의 관계 측정 : 개인적 차원과 집합적 차원의 지각비교를 통해
이완수** ; 최명일*** ; 유재웅****
**동서대학교 미디어커뮤니케이션 계열 교수, 주저자 (wansoo1960@gmail.com)
***남서울대학교 광고홍보학과 교수, 교신저자 (jhmi0410@nsu.ac.kr)
****을지대학교 홍보디자인학과 교수 (yoojw777@hanmail.net)

Estimating the Relationships among COVID-19 Pandemic Information Channel Use, Risk Perception, and Behavioral Response : A Comparison of Personal and Collective Perceptions
Wan Soo Lee** ; Myungil Choi*** ; Jae Woong Yoo****
**Professor, Division of Media & Communication, Dongseo University (wansoo1960@gmail.com)
***Professor, Department of Advertising & Public Relations, Namseoul University (jhmi0410@nsu.ac.kr)
****Professor, Department of Public Relations and Design, Eulji University (yoojw777@hanmail.net)
Funding Information ▼
초록

이 연구는 글로벌 팬데믹 코로나19에 대한 커뮤니케이션 이용 채널, 위험인식, 그리고 예방행동 변인 사이의 경로와 영향이 개인적 차원과 집합적 차원에 따라 어떤 차이가 있는지 비교분석했다. 이를 위해 먼저 대응표본 t-test를 통해 관련 변인이 개인적 차원과 집합적 차원에 따라 어떤 차이가 있는지, 그리고 경로분석(path analysis)을 통해 관련 변인 사이의 관계 경로와 영향을 측정했다. 분석결과, 코로나19 위험정보를 얻기 위해 개인적으로는 인터넷을 주로 이용한다고 평가한 반면에, 주변 사람들은 신문, TV와 함께 소셜 미디어를 주로 이용할 것이라고 추정했다. 정보를 얻는 수단으로써 소셜 커뮤니케이션에 대해서는 개인 자신과 다른 사람들 간에 차이가 없었다. 코로나19에 대한 위험인식에 대해서는 개인 자신보다 주변 사람들이 위험을 더 크게 지각할 것으로 추정했다. 그리고 행동반응에 있어서도 개인 자신보다 주변 사람들이 사회적 거리두기를 하거나 정부 정책에 대한 분노감을 더 크게 표출할 것으로 추정했다. 정보 이용채널에 따른 위험인식과 예방행동 사이의 확산 경로와 영향을 비교분석한 결과에서는 개인적 차원의 경우 TV 시청을 많이 할수록, 주변 사람들과 대화를 많이 할수록, 그리고 인터넷 이용을 많이 할수록 각각 코로나19에 대한 위험인식이 더 커졌다. 나아가 위험인식이 높을수록 대인 접촉회피가 커졌으며, 정부의 코로나 정책에 대한 분노도 커졌다. 집합적 차원의 경우 TV시청을 많이 할수록, 주변사람들과 대화를 많이 할수록, 그리고 소셜 미디어를 많이 이용할수록 코로나19 위험을 더 크게 지각할 것이라고 추정했다. 집합적 차원에 있어서도 위험인식이 사회적 거리두기와 정부의 코로나 정책에 대한 분노에 정적으로 영향을 미쳤다. 그리고 측정 변인 간의 경로가 서로 유의미한 차이를 보이는지 조절효과를 살펴본 결과에서는 소셜 미디어가 개인 자신이 아닌, 주변 사람들의 위험인식에 유일하게 영향을 미쳤다. 이 연구는 코로나19에 대한 정보 이용 채널별 위험인식과 예방행동 사이의 관계가 개인적 차원과 집합적 차원에 따라 차이가 있는지 살펴보고, 이를 바탕으로 이론적, 실무적 함의점을 논의했다.

Abstract

This study comparatively analyzed differences in the pathways and influences of usage of channels for communication on the global COVID-19, risk perception, and preventive behavior at the personal and collective levels. A response sample t-test was first performed to estimate differences in related variables at the personal and collective levels, and a path analysis was performed to establish the pathways and influences among the relevant variables. The results showed that in the personal-level analysis, respondents indicated that they used the Internet mainly to find information about COVID-19 risks, but predicted that other people primarily used social media, newspapers, and television. In terms of social communication as a means of acquiring information, no difference was found between the individual respondents and others. Respondents also predicted that others would perceive the risks associated with COVID-19 to be greater than they did. In terms of behavioral response, they predicted that others would avoid contact with others and express a stronger sense of anger toward government policies compared with themselves. At the personal level, comparative analysis of pathways and influences between risk perceptions and preventive behaviors according to information channel showed higher levels of TV viewing, conversations with others, and Internet usage to be associated with stronger perception of COVID-19 risks. Stronger risk perception was associated in turn with increased avoidance of interpersonal contact and anger toward government policies related to the pandemic. In the collective-level analysis, higher levels of TV viewing, conversations with others, and social media usage were associated with stronger perceptions of COVID-19 risks. Risk perception was also found to be positively associated with avoidance of interpersonal contact and anger toward government policies at the collective level. An examination of moderating effects to identify significant differences according to variable pathways showed a significant influence between social media usage and the risk perceptions of others, rather than those of the individual respondent. Theoretical and practical implications are discussed.

KeywordsCOVID-19, information channels, risk perception, behavioral response, impersonal influence hypothesis
키워드: 코로나19, 정보채널, 위험인식, 예방행동, 비개인적 영향 가설
Acknowledgments

This paper was supported by Dongseo University, “Dongseo Frontier Project” Research Fund of 2020(DSU-20200009)(이 논문은 2020년도 동서대학교 “Dongseo Frontier Project” 지원에 의해 이루어졌다).

References
1. Addi, A. R., Benksim, A., Amine, M., & Cherkaoui, M. (2020). COVID-19 outbreak and perspective in Morocco. Electron J Gen Med. 2020; 17 (4): em204.
2. Binder, A. R., Scheufele, D. A., Brossard, D., & Gunther, A. C. (2011). Interpersonal amplification of risk? Citizen discussions and their impact on perceptions of risks and benefits of a biological research facility. Risk Analysis: An International Journal, 31(2), 324-334.
3. Bomlitz, L. J., & Brezis, M. (2008). Misrepresentation of health risks by mass media. Journal of Public Health, 30(2), 202-204.
4. Brown, R., Coventry, L., & Pepper, G. (2021). Information seeking, personal experiences, and their association with COVID-19 risk perceptions: demographic and occupational inequalities. Journal of Risk Research, 24(3-4), 506-520.
5. Choi, D., Yoo, W., Noh, G., & Park, K. (2017). The impact of social media on risk perceptions during the MERS outbreak in South Korea. Computers in Human Behavior, 72.
6. Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591-621.
7. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10, 16598. https://doi.org/10.1038/s41598-020-73510-5
8. Davidson, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47, 1-15.
9. Fragkaki, I., Maciejewski, D. F., Weijman, E. L., Feltes, J., & Cima, M. (2021). Human responses to Covid-19: The role of optimism bias, perceived severity, and anxiety. Personality and Individual Differences, 176, 110781.
10. Gore, M. L., Siemer, W. F., Shanahan, J. E., Schuefele, D., & Decker, D. J. (2005). Effects on risk perception of media coverage of a black bear-related human fatality. Wildlife Society Bulletin, 33(2), 507–516.
11. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L.(2006), Multivariate data analysis, New Jersey: Pearson Prentice Hall.
12. Hart, P. S., Nisbet, E. C., & Shanahan, J. E. (2011). Environmental values and the social amplification of risk: An examination of how environmental values and media use influence predispositions for public engagement in wildlife management decision making. Society & Natural Resources, 24(3), 276–291.
13. Heydari, S. T., Zarei, L., Sadati, A. K., Moradi, N., Akbari, M., Mehralian, G., & Lankarani, K. B. (2021). The effect of risk communication on preventive and protective Behaviours during the COVID-19 outbreak: mediating role of risk perception. BMC Public Health, 21(1), 1-11.
14. Hussain, W. (2020). Role of social media in covid-19 pandemic. International Journal of Frontier Sciences, 4(2), 59-60.
15. Islam, A. N., Laato, S., Talukder, S., & Sutinen, E. (2020). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159, 120201.
16. Jahangiry, L., Bakhtari, F., Sohrabi, Z., Reihani, P., Samei, S., Ponnet, K., & Montazeri, A. (2020). Risk perception related to COVID-19 among the Iranian general population: an application of the extended parallel process model. BMC Public Health, 20(1), 1-8.
17. Jiang, X., Hwang, J., Shah, D. V., Ghosh, S., & Brauer, M. (2021). News Attention and Social-Distancing Behavior Amid COVID-19: How Media Trust and Social Norms Moderate a Mediated Relationship. Health Communication, 1-10. https://doi.org/10.1080/10410236.2020.1868064
18. Jung, T., & Brann, M. (2014). Analyzing the extended parallel process model and health belief model constructs in texting while driving: news coverage in leading US news media outlets. International Journal of Health Promotion and Education, 52(4), 210-221.
19. Kasperson, J. X., Kasperson, R. E., Pidgeon, N., & Slovic, P. (2003) The social amplification of risk: Assessing fifteen years of research and theory. In: N. Pidgeon, R. E. Kasperson, P. Slovic, (eds.) The Social Amplification of Risk. Cambridge, UK: Cambridge University, pp. 13–46.
20. Kasperson, R. E., & Kasperson, J. X. (1996). The social amplification and attenuation of risk. ANNALS of the American Academy of Political and Social Science. 545(1), 95-105.
21. Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., & Ratick, S. (1988). The social amplification of risk: A conceptual framework. Risk Analysis, 8(2), 177–187.
22. Leiss, W. (1996). Three Phases in the Evolution of Risk Communication Practice. Annals of the American Academy of Political and Social Science, 545(1), 85-94.
23. Liu, M., Zhang, H., & Huang, H. (2020). Media exposure to COVID-19 information, risk perception, social and geographical proximity, and self-rated anxiety in China. BMC Public Health, 20(1), 1-8.
24. Liu, P. L. (2021). COVID-19 information on social media and preventive behaviors: Managing the pandemic through personal responsibility. Social Science & Medicine, 277, 113928.
25. Liu, P. L., & Huang, L. V. (2020). Digital disinformation about COVID-19 and the third-person effect: examining the channel differences and negative emotional outcomes. Cyberpsychology, Behavior, and Social Networking, 23(11), 789-793.
26. Maestas, C. D., & Pollock, W. M. (2010). Measuring generalized risk orientation with a single survey item. Available at SSRN 1599867.
27. Maloney, E. K., Lapinski, M. K., & Witte, K. (2011). Fear appeals and persuasion: A review and update of the extended parallel process model. Social and Personality Psychology Compass, 5(4), 206-219.
28. Mazur, A. (1984). The journalists and technology: Reporting about love canal and Three Mile Island. Minerva, 22, 45-66.
29. Mobbs, D., Hagan, C. C., Dalgleish, T., Silston, B., & Prévost, C. (2015). The ecology of human fear: Survival optimization and the nervous system. Frontiers in Neuroscience, 9, 55. https://doi.org/10.3389/fnins.2015.00055.
30. Morton, T. A., & Duck, J. M. (2001). Communication and health beliefs: Mass and interpersonal influences on perceptions of risk to self and others. Communication Research, 28(5), 602-626.
31. Mutz, D. C. (1992). Impersonal influence: Effects of representations of public opinion on political attitudes. Political Behavior, 14(2), 89-122.
32. Mutz, D. C. (1998). Impersonal influence: How perceptions of mass collectives affect political attitudes. Cambridge University Press.
33. Ng, Y. J., Yang, Z. J., & Vishwanath, A. (2018). To fear or not to fear? Applying the social amplification of risk framework on two environmental health risks in Singapore. Journal of Risk Research, 21(12), 1487-1501.
34. Nguyen, H., & Nguyen, A. (2020). Covid-19 misinformation and the social(media) amplification of risk: A Vietnamese perspective. Media and Communication. 8(2), 444-447.
35. Nielsen, R. K., Fletcher, R., Newman, N., Brennen, J. S., & Howard, P. N. (2020). Navigating the ‘Infodemic’: How people in six countries access and rate news and information about coronavirus. Oxford: Reuters Institute for the study of journalism.
36. Palm, R., Bolsen, T., & Kingsland, J. T. (2021). The Effect of Frames on COVID-19 Vaccine Hesitancy. medRxiv.
37. Patterson, R. (1993). Collective identity, television and Europe. Nafional Idenfity and Europe, 1-7.
38. Piltch-Loeb, R., Savoia, E., Goldberg, B., Hughes, B., Verhey, T., Kayyem, J., ... & Testa, M. (2021). Examining the effect of information channel on COVID-19 vaccine acceptance. Plos One, 16(5), e0251095. https://doi.org/10.1371/journal.pone.0251095
39. Popova, L. (2012). The extended parallel process model: illuminating the gaps in research. Health Education & Behavior, 39(4), 455-473.
40. Reintjes, R., Das, E., Klemm, C., Richardus, J. H., Keßler, V., & Ahmad, A. (2016). “Pandemic public health paradox”: time series analysis of the 2009/10 influenza A/H1N1 epidemiology, media attention, risk perception and public reactions in 5 European countries. PloS One, 11(3), e0151258.
41. Renn O. (1991) Risk communication and the social amplification of risk. In: Kasperson R.E., Stallen P.J.M. (eds) Communicating Risks to the Public. Technology, Risk, and Society (An International Series in Risk Analysis), vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1952-5_14.
42. Renn, O., Burns, W. J., Kasperson, J. X., Kasperson, Roger, E., & Slovic, P. (1992). The Social Amplification of Risk: Theoretical Foundations and Empirical Applications. Journal of Social Issues, 48(4), 137-160.
43. Scopelliti, M., Pacilli, M. G., & Aquino, A. (2021). TV News and COVID-19: Media Influence on Healthy Behavior in Public Spaces. International Journal of Environmental Research and Public Health, 18(4), 1879.
44. Sharot, T. (2011). The optimism bias. Current Biology, 21(23), R941-R945.
45. Slovic, P. (1987). Perception of risk. Science, 230, 280-285.
46. Slovic, P. (Ed.). (2000). Risk, society, and policy series: The perception of risk. Earthscan Publications.
47. Snyder, L. B., & Rouse, R. A. (1995). The media can have more than an impersonal impact: The case of AIDS risk perceptions and behavior. Health Communication, 7(2), 125-145.
48. Strunk, D. R., Lopez, H., & DeRubeis, R. J. (2006). Depressive symptoms are associated with unrealistic negative predictions of future life events. Behaviour Research and Therapy, 44(6), 861-882.
49. Sundar, S. S., & Nass, C. (2001). Conceptualizing sources in online news. Journal of Communication, 51(1), 52-72.
50. Tai, Z., & Sun, T. (2007). Media dependencies in a changing media environment: The case of the 2003 SARS epidemic in China. New Media & Society, 9, 987-1009.
51. Tchuenche, J. M., & Bauch, C. T.(2012). Dynamics of infectious disease where media coverage influences transmission. ISRN Biomathematics.
52. Tsoy, D., Tirasawasdichai, T., & Kurpayanidi, K. I. (2021). Role of Social Media in Shaping Public Risk Perception during COVID-19 Pandemic: A Theoretical Review. International Journal of Management Science and Business Administration, 7(2), 35-41.
53. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
54. Van Bavel, J. J., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., & Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. nature human behaviour, 4(5), 460-471.
55. Wahlberg, A, A, F., & Sjoberg, L. (2000). Risk perception and the media. Journal of Risk Research, 3(1), 31-50.
56. Wang, P. W., Lu, W. H., Ko, N. Y., Chen, Y. L., Li, D. J., Chang, Y. P., & Yen, C. F. (2020). COVID-19-related information sources and the relationship with confidence in people coping with COVID-19: Facebook survey study in Taiwan. Journal of Medical Internet Research, 22(6), e20021.
57. Westerman, D., Van Der Heide, B., Klein, K. A., & Walther, J. B. (2008). How do people really seek information about others?: Information seeking across Internet and traditional communication channels. Journal of Computer-Mediated Communication, 13(3), 751-767.
58. Wirz, C. D., Xenos, M. A., Brossard, D., Scheufele, D., Chung, J. H., & Massarani, L. (2018). Rethinking social amplification of risk: Social media and Zika in three languages. Risk Analysis, 38(12), 2599-2624.
59. Wise, T., Zbozinek, T. D., Michelini, G., & Hagan, C. C. (2020). Changes in risk perception and protective behavior during the first week of the COVID-19 pandemic in the United States. Royal Society Open Science, 7(9), 1-13. https://doi.org/10.1098/rsos.200742
60. Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communications Monographs, 59(4), 329-349.
61. Woo, J., & Chen, H. (2016). Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus, 5(1), 1-19.
62. Wu, X., & Li, X. (2017). Effects of mass media exposure and social network site involvement on risk perception of and precautionary behavior toward the haze issue in China. International Journal of Communication, 11, 3975–3997.
63. Yang, J., & Tian, Y. (2021). “Others are more vulnerable to fake news than I Am”: Third-person effect of COVID-19 fake news on social media users. Computers in Human Behavior, 125, 106950.
64. Yang, L. (2015). Splicing noncoding RNAs from the inside out. Wiley Interdisciplinary Reviews: RNA, 6(6), 651-660.
65. Yeo, S. K., Cacciatore, M. A., Brossard, D., Scheufele, D. A., Runge, K., Su, L. Y., & Corley, E. A. (2014). Partisan amplification of risk: American perceptions of nuclear energy risk in the wake of the Fukushima Daiichi disaster. Energy Policy, 67, 727-736.
66. You, M., & Ju, Y. (2020). Comparing Outrage Effect on the Risk Perception of Climate Change versus Fine Dust. Health Communication, 35(13), 1678-1685.
67. Zhao, S., & Wu, X. (2021). From Information Exposure to Protective Behaviors: Investigating the Underlying Mechanism in COVID-19 Outbreak Using Social Amplification Theory and Extended Parallel Process Model. Frontiers in Psychology, 12, 1351.
부록Ⅰ. 국문 참고문헌
1. 김여라 (2010). 신종플루 뉴스 이용 정도가 개인 및 공중에 대한 건강보호 행위의도 에 미치는 영향에 관한 연구: 보호동기 이론을 중심으로. <한국언론정보학보>, 51호, 5-25.
2. 김영욱 (2014). <위험 커뮤니케이션>. 서울: 커뮤니케이션북스.
3. 김활빈·오현정·홍다예·심재철·장정헌 (2018). 미디어 이용이 신종 감염병에 대한 위험 인식과 예방행동 의도에 미치는 영향. <광고연구>, 119호, 123-152.
4. 류현숙 (2020). 미래위험을 둘러싼 위험인식과 대국민 소통 : 코로나19 사례를 중심으로. <FUTURE HORIZON>, 45호, 28-35.
5. 서정근·정일권 (2012). 국회 표결행위에 미치는 매스 미디어의 영향에 관한 연구: 머츠(Mutz)의 비대인적 영향력을 중심으로. <정치커뮤니케이션 연구>, 통권 25호, 87-130.
6. 서희정·양승찬 (2019). 비개인적 타자 정보 평가와 다수에 대한 경험이 개인의 행동의사에 미치는 영향. <정치정보연구>, 22권 1호, 127-162.
7. 유우현·정용국 (2016). 매스 미디어 노출과 메르스 예방행동 의도의 관계에서 대인 커뮤니케이션의 역할: 면대면 및 온라인 커뮤니케이션의 매개 및 조절 효과. <한국방송학보>, 30권 4호, 121-151.
8. 이보람·서경현 (2021). 불안과 감염병 대처와의 관계에서 위험지각 미디어 선택 행동억제의 이중 매개효과. <한국심리학회지: 건강>, 26권 1호, 91-107.
9. 이소은·박아란 (2020). 편향적 뉴스 이용과 언론 신뢰 하락: <Digital News Report 2020> 주요 결과. <Media Issue>, 6권 3호, 1-13.
10. 이소은·오세욱 (2020). 코로나(COVID-19) 관련 정보 이용 및 인식 현황. <Media Issue>, 6권 2호, 1-18.
11. 이완수 (2021). 코로나19 “인포데믹” 현상에 대한 이론적 고찰 : 커뮤니케이션학과 행동과학의 통합 적용. <커뮤니케이션 이론>, 17권 3호, 306-375.
12. 이완수·최명일·유재웅 (2020). 신종 코로나바이러스 발생에 따른 경제위기 평가에 대한 비개인적 영향가설 검증: 경제단위와 평가시점에 대한 지각분화를 중심으로. <한국언론학보>, 64권 5호, 286-318.
13. 이하나·황유리·정세훈 (2021). 미디어 이용자의 정보 검색과 공유 행동에 관한 연구: 성격 특성과 디지털 리터러시의 역할. <한국언론학보>, 65권 1호, 236-269.
14. 전영환·목진휴·김병준 (2016). 위험인식 및 정부신뢰가 원자력 정책 수용성과 만족도에 미치는 영향에 대한 연구. <정책분석평가학회보>, 26권 3호, 85-110.
15. 전종우 (2021). 코로나 위험지각에 영향을 미치는 미디어 배양효과와 개인의 문화 차이. <언론정보연구>, 58권 2호, 66-91.
16. 한국언론진흥재단 (2020). <2020 언론 수용자 조사>.