Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

Tomislav Pavlović; Flavio Azevedo; Koustav De; Julián C. Riaño-Moreno; Marina Maglić; Theofilos Gkinopoulos; Patricio Andreas Donnelly-Kehoe; César Payán-Gómez; Guanxiong Huang; Jaroslaw Kantorowicz; Michèle D. Birtel; Philipp Schönegger; Valerio Capraro; Hernando Santamaría-García; Meltem Yucel; Agustin Ibanez; Steve Rathje; Erik Wetter; Dragan Stanojević; Jan-Willem van Prooijen; Eugenia Hesse; Christian T. Elbaek; Renata Franc; Zoran Pavlović; Aleksandra Cichocka; André Krouwel

doi:10.1093/pnasnexus/pgac093

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

Tomislav Pavlović, Flavio Azevedo, Koustav De, Julián C. Riaño-Moreno, Marina Maglić, Theofilos Gkinopoulos, Patricio Andreas Donnelly-Kehoe, César Payán-Gómez, Guanxiong Huang, Jaroslaw Kantorowicz, Michèle D. Birtel, Philipp Schönegger, Valerio Capraro, Hernando Santamaría-García, Meltem Yucel, Agustin Ibanez, Steve Rathje, Erik Wetter, Dragan Stanojević, Jan-Willem van ProoijenEugenia Hesse, Christian T. Elbaek, Renata Franc, Zoran Pavlović, Aleksandra Cichocka, André Krouwel

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Original language	English
Article number	pgac093
Pages (from-to)	1-15
Number of pages	15
Journal	PNAS Nexus
Volume	1
Issue number	3
Early online date	5 Jul 2022
DOIs	https://doi.org/10.1093/pnasnexus/pgac093
Publication status	Published - Jul 2022

Funding

E.L. was financially supported by the VolkswagenStiftung (grant no. 98 525). Manos Tsakiris was financially supported by the NOMIS Stiftung (NOMIS Foundation). O.B. was financially supported by the Slovak Research and Development Agency (SRDA; Agentúra na podporu výskumu a vývoja; grant no. APVV-18-0218). P.L. was financially supported by the Medical Research Council (MRC; grant no. MR/P014097/1) and the Economic Social Research Council Impact Acceleration Award, University of Oxford. M.B.P. was financially supported by the Carlsberg Foundation (grant no. CF20-044). Michael Tyrala was financially supported by the HKUST IEMS research grant project, funded by EY. R.A.Z. was financially supported by the Netherlands Organization for Scientific Research (NWO; grant no. 440.20.003). Z.P. was financially supported by the Ministry of Education, Science, and Technological Development of the Republic of Serbia (grant no. 451-03-9/2021-14/200163). Y.Z. was financially supported by the National Natural Science Foundation of China (grant no. 71972065; 71832004; and 71872152), Universities in Hebei Province Hundred Outstanding Innovative Talents Support Program (grant no. SLRC2019002); and the Ministry of Education in China (grant no. 21JHQ088). B.S. was financially supported by the French National Research Agency (ANR; grant no. ANR-10-IDEX-0001-02 PSL*, ANR-10-LABX-0087 IEC, and ANR-17-EURE-0017 FrontCog). J.C. was financially supported by the IAST funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d'Avenir) programme (grant no. ANR-17-EURE-0010). M.H. was financially supported by the Slovak Research and Development Agency (grant no. APVV-17-0596). M.M. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. T.P. was financially supported by the Institute of Social Sciences Ivo Pilar and Croatian Science Foundation (grant no. DOK-01-2018), Zagreb; Croatia. I.M. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. R.F. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. A.I. was financially supported by the grants from Takeda (CW2680521); CONICET; ANID/FONDECYT Regular (1210195 and 1210176); FONCYT-PICT 2017-1820; ANID/FONDAP/15150012; Sistema General de Regalías (BPIN2018000100059), the Universidad del Valle (CI 5316); and the Multi-Partner Consortium to Expand Dementia Research in Latin America [ReDLat, supported by the National Institutes of Health, the National Institutes of Aging (R01 AG057234), the Alzheimer's Association (SG-20-725707), the Rainwater Charitable foundation—Tau Consortium, and the Global Brain Health Institute)]. P.S.B. was financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES; grant no. 1133/2019) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant no. 309905/2019-2). G.G.R. was financially supported by the São Paulo Research Foundation (grant no. 2019/26665-5). W.M.S. was financially supported by the São Paulo Research Foundation—FAPESP (grant no. 2019/27100-1). R.M.R. was financially supported by the Australian Research Council (grant no. DP180102384). M.A. was financially supported by the John Templeton Foundation (grant no. 61378). H.H.T. was financially supported by the Ministry of Science and Technology, Taiwan. M.-J.L. was financially supported by the Ministry of Science and Technology, Taiwan. J.J.V.B. was financially supported by the John Templeton Foundation. U.D. was financially supported by the QUT Centre for Behavioural Economics, Society and Technology (BEST). A.C. was financially supported by the National Science Center (grant no. 2018/29/B/HS6/02826). S.B. was financially supported by the ANR‐Labex IAST. F.C.A. was financially supported by the Research Council of Norway through its Centres of Excellence Scheme, FAIR project (grant no. 262675). B.C. was financially supported by the Social Sciences and Humanities Research Council (SSHRC). C.L. was financially supported by the Sistema Nacional de Investigadores, CONACyT (Mexico). R.T. was financially supported by the NOMIS Foundation Distinguished Scientist Award for the “Body & Image in Arts & Science” (BIAS) Project. T.O. was financially supported by the Aarhus University Research Foundation (grant no. 28207). D.S. was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Germany's Excellence Strategy (grant no. EXC 2052/1-390713894). M.A.J.A. was financially supported by the Biotechnology and Biological Sciences Research Council David Phillips Fellowship (grant no. BB/R010668/1). A.K. was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112) and the Academy of Finland (grant no. 323207). E.M.-M. was financially supported by the Contemporary Thinking and Innovation for Social Development (COIDESO), the University of Huelva, Huelva, Spain. T.S. was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112) and the Academy of Finland (grant no. 323207). C.L. was financially supported by the University of Vienna, COVID-19 Rapid Response grant, and the Austrian Science Fund (FWF, I3381). A.C.W. was financially supported by the Natural Sciences and Engineering Research Council of Canada. H.S. was financially supported by the Research Council of Norway, Centres of Excellence scheme -FAIR project (grant no. 262675). S.B. was financially supported by the School of Medicine and Health Sciences—Universidad del Rosario. W.C. was financially supported by the SSHRC grant (grant no. 506547). E.B. was financially supported by the PAI2018 project PROCOPE (Prosociality, Cognition, and Peer Effects) funded by the IMT School for Advanced Studies Lucca. V.H.D. was financially supported by the SSHRC grant (grant no. 506547). P.M. was financially supported by the Aarhus University Research Foundation (grant no. AUFF-E-201 9-9-4). A.F. was financially supported by the Slovak Research and Development Agency (grant no. APVV-17-0596). S.R. was financially supported by the Gates Cambridge Scholarship. M.J.A.W. was financially supported by the Social Science and Humanities Research Council of Canada (grant no. 435-2019-0692). A.O. was financially supported by the Swedish Research Council—consolidator grant (grant no. 2018-00877). J.F. was financially supported by the Natural Sciences and Engineering Research Council of Canada. L.C. was financially supported by the Batten Institute, the University of Virginia Darden School of Business. R.B.F. was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Germany's Excellence Strategy (grant no. EXC 2052/1-390713894). Open access funding was enabled and organized by Darden School of Business, Charlottesville, VA, USA. E.L. was financially supported by the VolkswagenStiftung (grant no. 98 525). Manos Tsakiris was financially supported by the NOMIS Stiftung (NOMIS Foundation). O.B. was financially supported by the Slovak Research and Development Agency (SRDA; Agentúra na podporu výskumu a vývoja; grant no. APVV-18-0218). P.L. was financially supported by the Medical Research Council (MRC; grant no. MR/P014097/1) and the Economic Social Research Council Impact Acceleration Award, University of Oxford. M.B.P. was financially supported by the Carlsberg Foundation (grant no. CF20-044). Michael Tyrala was financially supported by the HKUST IEMS research grant project, funded by EY. R.A.Z. was financially supported by the Netherlands Organization for Scientific Research (NWO; grant no. 440.20.003). Z.P. was financially supported by the Ministry of Education, Science, and Technological Development of the Republic of Serbia (grant no. 451-03-9/2021-14/200163). Y.Z. was financially supported by the National Natural Science Foundation of China (grant no. 71972065; 71832004; and 71872152), Universities in Hebei Province Hundred Outstanding Innovative Talents Support Program (grant no. SLRC2019002); and the Ministry of Education in China (grant no. 21JHQ088). B.S. was financially supported by the French National Research Agency (ANR; grant no. ANR-10-IDEX-0001-02 PSL∗, ANR-10-LABX-0087 IEC, and ANR-17-EURE-0017 FrontCog). J.C. was financially supported by the IAST funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) programme (grant no. ANR-17-EURE-0010). M.H. was financially supported by the Slovak Research and Development Agency (grant no. APVV-17-0596). M.M. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. T.P. was financially supported by the Institute of Social Sciences Ivo Pilar and Croatian Science Foundation (grant no. DOK-01-2018), Zagreb; Croatia. I.M. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. R.F. was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia. A.I. was financially supported by the grants from Takeda (CW2680521); CONICET; ANID/FONDECYT Regular (1210195 and 1210176); FONCYT-PICT 2017-1820; ANID/FONDAP/15150012; Sistema General de Regalías (BPIN2018000100059), the Universidad del Valle (CI 5316); and the Multi-Partner Consortium to Expand Dementia Research in Latin America [ReDLat, supported by the National Institutes of Health, the National Institutes of Aging (R01 AG057234), the Alzheimer’s Association (SG-20-725707), the Rainwater Charitable foundation—Tau Consortium, and the Global Brain Health Institute)]. P.S.B. was financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES; grant no. 1133/2019) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant no. 309905/2019-2). G.G.R. was financially supported by the São Paulo Research Foundation (grant no. 2019/26665-5). W.M.S. was financially supported by the São Paulo Research Foundation—FAPESP (grant no. 2019/27100-1). R.M.R. was financially supported by the Australian Research Council (grant no. DP180102384). M.A. was financially supported by the John Templeton Foundation (grant no. 61378). H.H.T. was financially supported by the Ministry of Science and Technology, Taiwan. M.-J.L. was financially supported by the Ministry of Science and Technology, Taiwan. J.J.V.B. was financially supported by the John Templeton Foundation. U.D. was financially supported by the QUT Centre for Behavioural Economics, Society and Technology (BEST). A.C. was financially supported by the National Science Center (grant no. 2018/29/B/HS6/02826). S.B. was financially supported by the ANR-Labex IAST. F.C.A. was financially supported by the Research Council of Norway through its Centres of Excellence Scheme, FAIR project (grant no. 262675). B.C. was financially supported by the Social Sciences and Humanities Research Council (SSHRC). C.L. was financially supported by the Sistema Nacional de Investigadores, CONACyT (Mexico). R.T. was financially supported by the NOMIS Foundation Distinguished Scientist Award for the “Body & Image in Arts & Science” (BIAS) Project. T.O. was financially supported by the Aarhus University Research Foundation (grant no. 28207). D.S. was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Germany’s Excellence Strategy (grant no. EXC 2052/1-390713894). M.A.J.A. was financially supported by the Biotechnology and Biological Sciences Research Council David Phillips Fellowship (grant no. BB/R010668/1). A.K. was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112) and the Academy of Finland (grant no. 323207). E.M.-M. was financially supported by the Contemporary Thinking and Innovation for Social Development (COIDESO), the University of Huelva, Huelva, Spain. T.S. was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112) and the Academy of Finland (grant no. 323207). C.L. was financially supported by the University of Vienna, COVID-19 Rapid Response grant, and the Austrian Science Fund (FWF, I3381). A.C.W. was financially supported by the Natural Sciences and Engineering Research Council of Canada. H.S. was financially supported by the Research Council of Norway, Centres of Excellence scheme -FAIR project (grant no. 262675). S.B. was financially supported by the School of Medicine and Health Sciences—Universidad del Rosario. W.C. was financially supported by the SSHRC grant (grant no. 506547). E.B. was financially supported by the PAI2018 project PRO-COPE (Prosociality, Cognition, and Peer Effects) funded by the IMT School for Advanced Studies Lucca. V.H.D. was financially supported by the SSHRC grant (grant no. 506547). P.M. was financially supported by the Aarhus University Research Foundation (grant no. AUFF-E-201 9-9-4). A.F. was financially supported by the Slovak Research and Development Agency (grant no. APVV-17-0596). S.R. was financially supported by the Gates Cambridge Scholarship. M.J.A.W. was financially supported by the Social Science and Humanities Research Council of Canada (grant no. 435-2019-0692). A.O. was financially supported by the Swedish Research Council—consolidator grant (grant no. 2018-00877). J.F. was financially supported by the Natural Sciences and Engineering Research Council of Canada. L.C. was financially supported by the Batten Institute, the University of Virginia Darden School of Business. R.B.F. was financially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the Germany’s Excellence Strategy (grant no. EXC 2052/1-390713894). Open access funding was enabled and organized by Darden School of Business, Charlottesville, VA, USA.

Funders	Funder number
ANR-10-LABX-0087 IEC
ANR-17-EURE-0017 FrontCog
ANR-Labex IAST
ANR‐Labex IAST
Centres of Excellence scheme
FAIR	262675
IAST	APVV-17-0596, ANR-17-EURE-0010
Institute of Social Sciences Ivo Pilar and Croatian Science Foundation	DOK-01-2018
NOMIS
QUT Centre for Behavioural Economics, Society and Technology
Sistema General de Regalías	BPIN2018000100059
Universities in Hebei Province Hundred Outstanding Innovative Talents Support Program	SLRC2019002
National Institutes of Health
National Institute on Aging	R01 AG057234
John Templeton Foundation	61378
Alzheimer's Association	SG-20-725707
Darden School of Business, University of Virginia
Takeda Pharmaceutical Company	CW2680521
Batten Institute for Innovation and Entrepreneurship, Darden School of Business, University of Virginia
Global Brain Health Institute
Scuola IMT Alti Studi Lucca	AUFF-E-201 9-9-4
Rainwater Charitable Foundation
School of Medicine and Health Sciences
Natural Sciences and Engineering Research Council of Canada
Social Sciences and Humanities Research Council of Canada	435-2019-0692
Medical Research Council	MR/P014097/1
Biotechnology and Biological Sciences Research Council	BB/R010668/1
Economic and Social Research Council
Australian Research Council	DP180102384
Deutsche Forschungsgemeinschaft	EXC 2052/1-390713894
Volkswagen Foundation	98 525
Agence Nationale de la Recherche	ANR-10-IDEX-0001-02 PSL
Fundação de Amparo à Pesquisa do Estado de São Paulo	2019/26665-5, 2019/27100-1
National Natural Science Foundation of China	71832004, 71972065, 71872152
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior	1133/2019
Ministry of Education of the People's Republic of China	21JHQ088
Academy of Finland	323207
Austrian Science Fund	I3381
Aarhus Universitets Forskningsfond	28207
Carlsbergfondet	CF20-044
Fondo Nacional de Desarrollo Científico y Tecnológico	1210195, FONCYT-PICT 2017-1820, ANID/FONDAP/15150012, 1210176
Consejo Nacional de Investigaciones Científicas y Técnicas
Universität Wien
Consejo Nacional de Ciencia y Tecnología
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	440.20.003
Conselho Nacional de Desenvolvimento Científico e Tecnológico	309905/2019-2
Jane ja Aatos Erkon Säätiö	170112
Narodowe Centrum Nauki	2018/29/B/HS6/02826
Vetenskapsrådet	2018-00877
Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja	451-03-9/2021-14/200163
Ministry of Science and Technology, Taiwan
Agentúra na Podporu Výskumu a Vývoja	APVV-18-0218
Gates Cambridge Trust
Norges forskningsråd
Universidad de Huelva
Universidad del Valle	CI 5316
NOMIS Stiftung
Universidad del Rosario	506547
Institute of BioMed-IT, Energy-IT and Smart-IT Technology (Best), Yonsei University
Sistema Nacional de Investigadores
Tata Institute of Social Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/pnasnexus/pgac093

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Pavlović, T., Azevedo, F., De, K., Riaño-Moreno, J. C., Maglić, M., Gkinopoulos, T., Donnelly-Kehoe, P. A., Payán-Gómez, C., Huang, G., Kantorowicz, J., Birtel, M. D., Schönegger, P., Capraro, V., Santamaría-García, H., Yucel, M., Ibanez, A., Rathje, S., Wetter, E., Stanojević, D., ... Krouwel, A. (2022). Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus, 1(3), 1-15. Article pgac093. https://doi.org/10.1093/pnasnexus/pgac093

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title = "Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning",

abstract = "At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.",

author = "Tomislav Pavlovi{\'c} and Flavio Azevedo and Koustav De and Ria{\~n}o-Moreno, {Juli{\'a}n C.} and Marina Magli{\'c} and Theofilos Gkinopoulos and Donnelly-Kehoe, {Patricio Andreas} and C{\'e}sar Pay{\'a}n-G{\'o}mez and Guanxiong Huang and Jaroslaw Kantorowicz and Birtel, {Mich{\`e}le D.} and Philipp Sch{\"o}negger and Valerio Capraro and Hernando Santamar{\'i}a-Garc{\'i}a and Meltem Yucel and Agustin Ibanez and Steve Rathje and Erik Wetter and Dragan Stanojevi{\'c} and {van Prooijen}, Jan-Willem and Eugenia Hesse and Elbaek, {Christian T.} and Renata Franc and Zoran Pavlovi{\'c} and Aleksandra Cichocka and Andr{\'e} Krouwel",

year = "2022",

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Pavlović, T, Azevedo, F, De, K, Riaño-Moreno, JC, Maglić, M, Gkinopoulos, T, Donnelly-Kehoe, PA, Payán-Gómez, C, Huang, G, Kantorowicz, J, Birtel, MD, Schönegger, P, Capraro, V, Santamaría-García, H, Yucel, M, Ibanez, A, Rathje, S, Wetter, E, Stanojević, D, van Prooijen, J-W, Hesse, E, Elbaek, CT, Franc, R, Pavlović, Z, Cichocka, A & Krouwel, A 2022, 'Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning', PNAS Nexus, vol. 1, no. 3, pgac093, pp. 1-15. https://doi.org/10.1093/pnasnexus/pgac093

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AU - Pavlović, Tomislav

AU - Azevedo, Flavio

AU - De, Koustav

AU - Riaño-Moreno, Julián C.

AU - Maglić, Marina

AU - Gkinopoulos, Theofilos

AU - Donnelly-Kehoe, Patricio Andreas

AU - Payán-Gómez, César

AU - Huang, Guanxiong

AU - Kantorowicz, Jaroslaw

AU - Birtel, Michèle D.

AU - Schönegger, Philipp

AU - Capraro, Valerio

AU - Santamaría-García, Hernando

AU - Yucel, Meltem

AU - Ibanez, Agustin

AU - Rathje, Steve

AU - Wetter, Erik

AU - Stanojević, Dragan

AU - van Prooijen, Jan-Willem

AU - Hesse, Eugenia

AU - Elbaek, Christian T.

AU - Franc, Renata

AU - Pavlović, Zoran

AU - Cichocka, Aleksandra

AU - Krouwel, André

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N2 - At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

AB - At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

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Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

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UN SDGs

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