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

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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 multi-national 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 was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible 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
Tomislav Pavlović1
Flavio Azevedo2
Koustav De3
Julián C. Riaño-Moreno4
Marina Maglić1
Theofilos Gkinopoulos5
Patricio Andreas Donnelly-Kehoe6
César Payán-Gómez7
Guanxiong Huang8
Jaroslaw Kantorowicz9
Michèle D. Birtel10
Philipp Schönegger11
Valerio Capraro12
Hernando Santamaría-García13
Meltem Yucel14
Agustin Ibanez15,16,17,18
Steve Rathje19
Erik Wetter20
Dragan Stanojević21
Jan-Willem van Prooijen22
Eugenia Hesse23
Christian T. Elbaek24
Renata Franc1
Zoran Pavlović25
Panagiotis Mitkidis24
Aleksandra Cichocka26
Michele Gelfand27
Mark Alfano28
Robert M. Ross28
Hallgeir Sjåstad29
John B. Nezlek30
Aleksandra Cislak31
Patricia Lockwood32,33
Koen Abts34
Elena Agadullina35
David M. Amodio36
Matthew A. J. Apps32
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John Jamir Benzon Aruta37
Sahba Besharati38
Alexander Bor39
Becky Choma40
William Cunningham41
Waqas Ejaz42
Harry Farmer43
Andrej Findor44
Biljana Gjoneska45
Estrella Gualda46
Toan L. D. Huynh47
Mostak Ahamed Imran48
Jacob Israelashvili49
Elena Kantorowicz-Reznichenko50
André Krouwel51
Yordan Kutiyski52
Michael Laakasuo53
Claus Lamm54
Jonathan Levy55,56
Caroline Leygue57
Ming-Jen Lin58
Mohammad Sabbir Mansoor59
Antoine Marie39
Lewend Mayiwar60
Honorata Mazepus61
Cillian McHugh62
Andreas Olsson63
Tobias Otterbring64
Dominic Packer65
Jussi Palomäki66
Anat Perry49
Michael Bang Petersen39
Arathy Puthillam67
Tobias Rothmund68
Petra C. Schmid69
David Stadelmann70
Augustin Stoica71
Drozdstoy Stoyanov72
Kristina Stoyanova73
Shruti Tewari74
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Bojan Todosijević75
Benno Torgler76
Manos Tsakiris77,78
Hans H. Tung79
Radu Gabriel Umbreș80
Edmunds Vanags81
Madalina Vlasceanu82
Andrew J. Vonasch83
Yucheng Zhang84
Mohcine Abad85
Eli Adler49
Hamza Alaoui Mdarhri85
Benedict Antazo86
F. Ceren Ay87
Mouhamadou El Hady Ba88
Sergio Barbosa89
Brock Bastian90
Anton Berg66
Michał Białek91
Ennio Bilancini92
Natalia Bogatyreva93
Leonardo Boncinelli94
Jonathan E. Booth95
Sylvie Borau96
Ondrej Buchel97,98
Chrissie Ferreira de Carvalho99
Tatiana Celadin100
Chiara Cerami101
Hom Nath Chalise102
Xiaojun Cheng103
Luca Cian104
Kate Cockcroft38
Jane Conway105
Mateo A. Córdoba-Delgado106
Chiara Crespi107
Marie Crouzevialle69
Jo Cutler108,109
Marzena Cypryańska30
Justyna Dabrowska110
Victoria H. Davis111
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John Paul Minda112
Pamala N. Dayley113
Sylvain Delouvée114
Ognjan Denkovski115
Guillaume Dezecache116
Nathan A. Dhaliwal117
Alelie Diato118
Roberto Di Paolo92
Uwe Dulleck119
Jānis Ekmanis120
Tom W. Etienne52
Hapsa Hossain Farhana121
Fahima Farkhari122,123
Kristijan Fidanovski124
Terry Flew125
Shona Fraser126
Raymond Boadi Frempong70
Jonathan Fugelsang127
Jessica Gale83
E. Begoña García-Navarro128
Prasad Garladinne74
Kurt Gray129
Siobhán M. Griffin62
Bjarki Gronfeldt26
June Gruber130
Eran Halperin49
Volo Herzon66
Matej Hruška44
Matthias F. C. Hudecek131
Ozan Isler119
Simon Jangard63
Frederik Jørgensen39
Oleksandra Keudel132
Lina Koppel133
Mika Koverola66
Anton Kunnari134
Josh Leota135
Eva Lermer136,137
Chunyun Li95
Chiara Longoni138
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Darragh McCashin139
Igor Mikloušić1
Juliana Molina-Paredes140
César Monroy-Fonseca141
Elena Morales-Marente142
David Moreau143
Rafał Muda144
Annalisa Myer145
Kyle Nash146
Jonas P. Nitschke54
Matthew S. Nurse147
Victoria Oldemburgo de Mello41
M. Soledad Palacios-Galvez148
Jussi Palomäki66
Yafeng Pan149
Zsófia Papp150
Philip Pärnamets63
Mariola Paruzel-Czachura151,152
Silva Perander153
Michael Pitman38
Ali Raza154
Gabriel Gaudencio Rêgo155
Claire Robertson156
Iván Rodríguez-Pascual148
Teemu Saikkonen157
Octavio Salvador-Ginez158
Waldir M. Sampaio155
Gaia Chiara Santi159
David Schultner160
Enid Schutte38
Andy Scott146
Ahmed Skali161
Anna Stefaniak162
Anni Sternisko156
Brent Strickland164
Brent Strickland163
Jeffrey P. Thomas95
Gustav Tinghög133
Iris J. Traast165
Raffaele Tucciarelli166
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Michael Tyrala167
Nick D. Ungson168
Mete Sefa Uysal169
Dirk Van Rooy170
Daniel Västfjäll171
Joana B. Vieira63,172
Christian von Sikorski173
Alexander C. Walker127
Jennifer Watermeyer174
Robin Willardt69
Michael J. A. Wohl162
Adrian Dominik Wójcik175
Kaidi Wu176
Yuki Yamada177
Onurcan Yilmaz178
Kumar Yogeeswaran83
Carolin-Theresa Ziemer68
Rolf A. Zwaan179
Paulo Sergio Boggio155
Ashley Whillans180
Paul A. M. Van Lange22
Rajib Prasad181
Michal Onderco182
Cathal O'Madagain85
Tarik Nesh-Nash183
Oscar Moreda Laguna52
Yordan Kutiyski52
Emily Kubin173
Mert Gümren184
Ali Fenwick185
Arhan S. Ertan186
Michael J. Bernstein187
Hanane Amara183
Jay Joseph Van Bavel156
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1Institute of Social Sciences Ivo Pilar, Zagreb, Croatia
2Department of Psychology, School of the Biological Sciences, Cambridge University, Cambridge,
United Kingdom
3Department of Finance and Quantitative Methods, Gatton College of Business & Economics,
University of Kentucky, Lexington, Kentucky, United States of America
4Faculty of Medicine, Cooperative University of Colombia, Villavicencio, Meta, Colombia
5Department of Philosophy and Social Studies, University of Crete, Rethymnon, Crete, Greece
6Department of Research and Development, Kozaca SA, Rosario, Santa Fe, Argentina
7Direccion Academica Sede la Paz, Universidad Nacional de Colombia - Sede de La Paz, Cesar,
Colombia
8Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong
9Department of Economics, Institute of Security and Global Affairs, Leiden University, The Hague,
Netherlands
10School of Human Sciences, University of Greenwich, London, United Kingdom
11Department of Philosophy, University of St Andrews, St Andrews, United Kingdom
12Department of Economics, Middlesex University London, London, United Kingdom
13Department of Psychiatry, Pontifical Xavierian University, Bogotá, Colombia
14Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United
States of America
15Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Peñalolén,
Chile
16Cognitive Neuroscience Center (CNC), University of San Andrés & CONICET, Buenos Aires,
Argentina, Buenos Aires, Argentina
17Global Brain Health Institute, University of California - San Francisco, San Francisco, California,
United States of America
18Global Brain Health Institute, Trinity College, Dublin, Ireland
19Department of Psychology, University of Cambridge, Cambridge, United Kingdom
20Department of Business Administration, Stockholm School of Economics, Stockholm, Sweden
21Department of Sociology, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia
22Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam,
Netherlands
23Departamento de Matemática y Ciencias, Universidad de San Andres, Victoria, Buenos Aires,
Argentina
24Department of Management, Aarhus University, Aarhus, Denmark
25Department of Psychology, Faculty of Philosophy, University of Belgrade, Belgrade, Serbia
26School of Psychology, University of Kent, Canterbury, United Kingdom
27Stanford Graduate School of Business, Stanford University, Stanford, California, United States of
America
28Department of Philosophy, Macquarie University, Macquarie Park, New South Wales, Australia
29Department of Strategy and Management, Norwegian School of Economics, Bergen, Norway
30Institute of Psychology, Center for Climate Action and Social Transformations, SWPS, University of
Social Sciences and Humanities, Warsaw, Poland
31Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
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32Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham,
United Kingdom
33Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
34Centre for Sociological Research, Katholieke Universiteit Leuven, Leuven, Belgium
35Faculty of Psychology, Higher School of Economics University, Moscow, Russia
36Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
37Department of Psychology, Sunway University, Petaling Jaya, Selangor, Malaysia
38Department of Psychology, University of the Witwatersrand, Johannesburg, Republic of South Africa
39Department of Political Science, Aarhus University, Aarhus, Denmark
40Department of Psychology, Toronto Metropolitan University, Toronto, Ontario, Canada
41Department of Psychology, University of Toronto, Toronto, Ontario, Canada
42Department of Mass Communication, National University of Science and Technology (NUST),
Islamabad, Islamabad Capital Territory, Pakistan
43Department of Psychology, University of Greenwich, London, United Kingdom
44Institute of European Studies and International Relations, Faculty of Social and Economic Sciences,
Comenius University, Bratislava, Slovakia
45Department of Psychology, Macedonian Academy of Sciences and Arts, Skopje, Republic of North
Macedonia
46Department of Sociology, Social Work and Public Health, University of Huelva, Huelva, Spain
47Department of Decision Analytics and Risk, University of Southampton, Southampton, United
Kingdom
48Department of Educational and Counselling Psychology, BRAC Institute of Educational and
Development, BRAC University, Dhaka, Bangladesh
49Department of Psychology, Hebrew University of Jerusalem, Jerusalem, Israel
50Rotterdam Institute of Law and Economics (RILE), Erasmus University Rotterdam, Rotterdam,
Netherlands
51Department of Communication Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
52Kieskompas (Election Compass), Amsterdam, Netherlands
53Department of Digital Humanities, University of Helsinki, Helsinki, Finland
54Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Wien, Austria
55Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
56Baruch Ivcher School of Psychology, Reichman University, Herzliya, Israel
57School of Psychology, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico
58Department of Economics, National Taiwan University, Taipei, Taiwan
59HEMS School, Kathmandu, Nepal
60Department of Leadership and Organizational Behaviour, BI Norwegian Business School, Oslo,
Norway
61Institute of Security and Global Affairs, Leiden University, The Hague, Netherlands
62Department of Psychology, University of Limerick, Limerick, Ireland
62Department of Psychology, University of Limerick, Limerick, Ireland
63Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
64Department of Management, University of Agder, Kristiansand, Norway
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65Department of Psychology, Lehigh University, Bethlehem, Pennsylvania, United States of America
66Department of Digital Humanities, University of Helsinki, Helsinki, Finland
67Department of Psychology, Monk Prayogshala, Powai, Mumbai, Maharashtra, India
68Department of Communication and Media Use, Friedrich Schiller University Jena, Jena, Germany
69Department of Management, Technology, and Economics, Swiss Federal Institute of Technology in
Zürich, Zürich, Switzerland
70Chair of Development Economics, University of Bayreuth, Bayreuth, Germany
71Department of Sociology, National School for Political and Administrative Studies (SNSPA),
Bucharest, Romania
72Department of Psychiatry and Medical Psychology, Medical University, Plovdiv, Bulgaria
73Division of Translational Neuroscience, Medical University, Plovdiv, Bulgaria
74Department of Humanities and Social Sciences, Indian Institute of Management, Indore, Madhya
Pradesh, India
75Department of Psychology, Institute of Social Sciences, Belgrade, Serbia
76School of Economics and Finance and Centre for Behavioural Economics, Society and Technology
(BEST), Queensland University of Technology, Brisbane City, Queensland, Australia
77Department of Psychology, Royal Holloway, University of London, Egham, United Kingdom
78Centre for the Politics of Feelings, School of Advanced Study, University of London, London, United
Kingdom
79Department of Political Science, National Taiwan University, Taipei, Taiwan
80Faculty of Political Science, National University of Political Studies and Public Administration,
Bucharest, Romania
81Psychology Department, University of Latvia, Riga, Latvia
82Department of Psychology, New York University, New York, New York, United States of America
83Department of Psychology, University of Canterbury, Christchurch, New Zealand
84School of Economics and Management, Hebei University of Technology, Tianjin, China
85School of Collective Intelligence, Mohammed VI Polytechnic University, Ben Guerir, Morocco
86Department of Psychology, Jose Rizal University, Mandaluyong, Metro Manila, Philippines
87Department of Economics, Telenor Research, Fornebu, Norway
88Cheikh Anta Diop University, Dakar, Senegal
89School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, Colombia
90Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria,
Australia
91Faculty of Historical and Pedagogical Sciences, University of Wroclaw, Wroclaw, Poland
92IMT School for Advanced Studies, Lucca, Italy
93Laboratory for Psychology of Social Inequality, Higher School of Economics University, Moscow,
Russia
94Department of Economics and Management, University of Florence, Florence, Italy
95Department of Management, London School of Economics and Political Science, London, United
Kingdom
96Department of Marketing, TBS Education, Toulouse, France
97The Institute for Sociology, Slovak Academy of Sciences, Bratislava, Slovakia
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98Social Policy Institute, Ministry of Labor, Family and Social Affairs of the Slovak Republic,
Bratislava, Slovakia
99Department of Psychology, Federal University of Santa Catarina, Florianopolis, Brazil
100Department of Economics, University of Bologna, Bologna, Italy
101IUSS Cognitive Neuroscience Center, University School for Advanced Studies, Pavia, Italy
102Central Department of Population Studies, Tribhuvan University, Kathmandu, Nepal
103School of Psychology, Shenzhen University, Shenzhen, China
104Department of Marketing, Darden School of Business, University of Virginia, Charlottesville,
Virginia, United States of America
105MRC Social, Genetic and Developmental Psychiatry Centre, Institute for Advanced Study in
Toulouse, Université Toulouse 1 Capitole, Toulouse Cedex 6, France
106Faculty of Medicine, Pontifical Xavierian University, Bogotá, Colombia
107U.O. Rho, Fondazione Luigi Clerici, Rho, Italy
108School of Psychology, University of Birmingham, Birmingham, United Kingdom
109School of Psychology, University of Oxford, Oxford, United Kingdom
110Department of Trade and Market Institutions, Cracow University of Economics, Kraków, Poland
111Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health,
University of Toronto, Toronto, Ontario, Canada
112Department of Psychology, Western University, London, Ontario, Canada
113Department of Psychology, University of California - Los Angeles, Los Angeles, California, United
States of America
114Department of Psychology, Université Rennes 2, Rennes, France
115Department of Communication Science, University of Amsterdam, Amsterdam, Netherlands
116Université Clermont Auvergne, Clermont-Ferrand, France
117UBC Sauder School of Business, University of British Columbia, Vancouver, British Columbia,
Canada
118Teacher Education Department, Cavite State University, General Trias, Cavite, Philippines
119School of Economics and Finance, Queensland University of Technology, Brisbane City,
Queensland, Australia
120Faculty of Education, Psychology and Art, University of Latvia, Riga, Latvia
121National Institute for the Intellectually Disabled and Autistic (NIIDA), Society for the Welfare of the
Intellectually Disabled (SWID Bangladesh), Dhaka, Bangladesh
122Department of Communication and Media Use, University of Münster, Münster, Germany
123Friedrich Schiller University Jena, Jena, Germany
124Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
125Department of Media and Communications, University of Sydney, Sydney, New South Wales,
Australia
126Medical School, Department of Psychiatry, University of the Witwatersrand, Johannesburg,
Republic of South Africa
127Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada
128Department of Nursing, University of Huelva, Huelva, Spain
129Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina,
United States of America
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130Department of Psychology and Neuroscience, College of Arts and Sciences, University of Colorado
Boulder, Boulder, Colorado, United States of America
131Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
132Kyiv School of Economics, Kyiv, Ukraine
133Department of Management and Engineering, Linköping University, Linköping, Sweden
134University of Helsinki, Helsinki, Finland
135Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
136Department of Business and Media Psychology, Ansbach University of Applied Sciences, Ansbach,
Germany
137Center for Leadership and People Management, Ludwig Maximilian University of Munich, Munich,
Germany
138Department of Marketing, Boston University, Questrom School of Business, Boston,
Massachusetts, United States of America
139School of Psychology, Dublin City University, Dublin, Ireland
140Pontifical Xavierian University, Bogotá, Colombia
141Seele Neuroscience, Mexico City, Mexico
142Department of Social, Developmental and Educational Psychology, University of Huelva, Huelva,
Spain
143School of Psychology, University of Auckland, Auckland, New Zealand
144Faculty of Economics, Maria Curie Sklodowska University, Lublin, Poland
145Department of Psychology, City University of New York (CUNY) Graduate Center, New York, New
York, United States of America
146Department of Psychology, University of Alberta, Edmonton, Alberta, Canada
147Australian National Centre for the Public Awareness of Science, Australian National University,
Canberra ACT, Australia
148COIDESO-Research Center of Contemporary Thinking and Innovation for Social Development,
University of Huelva, Huelva, Spain
149Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
150Department for Political Behavior, Centre for Social Sciences, Budapest, Hungary
151Institute of Psychology, University of Silesia in Katowice, Katowice, Poland
152Facultad de Psicología, Complutense University of Madrid, Madrid, Spain
153Department of Computer Science, University of Helsinki, Helsinki, Finland
154Department of Computer Science, Institute of Cognitive Science, University of Colorado Boulder,
Boulder, Colorado, United States of America
155Centro de Ciências Biológicas e da Saúde, Mackenzie Presbyterian University, São Paulo, Brazil
156Department of Psychology & Neural Science, New York University, New York, New York, United
States of America
157Department of Biology, Biodiversity Unit, University of Turku, Turku, Finland
158School of Psychology, Environmental Psychology Department, National Autonomous University of
Mexico, Mexico City, Mexico
159Department of Humanities and Life Sciences, University School for Advanced Studies, Pavia, Italy
160Department of Social Psychology, University of Amsterdam, Amsterdam, Netherlands
161Department of Global Economics & Management, University of Groningen, Groningen, Netherlands
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162Department of Psychology, Carleton University, Ottawa, Ontario, Canada
163Department of cognitive studies, Ecole normale supérieure, Institut Jean Nicod, Paris, France
164Africa Business School and The School of Collective Intelligence, UM6P, Rabat, Morocco
165Social Psychology Department, University of Amsterdam, Amsterdam, Netherlands
166Institute of Cognitive Neuroscience, University College London, London, United Kingdom
167Department of Asian and International Studies, City University of Hong Kong, Kowloon Tong, Hong
Kong
168Department of Psychology, Susquehanna University, Selinsgrove, Pennsylvania, United States of
America
169Department of Social Psychology, Friedrich Schiller University Jena, Jena, Germany
170Department of Design, University of Antwerp, Antwerp, Belgium
171Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden
172Department of Psychology, University of Exeter, Exeter, United Kingdom
173Department of Psychology, University of Koblenz-Landau, Landau, Germany
174Department of Speech Pathology and Audiology, University of the Witwatersrand, Johannesburg,
Republic of South Africa
175Institute of Psychology, Nicolaus Copernicus University, Torun, Poland
176Rady School of Management, University of California, San Diego, United States of America
177Faculty of Arts and Science, Kyushu University, Fukuoka, Japan
178Department of Psychology, Kadir Has University, Fatih/Istanbul, Turkey
179Department of Psychology, Erasmus University Rotterdam, Rotterdam, Netherlands
180Faculty of Negotiations, Organizations and Markets, Harvard Business School, Boston,
Massachusetts, United States of America
181Department of Economics, Vidyasagar College For Women, Kolkata, West Bengal, India
182Department of Public Administration and Sociology, Erasmus University Rotterdam, Rotterdam,
Netherlands
183Impact For Development, Casablanca, Morocco
184Department of Economics, Koc University, Sarıyer/Istanbul, Turkey
185Hult International Business School, Dubai, United Arab Emirates
186Department of International Trade, Bogazici University, Besiktas/Istanbul, Turkey
187Department of Psychological and Social Sciences, Penn State University Abington College,
Abington, Pennsylvania, United States of America
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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 multi-national 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 was found for morality as cooperation,
symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse
relationship was evident for the endorsement of conspiracy theories. However, we also found a non-
negligible 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.
Keywords: COVID-19, social distancing, hygiene, policy support, psychology, machine learning,
public health measures
Significance statement
Outcomes of this study suggest that morality-related factors, along with prosociality and individual
characteristics related to information processing and self-control, play an important role in determining
attitudinal and behavioral responses to the COVID-19 pandemic. However, a substantial variation in
the predictive contribution of included variables was observed. Therefore, the role of context (both in
terms of culture and stage of the pandemic) should not be underestimated. Nevertheless, this study
highlighted multiple factors relevant to the prevention of COVID-19 in different stages of the pandemic
and cultures, which makes it a good starting point for more complex and causal research designs.
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Introduction
The COVID-19 pandemic has caused significant loss of life, commodities, jobs, and disruption of
communities worldwide. As of March 2022, over 450 million infections and more than 6 million deaths
have been reported globally (WHO, 2022). As we write this paper, the daily number of new cases
worldwide exceeds one million. Given the lack of vaccination and treatment options, controlling the
spread of the SARS-CoV-2 virus in its early stages depended on preventive behaviors, such as
physical distancing (Thu et al., 2020) or hand and object disinfection (Alzyood et al., 2020, Meyers et
al., 2021). While governments across the globe rushed to implement the proposed measures, many
citizens resisted such change (Ryu et al., 2020, Van Zandwijk & Rasko, 2020). This is indicative of the
vital role of individual characteristics in the form of attitudes, abilities, traits, and perceptions in
compliance with preventive measures. Thus, decision-makers may benefit from insights from the
social and behavioral sciences that could explain who will adhere to or ignore advised measures (Van
Bavel et al., 2020).
Furthermore, nations vary in the strictness of preventive measures enacted by local governments and
the severity of the consequences of COVID-19: some countries report more than 100 deaths (e.g.,
Croatia, the UK), while others count less than one death (e.g., Bhutan, China) per 100,000 citizens
(Johns Hopkins Coronavirus Resource Center, n.d.). A recent cross-national analysis suggests that
many of these excess deaths in countries like the USA are the result of weak public health
infrastructure and a decentralized, inconsistent response to the pandemic (Bilinski & Emanuel, 2020).
This raises questions of how macro-level cultural variables might be associated with citizens’ health
attitudes and behaviors across nations.
The scientific community responded with numerous international research collaborations aimed at
explaining adherence to preventive measures from different perspectives. One group of researchers
(Travaglino & Moon, 2021) focused on cultural dimensions, self-awareness emotions, trust in
governmental actions, and political orientation as predictors of compliance in the USA, Italy, and
Korea. They found that horizontal collectivism was the only predictor of compliance significant in all
three countries. Similarly, other scholars (Biddlestone et al., 2020) identified collectivism's role in
promoting preventive behaviors. In terms of adherence to preventive measures, prosocial tendencies
emerged as a significant positive predictor, while perceiving others as violating preventive measures
was the most consistent negative predictor (Coroiu et al., 2020). Results from another study across
70 countries showed that trust in government, conscientiousness, and agreeableness predicted
engaging in preventive measures, with other variables having a negligible practical impact (Clark et
al., 2020). As research accumulates, interpreting and integrating findings from diverse research
streams with a variety of measures and samples presents another challenge for both scholars and
practitioners.
Due to their freedom of theoretical constraints, data-driven approaches might offer solutions to “grand
challenges” of existing theories, defined as complex problems with intertwined and evolving
underlying mechanisms (Eisenhardt et al., 2016) as they allow the effective use of highly dimensional
data (Igarashi et al., 2016). For instance, network analysis was used on data from the UK and the
Netherlands to explore the relationship between multiple constructs relevant for COVID-19 attitudes
and behaviors (Chambon et al., 2021). The perceived level of adherence to norms and efficacy of
preventive measures and support for these measures exhibited the strongest relationships with
COVID-19 preventive behaviors. On the other hand, applying random forests on more than 100
potentially relevant variables established different descriptive and injunctive norms and prosociality as
some of the most relevant predictors of behaviors (Van Lissa et al., 2022). Overall, the relevance of
prosociality and collectivism, both of which imply a willingness to make sacrifices for the benefit of the
community, has been emphasized throughout the literature (Biddlestone et al., 2020; Capraro et al.
2021; Coroiu et al., 2020; Travaglino & Moon, 2021; Van Bavel et al., 2022; Van Lissa et al., 2022).
However, this does not eliminate the role of other individual differences and capacities in adherence
to preventive measures (Clark et al., 2020; Van Lissa et al., 2022).
Our study expands on and contributes to the existing literature in three important ways. Firstly, we
brought together a diverse team of experts to select several key constructs from social, moral,
cognitive, and personality psychology that might be relevant to supporting public health
recommendations. Despite a proliferation of studies on predictors of attitudinal and behavioral
responses to the COVID-19 pandemic (see, for example, Zettler et al., 2021), research in this field is
still warranted. Hence, we sought to investigate attitudinal and behavioral responses in the first
pandemic wave, when uncertainty regarding the spread of the virus dominated societies. In
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conjunction with the existing findings, our study provides valuable evidence which can be utilized to
compare pandemic responses from different time points during the pandemic. Moreover, we sought to
statistically test the association between the three related but distinct outcomes – maintaining physical
hygiene, avoiding physical contact, and supporting governmental policies related to COVID-19. This
distinguishes our approach from prior research that employed a general factor of preventive behaviors
as it allowed us to gain insights both into attitudinal and behavioral responses to the measures aimed
at mitigating the spread of the virus. Secondly, we consider potential cultural differences in the
meaning of the studied constructs by establishing equivalence of factor scores through (partial) strong
invariance (see Putnick & Bornstein, 2016). Finally, to determine the efficacy of our independent
variables in explaining contact avoidance, hygiene maintenance, and COVID-19 policy support in
each country, we applied random forest-based regression algorithms appropriate for complex data
sets with possible non-linear and interactive relationships between variables (Breiman, 2001; Sage,
2018).
Overview
We focused on two specific research questions utilizing a large international sample of 51,404
participants from 69 countries from all continents except Antarctica. Firstly, we tested how precisely
(in terms of the explained variance) avoiding contact, maintaining hygiene, and policy support could
be predicted using a combination of variables from moral, social, personality, and cognitive
psychology, as well as socio-demographic variables. Secondly, we tested which of the included
variables provided a substantial contribution to the accuracy of our predictions. Descriptions of the
expected effects (of all study variables) based on theories or earlier studies are available as
Supplementary materials A1. Additionally, to evaluate the robustness of our findings, we conducted
additional analyses that took cultural differences and the pandemic stage during data collection into
account.
1 All supplementary files can be found online in the following OSF folder:
https://osf.io/cvkyr/?view_only=c88c0431224c4f878750875e599d2983
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Materials and Methods
International Collaboration on the Social & Moral Psychology of COVID-19 Project
The aim of the International Collaboration on the Social & Moral Psychology of COVID-19 (ICSMP
COVID-19) project is to examine and understand psychological factors related to the COVID-19
pandemic response. We launched the project in April 2020 via a social media call for national teams
that could collect samples in their own country. Over 230 scholars responded to the call. The main
questionnaire, created in English, was disseminated to each national team, responsible for translating
it to their local language (using the standard forward-backwards method). Each team collected the
data in their own country. The resulting datasets were then collated and analyzed altogether, and are
available online (Azevedo et al., 2022). The study received an umbrella ethics approval from the
University of Kent.
Participants
The analyzed sample consisted of 51,404 participants from 69 countries and territories, 25 of which
collected samples representative of their respective nations regarding age and gender (n = 22,064).
The remaining data were drawn from convenience samples. Following exclusion criteria set for the
purposes of this study (we excluded participants providing inaccurate response to the attention check,
participants who did not provide responses to more than one quarter of items, participants providing
the same response more than ten times in a row on the items of our predictors, participants who
chose “other” as their gender and participants completing the questionnaire unusually fast or
unusually slow, see Supplementary materials B and C), 7,615 participants were removed, resulting in
a sample of 43,789 (Mage = 43, SDage = 16; 52% females) participants for our analyses.
Measures
Unless otherwise indicated, participants responded on an 11-point scale with higher values indicating
higher levels of the measured concepts (after reversing the appropriate items). Prior to conducting
analyses that presumed grouping of participants, we achieved partial strong invariance for all of the
included multi-item scales. This was important to ensure that we measured the same constructs with
similar efficacy in each group (see Putnick & Bornstein, 2016). Detailed output on how the fit was
achieved can be found in Supplementary material C.
Individual-level variables
Criteria
Avoidance of physical contact during the coronavirus (COVID-19) pandemic was measured via five
items. Adequate fit (CFI = 0.979, RMSEA (95% CI upper limit) = 0.086, SRMR = 0.024, 2 = 0.69)
was achieved after correlating the residuals of the last two items (keeping distance and avoiding
handshakes).
Maintaining physical hygiene was measured via five items related to washing hands and other
behaviors related to personal hygiene. A single factor structure was retained (CFI = 0.999, RMSEA
(95% CI upper limit) = 0.037, SRMR = 0.007, 2 = 0.74) with correlated residuals of the first two items
(washing hands longer and more thoroughly).
Support for COVID-19 related policy decisions was measured with five items relating to restrictive
policies affecting five areas of everyday life. A single factor structure was retained (CFI = 0.989,
RMSEA (95% CI upper limit) = 0.098, SRMR = 0.016, 2 = 0.86) after correlating residuals of support
for forbidding public gatherings and unnecessary travel, and closing parks.
Predictors
Morality
Moral identity was measured using the 10-item moral identity scale (Aquino & Reed, 2002). The
original paper reports a two-factor model (Internalization and Symbolization), with acceptable internal
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consistency. The two-factor structure was confirmed in our study after correlating residuals of items 8
and 9, and 4 and 7. (CFI = 0.939, RMSEA (95% CI upper limit) = 0.084, SRMR = 0.067, 2internalization
= 0.68, 2symbolization = 0.75).
The moral circle scale (Waytz et al., 2019) assesses the moral expansiveness across 16 different
entities (human and non-human) deemed worthy of moral concern. Participants indicated the extent
of their moral circle, i.e., the circle for which they are concerned about right and wrong done towards
them, ranging from immediate family to all things in existence.
Morality as cooperation was measured using the Relevance subscale of the Morality-as-
Cooperation Questionnaire (MAC-Q; Curry et al., 2019), which measures the extent to which each of
the seven dimensions of cooperation is relevant when making moral judgments. One item per each of
its seven dimensions was used in this study. After excluding the items of fairness and property and
correlating residuals between helping a family member and showing courage and helping a family
member and uniting a community, a general factor of the relevance of cooperation in morality (CFI =
0.991, RMSEA (95% CI upper limit) = 0.066, SRMR = 0.014, 2 = 0.73) was extracted.
(Pro)social identification and attitudes
National identity was assessed with two items combined into a scale: “I identify as [nationality]”
(Postmes et al., 2013) and “Being a [nationality] is an important reflection of who I am” (see Cameron,
2004). The correlation among items was r = 0.69, and a single score was extracted using PAF.
Social belonging was measured using a four-item single-factor scale with excellent internal
consistency (Malone et al., 2012). A single factor structure (CFI = .988, RMSEA (95% CI upper limit) =
0.115, SRMR = 0.017, 2 = 0.78) was confirmed in this study after correlating the residuals between
first and third item.
Collective (national) narcissism was measured using three items of the original, single-factor
Collective Narcissism scale (Golec de Zavala et al., 2009). Invariance of this scale was tested along
with the endorsement of COVID-19 conspiracy theories (Sternisko et al., 2021), which was
measured using a single item for a denial conspiracy and three items for deflection conspiracies (e.g.,
“a hoax invented by interest groups for financial gains”). The three items related to collective
narcissism and the four items related to belief in conspiracy theories were modeled together and
yielded a clear two-factor structure (CFI = 0.988, RMSEA (95% CI upper limit) = 0.069, SRMR =
0.021, 2Conspiracies= 0.92 and 2Collective narcissism= 0.87).
Political orientation was measured using a single item, “Overall, what would be the best description
of your political views?”, on a scale ranging from very left-leaning (“0”) to very right-leaning (“10”).
COVID-19 risk perception was measured with two items asking participants to rate how likely it was
for them and for the average person to get infected with COVID-19 by April 30, 2021, on a slider scale
ranging from 0 (“impossible”) to 100 (“certain”). Based on their high correlation (r = 0.66), a single
component was extracted using PAF.
Individual dispositions
Individual grandiose narcissism was measured using the brief version of the Narcissistic
Admiration and Rivalry Questionnaire (Back et al., 2013), comprising two subcomponents, rivalry (R)
and admiration (A), which exhibited acceptable internal consistency. The scale achieved acceptable
fit after correlating residuals between items 3 and 6 reflecting rivalry (CFI = 0.986, RMSEA (95% CI
upper limit) = 0.068, SRMR = 0.020, 2= 0.69 for admiration and 2= 0.55 for rivalry).
Trait self-control was measured as a single-factor four-item scale (Tangney et al., 2004), with the
last two items being negatively worded. However, an adequate fit was not obtained even after
correlating residuals of the first two items (CFI = 0.988, RMSEA (95% CI upper limit) = 0.115, SRMR
= 0.017, 2 = 0.78).
Self-esteem was measured using the Single-Item Self-Esteem-Scale (SISE), which achieved good
test-retest reliability and was established as a viable alternative of longer self-esteem scales (Robins
et al., 2001).
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Trait optimism was measured using two items from the three-item optimism subscale of the Life
Orientation Test-Revised (Scheier et al., 1994). Based on their high correlation (r = 0.71), a single
factor was retrieved using PAF.
Open-mindedness, reflecting the acceptance of limitation of one’s knowledge and willingness to gain
new knowledge, was measured with a six-item scale of the Multidimensional measure of intellectual
humility (Alfano et al., 2017). The originally proposed single-factor structure achieved an acceptable fit
in our study (CFI = 0.998, RMSEA (95% CI upper limit) = 0.025, SRMR = 0.007) and was retained. It
exhibited questionable internal consistency (2 = 0.50).
Cognitive reflection was measured with a three-item test that measures the ability to inhibit intuitive
answers and engage in reflection to provide correct ones, adapted from Frederick (2005). Correct
answers were coded as “1” and incorrect as “0”, with a total scale ranging from 0 to 3.
Demographic factors and experiences
The MacArthur Scale of Subjective Social Status (Adler et al., 2000) was used to measure subjective
socio-economic status by asking participants to place themselves on an 11-rung ladder, with the top
rung representing individuals who are best off (in terms of education, jobs, and wealth), and the
bottom rung the ones worst off.
Participants were asked whether they had (coded as “1”) tested positive for COVID-19 and/or had a
close relative or acquaintance (friend, partner, family, colleague, etc.) who had tested positive for
COVID-19 (“1”) or not (“0”) by the time of data collection.
Multiple demographic factors were also collected. Participants were asked to indicate whether they
identify as “male”, “female”, or “other” and enter their age (in years). Additionally, participants’ marital
status had the following three options: married, single, in a relationship (recoded into married or in a
relationship (“1”) or other (“0”)), after which they indicated the number of children they had.
Participants were also asked to indicate their employment status (recoded into the employed,
students, or retired (“1”) or other (“0”)). Finally, participants indicated whether they lived in an urban
(coded as “1”) or rural setting (coded as “0”).
Analytical Procedure
This study was not preregistered. Our analytical approach consisted of multiple steps (see
Supplementary materials B-F) conducted in R (R Core Team, 2021). A detailed description of data
cleaning is presented in Supplementary material B, while used packages are listed at the beginning of
every Supplementary material in which they were used.
The psychometric properties of the applied measures were tested on the imputed data (see
Supplementary material C). We focused on testing the applied measures’ factor structure and internal
consistency. As the majority of the multi-item measures were taken from previously validated
instruments, CFAs with robust maximum likelihood estimator (MLR; Brosseau-Liard & Savalei, 2013;
Brosseau-Liard et al., 2012) and countries as clusters were applied using lavaan (Rosseel, 2012) to
test whether the proposed structures fit to the overall data. Modification indices were consulted when
theoretical models did not fit the data well.
We tested whether the obtained results were stable concerning the pandemic stage during data
collection. In the absence of any specific criterion, we initially attempted to group countries according
to the total number of COVID-19 cases per million inhabitants during the period of data collection,
calculated as the average of the number of cases per million at the start date of data collection and
the number of new cases per million at the end date of data collection2. In samples where only one
date was provided, we used the available information for that date. However, we noticed an unwanted
regularity in the grouping process - most countries with the total number of cases above the median
were European countries, and no countries from Africa were in this group. Thus, to minimize potential
cultural biases, we grouped the countries according to the Inglehart-Welzel cultural map (World
Values Survey 7, 2020) and selected the countries with the lowest and highest total number of cases
per million from each cultural region (Orthodox European countries, Protestant European countries,
Catholic European countries, English-Speaking countries, West & South Asian countries, Confucian
2 https://github.com/CSSEGISandData/COVID-19
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countries, African-Islamic countries, and Latin American countries) as representative. This resulted in
a group of countries in the early stage of the pandemic consisting of participants from Nigeria,
Slovakia, Australia, Bulgaria, the Philippines, and Nepal. On the other hand, a group of countries in
the advanced pandemic stage included participants from United Arab Emirates, Spain, Ireland,
Serbia, Brazil, and Singapore. Regarding Latin American countries, we considered only countries with
more than 150 participants as candidates, while no Protestant European countries were included due
to all of them being in the advanced stage of the pandemic during the data collection period (with a
total number of citizens infected per million exceeding 1000). Our two groups were highly distinctive
with respect to the total number of cases per million during the data collection (Mearly stage = 154.14;
Madvanced stage = 3520.87). In our attempt to further balance the analysis, we randomly selected the
same number of participants from each selected country, equal to the size of the smallest included
sample after the data cleaning (nUAE = 176).
Then, we checked the cross-group invariance of our multi-item measures.3 After achieving an
adequate fit by introducing changes suggested by modification indices, the cross-group invariance of
each obtained theoretical model was tested. Stepwise tests were further conducted. Firstly, configural
models were formed for each construct, followed by models with constrained item loadings to test
weak invariance, and ultimately models with constrained item loadings and intercepts to test strong
invariance. If the configural model achieved adequate fit, successive changes in fit indices with
respect to imposing restriction were used as a criterion for invariance. A CFI change of -0.015
accompanied by a change in RMSEA or SRMR of +.015 was considered as an indication of achieving
a higher level of invariance. If invariance was not achieved on the first attempt, modification indices
were consulted to achieve partial invariance. Finally, we extracted factor scores from models
reflecting strong invariance (where loadings and intercepts were constrained to form comparable
scores across countries) using the ten Berge correction to use them in further analyses.
Because two-item measures cannot be tested using CFA, factor analyses using principal axis
factoring were conducted to extract latent dimensions. In line with the factor scores based on strong
invariance, the analyses were conducted on the entire dataset used in a specific analysis.
Socio-demographic characteristics and moral circle were not scaled. Variables absent from a specific
national data set were replaced with a constant (i.e., the number of children in the Ghanaian data set
was set to median of other countries, while the residence data was coded as urban for participants
from Canada and Bulgaria).
The rest of the procedure was similar to the procedure applied by Van Lissa et al. (2022). After data
preparations, random forests were applied. Ranger function (Wright & Ziegler, 2017) was used to
apply random forests that served as a basis for partial dependence plots and permutation importance
metrics (see Altmann et al., 2010), which were used to interpret the relationships (see Supplementary
material D). Regarding the hyperparameters, the number of trees was set to 1000 and 2000, R2 was
chosen as the accuracy metric, permutation importance metrics were extracted as estimated variable
importance, the number of variables to test at each split ranged from five to twenty with an increment
of one, splitting was based on variance, while the minimum node sizes ranged from three to ninety-
nine with an increment of three. Holdout sample was used to ensure the robustness of findings: 20%
of the sample from each country formed a test set on which R2 and variable importance metrics (see
Supplementary materials E and F) were calculated.
3 We also conducted analyses with groups reflecting regions of the Inglehart-Welzel cultural map
(2020), which followed the described procedure. Due to space limitations, outputs of these analyses
can be found in Supplementary materials G.
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Results
Obtained R2 values of optimally tuned models were of weak to moderate magnitude both on the
complete data (R2contact = .134, R2hygiene = .200, R2policy = .146) and data consisting of samples
nationally representative regarding age and gender (R2contact = .172, R2hygiene = .256, R2policy = .124). In
the early stage of the pandemic, prediction of contact avoidance was negligible (R2contact = .045,
R2hygiene = .272, R2policy = .138). On the other hand, in the advanced stage of the pandemic, our models
led to a very imprecise prediction of maintaining hygiene (R2contact = .129, R2hygiene = -.043, R2policy =
.173). Therefore, we decided not to interpret the predictive contributions in models with maintaining
hygiene as the criterion on the sample reflecting the advanced stage of the pandemic. Nevertheless,
they are presented in the following paragraphs.
Figure 1. Permutation variable importance calculated with respect to representativeness of the
samples and stage of the pandemic
Results in Figure 1 show the importance metrics based on the models that yielded the highest R2 per
analysis. As permutation importance reflects a reduction in error, these plots are not directly
comparable. However, some common patterns can be observed.
In terms of avoiding contact (Figure 2), internalized moral identity provided the most consistent
contribution across analyses, followed by open-mindedness, collective narcissism, morality as
cooperation, symbolized moral identity, and self-control. Endorsement of conspiracy theories seems
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to have exhibited a stronger relationship with our criteria in the early stage of the pandemic than in the
advanced stage. In general, participants achieving higher scores on avoiding contact also achieved
higher scores on internalized moral identity, morality as cooperation, self-control, and open-
mindedness, respectively. These participants also exhibited lower endorsement of conspiracy
theories. Regarding collective narcissism and symbolized moral identity, it seems that the individuals
scoring around the midpoint reported higher contact avoidance compared to individuals scoring high
and those scoring low on the scale.
Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,
respectively.
Figure 2. Partial dependence plots depicting the relationships between our predictors and criteria
based on the complete data
Regarding hygiene maintenance (Figure 3), the most invariable contribution was found for social
belonging and morality as cooperation, followed by internalized and symbolized moral identity,
collective narcissism, and self-control. Gender differences in hygiene maintenance found on the
complete data and data based on representative samples were not detected in data organized
according to the stage of pandemic. Participants scoring higher on social belonging, internalized and
symbolized moral identity, collective narcissism, and self-control also scored higher on maintaining
hygiene. However, only the relationship between belonging and hygiene maintenance seemed linear
– all other lines reached a plateau at some point (usually around the midpoint), indicating participants
achieving the lowest scores on these factors also achieved the lowest scores on maintaining hygiene.
On the other hand, higher scores were related to higher reported hygiene maintenance among
participants scoring above the mean of morality as cooperation.
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Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,
respectively.
Figure 3. Partial dependence plots based on the data reflecting the early stage of pandemic
The most invariable predictors of policy support (Figure 4) were collective narcissism, internalized
moral identity, and self-control. Endorsement of conspiracy theories, symbolized moral identity,
possibly even morality as cooperation, and open-mindedness, seem to have exhibited a stronger
relationship with policy support in the early stages of the pandemic compared to the advanced stage.
Participants scoring higher on internalized moral identity and self-control generally were also more
supportive of policy measures. However, the relationships were not linear in the early pandemic stage
(and in the advanced stage in the context of self-control). The relationship between policy support and
collective narcissism was also complex - it was close to linear and positive in the advanced pandemic
stage, but in the early stages and on the complete data, it resembled an inverted-U-curve with a peak
around the mean. This indicates that participants scoring around the mean were most supportive of
restrictive policies, while those high and the ones low on collective narcissism were less supportive.
Participants showing more endorsement for COVID-19 conspiracy theories were less supportive,
while participants scoring higher on open-mindedness were more supportive of restrictive COVID-19
policies. The relationship between morality as cooperation and policy support was established only on
the complete data and indicated that only among those above the mean higher morality as
cooperation was related to higher policy support. The opposite was found for symbolized moral
identity – only among those lowest on this trait, the relationship between morality as cooperation and
policy support was linear and positive. No relationships were established around the mean or above
the mean.
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Note. Red, blue, and green represent avoiding contact, maintaining hygiene and policy support,
respectively.
Figure 4. Partial dependence plots based on the data reflecting the advanced stage of pandemic
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Discussion
Taking the machine learning approach, we provided several insights into social, psychological,
personality and cognitive factors in predicting COVID-19 responses. Although the nature of the
analyses (i.e., the dependence of importance estimates on error estimates which changes across
models) prevents us from direct comparisons of results across models, some consistent patterns were
observed.
Internalized moral identity was the most consistent predictor of COVID-19 attitudinal and behavioral
responses - the extent to which people perceived moral traits as central to their self-concept was
positively associated with their intentions to avoid physical contact, maintain hygiene, and support
policy measures aimed at mitigating the spread of the virus. Morality-as-cooperation was also
associated with the attitudinal and behavioral responses, most consistently in predicting hygiene
maintenance. These results suggest that maintaining hygiene, but also physical distancing and policy
support, were perceived as collective actions that benefit the group more than they benefit the self.
Symbolized moral identity was also associated with the criteria, but, interestingly, the relationship was
non-linear and strongest among participants scoring below the average of symbolized moral identity.
These findings may reflect the fact that individuals characterized by moderate or high symbolization of
moral identity prefer to be perceived as aligned with social norms, rather than actually adhering to
them (Winterich et al., 2013). However, the threshold at which the relationship becomes linear seems
to change with respect to the pandemic stage and specific criteria, indicating the need for further
research into these relationships. Overall, these findings are in line with previous research suggesting
that internalized moral identity is a relevant predictor of prosocial and cooperative intentions and
behavior, with more inconsistent results when examining the symbolization dimension of moral
identity (for a review, see Jennings et al., 2015; Winterich et al., 2013). The only variable related to
morality that did not substantially contribute to our criteria's prediction was the moral circle. Altogether,
these results indicate that morality represents an important factor in adherence to preventive
measures. Nevertheless, different aspects of morality provide different contributions to the prediction
of adherence to these measures.
Open-mindedness and self-control were positively associated with avoiding contact and supporting
policy, while self-control also exhibited a relatively steady, albeit weak, contribution to the prediction of
hygiene maintenance. Open-mindedness was conceptualized as a part of cognitive humility, which
reflects the virtue of being able to accept one’s fallibility and the willingness to accept information
contrary to one’s initial beliefs (Alfano et al., 2017; Spiegel, 2012), with some authors treating it as a
moral virtue (Arpaly, 2011; Song, 2018). Self-control is typically conceptualized as the capacity to
work effectively to reach goals, resisting short-term temptations (Fishbach & Shah 2006; Tangney et
al., 2004, ). Some authors have suggested that self-control goals are often moralized (Hofmann et al.,
2018). The relationship between open-mindedness and morality and between self-control and
morality may underlie the predictive contribution of open-mindedness and self-control established in
this study.
Social belonging was also established as a relevant predictor predominantly in terms of maintaining
hygiene, while collective narcissism also provided a substantial contribution to predicting policy
support and a less substantial contribution to predicting contact avoidance. On the one hand, ingroup
identification promotes acceptance of group norms (Livingstone et al., 2011), implying that findings on
social belonging could also reflect morality. On the other hand, the relationship between collective
narcissism and our criteria seems to be more complex, in line with the mixed evidence of previous
studies on the role of collective narcissism concerning various types of preventive behaviors such as
handwashing, physical distancing, and limiting leaving home (Nowak et al., 2020; Sternisko et al.,
2021). Namely, the evidence of a curvilinear relationship between collective narcissism and contact
avoidance and policy support might reflect the need of individuals high in collective narcissism to
establish and maintain a positive national image for the outside world (e.g., as model citizens, or
morally superior) (Cichocka, 2016; Cichocka & Cislak, 2020). However, at even higher levels of
collective narcissism, the need to assert and signal grandiosity and superiority in relation to various
threats (in this case, the virus) might manifest in lower support for restrictive preventive measures,
even at the cost of possible negative consequences for ingroup members (Cislak et al., 2018;
Marchlewska et al., 2020). This is also evident from the inverted-U curve in the context of policy
support, usually appearing slightly above the mean (except in the late stage of the pandemic, see
Figures 2 and 3). Overall, this suggests that while believing in ingroup potential may motivate
individuals to adhere to prosocial norms, irrational belief in superiority can undermine the support for
preventive measures that bring about short-term disturbance in the everyday ingroup dynamics.
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Additionally, conspiracy beliefs seem to be linked to contact avoidance and policy support, especially
in the early stage of the pandemic. Namely, endorsement of COVID-19 conspiracy theories was
associated with lower intentions to engage in physical distancing and lower policy support. Given that
conspiracy believers were found to be more self-centered (Hornsey et al., 2021) and less generous
(Alper et al., 2021) during the COVID-19 pandemic, this finding speaks in favor of viewing contact
avoidance as a form of prosocial action.
The presented findings suggest that prosociality and morality are relevant factors for understanding
physical distancing. This is in line with previous work on the role of prosociality on physical distancing
(e.g., Coroiu et al., 2020; Travaglino & Moon, 2021; Van Lissa et al., 2022; see Capraro et al. 2021 for
a review) and with the idea that personal norms, internal standards on what is right or wrong in a
given situation, play an important role in driving prosocial behavior (Capraro & Rand, 2018; Tappin &
Capraro, 2018; see Capraro & Perc, 2021, for a review). However, our results indicated a substantial
contextual variability, as well. While we focused on several most dependable and most substantial
predictors only to describe general patterns, it should be noted that multiple other factors provided a
contribution limited to a specific stage of the pandemic or specific culture (see Supplementary
materials G). This also implies that campaigns for increasing compliance with preventive measures in
future crises should be tailored to both the pandemic phase and the specifics of the culture in which
they plan to be implemented.
Generally, the obtained R2 values were lower than those reported by Van Lissa et al. (2022) in similar
analyses. In their study, injunctive norms and support for COVID-19 restrictive measures were found
to be two clearly dominant predictors of preventive behaviors, which may roughly approximate two
aspects of the Theory of Planned Behavior (Ajzen, 1991) - subjective norms and attitudes on the
specific behavior. We did not include these variables in our study, although policy support could
broadly be considered as attitudes regarding preventive measures. Conversely, we treated policy
support as one of the criteria rather than as one of the predictors, with contact avoidance, hygiene
maintenance, and policy support being moderately correlated (r = approx. .40). Thus, the simplest
explanation of the difference in the explained variance in our study compared to Van Lissa et al.
(2022) may reflect the difference in the extent to which the Theory of Planned Behavior has been
represented among predictors. Considered together, the two studies provide evidence in favor of the
Theory of Planned Behavior in the context of a global crisis.
Several limitations should be considered when interpreting our findings. Firstly, not all the national
samples were representative, and even the representative samples were not based on probabilistic
sampling, and consequently, some segments of society may have been underrepresented.
Furthermore, as the study was conducted online, our sample over-represents people with greater
access to internet-enabled technology, which may be a particularly important consideration in less-
developed countries (e.g., dissemination of conspiracy theories and fake news). Secondly, variability
in our criteria was heavily skewed in many countries (i.e., the vast majority of participants reported
high adherence to and support for preventive measures), which can be attributed to the first wave of
the pandemic during which the data were collected. Nevertheless, in some countries, data collection
was conducted during the peak of the first wave of the COVID-19 pandemic, while in other countries,
it was carried out at its beginning. Although we tried to operationalize the pandemic stage according
to the total number of infected individuals per million and took culture and sample size into account,
even such operationalization may not have eliminated all the potential sources of bias. The rough
similarity of the results based on representative and non-representative data, as well as data from
countries in different pandemic stages and data from countries grouped according to cultural zones
(see Supplementary materials G), provide arguments in favor of the validity of our findings;
nevertheless, the robustness of these more specific findings needs to be corroborated utilizing
different (i.e., longitudinal and nationally representative) samples. Furthermore, morality-as-
cooperation had to be modeled differently than proposed in the original papers to achieve invariance.
Additionally, as there are no conventional methods of testing the invariance of two-item and single-
item measures across cultures, scores on such items may be less precisely calculated than in the
case of multi-item measures. Finally, we focused on explaining variation in COVID-19 responses
without testing causality. It should be noted that we used cross-sectional, self-reported data which
may entail the desirability bias risk (Graeff, 2005). However, there is evidence that such desirability
bias does not play a key role, especially in self-reported measures like self-esteem, control, or
optimism (Caputo, 2017).
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Conclusion
Findings of our study indicate that the most effective predictors of COVID-19 responses, such as
avoiding physical contact, maintaining hygiene, and supporting restrictive COVID-19 policies, were
related to morality, prosociality, and traits and attitudes operationalizing self-control and information
processing. However, the predictive contribution of even the most invariant predictors substantially
varied with respect to the predicted type of response and cultural characteristics. While the research
design of this study prevents any causal conclusions, the results suggest that the interplay between
individual and contextual characteristics is relevant for understanding individual COVID-19 responses.
Ultimately, our findings can serve as a starting point for future, more nuanced, research on the
variables highlighted within our study. Hopefully, the growing body of research and accumulated
insights should lead to informed and efficient prevention and intervention programs for health-related
crises.
Data availability statement
The data that support the findings of this study are openly available in OSF at
http://doi.org/10.17605/osf.io/tfsza. These data have been used in multiple other manuscripts,
including the “National identity predicts public health support during a global pandemic” manuscript,
where prof. John Nezlek conducted the main analyses.
Declaration of competing interests
The authors have no competing interests to declare.
Author contributions
Conceptualization: Tomislav Pavlović and Jay van Bavel
Data curation: Tomislav Pavlović, Flavio Azevedo, Waldir M. Sampaio, Gabriel G. Rêgo, Paulo S.
Boggio
Formal analysis: Koustav De, Flavio Azevedo, Julian Riano-Moreno, Marina Maglić, Patricio Donnelly
Kehoe, César Payán-Gómez, Eugenia Hesse, Jaroslaw Kantorowicz, Steve Rathje, and Tomislav
Pavlović
Investigation (data collection): all authors
Methodology: Tomislav Pavlović and Jaroslaw Kantorowicz
Project administration: Tomislav Pavlović
Software: Koustav De, Flavio Azevedo, Julian Riano-Moreno, Marina Maglić, Patricio Donnelly
Kehoe, César Payán-Gómez, Eugenia Hesse, Jaroslaw Kantorowicz, Steve Rathje, and Tomislav
Pavlović
Supervision: Jay van Bavel, Marina Maglić, Michele Birtel, and Tomislav Pavlović
Validation: Tomislav Pavlović
Visualization: Jaroslaw Kantorowicz, Steven Rathje, and Tomislav Pavlović
Writing – original draft: Koustav De, Marina Maglić, Guanxiong Huang, Jaroslaw Kantorowicz, Michèle
D. Birtel, Philipp Schönegger, Valerio Capraro, Hernando Santamaría-García, Theofilos Gkinopoulos,
Meltem Yucel, Agustin Ibanez, Steve Rathje, Erik Wetter, Dragan T. Stanojevic, Jan-Willem van
Prooijen, Eugenia Hesse, Christian T. Elbaek, Panagiotis Mitkidis, Renata Franc, Zoran Pavlović.
Writing – review and editing: all authors
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Funding
Eva Lermer was financially supported by the VolkswagenStiftung (grant no. 98 525).
Manos Tsakiris was financially supported by the NOMIS Stiftung (NOMIS Foundation).
Ondrej Buchel was financially supported by the Slovak Research and Development Agency (SRDA)
(Agentúra na podporu výskumu a vývoja) (grant no. APVV-18-0218).
Patricia Lockwood 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.
Michael Bang Petersen 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.
Rolf A. Zwaan was financially supported by the Netherlands Organization for Scientific Research
(NWO) (grant no. 440.20.003).
Zoran Pavlović 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).
Yucheng Zhang was financially supported by the National Natural Science Foundation of China (grant
no. 71972065; 71832004; 71872152), Universities in Hebei Province Hundred Outstanding Innovative
Talents Support Program (grant no. SLRC2019002); Ministry of Education in China (grant no.
21JHQ088).
Brent Strickland 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).
Jane Conway 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).
Matej Hruška was financially supported by the Slovak Research and Development Agency (grant no.
APVV-17-0596).
Marina Maglić was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.
Tomislav Pavlović was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb;
Croatia.
Igor Mikloušić was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.
Renata Franc was financially supported by the Institute of Social Sciences Ivo Pilar, Zagreb; Croatia.
Agustin Ibanez 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), Universidad del
Valle (CI 5316); and the MULTI-PARTNER CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN
LATIN AMERICA [ReDLat, supported by National Institutes of Health, National Institutes of Aging
(R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable foundation - Tau
Consortium, and Global Brain Health Institute)].
Paulo Sergio Boggio was financially supported by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior - Brasil (CAPES) (grant no. 1133/2019) and Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq) (grant no. 309905/2019-2).
Gabriel Gaudencio Rêgo was financially supported by the São Paulo Research Foundation (grant no.
2019/26665-5).
Waldir M. Sampaio was financially supported by the São Paulo Research Foundation - FAPESP
(grant no. 2019/27100-1).
Robert M. Ross was financially supported by the Australian Research Council (grant no.
DP180102384).
Mark Alfano was financially supported by the John Templeton Foundation (grant no. 61378).
Hans H. Tung was financially supported by the Ministry of Science and Technology, Taiwan.
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Ming-Jen Lin was financially supported by the Ministry of Science and Technology, Taiwan.
Jay Joseph Van Bavel was financially supported by the John Templeton Foundation.
Uwe Dulleck was financially supported by the QUT Centre for Behavioural Economics, Society and
Technology (BEST).
Aleksandra Cislak was financially supported by the National Science Center (grant no.
2018/29/B/HS6/02826).
Sylvie Borau was financially supported by the ANRLabex IAST.
F. Ceren Ay was financially supported by the Research Council of Norway through its Centres of
Excellence Scheme, FAIR project (grant no. 262675).
Becky Choma was financially supported by the Social Sciences and Humanities Research Council
(SSHRC).
Caroline Leygue was financially supported by the Sistema Nacional de Investigadores, CONACyT
(Mexico).
Raffaele Tucciarelli was financially supported by the NOMIS Foundation Distinguished Scientist
Award for the ‘Body & Image in Arts & Science’ (BIAS) Project.
Tobias Otterbring was financially supported by the Aarhus University Research Foundation (grant no.
28207).
David Stadelmann was financially supported by the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany’s Excellence Strategy (grant no. EXC 2052/1
390,713,894).
Matthew A. J. Apps was financially supported by the Biotechnology and Biological Sciences Research
Council David Phillips Fellowship (grant no. BB/R010668/1).
Anton Kunnari was financially supported by the Jane & Aatos Erkko Foundation (grant no. 170112)
and the Academy of Finland (grant no. 323207).
Elena Morales-Marente was financially supported by the Contemporary Thinking and Innovation for
Social Development (COIDESO), University of Huelva, Huelva, Spain.
Teemu Saikkonen was financially supported by the Jane & Aatos Erkko Foundation (grant no.
170112) and the Academy of Finland (grant no. 323207).
Claus Lamm was financially supported by the University of Vienna, COVID-19 Rapid Response grant,
and the Austrian Science Fund (FWF, I3381).
Alexander C. Walker was financially supported by the Natural Sciences and Engineering Research
Council of Canada.
Hallgeir Sjåstad was financially supported by the Research Council of Norway, Centres of Excellence
scheme -FAIR project (grant no. 262675).
Sergio Barbosa was financially supported by the School of Medicine and Health Sciences -
Universidad del Rosario.
William Cunningham was financially supported by the SSHRC grant (grant no. 506547).
Ennio Bilancini was financially supported by the PAI2018 project PROCOPE (Prosociality, Cognition
and Peer Effects) funded by the IMT School for Advanced Studies Lucca.
Victoria H. Davis was financially supported by the SSHRC grant (grant no. 506547).
Panagiotis Mitkidis was financially supported by the Aarhus University Research Foundation (grant
no. AUFF-E-201 9-9-4).
Andrej Findor was financially supported by the Slovak Research and Development Agency (grant no.
APVV-17-0596).
Steve Rathje was financially supported by the Gates Cambridge Scholarship.
Michael J. A. Wohl was financially supported by the Social Science and Humanities Research Council
of Canada (grant no. 435-2019-0692).
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ORIGINAL UNEDITED MANUSCRIPT
Andreas Olsson was financially supported by the Swedish Research Council - Consolidator Grant
(grant no. 2018-00877).
Jonathan Fugelsang was financially supported by the Natural Sciences and Engineering Research
Council of Canada.
Luca Cian was financially supported by the Batten Institute, University of Virginia Darden School of
Business.
Raymond Boadi Frempong was financially supported by the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation) under Germany's Excellence Strategy (grant no. EXC 2052/1—
390713894).
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References
Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and
objective social status with psychological and physiological functioning: Preliminary data in healthy,
White women. Health psychology, 19(6), 586-592. https://doi.org/10.1037/0278-6133.19.6.586
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alfano, M., Iurino, K., Stey, P., Robinson, B., Christen, M., Yu, F., & Lapsley, D. (2017). Development
and validation of a multi-dimensional measure of intellectual humility. PLoS ONE, 12(8), e0182950.
https://doi.org/10.1371/journal.pone.0182950
Alper, S., Douglas, K., & Capraro, V. (2021). Conspiracy beliefs and generosity across 52 Countries
during the COVID-19 pandemic. PsyArXiv [preprint, version 2]. https://doi.org/10.31234/osf.io/fdyxr
Altmann, A., Toloşi, L., Sander, O., & Lengauer, T. (2010). Permutation importance: a corrected
feature importance measure. Bioinformatics, 26(10), 1340-1347.
https://doi.org/10.1093/bioinformatics/btq134
Alzyood, M., Jackson, D., Aveyard, H., & Brooke, J. (2020). COVID-19 reinforces the importance of
handwashing. Journal of Clinical Nursing, 29(15–16), 2760–2761. https://doi.org/10.1111/jocn.15313
Aquino, K., & Reed, A. II. (2002). The self-importance of moral identity. Journal of Personality and
Social Psychology, 83(6), 1423–1440. https://doi.org/10.1037/0022-3514.83.6.1423
Arpaly, N. (2011). Open-Mindedness As a Moral Virtue. American Philosophical Quarterly, 48(1), 75–
85. https://www.jstor.org/stable/23025075
[dataset] Azevedo, F., Pavlović, T., Rêgo, G. G. d., Ay, F. C., Gjoneska, B., Etienne, T., … Sampaio,
W. M. (2022, May 18). Social and moral psychology of COVID-19 across 69 countries. PsyArXiv.
https://doi.org/10.31234/osf.io/a3562
Back, M. D., Küfner, A. C. P., Dufner, M., Gerlach, T. M., Rauthmann, J. F., & Denissen, J. J. A.
(2013). Narcissistic admiration and rivalry: Disentangling the bright and dark sides of narcissism.
Journal of Personality and Social Psychology, 105(6), 1013–1037. https://doi.org/10.1037/a0034431
Biddlestone, M., Green, R., & Douglas, K. M. (2020). Cultural orientation, power, belief in conspiracy
theories, and intentions to reduce the spread of COVID-19. The British Journal of Social Psychology,
59(3), 663–673. https://doi.org/10.1111/bjso.12397
Bilinski, A., & Emanuel, E. J. (2020). COVID-19 and Excess All-Cause Mortality in the US and 18
Comparison Countries. JAMA, 324(20), 2100. https://doi.org/10.1001/jama.2020.20717
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
https://doi.org/10.1023/A:1010933404324
Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting Incremental Fit Indices for Nonnormality.
Multivariate Behavioral Research, 49(5), 460–470. https://doi.org/10.1080/00273171.2014.933697
Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An Investigation of the Sample Performance of Two
Nonnormality Corrections for RMSEA. Multivariate Behavioral Research, 47(6), 904–930.
https://doi.org/10.1080/00273171.2012.715252
Cameron, J. E. (2004). A Three-Factor Model of Social Identity. Self and Identity, 3(3), 239–262.
https://doi.org/10.1080/13576500444000047
Capraro, V., Boggio, P., Böhm, R., Perc, M., & Sjåstad, H. (2021). Cooperation and acting for the
greater good during the COVID-19 pandemic. In M. K. Miller (Ed.), The Social Science of the COVID-
19 Pandemic: A Call to Action for Researchers. Oxford University Press.
Capraro, V., & Perc, M. (2021). Mathematical foundations of moral preferences. Journal of The Royal
Society Interface, 18(175), 20200880. https://doi.org/10.1098/rsif.2020.0880
Capraro, V., & Rand, D. G. (2018). Do the Right Thing: Experimental evidence that preferences for
moral behavior, rather than equity or efficiency per se, drive human prosociality. Judgment and
Decision Making, 13(1), 99-111. https://eprints.mdx.ac.uk/25707/6/jdm171107.pdf
Downloaded from https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgac093/6631580 by guest on 08 July 2022
ORIGINAL UNEDITED MANUSCRIPT
Caputo, A. (2017). Social desirability bias in self-reported wellbeing measures: evidence from an
online survey. Universitas Psychologica. 16(2), 1–13. https://doi.org/10.11144/Javeriana.upsy16-
2.sdsw
Chambon, M., Dalege, J., Elberse, J. E., & van Harreveld, F. (2021). A Psychological Network
Approach to Attitudes and Preventive Behaviors During Pandemics: A COVID-19 Study in the United
Kingdom and the Netherlands. Social Psychological and Personality Science, 13(1), 233–245.
https://doi.org/10.1177/19485506211002420
Cichocka, A. (2016). Understanding defensive and secure in-group positivity: the role of collective
narcissism. European Review of Social Psychology, 27(1), 283–317.
https://doi.org/10.1080/10463283.2016.1252530
Cichocka, A., & Cislak, A. (2020). Nationalism as collective narcissism. Current Opinion in Behavioral
Sciences, 34, 69-74. https://doi.org/10.1016/j.cobeha.2019.12.013
Cislak, A., Wojcik, A. D., & Cichocka, A. (2018). Cutting the forest down to save your face: Narcissistic
national identification predicts support for anti-conservation policies. Journal of Environmental
Psychology, 59, 65-73. https://doi.org/10.1016/j.jenvp.2018.08.009
Clark, C., Davila, A., Regis, M., & Kraus, S. (2020). Predictors of COVID-19 voluntary compliance
behaviors: An international investigation. Global Transitions, 2, 76–82.
https://doi.org/10.1016/j.glt.2020.06.003
Coroiu, A., Moran, C., Campbell, T., & Geller, A. C. (2020). Barriers and facilitators of adherence to
social distancing recommendations during COVID-19 among a large international sample of adults.
PLoS ONE, 15(10), e0239795. https://doi.org/10.1371/journal.pone.0239795
Curry, O. S., Jones Chesters, M., & Van Lissa, C. J. (2019). Mapping morality with a compass:
Testing the theory of ‘morality-as-cooperation’ with a new questionnaire. Journal of Research in
Personality, 78, 106–124. https://doi.org/10.1016/j.jrp.2018.10.008
Eisenhardt, K. M., Graebner, M. E., & Sonenshein, S. (2016). Grand Challenges and Inductive
Methods: Rigor without Rigor Mortis. Academy of Management Journal, 59(4), 1113–1123.
https://doi.org/10.5465/amj.2016.4004
Fishbach, A., & Shah, J. Y. (2006). Self-control in action: Implicit dispositions toward goals and away
from temptations. Journal of Personality and Social Psychology, 90(5), 820–832.
https://doi.org/10.1037/0022-3514.90.5.820
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives,
19(4), 25–42. https://doi.org/10.1257/089533005775196732
Golec de Zavala, A., Cichocka, A., Eidelson, R., & Jayawickreme, N. (2009). Collective narcissism
and its social consequences. Journal of Personality and Social Psychology, 97(6), 1074–1096.
https://doi.org/10.1037/a0016904
Graeff, T. R. (2005). Response bias. In K. Kempf-Leonard (Ed.), Encyclopedia of Social
Measurement. (pp. 411–418). Elsevier Inc. https://doi.org/10.1016/B0-12-369398-5/00037-2
Hofmann, W., Meindl, P., Mooijman, M., & Graham, J. (2018). Morality and self-control: How they are
intertwined and where they differ. Current Directions in Psychological Science, 27(4), 286-291.
https://doi.org/10.1177/0963721418759317
Hornsey, M. J., Chapman, C. M., Alvarez, B., Bentley, S., Salvador Casara, B. G., Crimston, C. R., ...
& Jetten, J. (2021). To what extent are conspiracy theorists concerned for self versus others? A
COVID19 test case. European Journal of Social Psychology, 51(2), 285-293.
https://doi.org/10.1002/ejsp.2737
Igarashi, Y., Nagata, K., Kuwatani, T., Omori, T., Nakanishi-Ohno, Y., & Okada, M. (2016). Three
levels of data-driven science. Journal of Physics: Conference Series, 699, 012001.
https://doi.org/10.1088/1742-6596/699/1/012001
Jennings, P. L., Mitchell, M. S., & Hannah, S. T. (2015). The moral self: A review and integration of
the literature. Journal of Organizational Behavior, 36(S1), S104–S168.
https://doi.org/10.1002/job.1919
Downloaded from https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgac093/6631580 by guest on 08 July 2022
ORIGINAL UNEDITED MANUSCRIPT
Johns Hopkins Coronavirus Resource Center (n.d.). Mortality Analyses. Retrieved March 7, 2022,
from https://coronavirus.jhu.edu/data/mortality
Livingstone, A. G., Haslam, S. A., Postmes, T., & Jetten, J. (2011). “We Are, Therefore We Should”:
Evidence That In-Group Identification Mediates the Acquisition of In-Group Norms1. Journal of
Applied Social Psychology, 41(8), 1857–1876. https://doi.org/10.1111/j.1559-1816.2011.00794.x
Malone, G. P., Pillow, D. R., & Osman, A. (2012). The General Belongingness Scale (GBS):
Assessing achieved belongingness. Personality and Individual Differences, 52(3), 311–316.
https://doi.org/10.1016/j.paid.2011.10.027
Marchlewska, M., Cichocka, A., Jaworska, M., Golec de Zavala, A., & Bilewicz, M. (2020). Superficial
ingroup love? Collective narcissism predicts ingroup image defense, outgroup prejudice, and lower
ingroup loyalty. British Journal of Social Psychology, 59(4), 857-875.
https://doi.org/10.1111/bjso.12367
Meyers, C., Kass, R., Goldenberg, D., Milici, J., Alam, S., & Robison, R. (2021). Ethanol and
isopropanol inactivation of human coronavirus on hard surfaces. Journal of Hospital Infection, 107,
45–49. https://doi.org/10.1016/j.jhin.2020.09.026
Nowak, B., Brzóska, P., Piotrowski, J., Sedikides, C., Żemojtel-Piotrowska, M., & Jonason, P. K.
(2020). Adaptive and maladaptive behavior during the COVID-19 pandemic: The roles of Dark Triad
traits, collective narcissism, and health beliefs. Personality and individual differences, 167, 110232.
https://doi.org/10.1016/j.paid.2020.110232
Postmes, T., Haslam, S. A., & Jans, L. (2013). A single-item measure of social identification:
Reliability, validity, and utility. The British Journal of Social Psychology, 52(4), 597–617.
https://doi.org/10.1111/bjso.12006
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The
state of the art and future directions for psychological research. Developmental Review, 41, 71–90.
https://doi.org/10.1016/j.dr.2016.06.004
R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria. https://www.R-project.org/
Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring global self-esteem: Construct
validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and Social
Psychology Bulletin, 27(2), 151–161. https://doi.org/10.1177/0146167201272002
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical
Software, 48(1), 1–36. https://doi.org/10.18637/jss.v048.i02
Ryu, S., Hwang, Y., Yoon, H., & Chun, B. C. (2020). Self-Quarantine Noncompliance During the
COVID-19 Pandemic in South Korea. Disaster Medicine and Public Health Preparedness, 1–4.
https://doi.org/10.1017/dmp.2020.374
Sage, A. (2018). Random forest robustness, variable importance, and tree aggregation. [Doctoral
dissertation, Iowa State University]. Iowa State University Digital Repository.
https://doi.org/10.31274/etd-180810-6083
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and
trait anxiety, self-mastery, and self-esteem): A reevaluation of the Life Orientation Test. Journal of
Personality and Social Psychology, 67(6), 1063–1078. https://doi.org/10.1037//0022-3514.67.6.1063
Song, Y. (2018). The moral virtue of open-mindedness. Canadian Journal of Philosophy, 48(1), 65–
84. https://doi.org/10.1080/00455091.2017.1335566
Spiegel, J. S. (2012). Open-Mindedness and Intellectual Humility. Theory and Research in Education,
10(1), 27–38. https://doi.org/10.1177/1477878512437472
Sternisko, A., Cichocka, A., Cislak, A., & Van Bavel, J. J. (2021). National Narcissism predicts the
Belief in and the Dissemination of Conspiracy Theories During the COVID-19 Pandemic: Evidence
From 56 Countries. Personality and Social Psychology
Bulletin. https://doi.org/10.1177/01461672211054947
Downloaded from https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgac093/6631580 by guest on 08 July 2022
ORIGINAL UNEDITED MANUSCRIPT
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment,
less pathology, better grades, and interpersonal success. Journal of Personality, 72(2), 271–324.
https://doi.org/10.1111/j.0022-3506.2004.00263.x
Tappin, B. M., & Capraro, V. (2018). Doing good vs. avoiding bad in prosocial choice: A refined test
and extension of the morality preference hypothesis. Journal of Experimental Social Psychology, 79,
64–70. https://doi.org/10.1016/j.jesp.2018.06.005
Thu, T. P. B., Ngoc, P. N. H., Hai, N. M., & Tuan, L. A. (2020). Effect of the social distancing
measures on the spread of COVID-19 in 10 highly infected countries. The Science of the Total
Environment, 742, 140430. https://doi.org/10.1016/j.scitotenv.2020.140430
Travaglino, G. A., & Moon, C. (2021). Compliance and Self-Reporting During the COVID-19
Pandemic: A Cross-Cultural Study of Trust and Self-Conscious Emotions in the United States, Italy,
and South Korea. Frontiers in Psychology, 12, 565845. https://doi.org/10.3389/fpsyg.2021.565845
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. https://doi.org/10.1038/s41562-020-0884-z
Van Bavel, J. J., Cichocka, A., Capraro, V., Sjåstad, H., Nezlek, J. B., Pavlović, T., ... & Boggio, P. S.
(2022). National identity predicts public health support during a global pandemic. Nature
communications, 13, 517. https://doi.org/10.1038/s41467-021-27668-9
Van Lissa, C. J., Stroebe, W., vanDellen, M. R., Leander, N. P., Agostini, M., Draws, ... Bélanger, J. J.
(2022). Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention
Behaviors During the Early Phase of the Pandemic. Patterns.
https://doi.org/10.1016/j.patter.2022.100482
van Zandwijk, N. & Rasko, J. E. J. (2020). The COVID-19 outbreak: A snapshot from down under.
Expert Review of Anticancer Therapy, 20(6), 433–436.
https://doi.org/10.1080/14737140.2020.1766974
Waytz, A., Iyer, R., Young, L., Haidt, J., & Graham, J. (2019). Ideological differences in the expanse
of the moral circle. Nature Communications, 10(1), 4389. https://doi.org/10.1038/s41467-019-12227-0
WHO (2022). COVID-19 Weekly Epidemiological Update, Edition 83. Retrieved March 18, 2022, from
https://www.who.int/docs/default-source/coronaviruse/situation-
reports/20220315_weekly_epi_update_83.pdf?sfvrsn=194a6d66_4&download=true
Winterich, K. P., Aquino, K., Mittal, V., & Swartz, R. (2013). When moral identity symbolization
motivates prosocial behavior: The role of recognition and moral identity internalization. Journal of
applied psychology, 98(5), 759-770. https://doi.org/10.1037/a0033177
World Values Survey 7 (2020). The Inglehart-Welzel World Cultural Map 2020 [Provisional version].
Retrieved from
https://www.worldvaluessurvey.org/WVSContents.jsp?CMSID=findings&CMSID=findings
Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High
Dimensional Data in C++ and R. Journal of Statistical Software, 77, 1-17.
http://hdl.handle.net/10.18637/jss.v077.i01
Zettler, I., Lilleholt, L., Böhm, R., & Gondan, M. (2021). Comparing responses in repeated cross-
sectional and panel studies: Results across eight weeks during the first COVID-19 lockdown in
Denmark. Psychological Assessment, 33(8), 691–704. https://doi.org/10.1037/pas0001048
Downloaded from https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgac093/6631580 by guest on 08 July 2022
... Analyzing the direct relation between social support and the two different protective behaviors we might see that social support predicts both active protective behaviors and avoidant behaviors. This prediction is in line with a recent manuscript with data collected among 69 countries during the pandemic, where it was found that avoiding physical contact and maintaining hygiene were related to prosociality (Pavlović et al., 2022). The authors identified social belonging as a relevant predictor of hygiene maintenance behaviors and physical distancing. ...
... Even though the government called for the need to practice "social distancing" (although the meaning seems to be "physical distancing"), social support was revealed as a contributor to the adherence to responsible and precautionary behaviors. This finding is consistent with recent findings showing the importance of social support as a resource by contributing to responsible protection behaviors during the pandemic (Corral-Verdugo et al., 2021;Pavlović et al., 2022). ...
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