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Individual and collective protective responses during the early phase of the COVID-19 pandemic in 10 different countries: Results from the EUCLID online survey

Open AccessPublished:June 13, 2022DOI:https://doi.org/10.1016/j.ijid.2022.06.012

      Highlights

      • Behavioral changes and governmental strategies are needed to address COVID-19.
      • Protective behaviors and support for measures are often studied separately.
      • However, these individual and collective pathways are cross-amplifying.
      • Research should address connections between individual and collective pathways.

      Abstract

      Background

      In times of unprecedented infectious disease threats, it is essential to understand how to increase individual protective behaviors and support for collective measures. The present study therefore examines factors associated with individual and collective pathways.

      Methods

      Data was collected through an online survey from 4483 participants (70.8% female, M = 41.2 years) across 10 countries from April 15, 2020 to June 2, 2020 as part of the "EUCLID" project (https://euclid.dbvis.de). Structural equation modeling was used to examine individual and collective pathways across and within countries.

      Results

      Overall, the adoption of individual protective behaviors and support for collective measures were high. Risk perception on the individual level and perceived effectiveness at the collective level were positively associated with both individual protective behaviors and support for collective measures. Furthermore, the model explained considerable variance in individual (40.7%) and collective protective behaviors (40.8%) and was largely replicated across countries.

      Conclusions

      The study extends previous research by demonstrating that individual risk perception and perceived effectiveness of collective measures jointly affect individual protective health behaviors and support for collective measures. These findings highlight the need to jointly consider a variety of behavioral actions against infectious disease threats, acknowledging interactions between individual and collective pathways.

      Graphical abstract

      Keywords

      Abbreviations:

      WEIRD (Western, educated, industrialized, rich, and democratic), SEM (Structural equation model)

      Introduction

      The outbreak of COVID-19 has caused an international health emergency with large waves of infections (Johns Hopkins University, https://coronavirus.jhu.edu) and severely burdened health and economic systems (e.g.,
      • Sarkodie SA
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      ). Governments worldwide have tried to control the outbreak by implementing various containment and mitigation strategies. In spring 2020, many governments intensified their efforts to slow down the spread of COVID-19 and to reduce its impact on public health, the economy, and society. This included measures such as lockdowns, closing schools and nonessential businesses (e.g., restaurants), and mandatory protective behaviors, many of which were unfamiliar to most people (e.g., physical distancing). Individual behavior change and public support for governmental measures are key to the success of such strategies (
      • Khorram-Manesh A
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      Implementing public health strategies - The need for educational initiatives: A systematic review.
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      • Siegrist M
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      The impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases.
      ).
      In terms of individual behavior change, numerous studies have shown that personal risk perception is an essential motivational trigger for protective behavior change (
      • Gaube S
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      • Fischer P.
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      The concept of risk perception in health-related behavior theory and behavior change.
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      ). The relation between perceived risk and protective behavior has been underscored by meta-analyses on various measures of risk perception and their relationship to a range of protective intentions and behaviors (
      • Brewer NT
      • Chapman GB
      • Gibbons FX
      • Gerrard M
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      Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination.
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      ). Also studies conducted during the COVID-19 pandemic have examined this relationship with regard to various protective behaviors (for a review, see
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ), such as hygiene behavior (e.g.,
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      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
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      ), contact avoidance (e.g.,
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ;
      • Dryhurst S
      • Schneider CR
      • Kerr J
      • Freeman ALJ
      • Recchia G
      • van der Bles AM
      • Spiegelhalter D
      • van der Linden S.
      Risk perceptions of COVID-19 around the world.
      ;
      • Qin H
      • Sanders C
      • Prasetyo Y
      • Syukron M
      • Prentice E.
      Exploring the dynamic relationships between risk perception and behavior in response to the coronavirus disease 2019 (COVID-19) outbreak.
      ;
      • Siegrist M
      • Luchsinger L
      • Bearth A.
      The impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases.
      ;
      • Villinger K
      • Wahl DR
      • Debbeler LJ
      • Koller JE
      • Brünecke I
      • Lages NC
      • et al.
      Using Ecological Momentary Assessment to study variations in daily experiences and behaviors during the COVID-19 pandemic.
      ;
      • Xie K
      • Liang B
      • Dulebenets MA
      • Mei Y.
      The impact of risk perception on social distancing during the COVID-19 pandemic in China.
      ;
      • Yuan J
      • Zou H
      • Xie K
      • Dulebenets MA.
      An assessment of social distancing obedience behavior during the COVID-19 post-epidemic period in China: A cross-sectional survey.
      ), wearing a face mask (e.g.,
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ;
      • Dryhurst S
      • Schneider CR
      • Kerr J
      • Freeman ALJ
      • Recchia G
      • van der Bles AM
      • Spiegelhalter D
      • van der Linden S.
      Risk perceptions of COVID-19 around the world.
      ), and vaccinations (e.g.,
      • Caserotti M
      • Girardi P
      • Rubaltelli E
      • Tasso A
      • Lotto L
      • Gavaruzzi T.
      Associations of COVID-19 risk perception with vaccine hesitancy over time for Italian residents.
      ). From a theoretical perspective, risk perceptions are influenced by general facts about the hazard or pandemic situation (i.e., the number of cases or deaths), information and opinions provided by governmental sources, classic or social media (e.g.,
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ;
      • He S
      • Chen S
      • Kong L
      • Liu W.
      Analysis of risk perceptions and related factors concerning COVID-19 epidemic in Chongqing.
      ;
      • Malecki KMC
      • Keating JA
      • Safdar N.
      Crisis communication and public perception of COVID-19 risk in the era of social media.
      ;
      • Tsoy D
      • Tirasawasdichai T
      • Ivanovich Kurpayanidi K.
      Role of social media in shaping public risk perception during COVID-19 pandemic: A theoretical review.
      ), and personal experience with the hazard (
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ;
      • Weinstein ND.
      Effects of personal experience on self-protective behavior.
      ). Personal experience can range from infections within one's wider social network to infections in close social proximity (e.g., relatives;
      • Kollmann J
      • Benyamini Y
      • Lages NC
      • Renner B.
      The role of personal risk experience – An investigation of health and terrorism risk perception in Germany and Israel.
      ;
      • Weinstein ND.
      Effects of personal experience on self-protective behavior.
      ). It may be especially influential as it provides more vivid and concrete information about the hazard (
      • Weinstein ND.
      Effects of personal experience on self-protective behavior.
      ) and can trigger a set of disease-avoidance mechanisms known as the “Behavioral Immune System” (e.g.,
      • Koller JE
      • Villinger K
      • Lages NC
      • Brünecke I
      • Debbeler JM
      • Engel KD
      • Grieble S
      • Homann PC
      • Kaufmann R
      • Koppe KM
      • Oppenheimer H
      • Radtke VC
      • Rogula S
      • Stähler J
      • Renner B
      • Schupp HT.
      Stigmatization of Chinese and Asian-looking people during the COVID-19 pandemic in Germany.
      ;
      • Schaller M
      • Murray DR
      • Hofer MK.
      The Behavioural Immune System and pandemic psychology: The evolved psychology of disease-avoidance and its implications for attitudes, behaviour, and public health during epidemic outbreaks.
      ;
      • Schaller M
      • Park JH.
      The Behavioral Immune System (and why it matters).
      ).
      While individual protective behaviors such as thorough handwashing offer protection for oneself and one's social network, governmental strategies such as lockdowns aim at collective protection. Research on COVID-19 has emphasized the importance of the perceived effectiveness of governmental strategies for the support of collective protection measures (e.g.,
      • Chen CWS
      • Lee S
      • Dong MC
      • Taniguchi M.
      What factors drive the satisfaction of citizens with governments’ responses to COVID-19?.
      ;
      • Mækelæ MJ
      • Reggev N
      • Dutra N
      • Tamayo RM
      • Silva-Sobrinho RA
      • Klevjer K
      • et al.
      Perceived efficacy of COVID-19 restrictions, reactions and their impact on mental health during the early phase of the outbreak in six countries.
      ). Therefore, the perceived effectiveness of collective measures may be an important prerequisite for their support (
      • Chen CWS
      • Lee S
      • Dong MC
      • Taniguchi M.
      What factors drive the satisfaction of citizens with governments’ responses to COVID-19?.
      ), whereas risk perception might mainly determine the adoption of individual protective behaviors. Conversely, increasing individual risk perception could also amplify support for collective measures either directly (e.g.,
      • Siegrist M
      • Luchsinger L
      • Bearth A.
      The impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases.
      ) or indirectly via perceived effectiveness. This raises the question of whether individual risk perception also facilitates support for collective protection measures. Early studies during the pandemic suggest a link between risk perception and individuals’ satisfaction with governmental responses (e.g.,
      • Sabat I
      • Neuman-Böhme S
      • Varghese NE
      • Barros PP
      • Brouwer W
      • van Exel J
      • et al.
      United but divided: Policy responses and people’s perceptions in the EU during the COVID-19 outbreak.
      ). In contrast, higher perceived effectiveness of and support for collective protection measures may motivate people to adopt individual protective behaviors (
      • Siegrist M
      • Zingg A.
      The role of public trust during pandemics: Implications for crisis communication.
      ,

      The Independent Scientific Advisory Group for Emergencies. UK government messaging and its association with public understanding and adherence to COVID-19 mitigations: Five principles and recommendations for a COVID communication reset, 2020, www.independentsage.org/wp-content/uploads/2020/11/Messaging-paper-FINAL-1-1.pdf (accessed 24 November 2020).

      ). Individual and collective pathways may therefore cross-amplify their impact on individual and collective protection measures.

      The present study

      The present study aimed to investigate individual and collective pathways for protective behaviors and support for collective protection measures during the early phase of the COVID-19 pandemic in spring 2020 across ten countries. Specifically, we tested (a) how risk perception impacts the adoption of individual protective behaviors (“individual pathway”), (b) how the perceived effectiveness of collective protection measures impacts their support (“collective pathway”), and (c) how personal experience relates to both pathways and whether the individual and collective pathways are independent or cross-amplify their impact (Figure 3). In addition, the consistency of the proposed model was tested for ten countries, as they differed in their governmental responses to COVID-19 and their epidemiological situations.

      Method

      Data were collected within the “EUCLID” project (see: https://euclid.dbvis.de), which tracks risk perception, protective behaviors, and future expectations across countries throughout the COVID-19 pandemic. The project was approved by the ethics committee of the University of Konstanz (ID number: 07/2020) and adhered to the guidelines of the German Psychological Society and the Declaration of Helsinki. All participants gave written informed consent before their participation.

      Participants

      Data were collected between April 15, 2020 and June 2, 2020 in Costa Rica, Finland, Germany, Greece, Israel, Latvia, North Macedonia, Portugal, Slovenia, and Switzerland through online surveys (Qualtrics, April 2020). Participants were recruited through different strategies (e.g., social media and university participant pools), and participants in Finland, Germany, Slovenia, and Switzerland could take part in a lottery.
      Overall, data were collected from 4583 participants. Of these, n = 100 were excluded due to failed attention checks. The final sample includes 4483 participants (70.8% women) with an average age of 41.2 years (SD = 14.7, 18-89 years). Most participants (61.7%) were either employed or self-employed, and 17.7% were in training or education (see Table S1 and Figure 1 for country-specific sample characteristics).
      Figure 1
      Figure 1Number of participants, average age, and standard deviation, as well as percentage of genders and employment status overall and per country.

      Measures and materials

      The questionnaire was developed within the “EUCLID” project. Bilingual researchers and native speakers translated the original German version according to a standardized protocol and adapted it to country-specific requirements.
      Personal experience with COVID-19. Personal experience was assessed by two items which asked the participants to estimate the total number (current and past) of suspected and confirmed coronavirus infections among their acquaintances on a 6-point response scale from ‘no person’ (1), ‘1 person’ (2), ‘2 persons’ (3), ‘3–4 persons’ (4), ‘5–7 persons’ (5), to ‘8 or more persons’ (6).
      Risk perception. Risk perception was assessed using three items for perceived likelihood, perceived severity, and worry (cf.
      • Brewer NT
      • Chapman GB
      • Gibbons FX
      • Gerrard M
      • McCaul KD
      • Weinstein ND.
      Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination.
      ;

      Renner B, Hahn A, Schwarzer R. Risiko und Gesundheitsverhalten. Dokumentation der Meßinstrumente des Forschungsprojekts „Berlin Risk Appraisal and Health Motivation Study“ (BRAHMS), 1996, www.andre-hahn.de/downloads/pub/1996/1996_BRAHMS_Skalendokumentation.pdf (accessed 8 November, 2021).

      ;
      • Renner B
      • Reuter T.
      Predicting vaccination using numerical and affective risk perceptions: The case of A/H1N1 influenza.
      ;
      • Sun YC
      • Croyle RT.
      Level of health threat as a moderator of social comparison preferences.
      ;
      • Weinstein ND
      • Kwitel A
      • McCaul KD
      • Magnan RE
      • Gerrard M
      • Gibbons FX.
      Risk perceptions: Assessment and relationship to influenza vaccination.
      ). Specifically, participants were asked to estimate (a) their likelihood of contracting COVID-19 from ‘very unlikely’ (1) to ‘very likely’ (5), (b) the severity of COVID-19 for their health from ‘not at all serious (can be neglected)’ (1) to ‘very serious (life-threatening)’ (5), and (c) their worry about contracting COVID-19 from ‘not at all worried’ (1) to ‘very worried’ (5). A risk perception index was calculated as an averaged sum score.
      Individual protective behaviors. Adoption of six individual behaviors recommended by the

      World Health Organization. Coronavirus disease (COVID-19) advice for the public, 2020, www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public (accessed 4 December 2020).

      , including wearing a face mask, following sneezing and coughing rules, washing hands often or thoroughly, avoiding touching one's face, avoiding shaking hands, avoiding leaving home (i.e., leaving home only for essential needs) was assessed by asking the participants whether and how they had changed their respective behavior due to the coronavirus from ‘much less frequently than in the past’ (1) to ‘much more frequently than in the past’ (5). An individual protective behavior index across all behaviors was calculated as an averaged sum score.
      Perceived effectiveness of collective protection measures against COVID-19. The participants were asked to judge the effectiveness of four governmental measures from ‘very ineffective’ (1) to ‘very effective’ (5), including canceling public events, closing schools and daycare centers, social distancing, and implementing a lockdown. An effectiveness index across the governmental measures was calculated as an averaged sum score.
      Support for collective protection measures against COVID-19. The participants were asked to judge the collective protection measures implemented by their government against COVID-19 from ‘completely exaggerated’ (1), ‘exaggerated’ (2), ‘in moderation’ (3), ‘insufficient’ (4) to ‘completely insufficient’ (5). Scores between 3 and 5, where participants rated the implemented measures as appropriate or were supportive of even stronger collective measures, were categorized as ‘support for collective measures’.

      Statistical Analyses

      Missing values on core variables were imputed using predictive mean matching with the R package “mice” (version 3.13.0;
      • van Buuren S
      • Groothuis-Oudshoorn K.
      mice: Multivariate imputation by chained equations in R.
      ). Analyses were conducted using R (version 4.0.3;
      • R Core Team
      ).
      To investigate the impact of individual and collective pathways on individual protective behavior and collective protection measures, structural equation models (SEM) were calculated (see Figure 3) using the R package “lavaan” (version 0.6-9.1633;
      • Rosseel Y.
      lavaan: An R package for Structural Equation Modeling.
      ). One item assessing individual protective behavior (wearing a face mask) was excluded from the model because of a low factor loading (λ = .25) and a low corrected item-total correlation (r = .19). For the cross-country model comparison, multigroup SEMs were calculated to compare (1) a baseline model without any equality constraints, (2) a model assuming a comparable factor structure across countries (i.e., measurement invariance), and (3) a model additionally assuming comparable factor variances-covariances across countries (i.e., structural invariance; cf.
      • Byrne BM
      • van de Vijver FJR.
      Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence.
      ;
      • Horn JL
      • McArdle JJ.
      A practical and theoretical guide to measurement invariance in aging research.
      ). Robust CFI estimates were compared between models to test invariance (ΔCFI < 0.01;
      • Cheung GW
      • Rensvold RB.
      Evaluating goodness-of-fit indexes for testing measurement invariance.
      ). Goodness of fit indices for the overall, multigroup, and country-specific models are listed in Table 1.
      Table 1Goodness of fit indices for the overall model, the multigroup model, and each country.
      χ2dfχ2/dfRobust CFISRMRRobust RMSEA90% CI
      Overall model994.148112.270.950.0370.0550.052, 0.058
      Multigroup modela;b1687.507332.300.950.0470.0560.053, 0.060
      Costa Ricab152.30821.860.900.0670.0690.051, 0.085
      Finland179.22822.190.890.0650.0660.053, 0.079
      Germany460.29815.680.970.0350.0480.044, 0.052
      Greece148.60821.810.910.0540.0600.044, 0.075
      Israel146.66821.790.850.0980.0940.069, 0.118
      Latvia108.34811.340.940.0650.0520.020, 0.076
      North Macedonia166.43822.030.920.0620.0650.051, 0.079
      Portugal104.66811.290.950.0660.0450.010, 0.067
      Slovenia165.68822.020.920.0590.0620.048, 0.076
      Switzerland212.74812.630.900.0590.0790.066, 0.092
      CFI = comparative fit index; SRMR = standardized root-mean-squared residual; RMSEA = root-mean-square error of approximation.
      Robust estimates are based on the Satorra-Bentler correction. All χ2 are significant at P < .05. a Multigroup model without constraints. b Please note that since the model for Costa Rica yielded a negative variance for worry due to low correlations between the items of the latent factor of risk perception, it was not included in the multigroup model.

      Results

      Personal experience with COVID-19 was rather low, with participants on average knowing between 0 and 1 infected person. However, personal experience differed between countries, being lowest in Greece (suspected: M = 1.65, SD = 0.87/ confirmed: M = 1.31, SD = 0.72) and highest in Finland (suspected: M = 2.18, SD = 1.35/ confirmed: M = 1.70, SD = 1.08) and Portugal (suspected: M = 2.08, SD = 1.35/ confirmed: M = 1.75, SD = 1.18). For descriptive results see Table 2 and Figure 2.
      Table 2Study variables’ characteristics overall and per country.
      CountryPersonal experience: Suspected casesPersonal experience: Confirmed casesRisk perception (index)Adoption rate of individual protective behaviors (index)Perceived effectiveness of collective protection measures (index)Support for collective protection measures (% support)
      OverallM = 2.00 (SD = 1.25)M = 1.59 (SD = 0.98)M = 2.90 (SD = 0.82)M = 4.17 (SD = 0.54)M = 3.90 (SD = 0.90)75.7
      Costa RicaM = 1.69 (SD = 1.15)M = 1.62 (SD = 1.08)M = 3.30 (SD = 0.70)M = 4.21 (SD = 0.55)M = 4.49 (SD = 0.67)93.9
      FinlandM = 2.18 (SD = 1.35)M = 1.70 (SD = 1.08)M = 3.01 (SD = 0.73)M = 4.24 (SD = 0.44)M = 4.18 (SD = 0.65)88.8
      GermanyM = 2.08 (SD = 1.27)M = 1.61 (SD = 0.97)M = 2.81 (SD = 0.81)M = 4.15 (SD = 0.54)M = 3.76 (SD = 0.97)72.9
      GreeceM = 1.65 (SD = 0.87)M = 1.31 (SD = 0.72)M = 3.35 (SD = 0.75)M = 4.23 (SD = 0.52)M = 4.18 (SD = 0.68)68.3
      IsraelM = 1.72 (SD = 1.12)M = 1.45 (SD = 0.80)M = 2.67 (SD = 0.83)M = 4.10 (SD = 0.60)M = 4.44 (SD = 0.61)85.3
      LatviaM = 2.13 (SD = 1.35)M = 1.46 (SD = 0.90)M = 3.14 (SD = 0.76)M = 4.09 (SD = 0.54)M = 4.11 (SD = 0.69)85.3
      North MacedoniaM = 1.96 (SD = 1.14)M = 1.69 (SD = 1.03)M = 3.01 (SD = 0.80)M = 4.19 (SD = 0.52)M = 3.84 (SD = 0.86)72.6
      PortugalM = 2.08 (SD = 1.35)M = 1.75 (SD = 1.18)M = 3.36 (SD = 0.71)M = 4.48 (SD = 0.44)M = 4.20 (SD = 0.59)93.1
      SloveniaM = 1.81 (SD = 1.22)M = 1.44 (SD = 0.89)M = 2.60 (SD = 0.79)M = 4.08 (SD = 0.49)M = 3.84 (SD = 0.83)65.9
      SwitzerlandM = 1.90 (SD = 1.22)M = 1.61 (SD = 1.00)M = 2.68 (SD = 0.82)M = 4.04 (SD = 0.63)M = 3.73 (SD = 0.95)71.9
      Figure 2
      Figure 2Means and standard deviations of variables used as indicators of each construct in the SEMs overall and per country. The x-axis displays the range of the respective indicators.

      Individual pathway

      While the average risk perception (index) was in the mid-range of the scale (M = 2.90, SD = 0.82), adoption rates of individual protective behaviors were generally high, ranging from 88.6% for avoiding handshakes to 69.0% for staying at home. However, risk perception (F(9, 4461) = 36.48, P < .001, η2 = 0.07) and adoption rates of individual protective behaviors (F(9, 4473) = 11.9, P < .001, η2 = 0.02) differed between countries. While risk perception was lowest in Slovenia (M = 2.60, SD = 0.79) and highest in Costa Rica (M = 3.30, SD = 0.70), the rates of individual protective behaviors were lowest in Switzerland (M = 4.04, SD = 0.63) and highest in Portugal (M = 4.48, SD = 0.44).

      Collective pathway

      The perceived effectiveness of collective protection measures was high (M = 3.90, SD = 0.90), with most participants (75.7%) believing them to be effective. Lockdowns were rated as the least effective (M = 3.52, SD = 1.18), and the cancelation of public events as the most effective measures (M = 4.31, SD = 0.94). Similarly, the rate of support for collective protection measures was high (75.7%). However, countries differed both in the perceived effectiveness of (F(9, 4473)= 32.99, P < .001, η2 = 0.06) and support for (χ2(9) = 146.00, P < .001) collective protection measures. While perceived effectiveness was lowest in Switzerland (M = 3.73, SD = 0.95) and highest in Costa Rica (M = 4.49, SD = 0.67), support was lowest in Greece (68.3%) and highest in Costa Rica (93.9%).

      Individual and collective pathways

      Figure 3 shows the individual and collective pathways within the SEM. As expected, higher risk perceptions were related to more protective behaviors (β = 0.33, P < .001), supporting the “individual pathway”. The SEM also yielded evidence for the “collective pathway”, as higher perceived effectiveness of collective protection measures was related to higher support for such measures (β = 0.52, P < .001).
      Figure 3
      Figure 3Structural equation model of individual and collective pathways on individual protective behavior and collective protection measures (N = 4471).
      Importantly, the SEM also provides evidence for cross-pathways. Participants who felt more at risk from COVID-19 also believed that collective protection measures were more effective (β = 0.56, P < .001). Moreover, higher individual risk perception cross-amplified support for collective protection measures both directly (β = 0.17, P < .001) and indirectly via perceived effectiveness (β = 0.29, P < .001). Conversely, higher perceived effectiveness of collective protection measures facilitated the adoption of individual protective behaviors (β = 0.38, P < .001). While these findings demonstrate interconnections between the individual and collective pathways, only a small correlation between individual protective behavior and support for collective protection measures was observed (r = .12). Personal experience only had small positive effects on both pathways. Taken together, direct and cross-pathways explained 40.8% of the variance in individual protective behaviors and 40.7% of the variance in collective protection measures.
      Furthermore, we tested cross-country differences in a multigroup SEM (for country-specific results, please see Table S4). Inducing constraints further showed that the measurement model was comparable across the nine countries that were included (ΔCFI = 0.005), whereas the structural model differed between them (ΔCFI = 0.014). Importantly, however, the individual and collective pathways were generally replicated. Higher individual risk perception was associated with more individual protective behavior in all countries, except Israel (β = -0.07, P = .573), with effects ranging from β = 0.47 in Greece to β = 0.24 in Slovenia. A higher perceived effectiveness of collective protection measures was also related to greater support for them in all countries (0.58 ≥ β ≥ 0.26, Ps ≤ .024) except Greece (β = 0.10, P = .208). Furthermore, cross-pathways were observed. Higher individual risk perception was related to the higher perceived effectiveness of collective protection measures in all countries except Israel (β = 0.23, P = .057), ranging from β = 0.60 in Germany to β = 0.40 in Greece, and facilitated greater support for them both directly (0.29 ≥ β ≥ 0.16, Ps ≤ .047) in five (Germany, North Macedonia, Portugal, Slovenia, Switzerland) and indirectly via greater perceived effectiveness (0.34 ≥ β ≥ 0.22, Ps ≤ .001) in four of nine countries (Finland, Germany, North Macedonia, and Switzerland). Likewise, higher perceived effectiveness of collective protection measures facilitated the adoption of individual protective behaviors in all countries except Greece (β = 0.18, P = .064), ranging from β = 0.48 in Slovenia to β = 0.23 in Portugal. As in the general model, personal experience had little or no significant impact in all countries.

      Discussion

      The COVID-19 pandemic has highlighted the need to establish successful emergency responses at both the individual and collective levels. The present study proposes a model to examine individual and collective pathways for the adoption of individual protective behaviors and support for collective protection measures. Overall, the model explained a large amount of the variance in both individual protective behavior (40.8%) and collective protection measures (40.7%). Comparably small differences between countries emerged on both pathways. The replication of the overall structure of the model across countries is promising as countries vary substantially in their public health infrastructure and governmental responses, especially as best practices to address COVID-19, both individually and collectively, were limited in the early stages of the pandemic.
      Overall, reported rates of individual protective behaviors were high, and collective measures were generally perceived to be effective and largely supported. Interestingly, however, the introduction of lockdowns, i.e., the most intensive collective measure to reduce contact, was rated as the least effective measure in all countries. This subjective evaluation corresponds to retrospective cross-country data modeling, indicating that stay-at-home orders (i.e., lockdowns) only had small effects in addition to other large-scale collective measures during the first wave of COVID-19 (
      • Brauner JM
      • Mindermann S
      • Sharma M
      • Johnston D
      • Salvatier J
      • Gavenčiak T
      • et al.
      Inferring the effectiveness of government interventions against COVID-19.
      ). Furthermore, some variability between countries emerged, potentially reflecting different governmental strategies and country-specific messaging. For example, individual protective behaviors and the perceived effectiveness of collective measures were comparably low in Switzerland, a country with a liberal approach toward COVID-19. In contrast, both were comparably high in Portugal, which was hit hard during the first wave of COVID-19 and consequently introduced intensive collective measures.
      The present pattern of results reveals a cross-amplifying effect between individual protective behaviors and the perceived effectiveness of collective measures. Willingness to adopt individual protective behaviors was higher when perceived collective measures were perceived to be more effective. This pattern of results does not support the notion that people tend to “free ride” on collective measures (
      • Cato S
      • Iida T
      • Ishida K
      • Ito A
      • McElwain KM
      • Shoji M.
      Social distancing as a public good under the COVID-19 pandemic.
      ;
      • Yong JC
      • Choy BKC.
      Noncompliance with safety guidelines as a free-riding strategy: An evolutionary game-theoretic approach to cooperation during the COVID-19 pandemic.
      ), which would imply that people who are more confident in the protective value of collective measures minimize individual costs by relying on collective protection while refraining from taking individual protective measures. In line with this, research on crisis events such as mass emergencies (e.g., train accidents) and disasters (e.g., hurricanes) shows that, in the face of a shared threat, most people do not put themselves first and engage in exclusively selfish behaviors but rather respond prosocially, showing solidarity and cooperation (
      • Drury J
      • Cocking C
      • Reicher S.
      Everyone for themselves? A comparative study of crowd solidarity among emergency survivors.
      ;
      • Drury J
      • Reicher S
      • Stott C.
      COVID-19 in context: Why do people die in emergencies? It’s probably not because of collective psychology.
      ;
      • Rodríguez H
      • Trainor J
      • Quarantelli EL.
      Rising to the challenges of a catastrophe: The emergent and prosocial behavior following Hurricane Katrina.
      ;
      • Tekin S
      • Sager M
      • Bushey A
      • Deng Y
      • Uluğ ÖM.
      How do people support each other in emergencies? A qualitative exploration of altruistic and prosocial behaviours during the COVID-19 pandemic.
      ). Alternatively, following the concept of “risk compensation”, one could have assumed that people who perceive collective measures to be effective show less individual protective behavior because their perceived risk falls below their individual target level of risk, which they strive to maintain (
      • Hedlund J.
      Risky business: Safety regulations, risks compensation, and individual behavior.
      ). Although this may be of concern when collective measures are considered (
      • Mantzari E
      • Rubin GJ
      • Marteau TM.
      Is risk compensation threatening public health in the covid-19 pandemic?.
      ), it was not supported by the present findings. Therefore, it is important to combine all effective means to address COVID-19 rather than refraining from collective measures for fear of risk compensation.
      Furthermore, the model also showed a cross-amplified effect of individual risk perception on support for collective protection measures. The direct effect indicates that when people perceive the risk of COVID-19 as high, they are more willing to support collective measures (
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ;
      • Siegrist M
      • Luchsinger L
      • Bearth A.
      The impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases.
      ). However, it is important to note that while the direct effect was comparably small (β = 0.17), the indirect effect (β = 0.29) via perceived effectiveness was markedly larger, suggesting that support for collective measures is mediated by considerations of perceived effectiveness rather than being unconditional. Since these considerations could reflect a process similar to the weighing of response efficacy and response costs in the “Protection Motivation Theory” (
      • Maddux JE
      • Rogers RW.
      Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change.
      ,
      • Rogers RW.
      A Protection Motivation Theory of fear appeals and attitude change.
      ), costly collective measures may only be seen as justified if they are perceived as necessary and effective (cf.
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ;
      • Leder J
      • Pastukhov A
      • Schütz A.
      Social value orientation, subjective effectiveness, perceived cost, and the use of protective measures during the COVID-19 pandemic in Germany.
      ). This emphasizes the importance of public support, which requires transparent, timely, and effective public communication about the benefits, drawbacks, and expected mechanisms of collective measures.
      Despite the identified cross-amplifying interconnections between the individual and collective pathways, only a small correlation emerged between individual protective behavior and support for collective protection measures (r = .12). This contradicts a general support for protective measures, regardless of their nature, i.e., individual or collective. Rather, it suggests that both types of protection depend on different factors (e.g., risk perception, perceived effectiveness) even though they are part of the same continuum of behavioral actions. This finding could also be due to differing (i.e., self-protective or public-protective) motivations for individual protective behaviors and support for collective measures (cf.
      • Leder J
      • Pastukhov A
      • Schütz A.
      Social value orientation, subjective effectiveness, perceived cost, and the use of protective measures during the COVID-19 pandemic in Germany.
      ;
      • Liekefett L
      • Becker J.
      Compliance with governmental restrictions during the coronavirus pandemic: a matter of personal self-protection or solidarity with people in risk groups?.
      ). For instance, a person who feels relatively invulnerable to COVID-19 and therefore engages in relatively few protective behaviors might still endorse collective measures to protect vulnerable groups in society. Therefore, addressing motivation for self-protection and public protection may reinforce behavioral actions against COVID-19.
      While communications with the public could reinforce behavior and influence the perceived effectiveness of collective measures, how people perceive their risk may also depend on personal experience. Personal experiences have been shown to function as amplifiers of risk by reducing the degree of abstraction of a hazard, which is consequently perceived as closer and more threatening, thus increasing risk perception (
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ;
      • Dryhurst S
      • Schneider CR
      • Kerr J
      • Freeman ALJ
      • Recchia G
      • van der Bles AM
      • Spiegelhalter D
      • van der Linden S.
      Risk perceptions of COVID-19 around the world.
      ;
      • Kollmann J
      • Benyamini Y
      • Lages NC
      • Renner B.
      The role of personal risk experience – An investigation of health and terrorism risk perception in Germany and Israel.
      ;
      • Weinstein ND.
      Effects of personal experience on self-protective behavior.
      ). Surprisingly, however, personal experience only appears to have small effects in the present model. Although personal experience was generally low, the variability suggests that this cannot be fully explained by a floor effect. Instead, it could be explained by a moderating effect of disease severity and outcome that lowers people's risk perception when knowing someone who has recovered and increases it when knowing someone who has died (
      • Betsch C
      • Ulshöfer C
      • Renkewitz F
      • Betsch T.
      The influence of narrative v. statistical information on perceiving vaccination risks.
      ;
      • Leder J
      • Pastukhov A
      • Schütz A.
      Social value orientation, subjective effectiveness, perceived cost, and the use of protective measures during the COVID-19 pandemic in Germany.
      ;
      • Weinstein ND.
      Effects of personal experience on self-protective behavior.
      ). Thus, future studies should examine personal experience with COVID-19 more closely, including the severity and type of experience.
      In addition to the model and its implications, the applicability to other countries is of interest. While the model's overall structure was generally replicated in nine of ten countries, some differences emerged. Importantly, differences in significance levels of similar-sized effects could result from differences in sample size rather than qualitative differences between countries. Qualitative differences, by contrast, could be based on a variety of factors, including differences in the epidemiologic situation and collective measures. However, as the study did not aim to analyze differences between countries, we must be cautious in drawing conclusions about cross-country differences. In addition, the model could not be replicated for Costa Rica because of estimation problems resulting from unexpectedly low correlations among risk perception indicators during the assessment period of the present study. Nevertheless, the replication of the general structure of the model for most countries is noteworthy, especially given the relatively large number of countries (cf.
      • Byrne BM
      • van de Vijver FJR.
      Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence.
      ).
      The study has certain limitations. First of all, and although the cross-country examination of the model is a strength of the study, data only represent a snapshot of the rapidly changing and dynamic situation during the COVID-19 pandemic in the ten countries analyzed. Furthermore, because of the cross-sectional design, we can only hypothesize about the direction of effects in the model (cf.
      • Weinstein ND
      • Rothman AJ
      • Nicolich M.
      Use of correlational data to examine the effects of risk perceptions on precautionary behavior.
      ). Future research should preferably replicate and expand on the present findings using representative and longitudinal data, also including more non-WEIRD (non-WEIRD populations rather than WEIRD ones) populations and taking the epidemiologic situation in each country into account. Secondly, whereas the study focused on the main factors contributing to individual and collective pathways, other important aspects were not incorporated into the model. For instance, it would have been interesting to explore the influence of people's trust in their government, as several studies show its importance for compliance with behavioral recommendations and support of collective measures (e.g.,
      • Bargain O
      • Aminjonov U.
      Trust and compliance to public health policies in times of COVID-19.
      ;
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ;
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ;
      • Harring N
      • Jagers SC
      • Löfgren Å.
      COVID-19: Large-scale collective action, government intervention, and the importance of trust.
      ;
      • Siegrist M
      • Luchsinger L
      • Bearth A.
      The impact of trust and risk perception on the acceptance of measures to reduce COVID-19 cases.
      ). Similarly, classic and social media play an important role in shaping public perceptions of the risk of COVID-19 (e.g.,
      • Cipolletta S
      • Andreghetti GR
      • Mioni G.
      Risk perception towards COVID-19: A systematic review and qualitative synthesis.
      ;
      • He S
      • Chen S
      • Kong L
      • Liu W.
      Analysis of risk perceptions and related factors concerning COVID-19 epidemic in Chongqing.
      ;
      • Malecki KMC
      • Keating JA
      • Safdar N.
      Crisis communication and public perception of COVID-19 risk in the era of social media.
      ;
      • Tsoy D
      • Tirasawasdichai T
      • Ivanovich Kurpayanidi K.
      Role of social media in shaping public risk perception during COVID-19 pandemic: A theoretical review.
      ). Finally, social-cognitive factors such as self-efficacy or outcome expectancies (see e.g., the Health Action Process Approach;
      • Schwarzer R.
      Self-efficacy in the adoption and maintenance of health behaviors: theoretical approaches and a new model. In: Schwarzer R, Self-efficacy: Thought control of action.
      ) and interindividual differences (e.g., age and education level) can systematically affect the adoption of protective behaviors and the acceptance of more restrictive policies (
      • Betsch C
      • Korn L
      • Burgard T
      • Gaissmaier W
      • Felgendreff L
      • Eitze S
      • et al.
      The four weeks before lockdown during the COVID-19 pandemic in Germany: A weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.
      ). Future research could expand our model to provide a more complete picture of individual and collective pathways and their interactions.

      Conclusions

      The present study advances previous research by providing a more comprehensive view of perceptions and measures on the individual and collective levels, thereby revealing considerable connections between these pathways. Specifically, while the adoption of individual protective behavior and support for collective measures are mostly distinct, both are strengthened by risk perceptions at the individual level and effectiveness ratings at the collective level. This highlights the importance of considering the full range of behavioral actions against an infectious disease threat, taking interactions and the influence of perceptions on different levels into account. Pursuing an integrative view is important for communicating with the public to successfully address the current global crisis.

      Competing interests

      The authors have no competing interests to declare.

      Ethical approval statement

      The project was approved by the ethics committee of the University of Konstanz (ID number: 07/2020) and adhered to the guidelines of the German Psychological Society and the declaration of Helsinki. All participants gave written informed consent before their participation.

      Acknowledgments

      We would like to thank Joke M. Debbeler, Hannah Oppenheimer, Isabel Brünecke, Kai D. Engel, Sofia Grieble, Leonie Hartmann, Peer C. Homann, Sarah Höschele, Anna Katz, Robin Kaufmann, Kim M. Koppe, Jennifer Martens, Vanessa C. Radtke, Friederike Roelcke, Sarah Rogula, Wiebke Schneider, Leonie Schuhmacher, Johanna Stähler and Nelly Theiss for their valuable support and Tony Arthur for proofreading the manuscript.
      This study was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117–422037984 (the Centre for the Advanced Study of Collective Behaviour), and under FOR 2374 (RiskDynamics), and by the Bundesministerium für Bildung und Forschung (BMBF, Federal Ministry of Education and Research) under 01EL1820A (SMARTACT). The funding sources had no involvement in the study's design; the collection, analysis, and interpretation of data; the writing of the manuscript; and the decision to submit the manuscript for publication.

      Appendix. Supplementary materials

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