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 Table of Contents  
Year : 2023  |  Volume : 6  |  Issue : 3  |  Page : 93-104

Social trust and COVID-appropriate behavior: Learning from the pandemic

1 Production and Operations Management, School of Management, KIIT University, Bhubaneshwar, Odisha, India
2 Consultant Psychiatrist, Black Country Healthcare NHS Foundation Trust, Wolverhampton, UK
3 Production and Operations Management, School of Management, KIIT University; Assistant Admissions Officer, XIM University, Bhubaneshwar, Odisha, India

Date of Submission01-Oct-2022
Date of Decision08-Feb-2023
Date of Acceptance28-Mar-2023
Date of Web Publication18-Sep-2023

Correspondence Address:
Brajaballav Kar
School of Management, KIIT University, Bhubaneshwar, Odisha
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/shb.shb_183_22

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Introduction: General trust and trust in various social institutions/agents are argued to positively influence the outcome, more so, in a crisis. Mitigating a crisis requires actions from individuals, family, friends, co-workers, various policymaking, and implementing agencies, media, and other agencies with whom people interact. In the COVID-19 situation, people individuals did not have a choice but to access essential services even with the risk of infection. Personal experiences also guide individuals' trust in various social groups and are responsible for taking individual action of protecting themselves in the pandemic. To what extent people trusted various social groups and observed appropriate behavior is investigated in this research. Methods: Responses were collected through a structured, web-based questionnaire where respondents self-reported their trust in various social agents and the extent to which they observed COVID-appropriate behavior. Respondents primarily belonged to the eastern part of India. Results: This study finds significant demographic differences in observing appropriate behavior leading to an identification of a vulnerable group. Second, trust in the inner group (family, friends, neighbors, and co-workers among others) is least important whereas trust in professionals and administrative institutions is the most important. Trust in the central government, media, and politicians among others is counterproductive to observing the appropriate behavior. Conclusion: People repose higher trust in professionals and administrative institutions in a crisis situation. Professional and administrative leadership helps in more effective crisis management leading to better behavioral compliance of the public. Any other leadership may be ineffective or counter-productive.

Keywords: Crisis situation, health, social behavior, social trust

How to cite this article:
Kar B, Kar N, Panda MC. Social trust and COVID-appropriate behavior: Learning from the pandemic. Asian J Soc Health Behav 2023;6:93-104

How to cite this URL:
Kar B, Kar N, Panda MC. Social trust and COVID-appropriate behavior: Learning from the pandemic. Asian J Soc Health Behav [serial online] 2023 [cited 2023 Dec 2];6:93-104. Available from: http://www.healthandbehavior.com/text.asp?2023/6/3/93/385950

  Introduction Top

The pandemic COVID-19 management in India was complex. Huge population size, differing guidelines from the national and local governments, and the ground-level implementation process created complexity.[1] The constitution mandates health as a local governance issue. However, the national government imposed a lockdown across the nation. The local government did not have resources, selectively relaxed the lockdown norms, and did not have adequate institutional expertise that created barriers for the effective implementation of COVID-19 measures.[1] Local governments in different regions were from different political parties and political differences in implementation were reported. Such differences influenced public perception.

Activities during the initial period included lockdown, shutdown, patient isolation, contact tracing, centrally controlled allocation of hospitals, and treatment protocols. These activities required coordination and control from government officers, police, doctors, and hospitals among others;[2] these activities varied across regions and phases of the disease.[3] Inadequate hospital resources, initial confusion over the course of treatment, drug availability, vaccine discovery, approval, and administration created ambivalence. Pandemic was associated with infodemic by various media outlets including the spread of fake or misinformation.[4],[5] India being a multicultural country, and large religious gatherings of different faiths affected the pandemic response process. The use of masks and sanitization was mandatory and enforced by organizations, in addition, the government suggested various COVID-appropriate behavior (CAB) to be followed by the public.[2]

The citizen's compliance with CAB showed regional variations.[2] Public perception about political, health, and administrative institutions, the veracity of information, local situations, personal experiences, and exigencies among other factors influenced their compliance with CAB.[6] Serious health consequences prompted compliance, whereas socioeconomic demands necessitated a compromise. The risk of infection prompted distrust among people, and higher compliance with CAB, whereas the exigencies and the need to access service during the period required trust in different social institutions and higher compliance with CAB.[7] Thus, irrespective of the level of trust, a higher level of compliance to CAB was anticipated. However, the inconvenience caused due to restrictive policies was anticipated to create distrust. The role of media and religious leaders acting in contravention to restrictive policies was also anticipated to create distrust. The overall influence of trust in various factors resulting in CAB was likely to be confounding. Trust is conceptualized as a part of social capital. The social capital facilitates collective action, mobilization of resources, formation of network, acceptance, and compliance with measures taken to control COVID-19.

Research findings did not converge. The voluntary social distancing was greater in areas with higher civic capital and among individuals exhibiting a higher sense of civic duty.[8] However, the perceived sociability predicted higher COVID-19 mortality.[9] Compliance with lockdown required trust or concern or distrustful complacency.[10] The role of trust was found relevant while choosing between voluntary and enforced measures.[11] Trust in institutions, such as the health-care system, the police, and national governments among others, was found responsible for different levels of outcomes during the pandemic.[12] Institutional trust was associated with lower COVID-19 mortality.[9] Trust is vital when managing an outbreak.[13] Trust in both institutions and peers improves prosocial behavior.[14] Higher trust in the government regarding COVID-19 control was significantly associated with higher adoption of health behaviors (handwashing, avoiding crowded spaces, self-quarantine) and prosocial behaviors in specification curve analyses.[15] It is related to people's willingness to follow rules and guidelines containing infection and mortality.[16] Trust in politics and trust in science emerged as important predictors for the acceptance and adoption of protective measures.[17] Trust studies during the pandemic emphasized more on political institutions than trust among each other.[18] The relevance of trust in various other members, institutions, and policies to understand behavior was found missing in the literature.

This section discusses extant trust research on various factors in the context of the pandemic.


The acceptance and adoption of protective measures depended on sociodemographic variables, risk perception, and trust.[17] However, the findings were equivocal. One study found men and younger participants show lower acceptance and adoption of protective measures.[17] Old and healthy people were found to have more trust in their governments.[19] Higher education was also associated with a higher trust in government[20] but a contrary result indicated education and trust in governments to be negatively related.[19]


Significant distrust in the COVID-19 vaccine was reported in the U. S. due to the concern about testing, communication, speed, and process of approval.[21] The ideology of people influenced the vaccine attitude or immunization propensity and trust in medical experts.[22] Vaccine hesitancy was also related to demographics and trust in the government.[23]


Fake news undermined political discourses and scientific information required for necessary for decision-making processes.[24] Trust in media declined due to fake news which coincided with the increase in political trust.[4],[5] Media freedom enables a negative assessment of government activities.[20]


Trust in science predicted individuals' behavioral intentions across countries.[25] People in the pandemic reported higher trust in the following order science, trust in police, and politicians, and also reported higher levels of patriotism.[26]

Political institutions

The COVID-19 crisis increased trust in institutions.[27] Long-term support for local governments improves public trust and the effectiveness of the response.[28] The implementation and compliance with stay-at-home orders were found to be higher in high-trust counties than in low-trust counties.[29] The trust in the prime minister and institutions increased immediately after the declaration of lockdown, indicating that citizens tend to “rally around the flag” in times of crisis.[30] Norwegian success in handling the crisis was attributed to competent politicians, a high-trust society, reliable, and professional bureaucracy, and low population density.[31] Compared to other countries, New Zealanders trust the news higher, but a higher proportion of those did not trust the news.[32]

Interestingly, a significant reduction in mobility in European regions, higher levels of political trust, and stringent policies were associated with lower mortality in high-trust regions.[6] Conspiracy theory believers perceived government countermeasures as too strict during COVID-19.[20] Trust in the communication from the government showed a significant relationship with protective behavior and perception.[13] The government actions and guidelines intended to save lives but it severely restricted civil liberties; a higher political trust was likely to improve compliance but a higher social trust was likely to reduce the acceptance of such restrictions.[33] However, during normal times, social and political trust positively influences the proactive role of individuals and reduces the negative influence of external factors.[14]

Risk perception

The perceived risk of infection depended on personal exposure to COVID-19, prosocial values, trust in government, collective efficacy, trust in science, and medical professionals. The risk perception and adoption of preventive health-care behavior correlated significantly.[34] Efficacy prompted compliance without fear but, interpersonal and institutional trust motivated very little for compliance.[7] Contrary evidence also found that the number of infections was not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors.[25] Hong Kong had low levels of public trust and political legitimacy but showed a successful crisis response.[35]

Theoretical basis

Perceived trust in different groups and protective health behavior are two broad questions investigated in this research. The explanation for different social groups is based on the social identity theory and social categorization theory. The social identity theory proposes that the shared identity among individuals influences collective action. The social categorization theory indicates how people categorize themselves and others into different groups by detecting similar characteristics. The group features are attributed to individuals which determine inter- and intragroup behaviors. However, in the pandemic case, everyone was at risk, and the group concept of “us” versus “them” was vague. Perceived vulnerability was expected to determine the behavior.[36]

Four theories such as the health belief model, the protection motivation theory, the theory of reasoned action, and the subjective expected utility theory are used to explain health behavior.[37] The protection motivation theory proposes that perceived threat, coping-appraisal process, cost of precautionary action, perceived effectiveness of action, and self-efficacy are some of the significant components influencing the behavior. Communications arousing fear have a substantial impact on behavior. Inputs to the protection motivation theory include rewards, self-efficacy, information from environmental and intrapersonal sources, and prior experiences.[37]

The protection motivation theory and trust have been examined in the pandemic context. Social trust and government action have been studied for hotel stay,[38] information acquisition and trust,[39] trust in science and adherence recommendations,[19],[40],[41] trust in government and preventive health behavior.[42] The theory of protection motivation and group identity classification theories have not been investigated in the pandemic context. We propose that individuals categorize others into different trust groups and change their behavior to protect themselves in a crisis situation.

Research gap

The study was conducted in the eastern part of India which had the following peculiarities: (a) the local government in this region was from different political parties compared to the national government. This difference manifested in differing communications and acceptance of national government policies. Second, this region has less health infrastructure and is considered a poorer region of India. However, the state of Odisha was more proactive in implementing restrictions to contain the spread of disease. Overall, there were conflicts between the national and local governments which could have influence the trust perception.

Trust facilitates governance, and compliance, thus, influence outcomes, even when restrictive policies are implemented. The majority of trust research during pandemics has focused on political trust but the findings are not consistent.[43] Extant research conceptualizes political trust as a singular concept. However, citizens in different regions in India can have different political trust due to the multiparty system and different political parties forming the government at local and national levels. Furthermore, other social groups (police, religious leaders, friends, and co-workers among others) also played a significant role in influencing the outcome either positively or negatively. The resultant influence is not adequately emphasized in research. The outcomes based on trust have not been consistent, for example, greater compliance to lockdown measures due to trust remains an open question.[43] Such inconsistency prompted a claim that trust, social behavior, and risk perception require a nuanced understanding.[35] Similarly, the association between risk perception and trust has been proposed as a research question.[43]

Second, how people identified trust groups during the pandemic has not been identified. The pandemic can be considered a mass panic. A panic situation can be associated with irrationality, in which the applicability of social identity theory and self-categorization theory to identify groups to belong is questionable.[44] It is contended that the legitimacy and honesty of the authority build trust[44] and in the absence of personal history, social assurance influence trust.[45] How did people perceive the trusting groups needed investigation?

Finally, the literature admits a dearth in research on health-related trust in the low- and middle-income context.[45],[46] This study addressed the gaps by investigating the influence of trust on CAB during the pandemic. The trust was divided into general trust and trust during the pandemic. Trust in 16 different factors including social entities, professions, institutions, and groups were included in the study and elaborated in the data and methods section. We also investigated how CAB differs for demography, the presence of comorbid conditions, personal exposure to the virus, and different types of service usage during the pandemic.


Based on the research gaps, this study (a) identifies the level of trust in different factors considered in the study, (b) identifies common trust groups as perceived by respondents during pandemic, and (c) investigates the extent of voluntary CAB observed and its relationship with trust in various social entities.

  Methods Top

Population and sample

The study location was Bhubaneswar, the capital of Odisha, in eastern India. Every adult was considered part of the population. Initial list of respondents was generated from researchers' contact list. The objective of the sampling was to generate maximum valid responses during the period May 10 and June 26, 2021, coinciding with the highest incidences of COVID-19 in India, during the second wave. Thus, the sampling strategy was convenient and Snowball sampling. After the rejection of a few responses due to incomplete information, the sample size was 551.


The questionnaire was made available online, and its link was circulated on various social media platforms and mailed to individuals. Single response from a respondent was ensured in the questionnaire setting to one response by the login. Travel restrictions during the data collection period prevented physical data collection.

Ethical consideration

The ethical principles of informed consent, anonymity, voluntariness, and the option not to participate or to withdraw anytime without assigning any reason were adhered to. The survey did not seek any identifiable personal data. Since the survey investigated the general behavior (CAB) of adults during COVID-19, no specific approval from an ethics board was taken.


The instrument used had various components: a demographic section, services accessed during the pandemic, any exposure to COVID, the scale for general trust, trust in specific social institutions, and CAB.

Demographical variables included were gender, age, education, occupation, marital status, economic status of the family, family type (joint/nuclear), house type (apartment/shared/independent), and perceived crowdedness of the area. Other information collected were comorbid condition (Y/N), affected by COVID (myself, someone in the family, and someone close), and the services accessed during the COVID-19 period (visited markets, accessed hospital, traveled in public transport, stayed/dined in hotel, used personal care, and grooming services).

General trust and specific trust

Prior research cautioned that trust is difficult to define and measure.[47],[48] Specifically, the measurements are challenging during a crisis situation such as pandemic.[43] This study used the general trust scale,[49] where except for one item (most are honest), others were negatively worded, so the item responses were reversed. The five-point Likert scale items (1-Strongly disagree to 5-Strongly agree) were used; a higher score indicated a higher trust level. The items in the scale, mean score, and standard deviation (in brackets) were (T1) if given a chance, most people would try to take advantage of you (2.75 ± 1.24), (T2) most people are too busy looking out for themselves to be helpful (2.65 ± 1.13), (T3) you cannot trust strangers anymore (2.74 ± 1.24), (T4) when dealing with strangers, one is better off using caution before trusting them (2.38 ± 1.19), (T5) most people are basically honest (2.79 ± 1.10), and (T6) most people tell a lie when they can benefit doing so (2.59 ± 1.17). The grand mean and standard deviation of the scale items were 2.65 and 0.67. The internal consistency of the general trust scale was 0.59. The value corresponded to a lower range of acceptability.[50],[51] In addition, the questionnaire included two items such as “It is difficult to trust people during COVID” (score reversed to indicate trust, 2.81 ± 1.19) and “People voluntarily observe social distancing” (2.92 ± 1.34).

The levels of trust in specific social groups and institutions were asked from the respondents. Sixteen groups were doctors, scientists, hospitals, vaccines, police, state government, politician, religious leader, media, central government, government officers, employers, co-workers, neighbors, friends, and family members. The scale response was 1-lowest to 5-highest, a higher score indicated a higher level of trust.

COVID-appropriate behavior

The expected social behavior during COVID-19 was formed from the literature. We constructed the CAB scale based on the general guidelines of the government and the World Health Organization. The scale for CAB included six items such as stayed home (3.89 ± 1.25), restricted visit to crowded public places (3.81 ± 1.29), restricted visit of outsiders (3.38 ± 1.35), restricted visit to social events (3.66 ± 1.36), consumed immunity boosting products (3.66 ± 1.32), and postponed travel (3.81 ± 1.27). The overall scale mean was 3.71 and SD was 0.96. We avoided items relating to the use of masks and hand sanitization since it was made mandatory at different organizations. Thus, the CAB scale items used in this study were to an extent voluntary activity and were complied with by the respondents. The five-point Likert scale used 1-rarely and 5-most of the time indicating the level of compliance. A higher score indicated a higher level of compliance with the CAB. The reliability statistics were computed for CAB (Cronbach's alpha = 0.826, 6 items) and were found satisfactory.

Statistical analysis

Factor analysis was done to identify parsimonious factors from these specific groups and termed specific trust. The groups identified from the analysis were named based on their characteristics. We tested if the trust during COVID-19 was any different from the general trust. Correlations among behavior, general trust, and specific trust groups identified from factor analysis were calculated to understand their relationship. Differences in CAB concerning demographic and other groups were tested through t-test and analysis of variance (ANOVA).

Regression was carried out with CAB as the dependent variable and different types of trust and groups as independent variables to identify the overall impact on the dependent variable. The cutoff for statistical tests was considered at P ≤ 0.05.

  Results Top

The sample demography profile is indicated in [Table 1]. Maximum (around 23%) respondents belonged to the age group between 25 and 45 years, and around 29% had university-level education. Students constituted 21% of the sample. Twenty-nine percentage were married. Twenty-eight percentage indicated themselves in the middle-income group and 33% of the total sample belonged to a nuclear family.
Table 1: Demographic profile of the sample by gender (n=551)

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Trust groups

Principal component analysis was used with Varimax rotation and Kaiser normalization to extract factors from items. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.846 and the Bartlett's Test of Sphericity (Approx. Chi-square = 2102.613, df = 120, and P = 0.000) indicated adequate factorability of the items. Three factors were extracted that explained 48.8% of cumulative variance.

The factors were grouped from the rotated component matrix. Factor 1 explained the highest variance (18.67%). The first factor included hospitals (2.97 ± 1.2), doctors (3.43 ± 1.2), scientists (3.53 ± 1.2), vaccines (3.50 ± 1.2), police (3.03 ± 1.3), and local government (2.94 ± 1.2). This set treated individuals, offered advice, and implemented protocols in the crisis and can be considered “action set.” The reliability statistics (Cronbach's alpha = 0.752 and items = 6) indicated adequate reliability of the factors with an overall mean of 3.23 and standard deviation of 0.82). The second factor (16.48%) included government officers (2.88 ± 1.2), media (2.49 ± 1.3), religious leaders (2.32 ± 1.3), central government (2.83 ± 1.3), and politicians (2.15 ± 1.3). This group was not directly responsible for activities but influenced pandemic actions and communication and was considered “social influencer.” The items indicated internal consistency (Cronbach's alpha = 0.707 and items = 5, 2.53 ± 0.85). The third factor (13.65%) included family (4.01 ± 1.3), neighbors (2.93 ± 1.2), friends (3.51 ± 1.2), employers (3.02 ± 1.1), and co-workers (3.05 ± 1.1). The factor can be named as “inner group.” The trust level was higher for the inner group (3.30 ± 0.77). Prior research indicated in a situation of low uncertainty and small social distance improves dispositional trust.[52] Items in this group displayed internal consistency (Cronbach's alpha = 0.671 and items = 5).

The mean score of CAB among the two groups was compared by t-test [Table 2], and for multiple groups, the mean difference was compared by the analysis of variance and Tukey's Honestly Significant Difference (HSD) test [Table 3].
Table 2: Difference of COVID appropriate behavior by different groups

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Table 3: Comparison of COVID-appropriate behavior among multiple groups

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The CAB did not vary significantly between genders. Those who did not have comorbid conditions practiced the COVID restrictions more strictly. Similarly, those who had to go to visit mall/markets had to travel or used personal health services reported a significant increase in practicing COVID restrictions. Those who did not suffer from COVID or where someone close suffered reported a significant increase in the practice of COVID restriction.

The post hoc Tukey's HSD test indicated significant differences of mean between groups. The mean of CAB of senior citizens (age 60 and above) was the least and differed from all other age groups significantly (P < 0.05).

Respondents with no formal education displayed the least CAB and respondents with university education displayed the highest CAB. A higher level of education showed a higher level of CAB. The mean response of respondents with “no formal education” varied with respondents having college education (P < 0.01), university education (P < 0.01), and respondents with professional education (P < 0.05). The mean CAB of respondents with high school education was significantly less compared to individuals with college (P < 0.05) and university education (P < 0.01). Similarly, the CAB of college-educated individuals was significantly less compared to the CAB of individuals with university education (P < 0.05). The CAB of university-educated individuals was significantly higher compared to professionally educated persons (P < 0.05).

Students displayed the highest level score for CAB whereas businesspersons displayed the least score. The score for students differed from the score of business persons (P < 0.01) and unemployed (P < 0.01). Similarly, the CAB for employed was significantly higher compared to the score for businesspersons (P < 0.05).

Marital status and CAB score showed significant variations. The mean score for unmarried was the highest and a significant difference was observed with married (P < 0.01), widow–widower (P < 0.05), and separated (P < 0.05).

The CAB score varied by the economic status of the family. The upper-middle-income group had the highest score and below-poverty line (BPL) individuals had the lowest score. The score for BPL varied significantly with middle (P < 0.01) and upper-middle (P < 0.01) income groups. Similarly, the CAB score for the lower-middle-income group was significantly less compared to the score for the middle-income group (P < 0.05) and upper-middle-income group (P < 0.01).

The CAB score for individuals living in apartments was the highest and significantly different from individuals living in shared accommodation (P < 0.01). The CAB score for individuals living in shared accommodation was significantly less compared to the score of individuals living in independent houses (P < 0.01).

Individuals living in a moderately crowded area displayed the highest level of CAB which was significantly higher compared to the score for individuals living in a very crowded locality (P < 0.01). The CAB score for individuals living in very crowded areas was the least and it was significantly less compared to the score for individuals living in not crowded localities (P < 0.05).

The trust in people during COVID-19 (2.81 ± 1.19, n = 551) was significantly higher (t = 2.786, df = 1100, P = 0.005) than the general trust (2.65 ± 0.67, n = 551).

Pearson's coefficient of correlation was computed for various trust scores, risk of infection (2.87 ± 1.30), CAB, and perception of voluntary social distancing score to understand their relationships. Following significant correlations indicated that the CAB improved in the conditions of (a) general distrust (r = −0.454, P ≤ 0.01) as well as a distrust during COVID (r = −0.157, P ≤ 0.01), (b) trust in inner group members (r = 0.437, P ≤ 0.01), and (c) trust in the action set (r = 0.312, P ≤ 0.01; for example doctors, hospital, and police among others). The CAB was not significantly related to the social influencer group, perceived risk of infection, or voluntary social distancing. Thus, the behavior improved based on appropriate trust in general, in a specific situation, in the agents who can act or help, and in the inner group members. However, voluntary social distancing was weakly related to the trust in social influencers (r = 0.173, P ≤ 0.01) and general trust (r = 0.092, P ≤ 0.05), indicating the role of social influencers such as media and others. A correlation between trust during COVID and risk of infection (r = −0.100, P ≤ 0.05) indicated that people perceive a lower risk from a higher trustworthy person.

Predicting COVID-appropriate behavior

To understand how CAB varied with different sociodemographic variables and different types of trust, a regression analysis was conducted. Variables were entered in two blocks. The first block contained comorbid conditions, crowdedness, economic status family, gender, family type, marital status, house type, highest education, age group, and occupation. The second block contained the variables such as trust inner group, general trust, trust expert and admin, trust social influencer, risk of infection, trust during COVID, and voluntary social distancing. Variables were entered through the forward method.

The regression analysis generated five models [Table 4]. The improvement of adjusted R square was from 12.4% to 38.2%. The fifth model is noted below [Table 5].
Table 4: Regression model summary

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Table 5: Variables of regression model 5

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The overall regression for model 5 was statistically significant (R2 = 0.382, F (14,536) = 25.3, P = 0.000). The model excluded the following variables: risk of infection, trust during COVID, and voluntary social distancing.

Regression coefficients indicated that gender, the economic status of the family, house type, trust in inner group, general trust, trust in experts and administration, and trust in social influencer group significantly predicted CAB. Women, people from higher income groups, and those not sharing their living space are likely to display higher CAB. Higher trust in the inner group and experts, distrust in general, and distrust in social influencers are also likely to positively influence positive health behavior (CAB).

  Discussion Top

This study had three specific objectives: (a) the extent of trust on different institutions and agents during the pandemic, (b) based on the trust levels, what are different social groups, and (c) to what extent the COVID-appropriate behavior depended on the trust in different social groups and agents during the second wave.

Trust groups

Based on trust, respondents grouped different social agents and institutions into three groups such as “action set,” “social influencer,” and “inner group.” The “action set” group consisted of hospitals, doctors, vaccines, and the local government among others. This group explained the highest variance. The “social influencer” group, which explained the second highest variance, consisted of government officers, media, national government, and politicians. This group acted or influenced the behavior indirectly during the pandemic. The “inner group” consisting of family members, neighbors, friends, employers, and co-workers was of the lowest variance. Prior research suggested trust depends on disposition (trusting individuals), anticipation (who can be trusted), and situation factors; disposition factors play a minor role, anticipation factors play a larger but inconsistent role, and situational factors play the strongest role.[53] Some argue that trust in government, institutions, and professions is declining.[54] During one crisis, authors reported a reduction of trust in national institutions.[55] In the pandemic context, a lower priority of trust in the inner group and a higher priority of trust in the action set group are justified.

General trust

The scores for general trust items were lower than the neutral score (3) indicating a lower level of trust in general. Furthermore, the score for trust during COVID and voluntary social distancing was less than the neutral score. Trust has individualist–collectivist culture dimensions wherein the collectivist culture has a narrower trust radius.[56] Thus, a lower level of general trust can be explained by the cultural dimension of a collectivist culture.

COVID-appropriate behavior and demography

Respondents in this study reported a higher (>3) level of CAB. The CAB varied significantly across age groups, education, occupation, marital status, economic status, type of residence, or depending on the crowdedness of the area living in. Higher trust in the local government for pandemic control indicated higher adoption of health and prosocial behaviors.[15] Depending on their mean values, a low CAB score indicated the vulnerable group. Older citizens, respondents without formal education, businesspersons/unemployed, separated/widow or widower, respondents with below poverty line economic status, living in shared accommodation, and people living in crowded areas reported the lowest score. A possible indication that these groups could not observe appropriate behavior due to constraints and were vulnerable to the pandemic.

Respondents from nuclear families or without comorbid conditions displayed significantly higher CAB. This study did not find differences in CAB by gender but by age. This finding can be contrasted with another study where both age and gender difference was present.[17] Those who visited markets, hospitals, traveled in public transport, or availed of personal care and grooming services observed significantly higher CAB (P < 0.05). Similarly, those who did not contract the virus, but either a family member or someone close was infected practiced significantly higher CAB compared to the other group. The fear of infection, the possibility of exposure from various service access, and learning from other's experiences prompted a higher level of CAB. It is also pointed out that the impact of trust on consumer behavior in a safe and quotidian setting is negligible but it is significant under uncertainty.[57]

Relationship among various trust types

The correlation test indicated that the CAB was not correlated with the trust in the social influencer group, risk of infection, or voluntary social distancing, but it showed a negative correlation with the trust during COVID and general trust. This result is consistent with the proposition that a trusting individual is less likely to take protective measures. Second, protective behavior (CAB) is adopted individually and does not depend on what others may do in a similar situation.

Factors influencing behavior outcome

The regression results indicated that the CAB was significantly predicted by gender, economic status of the family, house type, trust in the inner group, general trust, trust in experts and administration, and trust in the social influencer group. Ability to observe the appropriate behavior is influenced by constraints such as living in shared accommodation. The CAB is influenced by the trust in inner group members, experts, and administration positive and significantly whereas it is negative and significantly influenced by the trust in social influencers and general trust. A lower general trust and higher appropriate behavior seem logical. The negative relationship with social influencers, however, requires some explanation. This factor includes government officers, media, religious leaders, central government, and politicians. The trust in government officers is group was highest in the group (<3), indicating distrust. Thus, a general distrust for these items in factor and a higher CAB are responsible for the negative coefficient. Local media use and lower compliance have been indicated in some research.[58] Media freedom and excessive criticism of government action in turn reduce trust.[20] Thus, items of these factors interact among themselves. In India, health is under the governance of the state; the central government has an advisory role whereas the state has an implementation role. It is also worth mentioning that different political parties rule the states in this study and the central government. The center-state conflict over disaster management is also noted in prior studies.[59] Similarly, the opposing effect of political and social trust on the acceptance of anti-pandemic policies also have been reported.[33] It is to be noted that restrictions were advised and imposed by the Central Government of India; the encroachment of personal liberty possibly reduced the trust.

This study finds that trust in experts and administration to positively influences the appropriate behavior consistent with the prior findings that trust in science was important predictor of the acceptance and adoption of protective measures.[17]

This article contributes by identifying the trust groups as “inner group,” “action set” group, and the “social influencer” groups during a crisis. Trust during the pandemic was significantly higher compared to general trust. The improvement in trust can be due to continual positive communication from the government and in social media that “We can defeat the coronavirus.” Furthermore, prior experience of the first wave could have contributed to the improved trust. Social trust manifests as social cohesion and improves health outcomes.[60] However, all social groups or institutions are not trusted similarly or uniformly. There can be distrust on some groups and it reflects negatively on the desired behavior. Trust in inner group members as well as in experts and authority influenced positively but trust in the social influencer group influenced CAB negatively. Further, a trusting person in general is not likely to adhere to the CAB guidelines.

Interestingly, the perceived risk of infection did not influence the appropriate behavior. This could mean overconfidence or underestimation of the risk, a moderating effect of prior experience is also likely. It was indicated in the value of the mean value of risk perception below the neutral level.


The generalizability of the study findings is limited by the sample chosen from the eastern part of India. Obvious behaviors such as using masks or sanitizing hands were not included in the question because it was mandatory and enforced, the inclusion could improve the reliability of CAB. The reliability of the general trust scale varied in a cross-cultural setting[61] and thus suggest review and adaptation in specific study. Second, the general trust during the pandemic situation was inconsistent and dynamic, it was not a normal social context, a possible reason for low reliability. The use of general trust scale in a crisis situation needs further substantiation.

  Conclusion Top

People trust professionals and administrative personnel during the pandemic and behaved accordingly, indicating that trust depends on the immediacy of need. Priorities accorded to specific trust groups are likely to be different depending on the need. The interaction among trust group members can be complex and context-specific and influence the outcome. An adversarial center-state relationship is likely to undermine the required appropriate behavior in a disaster. The type of intervention and public communication strategies should be based on trust.[25]

These findings raise several important questions for further investigation. Can a crisis be professionally managed across the federal structure to ensure increased trust and outcome? Inability to adopt appropriate behavior was significantly associated with demography and such groups were vulnerable groups in the pandemic. Prior identification of the vulnerable group and special provisions for such groups helps in crisis management, and policymakers need to take cognizance of it. Learning of trust-building measures should be emphasized in plan executions. Most importantly policymakers should heavily depend on professionals for policymaking and implementation in future health emergencies.


This research was funded by a grant from the Indian Council of Social Science Research. Project: Social Trust, SERVQUAL Model, and Service Consumption: A COVID-19 Study, File No: COVID/172/47/2020-21/ICSSR, Date March 1, 2021, awarded to Dr. Brajaballav Kar as principal investigator. The project was considered a survey by the faculty of the School of Management of KIIT University (KIIT-DU/Project-006/21). The authors acknowledge the support extended by Geriatric Care and Research Organization (GeriCaRe), India, and the Institute of Insight, UK, during the research.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Kar SK, Ransing R, Arafat SM, Menon V. Second wave of COVID-19 pandemic in India: Barriers to effective governmental response. EClinicalMedicine 2021;36:100915.  Back to cited text no. 1
Asrani P, Tiwari K, Eapen MS, Hassan MD, Sohal SS. Containment strategies for COVID-19 in India: Lessons from the second wave. Expert Rev Anti Infect Ther 2022;20:829-35.  Back to cited text no. 2
SOP on COVID-19 Containment & Management in Peri-Urban, Rural & Tribal areas. Ministry of Health & Family Welfare, Government of India; 2021. Available from: https://www.mohfw.gov.in/pdf/SOPonCOVID19Containment&ManagementinPeriurbanRural&tribalareas.pdf. [Last accessed on 2023 Feb 12]  Back to cited text no. 3
Ognyanova K, Lazer D, Robertson RE, Wilson C. Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power. Harv Kennedy Sch Misinformation Rev 2020;1:1-19. Available from: https://misinforeview.hks.harvard.edu/article/misinformation-in-action-fake-news-exposure-is-linked-to-lower-trust-in-media-higher-trust-in-government-when-your-side-is-in-power/. [Last accessed on 2022 Apr 04]. DOI: https://doi.org/10.37016/mr-2020-024.  Back to cited text no. 4
Melki J, Tamim H, Hadid D, Makki M, El Amine J, Hitti E. Mitigating infodemics: The relationship between news exposure and trust and belief in COVID-19 fake news and social media spreading. PLoS One 2021;16:e0252830.  Back to cited text no. 5
Bargain O, Aminjonov U. Trust and compliance to public health policies in times of COVID-19. J Public Econ 2020;192:104316.  Back to cited text no. 6
Jørgensen F, Bor A, Petersen MB. Compliance without fear: Individual-level protective behaviour during the first wave of the COVID-19 pandemic. Br J Health Psychol 2021;26:679-96.  Back to cited text no. 7
Barrios JM, Benmelech E, Hochberg YV, Sapienza P, Zingales L. Civic capital and social distancing during the COVID-19 pandemic. J Public Econ 2021;193:104310.  Back to cited text no. 8
Oksanen A, Kaakinen M, Latikka R, Savolainen I, Savela N, Koivula A. Regulation and trust: 3-month follow-up study on COVID-19 mortality in 25 European Countries. JMIR Public Health Surveill 2020;6:e19218.  Back to cited text no. 9
Lalot F, Heering MS, Rullo M, Travaglino GA, Abrams D. The dangers of distrustful complacency: Low concern and low political trust combine to undermine compliance with governmental restrictions in the emerging COVID-19 pandemic. Group Process Intergroup Relat 2022;25:106-21.  Back to cited text no. 10
Schmelz K. Enforcement may crowd out voluntary support for COVID-19 policies, especially where trust in government is weak and in a liberal society. Proc Natl Acad Sci U S A 2021;118:e2016385118.  Back to cited text no. 11
Lieberoth A, Lin SY, Stöckli S, Han H, Kowal M, Gelpi R, et al. Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey. R Soc Open Sci 2021;8:200589.  Back to cited text no. 12
Lim VW, Lim RL, Tan YR, Soh AS, Tan MX, Othman NB, et al. Government trust, perceptions of COVID-19 and behaviour change: Cohort surveys, Singapore. Bull World Health Organ 2021;99:92-101.  Back to cited text no. 13
Caferra R, Colasante A, Morone A. The less you burn, the more we earn: The role of social and political trust on energy-saving behaviour in Europe. Energy Res Soc Sci 2021;71:101812.  Back to cited text no. 14
Han Q, Zheng B, Cristea M, Agostini M, Bélanger JJ, Gützkow B, et al. Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: A cross-sectional and longitudinal study. Psychological Medicine 2023;53:149-59. Doi: https://doi.org/10.1017/S0033291721001306.  Back to cited text no. 15
Fancourt D, Steptoe A, Wright L. The cummings effect: Politics, trust, and behaviours during the COVID-19 pandemic. Lancet 2020;396:464-5.  Back to cited text no. 16
Dohle S, Wingen T, Schreiber M. Acceptance and adoption of protective measures during the COVID-19 pandemic: The role of trust in politics and trust in science. Soc Psychol Bull 2020;15:1-23.  Back to cited text no. 17
Wu C. Social capital and COVID-19: A multidimensional and multilevel approach. Chin Sociol Rev 2021;53:27–54.  Back to cited text no. 18
Gozgor G. Global evidence on the determinants of public trust in governments during the COVID-19. Appl Res Qual Life 2022;17:559-78.  Back to cited text no. 19
Rieger MO, Wang M. Trust in government actions during the COVID-19 crisis. Soc Indic Res 2022;159:967-89.  Back to cited text no. 20
Latkin CA, Dayton L, Yi G, Konstantopoulos A, Boodram B. Trust in a COVID-19 vaccine in the U.S.: A social-ecological perspective. Soc Sci Med 2021;270:113684.  Back to cited text no. 21
Baumgaertner B, Carlisle JE, Justwan F. The influence of political ideology and trust on willingness to vaccinate. PLoS One 2018;13:e0191728.  Back to cited text no. 22
Schernhammer E, Weitzer J, Laubichler MD, Birmann BM, Bertau M, Zenk L, et al. Correlates of COVID-19 vaccine hesitancy in Austria: Trust and the government. J Public Health (Oxf) 2022;44:e106-16.  Back to cited text no. 23
Kutscher S. Fake news and the illusion of truth: The influence of media on German political discourse in the wake of COVID-19. Sortuz Oñati J Emergent Socio Leg Stud 2022;11:142-69.  Back to cited text no. 24
Pagliaro S, Sacchi S, Pacilli MG, Brambilla M, Lionetti F, Bettache K, et al. Trust predicts COVID-19 prescribed and discretionary behavioral intentions in 23 countries. PLoS One 2021;16:e0248334.  Back to cited text no. 25
Sibley CG, Greaves LM, Satherley N, Wilson MS, Overall NC, Lee CHJ, et al. Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am Psychol 2020;75:618-30.  Back to cited text no. 26
Esaiasson P, Sohlberg J, Ghersetti M, Johansson B. How the coronavirus crisis affects citizen trust in institutions and in unknown others: Evidence from 'the Swedish experiment.' Eur J Polit Res 2021;60:748-60.  Back to cited text no. 27
Dutta A, Fischer HW. The local governance of COVID-19: Disease prevention and social security in rural India. World Dev 2021;138:105234.  Back to cited text no. 28
Brodeur A, Grigoryeva I, Kattan L. Stay-at-home orders, social distancing, and trust. J Popul Econ 2021;34:1321-54.  Back to cited text no. 29
Baekgaard M, Christensen J, Madsen JK, Mikkelsen KS. Rallying around the flag in times of COVID-19: Societal lockdown and trust in democratic institutions. Behav Public Adm 2020;3:1-12. Available from: https://journal-bpa.org. [Last accessed on 2022 Apr 04]. Doi: 10.30636/jbpa.32.172.  Back to cited text no. 30
Christensen T, Lægreid P. Balancing governance capacity and legitimacy: How the Norwegian government handled the COVID-19 crisis as a high performer. Public Adm Rev 2020;80:774-9.  Back to cited text no. 31
Myllylahti M, Treadwell G. In media we trust? A comparative analysis of news trust in New Zealand and other Western media markets. Kōtuitui N Z J Soc Sci Online 2022;17:90-100.  Back to cited text no. 32
Jäckle S, Trüdinger EM, Hildebrandt A, Wagschal U. A matter of trust: How political and social trust relate to the acceptance of COVID-19 policies in Germany. Ger Polit 2022. Doi: 10.1080/09644008.2021.2021510.  Back to cited text no. 33
Dryhurst S, Schneider CR, Kerr J, Freeman AL, Recchia G, Van Der Bles AM, et al. Risk perceptions of COVID-19 around the world. J Risk Res 2020;23:994-1006.  Back to cited text no. 34
Hartley K, Jarvis DS. Policymaking in a low-trust state: Legitimacy, state capacity, and responses to COVID-19 in Hong Kong. Policy Soc 2020;39:403-23.  Back to cited text no. 35
Venu AN, Sigroha S, Shankar S. Dynamics of social networks and collective behavior: A social identity approach. Front Hum Dyn 2021;3:1-3. Available from: https://www.frontiersin.org/articles/10.3389/fhumd. 2021.676190. [Last accessed on 2022 Oct 01]. Doi: https://doi.org/10.3389/fhumd.2021.676190.  Back to cited text no. 36
Floyd DL, Prentice-Dunn S, Rogers RW. A meta-analysis of research on protection motivation theory. J Appl Soc Psychol 2000;30:407-29.  Back to cited text no. 37
Hsieh YJ, Chen YL, Wang YC. Government and social trust versus hotel response efficacy: A protection motivation perspective on hotel stay intention during the COVID-19 pandemic. Int J Hosp Manag 2021;97:102991.  Back to cited text no. 38
Acar D, Kıcali ÜÖ. An integrated approach to COVID-19 preventive behaviour intentions: Protection motivation theory, information acquisition, and trust. Soc Work Public Health 2022;37:419-34.  Back to cited text no. 39
Hromatko I, Tonković M, Vranic A. Trust in science, perceived vulnerability to disease, and adherence to pharmacological and non-pharmacological COVID-19 Recommendations. Front Psychol 2021;12:664554. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg. 2021.664554/full. [Last accessed on 2022 Oct 01]. Doi: 10.3389/fpsyg.2021.664554.  Back to cited text no. 40
Mousoulidou M, Christodoulou A, Argyrides M, Siakalli M, Constantinou L. Trust in science and COVID-19. Encyclopedia 2022;2:602-16.  Back to cited text no. 41
Sarwar F, Imam H, Jameel HT, Panatik SA, Brannen DE. Unravelling the relationship between trust in government and voluntary adoption of preventative behaviour through health belief model: A cross-culture study. Kybernetes 2022. [doi: 10.1108/K-05-2022-0667].  Back to cited text no. 42
Devine D, Gaskell J, Jennings W, Stoker G. Trust and the coronavirus pandemic: What are the consequences of and for trust? An early review of the literature. Polit Stud Rev 2021;19:274-85.  Back to cited text no. 43
Carter H, Drury J, Amlôt R. Social identity and intergroup relationships in the management of crowds during mass emergencies and disasters: Recommendations for emergency planners and responders. Polic J Policy Pract 2020;14:931-44.  Back to cited text no. 44
Foddy M, Dawes R. Group-based trust in social dilemmas. In: Biel A, Eek D, Gärling T, Gustafsson M, editors. New Issues Paradig Res Soc Dilemmas. Boston, MA: Springer US; 2008. p. 57-71.  Back to cited text no. 45
Kar B, Satpathy S. Trust between physicians and older patients: Review and qualitative study. J Geriatr Care Res 2021;8:56-64.  Back to cited text no. 46
Larson HJ, Clarke RM, Jarrett C, Eckersberger E, Levine Z, Schulz WS, et al. Measuring trust in vaccination: A systematic review. Hum Vaccin Immunother 2018;14:1599-609.  Back to cited text no. 47
Cairney P, Wellstead A. COVID-19: Effective policymaking depends on trust in experts, politicians, and the public. Policy Des Pract 2021;4:1-14.  Back to cited text no. 48
Siegrist M, Gutscher H, Earle TC. Perception of risk: The influence of general trust, and general confidence. J Risk Res 2005;8:145-56.  Back to cited text no. 49
Raharjanti NW, Wiguna T, Purwadianto A, Soemantri D, Indriatmi W, Poerwandari EK, et al. Translation, validity and reliability of decision style scale in forensic psychiatric setting in Indonesia. Heliyon 2022;8:e09810.  Back to cited text no. 50
Ekolu SO, Quainoo H. Reliability of assessments in engineering education using Cronbach's alpha, KR and split-half methods. Glob J Eng Educ 2019;21:24-9.  Back to cited text no. 51
Goto SG. To trust or not to trust: Situational and dispositional determinants. Soc Behav Pers Int J 1996;24:119-31.  Back to cited text no. 52
Snijders C, Keren G. Do you Trust? Whom do you Trust? When do you Trust? Bingley, UK. Advances Group Processes Emerald Group Publishing Limited; 2001. p. 129-60.  Back to cited text no. 53
Swain C, Tait M. The crisis of trust and planning. Plan Theory Pract 2007;8:229-47.  Back to cited text no. 54
Roth F, Nowak-Lehmann DF, Otter T. Crisis and Trust in National and European Union Institutions: Panel evidence for the EU, 1999 to 2012; 2013. Available from: https://cadmus.eui.eu/handle/1814/26975. [Last accessed on 2022 Jun 09].  Back to cited text no. 55
van Hoorn A. Individualist – Collectivist culture and trust radius: A multilevel approach. J Cross Cult Psychol 2015;46:269-76.  Back to cited text no. 56
Dierks LH. Does trust influence consumer behaviour? German Journal of Agricultural Economics 2007;56(670-2016-45856):106-11.  Back to cited text no. 57
Wu Y, Shen F. Exploring the impacts of media use and media trust on health behaviors during the COVID-19 pandemic in China. J Health Psychol 2022;27:1445-61.  Back to cited text no. 58
Majumdar B, DasGupta S. Let Bengal be heard: Dealing with COVID and cyclone Amphan together. South Asian Hist Cult 2020;11:317-22.  Back to cited text no. 59
Jen MH, Sund ER, Johnston R, Jones K. Trustful societies, trustful individuals, and health: An analysis of self-rated health and social trust using the World Value Survey. Health Place 2010;16:1022-9.  Back to cited text no. 60
Jasielska D, Rogoza R, Zajenkowska A, Russa MB. General trust scale: Validation in cross-cultural settings. Curr Psychol 2021;40:5019-29.  Back to cited text no. 61


  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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