research paper about social norms

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• Published: 16 February 2024

Changes in social norms during the early stages of the COVID-19 pandemic across 43 countries

• Giulia Andrighetto 1 , 2 , 3   na1 ,
• Aron Szekely   ORCID: orcid.org/0000-0001-5651-4711 1 , 4   na1 ,
• Andrea Guido 1 , 2 , 5 ,
• Michele Gelfand 6 ,
• Jered Abernathy 7 ,
• Gizem Arikan   ORCID: orcid.org/0000-0002-2083-7321 8 ,
• Zeynep Aycan 9 , 10 ,
• Shweta Bankar 11 ,
• Davide Barrera   ORCID: orcid.org/0000-0002-0441-5073 4 , 12 ,
• Dana Basnight-Brown   ORCID: orcid.org/0000-0002-7200-6976 13 ,
• Anabel Belaus   ORCID: orcid.org/0000-0001-9657-8496 14 , 15 ,
• Elizaveta Berezina   ORCID: orcid.org/0000-0003-1972-8133 16 ,
• Sheyla Blumen   ORCID: orcid.org/0000-0002-9960-7413 17 ,
• Paweł Boski   ORCID: orcid.org/0000-0003-0984-5686 18 ,
• Huyen Thi Thu Bui 19 ,
• Juan Camilo Cárdenas   ORCID: orcid.org/0000-0003-0005-7595 20 , 21 ,
• Đorđe Čekrlija   ORCID: orcid.org/0000-0001-8177-8663 22 , 23 ,
• Mícheál de Barra   ORCID: orcid.org/0000-0003-4455-6214 24 ,
• Piyanjali de Zoysa   ORCID: orcid.org/0000-0002-7382-6503 25 ,
• Angela Dorrough   ORCID: orcid.org/0000-0002-5645-949X 26 ,
• Jan B. Engelmann   ORCID: orcid.org/0000-0001-6493-8792 27 ,
• Hyun Euh   ORCID: orcid.org/0000-0003-0972-1640 28 ,
• Susann Fiedler   ORCID: orcid.org/0000-0001-9337-2142 29 ,
• Olivia Foster-Gimbel   ORCID: orcid.org/0000-0002-4583-3060 30 ,
• Gonçalo Freitas   ORCID: orcid.org/0000-0001-5888-3000 31 ,
• Marta Fülöp 32 , 33 ,
• Ragna B. Gardarsdottir   ORCID: orcid.org/0000-0003-3368-4616 34 ,
• Colin Mathew Hugues D. Gill   ORCID: orcid.org/0000-0002-3225-246X 16 , 35 ,
• Andreas Glöckner   ORCID: orcid.org/0000-0002-7766-4791 26 ,
• Sylvie Graf   ORCID: orcid.org/0000-0002-7810-5457 36 ,
• Ani Grigoryan   ORCID: orcid.org/0000-0001-5453-2879 37 ,
• Katarzyna Growiec   ORCID: orcid.org/0000-0002-4448-2561 18 ,
• Hirofumi Hashimoto   ORCID: orcid.org/0000-0003-3648-9912 38 ,
• Tim Hopthrow   ORCID: orcid.org/0000-0003-2331-7150 39 ,
• Martina Hřebíčková   ORCID: orcid.org/0000-0002-8700-1326 36 ,
• Hirotaka Imada   ORCID: orcid.org/0000-0003-3604-4155 40 ,
• Yoshio Kamijo   ORCID: orcid.org/0000-0002-2184-9594 41 ,
• Hansika Kapoor   ORCID: orcid.org/0000-0002-0805-7752 42 ,
• Yoshihisa Kashima 43 ,
• Narine Khachatryan   ORCID: orcid.org/0000-0003-3590-7131 37 ,
• Natalia Kharchenko 44 ,
• Diana León   ORCID: orcid.org/0000-0003-4596-3858 45 ,
• Lisa M. Leslie 30 ,
• Yang Li   ORCID: orcid.org/0000-0002-8239-3279 46 ,
• Kadi Liik   ORCID: orcid.org/0000-0002-5166-9893 47 ,
• Marco Tullio Liuzza   ORCID: orcid.org/0000-0001-6708-1253 48 ,
• Angela T. Maitner   ORCID: orcid.org/0000-0003-3896-5783 49 ,
• Pavan Mamidi 11 ,
• Michele McArdle 8 ,
• Imed Medhioub   ORCID: orcid.org/0000-0003-4676-7330 50 ,
• Maria Luisa Mendes Teixeira   ORCID: orcid.org/0000-0002-0606-1723 51 ,
• Sari Mentser   ORCID: orcid.org/0000-0003-1520-8253 52 ,
• Francisco Morales   ORCID: orcid.org/0000-0003-0785-8838 53 ,
• Jayanth Narayanan   ORCID: orcid.org/0000-0003-2720-1593 54 ,
• Kohei Nitta 55 ,
• Ravit Nussinson   ORCID: orcid.org/0000-0002-7331-548X 56 , 57 ,
• Nneoma G. Onyedire   ORCID: orcid.org/0000-0002-4941-2300 58 ,
• Ike E. Onyishi 58 ,
• Evgeny Osin   ORCID: orcid.org/0000-0003-3330-5647 59 ,
• Seniha Özden 9 ,
• Penny Panagiotopoulou 60 ,
• Oleksandr Pereverziev 61 ,
• Lorena R. Perez-Floriano   ORCID: orcid.org/0000-0001-6898-7794 62 ,
• Anna-Maija Pirttilä-Backman   ORCID: orcid.org/0000-0002-7437-9645 63 ,
• Marianna Pogosyan 64 ,
• Jana Raver 65 ,
• Cecilia Reyna   ORCID: orcid.org/0000-0002-6097-4961 14 ,
• Ricardo Borges Rodrigues 66 ,
• Sara Romanò 12 ,
• Pedro P. Romero   ORCID: orcid.org/0000-0002-2616-4498 67 , 68 ,
• Inari Sakki   ORCID: orcid.org/0000-0001-8717-5804 63 ,
• Angel Sánchez   ORCID: orcid.org/0000-0003-1874-2881 69 , 70 ,
• Sara Sherbaji   ORCID: orcid.org/0000-0002-7815-8962 49 , 71 ,
• Brent Simpson   ORCID: orcid.org/0000-0001-9468-157X 7 ,
• Lorenzo Spadoni   ORCID: orcid.org/0000-0002-1208-2897 72 ,
• Eftychia Stamkou 73 ,
• Giovanni A. Travaglino   ORCID: orcid.org/0000-0003-4091-0634 40 ,
• Paul A. M. Van Lange   ORCID: orcid.org/0000-0001-7774-6984 74 ,
• Fiona Fira Winata 75 ,
• Rizqy Amelia Zein   ORCID: orcid.org/0000-0001-7840-0299 75 ,
• Qing-peng Zhang 76 &
• Kimmo Eriksson   ORCID: orcid.org/0000-0002-7164-0924 2 , 77 , 78  

Nature Communications volume  15 , Article number:  1436 ( 2024 ) Cite this article

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• Human behaviour

The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat.

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Introduction.

Societies vary extensively in the kinds and number of social norms—the unwritten social rules that guide behavior 1 , 2 —that they adopt and the extent to which people within those societies follow them. From religious ceremonies and dress codes to environmental conservation and infection-containment, we embrace an astonishing diversity of social norms. An influential theory proposes that societies with many strong social norms, and in which individuals who deviate from the script face severe social punishment, can be classified as tight, while those that are permissive, have few and weak social norms, and norm-breakers are subject to little punishment are known as loose 3 , 4 . Such differences in cultural tightness are also reflected in prevailing socio-political institutions and practices. Tighter countries, or regions, are likelier to have restrictive socio-political institutions (e.g., government, media, education, legal, and religious), stricter constraints across everyday situations (e.g., public park, library, restaurant, workplace, classroom), more incremental innovation, lower debt, and stronger metanorms (norms about punishment) among others 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 . Loose cultures are instead more open to new ideas, more predisposed to change and substantial innovation, but may have difficulties in facing collective risks. Indeed, recent work finds that looser societies had less success in limiting COVID-19 cases and deaths in the first stages of the pandemic 12 .

Given the broad practical and scientific importance of tightness-looseness, it is essential to understand what factors are associated with these differences across countries and cultures. Tightness-Looseness theory 3 contends that societies that have experienced chronic ecological and social threats—frequent disease, warfare, and environmental catastrophes—throughout history develop tighter cultures to maintain order and survive chaos and crises. In contrast, societies with less exposure to such ecological threats can afford to develop looser cultures that allow innovation and creativity at the cost of order. This core hypothesis, that social norm strength is related to the threats that nations have (or have not) historically encountered, is well supported by correlational evidence from cross-sectional surveys 3 , 6 , 7 , ethnographic datasets 8 , a long-term online experiment 13 , and a long-term survey about social distancing norms 14 . Moreover, computational models have shown that dramatic increases in threat cause tightening 15 . On the other hand, cultural evolution has been argued to be a slow process 16 , 17 , suggesting the alternative that norm strength is stable after a collective threat. The COVID-19 pandemic provides an opportunity to examine whether tightening naturally occurs or if culture remains stable in the early stages of a collective threat. This knowledge can help us not only predict the future responses of countries to similar situations and potentially identify effective interventions to deal with these crises but also to better anticipate social changes that can impact our societies for generations to come.

Here we address this question by studying a dataset on cultural tightness, social norms, and metanorms—norms about the punishment of norm-breakers 18 —and exploit variation in disease severity due to the COVID-19 pandemic to test whether tightening evolves after a collective threat. Specifically, we combine data from a survey collected between April–December 2019 (Wave 1) 5 prior to the pandemic with a repeat of the same survey, in the same countries and sampled from the same populations, that we conducted in March–July 2020 (Wave 2) during the first months of the COVID-19 pandemic. The combined data come from 30,431 respondents (samples from both students and non-students) and cover 55 cities in 43 countries (see Table  S1 for summary).

The follow-up data (Wave 2) were collected during the initial stages of the pandemic so they capture the early changes (or their stability) in norms that occurred. While this means that we cannot infer the long-run consequences of the pandemic on norms, it also presents important advantages. First, our data provide an insight into norm change under extreme circumstances—while social, political, and economic systems were in upheaval—which provides strong stimuli for change to occur potentially shaping norms. Put differently, if norm change occurs, then there is a good chance we should be able to observe this in the early stages. Second, early data give an insight into the non-equilibrium dynamics of how cultures move from one stable state to another. Third, we are able to test the boundaries of tightness-looseness theory in terms of timeline: our data indicate a lower bound on the time that may be needed for large-scale norm change to occur in response to pandemic threat. Fourth, endogeneity issues are reduced. Specifically, it reduces the possibility for other large-scale shocks to affect the data and the possibility of time varying factors (e.g. hospital infrastructure development) to confound our results.

To study whether a change in disease threat is associated with a change in norms, we study five outcomes. (i) Tightness-looseness: elicited using the standard six questions (e.g., “There are many social norms that people are supposed to abide by in this country”) with ratings standardized to control for response sets 3 , 5 . (ii) Situation-specific social norms’ strength: measured with disapproval of norm-breaking in four settings (e.g., listening to music on headphones at a funeral 19 ) and stealing shared resources 20 . (iii) Metanorm strength: for each of the prior scenarios respondents also rated the appropriateness of different responses to the norm-breaker by another individual (verbal confrontation, ostracism, gossip, physical punishment, and non-action) 5 , 18 . (iv) Frequency of punishing norm-breakers. (v) Hand washing norms: respondents indicated the situations (e.g., after shaking someone’s hand) in which people should wash their hands. Our core expectation is that these outcomes are higher after the emergence of COVID-19 than before.

These outcomes vary in their relevance to the COVID-19 pandemic. Hand hygiene is strongly related, stealing is partly related (i.e. stealing shared resources during a pandemic is particularly harmful), while others, such as listening to music on headphones at a funeral, are unrelated to the pandemic. Intuitively, norms most related to preventing disease spread should change the most. Yet tightness-looseness theory does not make such detailed predictions. Instead, it proposes the overarching hypothesis that norms and metanorms strengthen. Such a broad change may happen for two interlinked reasons: in the presence of threats, people rely more on social norms as heuristics to safely determine what to do and this increase in conformity leads to a general tightening 21 ; it is beneficial to have tight norms across the board since tightening even irrelevant norms can increase a general norm-following tendency that implies increased norm-following for the more relevant ones.

To gain a deeper insight into the mechanisms that may be associated with change, we exploit the heterogeneity across countries in their exposure to COVID-19 and we collected data on three pathways through which we conjecture that COVID-19 pandemics may shape norms. Two of these are the respondent’s beliefs about the prevalence of COVID-19 and their fear of COVID-19, as we conjecture that disease threat shapes the strength of norms through individuals’ perceptions. The final pathway concerns government policy. By implementing strict (or lenient) anti-disease policies, governments can signal to their citizens the severity of the threat. Moreover, they impose policies that change their citizens’ behavioral patterns (e.g., not shaking hands, socially isolating) and these may have consequences on social expectations and norms. While all countries in the sample have been exposed to the pandemic, the continuous variation in our collected measures helps shed light on the association between cultural change and intensity of COVID-19 pandemic. The study, including the hypotheses and analyses, was pre-registered with the Open Science Framework (see Methods).

Overall, we find that in the short term, the global threat posed by the COVID-19 pandemic was associated with a significant strengthening of social norms related to hand washing, a behavior highly relevant to limit disease spread. Contrary to our initial predictions, other established social norms governing our daily lives exhibit resilience and remain largely unchanged. In addition, cultural tightness slightly decreased accompanied by small decrease in punishment frequency. These findings suggest that the immediate impact of a global threat is selective in changing those norms that are directly relevant to cope with the threat and emphasizes the adaptive nature of societies in the face of a collective crisis.

Our analytic strategy proceeds in two stages. We first compare Wave 1 to Wave 2 averages using multilevel models with individual responses grouped on city and country. We then seek to identify the mechanisms associated with changes for only those outcomes that show significant associations which are robust across both models and sub-items. To do this we use the change across waves (Wave 2 - Wave 1) as the dependent variable as predicted by perceived prevalence, fear, and government stringency and use country-level observations and OLS regression models with heteroskedastic robust standard errors. Prevalence is measured using “What percent of people living in your province do you think have been infected with COVID-19?” and fear is the combination of three items (Cronbach’s α  = 0.84, see Methods for country-level variation). To capture variation in governmental policies, we use the Stringency Index from the Oxford COVID-19 Government Response Tracker 22 .This second stage of our analysis is similar in spirit to a difference-in-differences design but differs to the classical setup in that we have no entirely untreated control group—all countries in our sample were to some extent affected by the emergence of the COVID-19 pandemic—and instead of a treated and untreated group, we have many groups with different COVID-19 pandemic exposure levels. All analyses account for age, gender, and student status to control for any sample composition differences between the waves (see Methods). We also check whether deaths and cases, which account for the different levels of COVID-19 across countries, affect our results and find that they do not (see  Supplementary Materials ).

After our analyses were conducted, we added equivalence tests using the two one-sided tests procedure 23 , 24 , 25 to identify whether significant changes that we find are practically meaningful and if non-significant findings provide evidence for the absence of a meaningful change. In this procedure, we specify a series of smallest effect size of interest (SESOI) and then compare Wave 1 to Wave 2 changes and the mechanism associations to these SESOIs. Our SESOIs were set ex-post and not pre-registered and, given the lack of existing literature, or even data, concerning the changes in our outcome variables, there is large uncertainty about how the SESOI should be set (see Methods for discussion). Consequently, we use a benchmark-based approach and set the SESOI to Cohen’s d  = 0.1 (a small effect size 26 ) for our main individual-level analyses and β = ± 0.10 (a small effect size 26 ) for the mechanisms analyses (see Methods for details).

Tightness-Looseness

Tightness decreases (x̅ 1  = 1.90, x̅ 2  = 1.81; Fig.  1A ; Table  S1 ) although the effect size is small (Cohen’s d  = 0.11; b  = −0.028, 95% CI = [−0.047; −0.009], p  = 0.003; Table  S2 ), and the change is heterogeneous across countries (varying slope model: b  = −0.037, 95% CI = [−0.073; −0.001], p  = 0.042; random effect variance τ 11  = 0.01; Table  S2 ; Figure  S2 ). In most countries, the change is not significant (81.4%; 35/43), it is negative in 16.3% (7/43) and even positive in 2.3% (1/43) (Fig.  S2 ). Countries that have higher fear levels towards COVID-19 reduced their tightness the most ( b  = −0.081, 95% CI = [−0.157; −0.005], p  = 0.037; Table  S3 ) though this association is small. Perceived prevalence and government stringency are not significantly associated with change in tightness-looseness ( b  = −0.003, 95% CI = [−0.010; 0.003], p  = 0.306 and b  = 0.0003, 95% CI = [−0.002; 0.001], p  = 0.721, respectively; Table  S3 ).

( A ) tightness-looseness, ( B ) situation-specific norms, ( C ) metanorms, ( D ) punishing frequency and ( E ) hand washing norms. Tightness and punishing frequency slightly decrease  while hand washing norms increase after the emergence of the COVID−19 pandemic. Other social and metanorms display non-robust changes. Change in appropriateness items is computed by scaling the average change in each country to the maximum possible change. Hence, the index can take values from −1 to +1. Red and black dots depict sampled cities; red dots represent cities sampled in both waves while black dots refer to cities only sampled in Wave 2. Indonesia is not included in hand washing norm data because of a mistake in the survey translation (see Methods).

Situation-specific norms

Situation-specific norm strength decrease slightly from Wave 1 to Wave 2 (x̅ 1  = 1.15, x̅ 2  = 1.12; Fig.  1B ; Cohen’s d  = 0.04; b  = −0.017, 95% CI = [−0.028; −0.006], p  = 0.003; Table  S4 ) but this is not robust as it becomes non-significant when allowing for heterogeneous effects across countries (varying slope model: b  = −0.011, 95% CI = [−0.054; 0.033], p  = 0.628, τ 11  = 0.02; Table  S4 ; Fig.  S3 ). Analyses conducted on the five specific norm-breaking scenarios separately also show no consistent pattern (three are negative and two are positive) and the size of the changes is minimal (Table  S5 ). These results demonstrate that COVID-19 has no consistent effect on situation-specific norms, and, even where it does, the effect is minor.

We report similar findings for metanorms (Fig.  1C ). There is no significant change across the waves (x̅ 1  = 2.15, x̅ 2  = 2.17; Cohen’s d  = 0.03; b  = 0.006, 95% CI = [−0.001; 0.013], p  = 0.120; Table  S6 ; Fig.  S4 ) and there is little consistency across the different kinds of punishments: approval of ostracism slightly increases ( b  = 0.028, 95% CI = [0.015; 0.040], p  < 0.001; Table  S7 ) while gossip approval slightly decreases ( b  = −0.024, 95% CI = [−0.035; −0.013], p  < 0.001; Table  S7 ). Estimates from our models show no significant change in verbal confrontation, physical confrontation, and non-action (reverse coded) items.

Punishing frequency

In contrast, we find a statistically significant decrease in frequency of punishment (x̅ 1  = 3.00, x̅ 2  = 2.96; Fig. 1D ; Cohen’s d  = −0.07; b  = −0.034, 95% CI = [−0.047; −0.022], p  < 0.001;  Table  S8 ). This effect remains negative and significant with a varying slopes model ( b  = −0.031, 95% CI = [−0.059; −0.003], p  = 0.028, τ 11  = 0.01; Table  S8 ) and it is generally consistent across sub-items with the frequency of gossip ( b  = −0.091, 95% CI = [−0.112; −0.070], p  < 0.001; Table  S9 ) and confronting ( b  = −0.021, 95% CI = [−0.041; −0.002], p  = 0.035; Table  S9 ) both decreasing. Perhaps due to distancing and self-isolating measures, avoiding shows no significant change ( b  = 0.011, 95% CI = [−0.012; 0.034], p  = 0.335; Table  S9 ). Frequency of gossiping tended to decrease more in countries with a higher level of fear of COVID-19 ( b  = −0.139, 95% CI = [−0.261; −0.016], p  = 0.028; Table S10 ). The other change in punishing frequency categories, including the overall index, are not associated with the mechanism variables (Table  S10 ).

Hand washing norms

Hand washing norms increase on average (x̅ 1  = 4.07, x̅ 2  = 4.50; Fig. 1E ; Cohen’s d  = 0.32; b  = 0.420, 95% CI = [0.390; 0.450], p  < 0.001; Table  S11 ) and in almost every country (41 out of 42 countries, Fig.  1E ; all countries when considering only COVID relevant items, Fig.  S1 ). Results remain unchanged when accounting for country-level heterogeneity (varying slope model: b  = 0.433, 95% CI = [0.361; 0.506], p  < 0.001; τ 11  = 0.04; Table  S11 Fig.  S3 ). The increase is most strongly associated in the categories perceived to be relevant to reducing COVID-19 spread (Table  S12 ). Fear of COVID−19 accounts for most of the increase across all items ( b  = 0.040, 95% CI = [0.004; 0.076], p  = 0.032; Table  S13 ) and this effect becomes stronger when predicting only the change of COVID-relevant items ( b  = 0.092, 95% CI = [0.035; 0.148], p  = 0.002; Table  S13 ). Perceived prevalence does not predict hand washing norm change both when considering all items ( b  = 0.002, 95% CI = [−0.0003; 0.0049], p  = 0.085; Table  S13 ) and relevant items ( b  = 0.004, 95% CI = [−0.001; 0.008], p  = 0.086; Table  S13 ) but does so after shaking hands ( b  = 0.004, 95% CI = [0.001; 0.008], p  = 0.015; Table  S13 ). Governmental stringency does not predict change in hand washing norms ( b  = 0.0002, 95% CI = [−0.001; 0.001], p = 0.723; Table  S13 ).

Equivalence tests

For tightness-looseness, situation-specific norms, metanorms, and punishing frequency, we find that the between wave variation observed are statistically equivalent (all p  < 0.001) implying that the differences are statistically smaller than the SESOI we set. For hand washing norms, we find that the change is statistically greater than the SESOI, exceeding the upper equivalence bound (see Methods for details). For the mechanisms analyses, fear of COVID-19 is significantly associated with the outcomes of tightness-looseness and hand washing norms while all the other relevant mechanism coefficients are not significantly different to zero. Yet they all overlap with either the upper or lower equivalence bounds meaning that there is insufficient evidence to conclude a negligible effect (see Methods for details).

Our findings show that even a crisis as profound, global, and multifaceted as COVID-19 does not dramatically change the social norms of cultures in the short-term, except those believed to directly reduce disease spread, hand washing norms in this case. Nevertheless, and contrary to our expectations, we find a small decrease in tightness and punishing frequency and no significant robust changes in most social norms and metanorms in the early stages of the pandemic. Importantly, the non-significant findings are due to the absence of substantial changes and not because of a lack of power. What explains these results? One possibility is that the key prediction of tightness-looseness theory needs to be revised. Due to existing large-scale studies across multiple fields, which support the association between threat and tightness-looseness 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 and more broadly social norm strength 13 , 27 , 28 , we do not think this is the likeliest explanation. Instead, we think that there are more probable interpretations.

A distinct possibility is that cultural evolution is slow and extensive time is necessary between a collective threat and a subsequent change in cultures 16 , 17 . Indeed, if cultures do change slowly, we may expect specific cultural evolutionary mismatches—i.e., when traits that evolved in one environment become disadvantageous in a different environment 29 , 30 . Specifically, tight societies that have historically experienced threat may have traits that are better matched to dealing with a collective threat such as COVID-19, whereas looser societies would experience more of a cultural mismatch, as evidenced in 12 . Another interpretation is that different threats may tighten different norms, namely those most relevant to overcoming the specific immediate threats: pandemics may make hygiene norms stronger while earthquakes may, instead, increase norms of helping. This would be consistent with an experimental study which found that a risk of collective loss increased the strength of norms concerning cooperation 13 . Over time, this would create a mosaic of norms that together correspond to the emergent notion of tightness. If correct, cultures that face a variety of threats will be those that end up the tightest. Another possibility is that pathogen threats, which are abstract and invisible, have particular characteristics and produce different tightening dynamics than threats which are concrete and visible (e.g., earthquakes, terrorism, or warfare) 31 , 32 . The former are harder to assess, potentially causing uncertainty and panic that may have led to egoistic behavior during early stages of the pandemic. Indeed, as extensively reported by the mass media, there was hoarding of resources in the early stages of the pandemic 33 , 34 and recent work finds evidence for the erosion of social trust 35 .

These conclusions should also be considered in light of the limitations to our study. First, we use convenience samples (albeit both students and non-students). While this is unlikely to have substantial implications on our between-wave estimates, since the samples are broadly similar between the waves, it should be kept in mind when generalizing our findings to the broader populations. Specifically, it is possible that social norm change, or a lack thereof, occurred differently outside of cities, varied with socio-economic factors, or that younger people, who are overrepresented in our samples, experience fewer health-risks and our findings may not generalize to more senior people or those facing health issues. Second, our design allows us to avoid key endogeneity issues that are present in prior work, but cannot cleanly identify causal effects. More specifically, our first-stage analyses, comparing Wave 1 to Wave 2 averages, allows us to exclude reverse causality and country-constant confounders but it cannot exclude time-trends (e.g. changes in norm strength occurring over time irrespective of the pandemic). Our second-stage analyses, using perceived prevalence, fear, and government stringency to predict changes in the outcomes, reduces the possibility that such time-trends (or other confounding factors) are responsible for the observed changes as these would need to be correlated with our predictors and changes in social norms. In addition, we find little evidence for pre-existing time trends in tightness-looseness (see Methods and Fig.  S7 ). Still, we do not have the power in the mechanisms analyses to detect small effects and cannot entirely identify causality.

Our sample includes data from a first study wave collected before the breakout of the pandemic (April–December 2019, Wave 1 5 ) and data from a second wave (March–July 2020, Wave 2) that we collected during the initial stages of the COVID−19 emergence. For comparability of samples across waves and among countries, we set out to collect data from approximately 200 college students at least in a major city in each country, which was achieved in all countries (Table  S1 ). To assess the robustness of the country-level measures obtained from these samples, we complemented the main sampling strategy by collecting additional data from non-student samples.

When administering Wave 2, we aimed to collect data also from a subset of participants who took part in Wave 1 study. These participants were marked as “experienced” participants and were re-contacted (e.g. through laboratory recruitment systems). For six locations (Bosnia-Herzegovina, Canada, Colombia, Czech Republic, Italy, United States), we were able to recruit participants who had participated in Wave 1 but without matching their responses across waves. For two locations (Israel and Poland), we were able to uniquely identify participants and match their responses. Privacy and anonymity were nevertheless preserved in these samples. This allowed us to check whether experience of participation affects our findings. When specifically checking among participants matched across waves we find non-significant results that go in the same direction (see end of Methods).

In our analyses, we considered a response valid if a participant correctly passed an attention check placed at the end of the survey (i.e., participants had to click a specific item response). We discarded observations because of missing responses (4074 in Wave 1, 4660 in Wave 2) or failed attention checks (197 in Wave 1, 202 in Wave 2). We additionally excluded participants who declared an age under 18 (157 in Wave 1, 222 in Wave 2). The final dataset includes responses from 43 countries, 55 locations (six of which were sampled only in Wave 1, while only one sampled exclusively in Wave 2), and 30,431 valid respondents (see Table  S1 ).

We used the survey administered in 5 to preserve comparability, with the sole addition of a small number of questions (at the end of the survey precluding any effects on the prior questions) regarding COVID-19 fear and prevalence, desired Tightness-Looseness measures, generalized trust, and risk aversion. The survey was translated into 30 different languages, following the standard practice of independent translation and back-translation. The study was conducted anonymously online using Qualtrics. The English version of the survey is publicly available as part of our pre-registration ( https://osf.io/9ve4t ). Our study is a survey therefore no randomization occurred and some of the investigators were not blinded to the study’s hypotheses.

All participants gave their informed consent and we complied with all relevant ethical regulations. Approval of the study protocol was obtained from ethics committees and institutional review boards where required including for the University of Melbourne (Australia), Queen’s University at Kingston (Canada), Universidad de los Andes (Colombia), Institute of Psychology, Czech Academy of Sciences (Czech Republic), Universidad San Francisco de Quito (Ecuador), United Research Ethics Committee of Psychology (Hungary), Monk Prayogshala (India), Trinity College Dublin (Ireland), Open University of Israel (Israel), LUISS University (Italy), United States International University - Africa (Kenya), Sunway University (Malaysia), University of Amsterdam (Netherlands), SWPS University (Poland), Universidade de Lisboa (Portugal), National University of Singapore (Singapore), University of Colombo (Sri Lanka), Koc University (Turkey), American University of Sharjah (United Arab Emirates), Brunel University London (United Kingdom), University of Kent (United Kingdom), University of South Carolina (United States of America), and New York University (United States of America). Ethical approval was not sought in countries where the approval received for the study conducted in Wave 1 5 was considered sufficient or where local legislation did not require ethical approval in the first place.

Study preregistration

We pre-registered our study in two phases. Our initial pre-registration was submitted before data gathering ( https://osf.io/zvdkt/ ) (March 23rd 2020) and contained a design and provisional data analysis plan. Due to the short timeframe before data collection began, the analysis plan was only provisional. Our second pre-registration, which was submitted after the data were collected but before the data were examined or analyzed (October 22nd 2020), contains a detailed analysis plan that we completely followed ( https://osf.io/9ve4t ).

The hypotheses that we pre-registered and test are the following:

H1: Tightness-Looseness levels in Wave 2 will be higher on average than in Wave 1.

H2a: Perceived threat will be positively associated with change in tightness.

H2b: Perceived prevalence will be positively associated with change in tightness.

H2c: A stricter governmental response will be positively associated with change in tightness.

H3a: Punishments, on average, are perceived as more appropriate.

H3b: People are likelier to engage in punishing norm violations.

In addition to the aforementioned hypotheses, we investigate the differences in situation specific norms and a set of items measuring hand hygiene norms between waves 1 and 2 to provide a fuller understanding in social norm changes. Furthermore, to study the mechanisms for hand hygiene norms and punishment change, we complement our analyses by exploring the moderating role of perceived threat, COVID-19 prevalence, and governmental stringency on the change in hand hygiene norms and frequency of punishment, both of which show consistent changes from Wave 1 to Wave 2.

Survey measures

We measured the following variables through survey questions. These were elicited in both Wave 1 and Wave 2 unless stated otherwise.

Tightness-looseness scores

We compute tightness-looseness scores (TL) following individual-level standardization as in past work 3 , 5 . Standardization is needed to adjust for cross-cultural variation in response sets given that some cultures are more likely to provide extreme responses or acquiesce to survey items than others 3 , 36 . Following guidelines from cross-cultural psychology 36 , 37 , and from data published in the first wave 5 , we calculate appropriateness scores by averaging each individual’s responses to a large set of heterogeneous items (i.e. 50 appropriateness items that all used the same response scale, from extremely inappropriate to extremely appropriate). This score is then subtracted from participants’ responses in the tightness-looseness questionnaire (6 items from ref. 3 ). The final individual TL scores are computed by averaging the adjusted 6 items. After transformation, TL scores display an overall average x̅ = 1.85, standard deviation s = 0.81, min = −2.26, max = 5.25. Differently from 5 , we did not impute missing TL data. This resulted in tiny differences in TL scores between studies (difference between mean TL scores = 0.01) that do not affect the validity of our results. The correlation between our TL scores and those appearing in 5 is essentially perfect (Spearman test, r  = 0.997, p  < 0.001). Standardizing tightness-looseness scores does not affect our results (checked for all tightness-looseness analyses reported in the manuscript). Furthermore, the correlation between standardized and non-standardized measures of TL is high and significant ( r  = 0.84 for Wave 1 measures, r  = 0.85 for Wave 2 measures, p  < 0.001 in both cases).

Given our empirical interest in assessing the change in tightness-looseness associated with the emergence of the pandemic, we also checked whether TL scores changed or not between 2000–2003 (Wave 0), using data from 3 , and 2019 (Wave 1) 5 , and 2020 (Wave 2). We find that tightness-looseness scores have remained unchanged in almost all countries since 2000–2003 (Wave 0 to Wave 1: r  = 0.89; Wave 0 to Wave 2: r  = 0.88, all p < 0.001) and that there is strong stability in the ordering of countries (Kendall rank test, t  = 0.752, p  < 0.001, Fig.  S7 panels A, B) implying that TL is a stable measure. More formally, to check whether trends in TL scores were similar across our countries pre-pandemic, with respect to their post-pandemic COVID-19 intensity, we use the following model:

Where TL indicates tightness-looseness from country c , at time t; Wave are dummy variables indicating the study wave (Wave 1 or Wave 2; Wave 0 is the baseline), and Covid Severity is fear of COVID-19, perceived cases, actual COVID-19 cases, or COVID-19 deaths (we check each sequentially). If there are no systematic differences in trend pre-pandemic then δ 1  = 0. This would indicate that countries that were later affected by the pandemic with heterogenous intensities had TL change that followed the same pattern between Wave 0 and Wave 1. We find no evidence for systematic differences in trends of TL scores between 2000–2003 and 2019 according to later COVID-19 severity (Table  S14 ).

Participants’ appropriateness ratings are measured with their responses to five scenarios that cover potential norm-violating behavior in several domains concerning cooperation and out-of-place everyday behavior (see Analysis Plan of the pre-registration Analysis Plan). Ratings of the appropriateness of each item were elicited through a six-point scale, ranging from extremely inappropriate (coded 0) to extremely appropriate (coded 5). Average rating across countries is x̅ = 1.13, standard deviation s  = 0.60, min = 0, max = 5.

Metanorm scenarios

Metanorms were collected for each situation (five in total) based on survey items reported in our pre-registered analyses plan. Items covered five different punishment behaviors for each situation (hence, a total of 25 items, see Analysis Plan of pre-registration Analysis Plan), which are: verbal and physical confrontation, gossip, non-action (reverse coded) and ostracism, and we collected participants’ ratings of the appropriateness of each. Appropriateness was elicited through a six-point scale, ranging from extremely inappropriate (coded 0) to extremely appropriate (coded 5). Each punishment behavior is used as a separate dependent variable. Average appropriateness across countries is x̅ = 2.22, standard deviation s = 1.25, min = 0, max = 5.

We consider three survey items eliciting the frequency at which respondents engaging in confronting, gossiping, and ostracizing someone who behaves inappropriately. Frequency of punishment was elicited using a five-point scale ranging from never (coded 1) to always (coded 5). We analyzed these all together (with mixed effects at the scenario level) and also conducted separate analyses for each item separately. Average frequency of punishment across countries is x̅ = 2.98, standard deviation s = 0.59, min = 1, max = 5.

Hand washing norms

Our survey asked participants in which of six situations they think people should wash hands. These situations are: before eating a meal, after eating a meal, after defecating, after urinating, when they come home, and after shaking someone’s hand. Hand washing norms are analyzed using as both the number of situations considered as appropriate (number of ticks) as well as whether a participant considered a given situation as appropriate (participant ticked or not a given situation). Because of a translation mistake in our survey, one country (Indonesia) has been excluded from all the analyses of these items. Average number of appropriate situations across countries was x̅ = 4.28, standard deviation  s  = 1.30, min = 0, max = 6.

Fear of COVID-19

Our measure of COVID-19 fear comes from the Wave 2 survey. In particular, respondents answered three items: “How concerned are you by the spread of the new Coronavirus (COVID-19)?” “How much fear do you have by the spread of the Coronavirus?” “How dangerous do you think the Coronavirus is?”. Participants responded on a six-point scale. We then compute the average over items. Average COVID-19 fear is x̅ = 4.42, standard deviation s = 0.41, min = 3.42, max = 5.20. Following our pre-registration, we checked internal consistency of the items listed above reporting (Cronbach’s α = 0.84). We additionally computed Cronbach’s alphas for each country separately. Estimated values range from 0.58 (Kenya) to 0.90 (Poland) (see below for full list). The cross-country average is 0.80 ( s  = 0.07) which is close to the value obtained when merging all countries in our sample. Since estimated Cronbach alphas fall within the range of satisfactory internal consistency, throughout our main analyses, we averaged these items to create a single variable at the individual level. The only country with alpha <0.60 is Kenya; all our analyses reported in the manuscript are robust and do not substantially change when excluding Kenya from the dataset.

The full list of countries’ alphas is: ARE: 0.81, ARG: 0.76, ARM: 0.82, AUS: 0.78, BIH: 0.83, BRA: 0.79, CAN: 0.82, CHL: 0.80, CHN: 0.77, COL: 0.80, CZE: 0.85, DEU: 0.86, ECU: 0.75, ESP: 0.79, EST: 0.87, FIN: 0.84, GBR: 0.86, GRC: 0.85, HUN: 0.87, IDN: 0.83, IND: 0.71, IRL: 0.84, ISL: 0.77, ISR: 0.90, ITA: 0.86, JPN: 0.85, KEN: 0.58, KOR: 0.87, LKA: 0.63, MYS: 0.66, NGA: 0.65, NLD: 0.78, POL: 0.91, PRT: 0.88, RUS: 0.77, SAU: 0.84, SGP: 0.82, SWE: 0.80, TUR: 0.84, UKR: 0.89, USA: 0.82, VNM: 0.83. PER: items missing due to error in data collection.

Perceived COVID-19 prevalence

Our measure of disease prevalence was elicited with the Wave 2 survey question “What percent of people living in your province do you think have been infected with COVID-19? Please do not look up actual statistics to answer this question—just enter your best guess” (0–100). Average perceived COVID-19 prevalence across countries is x̅ = 21.87, standard deviation s = 7.05, min = 8.53, max = 42.65.

External measures

We measured the following variables through external data sources that we matched with our survey data.

Stringency Index

Our measure of the intensity of government response to COVID-19 is the Stringency Index from the Oxford COVID-19 Government Response Tracker 22 . The measure contains indicators reporting the severity of containment and closures (e.g. school and workplace closures and restrictions on gathering size; see items C1-C8 in ref. 23 ) and public information campaigns (item H1 in ref. 23 ). The Stringency Index can vary between 0 and 100. We match participants’ responses to our survey with Stringency Index data calculated on the same day. Average stringency across countries is x̅ = 78.12, standard deviation  s = 13.54, min = 32.77, max = 99.48.

Deaths and cases

We use COVID-19 deaths and cases per million from Our World in Data 38 (downloaded November 2020). Data were matched with participants’ responses to our survey based on day of response (thus case and deaths data run from March–July 2020). Average of deaths across countries and periods is x̅ = 47.88 per million,  standard deviation  s = 103.70, min = 0.05, max = 481.99. Average of cases across countries and periods is x̅ = 834.95 per million, standard deviation   s  = 1067.72, min = 1.98, max = 4389.68.

Computed measures

The following measures were computed based on changes between Wave 1 and Wave 2. In addition to the pre-registered test ΔTightness-Looseness, we did this only for those variables that showed robust changes between the waves (see Analyses).

ΔTightness-looseness, Δpunishing, and Δhand washing

When computing change in TL, we averaged individual scores for each country and compute the difference between Wave 2 and Wave 1 values (Wave 2–Wave 1). A similar procedure is followed for computing change in other items. For hand washing and punishing items (frequency of punishment) we computed changes across waves both for each individual item and for the average of all items.

We started by analyzing the between-wave changes in Tightness-Looseness, situation-specific norms, metanorms, punishing, and hand washing norms. Then, for those changes that are shown to be robust (across sub-items and model specifications, including with random slopes and with controls for COVID-19 cases and deaths), we examine the mechanisms predicting a change in our variables of interest (ΔTightness-Looseness, Δpunishing, and Δhand washing). The models used for both stages are outlined below. In addition to these models, we replicated all of our analyses with the addition of random slopes to allow for country-level variation of the effect associated with COVID-19 pandemic. For these, we additionally report τ 11 , the variance of the main parameter of interest ( Wave 2) to shed light on the heterogeneity of the effect due to COVID-19 pandemic among countries. Moreover, we also conducted these analyses controlling for deaths and cases (adjusted to each country population size) to account for the different levels of COVID-19 pandemic across the countries and this does not affect our results. For all coefficient estimates we report the results from two-sided t -tests. All tests meet the relevant assumptions. We do not adjust for multiple comparisons.

Tightness-looseness, situation-specific norms, and punishing

We use multilevel models with random intercepts at the individual ( n  ≈ 29,000), city ( n  = 55), and country ( n  = 43) level. Put formally, to test Hypothesis 1, we estimate the following multilevel model with varying intercepts at the country ( c ), city ( k ) and individual ( i ) level:

where Z is the vector of control variables to account for possible between-wave sample variation (age, gender, and student/non-student status), Wave 2 is a dummy variable taking value 1 when an observation was collected in Wave 2 and 0 otherwise. Our analyses for situation-specific norms, punishing, and hand washing norms follow the same model structure with the dependent variable changed to those variables.

We use multilevel models with random intercepts at the country ( c ), city ( k ), scenario ( s ), and individual ( i ) levels and implement the following model specification:

where A is the appropriateness score given by individual i to the punishment scenario s , in country c , city k . N is the average appropriateness at the location level that participants have given to the norm violation of scenario s (see also Methods in ref. 3 ) and Z is a vector of demographic controls (age, gender, and student/non-student status).

We used two approaches to test hand washing norms. First, to model the number of ticked categories we use the same model structure as Eq.  2 but with the dependent variable replaced with the number of ticks given by participant i , in county c , and city k . Second, to test the probability of ticking each single situation we use a multilevel logit regression with random intercepts at the country and city level:

Where H is the odds of participant i , in country c , and city k , ticking that it is appropriate to wash hands for a given setting. Z is a vector of demographic controls (age, gender, and student/non-student status).

ΔTightness-looseness, Δpunishing frequency, and Δhand washing

These analyses are conducted using heteroskedasticity-robust OLS regressions with observations at the country level. Observations are country-level as the dependent variable is Wave 1 to Wave 2 change in a given country. We do not use city-level because in a small number of countries different cities were sampled between Wave 1 and Wave 2. Put formally we estimate the following model for ΔTightness-Looseness:

where Fear is fear of COVID-19, PC is perceived cases of COVID-19, and SI is the Stringency Index from the Oxford COVID-19 Government Response Tracker.

We performed similar analyses for the change in hand washing and punishment. In particular, for the former, we conducted analyses for the change in the number of ticks for (i) all items, (ii) specifically for items that were not directly related to the COVID-19 pandemic (before meal, after meal, after defecating, and after urinating), (iii) specifically for items that are directly related to the pandemic (after shaking hands and after coming home), and (iv) each item separately that is directly related to the pandemic (Table  S12 ).

For the items measuring punishing frequency, we estimate the change in responses for each single item individually (Table  S9 ), and change in the mean of all of our 3 items (grand mean change) (Table  S10 ).

Tightness-looseness change for tracked participants

We were able to perfectly match responses to our survey across waves for two locations in our sample: Israel and Poland. Below, we report the results from a robustness check aimed to test tightness score decrease.

For our Israel sub-sample of tracked participants ( N  = 57), tightness scores decrease on average of 0.16 (Cohen’s d  = 0.17, Wilcoxon paired samples r  = 0.172), yet the change is not significant (Wilcoxon paired samples test, V  = 30, p  = 0.195). For our Poland sub-sample ( N  = 10), tightness scores decrease by about 0.12 (Cohen’s d  = 0.15), but the change is not significant (Wilcoxon paired samples test, V  = 30, p  = 0.85). We interpret results from our sub-samples as highly noisy but consistent with our general results from the full dataset showing a small decrease in tightness scores.

For 6 locations (Bosnia-Herzegovina, Canada, Colombia, Czech Republic, Italy, United States), we were able to distinguish responses coming from participants who previously participated in the first wave, but were not able to match the id of each responses. By running multilevel linear regression models, we report evidence of no significant change in tightness-looseness scores for these sub-populations ( b  = 0.046, p  = 0.222).

Power analysis

The main aim of this study was to examine whether the pandemic was associated with a systematic change in tightness-looseness (TL) scores compared to pre-pandemic scores. To make sure that our sample is large enough to detect small changes in TL, we compute the power achieved based on the mixed effects model in Eq. 2 . We adopt the common convention that a small effect be equivalent to a Cohen’s d of at least 0.10. From our sample, it means that the average TL score changes by at least 10% of its standard deviation, that is a change in TL of 0.08 (TL  s  = 0.80). By using the R package “simr”, we estimate the 95% CI of achieved power from the model in Eq. 2 to be 95% CI = [96.38; 100] (predictor “Wave2”, α = 0.05, 100 simulations).

We then perform sensitivity analysis to provide evidence of sufficient achieved power for models testing the change in TL scores. Given a sample of 28,374 individuals, a significance level of α = 0.05, and a desired power 0.80, we estimate the minimum detectable change in raw TL scores of 0.025 (equivalent to Cohen’s d  = 0.03).

We also perform sensitivity analysis for the proposed mechanisms variables (Eq.  5 ). Given a sample of 41 countries, a significance level of α = 0.05, and a desired power 0.80, we estimate the minimum detectable effect size f². Results show that the minimum effects that could be detected are of medium to large size f² = 0.2 (two sided) for the proposed mediating variables.

We performed equivalence tests for all the Wave 1 to Wave 2 change analyses following the two one-sided test (TOST) procedure 23 , 24 , 25 . To set the smallest effect size of interest (SESOI) it is recommended to use substantive motivations (e.g. prior findings in the literature) 23 , 24 . Yet, for our analyses, we were unable to identify clear substantive bases for setting the SESOI. For instance, comparable meta-norm measures do not exist, to our knowledge, while for tightness-looseness, there is only one other source for comparable large-scale cross-country data 3 but this is solely available in a transformed form making a comparison in mean change to our waves meaningless (see Supplementary Note  1 ). Given this absence of comparable prior empirical evidence for setting the SESOIs, we consider a Cohen’s d  = 0.10 as the SESOI for changes in our measures over time. While for all mechanism analyses, we considered standardized betas as effect size measure, and consider a threshold of β  = ±0.10 (a small effect size 26 ) as the SESOI benchmark for all mechanisms tested.

We conducted the TOST procedure (set at the 5% significance level) using the coefficients and standard errors derived from the model estimates displayed in the main text and supplementary materials . For example, when analyzing the SESOI for TL, we estimate the equivalent change Δ in the raw scale corresponding to d  = 0.10. The coefficient estimate and standard error are drawn from Model 1 (Table  S2 ) and the TOST procedure is applied. The SESOIs of all other norm measures are calculated by applying the same reasoning and the TOSTs are conducted in the same way. For each equivalence test, we report the smallest magnitude t- value from among the two one-sided tests performed.

Tightness-looseness

We find a significant difference between our estimate of TL change and the SESOI (Δ = ±0.08, t (28369) = 5.53, p  < 0.001) such that the relevant coefficient ( b  = −0.028, 90% CI = [−0.047; −0.009]) is contained within the upper and lower equivalence bounds. This indicates that although there is a significant decrease in TL from Wave 1 to Wave 2 the change is statistically equivalent.

We find a significant difference between our estimate of situation-specific norms change and the within-country SESOI (Δ = ±0.06, t (142531) = 7.802, p  < 0.001) such that the relevant coefficient ( b  = −0.017, 90% CI = [−0.028; −0.006]) is contained within the upper and lower equivalence bounds. This indicates that while we find a significant decrease in situation-specific norms from Wave 1 to Wave 2, the change is statistically equivalent.

We find a significant difference between our estimate of metanorms change and the SESOI (Δ = ±0.05, t (484665) = −12.925, p  < 0.001) such that the relevant coefficient ( b  = 0.006, 90% CI = [−0.001; 0.012]) is contained within the upper and lower equivalence bounds. This implies that the change in metanorms is not significant from Wave 1 to Wave 2 and statistically equivalent.

We find a significant difference between our estimate of punishing frequency change and the SESOI ( Δ = ±0.1, t (85490) = 9.603, p  < 0.001) such that the relevant coefficient ( b  = −0.034, 90% CI = [−0.047; −0.022]) is contained within the upper and lower equivalence bounds. This means that, although we find a statistically significant decrease in punishing frequency, the change is statistically equivalent.

We find a significant difference between our estimate of hand washing norms change and the SESOI (Δ = ±0.13, t (28134) = −49.84, p  < 0.001) such that the relevant coefficient ( b  = 0.420, 90% CI = [0.390; 0.450]) is above the upper equivalence bound. This implies that the change in hand washing norms is significant from Wave 1 to Wave 2 and not statistically equivalent.

Mechanism analyses

When running the equivalence tests for the factors included in the mechanism analysis of the change in TL scores , we find that all standardized coefficients of our factors (Fear of COVID-19, β = −0.283, 90% CI [−0.503; −0.063]; Perceived Prevalence, β = −0.201, 90% CI [−0.526; 0.124]; Gov. Stringency, β = −0.036, 90% CI [−0.205; 0.133]) overlap with either the upper or lower equivalence bounds. This means that there is insufficient evidence to conclude a negligible effect.

The same analyses run for the change in hand washing norms give similar results in terms of equivalence. The coefficient associated with Fear of COVID-19 ( β = 0.352, 90% CI = [0.087; 0.618]), Perceived Prevalence ( β = 0.343, 90% CI [0.017; 0.669]) as well as Gov. Stringency ( β = −0.058, 90% CI [−0.333; 0.216]) overlap with either the upper or lower bound of the equivalence interval indicating that there is insufficient evidence to conclude a negligible effect.

Likewise, results from the equivalence tests for the change in punishing frequency show that the coefficient associated with Fear of COVID−19 ( β = −0.080, 90% CI = [−0.2314; 0.071]), Perceived Prevalence ( β = 0.008, 90% CI [−0.241; 0.257]) as well as Gov. Stringency ( β = −0.096, 90% CI [−0.255; 0.062]) overlap with either the upper or lower bound of the equivalence interval indicating that there is insufficient evidence to conclude a negligible effect.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data generated in this study have been deposited in the Open Science Framework ( https://doi.org/10.17605/OSF.IO/STKFR ). Non-experimental data included in our datasets (i.e., intensity of government response to COVID-19 is the Stringency Index, COVID-19 deaths and cases per million) are taken from the Oxford COVID−19 Government Response Tracker 22 and Our World in Data 38 (downloaded November 2020). Wave 0 data are from 3 and  Wave 1 data are from 5 .

Code availability

The survey and analysis code are available at the Open Science Framework ( https://doi.org/10.17605/OSF.IO/STKFR ).

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Acknowledgements

Knut and Wallenberg Grant “How do human norms form and change?” 2016.0167. (G.An.). The Swedish Research Council grant “Norms & Risk: Do social norms help dealing with collective threats” 2021-06271 (G.An.). Ministero dell’Istruzione dell’Università e della Ricerca, PRIN 2017, prot. 20178TRM3F (D.B.). Universidad de Los Andes, Fondo Vicerrectoría de Investigaciones (J.-C.C.). Ministry of Innovation and Technology of Hungary, National Research, Development and Innovation Fund NKFIH-OTKA K135963 (M.F.). Grant 23-061770 S of the Czech Science Foundation (M.H. and S.G.). RVO: 68081740 of the Institute of Psychology, Czech Academy of Sciences (M.H. and S.G.). RA Science Committee, research project N.20TTSH-070 (A.Gr. and N.Khac.). Open University of Israel, 511687 (R.N.). HSE University Basic Research Program (E.O.). Project BASIC (PID2022-141802NB-I00) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” (A.Sá.). US Army Research Office Grant W911NF-19-1-910281 (B.S.). Netherlands Organisation for Scientific Research, 019.183SG.001 (E.S.). Netherlands Organisation for Scientific Research, VI.Veni.201 G.013 (E.S.). European Commission, Horizon 2020-ID 870827 (E.S.). UKRI Grant “Secret Power” No. EP/X02170X/1 awarded under the European Commission’s “European Research Council - STG” Scheme (G.A.T.).

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These authors contributed equally: Giulia Andrighetto, Aron Szekely.

Authors and Affiliations

Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy

Giulia Andrighetto, Aron Szekely & Andrea Guido

Institute for Futures Studies, Stockholm, Sweden

Giulia Andrighetto, Andrea Guido & Kimmo Eriksson

Institute for Analytical Sociology, Linköping University, Linköping, Sweden

• Giulia Andrighetto

Collegio Carlo Alberto, Turin, Italy

Aron Szekely & Davide Barrera

CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté, Dijon, France

Andrea Guido

Graduate School of Business and Department of Psychology, Stanford University, Stanford, USA

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How are We Influenced by Social Norms? Social Norms

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This chapter presents scientific studies on the influence of social norms on sustainable behavior, introduces various social norms, presents the results of a meta-analysis, and discusses implications for marketing practice.

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Cialdini, R. B., & Trost, M. R. (1998). Social infuence: Social norms, conformity and compliance. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (pp. 151–192). McGraw-Hill.

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Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of consumer Research, 35 (3), 472–482.

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Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The constructive, destructive, and reconstructive power of social norms. Psychological Science, 18 (5), 429–434.

White, K., Habib, R., & Hardisty, D. J. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing, 83 (3), 22–49.

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METHODS article

Culture and social norms: development and application of a model for culturally contextualized communication measurement (mc 3 m).

• 1 University of Arizona, Tucson, AZ, United States
• 2 Michigan State University, East Lansing, MI, United States
• 3 Cornell University, Ithaca, NY, United States
• 4 Emory University, Atlanta, GA, United States
• 5 Peking University, Beijing, China

Studies of social norms are common in the communication literature and are increasingly focused on cultural dynamics: studying co-cultural groups within national boundaries or comparing countries. Based on the review of the status quo in cross-cultural measurement development and our years of experience in conducting this research among a co-cultural group, this paper describes a Model for Culturally Contextualized Communication Measurement (MC 3 M) for intercultural and/or cross-cultural communication research. As an exemplar, we report on a program of research applying the model to develop a culturally derived measurement of social norms and the factors impacting the norm-behavior relationship for members of a unique population group (i.e., ethnically Tibetan pastoralists in Western China). The results provide preliminary evidence for the construct validity and reliability of the culturally derived measurements. The implications, benefits, and shortcomings of the MC 3 M model are discussed. Recommendations for advancing both conceptual and measurement refinement in intercultural and cross-cultural communication research are provided.

Introduction

Social norms research has rapidly garnered popularity in the past several decades in multiple disciplines, such as communication, social psychology, public health, and economics ( Chung and Rimal, 2016 ). Given the power of normative influence on perceptions and actions consistently shown in the body of literature ( Borsari and Carey, 2003 ; Rhodes et al., 2020 ), social norm theories, rooted in the U.S.-based research, are being applied in numerous cross-cultural contexts ( Mackie et al., 2015 ). Yet, problems persist with inconsistencies in the conceptual and operational definitions of norms ( Shulman et al., 2017 ), and findings of prior studies may be culturally bound ( Chung and Rimal, 2016 ).

To help fill this gap and advance scholarship on social norms and other culturally contextualized communication measurements, we combine qualitative and quantitative approaches to develop culturally derived measures of social norms in a unique population. 1 Specifically, we describe research that examines the nature of interpersonal communication as a basis for shaping social norms and normative perceptions ( Lapinski et al., 2021 ), using this information to derive a series of measures rooted in the cultural context. Data obtained from a multi-year project, including field visits, in-depth interviews, and household surveys about grassland conservation behaviors with ethnically Tibetan pastoralists on the Tibetan Plateau in Western China, are presented as the basis for conceptualization, scale development, and initial evidence for the validity of measures of social norms and related constructs from theories on the communication of social norms ( Rimal and Real, 2005 ; Lapinski et al., 2018 ). This population is the focus of our work because of their critical role in ecosystem conservation issues in Asia and as a marginalized cultural group ( Bessho, 2015 ; Bum, 2016 ). As the basis for this research, we offer a model derived from existing research and practice for the development or adaptation of constructs and measures for intercultural or cross-cultural communication research. The value of this paper is the presentation of a model for developing measures of social norms (and related scales in communication studies) accounting for cultural dynamics. This process is useful beyond the particular population studied here, as the detailed steps described in the model shed light on future research on similar issues (e.g., conservation and health) among marginalized groups or populations with unique historical and/or cultural backgrounds (e.g., indigenous people and ethnic minorities).

Conceptualizing and Measuring Social Norms in Cultural Context

Generally, social norms are “rules and standards that are understood by members of a group, and that guide or constrain social behavior without the force of law” ( Cialdini and Trost, 1998 , p. 152) shared through interpersonal and mediated communication ( Kincaid, 2004 ). Social norms can influence health, environmental, and philanthropic attitudes and behaviors and can be influenced through communication campaigns ( Shulman et al., 2017 ). International attention has focused on the use of social norm campaigns as key to social change on various issues (e.g., child marriage, female genital mutilation/cutting, food waste, vaccination) because these efforts involve changing beliefs and actions of an entire community or cultural group rather than those of individuals ( UNICEF, 2010 ).

Despite the growing popularity of social norms research, critical issues remain in literature, including vague conceptualizations of what constitutes a social norm and conflated definitions and inadequacies in the measures of different types of norms ( Shulman et al., 2017 ). These problems “impair our ability to understand what norms are, how they work, how they should be measured, and boundary conditions that dictate where norms should and should not be applied” ( Shulman et al., 2017 , p.1209). Meanwhile, the increasing trend of social norms research conducted as comparative studies or in countries other than the U.S. and Europe in recent years (e.g., Geber et al., 2019 ; Stamkou et al., 2019 ) has created a demand for new methods conceptualizing and measuring social norms and related constructs.

Indeed, what we know about norms may be impacted by the so-called WEIRD (Western, Educated, Industrialized, Rich, Democratic) phenomenon documented in psychological research ( Henrich et al., 2010 ). Shulman et al.’s (2017) examination of 832 empirical studies in English language journals found that most studies of social norms (82.4%) were conducted in the U.S. and Western Europe; similar findings exist in global development where few international studies address measurement development or fundamental conceptualization of norms ( Mackie et al., 2015 ).

Constructing valid and reliable measures of key study concepts is regarded as one of the most critical steps in empirical research. No matter how well-designed a study is, poor measurement of study constructs can yield errors in interpreting the results. When studies are designed to compare two cultures or to study communication patterns and processes in a unique population or co-cultural group within a larger group, the measurement challenges are compounded ( Croucher and Kelly, 2019 , 2020 ). Differences in the conceptualization of core study ideas, languages, values, and other factors lead to substantial challenges when researchers try to maximize conceptual and measurement equivalence, reliability, and construct validity of measurement for samples from co-cultural groups within national boundaries or across national boundaries ( Herdman et al., 1997 ; Steenkamp and Baumgartner, 1998 ; Davidov et al., 2018 ).

Because of the culturally bound nature of social norms, it is crucial for researchers to establish and clearly describe conceptualizations and measurements of norms embedded in the appropriate cultural and social context. By culturally bound, in this case, we mean that although social norms, as unwritten codes of conduct, appear to exist in all human cultures, their form and function vary by group, complicating measurement. A lack of culturally valid measurement may hinder progress in theory building, especially in identifying boundary conditions for theories.

Studies of social norms and cultural dynamics have focused on nation/country (e.g., Cialdini et al., 1999 ) or race/ethnicity (e.g., LaBrie et al., 2012 ) as a delimiting concept. We recognize the benefits and limitations of using country or nation as the sole proxy or operationalization of culture, despite the prevalence of this practice in cross-cultural research (c.f., Schaffer and Riordan, 2003 ).

Using country, race, or ethnicity to identify cultural groups is convenient, clear, and tidy; most people can self-identify these characteristics when asked with valid indicators in the measures. Yet, country and culture are incongruent under most conditions. Generally, multiple co-cultural groups exist under the same overarching national identity ( Orbe, 1997 ). As such, culture may function at the level of a nation-state, a co-cultural group within a nation-state, or any collective of people who share deep or surface-level cultural elements (termed a unique population ). For the current study, we draw from the intercultural communication literature and use the term “culture” to include communities of people with uniquely shared communication characteristics, perceptions, values, beliefs, and practices . Shared practices, ethnicity, and language serve as indicators for the cultural group, which is the focus of the present study; ethnically Tibetan pastoralists . This group shares the following characteristics: they are historically nomadic and engage in animal husbandry, and they have Tibetan ethnicity with the Kham Tibetan dialect as their primary language.

Because, fundamentally, culture influences how people view the world, identifying within-culture conceptualizations of key study constructs should be the first step in empirical inquiry. As unwritten implicit rules, social norms are formed, shaped, and reinforced through observation and interpersonal and mediated communication among a collective. Normative perceptions may be formed about both the prevalence of behavior (i.e., commonly called descriptive norms ; what is done by most members of a group) and what most people think to be appropriate or inappropriate behaviors (i.e., injunctive norms ; what is socially approved or disapproved; Cialdini et al., 1990 ). Hence, it is critical to acknowledge the socially and culturally shared nature of social norms, as people relate to in-group members within a specific culture. That is, social norms, by their nature, emanate from collectives within a system. As such, it is necessary to identify the influential people and in-groups who are most connected to particular decisions or behaviors in order to contextualize norms.

Some research demonstrates the culturally bound nature of conceptualizations of social norms and their communication (e.g., Jensen and Bute, 2010 ; Lapinski et al., 2015 ). Using in-depth interviews and observation, the literature indicates that key conceptualizations developed in one cultural context (like injunctive norms with social prescriptions for appropriate behavior) may not exist in the same form when examined through a different cultural lens ( Jensen and Bute, 2010 ). Likewise, the nature of interpersonal and mediated communication about what is approved behavior is constrained by the nature of the social system (Elwood et al., 2000; Lapinski et al., 2015 ) and connected to cultural predispositions ( Lapinski et al., 2019 ).

Developing culturally derived social norms measures is also critical to enhance both the internal and external validity of the existing corpus of research to account for culturally-based concepts and processes ( Mollen et al., 2010 ). Surprisingly little is written about how to develop reliable and valid culturally derived measures of communication concepts like social norms; instead, one must go to the literature in cross-cultural and organizational psychology to find scholarship addressing some of these issues (c.f., Schaffer and Riordan, 2003 ). In public health, there is a robust literature on the cross-cultural adaptation of scales; yet, Epstein et al. (2015) reviewed 31 studies making recommendations for cross-cultural adaptation (CCA) and concluded there was no consensus on best practices for adapting measures across cultural contexts.

In sum, identifying and refining the culturally derived conceptualization of social norms is the first step in developing methods for measuring these constructs. Measurement development is critical for expanding social norms research to account for cultural similarities and differences in order to enhance both internal and external validity in the corpus of research to account for culturally-based concepts and processes ( Mollen et al., 2010 ; Lapinski et al., 2019 ).

Studies of Social Norms in Cultural Context: Absolutism, Universalism, and Relativism

Various approaches to studying cultural dynamics in social normative influence are evidenced in the literature (c.f., Fischer et al., 2009 ; Lee and Green, 1991 ; Park and Levine, 1999 ). Many of these studies have involved comparative research designs in which data from a U.S. sample are compared to a sample(s) of people from another nation ( Shulman et al., 2017 ). The predominant theories that address social norms, such as the theory of reasoned action (TRA; Fishbein and Ajzen, 1975 ), the theory of planned behavior (TPB; Ajzen, 1991 ), focus theory of normative conduct ( Cialdini et al., 1990 ), social norms approach (SNA; Berkowitz, 2004 ), and theory of normative social behavior (TNSB; Rimal and Real, 2005 ), have been developed and tested primarily in the U.S. with measures of the core theoretical concepts constructed in English. Studies using these theories sometimes provide evidence for measurement reliability and validity of the study measures using data from samples, often of college undergraduates, in various regions of the U.S. (e.g., Cialdini et al., 1999 ; Jang, 2012 ).

It is when these theories and measures are applied in new cultural contexts that challenges may arise. That is, by moving existing normative concepts and measures into new cultural contexts, studies may fail to account for the dynamics of normative influence unique to the new context . A framework in cross-cultural psychology that can be applied to communication research describes three orientations to the cross-cultural adaptation of theories and measures, including absolutism, universalism, and relativism ( Herdman et al., 1997 ; Berry et al., 2002 ). Based on this framework, there are roughly three approaches to studying social norms in cultural context: 1 ) adoption of the conceptualization and measures from existing theories and using them with no modification in a new cultural context (absolutism); 2 ) using conceptualization and measures developed in one cultural context (often in the language of the researcher) and translating the measures into the primary language of the study participants or making other adjustments for cultural context (universalism), and 3 ) developing the study concepts and measures based on data (or dialogue) from within the cultural context in the language of participants for each cultural group included in the study (relativism). In each of these cases, the nuances of the study procedures and the reporting of the processes are different for each study. For example, studies may or may not report on: the development of conceptual definitions, translation and back translation of items, evidence for scale reliability or validity, or measurement invariance. In the following, we review and summarize examples of these orientations from across disciplines 2 and then propose a series of recommended practices derived from the existing literature, for culturally derived measurement of communication constructs.

Absolutism orientation assumes a minimal impact of “culture” on the constructs being studied (i.e., they are culture-free) because of the species-wide similarities among all human beings. As a result, standard instruments measuring the focal constructs are considered appropriate to be used in different cultures. This practice may result in a construct conceptualized and operationalized in one culture that is “imposed” directly onto another culture ( Berry et al., 2002 ). It involves adopting the conceptual definitions, study materials, and measures directly from prior research without substantial modifications 3 . It may include using measures from prior research in a particular country without any translation procedures or evidence for measurement construct validity or equivalence (e.g., Thøgersen and Ölander, 2006 ; Abikoye and Olley, 2012 ; Nguyen and Neighbors, 2013 ; Savani et al., 2015 ).

For example, Bobek et al. (2007) conducted an experimental study with participants recruited from Australia, Singapore, and the U.S. to examine the effects of social norms on tax compliance using Cialdini and Trost’s (1998) taxonomy of social norms. Factor analysis and scale reliability analysis were performed to establish evidence for the scales’ validity and reliability before proceeding to test hypotheses. However, across the three national samples, the constructs and measures were assumed to be equivalent, and a translation process was not described. 4 Likewise, using measures from the theory of planned behavior (TPB; Ajzen, 1991 ), Wan et al. (2018) examined the moderating effect of subjective norms on the behavioral intention of using urban green spaces among Hong Kong residents. The convergent and discriminant validity and reliability of the measures were assessed before testing the structural model. But, no survey translation information was described, although most people in Hong Kong speak Cantonese as their primary language, and only 4.3% of the population use English regularly ( GovHK, 2020 ).

Universalism

The universalism orientation acknowledges that culture substantially impacts how constructs are expressed and defined across cultures. Though this approach still assumes species-wide similarities (i.e., universal patterns), it accepts the idea that measurement needs to be adapted cross-culturally, given that the context-free constructs and measurements are difficult or impossible to obtain. In this approach, conceptual definitions and measures are developed in one cultural context, typically in English. Then the study materials and measures are translated into the country’s language in which the research is conducted. Evidence for back-translation, construct validity, and measurement equivalence may or may not be described. There are a few social norms studies that account for cultural dynamics using this method (e.g., Cialdini et al., 1999 ; Park and Levine, 1999 ; Boer and Westhoff, 2006 ; Fornara et al., 2011 ; Jang et al., 2013 ; Stamkou et al., 2019 ; Walter et al., 2019 ).

For example, Stamkou et al. (2019) examined the moderating effect of cultural collectivism and tightness on responses to norm violators in 19 countries. The conceptual definition of the key study constructs and the measures, including social norms, norm violations, individualism-collectivism, and tightness-looseness, were adapted from existing literature developed in the U.S and translated into each country’s official language following the procedures outlined by Brislin (1986) ; validity and reliability evidence was provided. Likewise, Jain et al. (2018) investigated the effect of descriptive and injunctive norms on condom use among young men in Ethiopia using norms measures from the TNSB ( Rimal and Real, 2005 ) translated into Amharic, Afan Oromo, and Tigrigna. Adaptations were made in the norm measures to account for cultural context, but measurement validity and reliability evidence was not presented. Limaye et al. (2012) reported similar process in Malawi; acceptable reliability of the scales was presented, but measurement validity evidence was not included.

The last orientation, relativism , assumes that because of the substantial role of culture in people’s cognitive thinking patterns and behaviors, it is impossible to use standard measurements across cultures; hence, local instruments developed within a specific culture should be adopted ( Herdman et al., 1997 ; Berry et al., 2002 ). In this approach, the conceptual definitions and measures are developed within the focal cultural group, often through collaborative processes and formative data collection. The language in which they are developed may be that of the focal country or region. Measurement construct validity and equivalence evidence may or may not be described (e.g., Babalola, 2007 ; Rimal et al., 2015 ; Yilma et al., 2020 ). For example, Rimal et al. (2019) developed a personal narrative-based intervention, including social norms messages targeting adolescent students in Serbia, to improve their driving behaviors using conceptual definitions and measurement based on theory and cultural context. Formative data (i.e., one-on-one interviews, focus groups, and reaction interviews) was conducted first to develop the intervention and the measures of core concepts, including descriptive and injunctive norms. Results showed acceptable reliability of the normative scales, but measurement validity evidence was not included.

In sum, the literature on social norms and cultural dynamics indicates a range of approaches to developing concepts and measurements in cultural context for both single and multi-culture studies.

Model for Culturally Contextualized Communication Measurement (MC 3 M)

Based on the research on culturally derived measurement (Hui and Triandis, 1985; Pedhazur and Schmelkin, 2013 ; Schaffer and Riordan, 2003 ; Steenkamp and Baumgartner, 1998 ), research on measurement model validation and equivalence ( Bollen, 2005 ), and our team’s international and cross-cultural research, we present a Model for Culturally Contextualized Communication Measurement (MC 3 M) containing a series steps for the development of quantitative measures in communication science taking a relativistic approach ( Figure 1 ) and use a variant of this model in the current research. Although we focus here specifically on social norms, we believe this model may benefit other communication research. In the following sections, we describe a series of studies to illustrate the process of applying the model to develop culturally derived social norm measures.

FIGURE 1 . Proposed model for communication measurement development in cultural context: model for culturally contextualized communication measurement (MC 3 M).

The program of research that we report here was conducted on the Tibetan Platea in the Tsangsum Yungyul (Tibetan) or Sanjiangyuan (Mandarin) area of China, located in southern Qinghai Province. This region is home to about 960,000 inhabitants, 90% of whom are ethnically Tibetan, and nearly 70% are pastoralists, sometimes nomadic, herding mainly yaks and sheep ( see Appendix A). Geographically, the territory is vast, with human settlements dispersed, making data collection in the region challenging. The terrain includes glaciers and high-altitude grasslands, which input to three of Asia’s major rivers, the Yellow, Yangtze, and Mekong providing freshwater to nearly a quarter of the world’s population. The population of this region is generally Tibetan Buddhist. Their position as a unique or co-cultural group within China makes Tibetan pastoralists an important group to study social influence processes. They play a key role in the future of this ecologically sensitive region, but studies conducted in this area are rare ( Shen and Tan, 2012 ).

Step 1: Identification of Key Constructs

Discussions with cultural informants, review of the scientific and gray literature about the study region, field visits, and collaborative discussions with project partners were the first stage of this project; Step 1 in the MC 3 M. The cross-cultural (U.S. multi-ethnic, Han, Tibetan team), cross-disciplinary (anthropology, communication, sustainability, conservation biology, economics) team shared an interest in interpersonal communication about social norms and their effects on conservation behavior and the role of financial incentives in promoting conservation behavior among ethnically Tibetan pastoralists.

The exploratory work conducted in Step 1 revealed results in many key activities and insights, two of which we highlight here. First, discussion with collaborators coupled with our searches of the scientific literature revealed little social science data on the population of interest. This is critical because it drove our approach to the methods we used throughout the remainder of the project. Second, the focal constructs, behaviors, core theory, and research questions/predictions were developed collaboratively based on this process. Animal husbandry behaviors and their impact on the grassland and water ecology were identified as both salient for the study population and conservation practice. Specifically, herding types of animals with less relative ecological impact, reducing herd size to have less impact on grassland quality, and modifying grazing patterns to protect sensitive areas were the behaviors examined; organized patrolling to reduce poaching of wild animals was also examined but is reported elsewhere.

Step 2: In-Depth Interviews

As the next step in developing measures of the normative dimensions and providing construct validity evidence in this cultural context, in-depth interviews were conducted (Step 2 in the MC 3 M). The purpose of the interviews was to determine whether or not and how normative information was communicated to members of our study population and the character of that information in order to identify conceptualizations of social norms. In addition, we sought to understand the conditions under which normative information was available, the people from whom normative information emanates, and expected outcomes for the focal behaviors. Eighty in-depth interviews were conducted with members of our study population; detailed results are reported in companion papers ( Lapinski et al., 2018 ; Lapinski et al., 2021 ). Interview data were analyzed via quantitative content analysis, thematic analysis, and network analysis.

The interviews provided the basis for understanding indigenous conceptualizations of injunctive and descriptive norms, outcome expectancies associated with the behaviors, important referent groups for information about our study topics. In brief, the findings from the interviews uncovered normative influence as one basis for social power ( Kelman, 1961 ) among members of the study community ( Lapinski et al., 2018 ) and three essential themes for conceptualizing social norms ( Lapinski et al., 2021 ): 1 ) a shared understanding of what the participants believe is typical in the community, particularly local herding groups or villages (descriptive norms); 2 ) what participants believe is approved and disapproved or expected in the community (injunctive norms), and the anticipated reactions of others to compliance or noncompliance with expectations; and 3 ) important referent groups for decisions about herding (normative referents). Key referents were identified as dependent on the nature of information (general information, advice-seeking, or problem-focused), including herding group members, other villagers, family, and people in positions of power (e.g., veterinarians, government officials, village leaders).

Step 3: Refining Conceptualizations

Based on the findings from the interviews, revised conceptual definitions (Step 3 in the MC 3 M) and quantitative items were developed (Step 4, described in the method) to investigate further the influences of social norms on behaviors guided by several existing theories of social norms ( Fishbein and Ajzen, 1975 ; Cialdini et al., 1990 ; Rimal and Real, 2005 ) and our prior research ( Lapinski et al., 2018 ). Based on the interview data, the conceptualizations of both normative constructs (i.e., perceived descriptive norms and perceived injunctive norms, provided earlier in this paper) have been modified slightly to be culturally appropriate. Consistent with prior research, perceived descriptive norms are conceptualized as pastoralists’ perceptions of the prevalence of referent others’ (herding group and village group member) behavior. Perceived injunctive norms are conceptualized as perceptions of the referent others’ opinions and expectations about behaviors. A common element in conceptualizations of social norms–that social sanctions exist for noncompliance with the norm–was not included in the definition because it was not evidenced in our data. The key referent groups for this behavior are the herding group (if the pastoralist belongs to one) or others from the same village (if the pastoralist does not herd with a herding group). Families have been incorporated into the herding group conceptualization, given the clear overlap revealed from the interview data between these two groups.

Outcome expectations, as well as group identification and group orientation, were considered as key constructs in the study because prior research has shown they enhance the influence of social norms and appear to be critical in studies of cultural dynamics ( Cruz et al., 2000 ; Lapinski et al., 2007 ) and conceptualizations were shaped based on the results of the in-depth interviews. Outcome expectation is conceptualized as beliefs of the potential losses or benefits related to the behavior and includes monetary and non-monetary outcomes. The types of outcomes identified in the interviews included changes to the grassland, changes to economic well-being, and changes to identity as a Tibetan ( Lapinski et al., 2021 ). Group identity refers to feelings of affinity with one’s social group and the desire to be connected to that group ( Rimal and Real, 2005 ). Group orientation refers to one’s connection to the collective (i.e., the extent to which one’s social groups are central to the decision-making process). Giving priority to group goals over personal goals may function to enhance the influence of social norms on behaviors since group-oriented individuals are guided by group goals and norms in order to maintain harmony within groups ( Lapinski et al., 2007 ). Finally, we conceptualized behavioral intention as a person’s readiness to perform a behavior ( Fishbein and Ajzen, 1975 ) and a possible outcome of normative influence.

These conceptualizations form the basis for the development of items designed to measure each of the constructs. A cross-sectional survey was conducted with our study population in order to complete Steps 4–7 in the MC 3 M: The hypothesis is proposed:

H: The measures of perceived descriptive norms (PDN), perceived injunctive norms (PIN), outcome expectations (OE), group identity (GID), group orientation (GO), and behavioral intentions (BI) will yield valid and reliable unidimensional scales.

Sampling and Participants

Participants were recruited from one city and three counties in the study region via network sampling by project partners ( see Appendix A). Yushu Prefecture is an area of 267,000 square kilometers, with a total population of 283,100 people (95.3 percent Tibetan). As of 2015, Yushu Prefecture has one city and five counties; our sample included: Yushu City, Zaduo County, Nangqian County, and Chengduo County. Because of the behaviors examined in this study, three filter questions were asked at the beginning of the survey to ensure that the participant 1 ) was a pastoralist, 2 ) with at least 10 yaks in their herd, and 3 ) was the primary decision-maker in the household (i.e., the head of the household). Only people who answered affirmatively to these questions were included in the sample. During data cleaning, one participant was removed from the data analysis because his household had fewer than 10 yaks.

In total, 360 Tibetan pastoralists (85% male) in 10 townships participated in the surveys 5 , with an average age of 45.85 ( SD = 12.29), ranging from 18 to 80. The average size of the household was 6.52 ( SD = 2.57), with an average number of 2.36 ( SD = 1.48) school-aged children and 2.31 ( SD = 1.48) family members who helped with herding. Regarding the level of education, on average, participants had 1.3 years ( SD = 2.36) of schooling (including public schools and monastery schools), ranging from 0 (illiterate; 68.1%) to 15 years. Nearly all (98.3%) reported owning only yaks; less than 1% had both yaks and sheep (three misssing responses). The average herd size of yaks was 40.87 ( SD = 28.27), ranging from 0 to 200. Approximately 20% of the participants ( n = 71) belonged to herding groups, and 9 (12.7%) of them reported themselves as the leader of the herding group.

Survey Instrument Development

Step 4: initial item development and cognitive interviews.

The survey items were developed by the project team based on the results of the in-depth interviews ( Lapinski et al., 2018 , 2021 ) and prior research on social norms-related variables (Step 4 in the MC 3 M). The scale items were developed via the procedures suggested by Hunter and Gerbing (1982) . Items were developed for each distinct dimension by examining the conceptual definitions of the constructs and by deriving content from the interviews. Multiple items were created for each construct in order to allow for subsequent statistical tests of construct validity ( Hunter and Gerbing, 1982 ). The item construction process resulted in a large pool of items reviewed for face validity by the researchers. To enhance conceptual equivalence ( Herdman et al., 1997 ), each question was discussed by study team members and revised based on the discussion. Items that matched the conceptual definition of the construct were retained. The measures were developed in English and Tibetan simultaneously, captured in English, and then translated into Tibetan with flexibility for local variations in the dialect. The instrument was then back-translated to English to check for accuracy in interpretation and to avoid cultural biases. Then, the study team members discussed the final version of questionnaire questions one by one ( see Appendix B for the detailed procedures of translation and back-translation).

Two groups of cognitive interviews (four participants per group) were conducted with local community members to pilot the survey instrument before the data collection. This qualitative approach, conducted prior to the quantitative data collection, helped researchers examine how the respondents process and interpret questions and identify the factors influencing their answers ( Cabral and Savageau, 2013 ). Due to the benefit of improving item interpretation and strengthening scale quality shown in numerous studies (e.g., Collins, 2003 ; Ryan et al., 2012 ), the cognitive interview has been recommended as a standard step in survey development, refinement, and adaptation.

During the cognitive interviews, participants were asked to evaluate the survey questions with the goal of increasing the clarity, meaningfulness, and cultural appropriateness of the questions. Modifications were made to question wording and question order, and some questions were eliminated. Although we developed the scales to use verbally administered Likert-type response scales ranging from 1 (strongly disagree) to 5 (strongly agree), based on the suggestions from local collaborators and cognitive interview participants, we adopted the strategy of using fingers (digits; commonly used among people in the sample in everyday life) as a response scale when asking about Likert-type questions (e.g., thumb = strongly agree; the little finger = strongly disagree), to help participant better understand the options. A “Not Sure” option was added based on the suggestions from the local collaborator and the feedback generated from the cognitive interviews.

Surveys were conducted by four ethnically-Tibetan enumerators who were native speakers of the Kham Tibetan dialect and also fluent in Mandarin Chinese. Enumerators received training on survey skills, survey instruments, and the protection of human subjects by the study team (Step 5 in the MC 3 M). The enumerators verbally administered all questions using the digit response scale described above and recorded the responses in booklets due to the low level of literacy among our potential participants 6 based on the exciting literature (e.g., John, 2000 ; Bangsbo, 2008 ), the fieldwork of our community collaborators in our study area over the years, and data from our previous interviews. To minimize unintended enumerator effects on the survey data, enumerators were trained not to provide any explanations to the survey questions other than clarification or to provide verbal or nonverbal reactions toward participants’ answers. Statistical analysis was conducted to ensure that no significant differences existed in study variables for different enumerators.

Upon approaching a potential participant, each enumerator first introduced him/herself and the purpose of the survey briefly. If the individual agreed to answer the initial eligibility questions, the enumerator would record the sex of the respondent through observation first and then ask the three filter questions mentioned above (i.e., a pastoralist with at least 10 yaks who is the head of their household). Once the participant was determined as eligible for the survey, the enumerator proceeded with the informed consent process, adapted to be culturally appropriate while retaining the key elements of consent. Participants were also provided with opportunities to ask questions before deciding to participate or not. If they agreed to participate, the enumerator would proceed to the main survey questions. First, each participant was asked if he/she belonged to a herding group. Based on the participant’s answer to this question, he/she was directed to the subsequent questions associated with a specific referent group (people in my herding group vs people in my village), measuring their perceived descriptive norms, perceived injunctive norms, group orientation, group identity, perceived outcome expectation, behavioral intentions of reducing their herd size and demographics. Based on local norms, participants did not receive incentives for participation.

Surveys were conducted in semi-private settings in Kham Tibetan dialect and lasted approximately 30 min each. Participants’ responses to each question were recorded on the survey paper in Mandarin Chinese by the surveyors and manually entered into the computer later by two research assistants who were fluent in both Chinese and English. Each research assistant first entered all the survey data independently, and then their data entry files were carefully compared to identify any inconsistencies caused by human error during the data entry process. Following several days of data collection, data were reviewed, and procedures were discussed to determine whether modifications were necessary; all study procedures were retained. One researcher who was tri-lingual (Kham Tibetan, Mandarin, and English) was responsible for quality control of the procedures and data. All procedures were approved by a university institutional review board.

Measurement

Likert-type scales ranging from 1 (strongly disagree) to 5 (strongly agree) were adopted, with an additional option “Not Sure” added; response scales were administered using the enumerators’ fingers as a guide. All survey items ( see Appendix D), including factor loadings, are presented in Table 1 . Items either focused on herding group members or village group members as the referent, the 5 years prior to the survey as the time period, and herd size reduction as the behavior. Because of the nature of the study procedures, which were conducted in the field in naturalistic conditions, without incentives, every effort was made to streamline the questionnaire content and number of items per dimension in order to avoid attrition. For all scales, items retained following confirmatory factor analysis were summed such that higher scores indicated greater levels of the variable.

TABLE 1 . The Measurement Model of the Six Constructs.

Establishment of Measurement Model

Based on Hunter and Gerbing (1982) , the development and evaluation of a measurement model via factor analysis procedures included three steps: 1 ) construction of the model, 2 ) estimation of the observed correlations among the variables/items in the model, and 3 ) comparison of the observed correlations among variables with the correlations predicted by the model. The measurement model was specified first based on a theory of the relationships among the items. Thus, it was appropriate to use confirmatory factor analysis (CFA) procedures to estimate the parameters of the models and provide construct validity evidence. These procedures are included in Step 6 of the MC 3 M in Figure 1 ; all scale items are presented in Table 1 , with items removed following measurement analysis designated.

Scales and Items

Perceived descriptive norms (PDN). Participants’ perceived prevalence of others’ behavior of reducing the herd size among their referent group (herding group or people in the same village) was assessed with four items. One item directly asking about how many yaks they think the most households in their herding group/village own was dropped as it failed the internal consistency test with a low factor loading.

Perceived injunctive norms (PIN). Participants’ perceptions of the referent others’ opinions and anticipations of them reducing the size of their herds were assessed with four items initially. Two items, including a reverse-coded item, were eliminated due to low factor loadings.

Group identity (GID). Participants’ perceived attitudinal similarity and closeness with their referent group (their herding group or people in the same village) was assessed with four items derived from Rimal and Real (2005) . One item measuring participants’ perceived closeness to their herding group/village was dropped as it failed the internal consistency test with a low factor loading.

Outcome expectations (OE). Expectations about behavioral outcomes were measured by four items, including a reverse-coded item measuring the perceived benefits associated with herd reduction behavior. The results indicated small correlations among all the items ( see Table 1 ). Hence, it was deemed inappropriate to compose the variable by summing the items. This variable was removed from the rest of the analysis assessing the validity and reliability of the scales.

Group orientation (GO). The extent to which one is oriented toward group goals as opposed to individual goals was measured by a four-item scale derived from Triandis’ (1995) individualism-collectivism (INDCOL) scale and prior research ( Lapinski et al., 2007 ), which has been modified for this study based on the in-depth interviews.

Behavioral intention (BI). Participants’ intent to engage in the study behavior of reducing the number of yaks in their herds was measured with three items initially, including a reverse-coded item measuring the intention to increase the number of yaks in their herds. One item was eliminated due to its low factor loading.

Demographics. Participants’ demographic information was collected at the end of the survey, including biological sex (observed and recorded by the enumerator), age, number of people in their households, number of children, level of education, and residence location (county and township).

Missing Data and “Not-Sure” Responses

Missing data and responses of “not sure” (NS) were scrutinized for patterns ( Rubin, 1976 ) because the population under study is rarely surveyed, and the scales are newly developed ( see detailed results in Appendix C). The findings show that NS answers are more prevalent among village groups than herding groups, accounting for 93.62% of the total NS answers, suggesting the influential power of one’s herding group as the source of clearer normative information. For measurement validation in the subsequent analyses, both the missing and the NS data were eliminated, and the pairwise deletion was employed to retain sufficient statistical power.

Construct Validity Assessment

CFA was conducted using the lessR package developed by Gerbing (2021) within R programming environment to provide evidence that the observed scale items measured the same theoretical constructs. Both internal consistency and parallelism ( Hunter and Gerbing, 1982 ) were tested to evaluate the unidimensionality of the measurement model. The a priori specified criteria for item retention for tests of internal consistency include both the pattern and magnitude of the errors between predicted and obtained correlations between items ( e < 0.20) and examination of the size of the factor loadings. Once items were eliminated from a factor, factors were reanalyzed to test the unidimensionality of the new factor. Behavioral intentions with three items 7 was not included in this test.

In testing the internal consistency among items designed to measure PDN, item #4 was dropped as it failed the internal consistency test with a low factor loading and large error for predicted and obtained inter-item correlations ( e > 0.20). Since there were only three items left after the elimination, this factor was not tested again for internal consistency. When testing items measuring PIN, items #3 (reverse-coded) and #4 were eliminated due to the low factor loadings and large errors yielded. Two items were retained. Likewise, when testing items measuring group identity, item #4 was eliminated due to the low factor loading and large error. As such, no further internal consistency test was conducted. For the items measuring OE, the results showed insufficient factor loadings of all items developed in this scale with large errors. Hence, we deemed it was inappropriate to compose the variable by summing up the items and removed this variable from the rest of the analysis.

For the items measuring GO, the test of internal consistency via CFA indicated a plausible four-item solution for the scale; all items were retained. All errors for predicted and obtained inter-item correlations were small ( e < 0.20, goodness of fit RMSE = 0.06).

Tests of parallelism were next conducted to estimate how items measuring the same factor are distinct from other factors. Instead of assessing macro-level correlations between scales, tests of parallelism are conducted at the level of individual items with a low tolerance for errors (i.e., the discrepancy between the predicted correlations and the observed correlations). Results from the parallelism test showed that the four-factor model solution was acceptable: Comparative Fit Index (CFI) = 0.94, Tucker-Lewis Index (TLI) = 0.91, Root Mean Square Error of Approximation (RMSEA) = 0.07, Standardized Root Mean Square Residual (SRMR) = 0.06, χ 2 (67) = 228.01, p < 0.00, all errors were below the a priori specified value of 0.20. The factor loading for each scale item was reported in Table 1 , in which the five-factor solution was clearly demonstrated.

Discriminant Validity of the Constructs

After establishing the measurement model, the relationships among the four constructs were examined to assess the discriminant validity, which refers to measurement items within different constructs that should be unrelated ( Hunter and Gerbing, 1982 ). See Table 2 for the correlations among the variables in both herding and village groups. The mean and standard deviation for each variable were also reported in the table.

TABLE 2 . Zero-Order Correlations, Means, and Standard Deviations of Measured Variables for Both Herding Group and Village Group Participants.

To assess discriminant validity, average variance extracted (AVE) was analyzed, which measures the amount of variance captured by a construct in relation to the amount of variance due to measurement error ( Fornell and Larcker, 1981 ). The formula for calculating AVE is as below:

where λ i is the factor loading of each measurement item on its corresponding construct, and ε i is the error measurement. A widely used criterion to assess discriminant validity is Fornell-Larcker criterion ( Fornell and Larcker, 1981 ), which suggested that based on the corrected correlations from the CFA model, the square root of a construct’s AVE should be larger than the coefficient of correlations between the specific construct and other constructs in the model–that is to say, a latent construct should explain better the variance of its own indicator rather than the variance of other latent constructs. Therefore, the square root of each construct’s AVE should have a greater value than the correlations with other latent constructs. If that is the case, discriminant validity is established on the construct level. In Table 2 , evidence is provided for the construct validity of the scales.

Measurement Invariance Tests

Since the survey questions pertained to different referent groups (herding group vs people in the same village), multi-group confirmatory factor analysis (MGCFA) was conducted using Mplus following procedures recommended by Byrne (2013) . These tests provide evidence that the observed scale indicators/items under study measured the same theoretical constructs (latent variables or factors) across the two groups of the sample. Without established measurement invariance, comparative analyses do not produce meaningful results, and results of differences between groups cannot be unambiguously interpreted ( Milfont and Fischer, 2015 ).

Firstly, a baseline model (Model 1) was established from each group without constraints imposed across the groups for configural invariance (i.e., pattern invariance test). Next, Model 2 examining metric invariance was tested by constraining the factor loadings to be equal across the two groups (i.e., weak measurement invariance test). Model 3 tested scalar invariance by constraining both the factor loadings and indicator/item intercepts equal across the two groups (i.e., strong measurement invariance test; Byrne, 2013 ). Results showed no significant changes in Chi-squares across the three models, indicating a satisfactory measurement equivalence across the two groups. This enabled us to compare mean scores for the underlying factors across groups in the later analysis. The results were reported in Table 3 .

TABLE 3 . Fit Indices for Measurement Invariance Tests between the Herding Group Members vs Village Group Members.

Reliability Assessment

Following the establishment of scale dimensionality, parallelism, and invariance, reliability was assessed via calculation of Cronbach’s alpha for each scale using SPSS v.25, with both the split data file based on the referent group (i.e., herding group vs village group) and the combined dataset. Hunter and Gerbing (1982) suggested that when establishing new measures, validity and reliability should be treated separately. Hence, it was necessary to establish the dimensionality of the scales before examining scale reliability.

In addition to Cronbach’s alpha, composite reliability (sometimes called construct reliability) was assessed as an indicator of internal consistency in scale items ( Netemeyer et al., 2003 ). By measuring the total amount of true score variance relative to the total scale score variance ( Brunner and SÜβ, 2005 ), it serves as an indicator of the shared variance among the observed variables used as an indicator of a latent construct ( Fornell and Larcker, 1981 ). Thresholds for composite reliability are up for debate, but as a general guideline ( Fornell and Larcker, 1981 ; Netemeyer et al., 2003 ), composite reliability of the constructs should be higher than 0.7; The formula ( Netemeyer et al., 2003 ) is:

where: λ i = completely standardized loading for the i th indicator, V(δ i ) = variance of the error term for the i th indicator, and p = number of indicators.

Results ( see Table 1 ) showed that coefficient alphas ranged from 0.60 to 0.93. Considering the uniqueness of the target culture group in this study and the fact that this was the very first study ever in which the measures were developed, the relatively lower-alpha scores for group orientation (α = 0.68) and behavioral intentions (α = 0.60) suggest that future use of these scales should correct estimates for unreliability due to error of measurement. The composite reliability estimates ranged from 0.77 to 0.91, providing additional evidence for scale reliability.

Ground Truthing Results

Step 7 in the MC 3 M is “ground truthing” of process, method, and findings throughout the entire course of the research with stakeholders, including cultural insiders. In the current study, this was accomplished in several key ways. First, by conducting cognitive interviewing and ongoing data and procedural quality checks during the course of the study, we accounted for perceptions of cultural insiders. Second, we regularly presented our procedures and progress to our community collaborators and enumerators to gain their input; changes to procedures were made when possible without compromising study rigor or validity. Third, the findings of the study were presented to people working in this region and on these topics prior to publication to discuss the findings and learn about their understanding of the study findings relative to their experience. Fourth, our project partners who work in this region and one of whom is a member of the population from which we sampled, were included in all publications and reviewed the content for consistency with their experience and understanding of the cultural context.

Noting the critical role of reliable and valid culturally derived measures for social norms constructs and the lack of models for developing measures in cultural context, the present study was designed to propose and apply a model to guide intercultural and cross-cultural communication researchers developing quantitative measures of study constructs. Specifically, this study contributed to the existing corpus of communication literature by offering the Model for Culturally Contextualized Communication Measurement (MC 3 M) to describe the process of developing measures for communication research involving unique populations. This model, derived from prior research in disciplines outside of communication and applied over several years in a program of research among ethnically Tibetan pastoralists, provides a clear path forward for researchers conducting studies of communication processes across or within cultures among marginalized or co-cultural groups. In addition to proposing and applying the MC 3 M, the results of this study provide preliminary evidence for measurement validity and reliability of measures of key social norms constructs. We first discuss the measurement development and findings using the MC 3 M process and then describe the utility and limitations of the MC 3 M.

Social Norms Measures

The development of the culturally contextualized measures of social norms constructs began with significant informal and formal information gathering processes and data collection. Existing social norms theories and measures (e.g., Cialdini et al., 1990 ; Lapinski and Rimal, 2005 ) and the culturally-contextualized conceptual definitions served as the basis for new item development and testing using a cross-sectional survey. The content evaluation was conducted by discussions among the multi-lingual, multi-cultural team members, translation and back-translation, and through cognitive interviews among participants from the study population. As a result, we modified questions, revised the response scale, and decided to use finger-counting as a way to describe the response scale to respondents. Continuous process and data quality monitoring during data collection contributed to the development of the measures.

Confirmatory factor analysis (CFA) provided initial evidence for the construct validity of the culturally derived social norm measures. Tests of internal consistency and parallelism indicated that the data were consistent with unidimensional factors measuring the two types of norms: descriptive norms and injunctive norms, as well as group identity, group orientation, and behavioral intentions. Notably, several items were removed from the scales for each of these constructs due to insufficient factor loadings suggesting the need for continued scrutiny of these items in future research. The items designed to measure outcome expectations failed to meet a priori standards, and as such, these items were removed from the final measurement analysis. Outcome expectations play a key role in enhancing the effects of social norms ( Chung and Rimal, 2016 ), and future research should consider improved measures of this construct appropriate to cultural context. The failure of these items is difficult to explain. The content of the items was derived from in-depth interviews, and the adoption of procedures described by Ajzen et al. (1995) for belief elicitation was included; the item administration followed the same procedures as other scales. Nonetheless, it is clear that the items appear to be measuring unique concepts and do not form a unidimensional scale.

Most of the scales exhibited reliability coefficients within generally accepted ranges. However, the scale measuring behavioral intentions is relatively low. Perhaps this is due to the small number of items measuring this dimension since alpha is a function of the number of items on a scale. Because of the study procedures and the need to keep the questionnaire to a reasonable length to recruit and retain study participants without incentives, minimal items per dimension were administered. The behavioral intention scale could benefit from additional item refinement in future research studying behaviors in a cultural context. As an important limitation: although we focused a great deal on identifying, conceptualizing, and understanding the behaviors under study in the in-depth interviews ( Lapinski et al., 2021 ), we did not focus our efforts on understanding our study community’s thinking about the concept of “intent.” This is something any legal scholar will remind us is complicated and perhaps culturally bound.

Because of the novelty of the study issue and information from our collaborators that most of our participants would not have the experience participating in research studies, a significant amount of time was spent reviewing and refining the item response scales. Ultimately, we decided to use digit counting and verbal descriptions of the responses. A “not sure” category was included in the scales, based on the cognitive interviewing process, and many participants used this option. The fact that many used this response option reinforces the importance of including it, but also makes the analysis and treatment of “not sure” responses complicated. It stands as a key limitation to our measures and will be explored carefully in future research. Reviewing the measurement literature for advice on how to handle these data, there was surprisingly little guidance. This represents an opening for future research on measurement and the development of response scales to be used when verbal administration of items is necessary, and populations may have little experience participating in research. This finding also highlights the utility of using cognitive interviewing to refine response scales and items.

Substantively, the “not sure” responses show that participants who were asked about village group members as the referent were more uncertain about what is considered normative behavior compared to those belonging to a herding group. These findings were consistent with the existing social norms and communication theories (e.g., Kincaid, 2004 ; Lapinski and Rimal, 2005 ; Mackie et al., 2015 ) on the critical role of physically or psychologically proximal groups in shaping, communicating, and maintaining normative information of certain behaviors.

The process described for developing, evaluating, and validating the culturally derived social norm measures presented in this study has valuable empirical and theoretical implications for researchers who intend to conduct studies of co-cultural groups or unique populations. The model delineating the specific steps in developing culturally derived communication measures, starting from identifying and refining culturally derived conceptualizations, is a major contribution of this paper. Although we focus specifically on social norms research among the Tibetan population, we believe this model may have relevance for other communication research issues targeting other populations.

The MC 3 M has a number of key benefits and limitations. First, it provides a roadmap to researchers who wish to combine qualitative and quantitative methods to study communication processes in cultural contexts by specifying a set of best practices for developing measures. It is particularly applicable for populations or issues with little existing communication research, such as what we describe here. Second, it is based on existing research and practice and meant to function as a nascent and evolvable model as research on measurement development in cultural context progresses in the field of communication. There are certain additions and changes that could be incorporated into this model, and it is the hope of the researchers that it will have heuristic value, evolving as new knowledge is generated. Third, it is directly designed to be applied to intercultural, cross-cultural, and global communication research, filling a gap in the literature that has been dominated by other disciplines.

The model is not without limitations. Most importantly, we recognize that implementing the entire model requires significant time, resources, and relationships in a community. Further, the measures developed using the model cannot be simply taken and used in other cultural contexts but can serve as a basis for adaptation in intercultural communication research among similar populations and for similar issues. The relativism approach taken in the MC 3 M represents a departure from some of the existing cross-cultural/intercultural research, in which absolutism or universalism approaches are commonly adopted, and measures are used in communities without adaptation. With this said, we acknowledge that absolutism or universalism may still be appropriate in certain study contexts, such as when the research constructs are likely to be less sensitive to the influence of cultural or social factors.

Nonetheless, it is crucial to recognize the substantial role of culture in people’s communication, cognitions, and behaviors ( Herdman et al., 1997 ; Berry et al., 2002 ). As such, we encourage researchers to develop quantitative measures derived within a specific cultural context following rigorous procedures. Measurement development and validation are critical for expanding social norms and other communication research accounting for cultural similarities and differences. Doing so can enhance both internal and external validity in the corpus of research to account for culturally-based concepts and processes ( Mollen et al., 2010 ; Croucher et al., 2019 ).

The continued increasing global interactions highlight the need for cross-cultural researchers to be particularly careful and attentive to the issues of adapting existing constructs, theories, and measures developed in one culture for use in other cultures, and such issues are applicable to a variety of research disciplines. Acknowledging that nuances of the research process are different for each study, we hope that the proposed Model for Culturally Contextualized Communication Measurement, as well as the case we have described in this study, could serve to stimulate advancement in both conceptual and measurement refinement in intercultural and cross-cultural communication research.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Michigan State University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the many years of collaborative work and approved it for publication. The authors would like to acknowledge Ariane Leclerq, Ed Glazer, and the team of interviewers, coders, and surveyors/enumerators for their assistance with this project.

This project would not have been possible without a grant from the Sustainable Michigan Endowed Project at Michigan State University # 2011001. Partial support was provided by the USDA National Institute of Food and Agriculture, Hatch project numbers MICL02244, MICL02173, and MICL02362, and by National Science Foundation Award #SMA-1328503.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2021.770513/full#supplementary-material

1 This study reports on a long-term program of research involving an interdisciplinary, intercultural team of scientists and non-governmental organization staff first supported by the U.S. National Science Foundation in 2013 and continuing with data collections from 2014 to 2016. The authors do not have financial conflicts of interest.

2 As a caveat, only studies published in English language journals are reviewed here. Further, we only use the information available about these studies in the published version of the paper which may be incomplete.

3 Minor adaptions of the scales may be involved to fit with the specific study scenarios or focal behaviors.

4 Although English is the dominant language in Australia and the U.S., Singapore’s national languages are English, Mandarin, Malay, and Tamil ( Department of Statistics Singapore, 2019 ).

5 The geographical distance between villages is very far with some hundreds of kilometers apart and the primary transportation relies on rough mountain roads, so obtaining the sample was challenging. The participants were recruited primarily through community events and snowball sampling strategies.

6 We were conducting research in a politically sensitive area in China (c.f., Huang, 2013 ) and participants were likely to be unfamiliar with surveys. As such, we used verbally administered surveys.

7 Based on the suggestions from our experienced local collaborators and cultural insiders, we had to keep the survey short by limiting the number of items for each scale as much as possible, due to the reasons that 1 ) our survey was verbally administered, which took a much longer time to complete compared to a written/online survey, and 2 ) our study group had never participated in any studies or completed any surveys. Items developed in each scale with closely shared meaning may confuse them when answering the questions.

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Keywords: unique populations, social norms, cross-cultural communication, culturally derived measures, measurement validation

Citation: Liu RW, Lapinski MK, Kerr JM, Zhao J, Bum T and Lu Z (2022) Culture and Social Norms: Development and Application of a Model for Culturally Contextualized Communication Measurement (MC 3 M). Front. Commun. 6:770513. doi: 10.3389/fcomm.2021.770513

Received: 03 September 2021; Accepted: 06 December 2021; Published: 03 January 2022.

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Copyright © 2022 Liu, Lapinski, Kerr, Zhao, Bum and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rain W. Liu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Power of Social Norms: Exploring the Influence of Cultural Factors on Economic Decision-Making

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