| Literature DB >> 35066337 |
Rhiannon MacDonnell Mesler1, Bonnie Simpson2, Jennifer Chernishenko3, Shreya Jain2, Lea H Dunn4, Katherine White5.
Abstract
Amidst the economic, political, and social turmoil caused by the COVID-19 pandemic, contrasting responses to government mandated and recommended mitigation strategies have posed many challenges for governments as they seek to persuade individuals to adhere to prevention guidelines. Much research has subsequently examined the tendency of individuals to either follow (or not) such guidelines, and yet a 'grey area' also exists wherein many rules are subject to individual interpretation. In a large study of Canadians (N = 1032, Mage = 34.39, 52% female; collected April 6, 2020), we examine how social dominance orientation (SDO) as an individual difference predicts individual propensity to 'bend the rules' (i.e., engaging in behaviors that push the boundaries of adherence), finding that SDO is significantly and positively associated with greater intentions toward rule-bending behaviors. We further find that highlighting a self-oriented or in-group identity enhances the relationship between SDO and rule-bending, whereas making salient a superordinate-level identity (e.g., Canada) attenuates this effect. Implications for theory and practice are discussed.Entities:
Keywords: COVID-19; Identity salience; Physical distancing; Social dominance orientation
Mesh:
Year: 2021 PMID: 35066337 PMCID: PMC8772264 DOI: 10.1016/j.actpsy.2021.103460
Source DB: PubMed Journal: Acta Psychol (Amst) ISSN: 0001-6918
Bivariate correlations, descriptive statistics, and Cronbach's alphas.
| Mean | SD | Alpha | Skewness | Kurtosis | SDO | ||
|---|---|---|---|---|---|---|---|
| SDO | 2.50 | 1.10 | 0.85 | 0.57 | −0.13 | r | |
| Intent | 1.37 | 0.46 | 0.87 | 3.46 | 19.15 | r | 0.20 |
| <.001 |
Correlation is significant at the 0.01 level (2-tailed).
Correlation is significant at the 0.05 level (2-tailed).
Regression results for moderation analysis.
| Regression coefficients (standard errors) analyses (N = 1032) | ||||||
|---|---|---|---|---|---|---|
| Coefficient | SE | t | LLCI | ULCI | ||
| Dependent variable model (DV = Mean ‘Rule Bending’ Intention) | ||||||
| Constant | 1.36 | 0.01 | 98.19 | <.01 | 1.3366 | 1.3912 |
| SDO | 0.08 | 0.01 | 6.53 | <.01 | 0.0579 | 0.1075 |
| Self vs. In-group | −0.00 | 0.02 | −0.14 | .89 | −0.0413 | 0.0356 |
| Self vs. Super. | −0.06 | 0.02 | −2.82 | .01 | −0.0948 | −0.0169 |
| SDO ∗ SelfInG | 0.02 | 0.02 | 1.07 | .29 | −0.0163 | 0.0550 |
| SDO ∗ SelfSup | −0.04 | 0.02 | −2.47 | .01 | −0.0788 | −0.0090 |
Model summary: R2 = 0.06, F(5,1026) = 12.20, p < .01.
Test of highest order unconditional interaction: R2 = 0.0057, F(2,1026) = 3.08, p = .047.
“Self vs. In-group = Self vs. In-group effect coding: self-identity [−1], in-group identity [1], superordinate [0].
“Self vs. Super.” = Self vs. Superordinate effect coding: self-identity [−1], in-group identity [0], superordinate [1].
Fig. 1Rule-bending intentions across levels of SDO.