Home Moral guidelines Moral parochialism and the causal assessment of transgressive harm in Seoul and Los Angeles

Moral parochialism and the causal assessment of transgressive harm in Seoul and Los Angeles

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Preliminary testing revealed no effect of gender, or gender-society interactions, on errors or causal attribution scores. Therefore, sex was removed from the analyses.

Misperception and consequences on reputation

Pooling companies, as expected, ratings of composite base errors and reputational consequences were positively correlated, r(120) = 0.64, pr(51) = 0.49 pr(68) = 0.74, p

Effects of society and context on perceived injustice

We performed a 2 (society) of 4 (transgression context) mixed model ANOVA. As assumed, there was a main effect of the transgression context, F(3357) = 22.61, pηp2= 0.16, with composite transgressions rated significantly more erroneous in the baseline condition than in the authority consent contexts (pηp2= 0.18), spatial distance (pηp2= 0.28), or Temporal distance (pηp2= 0.31). This main effect was qualified by two significant interaction effects between society and contextual condition, in which participants in Seoul rated composite transgressions as less erroneous from baseline than participants in Los Angeles in the spatial distance background, F(1, 119) = 7.51, p= 0.007, ηp2= 0.06, and in the context of temporal distance, F(1, 119) = 5.35, p= 0.022, ηp2= 0.04. There was no such interaction with respect to the context of the authority’s consent, p= 0.611. Both companies rated transgressions as significantly less erroneous compared to the baseline for all three settings (see Table 1).

Table 1 Context of transgression and composite error ratings.

There was also a main effect of society such that the Seoul sample rated transgressions as less bad on average in the context of spatial distance, F(1, 119) = 4.42, p= 0.038, ηp2= 0.04, 95% CI [0.02, 0.73]with a similar trend not significant in the context of temporal distance, F(1, 119) = 3.26, p= 0.074, ηp2= 0.03, 95% CI [− 0.03, 0.69], compared to the Los Angeles sample (see Table 1). There were no such company effects on base error ratings, p= 0.806, or error in the context of authority consent, p= 0.775.

Follow-up analyzes revealed that the same overall pattern was obtained in the individual transgression scenarios (see SI Tables S1 to S6). Transgressions were found to be significantly more erroneous at baseline than when spatially or temporally distant for all six scenarios in both societies, with one exception: there was no spatial distance effect for the “defamation” scenario. in the Los Angeles sample. Although authority consent also significantly reduced error ratings in many cases, this effect was not observed in the ‘injustice’, ‘unintentional harm’ or ‘defamation’ scenarios in the sample. of Seoul, nor in the “domestic beat” scenario in Los Angeles. to taste. These exceptions, while deviating from predictions, demonstrate that participants assessed scenarios and contexts in a way that cannot be explained by demand characteristics (i.e., inferring that they were “supposed” to tone down their error scores because they were repeatedly asked). The variation within the two samples in the relative magnitude of changes in error ratings in each setting also indicates that participants’ ratings reflect their moral feelings rather than demand effects.

Effects of Society on Causal Attribution

Between-subjects ANOVA revealed that, according to the hypothesis, participants in the Los Angeles sample rated the cause of the composite transgression as being significantly more attributable to the perpetrator compared to participants in the Seoul sample. , who tended to holistically attribute the cause of the transgression to the situation. This societal difference was highlighted using the two measures intended to assess causal attribution, in particularly large effects. To a greater extent than the Los Angeles participants, the Seoul participants indicated that most people in the same circumstances would have transgressed (i.Los Angeles: M= 4.13, South Dakota= 1.27; Seoul: M= 7.09, South Dakota= 0.74), 95% CI [2.57, 3.35], F(1, 119) = 226.06, pηp2= 0.66, and that the transgression was caused by the situation more than the personal qualities of the transgressor (i.e. the question Caused by the situation) (Los Angeles: M= 3.40, South Dakota= 1.42; Seoul: M= 7.06, South Dakota= 0.62), 95% CI [3.24, 4.08], F(1, 119) = 301.62, pηp2= 0.72 (see Figure 1). Follow-up tests revealed that the same pattern obtained for all six scenarios (see Table 2).

Figure 1

Differences between societies in causal attribution, averaging transgression scenarios. Compared to participants in Los Angeles, participants in Seoul rated transgressions as caused by situational circumstances more than the personal disposition of the transgressor (top), and more likely to have been committed regardless of who was present in the scene. the same situation (bottom). The contours of the violin plot illustrate the probability density of the kernel; the width of the shaded area represents the proportion of data within it and the black squares indicate the averages (see text for details).

Table 2 Ratings of the causes of transgressive harms by scenario and society.

Society, causal attribution and perceived error

There was no significant correlation between any of the dispositional items and the composite baseline error in either company, except for the issue Caused by location in the context of distance. time in Los Angeles (see Table 3), which appears to be driven by the Stealing Strangers and Market Cheating Scenarios (see Table S9; for correlations between causal ratings and error ratings in each context condition for each scenario, see SI Tables S7 to S10.).

Table 3 Correlations between composite causal attributions and error ratings.

Null effect of the company on the reputational consequences of the transgression

A between-subjects ANOVA revealed that, despite the large differences in dispositional causal attribution to situation, there was no significant difference in ratings of the mean consequences of the transgression on reputation between the sample of Seoul (M= 2.59, South Dakota= 0.83) and the Los Angeles sample (M= 2.43, South Dakota= 0.77), p= 0.282. Similarly, follow-up testing revealed no significant differences between companies in the estimated reputational consequences in any of the individual scenarios, Home Battery, which the Los Angeles sample rated as having lower reputational consequences. to those of the South Korean sample (see SI Table S11) .

Company, causal attribution and reputational consequences of transgression

We then assessed the correlations between the estimated reputational consequences and each causal attribution measure. None of the composite causal attribution ratings were significantly associated with estimated reputational consequences when analyzed separately within each company (Others would also violate: Seoul: r(51) = 0.15, p= 0.292; Los Angeles: r(68) = 0.04, p= 0.749; Caused by the situation: Seoul: r(51) = 0.25, p= 0.078; Los Angeles: r(68) = 0.22, p= 0.064. (However, see SI Table S12 for analyzes revealing exceptions: two scenarios in which positive correlations were observed in the Los Angeles sample, and one scenario in which a positive association was observed in the Seoul sample. ).