Theories of moralization argue that moral relevance varies due to inter-individual differences, domain differences, or a mix of both. Predictors associated with these sources of variation have been studied in isolation to assess their unique contribution to moralization. Across three studies (N-Study1 = 376; N-Study2a = 621; N-Study2b = 589), assessing attitudes towards new big data technologies, we found that moralization is best explained by theories focusing on inter-individual variation (similar to 29%) and intra-individual variation across technology domains (similar to 49%), and less by theories focusing on differences between technology domains (similar to 6%). We simultaneously examined 15 inter-individual and 16 intra-individual predictors that potentially explain this variation. Predictors directly relevant to the technologies (e.g., justice concerns), cognitive styles (e.g., faith in intuition), and emotional reactions (e.g., anger) best explain variation in moral relevance. Accordingly, scholars should simultaneously adopt and adapt moralization theories related to inter-individual and intra-individual differences across domains rather than in isolation.
|Journal||European Journal of Social Psychology|
|Publication status||Published - 2022|
- big data
- justice sensitivity
- moral conviction
- moral foundations
- moral relevance