It is increasingly recognized that team diversity with respect to various social categories (e.g., gender, race) does not automatically result in the cognitive activation of these categories (i.e., categorization salience), and that factors influencing this relationship are important for the effects of diversity. Thus, it is a methodological problem that no measurement technique is available to measure categorization salience in a way that efficiently applies to multiple dimensions of diversity in multiple combinations. Based on insights from artificial intelligence research, we propose a technique to capture the salience of different social categorizations in teams that does not prime the salience of these categories. We illustrate the importance of such measurement by showing how it may be used to distinguish among diversity-blind responses (low categorization salience), multicultural responses (positive responses to categorization salience), and intergroup-biased responses (negative responses to categorization salience) in a study of gender and race diversity and the gender by race faultline in 38 manufacturing teams comprising 239 members.
Bibliographical noteFunding: Research Grant ECO2012-33081 from the Ministry of Economy and Competitiveness
- multivariate analysis
- computational modeling
- team diversity
- categorization salience