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Affirmative Action Support in an Organization

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  • A. Olu Oyinlade

Abstract

Underpinned by the assumption that people would support affirmative action based on self-interests, and/or when they have high job security not to be threatened by the policy, this study investigated the likelihood that workers would differentially support affirmative action by their demographic attributes. Analyses of three demographic models—social, organizational, and combined(social plus organizational)—were used to determine predictors of support for affirmative action. Findings of the third (combined) model indicated that organizational tenure (an organizational demographic variable) and educational completion (a social demographic variable), respectively, were the two strongest predictors of support for affirmative action. This study suggested that factors of achievement, rather than race-ethnicity or gender, were the strongest predictors of support for affirmative action. This finding may be useful to personnel and human resources leaders in designing programs for employee acceptance of affirmative action programs.

Suggested Citation

  • A. Olu Oyinlade, 2013. "Affirmative Action Support in an Organization," SAGE Open, , vol. 3(4), pages 21582440135, December.
    • Handle: RePEc:sae:sagope:v:3:y:2013:i:4:p:2158244013516156
    DOI: 10.1177/2158244013516156
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    References listed on IDEAS

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    1. Shteynberg, Garriy & Leslie, Lisa M. & Knight, Andrew P. & Mayer, David M., 2011. "But Affirmative Action hurts Us! Race-related beliefs shape perceptions of White disadvantage and policy unfairness," Organizational Behavior and Human Decision Processes, Elsevier, vol. 115(1), pages 1-12, May.
    2. C. Carl Pegels & Yong I. Song & Baik Yang, 2000. "Management heterogeneity, competitive interaction groups, and firm performance," Strategic Management Journal, Wiley Blackwell, vol. 21(9), pages 911-923, September.
    3. Barbara S. Lawrence, 1997. "Perspective---The Black Box of Organizational Demography," Organization Science, INFORMS, vol. 8(1), pages 1-22, February.
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    1. A. Olu Oyinlade & Alex Losen, 2014. "Extraneous Effects of Race, Gender, and Race-Gender Homo- and Heterophily Conditions on Data Quality," SAGE Open, , vol. 4(1), pages 21582440145, February.

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