Racial injustice in employment demands the attention of business organizations because it profoundly shapes our life prospects. While comparing the ideal of perfectly equal opportunity with its invariably imperfect alternatives can impede reform, the true challenge lies in addressing persistent inequities as we strive for equality. This article introduces “shared inequity” as a frame of reference for assessing workplace racial disparities and emphasizing a collective responsibility to remedy systemic issues. In critiquing an exaggerated notion of meritocracy, I emphasize that structural (...) racism, rather than just individual acts, facilitates collective advantages, especially for White males. Hence, it is morally fitting to frame racial justice in employment, not only in terms of perfect equality, but also in terms of justly sharing an imbalance that cannot be corrected without counterbalance. The “shared inequity” lens offers a more realistic and just approach to pursuing racial justice in the workplace. (shrink)

In this article, the co-editors of the corporate responsibility: quantitative issues section of the journal provide an overview of the quantitative CSR field and offer some new perspectives on where the field is going. They highlight key issues in developing impactful, theory-driven, and ethically grounded research and call for research that examines complex problems facing businesses and the society (e.g., big data and artificial intelligence, political polarization, and the role of CSR in generating social impact). By examining topics that are (...) under-researched, forward-looking, and socially oriented, scholars can expand the boundary of CSR’s substantive domain and produce research that helps businesses act in a long-term, socially responsible way in this quickly evolving, turbulent environment. They also discuss ways to enhance the methodological rigor of quantitative CSR research and encourage scholars to employ cutting-edge, innovative methods to shed light on the micro-level mechanisms of CSR and reveal patterns and relationships hidden in unstructured big data. (shrink)