@article {1971291, title = {Bias in context: Small biases in hiring evaluations have big consequences.}, journal = {Journal of Management}, year = {2021}, month = {2021}, abstract = {It is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars. In this study, we sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, we conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch our investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts. Consistent with prior research, we found evidence of small gender bias effects (d = -0.30) and large qualification effects (d = 1.61) on hiring managers{\textquoteright} evaluations of candidate hireability. We then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions. Collectively, our simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, we found contextual factors can alter but cannot obviate the consequences of biased evaluations,}, keywords = {Business Analytics, Management}, url = {https://doi.org/10.1177/0149206320982654}, author = {Hardy,Jay and Tey,K. S. and Wilson,Cyrus and Martell,Richard and Olstad,Andrew and Uhlmann,Eric Luis} } @article {1981341, title = {THOUGHT-LEADERS" ON GENDER BIAS IN THE WORKPLACE}, year = {2015}, month = {2015}, address = {NEW YORK}, keywords = {Management}, author = {Martell,Richard} } @article {1981336, title = {What Role Do Men Play in Women{\textquoteright}s Leadership Development?}, year = {2015}, month = {2015}, address = {Vancouver, Canada}, keywords = {Management}, author = {Martell,Richard} } @article {1981351, title = {The adverse effects of implicit bias and micro-inequities in the workplace: Much ado about something}, year = {2013}, month = {2013}, address = {St. George Utah}, keywords = {Management}, author = {Martell,Richard} } @article {1981346, title = {A multilevel emergent theory of gender segregation in organizations.}, year = {2013}, month = {2013}, address = {Orlando, Florida}, keywords = {Management}, author = {Martell,Richard} } @article {1973251, title = {From bias to exclusion: A multilevel emergent theory of gender segregation in organizations. Research in Organization Behavior, 32, 137-162.}, journal = {Research in Organizational Behavior}, volume = {32}, year = {2012}, month = {2012}, pages = {137-162}, address = {Elsevier}, abstract = {From bias to exclusion: A multilevel emergent theory of gender segregation in organizations

2012
Richard F. Martell | Cynthia G. Emrich | James Robison-Cox

Abstract: This article presents a multilevel emergent theory of organizational segregation linking gender bias in performance assessment (a micro-level phenomenon) to gender segregation in organizations (a macro-level phenomenon). Based on an integration of multilevel research, emergence and signaling theory, we propose the following: (a) gender segregation in organizations is an emergent phenomenon that arises from the collective behavior of individuals who express only a small bias in favor of males, in concert with the signals governing promotion decisions and organizational mobility; (b) the emergence of a gender-segregated organization is often unintentional and the bottom{\textendash}up and top{\textendash}down processes that produce segregation are difficult to see; and (c) agent-based modeling is especially well-suited for illuminating the dynamics of bias that produce gender-segregated organizations. This multilevel emergent-based theory contributes to the research literature on organizational stratification by: (a) revealing the manner in which micro-level and macro-level forces conspire, oftentimes unwittingly, to produce gender-segregated organizations; (b) providing new and very different directions for future research on gender segregation that rely on agent-based modeling; and, most importantly, (c) moving a 30-year debate over the {\textquotedblleft}real-world{\textquotedblright} impact of gender bias that continues to occupy the field of human resource management and, most recently, Supreme Court justices on to more fertile ground.}, keywords = {Management}, url = {http://www.journals.elsevier.com/research-in-organizational-behavior/}, author = {Martell,Richard} } @article {1981356, title = {Linking bias to exclusion using computational modeling.}, year = {2012}, month = {2012}, address = {Winnipeg, Canada}, keywords = {Management}, author = {Martell,Richard} }