02094nas a2200193 4500008004100000245007900041210006900120260000900189520150600198653002301704653001501727100001501742700001601757700001801773700002101791700001901812700002401831856004501855 2021 eng d00aBias in context: Small biases in hiring evaluations have big consequences.0 aBias in context Small biases in hiring evaluations have big cons c20213 aIt 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’ 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,10aBusiness Analytics10aManagement1 aHardy, Jay1 aTey, K., S.1 aWilson, Cyrus1 aMartell, Richard1 aOlstad, Andrew1 aUhlmann, Eric, Luis uhttps://doi.org/10.1177/014920632098265400398nas a2200109 4500008004100000245005300041210005100094260001900145653001500164100002100179856008800200 2015 eng d00aTHOUGHT-LEADERS" ON GENDER BIAS IN THE WORKPLACE0 aTHOUGHTLEADERS ON GENDER BIAS IN THE WORKPLACE aNEW YORKc201510aManagement1 aMartell, Richard uhttps://stage.business.oregonstate.edu/biblio/thought-leaders-gender-bias-workplace00437nas a2200109 4500008004100000245006100041210005900102260002800161653001500189100002100204856010200225 2015 eng d00aWhat Role Do Men Play in Women's Leadership Development?0 aWhat Role Do Men Play in Womens Leadership Development aVancouver, Canadac201510aManagement1 aMartell, Richard uhttps://stage.business.oregonstate.edu/biblio/what-role-do-men-play-womens-leadership-development00525nas a2200109 4500008004100000245010500041210006900146260002600215653001500241100002100256856013800277 2013 eng d00aThe adverse effects of implicit bias and micro-inequities in the workplace: Much ado about something0 aadverse effects of implicit bias and microinequities in the work aSt. George Utahc201310aManagement1 aMartell, Richard uhttps://stage.business.oregonstate.edu/biblio/adverse-effects-implicit-bias-and-micro-inequities-workplace-much-ado-about-something-000468nas a2200109 4500008004100000245007300041210006900114260002700183653001500210100002100225856011200246 2013 eng d00aA multilevel emergent theory of gender segregation in organizations.0 amultilevel emergent theory of gender segregation in organization aOrlando, Floridac201310aManagement1 aMartell, Richard uhttps://stage.business.oregonstate.edu/biblio/multilevel-emergent-theory-gender-segregation-organizations-002248nas a2200145 4500008004100000245014500041210006900186260001900255300001200274490000700286520169900293653001501992100002102007856007402028 2012 eng d00aFrom bias to exclusion: A multilevel emergent theory of gender segregation in organizations. Research in Organization Behavior, 32, 137-162.0 aFrom bias to exclusion A multilevel emergent theory of gender se aElsevierc2012 a137-1620 v323 aFrom 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–up and top–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 “real-world” 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.10aManagement1 aMartell, Richard uhttp://www.journals.elsevier.com/research-in-organizational-behavior/00437nas a2200109 4500008004100000245006000041210005900101260002700160653001500187100002100202856010400223 2012 eng d00aLinking bias to exclusion using computational modeling.0 aLinking bias to exclusion using computational modeling aWinnipeg, Canadac201210aManagement1 aMartell, Richard uhttps://stage.business.oregonstate.edu/biblio/linking-bias-exclusion-using-computational-modeling-0