How Journals Can Facilitate the Study of Underlying Situational Characteristics Distinguishing Worker and Professional Samples

Abstract

Bergman and Jean's (2016) focal article decries the limited research attention of industrial and organizational (I-O) psychologists on “workers”—that is, employees such as wage earners, frontline workers, and contractors, who do not fill professional, managerial, or executive positions. We agree. In addition to the scientific and moral benefits of studying workers, there is a practical imperative. An academic discipline that comes across as being disinterested in workers may leave itself open to charges of being the “handmaiden” of management (Hulin, 2002, p. 12). Moreover, such an academic discipline may be ill prepared to provide evidence-based contributions to important societal debates on topics such as income inequality and immigration.

From Handmaidens to POSH Humanitarians: The Case for Making Human Capabilities the Business of I-O Psychology

“…As a proxy for the professional nature of work, we utilized typical educational requirements in an occupation. We observed a substantial negative correlation ( r = –.61, n = 689, p < .001) between educational requirements and occupational situational constraints in support of Green and Dalal (2016)’s reasoning that professionals (who predominate in occupations with higher levels of education) would hold occupations with lower levels of situational strength. When it comes to the connection between social discrimination and situational constraints, we note the close conceptual connection between exclusion from society due to discrimination and the reduction in available ways one might be freely able to participate in that society (Dalal, 2010).…”

“…In particular, based on both research and commentary in I-O psychology (Green & Dalal, 2016; Meyer, Dalal, & Bonaccio, 2009; Meyer, Dalal, & Hermida, 2010) and scholarship from the field of development economics (Sen, 1999), we propose that gradients in poverty are likely to be associated with variation in the extent to which individual freedom is constrained by situations. Green and Dalal (2016) highlighted that professionals are likely to enjoy different levels of situational strength than other workers. Situational strength can be defined as implicit or explicit cues provided regarding the desirability of various forms of behavior (Meyer et al, 2010).…”

“…We now turn to the reasons why a relative neglect of poverty is a problem for science and practice in I-O psychology. As before, we acknowledge and attempt to synthesize observations by previous commentators who have done an excellent job of reviewing problems associated with the various facets of I-O psychology's POSH bias (Bergman & Jean, 2016; Gelfand et al, 2008; Green & Dalal, 2016; Lefkowitz, 2016; Ruggs et al, 2013; Saxena et al, 2015). We divide this conversation into issues relating to research and practice.…”

“…Not only might a POSH bias obscure certain phenomena or change the meaning of certain variables, but the moderating role of poverty might go underappreciated. In particular, based on both research and commentary in I-O psychology (Green & Dalal, 2016; Meyer, Dalal, & Bonaccio, 2009; Meyer, Dalal, & Hermida, 2010) and scholarship from the field of development economics (Sen, 1999), we propose that gradients in poverty are likely to be associated with variation in the extent to which individual freedom is constrained by situations. Green and Dalal (2016) highlighted that professionals are likely to enjoy different levels of situational strength than other workers.…”

The Double-Edged Sword of Big Data in Organizational and Management Research

“…Accordingly, Big Data can transcend the usual samples and afford OMS with a more inclusive empirical assessment and theoretical understanding of workplace experiences and domains, which can then be broadly applied across people, jobs, industries, and cultures. As a result, OMS may contribute more evidence-based insights to topics of societal importance such as income inequality, work automation, and immigration across different relevant but often underrepresented populations (Green & Dalal, 2016). Where it makes sense for the research question and assuming ethical obligations can be met, we encourage researchers to explore how Big Data can help maximize the external validity and utility of their findings through truly representative samples, if not population-level data…”