Corporate social responsibility (CSR) focuses on many types of stakeholders and outcomes, including stakeholders outside of the organization and outcomes that go beyond financial results. Thus, CSR ...expands the notion of work to go beyond a task, job, intraindividual, intraorganizational, and profit perspective and provides an ideal conduit for individuals to seek and find meaningfulness through work. We adopt a person-centric conceptualization of CSR by focusing on sensemaking as an underlying and unifying mechanism through which individuals are proactive and intentional agents who search for and find meaningfulness through work. Our conceptualization allows us to understand variability in CSR effects due to variability in employee sensemaking and the meaningfulness employees experience from CSR; highlight synergies across disconnected theories and streams of research originating in different disciplines and at the intraindividual, intraorganizational, and extraorganizational levels of analysis; and propose new research directions for the future in the form of propositions and research questions. By using sensemaking as a unifying underlying process, the proposed conceptualization explains how individuals find meaningfulness through work and, consequently, when and why employees experience CSR in a particular manner—resulting in more or less positive outcomes for themselves, their organizations, and external stakeholders. Our proposed model could also be used in other individual-level research domains that would benefit from (a) placing people and their search for meaningfulness center stage and (b) focusing on the role that same-level and cross-level interactions among intraindividual, intraorganizational, and extraorganizational sensemaking factors play in the process.
The use of Amazon’s Mechanical Turk (MTurk) in management research has increased over 2,117% in recent years, from 6 papers in 2012 to 133 in 2019. Among scholars, though, there is a mixture of ...excitement about the practical and logistical benefits of using MTurk and skepticism about the validity of the data. Given that the practice is rapidly increasing but scholarly opinions diverge, the Journal of Management commissioned this review and consideration of best practices. We hope the recommendations provided here will serve as a catalyst for more robust, reproducible, and trustworthy MTurk-based research in management and related fields.
Understanding boundary conditions, or situations when relations between variables change depending on values of other variables, is critical for theory advancement and for providing guidance for ...practice. Metaregression is ideally suited to investigate boundary conditions because it provides information on the presence and strength of such conditions. In spite of its potential, results of our review of 63 metaregression articles published in the Journal of Management, Journal of Applied Psychology, Personnel Psychology, Journal of Management, Academy of Management Journal, and Strategic Management Journal uncovered a surprising lack of transparency, frequently implemented erroneous practices, and a lack of attention to important methodological choices. Results also suggest that many substantive conclusions are ambiguous at best and, unbeknownst to authors and readers, potentially misleading. Drawing from our review of the substantive literature as well as the latest statistical and methodological research, we offer evidence-based best-practice recommendations on how to conduct and report the results of a metaregression study. We offer recommendations on calculating statistical power and heterogeneity, choosing an appropriate model, testing boundary condition hypotheses, adjusting R2 for known variance, explaining methodological choices, and reporting and interpreting model coefficients and other results. Also, we conducted two illustrative metaregression studies that incorporate all of our recommendations with accompanying syntax and data. Our recommendations can be used by authors, readers, journal editors, and reviewers wishing to conduct and evaluate metaregression studies, as well as practitioners interested in understanding conditions under which organizational practices are more or less likely to be effective.
The use of control variables plays a central role in organizational research due to practical difficulties associated with the implementation of experimental and quasi‐experimental designs. As such, ...we conducted an in‐depth review and content analysis of what variables, and why such variables are controlled for, in 10 of the most popular research domains (task performance, organizational citizenship behaviors, turnover, job satisfaction, organizational commitment, employee burnout, personality, leader‒member exchange, organizational justice, and affect) in organizational behavior/human resource management (OB/HRM) and applied psychology. Specifically, we examined 580 articles published from 2003 to 2012 in AMJ, ASQ, JAP, JOM, and PPsych. Results indicate that, across research domains with clearly distinct theoretical bases, the overwhelming majority of the more than 3,500 controls identified in our review converge around the same simple demographic factors (i.e., gender, age, tenure), very little effort is made to explain why and how controls relate to focal variables of interest, and control variable practices have not changed much over the past decade. To address these results, we offer best‐practice recommendations in the form of a sequence of questions and subsequent steps that can be followed to make decisions on the appropriateness of including a specific control variable within a particular theoretical framework, research domain, and empirical study. Our recommendations can be used by authors as well as journal editors and reviewers to improve the transparency and appropriateness of practices regarding control variable usage.
This article provides a review of the training and development literature since the year 2000. We review the literature focusing on the benefits of training and development for individuals and teams, ...organizations, and society. We adopt a multidisciplinary, multilevel, and global perspective to demonstrate that training and development activities in work organizations can produce important benefits for each of these stakeholders. We also review the literature on needs assessment and pretraining states, training design and delivery, training evaluation, and transfer of training to identify the conditions under which the benefits of training and development are maximized. Finally, we identify research gaps and offer directions for future research.
We describe experimental vignette methodology (EVM) as a way to address the dilemma of conducting experimental research that results in high levels of confidence regarding internal validity but is ...challenged by threats to external validity versus conducting nonexperimental research that usually maximizes external validity but whose conclusions are ambiguous regarding causal relationships. EVM studies consist of presenting participants with carefully constructed and realistic scenarios to assess dependent variables including intentions, attitudes, and behaviors, thereby enhancing experimental realism and also allowing researchers to manipulate and control independent variables. We describe two major types of EVM aimed at assessing explicit (i.e., paper people studies) and implicit (i.e., policy capturing and conjoint analysis) processes and outcomes. We offer best practice recommendations regarding the design and implementation of EVM studies based on a multidisciplinary literature review, discuss substantive domains and topics that can benefit from implementing EVM, address knowledge gaps regarding EVM such as the need to increase realism and the number and diversity of participants, and address ways to overcome some of the negative perceptions about EVM by pointing to exemplary articles that have used EVM successfully.
A growing body of empirical evidence in the management literature suggests that antecedent variables widely accepted as leading to desirable consequences actually lead to negative outcomes. These ...increasingly pervasive and often countertheoretical findings permeate levels of analysis (i.e., from micro to macro) and management subfields (e.g., organizational behavior, strategic management). Although seemingly unrelated, the authors contend that this body of empirical research can be accounted for by a meta-theoretical principle they call the too-much-of-a-good-thing effect (TMGT effect). The authors posit that, due to the TMGT effect, all seemingly monotonic positive relations reach context-specific inflection points after which the relations turn asymptotic and often negative, resulting in an overall pattern of curvilinearity. They illustrate how the TMGT effect provides a meta-theoretical explanation for a host of seemingly puzzling results in key areas of organizational behavior (e.g., leadership, personality), human resource management (e.g., job design, personnel selection), entrepreneurship (e.g., new venture planning, firm growth rate), and strategic management (e.g., diversification, organizational slack). Finally, the authors discuss implications of the TMGT effect for theory development, theory testing, and management practice.
Most management theories include hypotheses about interaction effects (i.e., the relation between two variables depends on values of another), but it is common for articles to present results that ...make it difficult to evaluate the nature, strength, and importance of the effect. We offer recommendations for improving the reporting of interaction effects by focusing on (a) visualizations, (b) effect size estimates, and (c) assessments of the nature, meaning, and importance of interactions for theory and practice.
Meta-analyses summarize a field’s research base and are therefore highly influential. Despite their value, the standards for an excellent meta-analysis, one that is potentially award-winning, have ...changed in the last decade. Each step of a meta-analysis is now more formalized, from the identification of relevant articles to coding, moderator analysis, and reporting of results. What was exemplary a decade ago can be somewhat dated today. Using the award-winning meta-analysis by Stahl et al. (Unraveling the effects of cultural diversity in teams: A meta-analysis of research on multicultural work groups. Journal of International Business Studies, 41(4):690–709, 2010) as an exemplar, we adopted a multi-disciplinary approach (e.g., management, psychology, health sciences) to summarize the anatomy (i.e., fundamental components) of a modern meta-analysis, focusing on: (1) data collection (i.e., literature search and screening, coding), (2) data preparation (i.e., treatment of multiple effect sizes, outlier identification and management, publication bias), (3) data analysis (i.e., average effect sizes, heterogeneity of effect sizes, moderator search), and (4) reporting (i.e., transparency and reproducibility, future research directions). In addition, we provide guidelines and a decision-making tree for when even foundational and highly cited meta-analyses should be updated. Based on the latest evidence, we summarize what journal editors and reviewers should expect, authors should provide, and readers (i.e., other researchers, practitioners, and policymakers) should consider about meta-analytic reviews.
We offer best-practice recommendations for journal reviewers, editors, and authors regarding data collection and preparation. Our recommendations are applicable to research adopting different ...epistemological and ontological perspectives—including both quantitative and qualitative approaches—as well as research addressing micro (i.e., individuals, teams) and macro (i.e., organizations, industries) levels of analysis. Our recommendations regarding data collection address (a) type of research design, (b) control variables, (c) sampling procedures, and (d) missing data management. Our recommendations regarding data preparation address (e) outlier management, (f) use of corrections for statistical and methodological artifacts, and (g) data transformations. Our recommendations address best practices as well as transparency issues. The formal implementation of our recommendations in the manuscript review process will likely motivate authors to increase transparency because failure to disclose necessary information may lead to a manuscript rejection decision. Also, reviewers can use our recommendations for developmental purposes to highlight which particular issues should be improved in a revised version of a manuscript and in future research. Taken together, the implementation of our recommendations in the form of checklists can help address current challenges regarding results and inferential reproducibility as well as enhance the credibility, trustworthiness, and usefulness of the scholarly knowledge that is produced.
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