The authors review the corporate social responsibility (CSR) literature based on 588 journal articles and 102 books and book chapters. They offer a multilevel and multidisciplinary theoretical framework that synthesizes and integrates the literature at the institutional, organizational, and individual levels of analysis. The framework includes reactive and proactive predictors of CSR actions and policies and the outcomes of such actions and policies, which they classify as primarily affecting internal (i.e., internal outcomes) or external (i.e., external outcomes) stakeholders. The framework includes variables that explain underlying mechanisms (i.e., relationship-and value-based mediator variables) of CSR-outcomes relationships and contingency effects (i.e., people-, place-, price-, and profile-based moderator variables) that explain conditions under which the relationship between CSR and its outcomes change. The authors' review reveals important knowledge gaps related to the adoption of different theoretical orientations by researchers studying CSR at different levels of analysis, the need to understand underlying mechanisms linking CSR with outcomes, the need for research at micro levels of analysis (i.e., individuals and teams), and the need for methodological approaches that will help address these substantive knowledge gaps. Accordingly, they offer a detailed research agenda for the future, based on a multilevel perspective that aims to integrate diverse theoretical frameworks as well as develop an understanding of underlying mechanisms and microfoundations of CSR (i.e., foundations based on individual action and interactions). The authors also provide specific suggestions regarding research design, measurement, and data-analytic approaches that will be instrumental in carrying out their proposed research agenda.

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.

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.

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-muchof-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.

Multilevel modeling allows researchers to understand whether relationships between lower-level variables (e.g., individual job satisfaction and individual performance, firm capabilities and performance) change as a function of higher-order moderator variables (e.g., leadership climate, market-based conditions). We describe how to estimate such cross-level interaction effects and distill the technical literature for a general readership of management researchers, including a description of the multilevel model building process and an illustration of analyses and results with a data set grounded in substantive theory. In addition, we provide 10 specific best-practice recommendations regarding persistent and important challenges that researchers face before and after data collection to improve the accuracy of substantive conclusions involving cross-level interaction effects. Our recommendations provide guidance on how to define the cross-level interaction effect, compute statistical power and make research design decisions, test hypotheses with various types of moderator variables (e.g., continuous, categorical), rescale (i.e., center) predictors, graph the cross-level interaction effect, interpret interactions given the symmetrical nature of such effects, test multiple cross-level interaction hypotheses, test cross-level interactions involving more than two levels of nesting, compute effect-size estimates and interpret the practical importance of a crosslevel interaction effect, and report results regarding the multilevel model building process.

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.

The authors conducted a 30-year review (1969-1998) of the size of moderating effects of categorical variables as assessed using multiple regression. The median observed effect size (f(2)) is only .002, but 72% of the moderator tests reviewed had power of .80 or greater to detect a targeted effect conventionally defined as small. Results suggest the need to minimize the influence of artifacts that produce a downward bias in the observed effect size and put into question the use of conventional definitions of moderating effect sizes. As long as an effect has a meaningful impact, the authors advise researchers to conduct a power analysis and plan future research designs on the basis of smaller and more realistic targeted effect sizes.

The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier definitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, interesting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommendations can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.