Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.

This article encourages transparency in the reporting of meta-analytic procedures. Specifically, we highlight aspects of meta-analytic search, coding, data presentation, and data analysis where published meta-analyses often fall short in presenting sufficient information to allow replication. We identify opportunities where reviewers can request additional information or analyses that will enhance transparent reporting practices and facilitate the evaluation of quality in meta-analytic reporting. We focus on concerns specific to (or prevalent in) meta-analyses conducted in organizational research. In doing so, we reference a number of existing and emerging techniques, highlighting their contribution to meta-analysis while emphasizing key information reviewers may request. Our focus is primarily on meta-analyses, but secondary uses of meta-analytic data are also considered. We conclude by providing a checklist for reviewers in an effort to facilitate the review process as it pertains to the goals of transparency and replicability.

Systematic reviews of academic research have not impacted management practice as much as many researchers had hoped. Part of the reason is that researchers and managers differ significantly in their knowledge systems—in both what they know and how they know it. Researchers can overcome some of these challenges by including managers as knowledge partners in the research endeavor; however, doing so is rife with challenges. This article seeks to answer, how can researchers and managers navigate the tensions related to differences in their knowledge systems to create more impactful systematic reviews? To answer this question, we embarked on a data-guided journey of the experience of the Network for Business Sustainability, which had undertaken 15 systematic reviews that involved researchers and managers. We interviewed previous participants of the projects, observed different systematic review processes, and collected archival data to learn more about researcher-manager collaborations in the systematic review process. This article offers guidance to researchers in imbricating academic with practical knowledge in the systematic review process.

The empirical study of change has proven to be one of the most vexing challenges in organizational science. Fortunately, contemporary methodologies originating from developmental psychology may provide a potential solution and are consequently working their way into the literature. In particular, organizational researchers are increasingly employing variations of latent change score (LCS) models to address questions regarding change, development, and dynamics. Although these models may indeed be used to reliably study change, development, and dynamics, many studies utilizing these models—and published in premier outlets—are characterized by questionable methodological choices, improper modeling procedures, and suboptimal research designs. Thus, the purpose of the present article is to (a) provide a critical review of LCS models, (b) outline appropriate modeling procedures (with corresponding Mplus and R syntax), (c) compare and contrast LCS modeling with other analytical techniques, and (d) delineate best practices. Ultimately, we endorse the use of LCS models by organizational researchers interested in studying longitudinal phenomena. However, we also heed researchers to do so judiciously because their misuse may lead to their unwarranted rejection by the field.

Although numerous books and articles provide toolkit approaches to explain how to conduct literature reviews, these prescriptions regard literature reviewing as the production of representations of academic fields. Such representationalism is rarely questioned. Building on insights from social studies of science, we conceptualize literature reviewing as a performative endeavor that co-constitutes the literature it is supposed to “neutrally” describe, through a dual movement of re-presenting—constructing an account different from the literature, and intervening—adding to and potentially shaping this literature. We discuss four problems inherent to this movement of performativity— description, explicitness, provocation, and simulacrum—and then explore them through a systematic review of 48 reviews of the literature on corporate social responsibility (CSR) for the period 1975 to 2019. We provide evidence for the performative role of literature reviewing in the CSR field through both re-presenting and intervening. We find that reviews performed the CSR literature and, accordingly, the field’s boundaries, categories, and priorities in a self-sustaining manner. By reflexively subjecting our own systematic review to the four performative problems we discuss, we also derive implications of performative analysis for the practice of literature reviewing.

Although the rising popularity of methodological templates has yielded an increasing interest in qualitative research, we discuss how the misuse of methodological templates can diminish the quality of research. As an alternative, we propose methodological bricolage as an organizing metaphor for how to do qualitative methods, which involves the combining of analytic moves for the purpose of solving a problem or problems tailored to one’s own research project. To develop a methodological bricolage approach, we draw on our own research as well as a broader set of qualitative research articles to illustrate how authors arrange various methodological moves to create an effective arrangement that communicates trustworthiness. We outline the benefits of methodological bricolage and some cautions in using this approach.

IR-tree models assume that categorical item responses can best be explained by multiple response processes. In the present article, guidelines are provided for the development and interpretation of IR-tree models. In more detail, the relationship between a tree diagram, the model equations, and the analysis on the basis of pseudo-items is described. Moreover, it is shown that IR-tree models do not allow conclusions about the sequential order of the processes, and that mistakes in the model specification can have serious consequences. Furthermore, multiple-group IR-tree models are presented as a novel extension of IR-tree models to data from heterogeneous units. This allows, for example, to investigate differences across countries or organizations with respect to core parameters of the IR-tree model. Finally, an empirical example on organizational commitment and response styles is presented.

In experiments, researchers commonly allocate subjects randomly and equally to the different treatment conditions before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest machine learning algorithms for subject allocation in experiments. Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo simulations to compare its efficiency with randomized controlled trials that have a fixed and balanced subject allocation. Our findings indicate that a randomized allocation based on Bayesian multi-armed bandits is more efficient and ethical in most settings. We develop recommendations for researchers and discuss the limitations of our approach.

Marks, Mathieu, and Zaccaro (2001) advanced a theory and framework of team processes that has garnered much attention and guided numerous investigations. They proposed that 10 first-order constructs (e.g., strategy formulation, coordination, conflict management) would map to three second-order constructs (i.e., transition, action, and interpersonal). Despite the popularity of this framework, we are unaware of any validated multiitem measures of the team processes they identified. Accordingly, we develop and demonstrate content and construct validity of 50-, 30-, and 10-item versions of a survey measure of team processes. Using data from over 700 teams, we test Marks et al.’s higher-order model and find results that are largely consistent with both their 10 first-order dimensions and the 10:3 second-order framework. Using samples of global virtual knowledge teams and health care employees, we provide evidence of the discriminant validity of our team process measure versus a measure of team empowerment. We provide recommendations for the use of these measures in future research and practice and encourage their use as part of a portfolio of measures of team processes.