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The use of moving images to generate data for behavioral analysis has long been a methodology available to organizational researchers. In this article, we draw from previous research in team dynamics to describe and discuss various methodological approaches to using video recorded behavior as a source of quantitative data. More specifically, we identify and examine key decision points for researchers and illustrate benefits and drawbacks to consider. The article concludes with suggestions for ways in which quantitative video-based approaches could be improved.

Natural language processing (NLP) techniques are becoming increasingly popular in industrial and organizational psychology. One promising area for NLP-based applications is scale development; yet, while many possibilities exist, so far these applications have been restricted—mainly focusing on automated item generation. The current research expands this potential by illustrating an NLP-based approach to content analysis, which manually categorizes scale items by their measured constructs. In NLP, content analysis is performed as a text classification task whereby a model is trained to automatically assign scale items to the construct that they measure. Here, we present an approach to text classification—using state-of-the-art transformer models—that builds upon past approaches. We begin by introducing transformer models and their advantages over alternative methods. Next, we illustrate how to train a transformer to content analyze Big Five personality items. Then, we compare the models trained to human raters, finding that transformer models outperform human raters and several alternative models. Finally, we present practical considerations, limitations, and future research directions.

Researchers are frequently concerned that people respond to questions on sensitive topics (e.g., those involving money, criminal activity, sexual behavior) in a way that makes them look more socially desirable than they are. For decades, the technique known as "policy capturing" (or "judgment analysis") has been recommended as a solution to socially desirable responding (i.e., "faking good"). Surprisingly, however, until now, the extent to which policy capturing actually reduces socially desirable responding had not been tested empirically in a comprehensive manner. We examined the importance respondents assigned to several job characteristics, some of which (e.g., pay, schedule flexibility) tend to be susceptible to socially desirable responding. We compared responses obtained from policy capturing to those from four traditional self-report techniques (i.e., Likert-type, forced choice, ranking, and points distribution) across four instructional sets: instructions to respond honestly, warnings not to respond dishonestly, instructions to respond in a socially desirable manner, and no specific instructions. Results from both between-subject and within-subject comparisons indicated that policy capturing was indeed much more resistant than any of the traditional self-report techniques to socially desirable responding.

Much of our knowledge of team information processing has been influenced by the hidden-profile paradigm. In this review, we employ the input–mediator–outcome (IMO) team effectiveness framework to organize a systematic and comprehensive review of the knowledge accumulated in this area during the last three decades. The use of the IMO framework highlights important aspects of team dynamics that have received limited attention in past studies. Building on our analysis of the literature, we discuss significant theoretical questions that remain to be answered and propose methodological changes that would broaden and enhance our current understanding of team information processing. We suggest that the hidden-profile paradigm has reached maturity in terms of the permutations of Stasser and Titus's original conceptualization and conclude by proposing that future research should move toward exploring novel settings that move closer toward embracing the dynamic and complex nature of team information processing.

Organizational researchers are now making widespread use of ecological momentary assessments but have not yet taken the logical next step to ecological momentary interventions, also called Just-in-Time Adaptive Interventions (JITAIs). JITAIs have the potential to test within-person causal theories and maximize practical benefits to participants through two developmental phases: The microrandomized trial and the randomized controlled trial, respectively. In the microrandomized trial design, within-person randomization and experimental manipulation maximize internal validity at the within-person level. In the randomized controlled trial design, interventions are delivered in a timely and ecological manner while avoiding unnecessary and ill-timed interventions that potentially increase participant fatigue and noncompliance. Despite these potential advantages, the development and implementation of JITAIs require consideration of many conceptual and methodological factors. Given the benefits of JITAIs, but also the various considerations involved in using them, this review introduces organizational behavior and human resources researchers to JITAIs, provides guidelines for JITAI design, development, and evaluation, and describes the extensive potential of JITAIs in organizational behavior and human resources research.

Previous research asserts that teams working in routine situations pass through performance episodes characterized by action and transition phases, while other evidence suggests that certain team behaviors significantly influence team effectiveness during nonroutine situations. We integrate these two areas of research—one focusing on the temporal nature of team episodic performance and the other on interaction patterns and planning in teams—to more fully understand how teams working in dynamic settings successfully transition across routine and nonroutine situations. Using behavioral data collected from airline flight crews working in a flight simulator, we find that different interaction pattern characteristics are related to team performance in routine and nonroutine situations, and that teams engage in more contingency, in-process planning behavior during routine versus nonroutine situations. Moreover, we find that the relationship between this in-process planning and subsequent team adaptiveness is curvilinear (inverted U-shaped). That is, team contingency or in-process planning activity may initially increase team adaptiveness, but too much planning has adverse effects on subsequent performance.