Positive organizational scholarship has led to a growing interest in the critical role of positive emotions for the lives of both workers and organizations. We review and integrate the different perspectives on positive emotions (i.e., positive valence, positive emotion regulation strategies, and positive adaptive function) and the four main mechanisms (i.e., cognition, affect, behavior, and physiology) that lead to positive organizational outcomes. There is growing evidence that positive emotions influence variables vital for workplace success such as positive beliefs, creativity, work engagement, positive coping, health, teamwork and collaboration, customer satisfaction, leadership, and performance. We additionally review dynamic features of positive emotions (i.e., intraindividual variability, reactivity, inertia, cycles, feedback loops) and their relation to psychological and work outcomes. Finally, we discuss additional questions and future directions for consideration.
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 paper briefly reviews the current state of meta-analytics research and suggests two innovations for dramatically increasing the efficiency and robustness of meta-analytic practice while simultaneously extending meta-analyses’ “shelf life” in the face of continually accumulating evidence.
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