Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output.

The goal of the current study was to investigate the relationships among psychological resources, career barriers, and job search self-efficacy in a sample of post-2014 Syrian refugees. Participants included 330 refugees in Greece and the Netherlands. Data were obtained using paper-based surveys, with all measures translated into Arabic. Drawing from career construction theory (Savickas, 2005), we hypothesized that adaptive readiness, operationalized in terms of psychological capital, would be positively related to job search self-efficacy through career adaptability. In addition, social and administrative career barriers were hypothesized to moderate the first stage of the indirect effect between psychological capital and job search self-efficacy, such that this relationship is weaker when refugees experience higher career barriers. Results indicated that individuals with higher psychological capital more confidently engaged in job search behavior in the destination country, mostly due to their enhanced career adaptability. However, this relationship weakened when participants experienced higher social barriers and strengthened when they experienced higher administrative barriers. The findings provide further support for the career construction model of adaptation (Savickas & Porfeli, 2012) and pinpoint career adapt-ability resources as critical self-regulatory strengths that help individuals in this particularly vulnerable group adapt to occupational transitions. Moreover, the results highlight the potentially detrimental role of social barriers in this process. Based on the results, we offer implications for formulating training and career construction theory-based career counseling focused on enhancing career adaptability and psychological capital.

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This paper studies the relationship between a vacancy population obtained from web crawling and vacancies in the economy inferred by a National Statistics Office (NSO) using a traditional method. We compare the time series properties of samples obtained between 2007 and 2014 by Statistics Netherlands and by a web scraping company. We find that the web and NSO vacancy data present similar time series properties, suggesting that both time series are generated by the same underlying phenomenon: the real number of new vacancies in the economy. We conclude that, in our case study, web-sourced data are able to capture aggregate economic activity in the labor market.