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.

The concept of meaningful work has recently received increased attention in philosophy and other disciplines. However, the impact of the increasing robotization of the workplace on meaningful work has received very little attention so far. Doing work that is meaningful leads to higher job satisfaction and increased worker well-being, and some argue for a right to access to meaningful work. In this paper, we therefore address the impact of robotization on meaningful work. We do so by identifying five key aspects of meaningful work: pursuing a purpose, social relationships, exercising skills and self-development, self-esteem and recognition, and autonomy. For each aspect, we analyze how the introduction of robots into the workplace may diminish or enhance the meaningfulness of work. We also identify a few ethical issues that emerge from our analysis. We conclude that robotization of the workplace can have both significant negative and positive effects on meaningful work. Our findings about ways in which robotization of the workplace can be a threat or opportunity for meaningful work can serve as the basis for ethical arguments for how to-and how not to-implement robots into workplaces.

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.

Starting from the notion that work is an important part of who we are, we extend existing theory making on the interplay of work and identity by applying them to (so called) atypical work situations. Without the contextual stability of a permanent organizational position, the question "who one is" will be more difficult to answer. At the same time, a stable occupational identity might provide an even more important orientation to one's career attitudes and goals in atypical employment situations. So, while atypical employment might pose different challenges on identity; identity can still be a valid concept to assist the understanding of behaviour, attitudes and well-being in these situations. Our analysis does not attempt to 'reinvent' the concept of identity, but will elaborate how existing conceptualisations of identity as being a multiple (albeit perceived as singular), fluid (albeit perceived as stable), and actively forged (as well as passively influenced) construct that can be adapted to understand the effects of atypical employment contexts. Furthermore, we suggest three specific ways to understand the longitudinal dynamics of the interplay between atypical employment and identity over time: passive incremental, active incremental and transformative change. We conclude with key learning points and outline a few practical recommendations for more research into identity as an explanatory mechanism for the effects of atypical employment situations.

The essential and significant components of one's job performance, such as facts, principles, and concepts are considered as job knowledge. This paper provides a framework for forging links between the knowledge, skills, and abilities taught in vocational education and training (VET) and competence prerequisites of jobs. Specifically, the study is aimed at creating an ontology for the semantic representation of that which is taught in the VET, that which is required on the job, and how the two are related. In particular, the creation of a job knowledge (Job-Know) ontology, which represents task and knowledge domains, and the relation between these two domains is discussed. Deploying the Job-Know ontology facilitates bridging job and knowledge elements collected from various sources (such as job descriptions), the identification of knowledge shortages and the determination of mismatches between the task and the knowledge domains that, in a broader perspective, facilitate the bridging requirements of labor market and education systems.