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.

No abstract

The COVID-19 pandemic, apart from its health and economic impacts, has become a new contributor to marine litter. Pollution by personal protective equipment (PPE) in the environment has been recorded in different parts of the world. However, no such data is available from the Philippines. We present the first findings of a marine litter survey using an aerial drone on a beach in Davao Gulf Mindanao, Philippines, showing the first quantification of marine litter associated with COVID-19. Marine litter density was recorded at 0.7 items/m2, with plastics making up most of the identified litter. Disposable face masks made up 2% of the total litter, having a density of 0.014 items/m2. The presence of discarded PPE is a source of concern. Given that the use of PPE will continue, the number of PPE in the marine environment is expected to increase in the future. This study highlights the need to greatly improve the solid waste management of areas straddling Davao Gulf, especially in dealing with wastes that are associated with COVID-19.

Text data pertaining to peoples' careers have proliferated in the past few decades. Due to the digitization of job search, recruitment, and the development of HR systems, it is relatively easy to access and obtain large datasets containing information about jobs or other work-related information at the micro (individual), meso (institutional), and macro (regional, national and global) levels, or some combination thereof. Examples of text data that may be used to study careers include (auto)biographies, résumés, posts in professional social networking sites, online job boards, public surveys, interview transcripts, personal diary entries, and even academic publications. Of particular interest are job vacancies, as aside from education and job experience, they also contain information about individuals' roles, responsibilities, knowledge, skills, and abilities, which comes with the promise of adding specificity and context to the career domain, which has come to be dominated by reductionist and generalist approaches to operationalizing key constructs. Online forums and social media also provide data relevant to the study of careers since employees use these platforms to voice their ongoing opinions and sentiments about their past and present employers.As a way to characterize big text data we can use the framework of the four "V"s of Big Data: Volume, Velocity, Variety, and Veracity (De Mauro, Greco, & Grimaldi, 2015). The sheer Volume of the available text data on careers is unprecedented, and far beyond the traditional qualitative and quantitative datasets in careers research. It is oftentimes not possible to store these data locally on a single computer (e.g. a desktop) and to use traditional analytical software and methods for their analysis. Furthermore, the rate at which data about work and careers is generated (Velocity) is also growing. One should simply think about the number of public status or CV updates on popular professional/social networking sites such as LinkedIn or Facebook, or the number of vacancy announcements posted to the Internet on a daily basis. Data also comes in many different guises (Variety), and are hardly ever produced with the primary aim of facilitating the conduct of research. Therefore, substantial effort must be invested to process different data types and forms in order to make the data suitable for analysis. A final challenge lies in the question of data and data-source integrity (Veracity), which also needs to be carefully considered when one wants to generate valid insights from textual data.The abundance of "big" text data containing information about careers also offers new avenues for careers research and paves the way for the development of bespoke