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A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

This literature review is on advanced computer analytics, which is a major trend in the field of Human Resource Management (HRM). The authors focus specifically on computer-assisted text analysis (CATA) because text data are a prevalent yet vastly underutilized data source in organizations. The authors gathered 341 articles that use, review, or promote CATA in the management literature. This review complements existing reviews in several ways including an emphasis on CATA in the management literature, a description of the types of software and their advantages, and a unique emphasis on findings in employment. This examination of CATA relative to employment is based on 66 studies (of the 341) that bear on measuring constructs potentially relevant to hiring decisions. The authors also briefly consider the broader machine learning literature using CATA outside management (e.g., data science) to derive relevant insights for management scholars. Finally, the authors discuss the main challenges when using CATA for employment, and provide recommendations on how to manage such challenges. In all, the authors hope to demystify and encourage the use of CATA in HRM scholarship.

This chapter explores the use of critical discourse analysis (CDA) within higher education research. CDA is an approach to studying language and its relation to power, ideology and inequality. Within CDA, texts are not analysed in isolation, but as part of the institutional and discoursal practices in which they are embedded. Within the broad field of educational research, CDA has been increasingly used to explore the relationship between language and society; higher education research appears to be experiencing a similar turn to CDA. The chapter begins with an overview of CDA, outlining its origins, and discussing its position as both a theory and a method. A review of CDA-related higher education research follows. The review aims to show the scope and potential of CDA in the study of higher education. The chapter closes with recommendations for future work to develop and extend the use of CDA within higher education research.

This research paper explores the roles of electronic texts in research projects in the humanities and seeks to deepen the understanding of the nature of scholars' engagement with e-texts. The study used qualitative methodology to explore engagement of scholars in literary and historical studies with primary materials in electronic form (i.e., e-texts). The study revealed a range of scholars' interactions with e-texts during the whole research process. It uncovered a particular pattern of information-seeking practices in electronic environments called netchaining and the main types of uses and contributions of e-texts to research projects. It was found that e-texts play support and substantive roles in the research process. A number of influences from electronic environment are identified as challenges and aids in working with e-texts. The study does not have statistical significance. It indicates a need for further research into scholarly practices, training requirements, and new forms of service provision. Study results are relevant for the development of digital collections, information services, educational programs, and other forms of support for the use of technology in research. The results can be also used to inform approaches to text encoding and development of electronic information systems and have implications for organizational and industry policies. The study found a range of scholars' interactions and forms of intellectual engagement with e-texts that were not documented and analyzed by earlier studies. It provides insights into disciplinary variations in the humanities and contributes to the understanding of scholarly change catalyzed by information technology.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

This article examines the discourse of appointment, promotion, and tenure (APT) documents for academic librarians. Discourse analysis can illuminate the social role of language, social systems, and social practices.

This qualitative research analyzes the APT documents for librarians from a group of US universities (n = 50) whose librarians are tenured faculty (n = 35). Linguistic features were examined to identify genre (text type) and register (language variety) characteristics.

The documents showed strong relationships with other texts; vocabulary from the language of human resources (HR); grammatical characteristics such as nominalization; passive constructions; few pronouns; the “quasi-synonymy” of series of adjectives, nouns, or verbs; and expression of certainty and obligation. The documents have a sociolinguistic and social semiotic component. In using a faculty genre, librarians assert solidarity with other faculty, while the prominent discourse of librarians as practitioners detracts from faculty solidarity.

This research is limited to librarians at US land grant institutions. It has implications for other research institutions and other models of librarian status.

This research can help academic librarians fulfill their obligations by understanding how values encoded in these documents reflect positive and negative approaches.

Higher education and academic librarianship are in a state of flux. Understanding the discourse of these documents can help librarians encode appropriate goals and values. Little has been written on the discourse of librarianship. This is a contribution to the understanding of librarians as a discourse community and of significant communicative events.

Kathy Charmaz, a developer of constructivist grounded theory (CGT), advocated the use of grounded theory as a tool to attain social justice through research. We developed the grounded text mining approach (GTxA) method by integrating Charmaz's CGT with text mining. This technique is aimed at facilitating the systematic and comprehensive understanding of textual data. GTxA helps researchers engage in abductive reasoning by encouraging them to transition between CGT and text mining analyses. This chapter illustrates an example of how GTxA was utilized when a group of researchers analyzed depositions and semi-structured interview data of a defendant in an actual criminal case in Japan and, thus, detected the possibility of a coerced false confession. The chapter concludes by encouraging researchers to utilize GTxA for attaining social justice.