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Much of contemporary institutional theory rests on the identification of structured, coherent, and encompassing logics, and from there proceeds to examine multilevel dynamics or the relationship between logics in a field. Less research directly studies the internal properties and dynamics of logics and how they are structured over time. In this paper, we propose a method for understanding the content and organization of logics over time. We advocate for an analysis of logics that is grounded in a repertoire view of culture (Swidler, 1986; Weber, 2005). This approach involves identifying the set of cultural categories that can make up logics, and measuring empirically the dimensions that mark a cultural system as more or less logic-like. We discuss several text analytic approaches suitable for discourse data, and outline a seven-step method for describing the internal organization of a cultural repertoire in term of its “logic-ness.” We provide empirical illustrations from a historical analysis of the field of alternative livestock agriculture. Our approach provides an integrated theoretical and methodological framework for the analysis of logics across a range of settings.

Much of contemporary institutional theory rests on the identification of structured, coherent, and encompassing logics, and from there proceeds to examine multilevel dynamics or the relationship between logics in a field. Less research directly studies the internal properties and dynamics of logics and how they are structured over time. In this paper, we propose a method for understanding the content and organization of logics over time. We advocate for an analysis of logics that is grounded in a repertoire view of culture (Swidler, 1986; Weber, 2005). This approach involves identifying the set of cultural categories that can make up logics, and measuring empirically the dimensions that mark a cultural system as more or less logic-like. We discuss several text analytic approaches suitable for discourse data, and outline a seven-step method for describing the internal organization of a cultural repertoire in term of its “logic-ness.” We provide empirical illustrations from a historical analysis of the field of alternative livestock agriculture. Our approach provides an integrated theoretical and methodological framework for the analysis of logics across a range of settings.

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.

Stakeholders and their organizations are increasingly involved in governance of higher education, not only within institutions or at system level, but also in various supra-national and intergovernmental processes. For these, as well as pragmatic reasons (ease of access and relatively simple methods for analysis), this chapter advocates for a more systematic approach to studying stakeholder organizations, their participation in and impact on governance of higher education. Specifically, the chapter: (1) provides a three-fold nested conceptualization of policy positions of stakeholder organizations, comprising issues, preferences concerning these issues, and the normative basis utilized to legitimize said preferences; (2) presents advantages and disadvantages of different methodological approaches to analyzing policy positions of stakeholder organizations, including qualitative and quantitative content analysis, employing either human coding or computer-assisted coding of policy documents; and (3) highlights different insights one can gain from analyzing policy positions of stakeholder organizations. It combines (thus far limited) insights from higher education studies with the more generic literature on interest groups, and uses examples from European level stakeholder organizations to illustrate its points.

This chapter analyzes #YesAllWomen, one of the largest, most visible, feminist Twitter events of recent years. Though hashtags and other forms of digital activism are not always taken seriously as politics, in this project we investigate #YesAllWomen and its recirculation through media and public blogs, as an important instance of contemporary feminist discursive activism. Specifically, we argue that the hashtag functioned, first, as a site of collective identity for participants, and we describe some of the ways in which this identity building was achieved, and second, we argue that through its links to and recirculation by other platforms and media, #YesAllWomen also functioned as a public protest or agenda-building event with impact on public discourse beyond Twitter. Our project draws on content and discourse analysis methods to analyze the #YesAllWomen hashtag and to trace its interaction with other discourses such as news and blogs, including an automated content analysis of almost two million tweets and an analysis of a sample of 251 media and blog stories. We note that contemporary feminists are using digital media, in this case a Twitter hashtag, to achieve many of the same discursive goals of knowledge building and critique that have previously been achieved using other communications strategies such as consciousness-raising groups, publishing collectives, media strategies, and zaps.

Researchers are continually trying to find reliable fraud indicators (e.g., Beasley, 1996) and some are working on building fraud prediction models (e.g., Spathis, 2002) to aid auditors in fraud detection. With this same goal of predicting fraud in mind, the purpose of this study is to explore the potential of qualitative fraud risk indicators. Content analysis is used in analyzing the Management's Discussion and Analysis (MDA) section of the annual report to identify potential indicators of deception to increase the likelihood of fraud detection in a timelier manner than current quantitative models.

By examining asynchronous communication contained in annual reports for companies required by the SEC to restate their financial statements, patterns of key linguistic characteristics were identified and compared to those used by companies not required to restate. Findings evidence significant differences on several dimensions. Using language cues for detection of deception has the advantage over quantitative methods of providing a more timely method of determining deception. Quantitative models often cannot detect deception until the effects are validated by financial impairment.

Implications of the findings suggest that qualitative methods of deception detection may provide an earlier, and thus more useful, method of the detection of fraud. The results of this study should provide stakeholders with a set of indicators to aid in identifying misstated information. This approach is also one that can be generalized to other written documents used to predict fraudulent communication.

This chapter discusses the use of qualitative content analysis in the field of hospitality and tourism. The primary objective of this chapter is to draw attention to the use of a content analysis approach for the treatment of data. As a further objective, the chapter contributes to current knowledge by underscoring a qualitative content analysis approach that would be of benefit to hospitality and tourism scholars. Overall, the chapter serves to inform hospitality and tourism scholars of how to increase the trustworthiness of qualitative content analysis approaches, which is seen as one of the most crucial impediments to its use.

This chapter conceptualizes computational methods across three related, yet distinct approaches: (1) Social Simulation, (2) Data Science, and (3) Big Data. Group communication research is then situated and reviewed along these three lines of research. Although some areas have considerable visibility (e.g., network analysis, text mining), some areas are less visible in group communication research (e.g., Social Simulation, Big Data designs). The chapter concludes with suggestions for issues regarding reliability, validity, and ethics.

We know a great deal about the ways in which routines of news coverage may bias newspaper content, but little about how different article retrieval practices influence newspaper data assembled by scholars. Using the New York Times as a source of data on social movement activity, we compare depictions of protest by the African-American Civil Rights movement over time produced using the two most common article retrieval methods: index versus full-story coding. Full-story coding clearly offers more depth and greater breadth in terms of the events identified. Moreover, many of the same event characteristics associated with selection bias in newspaper reporting (e.g., size and confrontational nature of a protest event, presence of counter-demonstrators or police, and event sponsorship by a recognized social movement organization) are selected upon again when stories are indexed by New York Times staff.