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While the motivation for collecting art has received considerable attention in the literature, less is known about the characteristics of the typical art collector. This paper aims to explore these characteristics to develop a typology of art consumers using a mixed method approach over several studies.

This is achieved by analyzing qualitative data, gathered via semi-structured interviews of art collectors, and quantitatively by means of natural language processing analysis and automated text analysis and using correspondence analysis to analyze and present the results.

The study’s findings reveal four distinct clusters of art collectors based on their “Big Five” personality traits, as well as uncovering insights into how these types talk about their possessions.

In addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of consumers that managers can use for market segmentation and target marketing decisions in other markets. The paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating the “doubling” procedure to deal with percentile data.

In addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of art collectors that managers can use for market segmentation and target marketing decisions. The paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating a non-traditional application of correspondence analysis using the “doubling” procedure. Buyer behavior in the fine art market is not exhaustively studied. By understanding the personality traits of consumers in the art market, sales forces can better provide assistance and product to consumers. Further, understanding the personalities of consumers is better for art retail spaces to better serve consumers.

This paper demonstrates a unique mixed methods approach to analyzing unstructured qualitative data. It shows how text data can be used to identify measurable market segments for which targeted strategies can be developed.

This paper aims to report the findings of a study into the automated text analysis of student feedback comments to assist in investigating a high volume of qualitative information at various levels in an Australian university. It includes the drawbacks and advantages of using selected applications and established lexicons. There has been an emphasis on the analysis of the statistical data collected using student surveys of learning and teaching, while the qualitative comments provided by students are often not systematically scrutinised. Student comments are important, as they provide a level of detail and insight that are imperative to quality assurance practices.

The paper outlines the process by which the institution researched, developed and implemented the automated analysis of student qualitative comments in surveys of units and teaching.

The findings indicated that there are great benefits in implementing this automated process, particularly in the analysis of evaluation data for units with large enrolments. The analysis improved efficiency in the interpretation of student comments. However, a degree of human intervention is still required in creating reports that are meaningful and relevant to the context.

This paper is unique in its examination of one institution’s journey in developing a process to support academics staff in interpreting and understanding student comments provided in surveys of units and teaching.

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 study aims to focus on automated text analyses (ATAs) of sustainability and integrated reporting as a recent approach in empirical–quantitative research.

Based on legitimacy theory, the author conducts a structured literature review and includes 38 quantitative peer-reviewed empirical (archival) studies on specific determinants and consequences of sustainability and integrated reporting. The paper makes a clear distinction between analyses of reports due to readability, tone, similarity and specific topics. In line with prior studies, it is assumed that more readable reports with less tone and similarity relate to increased reporting quality.

In line with legitimacy theory, there are empirical indications that specific corporate governance variables, other firm characteristics and regulatory issues have a main impact on the quality of sustainability and integrated reporting. Furthermore, increased reporting quality leads to positive market reactions in line with the business case argument.

The author deduces useful recommendations for future research to motivate researchers to include ATA of sustainability and integrated reports. Among others, future research should recognize sustainable and behavioral corporate governance determinants and analyze other stakeholders’ reactions.

As both stakeholders’ demands on sustainability and integrated reporting have increased since the financial crisis of 2008–2009, firms should increase the quality of reporting processes.

This analysis makes major contributions to prior research by including both sustainability and integrated reporting, based on ATA. ATAs play a prominent role in recent empirical research to evaluate possible drivers and consequences of sustainability and integrated reports. ATA may contribute to increased validity of empirical–quantitative research in comparison to classical manual content analyses, especially due to future CSR washing analyses.

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 chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

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.

The purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (AI)-supported ways to analyze content (primarily text) in marketing and consumer research. A further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data.

This conceptual paper maps our methods used for content analysis in marketing and consumer research.

This paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. The main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of AI-supported tools for all types of content and media.

This paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. It concludes that, much like in the past, today’s research focuses on the producers of the words than the words themselves and urges researchers to use AI and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.