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Article
Publication date: 5 February 2018

Elizabeth Santhanam, Bernardine Lynch and Jeffrey Jones

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Quality Assurance in Education, vol. 26 no. 1
Type: Research Article
ISSN: 0968-4883

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Article
Publication date: 2 January 2020

Christine S. Pitt, Anjali Suniti Bal and Kirk Plangger

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…

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3141

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

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Article
Publication date: 27 February 2020

Jan Kietzmann and Leyland F. Pitt

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…

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924

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Originality/value

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.

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Book part
Publication date: 24 July 2020

Emily D. Campion and Michael A. Campion

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…

Abstract

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.

Details

Research in Personnel and Human Resources Management
Type: Book
ISBN: 978-1-80043-076-1

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Book part
Publication date: 1 January 2013

Klaus Weber, Hetal Patel and Kathryn L. Heinze

Much of contemporary institutional theory rests on the identification of structured, coherent, and encompassing logics, and from there proceeds to examine multilevel…

Abstract

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.

Details

Institutional Logics in Action, Part B
Type: Book
ISBN:

Keywords

Open Access
Article
Publication date: 26 July 2021

David D’Acunto, Serena Volo and Raffaele Filieri

This study aims to explore US hotel guests’ privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly…

Abstract

Purpose

This study aims to explore US hotel guests’ privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly discussed by guests in their reviews and to examine the influence of cultural proximity on privacy concerns.

Design/methodology/approach

This study combined automated text analytics with content analysis. The database consisted of 68,000 hotel reviews written by US guests lodged in different types of hotels in five European cities. Linguistic Inquiry Word Count, Leximancer and SPSS software were used for data analysis. Automated text analytics and a validated privacy dictionary were used to investigate the reviews by exploring the categories, themes and attributes of privacy concerns. Content analysis was used to analyze the narratives and select representative snippets.

Findings

The findings revealed various categories, themes and concepts related to privacy concerns. The two most commonly discussed categories were privacy restriction and outcome state. The main themes discussed in association with privacy were “room,” “hotel,” “breakfast” and several concepts within each of these themes were identified. Furthermore, US guests showed the lowest levels of privacy concerns when staying at American hotel chains as opposed to non-American chains or independent hotels, highlighting the role of cultural proximity in privacy concerns.

Practical implications

Hotel managers can benefit from the results by improving their understanding of hotel and service attributes mostly associated with privacy concerns. Specific suggestions are provided to hoteliers on how to increase guests’ privacy and on how to manage issues related to cultural distance with guests.

Originality/value

This study contributes to the hospitality literature by investigating a neglected issue: on-site hotel guests’ privacy concerns. Using an unobtrusive method of data collection and text analytics, this study offers valuable insights into the categories of privacy, the most recurrent themes in hotel guests’ reviews and the potential relationship between cultural proximity and privacy concerns.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 8
Type: Research Article
ISSN: 0959-6119

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Book part
Publication date: 20 September 2018

Arthur C. Graesser, Nia Dowell, Andrew J. Hampton, Anne M. Lippert, Haiying Li and David Williamson Shaffer

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a…

Abstract

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.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
ISBN: 978-1-78754-474-1

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Article
Publication date: 6 December 2019

Hsiu-Yuan (Jody) Tsao, Colin L. Campbell, Sean Sands, Carla Ferraro, Alexis Mavrommatis and Steven (Qiang) Lu

This paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of…

Abstract

Purpose

This paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of consumer-generated textual data. The authors term this method scale-directed text analysis.

Design/methodology/approach

The method first develops a dictionary of words related to specific dimensions of a construct that is used to assess textual data from any source for a specific meaning. The method explicitly recognizes both specific words and the strength of their underlying sentiment.

Findings

Results calculated using this new approach are statistically equivalent to responses to traditional marketing scale items. These results demonstrate the validity of the authors’ methodology and show its potential to complement traditional survey approaches to assessing marketing constructs.

Research limitations/implications

The method we outline relies on machine learning and thus requires either large volumes of text or a large number of cases. Results are reliable only at the aggregate level.

Practical implications

The method detail provides a means of less intrusive data collection such as through scraped social media postings. Alternatively, it also provides a means of analyzing data collected through more naturalistic methods such as open-response forms or even spoken language, both likely to increase response rates.

Originality/value

Scale-directed text analysis goes beyond traditional methods of conducting simple sentiment analysis and word frequency or percentage counts. It combines the richness of traditional textual and sentiment analysis with the theoretical structure and analytical rigor provided by traditional marketing scales, all in an automatic process.

Details

European Journal of Marketing, vol. 54 no. 3
Type: Research Article
ISSN: 0309-0566

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Article
Publication date: 31 January 2018

Meena Rambocas and Barney G. Pacheco

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market…

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3663

Abstract

Purpose

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

Design/methodology/approach

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

Findings

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

Practical implications

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

Originality/value

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

Details

Journal of Research in Interactive Marketing, vol. 12 no. 2
Type: Research Article
ISSN: 2040-7122

Keywords

Content available
Book part
Publication date: 19 October 2020

Anat Rafaeli, Galit Bracha Yom Tov, Shelly Ashtar and Daniel Altman

Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research…

Abstract

Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data science colleagues and to show how such collaborations can expand the scope of research on emotion in service delivery.

Design/methodology/approach: Uses archived resources available at http://LivePerson.com to extract data based in genuine service conversations between agents and customers. We refer to these as “digital traces” and analyze them using computational science models.

Findings: Although we do not test significance or causality, the data presented in this chapter provide a unique lens into the dynamics of emotions in service; results that are not obtainable using traditional research methods.

Research limitations/implications: This is a descriptive study where findings unravel new dynamics that should be followed up with more research, both research using traditional experimental methods, and digital traces research that allows inferences of causality.

Practical implications: The digital data and newly developed tools for sentiment analyses allow exploration of emotions in large samples of genuine customer service interactions. The research provides objective, unobtrusive views of customer emotions that draw directly from customer expressions, with no self-report intervention and biases.

Originality/value: This is the first objective and detailed depiction of the actual emotional encounters that customers express, and the first to analyze in detail the nature and content of customer service work.

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