Search results

1 – 10 of over 66000
Book part
Publication date: 18 January 2023

Shane W. Reid, Aaron F. McKenny and Jeremy C. Short

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational…

Abstract

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.

Article
Publication date: 18 January 2008

Elaine G. Toms and Heather L. O'Brien

The purpose of this paper is to understand the needs of humanists with respect to information and communication technology (ICT) in order to prescribe the design of an…

3262

Abstract

Purpose

The purpose of this paper is to understand the needs of humanists with respect to information and communication technology (ICT) in order to prescribe the design of an e‐humanist's workbench.

Design/methodology/approach

A web‐based survey comprising over 60 questions gathered the following data from 169 humanists: profile of the humanist, use of ICT in teaching, e‐texts, text analysis tools, access to and use of primary and secondary sources, and use of collaboration and communication tools.

Findings

Humanists conduct varied forms of research and use multiple techniques. They rely on the availability of inexpensive, quality‐controlled e‐texts for their research. The existence of primary sources in digital form influences the type of research conducted. They are unaware of existing tools for conducting text analyses, but expressed a need for better tools. Search engines have replaced the library catalogue as the key access tool for sources. Research continues to be solitary with little collaboration among scholars.

Research limitations/implications

The results are based on a self‐selected sample of humanists who responded to a web‐based survey. Future research needs to examine the work of the scholar at a more detailed level, preferably through observation and/or interviewing.

Practical implications

The findings support a five‐part framework that could serve as the basis for the design of an e‐humanist's workbench.

Originality/value

The paper examines the needs of the humanist, founded on an integration of information science research and humanities computing for a more comprehensive understanding of the humanist at work.

Details

Journal of Documentation, vol. 64 no. 1
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 30 October 2007

John Ferguson

The purpose of this paper is to elaborate on John B. Thompson's “tripartite approach” for the analysis of mass media communication, highlighting how this methodological…

3642

Abstract

Purpose

The purpose of this paper is to elaborate on John B. Thompson's “tripartite approach” for the analysis of mass media communication, highlighting how this methodological framework can help address some of the shortcomings apparent in extant studies on accounting which purport to analyse accounting “texts”.

Design/methodology/approach

By way of example, the paper develops a critique of an existing study in accounting that adopts a “textually‐oriented” approach to discourse analysis by Gallhofer, Haslam and Roper. This study, which is informed by Fairclough's version of critical discourse analysis (CDA), undertakes an analysis of the letters of submission of two business lobby groups regarding proposed takeovers legislation in New Zealand. A two‐stage strategy is developed: first, to review the extant literature which is critical of CDA, and second, to consider whether these criticisms apply to Gallhofer et al. Whilst acknowledging that Gallhofer et al.'s (2001) study is perhaps one of the more comprehensive in the accounting literature, the critique developed in the present paper nevertheless highlights a number of limitations. Based upon this critique, an alternative framework is proposed which allows for a more comprehensive analysis of accounting texts.

Findings

The critique of Gallhofer et al.'s study highlights what is arguably an overemphasis on the internal characteristics of text: this is referred to by Thompson as the “fallacy of internalism”. In other words, Gallhofer et al. draw inferences regarding the production of the letters of submission from the texts themselves, and make implicit assumptions about the likely effects of these texts without undertaking any formal analysis of their production or reception, or without paying sufficient attention to the social and historical context of their production or reception.

Originality/value

Drawing on Thompson's theory of mass communication and his explication of the hermeneutical conditions of social‐historical enquiry, the paper outlines a range of theoretical considerations which are pertinent to researchers interested in studying accounting texts. Moreover, building on these theoretical considerations, the paper delineates a coherent and flexible methodological framework, which, it is hoped, may guide accounting researchers in this area.

Details

Accounting, Auditing & Accountability Journal, vol. 20 no. 6
Type: Research Article
ISSN: 0951-3574

Keywords

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

Keywords

Article
Publication date: 29 April 2022

Chih-Ming Chen, Szu-Yu Ho and Chung Chang

This study aims to develop a hierarchical topic analysis tool (HTAT) based on hierarchical Latent Dirichelet allocation (hLDA) to support digital humanities research that…

Abstract

Purpose

This study aims to develop a hierarchical topic analysis tool (HTAT) based on hierarchical Latent Dirichelet allocation (hLDA) to support digital humanities research that is associated with the need of topic exploration on the Digital Humanities Platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW). HTAT can assist humanities scholars on distant reading with analysis of hierarchical text topics, through classifying time-stamped texts into multiple historical eras, conducting hierarchical topic modeling (HTM) according to the texts from different eras and presenting through visualization. The comparative network diagram is another function provided to assist humanities scholars in comparing the difference in the topics they wish to explore and to track how the concept of a topic changes over time from a particular perspective. In addition, HTAT can also provide humanities scholars with the feature to view source texts, thus having high potential to be applied in promoting the effectiveness of topic exploration due to simultaneously integrating both the topic exploration functions of distant reading and close reading.

Design/methodology/approach

This study adopts a counterbalanced experimental design to examine whether there is significant differences in the effectiveness of topic inquiry, the number of relevant topics inquired and the time spent on them when research participants were alternately conducting text exploration using DHP-LCLW with HTAT or DHP-LCLW with Single-layer Topic Analysis Tool (SLTAT). A technology acceptance questionnaire and semi-structured interviews were also conducted to understand the research participants' perception and feelings toward using the two different tools to assist topic inquiry.

Findings

The experimental results show that DHP-LCLW with HTAT could better assist the research participants, in comparison with DHP-LCLW with SLTAT, to grasp the topic context of the texts from two particular perspectives assigned by this study within a short period. In addition, the results of the interviews revealed that DHP-LCLW with HTAT, in comparison with SLTAT, was able to provide a topic terms that better met research participnats' expectations and needs, and effectively guided them to the corresponding texts for close reading. In the analysis of technology acceptance and interview data, it can be found that the research participants have a high and positive tendency toward using DHP-LCLW with HTAT to assist topic inquiry.

Research limitations/implications

The Jieba Chinese word segmentation system was used in the Mr. Lo Chia-Lun’s Writings Database in this study, to perform word segmentation on Mr. Lo Chia-Lun’s writing texts for topic modeling based on hLDA. Since Jieba word segmentation system is a lexicon based word segmentation system, it cannot identify new words that have still not been collected in the lexicon well. In this case, the correctness of word segmentation on the target texts will affect the results of hLDA topic modeling, and the effectiveness of HTAT in assisting humanities scholars for topic inquiry.

Practical implications

An HTAT was developed to support digital humanities research in this study. With HTAT, DHP-LCLW provides hmanities scholars with topic clues from different hierarchical perspectives for textual exploration, and with temporal and comparative network diagrams to assist humanities scholars in tracking the evolution of the topics of specific perspectives over time, to gain a more comprehensive understanding of the overall context of the texts.

Originality/value

In recent years, topic analysis technology that can automatically extract key topic information from a large amount of texts has been developed rapidly, but the topics generated from traditional topic analysis models like LDA (Latent Dirichelet allocation) make it difficult for users to understand the differences in the topics of texts with different hierarchical levels. Thus, this study proposes HTAT which uses hLDA to build a hierarchical topic tree with a tree-like structure without the need to define the number of topics in advance, enabling humanities scholars to quickly grasp the concept of textual topics and use different hierarchical perspectives for further textual exploration. At the same time, it also provides a combination function of temporal division and comparative network diagram to assist humanities scholars in exploring topics and their changes in different eras, which helps them discover more useful research clues or findings.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 10 May 2022

Qiang Cao, Xian Cheng and Shaoyi Liao

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning…

Abstract

Purpose

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

Design/methodology/approach

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

Findings

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

Originality/value

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 19 January 2021

Chih-Ming Chen, Chung Chang and Yung-Ting Chen

Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study…

Abstract

Purpose

Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.

Design/methodology/approach

With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.

Findings

The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.

Research limitations/implications

Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.

Practical implications

This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.

Originality/value

At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 1 June 2001

Marcus Schmidt

Explores the appropriateness of quantifying focus group discussions. Basic problems involved in quantification of text are addressed. Prior approaches are discussed and…

1162

Abstract

Explores the appropriateness of quantifying focus group discussions. Basic problems involved in quantification of text are addressed. Prior approaches are discussed and briefly evaluated. A new neural network algorithm for analyzing excerpts from in‐depth interviews is presented. Keywords (brand names, values, etc.) are identified by the analyst. The entire text is scanned and a “covariance” matrix with weights expressing pairwise associations between words is established. This matrix can be used as input data set for advanced statistical analysis. The small illustrative study involves a focus group dealing with a holiday cruise. Results are inspected using multivariate analysis. Findings seem to make good sense. It is doubtful, though, that quantitative techniques will replace well‐established qualitative approaches. On the contrary, quantitative methods for textual analysis may supplement and improve insight gained from qualitative scrutiny. Recommendations for future research are discussed.

Details

Qualitative Market Research: An International Journal, vol. 4 no. 2
Type: Research Article
ISSN: 1352-2752

Keywords

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

Keywords

Article
Publication date: 17 November 2022

Sungwon Oh, Min Jae Park, Tae You Kim and Jiho Shin

This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the…

Abstract

Purpose

This study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.

Design/methodology/approach

First, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.

Findings

This study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.

Originality/value

This study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

Keywords

1 – 10 of over 66000