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1 – 10 of over 8000Birol Yıldız and Şafak Ağdeniz
Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show…
Abstract
Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.
Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.
Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.
Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.
Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).
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Francisco Villarroel Ordenes and Shunyuan Zhang
The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical…
Abstract
Purpose
The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.
Design/methodology/approach
On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.
Findings
The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.
Research limitations/implications
This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.
Practical implications
The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.
Originality/value
The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).
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Salam Abdallah and Ashraf Khalil
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…
Abstract
Purpose
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.
Design/methodology/approach
This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.
Findings
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.
Originality/value
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.
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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.
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Carla Bonato Marcolin, Eduardo Henrique Diniz, João Luiz Becker and Henrique Pontes Gonçalves de Oliveira
In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to…
Abstract
Purpose
In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to compare human and machine analyses, highlighting the challenges and opportunities of both approaches.
Design/methodology/approach
This study applied qualitative secondary analysis (QSA) with machine learning-based text mining on qualitative data from 25 interviews previously analyzed with traditional qualitative content analysis.
Findings
By analyzing both techniques' strengths and weaknesses, this study complements the results from the original research work. The previous human model failed to point to a particular aspect of the case, while the machine analysis did not recognize the sequence of time in the interviewee's discourse.
Originality/value
This study demonstrates that combining content analysis with text mining techniques improves the quality of the research output. Researchers may, therefore, better handle biases from humans and machines in traditional qualitative and quantitative research.
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This study aims to analyze four text-mining studies of quality management (QM) to illustrate and problematize how the research on quality has informed the quality paradigm since…
Abstract
Purpose
This study aims to analyze four text-mining studies of quality management (QM) to illustrate and problematize how the research on quality has informed the quality paradigm since the 1980s. By understanding history, one can better manage current developments.
Design/methodology/approach
The findings are based on a meta-analysis of four text-mining studies that explore and describe 11,579 research entries on quality between 1980 and 2017.
Findings
The findings show that the research on quality during the past 30 years form a research paradigm consisting of three operational paradigms: an operative paradigm of backend quality orbiting around QM, total QM (TQM) and service quality; an operative paradigm of middle-way quality, circling around the International Organization for Standardization (ISO), business excellence frameworks (BEFs) and quality awards; and an operative paradigm of frontend quality, revolving around reliability, costs and processes. The operative paradigms are interconnected and complementary; they also show a divide between a general management view of quality and a hands-on engineering view of quality. The findings indicate that the research on quality is a long-lived standalone paradigm, supporting the notion of quality being a genuine academic entity, not a fashion or fad.
Research limitations/implications
The empirical basis of the study is four text-mining studies. Consequently, the results and findings are based on a limited number of findings.
Originality
Text-mining studies targeting research on quality are scarce, and there seem to be no prior models that depict the quality paradigm based on such studies. The perspectives presented here will advance the existing paradigmatic discourse. The new viewpoints aim to facilitate and deepen the discussion on current and future directions of the paradigm.
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Yuyan Luo, Tao Tong, Xiaoxu Zhang, Zheng Yang and Ling Li
In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for…
Abstract
Purpose
In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.
Design/methodology/approach
The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.
Findings
In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.
Originality/value
Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.
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Zhao Duan, Yajuan He and Yuan Zhong
Based on the text mining tools, this paper aims to propose a new method to evaluate the subjectivity and objectivity of corporate social responsibility information disclosure.
Abstract
Purpose
Based on the text mining tools, this paper aims to propose a new method to evaluate the subjectivity and objectivity of corporate social responsibility information disclosure.
Design/methodology/approach
The authors build up a text subjectivity evaluation model of corporate social responsibility reports through meta-analysis; a text mining is conducted to all sample CSR reports released by Chinese listed companies untill March 2016[1]. Furthermore, the authors made an overall and quantitative analysis of the situation which contained changing state, characteristics and abnormal value on the subjectivity and objectivity of information disclosure.
Findings
The results show that the subjectivity scores of social responsibility reports of Chinese listed companies are generally in a normal distribution. The diagram turns out to be a rising trend over the years and increases linearly from 2011 to 2013. Also, the industry heterogeneity and policy control are the main reasons for the formation of the differences, which are significant between different industries and different years.
Originality/value
This paper provides not only an important empirical basis for the research of corporate social responsibility but also a new idea for the non-financial information disclosure as well as objective evaluation of normative text.
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Keng Yang, Hanying Qi and Qian Huang
Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to…
Abstract
Purpose
Existing studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.
Design/methodology/approach
An empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.
Findings
The study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.
Originality/value
This study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.
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Mohammadreza Esmaeili Givi, Mohammad Karim Saberi, Mojtaba Talafidaryani, Mahdi Abdolhamid, Rahim Nikandish and Abbas Fattahi
The Journal of Intellectual Capital (JIC) celebrated its 20th anniversary in 2020. Therefore, the present study aims to provide a general overview of the history and key trends in…
Abstract
Purpose
The Journal of Intellectual Capital (JIC) celebrated its 20th anniversary in 2020. Therefore, the present study aims to provide a general overview of the history and key trends in this journal during 2000–2019.
Design/methodology/approach
Two types of citation and textual data during a 20-year journal period were retrieved from the Scopus database. The citation structures and contents were explored based on a combination of bibliometric analysis, altmetric analysis and text mining. The journal themes and trends of their changes were analyzed through citation bursts, mapping and topic modeling. To make a better comparison, the text mining process for the topic modeling of the IC field was performed in addition to the topic modeling of JIC.
Findings
Bibliometric analysis indicated that JIC has experienced a remarkable growth in terms of the number of publications and citations over the last 20 years. The results indicated that JIC plays a significant role among IC researchers. Additionally, a large number of researchers, institutes and countries have made contributions to this journal and cited its research papers. Altmetric analysis showed that JIC has been shared in different social media such as Twitter, Facebook, Wikipedia, Mendeley, Citeulike, news and blogs. Text mining abstract of JIC articles indicated that “measurement,” “financial performance” and “IC reporting” have the relative prevalence with increasing trends over the past 20 years. In addition, “research trends” and “national and international studies” had a stable trend with low thematic share.
Research limitations/implications
The findings have important implications for the JIC editorial team in order to make informed decisions about the further development of JIC as well as for IC researchers and practitioners to make more valuable contributions to the journal.
Originality/value
Using bibliometric analysis, altmetric analysis and text mining, this study provided a systematic and comprehensive analysis of JIC. The simultaneous use of these methods provides an interesting, unique and suitable capacity to analyze the journals by considering their various aspects.
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