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Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining.

The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships.

The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results.

Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.

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.

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.

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.

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.

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.

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).

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

Background: Amid increasing leadership failures in the global business context, the mining industry is one of the industries with many adverse incidents, affecting employee safety, the environment, and surrounding communities. Emerging economies tend to have unique socio-economic challenges and greater relative economic dependence on mining, presenting unique challenges to leaders. The purpose of this research was to study the realities of responsible leadership in the mining industry in an emerging economy.

Methods: A qualitative research study, consisting of semi-structured interviews was conducted. Nine senior mine managers were selected to represent perspectives from different operations and mining houses. Data was gathered from August to October 2020 in South Africa, an emerging economy with significant mining operations. A thematic analysis of interview transcripts was conducted through the use of software, rendering five themes, with 12 sub-themes.

Results: The research found that requirements on mining leaders in emerging economies demand consistent balancing of a complex set of competing risks, whilst attending to paradoxical requirements among operations, and internal and external stakeholders. Leaders face several competing requirements from stakeholders, the environment, mining practices, and time frames. Responsible leaders must navigate a paradoxical maze of needs and time horizons, with several conflicting forces and dilemmas, and dichotomous relationships. Responsible leadership in the mining industry of an emerging economy is a proverbial minefield of paradoxes and dilemmas between responsible intentions and practical realities. These paradoxes and dilemmas are specifically acute in the context of emerging economies due to the dire socio-economic situations. A total of 10 competencies emerged as essential responsible leadership requirements in this context.

Conclusions: The study provides an in-depth understanding of the intricacies of responsible leadership in the mining industry of an emerging economy. This understanding will contribute to capacitating leaders in the mining industries of emerging economies to act responsibly.

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.

The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

The purpose of this paper is to explore operation influencing factors of mining. To collect gaps of study and to form a thematic representation of principal influencing factors and their unique influencing factors.

Articles were collected from different sources from 1974 to 2019 consisting of research articles, technical papers, expert blogs, working papers and conference papers covering various disciplines from psychology, human resource, finance and economics to mining engineering. Mining operation influencing factors were noted down. Four massively deposed mines were visited to observe the sequence of mining process. The field experts were also consulted to identify factors influencing their respected industry. Gaps were observed while comparing with the reviewed articles and opinions of field experts. Finally, senior experts were consulted to identify unique factors from the final list prepared and a framework of seven thematic categories consisting of unique factors was formulated.

A total of 197 sub-factors were collected from literature review and 2 sub-factors from Indian Mining experts during field study. These 199 sub-factors were initially categorised as 48 factors and one more factor was collected from Indian field experts. Finally, these 49 factors were thematically represented as principal factors and termed as operation, marketing and management, human resource, finance, resource and utility, corporate affairs and corporate social responsibility and environment.

This study can be very helpful in the direction of different qualitative and quantitative studies, as the factors and sub-factors groups are identified.

The paper fulfils an identified need to provide a holistic review for understanding and documenting principal factors, unique factors and sub-factors those influence mining operation, profitability or sustainability issues of mines.

As for academic papers, the customary methods for assessing the impact of books are based on citations, which is straightforward but limited to the coverage of databases. Alternative metrics can be used to avoid such limitations, such as blog citations and library holdings. However, content-level information is generally ignored, thus overlooking users’ intentions. Meanwhile, abundant academic reviews express scholars’ opinions on books, which can be used to assess books’ impact via fine-grained review mining. Hence, this study aims to assess books’ use impacts by conducting content mining of academic reviews automatically and thereby confirmed the usefulness of academic reviews to libraries and readers.

Firstly, 61,933 academic reviews in Choice: Current Reviews for Academic Libraries were collected with three metadata metrics. Then, review contents were mined to obtain content metrics. Finally, to identify the reliability of academic reviews, Choice review metrics and other assessment metrics for use impact were compared and analysed.

The analysis results reveal that fine-grained mining of academic reviews can help users quickly understand multi-dimensional features of books, judge or predict the impacts of mass books, so as to provide references for different types of users (e.g. libraries and public readers) in book selection.

Book impact assessment via content mining can provide more detail information for massive users and cover shortcomings of traditional methods. It provides a new perspective and method for researches on use impact assessment. Moreover, this study’s proposed method might also be a means by which to measure other publications besides books.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts.

The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts.

At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice.

There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.