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The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

In recent years, Industry 4.0 has received immense attention from academic community, practitioners and the governments across nations resulting in explosive growth in the publication of articles, thereby making it imperative to reveal and discern the core research areas and research themes of Industry 4.0 extant literature. The purpose of this paper is to discuss research dynamics and to propose a taxonomy of Industry 4.0 research landscape along with future research directions.

A data-driven text mining approach, Latent Semantic Analysis (LSA), is used to review and extract knowledge from the large corpus of the 503 abstracts of academic papers published in various journals and conference proceedings. The adopted technique extracts several latent factors that characterise the emerging pattern of research. The cross-loading analysis of high-loaded papers is performed to identify the semantic link between research areas and themes.

LSA results uncover 13 principal research areas and 100 research themes. The study discovers “smart factory” and “new business model” as dominant research areas. A taxonomy is developed which contains five topical areas of Industry 4.0 field.

The data set developed is based on systematic article refining process which includes the keywords search in selected electronic databases and articles limited to English language only. So, there is a possibility that other related work may not be captured in the data set which may be published in other than examined databases and are in non-English language.

To the best of the authors’ knowledge, this study is the first of its kind that has used the LSA technique to reveal research trends in Industry 4.0 domain. This review will be beneficial to scholars and practitioners to understand the diversity and to draw a roadmap of Industry 4.0 research. The taxonomy and outlined future research agenda could help the practitioners and academicians to position their research work.

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.

The purpose of this paper is twofold: first the paper aims to sketch the theoretical basis for the use of electronic portfolios for prior learning assessment; second it endeavours to introduce latent semantic analysis (LSA) as a powerful method for the computation of semantic similarity between texts and a basis for a new observation link for prior learning assessment.

A short literature review about e‐assessment was conducted with the result that none of the reviews included new and innovative methods for the assessment of open responses and narrative of learners. On a theoretical basis the connection between e‐portfolio research and research about prior learning assessment is explained based on existing literature. After that, LSA is introduced and several examples from similar educational applications are provided. A model for prior learning assessment on the basis of LSA is presented. A case study at the Open University of The Netherlands is presented and preliminary results are discussed.

A first inspection of the results shows that the similarity measurement that is produced by the system can differentiate between learners who sent in different material and between the learning activities and chapters.

The paper is original because it combines research from natural language processing with very practical educational problems in higher education and technology‐enhanced learning. For faculty members the presented model and technology can help them in the assessment phase in an APL procedure. In addition, the presented model offers a dynamic method for reasoning about prior knowledge in adaptive e‐learning systems.

The purpose of this paper is to analyze the content of the statements that are released by the Federal Open Market Committee (FOMC) after its meetings, identify the main textual associative patterns in the statements and examine their impact on the US treasury market.

Latent semantic analysis (LSA), a language processing technique that allows recognition of the textual associative patterns in documents, is applied to all the statements released by the FOMC between 2003 and 2014, so as to identify the main textual “themes” used by the Committee in its communication to the public. The importance of the main identified “themes” is tracked over time, before examining their (collective and individual) effect on treasury market yield volatility via time-series regression analysis.

We find that FOMC statements incorporate multiple, multifaceted and recurring textual themes, with six of them being able to characterize most of the communicated monetary policy in the authors’ sample period. The themes are statistically significant in explaining the variation in three-month, two-year, five-year and ten-year treasury yields, even after controlling for monetary policy uncertainty and the concurrent economic outlook.

The main research implication of the authors’ study is that the LSA can successfully identify the most economically significant themes underlying the Fed’s communication, as the latter is expressed in monetary policy statements. The authors feel that the findings of the study would be strengthened if the analysis was repeated using intra-day (tick-by-tick or five-minute) data on treasury yields.

The authors’ findings are consistent with the notion that the move to “increased transparency” by the Fed is important and meaningful for financial and capital markets, as suggested by the significant effect that the most important identified textual themes have on treasury yield volatility.

This paper makes a timely contribution to a fairly recent stream of research that combines specific textual and statistical techniques so as to conduct content analysis. To the best of their knowledge, the authors’ study is the first that applies the LSA to the statements released by the FOMC.

The reviews of online tourism have not been taken advantage of effectively because the text data of such reviews is enormous and its current, in-depth research is still in infancy. Therefore, it is expected that the text data could be processed by the method of text mining to better understand the implicit information. The purpose of this paper is to contribute to tourism practitioners and tourists to conveniently use the texts through appropriate visualization processing techniques. In particular, time-changing reviews can be used to reflect the changes in tourists’ feedback and concerns.

Latent semantic analysis is a new branch of semantics. Every term in the document can be regarded as a single point in multi-dimensional space. When a document with semantics comes into such space, the distribution of the document is not random, but will obey some type of semantic structure.

First, overall grasping for the big data is applicable. Second, propose a direct method is proposed that allows more non-language processing researchers or proprietors to use the data. Lastly, the results of changes in different spans of times are investigated.

This paper proposes an approach to disclose a significant number of travel comments from different years that may generate new ideas for tourism. The authors put forward a processing approach to deal with large amounts of texts of comments. Using the case study of Mt. Lushan, the various changes of travel reviews over the years are successfully visualized and displayed.

The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).

The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).

Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.

This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.

To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.

This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

The purpose of this paper is to quantify Toyota's managerial values known as the Toyota Way to understand the cultural aspects of the Toyota production system (TPS).

The research methodology in this paper utilizes latent semantic analysis and singular value decomposition to analyze corporate memory documents to determine from organizational view how TPS is prescribed ideally to achieve Toyota's culture.

This work shows that the Toyota Way heavily centers on the principle of Genchi Genbutsu which is the practice of seeing problems first hand. Findings also show that Toyota's widely popularized Kaizen philosophy is de‐emphasized compared to team work and respect for people. Toyota's culture is somewhat balanced between individualism and collectivism which disagrees with most national Asian cultures. Finally, results show that Toyota reinforces both long‐ and short‐term orientations which disagree with most national views of Japan's national culture.

Future work using latent semantic analysis should include a broader spectrum of literature on which to perform the analysis. This analysis is limited to developing theories about Toyota's culture but does not actually describe the culture that exist in the workplace.

This work provides a broad guideline with which to structure a lean culture. It provides the reader with knowledge of what parts of a corporate culture to deem the most significant. Improving upon each of these company values with the weighted significance elicited in this document could provide a positive impact within an organization.

The methodology used in this paper is a brand new, fledgling technique that could provide significant improvements in studying lean cultures. The concepts of this technique will be useful to researchers in this field and the results will be of value to management who wish to create a more efficient organization.

This paper aims to elaborate a set of characteristics that conceptualize and qualify a disruptive business model.

The literature on disruptive business models will be analyzed using the latent semantic analysis (LSA) technique, complemented by content analysis, to obtain a more precise qualification and conceptualization regarding disruptive business models.

The results found described concepts already described in the theory. However, such findings, highlighted by the LSA, bring new perspectives to the analysis of the disruptive business models, little discussed in the literature and which reveal important considerations to be made on this subject.

It should be noted, about the technique used, a limitation on the choice of the number of singular values. For this to be a problem in the open literature, the authors tried to work not just with the cost-benefit ratio given the addition of each new dimension in the analysis, as well as a criterion of saturation of the terms presented.

The presentation of this set of characteristics can be used as a validation tool to identify if a business is or is not a disruptive business model by managers.

The originality of this paper is the achievement of a consolidated set of characteristics that conceptualize and qualify the disruptive business models by conducting an in-depth analysis of the literature on disruptive business models through the LSA technique, considering the difficulty of obtaining precise concepts on this subject in the literature.

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.