The purpose is to bring together all bibliographic references of the published literature on electronic books (e‐books) and related technologies in one source so that it will save…
Abstract
Purpose
The purpose is to bring together all bibliographic references of the published literature on electronic books (e‐books) and related technologies in one source so that it will save time for others in conducting literature searches and reviewing the developments.
Design/methodology/approach
The information included in this bibliography is collected systematically from all the published sources in the world such as journal articles, conference papers, conference proceedings, books, reports and PhD theses on e‐books until the last quarter of 2004. Mainly it covers e‐books, e‐books publishing, the impact of e‐books on different types of users, e‐book publishing techniques and trends, e‐book user interfaces and other technologies related to e‐publications.
Findings
As computer usage continues to grow exponentially, the desire of users to use electronic publications (e‐publications) has also increased tremendously. This has led to the publication of materials in electronic form as e‐publications on both CD‐ROMs and web. The e‐book is one of the several forms of e‐publications and its popularity has been growing steadily for the past decade.
Originality/value
This bibliography will be useful to all researchers conducting research in any areas related to e‐books and e‐book publishing.
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K.W.E. Cheng, K.F. Kwok, S.L. Ho and Y.L. Ho
Calculation of the winding losses of high frequency transformer based on one‐dimensional field analysis is normally inapplicable for complex winding configurations. This paper…
Abstract
Calculation of the winding losses of high frequency transformer based on one‐dimensional field analysis is normally inapplicable for complex winding configurations. This paper presents a matrix modeling method which can produce a generalized mechanism to solve the AC winding losses. The transformer is modeled in a matrix connected filament. This is an alternative method to the finite element and is a filament approach. Experimental results and computation results using the proposed method are found to have good agreement.
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This study aims to contribute novel insights into understanding and mitigating the harmful consequences of abusive supervision (AS) by examining the association between AS…
Abstract
Purpose
This study aims to contribute novel insights into understanding and mitigating the harmful consequences of abusive supervision (AS) by examining the association between AS experiences, revenge, forgiveness, and the moderating role of emotional intelligence (EI). The key argument is that employees' EI can influence the AS experience through affective processes, countering supervisors' abusive behaviors.
Methodology
A between-person scenario-based experiment was conducted with 366 participants divided into AS and control groups. The study explored the association between AS experience and revenge/forgiveness, mediated by core affect (valence and activation). EI abilities were measured as a moderator. Data analysis examined the relationships and interactions among AS, revenge/forgiveness, EI, and affective experiences.
Findings
The study reveals significant findings indicating that AS experiences were positively associated with revenge and negatively associated with forgiveness. The mediation analysis confirmed the role of core affect in these relationships. EI emerged as a moderator, shaping the association between AS experiences and revenge/forgiveness. Importantly, participants with higher EI exhibited lower revenge intentions, demonstrating the potential of EI to mitigate the adverse effects of AS. Unexpectedly, individuals with high EI also expressed fewer forgiveness intentions.
Originality/Value
This study provides a comprehensive understanding of how employees can effectively counterbalance the impact of AS through higher levels of strategic EI. Examining core affect as a mediator offers novel insights into coping mechanisms in response to AS experiences and their consequences.
Limitations
The study acknowledges several limitations, as the scenarios may only partially capture the complexities of real-life AS situations. The focus on a specific context and the sample characteristics limit the generalizability of the findings. Future research should explore diverse organizational contexts and employ longitudinal designs.
Implications
The findings have practical implications for organizations as enhancing employees' EI skills through training programs interventions and integrating EI into organizational culture and leadership conduct.
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This chapter will provide an overview of the lived experience and peer support context and draws on the origins of peer work in mental health arenas. The recovery movement will be…
Abstract
This chapter will provide an overview of the lived experience and peer support context and draws on the origins of peer work in mental health arenas. The recovery movement will be discussed and peer support will be put in context as an alternative/adjunct/complimentary role to the predominant biomedical model. What is the role of peer support in mental health settings? What is it that a peer does on a day-to-day basis? What are the principles and practices that a person with lived experiences engages in to operationalise peer support? What are the outcomes associated with peer support working and what does peer work look like when it works well? What type of settings does the peer work in and what teams are they a part of? This chapter explores some of the challenges peers face when integrating into teams and organisations. The dominance of the biomedical model will be discussed and how this can potentially impact on the peer's role in these settings.
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There has been almost no scholarly work on the challenges of money laundering in sovereign states that use the US dollar as their currency of choice. This study aims to break the…
Abstract
Purpose
There has been almost no scholarly work on the challenges of money laundering in sovereign states that use the US dollar as their currency of choice. This study aims to break the silence by highlighting how money laundering thrives in these situations mainly due to lack of or weak regulation of the US dollar by both the adopting states and the USA.
Design/methodology/approach
The research depended on various secondary data sources. It is an adapted academic version of a shorter piece for a professional magazine for professionals in the Anti-Money Laundering (AML) Field.
Findings
Preliminary findings show that due to the lack of regulation of the US dollar in dollarized economies, unscrupulous politicians, organized criminal gangs and multinational corporations among others can use a variation of the Black Market Peso Exchange (BMPE) to counteract money laundering controls and launder ill-gotten gains from crimes such as corruption, transnational crimes and tax evasion. Furthermore, ordinary citizens, migrant workers and small businesses avoiding stringent exchange rates are also using the black market, posing a further challenge to the law enforcement authorities.
Practical implications
The practical implications of this paper relate to how the mutations of money laundering techniques, as they are adopted by criminals, to operate in different conditions are making it difficult not only to dictate but also to address using traditional AML techniques.
Social implications
BMPE has far reaching social consequences. Hence, this study is significant to instigate a search for solutions and for further detailed studies into the money laundering techniques in countries that do not have a sovereign currency.
Originality/value
To the best of the authors’ knowledge, this is the first paper to discuss the unique challenges faced by countries that have adopted the US dollar for domestic use. The paper also shows how dollarization is a modest reminder that money laundering technique such as the BMPE can evolve to counter the legislative and regulatory environment of the various jurisdictions in which they are laundered.
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Bufei Xing, Haonan Yin, Zhijun Yan and Jiachen Wang
The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and…
Abstract
Purpose
The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.
Design/methodology/approach
This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.
Findings
The experiment results show that the proposed method outperforms the baseline methods.
Originality/value
This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.
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Toong Khuan Chan and Abdul-Rashid Abdul-Aziz
The purpose of this paper is to characterise the financial performance and to identify the operating strategies of property development companies in Malaysia during the 2008…
Abstract
Purpose
The purpose of this paper is to characterise the financial performance and to identify the operating strategies of property development companies in Malaysia during the 2008 global financial crisis (GFC).
Design/methodology/approach
The research approach includes a comprehensive analysis of the financial statements and annual reports of 35 property development companies listed on the Kuala Lumpur stock exchange. The financial statements were analysed to evaluate the financial performance of these companies and to assess the severity of the impact of the GFC on revenues and profits. The operating strategies were determined from a content analysis of the statement to shareholders.
Findings
An aggregated analysis of the financial performance indicates a 23 per cent decline in net profit in 2008. Classifying these companies into two separate sets of distressed and non-distressed companies showed that poor financial performance and a high debt-to-equity ratio pre-GFC led to continuing poor performance during the GFC period and beyond. Survival strategies adopted by distressed companies include the disposal of assets to improve cash flow, refinancing loans, delaying the launch of new projects and reducing their workforce. Non-distressed companies adopted growth strategies such as purchasing land for development, focusing their offerings towards high-end products, vertically integrating and diversification.
Practical implications
The increased understanding of the financial performance and operational strategies will allow managers of property development companies to improve financial management and adopt appropriate strategies in response to the impact of future financial distress.
Originality/value
The study presented in this paper is the first to analyse the financial performance of Malaysian public-listed property development companies during the period of the 2008 GFC and to link their financial performance to operational strategies.
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Mariem Bounabi, Karim Elmoutaouakil and Khalid Satori
This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency…
Abstract
Purpose
This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals.
Design/methodology/approach
After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms.
Findings
Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%.
Originality/value
The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.
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Chao Wang, Xiaoyan Jiang, Qing Li, Zijuan Hu and Jie Lin
Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There…
Abstract
Purpose
Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There exist a lot of weak expert-generated texts on the Internet of their own subjective evaluations of products. Analyzing these texts can indirectly extract the opinions of weak experts and transform them into decision-support information that assists product designers in understanding the market.
Design/methodology/approach
In social networks, a subset of users, termed “weak experts”, possess specialized knowledge and frequently share their product experiences online. This study introduces a comparative opinion mining framework that leverages the insights of “weak experts” to analyze user opinions.
Findings
An automotive product case study demonstrates that evaluations based on weak expert insights offer managerial insights with a 99.4% improvement in timeliness over traditional expert analyses. Furthermore, in the few-shot sentiment analysis module, with only 10% of the sample, the precision loss is just 1.59%. In addition, the quantitative module of specialization weighting balances low-specialization expert opinions and boosts the weight of high-specialization weak expert views. This new framework offers a valuable tool for companies in product innovation and market strategy development.
Originality/value
This study introduces a novel approach to opinion mining by focusing on the underutilized insights of weak experts. It combines few-shot sentiment analysis with specialization weighting and AHP, offering a comprehensive and efficient tool for product evaluation and market analysis.
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Jun Liu, Sike Hu, Fuad Mehraliyev, Haiyue Zhou, Yunyun Yu and Luyu Yang
This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into…
Abstract
Purpose
This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.
Design/methodology/approach
This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.
Findings
The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.
Research limitations/implications
These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.
Originality/value
This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.