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This paper describes one of the new package cooling technology concepts using low melting point alloys in order to perform high density packaging. Two kinds of cooling alloy materials, Bi/Sn/In and Bi/Pb/Sn/ln, whose melting points were less than 80°C and whose costs were low, were selected. The experimental substrate sample was fabricated by greensheet technology on which a tungsten metallised resistor heater was formed. Two kovar weld rings were brazed together to the top side and back side surfaces of the substrate individually. One kovar metal shell was laser welded to the top side weld ring in order to protect many devices. Another kovar metal shell, with a hole in the centre, was laser welded to the back side weld ring. The low melting point alloy was melted and poured into the back side kovar shell through the hole in a liquid state. After it was cooled and changed into a solid state, the hole was sealed hermetically with a small kovar metal cap by a laser beam. The authors performed a thermal experiment and confirmed that the substrate back surface temperature was fixed at the cooling alloy material's melting point for several minutes by thermal absorption while the low melting point alloy phase changed from its original solid state into a liquid state. This new package cooling technology is extremely useful for a high power motor drive circuit package which consists of many high power transistor chips and other analogue IC chips, and whose motor drive operation is performed intermittently for several minutes with some interval times.

This paper examines the acceptance, of a group of 79 students, of an educational forum, used for mobile and distance learning, that has been modified to include empathic characteristics and affective principles.

With this study is proposed that the introduction of empathic and affective principles in educational forums is a useful and effective way to increase students’ participation and motivation in educational contexts. After an analysis of existing literature and research on the subject of empathic technology, the unified theory of acceptance and use of technology (UTAUT) was used as a framework for the research model. The analysis of their acceptance is done via an extended version of the UTAUT that focuses on the use of the variable attitude toward technology and uses gender, age and experience as moderators. A partial least square technique has been used to test the nine hypotheses.

The results confirmed three of the nine hypotheses: performance expectancy and effort expectancy had a positive influence on the students attitudes towards empathic forums, while the effect of social influence and facilitating conditions was considered insignificant; social influence had a positive influence on the students’ behavioral intention to use emphatic forums, while attitude toward technology, performance expectancy, facilitating conditions and effort expectancy were considered not relevant.

The focus of this study was the influence of attitude toward empathic forums, used for mobile and distance learning, and the results establish the grounds for future research on attitude as an important determinant of technology acceptance.

The purpose of this paper is to propose a new approach for organizing texts for researchers based on projection from researcher space (consisting of reasoning ability and research ability) onto text space (consisting of text features).

The projection from researcher space onto text space is performed on the grounds of the differences between the sets of essential text features which are consistent, respectively, for the reasoning ability (as researcher's existing ability) and the research ability (as researcher's desired ability) using the concept of a dependency graph.

Through the projection from researcher space onto text space, one can expect to find an effective text which can help a person with certain reasoning ability acquire a certain research ability in the related domain.

Acquisition of reasoning ability (ies) may call for a comprehensive questionnaire or test protocol whose validation by the expert may not necessarily be convenient. Appropriate questionnaires/test protocols, as well as knowledge‐based models for the text features at different layers (to assure derivation of more reliable features), is suggested as future work.

A salient benefit of the proposed approach is its flexibility in responding to a wide range of users with different models. It thus can be used as an efficient tool for online e‐learning and e‐research purposes in cyber‐learning environment.

The originality of the paper mostly lies in the concept of projection from researcher space onto text space as an approach for provision of appropriate text features; and the application of dependency graph as a potential means for identifying those text features whose prerequisites exist in the research abilities of the researcher.

The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.

The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.

The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.

The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.

The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

The purpose of this paper is to consider how recent changes in Japanese labour management and industrial relations are affecting its traditional “stakeholder‐oriented” governance model, where special recognition was given to company employees.

This paper critically analyses a range of recently published (2000‐2006) works on Japanese labour management and industrial relations with a view to predicting future trends in Japanese employment practices.

The paper finds that Japanese firms are breaking with tradition and experimenting with Western style employment practices. Changes in such practices indicate a move away from the traditional model of governance. It may be that a hybrid model of governance is emerging.

This paper fulfils an identified information/resources need and contributes to the study of recent changes and developments in corporate governance in South East Asian countries.

The purpose of this paper is to conceptualise and discuss the possible insights that can be generated for product development by analysing the user-generated content available from various social media platforms.

The paper reviews the role of user generated content in developing products and its features (e.g. appearance and shape). It delineates the directions in which the relationship between social media content and customer oriented concepts evolve while developing successful new products.

The review and arguments presented in this paper suggest that the social media approach adds more value than the traditional approaches for obtaining insights about the products. Availability of users’ opinions and information about existing products provide insights for the improvement in the product design process. Co-creation and self-construal are important components that are based on customer engagement and customer behaviour, respectively, in the product design and development.

As social media creates new ways of communication with users, businesses can include users into the product development process to improve and refine their products or for making the next generation of products.

This paper suggests a new approach in getting useful insights about the products from user-generated contents. This way of using social media helps businesses to move forward from the traditional product development paradigms.

Purpose – Social relations play an important role in a real community. Interaction patterns reveal relations among actors (such as persons, groups, firms), which can be merged to produce valuable information such as a network structure. This paper aims to present a new approach to extract inter‐firm networks from the web for further analysis. Design/methodology/approach – In this study extraction of relations between a pair of firms is obtained by using a search engine and text processing. Because names of firms co‐appear coincidentally on the web, an advanced algorithm is proposed, which is characterised by the addition of keywords (“relation keywords”) to a query. The relation keywords are obtained from the web using a Jaccard coefficient. Findings – As an application, a network of 60 firms in Japan is extracted including IT, communication, broadcasting, and electronics firms from the web and comprehensive evaluations of this approach are shown. The alliance and lawsuit relations are easily obtainable from the web using the algorithm. By adding relation keywords to named pairs of firms as a query, It is possible to collect target pages from the top of web pages more precisely than by only using the named pairs as a query. Practical implications – This study proposes a new approach for extracting inter‐firm networks from the web. The obtained network is useful in several ways. It is possible to find a cluster of firms and characterise a firm by its cluster. Business experts often make such inferences based on firm relations and firm groups. For that reason the firm network might enhance inferential abilities on the business domain. Also we might use obtained networks to recommend business partners based on structural advantages. The authors' intuition is that extracting a social network might provide information that is only recognisable from the network point of view. For example, the centrality of each firm is identified only after generating a social network. Originality/value – This study is a first attempt to extract inter‐firm networks from the web using a search engine. The approach is also applicable to other actors, such as famous persons, organisations or other multiple relational entities.

Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA).

The paper presents a temporal trend detection algorithm – vector space model – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies.

The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent.

Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics.

This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.

In an era of rapidly expanding digital content, the number of e‐documents and the amount of knowledge frequently overwhelm the R&D teams and often impede intellectual property management. The purpose of this paper is to develop an automatic patent summarization method for accurate knowledge abstraction and effective R&D knowledge management.

This paper develops an integrated approach for automatic patent summary generation combining the concepts of key phrase recognition and significant information density. Significant information density is defined based on the domain‐specific key concepts/phrases, relevant phrases, title phrases, indicator phrases and topic sentences of a given patent document.

The document compression ratio and the knowledge retention ratio are used to measure both quantitative and qualitative outcomes of the new summarization methodology. Both measurements indicate the significant benefits and superior results of the method.

In order to implement the methodology with practical success, the accurate and efficient pre‐processing of identifying key concepts and relevant phrases of patent documents is required. The approach relies on a powerful text‐mining engine as the pre‐process module for key phrase extraction.

The methodology helps R&D companies consistently and automatically process, extract and summarize the core knowledge of related patent documents. This enabling technology is critical to R&D companies when they are competing to create new technologies and products for short life cycle marketplaces.

This research addresses a new perspective in R&D knowledge management, particularly in solving the knowledge‐overloading issue. The methodology helps R&D collaborative teams consistently to summarize the core knowledge of patent documents with efficiency. Efficient R&D knowledge management helps the firm to take advantage of IP positioning while avoiding patent conflict and infringement.

The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco.

The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model.

ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources.

This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.