Search results

Prior studies in citation-based journal rankings tend to be static to compare across journals. One journal may be judged better in citations than other journals at some points in time but not at the others. The assumption that the citation distribution is normally distributed and that the citation observations are independent and identically distributed (i.i.d.) may not be appropriate. The paper aims to discuss these issues.

This study uses a stochastic dominance (SD) analysis, which overcomes the dynamic nature of changes in citation over time. The SD method proposed by Linton, Maasoumi, and Whang (hereafter LMW, 2005) does not require the data to be i.i.d. We use the LMW method to compare the relative ranking of 23 finance journals using citations for all articles from them during 1990-2010.

The study indicates that the citation distribution changes over time. Thus a SD analysis is a better approach for a comparison of journal ranking. The findings unambiguously place JF, JFE, RFS, JFQA, and JFI in the top five spots of the finance journal ranking. The “near-top” journals, such as JBF, JCF, and FM, are not clear cut in the SD analysis.

The results confirm that ranking for the lower ranked journals may change over time especially, but the top three journals appear to be robust across methods and over time.

The results of SD analysis provides more convincing evidence on finance journal ranking and could be useful to rank academic institutions, faculty research quality, and help the authors to decide what to read and which journals are influential.

The semiconductor market exceeded US$250 billion worldwide in 2010 and has had a double‐digit compound annual growth rate (CAGR) in the last 20 years. As it is located far upstream of the electronic product market, the semiconductor industry has suffered severely from the “bullwhip” effect. Therefore, effective e‐based supply chain management (e‐SCM) has become imperative for the efficient operation of semiconductor manufacturing (SM) companies. The purpose of this research is to define and analyze the key success factors (KSF) for e‐SCM system implementation in the semiconductor industry.

A hierarchy of KSFs is defined first by a combination of a literature review and a focus group discussion with experts who successfully implemented an inter‐organizational e‐SCM project. Fuzzy analytic hierarchy process (FAHP) is then employed to rank the importance of these identified KSFs. To confirm the research result and further explore the managerial implications, a second in‐depth interview with the e‐SCM project executives is conducted.

The KSF hierarchy is constructed with two levels: a top‐level consisting of four dimensions and a detailed‐level consisting of 15 individual factors. The research shows that, in the top‐level, strategy is the most critically successful dimension followed by process, organization, and technical; whereas in the detailed‐level, the top management commitment, clear project goal and business requirements, and business process re‐engineering are the top three critical successful factors.

Research surveys and interviews were conducted with two leading companies: Taiwan Semiconductor Manufacturing Company (TSMC) and ASE; they are the largest front‐end and back‐end SM companies in the world, respectively. Although the data collected was primarily based on the experience of one successful e‐SCM project, the significant roles of these two companies and compelling contribution made by the e‐SCM project leading to the research resulted in valuable guidelines for the companies in the semiconductor industry and a useful reference for companies in other manufacturing industries.

e‐SCM system has a high failure rate and there is little literature discussing the KSF of e‐SCM implementation from a holistic view for certain industries. This paper not only provides a structured and comprehensive list of KSFs but also illustrates the application of the most critical factors by examples. In addition to the contributions made to industries, the research results can also serve as a foundation for related academic research when comparing the KSFs of implementing e‐SCM by different industries.

The purpose of this paper is to examine how service failure affects customers’ negative response and how service recovery affects perceived justice in the context of different relationship norms.

It includes four studies that examine how relationships influence customer reactions to service failures. In study 1, the paper examines how service failures affect customers’ negative reaction. In study 2, the paper examines how service recoveries influence perceived justice. Study 3 and study 4 test the robustness of the results of study 1 and study 2. All studies have a 2×2 between-subjects design.

The results show that individuals in exchange relationships experience a stronger feeling of betrayal than those in communal relationships during service failures. Further, individuals feel more betrayed and show greater negative responses during process failures. They perceive greater justice when offered physical recoveries, which, in turn, contributes to higher service-recovery satisfaction.

This study was conducted in Taiwan. Customer reactions to service failures may vary according to cultural and environmental contexts.

Service providers are encouraged to cultivate relationships with customers and identify different types of customers to compensate them more effectively, according to their preferences.

This study introduces relationship norms to investigate consumer responses to service failures. The main contributions are twofold; it investigates the effect of relationship norms on customer responses to service-failure types and service-recovery types.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

This paper aims to examine the operating frontier, trajectory and absorptive capacity influencing proactive and reactive dimension of supply chain resilience and implementation in inter-organizational relationships.

This paper presents a research model comprises six research hypotheses with five constructs, including trajectory, absorptive capacity, operating frontier, proactive and reactive dimension of supply chain resilience. The hypotheses are tested on data collected from 297 senior managers of Taiwanese manufacturing firms, using structural equation modeling.

The study provides insights into how supply chain members can reinforce their operating frontier, trajectory and absorptive capacity activities to improve proactive and reactive dimension of supply chain resilience.

The resultant findings cannot be generalized for all forms of supply chains, as they exclusively reflect those in Taiwan. With the research model developed, cross-industrial studies on various forms of supply chains would be worth conducting to investigate whether their inter-relationship effects differ in relation to inter-organizational supply chain resilience.

This study provides multiple insights for managers and practices seeking to improve inter-organizational supply chain resilience, which have become increasingly popular because their factors enhance coping strategies to achieve corporate goals. The proactive and reactive dimension of supply chain resilience can be effectively improved by enhancing trajectory, absorptive capacity and operating frontier.

This empirical research attempted to fill the gaps created by trajectory and resource-based perspectives in inter-organizational supply chain resilience. This study reveals how supply chain members can reinforce the factors of their coping strategies (i.e. operating frontier, trajectory and absorptive capacity) to make significant improvements, which is not dealt with in previous studies.

The categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification.

The proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.

Examples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures.

The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.

The evolution of technology has become the mainstream of the current technological innovation era. Technological change is organized in its unique pattern and a new approach that takes place in a systematic and selective manner. Such change is generally molded with the amalgamation of various factors, namely, economic, social or scientific and technological. This paper aims to focus on identifying technological trajectories in a technological ecosystem with the case of m-payment technology.

This study constructs a patent citation network for mobile payment service technology through patent citation data and identifies the main evolution process using the main path analysis of the network. The scope of this study focuses on key innovation using social network analysis and patent citation network, validated using the case of a mobile payment system and analyzing its technological trajectory.

Analyzing technology evolution provides a greater insight of the overall technology landscape to the researcher and practitioner. Analyzing the m-payment technology landscape gives three main categories of m-payment systems: the mobile financial transaction system), the payee mobile device payment selection system and e-wallet services.

The novelty of this research lies in the process of identifying technological evolution using social network and patent citation network analysis. The case of m-payment technology ecosystem is studied quantitatively which is not explored by previous researchers. This research provides a way to develop the main path technology of innovative products or services to identify technology evolution using the case of m-payment landscape.

The trend of electronic products toward lighter, thinner, and faster transmission is challenging the printed circuit board industry to incorporate high density interconnection technology (such as build‐up and semi‐additive processes). Micro stacked via is one technology utilized to produce high‐density structures. Dielectric resin, conductive paste or via plating are usually applied for the filling process. As compared with other filling methods, via filling plating technology has advantages in offering a shorter process and higher reliability. This paper discusses the influence of different equipment design, operating conditions and additives on via filling plating technology.

This study aims to propose a methodology by integrating three approaches, namely, internal core technology, external knowledge flow and industrial technology development to help companies improve their decision-making quality for technology planning and enhance their research and development (R&D) portfolio efficiency.

The primary focus of this study is thin-film solar technology and patent data is retrieved from the United States Patent and Trademark Office (USPTO) database. This study presents a methodology based on the proposed integrated analysis method, constructed with patent indicators, centrality analysis of social networks and main path analysis.

The results of this study can be itemized as – the core technological competency: companies involved in two specific technology fields have lower strength in R&D portfolio than leading companies with single-core technology. Knowledge flow: most companies in a network are knowledge producers/absorbers and technological development: diverse source and sink nodes were identified in the global main path during 1997-2003, 2004-2010 and 2011-2017.

Latecomer companies can emulate leaders’ innovation and enhance their technological competence to seek niche technology. Using the global main path, companies monitor outdated technologies that can be replaced by new technologies and aid to plan R&D strategy and implement appropriate strategic decisions avoiding path dependency.

The knowledge accumulation process helps in identifying the change of position and the role of companies; understanding the trend of industrial technology knowledge helps companies to develop new technology and direct strategic decisions. The novelty of this research lies in the integrated approach of three methods aiding industries to find their internal core technical competencies and identify the external position in the competitive market.

The aim of this paper is to employ the perspectives of uses and gratification theory and experiential marketing to investigate the antecedents of virtual community (VC) members’ attitude formation and knowledge‐contribution intention through the sense of virtual community (SOVC) and the interaction between their motivations and experiences in a peer‐to‐peer problem solving (P3) VC.

This study chose a well‐known professional IT VC in Taiwan, the ITHelp community, as the target for data collection. An online survey linked from the homepage of this community was used through the cooperation of the vendor of this community. This study employs the partial least squares (PLS) method to examine the research model.

The results show that members’ attitudes toward their VCs are determined by the interaction between their motivations for and experiences with P3 VC usages. Moreover, SOVC plays full mediating roles in the relationship between attitude toward P3 VC and knowledge‐contribution intention.

VC managers need to aim at creating pleasant experiences for their members and foster their belongingness and consciousness to form higher SOVC. The conclusions are restricted to a VC that involves IT‐related issues, which focuses problem solving rather than being socially oriented. Members of IT P3 VCs are usually highly innovative and enthusiastic about new IT products, which is quite different from the membership of other P3 VCs.

Extant studies seldom considered the effects of SOVC and members’ experiences on knowledge‐contribution behavior. The mediating role of SOVC and the interaction between motivations and experience can enhance our understanding about online knowledge‐contribution behavior.