Earticle

Contemporary research on information technology (IT) continuance is plagued by inadequate understanding of the continuance concept and inappropriate use of theories for studying this phenomenon. Following a review of the IT continuance literature, this paper identifies some of the extant misconceptions about continuance research and suggests theoretical avenues for advancing this research in a meaningful manner. Based on these insights, an extended expectation-confirmation theoretic model of IT continuance is proposed.

This paper proposes a new framework to predict decision performance by investigating the cognitive fit of decision makers. We assume that every decision maker has two kinds of schema: emotional and rational. Cognitive fit is believed to have a close relationship with the two schemata and decision performance. In the literature on decision performance there is few studies investigating the relationship between the two schemata and cognitive fit. Therefore, our research purposes are twofold: (1) to provide a theoretical basis for the proposed framework describing the causal relationships among the two schemata, cognitive fit, and decision performance, and (2) to empirically prove its validity in the application to an Internet shopping environment. Based on the questionnaires from 104 respondents, we used a second order, confirmatory factor analysis (CFA) model to extract valid constructs, and a structural equation model (SEM) to calculate path coefficients and prove the statistical validity of our proposed research model. The experimental results supported our research model.

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

These days, firms are focusing on the improvement of relationships with business partners. The supply chain integrations are taking critical role in improving the relationships with business partners. In accordance with the development of the IT technology, it became possible for firms not only to integrate inner parts of the organization, but also to integrate the company with other organizations in the supply chain. Therefore, in e-Biz environments, it is imperative for firms to strengthen the core capacity through the supply chain, and to precisely determine the components of the determinants of e-Business integration which impact the firm performance. This study analyzed determinants that have impacts on e-business integration in e-business capacity perspectives in competitive environments. This study based on the premise that the resources and capacities that Grant[1991] and Hart[1995] emphasized do not directly influence the corporate performance. This study focused on the fact that corporate must create core competencies based on these capacities to establish competitive edge. Therefore, this study model analyzed to find out which e-Biz competencies are needed to integrate e-Biz according to competitive environment elements. This study designed to empirically analyze the impact of the e-Biz competencies to the e-Biz integration and to the corporate performance. Independent variables of this study-IT management, partner management, e-Biz knowledge, e-Biz establishment and proliferation, process innovation-are selected based on precedent studies on e-Biz competencies. We selected intermediate variables to verify that e-Biz competencies do not have direct impact on the corporate performance, but have impact on the e-Biz integration, which is intermediate effect. That is to verify that if the components of supply chain improve the integration level using e-Biz competencies, the overall supply chain performances will improve. Dependent variables are selected to verify that e-Biz integration has impacts on corporate performances. This study used factor analysis, path analysis, moderating effect analysis as statistical tests. First, we used exploratory factor analysis and confirmatory factor analysis to analyze reliability and validity. Because e-Biz competencies are presented variously by preceding studies, we used SPSS16.0 to verify if survey questionnaire used by theoretical backgrounds is properly composed. Second, we tested the property of structure model by AMOS. We did path analysis using AMOS16.0 to test structure that is composed of e-Biz competencies and e-Biz integration. Last, we tested moderating effects of measure factors. We analyzed 163 domestic companies to find out many significant suggestive points. First, relationship improvement capacity, e-business knowledge sharing capacity with business partners, and process innovation capacity are adopted as determinants of differentiation and competitive edges against competing firms. Second, e-business knowledge sharing capacity, and process innovation capacity are analyzed as the determinants of e-business integration in the firm which demand fluctuation in the market is high. On the other hand, among the determinants that require capturing ideas on new products, and strengthening the technological power, process innovation capacity are adopted as the determinants. These results provide us the foundation that the determinants that we have analyzed can impact the supply chain integration strategies which take into account the competitive environments.

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful in a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with more expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are PageRank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both PageRank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable. In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual interactions between entities, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as “sent through twitter” or “registered as a friend,” are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.