Collaborative Filtering-based Context-Aware Document-Clustering (CF-CAC) Technique

Document clustering is an intentional act that should reflect an individual\u27s preference with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user\u27s categorization context. In response, Yang & Wei (2007) propose a Context-Aware document Clustering (CAC) technique that takes into consideration a user\u27s categorization preference relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. However, the CAC technique encounters the problem of small-sized anchoring terms. To overcome this shortcoming, we extend the CAC technique and propose a Collaborative Filtering-based Context-Aware document-Clustering (CF-CAC) technique that considers not only a target user\u27s but also other users\u27 anchoring terms when approximating the categorization context of the target user. Our empirical evaluation results suggest that our proposed CF-CAC technique outperforms the CAC technique

Single-Class Learning for Spam Filtering: An Ensemble Approach

Spam, also known as Unsolicited Commercial Email (UCE), has been an increasingly annoying problem to individuals and organizations. Most of prior research formulated spam filtering as a classical text categorization task, in which training examples must include both spam emails (positive examples) and legitimate mails (negatives). However, in many spam filtering scenarios, obtaining legitimate emails for training purpose is more difficult than collecting spam and unclassified emails. Hence, it would be more appropriate to construct a classification model for spam filtering from positive (i.e., spam emails) and unlabeled instances only; i.e., training a spam filter without any legitimate emails as negative training examples. Several single-class learning techniques that include PNB and PEBL have been proposed in the literature. However, they incur fundamental limitations when applying to spam filtering. In this study, we propose and develop an ensemble approach, referred to as E2, to address the limitations of PNB and PEBL. Specifically, we follow the two-stage framework of PEBL and extend each stage with an ensemble strategy. Our empirical evaluation results on two spam-filtering corpora suggest that the proposed E2 technique exhibits more stable and reliable performance than its benchmark techniques (i.e., PNB and PEBL)

THE EFFECT OF INNOVATION STRATEGY ON POST-M&A INNOVATION PERFORMANCE: AN EVIDENCE FROM PHARMACEUTICAL INDUSTRY

M&A is a popular strategy for pharmaceutical industry due to high R&D risk and costs. Prior research related to post-M&A performance mainly focused on the financial and technology resource perspectives. This study aims to provide a new perspective of innovation strategy which is inspired by the research of March (1991), who noted the difference between exploration and exploitation. Moreover, we build the bridge between M&A and innovation strategy by applying the resource-based view theory. We argue that the acquirer’s exploration strategy will negatively influence the post-M&A innovation performance and the innovation strategy similarity between the acquirer and the target is beneficial for future innovation. Furthermore, we hypothesize that there is a negatively moderating effect caused by the acquirer’s exploration strategy on the effect of innovation strategy similarity. On the basis of 89 M&A deals in the pharmaceutical industry, our empirical results suggest two important findings. First, post- M&A innovation performance is influenced by acquirer’s innovation strategy, more specifically, acquirer’s exploration is harmful for post-M&A innovation. Second, the similarity effect is moderated by acquirer’s innovation strategy. Precisely, acquirer’s exploration will diminish the positive effect of similarity

Context-aware Document-clustering Technique

Document clustering is an intentional act that should reflect individuals’ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective documentclustering techniques need to take into account a user’s categorization context defined by or relevant to the target task under consideration. However, existing document-clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate context-aware document-clustering. In response, we propose a Context-Aware document-Clustering (CAC) technique that takes into consideration a user’s categorization preference (expressed as a list of anchoring terms) relevant to the context of a target task and subsequently generates a set of document clusters from this specific contextual perspective. Our empirical evaluation results suggest that our proposed CAC technique outperforms the pure content-based document-clustering technique

How and When Review Length and Emotional Intensity Influence Review Helpfulness: Empirical Evidence from Epinions.com

Although longer reviews are generally considered more helpful, no research has investigated whether “the more the better” also applies to the expression of emotions. This paper explores the distinct effects of review length and emotional intensity. We propose that, in contrast to review length, the intensity of emotions has a negative effect on review helpfulness, and that this effect only applies to positive emotions. Additionally, drawing on elaboration likelihood model and the literature on the social functions of emotions, we predict that the respective effects of review length and emotional intensity are moderated by reviewer trustworthiness and the difficulty of reading review content. To test these hypotheses, we collected a rich data set from Epinions.com - a leading provider of consumer reviews. Our findings reveal the importance of taking the intensity of emotions into consideration when evaluating review helpfulness, and the results carry important practical implications

Guest Editors’ Introduction

The Pacific Asia Conference on Information Systems (PACIS), sponsored by the Association for Information Systems (AIS), is the premier annual information systems conference in the region. It aims to provide a high quality forum for researchers and practitioners to exchange research findings and practices on key issues in information systems and management. PACIS 2010 was held in Taipei, Taiwan in July 2010 and its theme is “Service Science in Information Systems Research.” In response to the transition of the global economy from the manufacturing to service-dominated economy, service science is emerging as a new and exciting paradigm. It represents a melding of information technology with an understanding of business processes and human behaviors for improving service operations, delivery, innovation, and ultimate values to customers. At the emerging stage of service science, it is essential for information systems (IS) researchers and practitioners to help shape what service science is by fusing IS research into inquiries of service science. Meanwhile, the service-centric view of servicescience may open up exciting opportunities and unique challenges to IS research. Hence, PACIS 2010 aims to facilitate the dialogues among IS professionals in academic and industries to exchange insights on issues related to service science in IS research as well as IS research to service science