As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, individuals and organizations frequently organize their documents into categories that facilitate document management and subsequent access and browsing. Document clustering is an intentional act that should reflect individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document clustering must consider individual preferences and needs to support personalization in document categorization. In this paper, we present an automatic document-clustering approach that incorporates an individual's partial clustering as preferential information. Combining two document representation methods, feature refinement and feature weighting, with two clustering methods, precluster-based hierarchical agglomerative clustering (HAC) and atomic-based HAC, we establish four personalized document-clustering techniques. Using a traditional content-based document-clustering technique as a performance benchmark, we find that the proposed personalized document-clustering techniques improve clustering effectiveness, as measured by cluster precision and cluster recall.

Analysis of prevalent document management practices shows the popular use of categories (e.g., folders) to organize documents for subsequent searches and retrievals. The coherence and distinction of an existing document category can diminish considerably as influxes of new documents arrive over time. The complexity of and effort requirements for document-category management favor an automated approach that can be supported by appropriate document-clustering techniques. A review of the extant literature shows a predominant focus on document content analysis in automated document-category management, which cannot preserve the user's document-grouping preferences. This research develops two advanced evolution-based techniques for preserving user preferences in their management of document categories. The first technique (CE2), which supports the automated evolution of a set of flat (i.e., nonhierarchical) document categories, extends a promising evolution-based technique (category evolution, CE) by addressing its fundamental limitations inherent to the use of holistic measures. The second technique, category hierarchy evolution (CHE), is developed on the basis of CE2 to support scenarios where document categories are organized with a hierarchical structure. Empirical evaluations of the effectiveness of each technique in various category evolution scenarios created using two different document corpora (i.e., news documents from Reuters and research articles from the ACM digital library), as compared with those of associated salient techniques for benchmark purposes, show that CE2 and CHE outperform their respective benchmark techniques. Their performance is reasonably robust and appears more effective when the quality (coherence) of the previously created categories does not deteriorate excessively. According to our results, the evolution-based approach is viable, appealing, and capable of preserving user preferences in automatic reorganizations of document categories.

This study examines key drivers of firms’ green information technology (IT) practices. A hierarchical view, premised in institutional theory and competitive dynamics, leads to a model that explains firms’ practices. This model includes factors that pertain to the environment (environmental awareness and government regulations), industry (industry norms and competitors’ green practices), and firm (customers’ and equity holders’ attitudes and internal readiness) levels. Survey data collected from 304 major firms in Taiwan are used to test the model and hypotheses. In particular, attitudes of a firm’s customers and equity holders, as well as its internal readiness, directly influence its green IT practices, while also channeling the effects of important contextual factors. Among the contextual factors considered herein, environmental awareness and industry norms influence firms’ practices both directly and indirectly. The overall results highlight the significance of contextual factors and underscore the mediating roles of firm-specific considerations. According to our findings, firms should implement strategic goals and make resource allocations toward green IT practices that are aligned with the industry-wide atmosphere and general public’s awareness of environmental protection.

Word mismatch represents a fundamental information retrieval challenge that has become increasingly important as electronic document repositories (e.g., Web resources, digital libraries) grow in number and sheer volume. In general, word mismatch refers to the phenomenon in which a concept is described by different terms in user queries and in source documents. Query expansion represents a promising avenue to address such problems. Previous research predominantly approaches query expansion on the basis of global or local analysis. However, these approaches emphasize a global perspective rather than taking a topic-specific view of term associations. As a consequence, their effectiveness can be severely constrained when the document corpus spans a diverse set of topics. In this study, we propose a topic-based approach for query expansion and develop and empirically evaluate two novel methods—namely, nonfuzzy and fuzzy topic-based query expansion—to address word mismatch problems. According to our evaluation results, the proposed topic-based approach is more effective than a benchmark global analysis method, particularly when user queries consist of multiple query terms.

The insurance business is characterized by complicated transactional interrelationships among various stakeholders involved in insurance-related activities. Given this unique nature, the century-old challenge in the insurance industry is to effectively reduce transaction costs among the stakeholders while maintaining business privacy and trust. Although blockchain is a promising technology to mitigate this challenge, two technical issues, namely (1) inefficiency in data auditing and (2) difficulty in verifying encrypted data, are of strategic importance when applying blockchain to the insurance industry. To address these technical challenges, we propose an innovative blockchain-based technical model, InsurModel, in the context of newly initiated long-term care insurance in China. Specifically, we utilize cryptographical methods including “zero-knowledge-proof” to 1) represent business interdependence and 2) verify confidential business information without disclosure of specifics. We demonstrate the scalability and applicability of InsurModel and explore its strategic implications in constraining adverse behaviors of the stakeholders.