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Date

22. 7.2006 15:26:24

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.342-346

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Abstract

Knowledge Management Systems (KMS) have become increasingly popular as a knowledge-sharing tool in contemporary corporations. Enticing employees to seek knowledge from KMS remains an important concern for researchers and practitioners. Trust has been widely recognized in many studies as an important enabling factor for seeking knowledge; however, the role of trust in promoting knowledge-seeking behavior using KMS has not been adequately addressed. Drawing upon the extant literature on trust and information technology adoption, this article examines the relationships between the knowledge seekers' trust in the community of KMS users, their perceptions toward the system (perceived usefulness and perceived seeking efforts), and the intention to continually use the KMS. The results reveal that trust in the community of KMS users does not directly affect the employees' knowledge-seeking continuance intention; rather, it happens indirectly through a mediated effect of perceived usefulness of the KMS. Furthermore, we find that trust seems to be a stronger determinant of perceived usefulness than of perceived seeking efforts. Our study thus demonstrates the indirect, but still crucial, role of trust in knowledge-seeking behavior in the context of corporate KMS usage. Other findings and the implications of this study for both researchers and practitioners are correspondingly discussed.

Date

22. 3.2009 13:01:44

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.526-537

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Abstract

Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.

Source

Information processing and management. 54(2018) no.6, S.1002-1021

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Abstract

A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461

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Source

Journal of the American Society for Information Science and Technology. 60(2009) no.5, S.997-1011

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Source

Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.356-371