Fuzzy logic based model to measure knowledge sharing

Zadjabbari, Behrang; Mohseni, Shahriar; Wongthongtham, Pornpit

doi:10.1109/dest.2009.5276692

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The purpose of this model is to determine the motives of people in sharing their knowledge and also to examine whether the combination of competence-based and benevolence-based trusts has effect on knowledge sharing (Zadjabbari, 2009). PET: is the degree of confidence, trust or willingness between the knowledge source and the knowledge seekers (Davis, 1989 andDavies et al, 1989).…”

Section: Trust-based Knowledge Sharing Adoption Model (Tbksam)mentioning

confidence: 99%

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

Folorunso¹

2015

CIS

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In this paper, a fuzzy expert system-based Knowledge Sharing Trust Level(KSTL) measurement adoption model is presented. The KSTL was modeled using four input variables, developed from Technology Acceptance Model constructs namely; Perceived Trust Towards Competence, Perceived Trust Towards Benevolence, Perceived Trust Barrier for Sharing, External Cue Towards Trust to determine KSTL. A KSTL-fuzzy algorithm was developed using a trust metric equation at the preprocessing stage and implemented using Matlab 7.6.0 to compute KSTL crisp_value. The results obtained provided a useful understanding about the degree of trust among Community of Practice practicing knowledge-sharing. The proposed work was found to be dynamic, as the computed KSTL fluctuates with changes in the input variables. The simulated results demonstrate the effectiveness of the model in measuring trust level in knowledge-sharing applications.

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Section: Trust-based Knowledge Sharing Adoption Model (Tbksam)mentioning

confidence: 99%

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

Folorunso¹

2015

CIS

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“…In [30] is presented a fuzzy temporal model integrated with an ontology model to allow annotating ontology definitions with time specifications. Another successful application of FL to emulate human behaviour is the application of Fuzzy Logic to measure knowledge sharing, namely the confidence and knowledge complexity level [31].…”

Section: Design Experimentsmentioning

confidence: 99%

Semantic Adaptation of Knowledge Representation Systems

Lucena

Sarraipa

Jardim‐Gonçalves

2013

IFIP Advances in Information and Communication Technology

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Abstract. Due to the worldwide diversity of enterprises, a high number of ontologies representing the same segment of reality which are not semantically coincident have appeared. To solve this problem, a possible solution is to use a reference ontology to be the intermediary in the communications between the community enterprises and to outside. Since semantic mappings between enterprise's ontologies are established, this solution allows each of the enterprises to keep internally its own ontology and semantics unchanged. However information systems are not static, thus established mappings become obsolete with time. This paper's presents a PhD research with the objective to identify a suitable approach that combines semantic mappings with user's feedback, providing an automatic learning to ontologies & enabling autoadaptability and, consequently, dynamism to the information systems.

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“…The work described in [12] instead, exploits the theory of Fuzzy Cognitive Time Maps (FCTMs) into modeling and evaluating trust relationships in the virtual enterprises setting. Similarly to our approach, a fuzzy inference system has been exploited [13] to model trust measurement. The approach has been applied to improve knowledge sharing within a community to lead organizations to high level of productivity: the fuzzy system returns the degree of knowledge sharing.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy agent-based approach to trust-based competency management

Gaeta

Loia

Senatore

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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In an era in which organizations increasingly consider the competencies of their employees as a crucial resource, human management becomes a key activity for improving staff and business performances. Important is also knowing who knows what inside the organization, so that project teams are assembled as the right mix of skill, knowledge and workforce abilities. At the same time, trust, an essential component, related to understanding interpersonal and group behavior, is an indisputable prerequisite for organizational effectiveness, in terms of socialcognitive capital, global competency as well as economic exchange and social and political stability. This paper defines an approach to support competency-based management by providing recommendations about the reliability of a worker in terms of trust information and own competencies. The approach lies on an agent-based architecture which supervises the Human Resources Management (HRM). Taskoriented agents monitor the employees' profiles and capabilities by maintaining update the competencies and the trusts in the organizational social network. Particularly an agent endowed by fuzzy reasoning capabilities provides recommendation about workers' competencies in HRM decision-making processes.

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Fuzzy logic based model to measure knowledge sharing

Cited by 3 publications

References 11 publications

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

Semantic Adaptation of Knowledge Representation Systems

A fuzzy agent-based approach to trust-based competency management

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