Knowledge Sharing Adoption Model Based on Artificial Neural Networks

Knowledge Sharing Adoption Model Based on Artificial Neural Networks

Olusegun O. Folorunso (University of Agriculture Abeokuta, Nigeria), Rebecca Opeoluwa Vincent (University of Agriculture Abeokuta, Nigeria), Adewale Akintayo Ogunde (Redeemer’s University (RUN), Nigeria) and Benjamin Agboola (University of Agriculture Abeokuta, Nigeria)
Copyright: © 2010 |Pages: 14
DOI: 10.4018/jea.2010100101
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Abstract

Knowledge Sharing Adoption Model called (KSAM) was developed in this paper using Artificial Neural Networks (ANN). It investigated students’ Perceived Usefulness and Benefits (PUB) of Knowledge Sharing among students of higher learning in Nigeria. The study was based on the definition as well as on the constucts related to technology acceptance model (TAM). A survey was conducted using structured questionnaire administered among students and analysed with SPSS statistical tool; the results were evaluated using ANN. The KSAM includes six constucts that include Perceived Ease Of Sharing (PEOS), Perceived Usefulness and Benefits (PUB), Perceived Barriers for Sharing (PBS), External Cues to Share (ECS), Attitude Towards Sharing (ATT), and Behavioral Intention to Share (BIS). The result showed that Students’ PUB must be raised in order to effectively increase the adoption of Knowledge Sharing in this domain. The paper also identified a myriad of limitations in knowledge sharing and discovered that the utilization of KSAM using ANN is feasible. Findings from this study may form the bedrock on which further studies can be built.
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Literature Review

Technology acceptance model (TAM) and knowledge sharing adoption model (KSAM) are two models which are in some aspects, complementary. Therefore, it would be of great significance if they are integrated to investigate the adoption of knowledge sharing. The follwoing sections reviews these models.

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