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Knowledge Sharing Adoption Model Based on Artificial Neural Networks

Knowledge Sharing Adoption Model Based on Artificial Neural Networks

Olusegun O. Folorunso, Rebecca Opeoluwa Vincent, Adewale Akintayo Ogunde, Benjamin Agboola
Copyright: © 2010 |Volume: 2 |Issue: 4 |Pages: 14
ISSN: 1937-9633|EISSN: 1937-9641|EISBN13: 9781613502518|DOI: 10.4018/jea.2010100101
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MLA

Folorunso, Olusegun O., et al. "Knowledge Sharing Adoption Model Based on Artificial Neural Networks." IJEA vol.2, no.4 2010: pp.1-14. http://doi.org/10.4018/jea.2010100101

APA

Folorunso, O. O., Vincent, R. O., Ogunde, A. A., & Agboola, B. (2010). Knowledge Sharing Adoption Model Based on Artificial Neural Networks. International Journal of E-Adoption (IJEA), 2(4), 1-14. http://doi.org/10.4018/jea.2010100101

Chicago

Folorunso, Olusegun O., et al. "Knowledge Sharing Adoption Model Based on Artificial Neural Networks," International Journal of E-Adoption (IJEA) 2, no.4: 1-14. http://doi.org/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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