Modelling a Semantic Knowledge Management System for Collaborative Learning Environment Using a Structural Equation Modelling

Zaihisma Che Cob, Rusli Abdullah

Abstract


Effective knowledge management system (KMS) should be able to deliver relevant knowledge to the right knowledge user at the right time. However, current KMS still largely relies on human efforts to access, extract and filter information pertinent to their knowledge need, resulted in inefficient process especially in collaborative learning environment. Effective KMS requires the identification of proper technology designed with the right system features to support the knowledge management (KM) activities to ensure that the goals of KM will be achieved. This study analyzed the proposed Semantic KMS Model for Collaborative Learning Environment using structural equation modelling (SEM) to test the effects of the model constructs in achieving the KM goals of KMS used in organizations. The model build upon comprehensive reviews of existing models in literature, and a prototype called Semantic Knowledge Management System for Collaborative Learning (SKMSCL) is developed to translate the constructs into KMS features. A post-implementation survey was conducted to assess the semantic KMS prototype in terms of the system quality, knowledge quality and the semantic KMS features identified, and how well the SKMSCL support the KM goals in comparison with the current KMS used in higher learning institutions (HLIs). Data was collected via questionnaire from a private university who participated in this study. Since there were no references can be found on the relationship between KMS knowledge quality, system quality and semantic KMS features and KM Goals, eleven research questions are derived from the model rather than hypotheses. In summary, findings indicated that seven out of eleven research questions tested are significant and supported by the findings.

Keywords


Knowledge Management; Knowledge Management System; Semantic Knowledge Management;

Full Text:

PDF

References


M. Alavi and D. E. Leidner, “Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,” MIS Q., vol. 25, no. 1, pp. 107–136, 2001.

K. M. Wiig, “Journal of Knowledge Management Emerald Article : What future knowledge management users may expect What future knowledge management users may expect,” no. 1999, 2008.

T. Berners-Lee, J. Hendler, and O. Lassila, “The semantic web,” Sci. Am., vol. 284, no. 5, pp. 28–37, 2001.

A. Kohlhase and M. Kohlhase, “Semantic knowledge management for education,” Proc. IEEE, vol. 96, no. 6, pp. 970–989, 2008.

Z. Che Cob, R. Abdullah, H. Risidi, and M. Z. Mohd Nor, “Preliminary Study on Semantic Knowledge Management Model for Collaborative Learning,” ARPN J. Eng. Appl. Sci., vol. 10, no. 2, pp. 442–450, 2015.

J. Cohen, Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, Seventh Ed. Prentice Hall, Upper Saddle River, New Jersey, 2010.

J. Joo and S. M. Lee, “Adoption of the Semantic Web for overcoming technical limitations of knowledge management systems,” Expert Syst. Appl., vol. 36, no. 3, pp. 7318–7327, Apr. 2009.

J.-M. Ringle, C.M., Wende, S., and Becker, SmartPLS 3. 2015.

J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publication: Los Angeles, 2014.

Z. Che Cob, R. Abdullah, S. Mohd Drus, and N. Ali, “Che Cob, Zaihisma, et al. "System requirement specifications for a semantic knowledge management system for collaborative learning environment,” Knowledge Management International Conference (KMICe) 2016, 29 – 30 August 2016, Chiang Mai, Thailand pp. 229– 234, 2016.

C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, 1981.

N. K. Malhotra and S. Dash, Marketing Research an Applied Orientation (Paperback). London: Pearson Publishing, 2011.

L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model., vol. 6, pp. 1–55., 1999.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131