Developing TOPSIS method using statistical normalization for selecting knowledge management strategies
Abstract: Numerous companies
are expecting their knowledge management (KM) to be performed effectively in
order to leverage and transform the knowledge into competitive advantages.
However, here raises a critical issue of how companies can better evaluate and
select a favorable KM strategy prior to a successful KM implementation.
Design/methodology/approach: An extension of TOPSIS, a multi-attribute
decision making (MADM) technique, to a group decision environment is
investigated. TOPSIS is a practical and useful technique for ranking and
selection of a number of externally determined alternatives through distance
measures. The entropy method is often used for assessing weights in the TOPSIS
method. Entropy in information theory is a criterion uses for measuring the
amount of disorder represented by a discrete probability distribution.
According to decrease resistance degree of employees opposite of implementing a
new strategy, it seems necessary to spot all managers’ opinion. The normal
distribution considered the most prominent probability distribution in
statistics is used to normalize gathered data.
Findings: The results of this study show that by considering 6 criteria
for alternatives Evaluation, the most appropriate KM strategy to implement in our company was ‘‘Personalization’’.
Research limitations/implications: In this research, there are some
assumptions that might affect the accuracy of the approach such as normal
distribution of sample and community. These assumptions can be changed in
future work.
Originality/value: This paper proposes an effective solution based on
combined entropy and TOPSIS approach to help companies that need to evaluate
and select KM strategies. In represented solution, opinions of all managers is
gathered and normalized by using standard normal distribution and central limit
theorem.
Author: Amin Zadeh Sarraf, Ali
Mohaghar, Hossein Bazargani
Journal Code: jptindustrigg130039