Forecasting the probability of successful knowledge management by consistent fuzzy preference relations

This paper presents an analytic hierarchy prediction model based on the consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the success of Knowledge Management (KM) implementation, forecasting the possibility of successful KM project, as well as identifying the actions necessary before initiating KM. Pairwise comparisons are utilized to obtain the priority weights of influential factors and the ratings of two possible outcome (success and failure). The subjectivity and vagueness within the prediction process are dealt with using linguistic variables quantified in an interval scale [0, 1]. By multiplying the weights of influential factors and the ratings of possible outcome, predicted success/failure values are determined to enable organizations to decide whether to initiate knowledge management, inhibit adoption or take remedial actions to enhance the possibility of successful KM project. This proposed approach is demonstrated with a real case study involving seven major influential factors assessed by eleven evaluators solicited from a semiconductor engineering incorporation located in Taiwan.