| Summary: | 博士 === 義守大學 === 資訊工程學系博士班 === 95 === Knowledge Management (KM) implementation involves innovation and reformation for organizations. Knowledge management implementation requires not only a substantial investment, but also changes the organizational culture and structure. Before embarking on knowledge management, thorough and careful plans are critical to ensure the implementation achieves the intended objectives of accruing profit and enhancing competitiveness for organizations. Therefore, this study proposes an analytic hierarchy prediction model based on the additive reciprocal consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the success of knowledge management, measure the possibility of successful knowledge management project, as well as identify the policies necessary before initiating knowledge management under an uncertain and vague environment. Pairwise comparisons are utilized to obtain the importance weights of influential factors and the priority ratings of two possible outcomes (success/failure). Subjectivity and vagueness within the measuring process are dealt with using linguistic variables quantified in a scale of [0-1]. By multiplying the importance weights of influential factors and the priority ratings of two possible outcomes, 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 knowledge management project. This proposed approach is demonstrated with a real case study solicited from the Power-Max Liquid Crystal Display (LCD) Manufacturing Corporation located in Taiwan.
The empirical results not only demonstrate that the five most important influential factors are organizational culture (0.205 ), application of information technology (0.188 ), leadership of superintendents (0.155 ), audit and assessment (0.140 ), staff character (0.127 ), but also reveal the applicability and feasibility of additive reciprocal consistent fuzzy preference relations for solving complicated hierarchical multiple attribute prediction problems. The predicted values demonstrate that the possibility of successful knowledge management implementation (0.655) is roughly 1.9 times than that of failure knowledge management implementation (0.345 ). This study therefore provides a compromised suggestion that the Power-Max LCD Manufacturing Corporation should decide to initiate knowledge management and simultaneously implement some remedial improvement policies to enhance the poor performance perspectives of this corporation for increasing the chance of successful knowledge management implementation.
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