Abstract
In Higher Education Institutions (HEI), the process of retaining leadership succession is critical since it has involved the process in choosing the right person. The purpose is to steer the institutions to sustain organizations’ excellence for academic leadership and management (ALM) position. Many ALM of Malaysia HEIs are struggling to find the right successor to replace their roles as they do not have yet any firm criteria in evaluating the competence among their potential successors in their home institutions. This study aims to propose a multi criteria tacit knowledge acquisition framework (MC-TKAF) for supporting talent development intervention program in Malaysia HEIs. It will be based on cognitive apprenticeship, socialization and informal learning theory which mostly used in acquiring knowledge from expertise to overcome talent bottleneck among novice. The main process of this study will use Fuzzy Delphi among ALM in Malaysian HEIsto get consensus judgement about the right indicator to evaluate tacit knowledge competence. Three phases involved are: Phase 1 is to analyze the existing Tacit Knowledge Acquisition (TKA) by finding the suitable parameters to construct intended framework, Phase 2 is to use the findings in Phase 1 in order to develop a new framework of Tacit Knowledge Acquisition Framework (TKAF) that suits with HEI environment. Finally, Phase 3 is to evaluate the practicality of Tacit Knowledge Acquisition Framework (TKAF) by using Multi Criteria Decision Making (MCDM) approach in supporting Talent Development Intervention Program. The objective of this paper is to propose the multi criteria tacit acquisition framework by using MCDM technique as a talent performance indicator. This paper basically will focus on Phase 1 of the research design. The constructed indicators in this paper could be served as a reference for the HEI industries to establish applicable talent performance indicators according to the properties of each TKA used.
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More From: International Journal of Advanced Trends in Computer Science and Engineering
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