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Title: An Improved Intellectual Capital Management Method for Selecting and Prioritizing Intangible-Related Aspects: A Case Study of Small Enterprise in Thailand
Authors: Ratapol Wudhikarn
Danaitun Pongpatcharatorntep
Authors: Ratapol Wudhikarn
Danaitun Pongpatcharatorntep
Keywords: Mathematics
Issue Date: 1-Feb-2022
Abstract: This study develops a new integrated approach for improving deficiencies relating to executives’ intuitive or illogical decisions, mainly found in past intellectual capital management (ICM) methods. To simultaneously rectify several flaws, the process model of intellectual capital (IC), a traditional ICM method, is integrated using decision science methods—the analytic network process (ANP) and quality function deployment (QFD). The process model of IC is adopted as a core procedure of the proposed ICM approach. ANP is integrated to improve the ability to consider relationships among the IC critical factors and their impacts, while QFD is included to facilitate the systematic consideration and identification of correlations, linkages, and impacts between all IC-related elements from the business concept to strategic plans. The proposed method was applied to two case studies in one real enterprise in Thailand. The results of the implementation reveal the priorities of all IC-related aspects, and the first priority of key success factors (KSFs), key performance indicators (KPIs), and action plans (APs) are all associated with the organization in the structural capital dimension. The results demonstrate that the method may offer advantages with respect to the conceptual expectations and may prioritize critical IC factors and identify their weights. Furthermore, the improved method could indicate the correlations and impacts between related elements, such as critical factors and associated indicators. This study proposes a new comprehensive and systematic management framework by integrating different concepts-decision science methods and the ICM method. To the best of the authors’ knowledge, this improved approach has not been explored or proposed in earlier studies.
ISSN: 22277390
Appears in Collections:CMUL: Journal Articles

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