Enhancing corporate knowledge management and sustainable development: An inter-dependent hierarchical structure under linguistic preferences
With the United Nations’ announcement of the 2030 sustainable development goals agenda, the electronic industry is striving to improve corporate sustainability performance. Studies have attempted to consider knowledge management to promote corporate sustainability performance. However, the possible...
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Main Authors: | , , , , , |
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Format: | text |
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Animo Repository
2019
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Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/1692 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2691/type/native/viewcontent |
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Institution: | De La Salle University |
Summary: | With the United Nations’ announcement of the 2030 sustainable development goals agenda, the electronic industry is striving to improve corporate sustainability performance. Studies have attempted to consider knowledge management to promote corporate sustainability performance. However, the possible linkages and potential benefits for bridging knowledge management and corporate sustainability performance are still lacking in previous discussions. To address this gap, this study proposes a hybrid method that integrates a fuzzy synthetic method with an analytical network process to develop an inter-dependent hierarchical framework. The results indicate that the relation, operation and economy aspects must align with the priority goal of achieving better corporate sustainability performance. Improvements of these aspects rely on the knowledge management of developing rigid external collaboration, adopting information technology-supported processes and expanding capital for developing knowledge management. This study contributes to (1) bridging knowledge management and corporate sustainability performance; (2) providing a hybrid method to develop a hierarchical framework; and (3) giving specific directions for guiding improvements. Theoretical and managerial implications are discussed. © 2019 Elsevier B.V. |
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