Knowledge capabilities in supply chain networks: a taxonomy

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Author(s)
Ogulin, Robert
Guzman, Gustavo
Nuwangi, Subasinghage Maduka
Griffith University Author(s)
Year published
2020
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Purpose: This paper aims to develop a conceptual taxonomy for building requisite knowledge capabilities for different supply chain network (SCN) types. Specifically, it examines knowledge capabilities required for three types of SCNs: efficient, collaborative and agile SCNs. Design/methodology/approach: This paper integrates two bodies of thought (i.e. knowledge management and organisational learning) and applies them to SCNs. An abductive research process is used to develop this conceptual taxonomy. Findings: The conceptual taxonomy details three archetypical knowledge capabilities – exploitation, exploration and ambidextrous. ...
View more >Purpose: This paper aims to develop a conceptual taxonomy for building requisite knowledge capabilities for different supply chain network (SCN) types. Specifically, it examines knowledge capabilities required for three types of SCNs: efficient, collaborative and agile SCNs. Design/methodology/approach: This paper integrates two bodies of thought (i.e. knowledge management and organisational learning) and applies them to SCNs. An abductive research process is used to develop this conceptual taxonomy. Findings: The conceptual taxonomy details three archetypical knowledge capabilities – exploitation, exploration and ambidextrous. Those knowledge capabilities are required for efficient, collaborative and agile SCNs, respectively. Research limitations/implications: This paper is conceptual and theory-based. The next stages of the research seek to further strengthen the explanatory value of the taxonomy through empirical validation. Practical implications: The taxonomy developed in this paper provides a valuable and pragmatic tool for managerial decision-making in the context of SCNs. Specifically, it provides a roadmap for practitioners since the study develops an understanding of the relationship between knowledge capabilities and types of SCNs. Originality/value: This is one of the earliest studies that attempt to unearth requisite knowledge capabilities for different types of SCNs.
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View more >Purpose: This paper aims to develop a conceptual taxonomy for building requisite knowledge capabilities for different supply chain network (SCN) types. Specifically, it examines knowledge capabilities required for three types of SCNs: efficient, collaborative and agile SCNs. Design/methodology/approach: This paper integrates two bodies of thought (i.e. knowledge management and organisational learning) and applies them to SCNs. An abductive research process is used to develop this conceptual taxonomy. Findings: The conceptual taxonomy details three archetypical knowledge capabilities – exploitation, exploration and ambidextrous. Those knowledge capabilities are required for efficient, collaborative and agile SCNs, respectively. Research limitations/implications: This paper is conceptual and theory-based. The next stages of the research seek to further strengthen the explanatory value of the taxonomy through empirical validation. Practical implications: The taxonomy developed in this paper provides a valuable and pragmatic tool for managerial decision-making in the context of SCNs. Specifically, it provides a roadmap for practitioners since the study develops an understanding of the relationship between knowledge capabilities and types of SCNs. Originality/value: This is one of the earliest studies that attempt to unearth requisite knowledge capabilities for different types of SCNs.
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Journal Title
Journal of Knowledge Management
Volume
24
Issue
3
Copyright Statement
© 2020 Emerald. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Information and computing sciences
Commerce, management, tourism and services
Science & Technology
Social Sciences
Information Science & Library Science