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Linking big data analytics capability and sustainable supply chain performance: mediating role of knowledge development
Purpose Drawing on the dynamic capability view, this study aims to examine the relationships between big data analytics capability (BDAC) and sustainable supply chain performance (SSCP) by exploring the mediating effects of knowledge development (KD) in terms of knowledge acquisition, information di...
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Published in: | Management research news 2024-02, Vol.47 (4), p.512-536 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Purpose
Drawing on the dynamic capability view, this study aims to examine the relationships between big data analytics capability (BDAC) and sustainable supply chain performance (SSCP) by exploring the mediating effects of knowledge development (KD) in terms of knowledge acquisition, information distribution, shared meaning and achieved memory.
Design/methodology/approach
Data were collected by questionnaire survey from 300 manufacturing organizations. Structural equation modeling was used to test the research hypotheses.
Findings
It was found that all the dimensions of KD were positively related to BDAC and SSCP. Although no direct association was established between BDAC and SSCP, the empirical findings indicated that all the dimensions of KD fully mediated the relationship between BDAC and SSCP. This highlights that organizations need to harness KD because developing BDAC alone may not be sufficient.
Originality/value
No previous research has explored how KD dimensions such as knowledge acquisition, information distribution, shared meaning and achieved memory mediate the relationship between BDAC and SSCP. This paper addresses this gap in the literature and contributes to the existing debate to better understand the conditions in which BDAC affects SSCP. Pointers for future research are also identified. |
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ISSN: | 2040-8269 2040-8277 2040-8269 |
DOI: | 10.1108/MRR-01-2023-0018 |