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Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach

Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach
Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach

Although supply chain ecosystem health (SCE Health) is receiving attention in relation to environmental uncertainty, its conception and measurement are largely undocumented, and how to pursue SCE Health under environmental turbulence is unclear. Supply chain learning (SCL) is an important way to build dynamic capabilities, and whether it can empower the achievement of SCE Health is worthy of investigative study. Therefore, grounded in the dynamic capabilities theory, a survey data-based structural equation modelling (SEM) approach is employed. Based on four experts’ opinions and an in-depth literature review, 47 measurement items (11 for SCL, 28 for SCE Health, and 8 for environmental turbulence) were identified in the questionnaire design. Further, 208 valid questionnaires from the field survey of supply chain management (SCM)-related firms in China were collected and used for SEM analysis. The results show that the internal learning of SCL stimulates its external learning. SCL empowers the pursuit of SCE Health, which is strengthened under higher environmental turbulence. The theoretical framework and results also derive practical insights and support from 11 interviewees of five companies.

dynamic capabilities theory, ecosystem health, environmental turbulence, structural equation modelling, Supply chain learning, >
0020-7543
Wang, Liukai
1ea2c3e6-95e6-4912-952a-54a53e2da059
Kong, Xinyi
25ee28a9-2491-4b7d-9a38-d5e507c4318a
Wang, Weiqing
80f1fd16-ca01-426a-9569-3b21e17b3299
Gong, Yu
86c8d37a-744d-46ab-8b43-18447ccaf39c
Wang, Liukai
1ea2c3e6-95e6-4912-952a-54a53e2da059
Kong, Xinyi
25ee28a9-2491-4b7d-9a38-d5e507c4318a
Wang, Weiqing
80f1fd16-ca01-426a-9569-3b21e17b3299
Gong, Yu
86c8d37a-744d-46ab-8b43-18447ccaf39c

Wang, Liukai, Kong, Xinyi, Wang, Weiqing and Gong, Yu (2023) Pursuing supply chain ecosystem health under environmental turbulence: a supply chain learning approach. International Journal of Production Research. (doi:10.1080/00207543.2023.2235019).

Record type: Article

Abstract

Although supply chain ecosystem health (SCE Health) is receiving attention in relation to environmental uncertainty, its conception and measurement are largely undocumented, and how to pursue SCE Health under environmental turbulence is unclear. Supply chain learning (SCL) is an important way to build dynamic capabilities, and whether it can empower the achievement of SCE Health is worthy of investigative study. Therefore, grounded in the dynamic capabilities theory, a survey data-based structural equation modelling (SEM) approach is employed. Based on four experts’ opinions and an in-depth literature review, 47 measurement items (11 for SCL, 28 for SCE Health, and 8 for environmental turbulence) were identified in the questionnaire design. Further, 208 valid questionnaires from the field survey of supply chain management (SCM)-related firms in China were collected and used for SEM analysis. The results show that the internal learning of SCL stimulates its external learning. SCL empowers the pursuit of SCE Health, which is strengthened under higher environmental turbulence. The theoretical framework and results also derive practical insights and support from 11 interviewees of five companies.

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Accepted/In Press date: 3 July 2023
e-pub ahead of print date: 23 July 2023
Published date: 23 July 2023
Keywords: dynamic capabilities theory, ecosystem health, environmental turbulence, structural equation modelling, Supply chain learning, >

Identifiers

Local EPrints ID: 481347
URI: http://eprints.soton.ac.uk/id/eprint/481347
ISSN: 0020-7543
PURE UUID: 2bda39b8-e301-49a2-b1bd-3907963d7f9c
ORCID for Yu Gong: ORCID iD orcid.org/0000-0002-5411-376X

Catalogue record

Date deposited: 23 Aug 2023 17:02
Last modified: 18 Mar 2024 03:40

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Contributors

Author: Liukai Wang
Author: Xinyi Kong
Author: Weiqing Wang
Author: Yu Gong ORCID iD

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