Boon-itt, S, Wong, CY orcid.org/0000-0002-4933-1770 and Wong, CWY (2017) Service supply chain management process capabilities: Measurement development. International Journal of Production Economics, 193. pp. 1-11. ISSN 0925-5273
Abstract
The role of supply chain management processes in achieving competitive advantages in the service industry has been widely discussed. However, due to the lack of valid measurement scales, the effects of service supply chain management (SSCM) process capability cannot be ascertained. This study aims to develop and validate measurement scales for SSCM process capability constructs. The measurement scales were initially developed by literature review, and refined by Q-sort method. The SSCM process capability is a seven-dimensional construct; each dimension consists of a collection of unidimensional multi-item scales. Confirmatory factor analyses of a large-scale survey confirmed the unidimensionality, reliability, and validity of the multidimensional construct of seven SSCM process capabilities. The validated measurement scales lay a crucial foundation for advancing knowledge of the service supply chain by enabling future empirical studies in the field, which previously relied on largely conceptual frameworks and descriptive accounts of SSCM processes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier B.V. This is an author produced version of a paper published in International Journal of Production Economics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Service supply chain; Process capability; Scale development; Empirical measurement methodology |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) > Logistics, Info, Ops and Networks (LION) (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Jun 2017 09:42 |
Last Modified: | 21 Dec 2018 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ijpe.2017.06.024 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118142 |