The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective
Abstract
Purpose: The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency. Design/methodology/approach: Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression. Findings: The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT. Research limitations/implications: This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements. Practical implications: The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities. Originality/value: This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.
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Hallikas, J., Immonen, M., & Brax, S. (2021) · Supply Chain Management: An International Journal · doi:10.1108/scm-05-2020-0201 · Cited by 218
Akter, S., Bandara, R., Hani, U., Fosso Wamba, S., Foropon, C., & Papadopoulos, T. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.01.020 · Cited by 193
Examining the impact of cloud ERP on sustainable performance: A dynamic capability view
Gupta, S., Meissonier, R., Drave, V. A., & Roubaud, D. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.10.013 · Cited by 175
Metadata
- Authors
- Zhu, S., Song, J., Hazen, B. T., Lee, K., & Cegielski, C.
- Venue
- International Journal of Physical Distribution & Logistics Management
- Publisher
- Emerald
- Published
- 2018-01-18
- Volume
- 48
- Issue
- 1
- Pages
- 47-68
- ISSN
- 0960-0035 (ISSN-L)
- OpenAlex
- W2784191413
- Cited by
- 294
- References
- 11
- FWCI
- 30.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhu, S., Song, J., Hazen, B. T., Lee, K., & Cegielski, C. (2018). How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective. International Journal of Physical Distribution & Logistics Management. https://doi.org/10.1108/ijpdlm-11-2017-0341
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