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Cited by 258

The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

Adoption of cloud computing technologies in supply chains: An organizational information processing theory approach

Cegielski, Casey G., Allison Jones‐Farmer, L., Wu, Yun, Hazen, Benjamin T.·2012·The International Journal of Logistics Management

Abstract

Purpose: The purpose of this paper is to employ organizational information processing theory to assess how a firm's information processing requirements and capabilities combine to affect the intention to adopt cloud computing as an enabler of electronic supply chain management systems. Specifically, the paper examines the extent to which task uncertainty, environmental uncertainty, and inter‐organizational uncertainty affect intention to adopt cloud computing technology and how information processing capability may moderate these relationships. Design/methodology/approach: The paper uses a multiple method approach, thus examining the hypothesized model with both quantitative and qualitative methods. To begin, the paper incorporates a Delphi study as a way in which to choose a practically relevant characterization of the moderating variable, information processing capability. The authors then use a survey method and hierarchical linear regression to quantitatively test their hypotheses. Finally, the authors employ interviews to gather additional qualitative data, which they examine via use of content analysis in order to provide additional insight into the tenability of the proposed model. Findings: The quantitative analysis suggests that significant two‐way interactions exist between each independent variable and the moderating variable; each of these interactions is significantly related to intention to adopt cloud computing. The qualitative results support the assertion that information processing requirements and information processing capability affect intention to adopt cloud computing. These findings support the relationships addressed in the hypothesized model and suggest that the decision to adopt cloud computing is based upon complex circumstances. Research limitations/implications: This research is limited by the use of single key informants for both the quantitative and qualitative portions of the study. Nonetheless, this study enhances understanding of electronic supply chain management systems, and specifically cloud computing, through the application of organizational information processing theory. The authors’ mixed‐methods approach allowed them to draw more substantive conclusions; the findings provide a theoretical and empirical foundation for future research in this area, and also suggest the use of additional theoretical perspectives. Practical implications: This study provides insight that can help supply chain managers to better understand how requirements, when coupled with capabilities, may influence the decision to adopt cloud computing as an enabler of supply chain management systems. Originality/value: As an emerging technology, cloud computing is changing the form and function of information technology infrastructures. This study enhances the understanding of how this technology may diffuse within the supply chain.

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Metadata

Authors
Cegielski, C. G., Allison Jones‐Farmer, L., Wu, Y., & Hazen, B. T.
Venue
The International Journal of Logistics Management
Publisher
Emerald
Published
2012-08-10
Volume
23
Issue
2
Pages
184-211
ISSN
0957-4093 (ISSN-L)
OpenAlex
W1558467047
Cited by
258
References
83
FWCI
37.64
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adoption of cloud computing technologies in supply chains: An organizational information processing theory approach. https://app.cochat.ai/papers/10.1108/09574091211265350
The paper
Cegielski, C. G., Allison Jones‐Farmer, L., Wu, Y., & Hazen, B. T. (2012). Adoption of cloud computing technologies in supply chains: An organizational information processing theory approach. The International Journal of Logistics Management. https://doi.org/10.1108/09574091211265350

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.