Drivers and consequences of an innovative technology assimilation in the supply chain: cloud computing and supply chain integration
Abstract
Cloud Computing are innovative technologies that are being applied in the main business functions in the supply chain. This study aims to reveal the determinant factors (drivers and a relevant outcome) of the level of use or assimilation of Cloud Computing in the supply chain. In order to test three hypotheses we conducted an empirical study in 484 companies from sectors in an intermediate position in the supply chain. The data gathering method consisted of a telephone survey using a computerised system (CATI). We used structural equation modelling (SEM) to test the hypotheses. The empirical study reveals that Advanced Manufacturing Technologies pursuing the internal efficiency of the supply chain (Intra-organisational IT) and IT for capabilities in e-business/e-commerce seeking external connection of the supply chain with other companies (Inter-organisational IT) are drivers of Cloud Computing assimilation. Furthermore, supply chain integration is one of the major consequences of Cloud Computing assimilation in the supply chain.
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This theory has been used extensively in extant literature concerning assimilation of cloud computing, ERP assimilation, tracking technology, RFID technology in different management disciplines.
“This theory has been used extensively in extant literature concerning assimilation of cloud computing (Liang, Ki, Zhang & Li, 2019; Maqueira et al., 2019), ERP assimilation (Shao, Feng & Hu, 2017), tracking technology (Basole & Nowak, 2018), RFID technology (Hossain et al., 2016, Wei, Lowry & Seedorf, 2015) in different management disciplines.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Maqueira et al., 2019
These four stages are considered distinct variables in the assimilation literature.
“These four stages are considered distinct variables in the assimilation literature”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Maqueira et al., 2019
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AI-augmented HRM: Antecedents, assimilation and multilevel consequences
Prikshat, V., Malik, A., & Budhwar, P. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100860 · Cited by 200 · see citing works
A cloud-based supply chain management system: effects on supply chain responsiveness
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An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101617 · Cited by 106 · see citing works
- Authors
- Manuel Maqueira, J., Moyano-Fuentes, J., & Bruque, S.
- Affiliations
- Department of Business Organization, Marketing and Sociology, University of Jaen, Jaén, Spain
- Universidad de Jaén (ES)
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2018-10-08
- Volume
- 57
- Issue
- 7
- Pages
- 2083-2103
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2896056280
- Cited by
- 94
- References
- 7
- Citations by year
- 2026: 2 · 2025: 20 · 2024: 17 · 2023: 12 · 2022: 16 · 2021: 16 · 2020: 9 · 2019: 2
- FWCI
- 10.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Production Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Ministerio de Economía y Competitividad (ECO-2015-65874-P)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Quality and Supply Management
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Management Information Systems › ERP Systems Implementation and Impact
- Keywords
- cloud computing, supply chain integration, technology assimilation, advanced manufacturing technology, e-commerce, supply chain management, technology adoption factors
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