The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.
Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities
Abstract
ABSTRACT The significance of big data analytics-powered artificial intelligence has grown in recent years. The literature indicates that big data analytics-powered artificial intelligence has the ability to enhance supply chain performance, but there is limited research concerning the reasons for which firms engaging in manufacturing activities adopt big data analytics-powered artificial intelligence. To address this gap, our study employs institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities. We tested the research hypothesis using primary data collected from 219 automotive and allied manufacturing companies operating in South Africa. The contribution of this work lies in the statistical validation of the theoretical framework, which provides insight regarding the role of institutional pressures on resources and their effects on the adoption of big data analytics-powered artificial intelligence, and how this affects sustainable manufacturing and circular economy capabilities under the moderating effects of organizational flexibility and industry dynamism.
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Cited by
Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120880 · Cited by 825
Self-citationChauhan, C., Parida, V., & Dhir, A. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121508 · Cited by 726
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.069 · Cited by 531
Denicolai, S., Zucchella, A., & Magnani, G. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120650 · Cited by 503
Rodríguez-Espíndola, O., Cuevas-Romo, A., Chowdhury, S., Díaz-Acevedo, N., Albores, P., Despoudi, S., Malesios, C., & Dey, P. (2022) · International Journal of Production Economics · doi:10.1016/j.ijpe.2022.108495 · Cited by 459
Metadata
- Authors
- Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 163
- Pages
- 120420
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W3097839885
- Cited by
- 910
- References
- 138
- FWCI
- 52.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120420
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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