The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context
Open accessAbstract
The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Firm Resources and Sustained Competitive Advantage
Barney, J. (1991) · Journal of Management · doi:10.1177/014920639101700108 · Cited by 48,334
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Estimating nonresponse bias in mail surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.1177/002224377701400320 · Cited by 10,390
Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology
Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996) · Administrative Science Quarterly · doi:10.2307/2393988 · Cited by 8,499
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“…complex situations at high speed, whilst processing large amounts of data, and to learn and interact like humans (Duan, Edwards, & Dwivedi, 2019; Dwivedi et al., 2021; Gupta, Kar, Baabdullah, & Al-Khowaiter, 2018; Kelly, 2015) and Artificial intelligence-powered supply chain analytics (AI-SCA)…”
Background
Cited by
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024) · Sustainable Development · doi:10.1002/sd.2773 · Cited by 420
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023) · Annals of Operations Research · doi:10.1007/s10479-022-04983-y · Cited by 409
Fosso Wamba, S. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102544 · Cited by 269
Chen, D., Esperança, J. P., & Wang, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.884830 · Cited by 198
Hao, X., & Demir, E. (2024) · Journal of Modelling in Management · doi:10.1108/jm2-01-2023-0009 · Cited by 92
Metadata
- Authors
- Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M.
- Venue
- Industrial Marketing Management
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 96
- Pages
- 135-146
- ISSN
- 0019-8501 (ISSN-L)
- OpenAlex
- W3164963453
- Cited by
- 255
- References
- 30
- FWCI
- 36.69
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. https://app.cochat.ai/papers/10.1016/j.indmarman.2021.05.003
- The paper
- Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M. (2021). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2021.05.003
Data sources
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.
