Digital innovation, data analytics, and supply chain resiliency : a bibliometric-based systematic literature review |
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Author: | Iftikhar, Anas1; Ali, Imran2; Arslan, Ahmad3; |
Organizations: |
1International Lecturer in Logistics & Supply Chain Management, Lancaster University Management School, Lancaster University, Lancaster, United Kingdom 2Lecturer in Operations and Innovation Management, School of Business & Law, Central Queensland University, Rockhampton, Australia 3Oulu Business School, University of Oulu, Oulu, Finland
4Birmingham Business School, University of Birmingham, Birmingham, UK
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Format: | article |
Version: | published version |
Access: | open |
Online Access: | PDF Full Text (PDF, 1.4 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2022052338119 |
Language: | English |
Published: |
Springer Nature,
2022
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Publish Date: | 2022-05-23 |
Description: |
AbstractIn recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners’ awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place. see all
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Series: |
Annals of operations research |
ISSN: | 0254-5330 |
ISSN-E: | 1572-9338 |
ISSN-L: | 0254-5330 |
Issue: | Online first |
DOI: | 10.1007/s10479-022-04765-6 |
OADOI: | https://oadoi.org/10.1007/s10479-022-04765-6 |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
512 Business and management |
Subjects: | |
Funding: |
Open Access funding enabled and organized by CAUL and its Member Institutions. |
Copyright information: |
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
https://creativecommons.org/licenses/by/4.0/ |