Data strategies for global value chains: Hybridization of small and big data in the aftermath of COVID-19
| Item Type | Journal paper |
| Abstract | Senior executives must make strategic decisions on (re)configuring global value chains (GVCs) in a post-COVID-19 world, with digital technologies playing a decisive role in enabling decision-making on both the reconfigurations and their implementation. Against this backdrop, the paper explores in depth how executives can leverage and combine big and small data analytics into their GVC (re-)configuration decisions. We draw on a longitudinal single-case study of an analytics firm supporting decision makers in agri-food GVCs, enriched through multiple interviews with experts from the industry. Our analysis reveals an interesting shift towards hybrid data strategies combining big and small data to arrive at new forms of decision-making processes after the outbreak of COVID-19. Through this hybridization, executives aim to improve their understanding of GVCs as well as their own agility and flexibility in decision-making to ensure GVC resilience and efficiency. |
| Authors | Rengarajan, Srinath; Narayanamurthy, Gopalakrishnan; Moser, Roger & Pereira, Vijay |
| Journal or Publication Title | Journal of Business Research |
| Language | English |
| Subjects | business studies information management |
| HSG Classification | contribution to scientific community |
| Refereed | Yes |
| Publisher | Elsevier |
| Volume | 144 |
| Page Range | 776-787 |
| ISSN | 0148-2963 |
| Publisher DOI | https://doi.org/10.1016/j.jbusres.2022.02.042 |
| Depositing User | Prof. Dr. Roger Moser |
| Date Deposited | 05 Feb 2023 23:02 |
| Last Modified | 28 Feb 2023 12:42 |
| URI: | https://www.alexandria.unisg.ch/publications/268952 |
DownloadFull text not available from this repository.CitationRengarajan, Srinath; Narayanamurthy, Gopalakrishnan; Moser, Roger & Pereira, Vijay Data strategies for global value chains: Hybridization of small and big data in the aftermath of COVID-19. Journal of Business Research, 144 776-787. ISSN 0148-2963 Statisticshttps://www.alexandria.unisg.ch/id/eprint/268952
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