Resource Re-Orchestration and Firm Survival in Crisis Periods: The Role of Business Models of Technology MNEs during COVID-19

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Publication date
2023-07Keyword
Business modelsArtificial intelligence
Machine learning
Digitalisation
Agility
Resource orchestrations
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© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Peer-Reviewed
YesOpen Access status
openAccessAccepted for publication
01/05/2023
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Using data from world-leading digital-driven/technology multinational enterprises (DTMNEs), we draw from the resource orchestration theory to investigate the associations between business model (BM) drivers and firm performance during crisis periods. Drawing on data from the COVID-19 pandemic period, we deploy diverse analytical approaches including multivariate linear regressions and aggregated composite index statistical methods in examining how the BMs of our sampled DTMNEs drive firm performance. Our study highlights six methodological approaches that can be utilised by decision-makers in examining which variables in their BM drive better firm performance. Our findings revealed that the principal component analysis and multicriteria decision analysis (PROMETHEE methods) that espouse the use of aggregate composite index can provide significant and consistent predictive results in comparison to the traditional linear methods when examining the association between BM and firm performance during crisis periods. The paper provides policy and managerial implications on how firms and decision-makers can bolster business continuity, resilience, and plasticity by using analytical lenses that identify optimum resource orchestration during crises.Version
Published versionCitation
Attah-Boakye R, Adams K, Hernandez-Perdomo E et al (2023) Resource Re-Orchestration and Firm Survival in Crisis Periods: The Role of Business Models of Technology MNEs during COVID-19. Technovation. 125: 102769.Link to Version of Record
https://doi.org/10.1016/j.technovation.2023.102769Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.technovation.2023.102769