Resource Re-orchestration and firm survival in crisis periods: The role of business models of technology MNEs during COVID-19

Attah-Boakye, Rexford; Adams, Kweku; Hernandez-Perdomo, Elvis; Yu, Honglan; Johansson, Jeaneth

Abstract

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.

Citation

Attah-Boakye, R., Adams, K., Hernandez-Perdomo, E., Yu, H., & Johansson, J. (2023). Resource Re-orchestration and firm survival in crisis periods: The role of business models of technology MNEs during COVID-19. Technovation, 125, Article 102769. https://doi.org/10.1016/j.technovation.2023.102769

• Journal Article Type: Article
• Acceptance Date: May 1, 2023
• Online Publication Date: Jun 1, 2023
• Publication Date: 2023-07
• Deposit Date: Jun 6, 2023
• Publicly Available Date: Jun 6, 2023
• Journal: Technovation
• Print ISSN: 0166-4972
• Electronic ISSN: 1879-2383
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 125
• Article Number: 102769
• DOI: https://doi.org/10.1016/j.technovation.2023.102769
• Keywords: Business models; Artificial intelligence; Machine learning; Digitalisation; Agility; Resource orchestrations
• Public URL: https://nottingham-repository.worktribe.com/output/21630413
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0166497223000809
• Additional Information: This article is maintained by: Elsevier; Article Title: Resource Re-orchestration and firm survival in crisis periods: The role of business models of technology MNEs during COVID-19; Journal Title: Technovation; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.technovation.2023.102769; Content Type: article; Copyright: © 2023 The Authors. Published by Elsevier Ltd.