open access publication

Review, Early Access, 2023

Unpacking the complexities of crisis innovation: a comprehensive review of ecosystem-level responses to exogenous shocks

REVIEW OF MANAGERIAL SCIENCE, ISSN 1863-6683, 1863-6683, 10.1007/s11846-023-00709-x

Contributors

Brem, Alexander 0000-0002-6901-7498 [1] [2] Nylund, Petra (Corresponding author) [2] Roshani, Saeed 0000-0001-5851-2867 [3]

Affiliations

  1. [1] Univ Southern Denmark, Dept Technol & Innovat, Als 2, DK-6400 Sonderborg, Denmark
  2. [NORA names: SDU University of Southern Denmark; University; Denmark; Europe, EU; Nordic; OECD];
  3. [2] Univ Stuttgart, Inst Entrepreneurship & Innovat Sci, Pfaffenwaldring 19, D-70569 Stuttgart, Germany
  4. [NORA names: Germany; Europe, EU; OECD];
  5. [3] Allameh Tabatabai Univ, Dept Technol & Entrepreneurship Management, Tehran 489684511, Iran
  6. [NORA names: Iran; Asia, Middle East]

Abstract

Innovation in times of crisis has experienced a flood of research in the wake of recent events. These studies are dispersed over a broad range of fields and do not adequately reflect earlier research or prior crises. To encourage the convergence of related literature streams, we define crisis innovation as an ecosystem-level process to meet the needs of-and overcome the resource constraints derived from-an exogenous shock. We then conduct a systematic literature review aided by machine learning techniques, specifically utilizing topic modeling. We derive a taxonomy of crisis innovation, which represents innovation as a response to societal crisis, funding crisis, financial crisis, economic crisis, digitalization, transformation, political crisis, strategy crisis, and organizational crisis. We find that crisis innovation drives digitalization through increased motivation for open and ecosystem innovation, but also that the dynamic network structures required for lasting digital transformation are often not implemented during crisis.

Keywords

Crisis innovation, Digital transformation, Innovation ecosystem, Innovation management, Natural language processing, O32, Topic modeling

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