Data-driven growth and business model transformation: how startups unlock resilience in turbulent times
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04130081%3A_____%2F25%3AN0000031" target="_blank" >RIV/04130081:_____/25:N0000031 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11846-025-00957-z" target="_blank" >https://link.springer.com/article/10.1007/s11846-025-00957-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11846-025-00957-z" target="_blank" >10.1007/s11846-025-00957-z</a>
Alternative languages
Result language
angličtina
Original language name
Data-driven growth and business model transformation: how startups unlock resilience in turbulent times
Original language description
This study aims to explore how startups can enhance resilience through business model transformation (BMT) driven by data-driven methodologies during crises. Organizational resilience, defined as the ability to adapt, recover, and thrive amidst adverse conditions, has become a strategic imperative in an era marked by repeated global disruptions. While previous research has focused on adaptive and absorptive paths to resilience, our work highlights the synergistic potential of integrating these approaches with data-driven growth, in the specific and under-researched context of platform-based startups. Adopting a qualitative research design, we conduct a multiple case study of four platform-based startups from diverse sectors. Our findings reveal that data-driven growth facilitates continuous experimentation, real-time learning, and agile decision-making, enabling organizations to identify new market opportunities, address customer pain points, and pivot their value propositions effectively. By fostering dynamic capabilities, such as sensing, seizing, and reconfiguring resources, this approach enhances both short-term adaptability and long-term competitiveness. The study contributes to the resilience, business model, and growth literature by advancing our understanding of how data-driven methodologies can act as a catalyst for BMT and organizational resilience, offering actionable insights for both theory and practice. Our framework underscores the strategic importance of integrating growth hacking principles into business model innovation to build robust organizational resilience in an increasingly turbulent environment.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
REVIEW OF MANAGERIAL SCIENCE
ISSN
1863-6683
e-ISSN
1863-6691
Volume of the periodical
neuveden
Issue of the periodical within the volume
08 December 2025
Country of publishing house
DE - GERMANY
Number of pages
28
Pages from-to
1-28
UT code for WoS article
001631597400001
EID of the result in the Scopus database
2-s2.0-105024139487