Data-driven growth and business model transformation: how startups unlock resilience in turbulent times

Abstract 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.