Adapting Growth Models for Digital Startups: Empirical Evidence and Directions for Digital Entrepreneurship Research
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Proceedings of ICIS 2024. The Association for Information Systems (AIS), 2024.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Adapting Growth Models for Digital Startups
T2 - Empirical Evidence and Directions for Digital Entrepreneurship Research
AU - Tschoppe, Nils Johann
AU - Drews, Paul
PY - 2024
Y1 - 2024
N2 - We contribute to digital entrepreneurship research by adapting growth models to better reflect the dynamics of entrepreneurial growth trajectories and increasing digital technology pervasion in digital startups. By grounding our findings in a dataset that includes interviews with founders and executive managers of 24 digital startups from eight countries, we propose three directions for advancing theory that supports a better understanding of the complexity and ambiguity of digital startups when growing. Directions I and II aim at revising and extending established growth models to support research on attributes that better capture the ubiquitous digital technology pervasion in digital startups. In direction III, we explore seminal research on dynamic states that contradict the dominant view of deterministic stages in growth model research and illustrate the need to integrate both approaches when designing new theoretical growth models by presenting four archetypes of digital infrastructure evolution.
AB - We contribute to digital entrepreneurship research by adapting growth models to better reflect the dynamics of entrepreneurial growth trajectories and increasing digital technology pervasion in digital startups. By grounding our findings in a dataset that includes interviews with founders and executive managers of 24 digital startups from eight countries, we propose three directions for advancing theory that supports a better understanding of the complexity and ambiguity of digital startups when growing. Directions I and II aim at revising and extending established growth models to support research on attributes that better capture the ubiquitous digital technology pervasion in digital startups. In direction III, we explore seminal research on dynamic states that contradict the dominant view of deterministic stages in growth model research and illustrate the need to integrate both approaches when designing new theoretical growth models by presenting four archetypes of digital infrastructure evolution.
M3 - Article in conference proceedings
BT - Proceedings of ICIS 2024
PB - The Association for Information Systems (AIS)
ER -