From Complexity to Clarity

A Taxonomy of GovTech Startup Business Models for Public Sector Innovation

Abstract

Digital transformation in public administration is increasingly driven by GovTech startups, which leverage innovative technologies to address complex societal and administrative challenges. Despite their growing importance, the business models underlying these startups remain poorly understood, limiting their effective integration into public sector ecosystems. While existing research focuses on defining GovTech and exploring its potential, little attention has been paid to the systematic analysis of the business models that enable these startups to align technological innovation with the unique requirements of public administration. Addressing this gap, our study develops a taxonomy of GovTech business models to provide clarity and actionable insights for stakeholders. Our research identifies 11 dimensions and 55 characteristics that structure the business models of GovTech startups, emphasizing meta-dimensions such as value proposition, value creation and delivery, and value capture. These insights offer a comprehensive framework to classify and analyse the diverse approaches within the GovTech sector. By analysing 85 GovTech startups in Germany through an iterative taxonomy development process, we reveal distinct patterns in areas such as funding strategies, stakeholder alignment, technological enablers, and operational scope. The taxonomy highlights how GovTech startups address key challenges like citizen engagement, operational efficiency, and compliance assistance, providing practical examples of successful implementations. This study contributes to both theory and practice by offering a structured understanding of GovTech business models. For researchers, the taxonomy provides a foundation for further exploration of this emerging field. For practitioners and policymakers, it serves as a strategic tool to foster public-private collaboration, optimize innovation adoption, and address public sector challenges in a dynamic environment.