Self-reinforcing barriers to municipal digital government: A system dynamics perspective

Abstract

Municipal governments have invested substantially in digital government endeavors, yet progress in public sector digital transformation remains surprisingly slow. Although research has repeatedly identified, categorized and reclassified barriers while also proposing numerous strategies to address them, the same obstacles continue to reappear across ongoing digitalization efforts. This persistence suggests that existing knowledge about barriers and their remedies remains insufficient. To address this gap, this study adopts a systemic perspective to explain why the barriers to municipal digital government persist, even after being repeatedly identified and targeted. We conducted 56 semi-structured interviews with 67 municipal employees and employees of GovTech providers in Germany and analyzed them through an inductive multi-stage coding process. Using system dynamics principles, we reconstruct the shared mental model through which actors perceive, explain and interlink barriers into a causal map. The resulting model reveals a dense web of interdependencies among barriers that form extended causal chains and self-reinforcing feedback loops. This perspective provides a dynamic and system-level explanation of persistence that reconceptualizes barriers not as discrete factors, but as generative mechanisms that sustain digital stagnation through their mutual reinforcement.