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

Municipal governments have invested substantially in digital government endeavors, yet progress in public sec-
tor 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. ... mehrThis 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.