Context matters: How national settings shape public sector digital transformation
Fatemeh Ahmadi Zeleti & Lukasz Porwol
What the paper says
Public sector organizations in remote and sparsely populated regions face persistent barriers in adopting digital innovation, from limited broadband and human capacity to rigid bureaucratic processes. This teaching case examines how peripheral public sector contexts can build innovation readiness for digital transformation by using the INNOCAP framework – a model developed under the Interreg Northern Periphery and Arctic (NPA) Programme. Derived from an empirical analysis of 32 real-world digital initiatives across NPA regions, the INNOCAP framework identifies four critical dimensions for capacity-building: Digital Solutions , Procurement of Innovative Solutions , Delivery Models , and Skills . The case explores each dimension through evidence from the 32 case studies, highlighting common challenges (e.g. fragmented pilot projects, inflexible procurement rules, skill shortages) and successful strategies (e.g. co-creation with citizens, adaptive contracting, investment in training) observed in these communities. Comparative insights illustrate how different countries emphasize different aspects of innovation depending on their local context. By analysing this case, students in graduate or executive programs will gain a nuanced understanding of how technology-driven public sector innovation is not just about digital tools, but also about governance readiness: the structures, processes, and skills needed to turn isolated experiments into sustainable, public value-creating transformations. The case is designed to support classroom discussion and individual assignments on digital government, innovation management, and policy implementation in challenging, resource-constrained environments.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.