Unpacking the citizen role in digital government maturity models
Åsa Agneta Waara et al.
What the paper says
Purpose This study aims to examine and illustrate the landscape of citizens’ roles in digital government maturity models (DGMM) based on public value positions. It seeks to answer: How are citizens’ roles conceptualized and integrated into DGMM? Design/methodology/approach This study uses an interpretive qualitative content analysis. This study examined 18 DGMMs, analysing both their explicit and implicit representations of citizens. Using Distel and Lindgren’s (2023) extended e-government value position framework as a theoretical lens, the authors positioned the role of citizens within these models. Findings The study reveals that DGMMs vary in how they view citizens’ roles. While all four value ideals acknowledge citizens, models emphasizing efficiency and professionalism tend to regard them as passive participants, whereas service and engagement ideals promote more active and participatory roles. Research limitations/implications The analysis examines 18 DGMMs. While diverse, it may not reflect all emerging models or regional variations. Citizen role interpretation involves some subjectivity, despite collaborative efforts for consistency. Practical implications The findings provide a framework for understanding citizens’ roles through different value positions, supporting the public sector in strengthening its citizen focus. Social implications The study offers insights for national and local levels on including citizens in digital transformation, contributing to greater awareness of their diverse roles in digital government maturity. Originality/value This study presents a novel synthesis of citizens’ roles within the extended public value framework, opening new avenues for research and policy development.
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.