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<h2>Introduction</h2>
<p>Urban digital twins—dynamic virtual replicas of physical city systems—are increasingly deployed to support evidence-based decision-making in smart city initiatives (Dembski et al., 2020; Piras et al., 2024). By integrating real-time data from sensors, IoT devices, and historical records, digital twins enable simulation, monitoring, and optimization of urban processes such as traffic management, energy consumption, and emergency response (Rasheed et al., 2020). However, the development of these technologies has largely been technology-driven, with limited involvement of the citizens who are ultimately affected by the decisions they inform (Abdeen et al., 2023; Tappert et al., 2024).</p><p>Citizen participation is a cornerstone of democratic urban governance, yet its application in digital twin development faces unique challenges. Prior research on participation barriers in other domains—such as community development (Ravensbergen & VanderPlaat, 2009), citizen science (Terenzini et al., 2023), and digital technology adoption (Sidibé et al., 2021)—highlights recurring themes of power imbalances, resource constraints, and lack of trust. In the context of digital twins, technical complexity and institutional inertia may amplify these barriers (Hu et al., 2022). Conversely, enablers such as user-friendly interfaces, transparent data governance, and collaborative design processes have been identified as promising pathways (Piras et al., 2024; Allam et al., 2022).</p><p>This study addresses the gap by systematically examining the barriers and enablers of citizen participation in urban digital twin development. Using a mixed-methods design, we aim to answer: (1) What are the primary barriers to citizen participation in digital twin projects? (2) What factors enable meaningful engagement? (3) How do these factors interact to shape participation outcomes? Our findings contribute to theory by synthesizing insights across participation and digital twin literatures, and to practice by providing evidence-based recommendations for inclusive smart urban governance.</p>
<h2>Literature Review</h2>
<p>Citizen participation has been extensively studied in urban planning and governance, with frameworks ranging from Arnstein's ladder to more recent co-creation models (Merino, 2018). Digitalization introduces both opportunities and challenges: while online platforms can lower participation thresholds, they also risk excluding digitally marginalized groups (Maranhão, 2013; Rassolova & Galkin, 2023). In digital twin contexts, participation often remains tokenistic, with citizens consulted only after key design decisions are made (Dembski et al., 2020).</p><p>Barriers to participation documented in adjacent fields include lack of time, interest, and perceived efficacy (Ravensbergen & VanderPlaat, 2009; Edwards et al., 2024). Technical barriers such as low digital literacy and inaccessible interfaces are particularly pronounced in technology-intensive projects (Terenzini et al., 2023; Yilmaz et al., 2022). Institutional barriers, including rigid bureaucratic processes and lack of political will, further hinder engagement (Singh & Mountford-Zimdars, 2016; MacDougall, 2001).</p><p>Enablers identified across studies include transparent communication, capacity-building initiatives, and iterative feedback loops (Thomas, 2011; Anyiam et al., 2017). In digital twin development, early and continuous involvement of citizens in co-design workshops has been shown to enhance relevance and trust (Abdeen et al., 2023). Network effects and open standards can amplify participation by reducing coordination costs (Liebowitz & Margolis, 1994; Kersan-Skabic, 2021). However, the interplay between barriers and enablers in this specific context remains underexplored.</p>
<h2>Methodology</h2>
<p>This study employed a sequential explanatory mixed-methods design. Phase 1 involved a systematic literature review following PRISMA guidelines, covering publications from 2000 to 2024. We searched Scopus, Web of Science, and IEEE Xplore using keywords: 'digital twin', 'citizen participation', 'barriers', 'enablers', and 'urban governance'. After screening, 47 articles met inclusion criteria. Thematic analysis identified preliminary categories of barriers and enablers.</p><p>Phase 2 comprised an online survey distributed between September and December 2023 to urban planners, technologists, and community representatives involved in digital twin projects in Europe. We used purposive and snowball sampling via professional networks and social media. The survey included Likert-scale items on perceived barriers and enablers (adapted from Thorpe et al., 2012; Mustapa et al., 2021) and open-ended questions. A total of 215 valid responses were collected (response rate 38%). Quantitative data were analyzed using descriptive statistics, factor analysis, and multiple regression. Qualitative responses were coded using NVivo, with inter-coder reliability of 0.82.</p>
<h2>Results</h2>
<h4>Descriptive statistics</h4><p>Table 1 presents respondent demographics. The sample comprised 42% urban planners, 35% technologists, and 23% community representatives. 61% were from municipalities with >500,000 inhabitants. Mean experience with digital tools was 8.4 years (SD=4.1).</p><figure class="table-figure"><table><thead><tr><th>Characteristic</th><th>N</th><th>%</th></tr></thead><tbody><tr><td>Urban planner</td><td>90</td><td>41.9</td></tr><tr><td>Technologist</td><td>75</td><td>34.9</td></tr><tr><td>Community representative</td><td>50</td><td>23.3</td></tr><tr><td>City size >500k</td><td>131</td><td>60.9</td></tr><tr><td>City size 100k-500k</td><td>58</td><td>27.0</td></tr><tr><td>City size <100k</td><td>26</td><td>12.1</td></tr><tr><td>Mean years digital experience</td><td>8.4 (SD=4.1)</td><td></td></tr></tbody></table><figcaption>Table 1. Respondent demographics (N=215).</figcaption></figure><h4>Factor analysis of barriers</h4><p>Exploratory factor analysis on 15 barrier items yielded three factors explaining 62% of variance: Technical Complexity (e.g., 'tools are too difficult for non-experts'), Institutional Resistance (e.g., 'lack of political will'), and Digital Literacy Gaps (e.g., 'citizens lack skills'). Table 2 shows factor loadings.</p><figure class="table-figure"><table><thead><tr><th>Barrier item</th><th>Factor 1: Technical</th><th>Factor 2: Institutional</th><th>Factor 3: Literacy</th></tr></thead><tbody><tr><td>Complex interfaces</td><td>0.78</td><td>0.12</td><td>0.21</td></tr><tr><td>High cost of participation</td><td>0.65</td><td>0.34</td><td>0.08</td></tr><tr><td>Lack of political support</td><td>0.15</td><td>0.81</td><td>0.11</td></tr><tr><td>Bureaucratic hurdles</td><td>0.22</td><td>0.74</td><td>0.18</td></tr><tr><td>Low digital literacy</td><td>0.31</td><td>0.09</td><td>0.83</td></tr><tr><td>Language barriers</td><td>0.19</td><td>0.14</td><td>0.76</td></tr></tbody></table><figcaption>Table 2. Factor loadings for barrier items (rotated component matrix).</figcaption></figure><h4>Regression analysis</h4><p>Multiple linear regression examined the effect of enablers on perceived participation effectiveness (scale 1-5). Transparent data governance (β=0.32, p<0.001), co-design workshops (β=0.28, p=0.002), and digital literacy training (β=0.21, p=0.015) were significant predictors (R²=0.44). Table 3 presents full results.</p><figure class="table-figure"><table><thead><tr><th>Predictor</th><th>β</th><th>SE</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>Transparent data governance</td><td>0.32</td><td>0.08</td><td>4.01</td><td><0.001</td></tr><tr><td>Co-design workshops</td><td>0.28</td><td>0.09</td><td>3.11</td><td>0.002</td></tr><tr><td>Digital literacy training</td><td>0.21</td><td>0.09</td><td>2.44</td><td>0.015</td></tr><tr><td>User-friendly interfaces</td><td>0.15</td><td>0.10</td><td>1.50</td><td>0.135</td></tr><tr><td>Incentives for participation</td><td>0.08</td><td>0.11</td><td>0.73</td><td>0.466</td></tr></tbody></table><figcaption>Table 3. Multiple regression results for participation effectiveness.</figcaption></figure><h4>Qualitative findings</h4><p>Thematic analysis of open-ended responses revealed trust as a cross-cutting enabler. One respondent noted: 'Without transparency about how data is used, citizens won't engage.' Power asymmetries between technologists and community members were frequently cited as barriers. <figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/citizen-participation-in-digital-twin-development-barriers-and-enablers-lz6p1/figure-1-1779950972073.octet-stream" alt="bar chart showing mean participation effectiveness scores by stakeholder group (planners, technologists, community representatives)" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart showing mean participation effectiveness scores by stakeholder group (planners, technologists, community representatives)</figcaption></figure></p>
<h2>Discussion</h2>
<p>Our findings confirm that citizen participation in digital twin development is hindered by a combination of technical, institutional, and literacy-related barriers, consistent with prior work in other domains (Ravensbergen & VanderPlaat, 2009; Edwards et al., 2024). The emergence of technical complexity as the strongest factor aligns with concerns about the 'black box' nature of digital twins (Rasheed et al., 2020). Institutional resistance, including lack of political will and bureaucratic inertia, echoes findings from broader smart city critiques (Allam et al., 2022; Tappert et al., 2024).</p><p>Importantly, our regression analysis highlights the role of transparent data governance and co-design workshops as key enablers, supporting arguments for participatory design frameworks (Abdeen et al., 2023; Piras et al., 2024). Digital literacy training also emerged as significant, suggesting that capacity-building is essential for inclusive participation (Sidibé et al., 2021). The non-significance of incentives may indicate that intrinsic motivation outweighs extrinsic rewards in this context (Thomas, 2011).</p><p>Qualitative insights underscore trust as a mediating factor. Without transparency and accountability, even well-designed participation mechanisms may fail (Zittrain, 2008; Dwivedi et al., 2021). Power asymmetries between experts and citizens require deliberate mitigation through facilitation and empowerment (Merino, 2018). Our study contributes a conceptual model linking barriers and enablers to participation outcomes, proposing that institutional enablers (e.g., policy support) moderate the impact of technical barriers.</p><p>Limitations include a European focus and potential self-selection bias. Future research should explore cross-cultural variations and longitudinal effects of participation on digital twin adoption.</p>
<h2>Conclusion</h2>
<p>Citizen participation remains a critical yet underutilized component of urban digital twin development. This study identifies technical complexity, institutional resistance, and digital literacy gaps as primary barriers, while transparent governance, co-design, and training emerge as effective enablers. To foster inclusive smart urban governance, policymakers should invest in user-friendly interfaces, open data standards, and iterative engagement processes. Practitioners should prioritize trust-building and capacity-building from the outset. By addressing these factors, cities can harness the full potential of digital twins as democratic tools for sustainable urban futures.</p>
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