Bridging the Capability Gap: A Multidimensional Maturity Model for Smart City Development in German Municipalities

Abstract

Municipal smart city programs remain hampered by conceptual fragmentation and the absence of validated, context-specific maturity assessments. We develop the Smart Municipality Maturity Model (SMMM) via a design-science process, synthesizing 183 publications and adapting a practitioner-oriented self-assessment with 99 binary items across ten dimensions. Validation proceeded in four stages: expert review, industry validation, a pilot with 24 municipalities, and a large-scale rollout to 1136 municipalities. The five-level model yields comparable maturity scores and reveals a structural capability gap—governance and strategy outpace foundational technical capacities, especially digital infrastructure and data management. Maturity rises with municipality size, yet leadership, partnerships, and innovation culture act as moderators. The SMMM represents one of the first empirically validated, large-scale maturity assessments tailored to municipal administrations, providing a robust analytical basis for diagnosing capability gaps at scale. Its architecture directly supports municipal policy by translating conceptual smart city ambitions into measurable, comparable operational capacities and by enabling more targeted, evidence-driven interventions. The SMMM provides a low-burden instrument for self-assessment, peer benchmarking, and evidence-based policy design. Closing the identified capability gap requires capability-first investment and more explicit integration of cybersecurity and data privacy in future models and municipal practice.

1. Introduction

Smart city initiatives have become a default response to mounting urban pressures. Yet the field remains conceptually fragmented, with multiple overlapping definitions and shifting emphases across technology, sustainability, and citizen-centric goals [1,2,3]. This heterogeneity impedes cumulative knowledge and consistent assessment. Concerns central to instrumented cities—such as data protection and cybersecurity—are often underweighted in definitional treatments, complicating governance and risk management [1,2,3,4].

For municipal administrations, the practical problem is sharper: existing maturity frameworks are frequently too abstract, mathematically heavy, or insufficiently validated with end-users, which undermines comparability and decision support [5,6,7]. In short: much ambition, thin instrumentation.

This article introduces the Smart Municipality Maturity Model (SMMM), a practitioner-oriented framework tailored to municipal governance and operations. Methodologically, we follow the Design Science Research method to design, instantiate, and evaluate the artifact in real-world contexts [8,9]. The SMMM operationalizes ten assessment dimensions with 99 binary items and five maturity levels; language and logic are simplified for administrative use [10,11,12]. Validation proceeds in four stages—expert review, industry validation, a pilot with 24 municipalities, and large-scale deployment across 1136 municipalities—yielding comparable scores and robust patterns [11]. We position SMMM alongside established public-sector frameworks to enable self-assessment, peer benchmarking, and policy design [13,14].

Research Questions and Rationale: We derive the following two questions from four persistent gaps in the literature and practice—definitional ambiguity, validation deficits, context-specificity, and usability [5,6,7,10]:

• RQ1 (Model Development and Validation). How can a comprehensive, empirically validated Smart City Maturity Model (SMMM) be developed and tested to meet the administrative and governance requirements of German municipalities?

• Rationale: addresses validation deficits and context-specificity through a DSR pipeline and staged evaluation [8,9,10,11].

• RQ2 (Empirical Pattern and Capability Gap). How is digital maturity distributed across the ten SMMM dimensions in German municipalities, and what evidence substantiates a systemic capability gap between strategy/governance and foundational technical capacity (infrastructure, data management)?

• Rationale: targets comparability and practical steering by quantifying where implementation bottlenecks bind outcomes [10,11,13,14].

Contributions: We synthesize the conceptual landscape; deliver an operational, validated instrument; and provide quantitative evidence of a structural capability gap that informs capability-first investment and the explicit integration of security/privacy in future assessments and practice [4,10,11,13,14].

2. Materials and Methods

2.1. Research Design

We adopted a Design Science Research (DSR) approach to design, instantiate, and evaluate the Smart Municipality Maturity Model (SMMM) in municipal practice [8,9]. The artifact is a self-assessment instrument oriented to German municipalities. The unit of analysis is the municipality, not individuals. The design choices follow established guidance on maturity-model development regarding usefulness, phases, and evaluation heuristics [5,6,7]. Our goal was not only conceptual completeness but also administrative usability.

The development of the SMMM followed a Design Science Research (DSR) approach to ensure both scientific rigor and practical relevance [6]. DSR provides a structured methodology for creating and evaluating artifacts such as models, frameworks, and instruments [8]. In our context, DSR guided the conceptualization, iterative refinement, and validation of the maturity model. The process comprised six interconnected phases as follows: (1) problem identification and scoping based on practitioner needs; (2) consolidation of existing knowledge through the systematic literature review; (3) design of initial dimensions and indicators using insights from prior work and practitioner-oriented instruments; (4) artifact development and iterative refinement during expert workshops; (5) evaluation through a multi-stage validation pipeline; and (6) deployment in a large-scale real-world setting.

The four-stage evaluation procedure—expert review, industry validation, pilot implementation in 24 municipalities, and final rollout to more than 1100 municipalities—corresponds to the iterative evaluation and problem–solution fit emphasized in DSR. Feedback loops in each phase informed refinements to dimensions, item phrasing, and model logic, ensuring that the artifact remained both theoretically grounded and practically applicable. In line with DSR principles, the final SMMM therefore represents an empirically validated solution to a clearly defined organizational problem: the need for a standardized, low-burden instrument for assessing municipal digital maturity.

2.2. Literature Review

A structured review synthesized 183 publications to derive requirements, concepts, and indicators relevant to municipal smart city capabilities. To ensure transparency and methodological rigor, we applied a multi-stage review protocol inspired by established guidelines for systematic literature reviews in information systems research and aligned with the procedures documented in prior work on maturity-model and instrument development [11,15,16]. The review process consisted of the following five steps: (i) definition of the conceptual scope, (ii) development of search strings, (iii) screening of titles, abstracts, and full texts, (iv) coding and classification of constructs, and (v) synthesis of dimensions and indicators.

Search strings combining “Smart City”, “Smart Municipality”, “Maturity Model”, “Digital Transformation”, and related terms were applied across major academic databases, including Google Scholar, ScienceDirect, SpringerLink, and complementary gray-literature sources. All retrieved publications were screened for relevance using the three-stage protocol recommended by vom Brocke et al. (2015): title relevance, abstract relevance, and full-text relevance [16]. Backward and forward snowballing ensured that influential earlier studies and recent publications were included.

Although the search process initially identified 183 publications, the subsequent three-stage screening procedure—title relevance, abstract review, and full-text assessment—led to the exclusion of 24 sources that were redundant, conceptually peripheral, not peer-reviewed, or not focused on municipal smart city capability development. The resulting corpus of 159 relevant publications served as the basis for coding and synthesis. Each of the 159 retained sources was coded using a structured template capturing the following: (i) conceptual themes, (ii) capability constructs, (iii) operational indicators, (iv) maturity-related characteristics, and (v) applicability to municipal contexts. The coding scheme followed the analytical procedure established in previous design-oriented reviews (e.g., [15]), ensuring consistent extraction of constructs and facilitating cross-publication comparison.

The synthesis of the coded material resulted in the following three outputs:

(1)

a consolidated set of ten capability dimensions covering strategy, governance, data, infrastructure, services, operations, standards, innovation, and performance;

(2)

an operational item pool from which the final 99 binary indicators were derived;

(3)

a set of design requirements used in the subsequent conceptualization of the SMMM.

The ten dimensions represent categories repeatedly identified across the literature corpus as foundational elements of municipal smart city capability. The 99 binary items were developed by adapting and enhancing an existing practitioner-oriented instrument [10] and validating the resulting item set against the coded evidence from the review. This ensured both conceptual completeness and practical usability.

A complete list of all relevant reviewed publications (n = 159) is provided in Appendix B to ensure full traceability of the literature base and to support replication and reuse in future research.

2.3. Model Development

The model development followed the DSR-guided design and evaluation process outlined in Section 2.1, translating the synthesized literature evidence and practitioner insights into a structured maturity model. Building on prior smart city guidance and municipal practice sources [10,11,13,14], we developed a practitioner-oriented maturity model with five levels and ten assessment dimensions. Language and item wording were simplified to fit administrative contexts without diluting diagnostic value. The instrument operationalizes the following dimensions: Strategy and Vision; Stakeholder Engagement; Partners and Intelligent Services; Digital Infrastructure; IT and Digital Data Strategy; Data Accessibility; ICT Operations; Standards; Innovation Ecosystem; and Performance Management (dimension labels follow the terminology established during pretests and expert review) [11]. Figure 1 summarizes the ten assessment dimensions of the SMMM/the architecture of the SMMM. The full content of the instrument (99 binary items) is provided in Appendix A.2 The Smart Municipality Maturity Model Assessment Framework.

2.4. Instrument Structure and Scoring

The questionnaire comprises 99 binary (yes/no) items with light conditional branching to reduce burden while preserving coverage [11]. Dimension scores are simple sums of the respective items; the overall maturity score is the unweighted sum (0–99). Five maturity levels map to score ranges 0–20, 21–40, 41–60, 61–80, and 81–99. We chose equal weights for interpretability and comparability in public-sector use. We did not apply population weights; each municipality counts once. Guidance notes clarify typical patterns and edge cases (e.g., when high governance scores coincide with weak infrastructure) [11]. To aid interpretation, Figure 2 summarizes the five SMMM maturity levels and their score thresholds (0–99). The level labels are used consistently throughout the paper and anchor all subsequent reporting and comparisons.

2.5. Validation Pipeline

Validation proceeded in the following four stages:

• Expert review to assess clarity and content coverage;
• Industry validation with a public IT provider to ensure operational relevance;
• Pilot with 24 municipalities to test usability and scoring patterns;
• Large-scale deployment across 1136 municipalities (≈3400 decision-makers) to assess robustness and comparability [11].

Design decisions were cross-checked against established public-sector maturity frameworks to maintain compatibility and interpretability for practitioners [13,14].

2.6. Data Collection and Sample

Data were collected via a web-based questionnaire distributed to all municipalities in Baden-Württemberg (Germany). Reporting uses population-based categories common to state administration (e.g., <7500; 7500–20,000; large district towns; urban districts; rural districts). Responses were aggregated at the municipal level; no personally identifiable information was analyzed [11].

2.7. Measures and Variables

For each dimension, we compute a dimension score (0–10). The overall maturity is the sum of all dimensions (0–99) with level assignment as in Section 2.4. We also record population class (categorical) to explore size–maturity patterns. Additional indicators (e.g., self-reported leadership commitment and partnership density) are used descriptively, where available, to contextualize maturity profiles [11].

2.8. Analysis Plan

Analyses focus on descriptive statistics (means and dispersion) and between-group comparisons across population classes. We check distributional assumptions to choose parametric or non-parametric tests. To support diagnosis, we report dimension-level profiles that highlight potential capability gaps between governance/strategy dimensions and foundational technical capacity (infrastructure and data management) [10,11,13,14]. Exact test specifications and robustness checks are reported in Section 3. All descriptive means are computed as unweighted averages at the municipality level (each municipality contributes equally); no population weighting is applied.

2.9. Methodological Limitations

The instrument is self-assessed, which can introduce optimism or uneven interpretation despite binary phrasing. We explored robustness against uniformly optimistic responses; results are directionally unchanged. Equal weights improve transparency but may understate dimension importance in specific contexts; this is a deliberate usability trade-off. Finally, external validity is strongest for German municipalities with similar administrative structures; transfer to other countries should include local content validation against prevailing frameworks [13,14].

3. Results

This section presents the empirical patterns from the large-scale deployment of the SMMM in Baden-Württemberg. We begin with the distribution of maturity scores by population class, then examine dimension-level profiles that surface a structural capability gap, and finally provide a focused subgroup contrast. Reporting aligns with the SMMM architecture (five levels, ten dimensions, and 99 binary indicators) and the staged validation described earlier [8,9,11]. Data for

Table 1. Smart city maturity scores by population category (mean, SD, and range).

Population Category	Number of Municipalities	Average Maturity Score	Standard Deviation	Range
<7500 inhabitants	766	34.2	12.8	8–72
7500–20,000 inhabitants	233	42.7	15.3	15–78
Large district towns	93	56.4	18.2	22–89
Urban districts	9	67.8	14.6	45–86
Rural districts	35	61.3	16.9	28–84

Data from the validation with German municipalities in Baden-Württemberg.

and

Table 2. Average maturity scores by dimension and population category.

Dimension	Small (<7500)	Medium (7500–20,000)	Large District Towns	Urban Districts	Rural Districts
Smart Strategy and Vision	6.2	7.1	8.3	8.9	8.0
Stakeholder Engagement	5.8	6.4	7.6	8.2	7.3
Intelligent Services	4.3	5.7	7.2	8.1	6.8
Digital Infrastructure	2.9	4.1	6.3	7.4	5.6
Data Strategy	3.1	4.5	6.8	7.8	6.2
Data Accessibility	2.4	3.8	5.9	7.2	5.1
ICT Operations	3.6	4.9	6.7	7.6	6.4
Standards	4.2	5.3	6.9	7.7	6.6
Innovation Ecosystem	2.8	4.2	6.4	7.5	5.9
Performance Management	5.1	6.2	7.4	8.3	7.1

Data from the validation with German municipalities in Baden-Württemberg.

originate from the study dataset underlying the manuscript scaffold.

3.1. Overall Distribution of Maturity by Population Class

Average maturity rises with municipality size, but dispersion within classes is large. Table 1 summarizes the following pattern: Urban Districts average 67.8 (SD 14.6, range 45–86), Rural Districts 61.3 (SD 16.9, 28–84), Large District Towns 56.4 (SD 18.2, 22–89), Medium Municipalities 42.7 (SD 15.3, 15–78), and Small Municipalities 34.2 (SD 12.8, 8–72) (all values from the study dataset). The gradient is compatible with resource endowments, specialization, and administrative scope in larger jurisdictions [1,17,18,19], yet the wide ranges in every class warn against a deterministic account. Governance capacity, leadership, partnerships, and an innovation culture appear to moderate capability accumulation beyond raw size effects [20,21,22,23]. Figure 3 visualizes the size gradient in overall maturity; the full statistics (mean, SD, range) appear in Table 1, while

Table A2. Average overall maturity by municipality type (0–99).

Municipality Type	Average Overall Maturity (0–99)
Urban Districts	67.8
Rural Districts	61.3
Large Towns	56.4
Medium Municipalities	42.7
Small Municipalities	34.2

lists the means used for the figure.

3.2. Dimension Profiles: Governance Leads; Infrastructure and Data Lag

Aggregate dimension means exposing a systematic imbalance across municipalities. Governance- and strategy-oriented areas—Strategy and Vision and Stakeholder Engagement—sit near the top, while Digital Infrastructure and Data Accessibility form the lower bound. This profile is consistent with the conceptual trajectory of the field, where governance and institutional arrangements have grown central alongside technology and people [20,22,23,24], and with indicator-centric frameworks that codify operational capabilities unevenly across domains [13,14,25,26,27].

Figure 4 displays municipality-level (unweighted, equal-weight) means across the ten SMMM dimensions (0–10). A consistent profile emerges: governance and strategy lead; infrastructure and data access lag.

The numerical backbone appears in Table 2, which reports means by population class. For Small Municipalities (<7500), Strategy and Vision averages 6.2, whereas Digital Infrastructure and Data Accessibility average 2.9 and 2.4. For Urban Districts, the corresponding values are 8.9, 7.4, and 7.2. We refer to this persistent asymmetry as a strategic–technical capability gap [1,4,17,18,28,29,30]. Table 2 provides the class-specific breakdown, and

Table A3. Dimension-level averages across all municipalities (0–10).

Dimension	Average Score (0–10)
Strategy & Vision	8.2
Stakeholder Engagement	6.9
Digital Infrastructure	4.1
Data Strategy	3.8
Data Accessibility	3.2

lists the headline means shown here.

3.3. Subgroup Contrast: Urban Districts Versus Small Municipalities

Figure 5 contrasts Urban Districts with Small Municipalities across the ten SMMM dimensions. The largest separations occur in Digital Infrastructure and Data Accessibility, whereas Strategy and Vision and Stakeholder Engagement are comparatively high in both groups. Urban Districts substantially outperform Small Municipalities in the technical/data core—Digital Infrastructure (7.4 vs. 2.9) and Data Accessibility (7.2 vs. 2.4)—while both maintain comparatively higher scores in Strategy and Vision (8.9 vs. 6.2). In short, intent outpaces capability: strategies and stakeholder processes are often in place, but the backbone for scalable implementation lags [28,29,30].

3.4. Robustness and Sensitivity

Three checks support the capability-gap interpretation. First, within-class dispersion remains high even among resource-rich jurisdictions (e.g., Urban Districts 45–86), arguing against a purely resource-deterministic view [1,17,18,19]. Second, the imbalance appears both in the aggregate profile (Figure 4) and in the subgroup contrast (Figure 5), indicating cross-metric coherence. Third, the pattern coheres with the instrument’s design (ten dimensions, 99 binary items) and the four-stage validation pipeline, reducing the odds of a measurement artifact [6,7,8,9,11].

4. Discussion

This study sets out to develop and validate a municipality-specific maturity model and to assess the empirical distribution of digital capabilities across German municipalities. The results indicate a robust strategic–technical capability gap: strategy and stakeholder-related capacities lead, while technical and data foundations lag (notably Digital Infrastructure and Data Accessibility). Below, we interpret this gap, position the contribution within the literature, compare SMMM with recent models (2023–2025), and discuss practical implications, limitations, and future research.

While this study deliberately focuses on digital and data-related municipal capabilities, we fully acknowledge that contemporary smart city research encompasses a broader set of domains, including sustainability, environmental quality, social well-being, mobility, and urban resilience. These perspectives are essential for a comprehensive understanding of urban development; however, they fall outside the operational mandate of the target respondent group and thus beyond the reliable scope of a 99-item self-assessment instrument. Incorporating such additional domains would likely have diluted measurement validity, increased response burden, and produced incomplete or inconsistent returns. For these reasons, the SMMM concentrates on foundational digital capability maturity, while sustainability-oriented and quality-of-life dimensions remain important areas for complementary future research and model extensions.

4.1. Interpretation of the Systemic Capability Gap

The pattern documented in Section 3 is best interpreted as a capability gap rather than a willingness gap. Municipalities—across size classes—report comparatively strong Strategy and Vision and Stakeholder Engagement, suggesting political will, governance intent, and participatory processes are present. What constrains impact is the backbone for execution: connectivity, data plumbing, operational ICT routines, and the capacity to make data usable at scale. This reading is consistent with recent reviews that detect a governance turn in the smart city literature alongside persistent shortfalls in operational capacity and measurement standardization [19,28,29,30]. It also aligns with the definitional landscape: security and privacy are explicitly mentioned in only ~9% of definitions, a systemic blind spot that plausibly diffuses into practice, leaving foundational safeguards underweighted [4].

A notable aspect of this imbalance is that governance and strategy tend to mature earlier than technical and data-related capabilities in many municipalities. This reflects structural conditions in the public sector: strategic conversations, role clarification, and governance reforms commonly precede substantial technological investment, especially in small and medium-sized administrations. Limited financial resources, fragmented legacy infrastructures, and scarce specialist personnel constrain the adoption of advanced technologies such as Digital Twins. As a result, municipalities are often at a much earlier stage of digital transformation than implied by high-end smart city discourse. The SMMM is therefore intentionally positioned at the level of foundational capability maturity, as these conditions must be in place before advanced technologies can be meaningfully deployed or evaluated.

Interpreting the misalignment as a capability gap has two organizational implications. First, organizational learning must privilege proceduralization (e.g., patching, backups, continuity testing, access control, and monitoring) and codified data stewardship before layering on more “smart” services. Second, data literacy—from senior leadership through to front-line units—needs to extend beyond dashboards to issues of data quality, provenance, metadata, and security/privacy by design [4,20]. The working hypothesis that intent outpaces capacity is therefore supported by both the descriptive evidence and the direction of recent syntheses [19,28,29,30].

4.2. Theoretical Contributions and Alignment with the Literature

The paper contributes to digital governance by showing, on a large empirical base, that governance-proximate dimensions often mature faster than infrastructural/data ones—evidence for an asymmetric trajectory of municipal digitalization [19,28,29,30]. Conceptually, this extends integrative smart city governance frameworks (technology–people–institutions; multi-pillar models) by coupling them to an empirically validated, practitioner-usable instrument [13,14,25,26,27]. Methodologically, the model follows a Design Science Research approach with four validation stages (expert, industry, pilot, and large-scale deployment), addressing common critiques on external validity and usability in maturity-model research [5,6,7,8,9]. The SMMM thus adds to the maturity-model canon by exemplifying low-cost self-assessment with standardized comparability while retaining diagnostic value (99 binary items across ten dimensions).

4.3. Comparison with Recent and Representative Maturity Models (2019–2025)

Positioning logic: Table 3 positions the SMMM vis-à-vis representative models (2019–2025). It summarizes widely cited and practice-relevant approaches that frame municipal “smartness” from different angles—corporate social responsibility and sustainability; benchmark-oriented practice toolkits; stage-based urban development; multicriteria index construction; vendor-supported self-assessment; and capability/process awareness.

Reading them together clarifies what each contributes—and, equally, what remains underserved—relative to the Smart Municipality Maturity Model (SMMM) used in this study (ten dimensions, 99 binary items; four-stage validation; large-N municipal deployment). The latter is purpose-built for low-burden self-assessment and peer benchmarking in municipal administrations [31,32,33,34,35,36,37]; see also Section 2 and

Table A1. Assessment Framework: Dimensions and Contents of Municipal Digital Maturity Level Questions. Source: Adapted and enhanced from Gassmann et al., 2019 [12].

Item	Category 1: Smart Strategy and Vision
1.1	Clarification on whether the municipality possesses a distinct vision for its future development.
1.2	Definition of specific goals, priorities, and necessary actions regarding the future vision.
1.3	Awareness of the primary challenges the municipality is currently facing.
1.4	Opportunity for all stakeholders (e.g., citizens) to participate in developing the vision.
1.5	Execution of a strengths/weaknesses and environmental analysis (e.g., SWOT, PESTEL).
1.6	Consideration of citizens’ needs within the strategic framework.
1.7	Analysis of the specific skills required for the planned projects.
1.8	Usage of terminology that is comprehensible to all involved stakeholders.
1.9	Application of data analytics or comparable methods within the decision-making process.
1.10	Utilization of data analytics insights for making decisions or adapting political guidelines.
Item	Category 2: Stakeholder Engagement
2.1	Involvement of representatives from affected departments, private sector experts, and citizens.
2.2	Active participation of citizens in the planning processes.
2.3	Acting in accordance with generally accepted decisions.
2.4	Informing stakeholders about decisions that have been made.
2.5	Consideration of diverse stakeholder perspectives to improve services.
2.6	Existence of a digital inclusion strategy to ensure access for all societal segments.
2.7	Implementation and usage of new proposals submitted by citizens and businesses.
2.8	Integration of digital and non-digital channels (e.g., workshops) to foster participation.
2.9	Mapping of the municipality’s stakeholder environment.
2.10	Regular measurement of citizen satisfaction regarding services and quality of life.
Item	Category 3: Partners and Intelligent Services
3.1	Usage of strategies to ensure collaboration across different departments.
3.2	Regular review of partners and service providers (e.g., regarding scope of services).
3.3	Existence of a portfolio containing cross-departmental projects.
3.4	Development of employee skills in agile management, UX, or digital business models.
3.5	Focus on agility and innovation in the development of new citizen services.
3.6	Existence of smart pilot projects within this context.
3.7	Reorganization of departments and offices to align with smart service goals.
3.8	Conduct of cost-benefit analyses for smart projects to ensure efficient resource allocation.
3.9	Exchange of information with other municipalities to learn from experiences.
3.10	Prioritization of compatible systems and shared data sources to reduce departmental barriers.
Item	Category 4: Digital Infrastructure
4.1	Plans for joint investments in installing networked facilities with sensors (e.g., emissions, fill levels).
4.2	Existence of a register listing all physical and digital assets for management purposes.
4.3	Equipment of existing physical assets with sensors for data collection.
4.4	Future maintenance plans for assets that are currently installed or managed by third parties.
4.5	Knowledge regarding the ownership of assets within the municipality.
4.6	Use of predictive methods for proactive asset management.
4.7	Connections between public and private infrastructures.
4.8	Accessibility of asset data to all service providers to improve public services.
4.9	Support of service provision through real-time asset data.
Item	Category 5: IT and Digital Data Strategy
5.1	Active management of data, for instance through a data management system (cloud/network).
5.2	Analysis of datasets to gain insights into service delivery or resource utilization.
5.3	Usage of data from various sources to achieve maximum coverage.
5.4	Implementation of standards for data management.
5.5	Definition of compatibility between systems and data sources as a prerequisite.
5.6	Active search for methods to collect more data in real-time.
5.7	Agreements with partners regarding data exchange, security, and management standards.
5.8	Continuous development, monitoring, and review of data processes using agile methods.
5.9	Usage of data for predictive models (e.g., AI, Big Data) to improve services.
5.10	Usage of real-time data to react to unpredictable events.
Item	Category 6: Data Accessibility
6.1	Accessibility of municipal data to the general public.
6.2	Operation of an Open Data platform.
6.3	Consideration of user-friendliness during the creation of the platform (if applicable).
6.4	Existence of physical and virtual spaces to support data communities.
6.5	Definition of goals, results, and appropriate metrics for data exchange.
6.6	Commissioning of internal/external organizations to remove barriers to data exchange.
6.7	Accessibility of administrative and partner data (e.g., utilities) via a data hub.
6.8	Cross-departmental accessibility of data.
6.9	Public accessibility of performance indicators.
6.10	Publication of data in the agreed-upon format.
Item	Category 7: ICT Operations
7.1	Support of smart strategic goals by the existing (digital) infrastructure.
7.2	Provision of access to next-generation broadband networks (wireless/fixed).
7.3	Construction of holistically networked facilities (e.g., e-mobility charging).
7.4	Continuous review of the Information and Communication Technology (ICT) infrastructure.
7.5	Existence of a direct reaction mechanism for ICT system disruptions.
7.6	Capacity of ICT structure to support increasing numbers of sensors and devices.
7.7	Analysis of the existing ICT infrastructure.
7.8	Standardization of ICT facilities.
7.9	Capability of the digital infrastructure to generate real-time data.
7.10	Favorable location of the ICT infrastructure for the municipality.
Item	Category 8: Standards
8.1	Consideration of using open standards (barrier-free data access).
8.2	Dependency of service provider selection on their willingness to adopt chosen standards.
8.3	Usage of standards that promote cross-departmental collaboration and decision-making.
8.4	Usage of standards when building essential technology infrastructure.
8.5	Usage and encouragement of uniform technical vocabulary from standardization bodies.
8.6	Identification of key areas for efficient collaboration of compatible systems.
8.7	Discussion of digital infrastructure development with national/international standard bodies.
8.8	Usage of new standards to break established thought patterns in administration/industry.
8.9	Identification of bottlenecks that can be eliminated through standards.
8.10	Familiarity with various initiatives (e.g., Smart City) of standardization bodies.
Item	Category 9: Innovation Ecosystem
9.1	Involvement of the municipality in promoting innovation.
9.2	Existence of an open incubator to promote new ideas and innovations.
9.3	Investment in innovation to promote competition (e.g., via hackathons).
9.4	Exchange of ideas with start-ups, universities, and SMEs in the city.
9.5	Encouragement of citizens to participate in the innovation movement.
9.6	Taking of calculated risks to implement new ideas.
9.7	Promotion of innovative, data-driven start-ups to drive public sector reforms.
9.8	Accessibility of prototypical smart solutions to citizens.
9.9	Focus of innovations on the specific needs of the municipality.
9.10	Organization of events for citizens to expand networks and exchange ideas.
Item	Category 10: Performance Management
10.1	Use of results-oriented performance indicators (KPIs) to monitor services and processes.
10.2	Transparency of the performance monitoring process and KPI collection.
10.3	Knowledge of the smart administration’s maturity level (e.g., via assessments).
10.4	Familiarity with ISO-37122 indicators for sustainable urban development.
10.5	Existence of a generally accepted evaluation framework with associated figures.
10.6	Existence of clearly defined baseline values for all goals.
10.7	Existence of measurable performance criteria for all intended goals.
10.8	Involvement of different stakeholders in the performance monitoring process.
10.9	Measurement of the ""quality of life"" within the municipality.
10.10	Use of monitoring results to exchange info with other cities and learn from success/failure.

for instrument details.

Table 3. Representative maturity models (2019–2025): focus, validation method, and intended use.

Model/Source (Ref)	Primary Focus	Validation/Evidence	Intended Use/Scope	Notes vs. SMMM
Sulman et al., 2021 [31]	CSR/sustainability lens for smart-city maturity	Conceptual development + structured review; illustrative operationalization	Conceptual robustness; strengthens sustainability & governance perspective	Rich CSR framing; heavier for routine self-assessment than SMMM
TM Forum TR259, 2019/20 [32]	Industry benchmark model; practitioner vocabulary	Ecosystem adoption; iterative releases; practitioner pilots	Cross-city benchmarking; vendor/ecosystem alignment	Strong for benchmarking; less diagnostic on admin-internal gaps; SMMM adds transparent item-level scoring
Caird & Hallett, 2019 [33]	Stage-based urban development/evaluation design	Conceptual synthesis; cases/examples	Policy sequencing; maturity-phase logic	SMMM operationalizes 5 levels + 10 dimensions with validated items
Ghazinoory et al., 2024 [37]	Meta-review; synthesized multidimensional framework	Systematic review & synthesis	Landscape mapping; international comparability	SMMP provides deployable, large-N instrument aligned with the synthesized dimensions
Aragão et al., 2023 [34]	Multicriteria (TOPSIS) smart-city maturity index	MODM/TOPSIS; country dataset	Quantitative ranking where indicator data exist	Needs data & analytics capacity; SMMM lowers burden via binary self-assessment
UrbanTide, 2023 [38]	Vendor-supported self-assessment (EU practice)	Co-developed with Scottish partners; field deployment	Municipal self-diagnosis; investment road-mapping	Similar usability space; SMMM adds uniform item wording, transparent scoring, and state-wide scale
Ajoudanian & Aboutalebi, 2025 [35]	Capability/process-aware CMM for digital transformation	Conceptual + case-oriented/process evidence	Process design; ICT-transformation alignment	Complementary: SMMM shows where gaps are; process-CMMs guide how to fix them
Anschütz, Ebner & Smotnik, 2024 [36]	Maturity model tailored to small/medium cities	Design-science; expert interviews + municipal case (Bad Hersfeld)	SME-scale administrations; context fit under constraints	SMMM complements with large-N deployment and population-class benchmarks

Table 3. Representative maturity models (2019–2025): focus, validation method, and intended use.

Model/Source (Ref)	Primary Focus	Validation/Evidence	Intended Use/Scope	Notes vs. SMMM
Sulman et al., 2021 [31]	CSR/sustainability lens for smart-city maturity	Conceptual development + structured review; illustrative operationalization	Conceptual robustness; strengthens sustainability & governance perspective	Rich CSR framing; heavier for routine self-assessment than SMMM
TM Forum TR259, 2019/20 [32]	Industry benchmark model; practitioner vocabulary	Ecosystem adoption; iterative releases; practitioner pilots	Cross-city benchmarking; vendor/ecosystem alignment	Strong for benchmarking; less diagnostic on admin-internal gaps; SMMM adds transparent item-level scoring
Caird & Hallett, 2019 [33]	Stage-based urban development/evaluation design	Conceptual synthesis; cases/examples	Policy sequencing; maturity-phase logic	SMMM operationalizes 5 levels + 10 dimensions with validated items
Ghazinoory et al., 2024 [37]	Meta-review; synthesized multidimensional framework	Systematic review & synthesis	Landscape mapping; international comparability	SMMP provides deployable, large-N instrument aligned with the synthesized dimensions
Aragão et al., 2023 [34]	Multicriteria (TOPSIS) smart-city maturity index	MODM/TOPSIS; country dataset	Quantitative ranking where indicator data exist	Needs data & analytics capacity; SMMM lowers burden via binary self-assessment
UrbanTide, 2023 [38]	Vendor-supported self-assessment (EU practice)	Co-developed with Scottish partners; field deployment	Municipal self-diagnosis; investment road-mapping	Similar usability space; SMMM adds uniform item wording, transparent scoring, and state-wide scale
Ajoudanian & Aboutalebi, 2025 [35]	Capability/process-aware CMM for digital transformation	Conceptual + case-oriented/process evidence	Process design; ICT-transformation alignment	Complementary: SMMM shows where gaps are; process-CMMs guide how to fix them
Anschütz, Ebner & Smotnik, 2024 [36]	Maturity model tailored to small/medium cities	Design-science; expert interviews + municipal case (Bad Hersfeld)	SME-scale administrations; context fit under constraints	SMMM complements with large-N deployment and population-class benchmarks

Corporate Social Responsibility (CSR) and sustainability lens: CSR-oriented maturity models, such as Suliman et al. (2021), focus on assessing an organization’s social, ethical, and environmental responsibility [31]. Suliman et al. (2021) integrate CSR principles into a smart city maturity construct and advocate a full-assessment approach [31]. This strengthens the sustainability and governance dimensions of “smartness” and offers a conceptually rich instrument; however, it is comparatively heavyweight for routine self-assessment in smaller administrations and less oriented toward rapid, repeated diagnostics.

In contrast, the SMMM trades breadth for usability—binary items and clear thresholds—enabling frequent, comparable assessments across many municipalities (see Section 2). While the CSR approach provides useful perspectives for sustainability-oriented evaluation, CSR was not a conceptual foundation for the SMMM because the model was designed to capture digital and data-related municipal capabilities.

Benchmark-oriented practice models: The TM Forum TR259 (2019/2020 release series) is an internationally recognized benchmark model designed for practitioner uptake and cross-city comparison. Its strengths are an actionable vocabulary and ecosystem alignment; its limitations are proprietary evolution and a benchmarking focus that can obscure fine-grained, administration-internal capability gaps [32]. SMMM complements this by providing transparent scoring and dimension-level diagnostics for internal steering while still supporting external benchmarking at the state level [32].

Stage-based urban development: Caird et al. (2019) emphasize that cities progress through distinct maturity phases, a framing that legitimizes staged policy design and sequencing [33]. SMMM adopts the same intuition—five maturity levels—but couples it to ten operational dimensions and a validated item set that surfaces exactly where progress stalls (e.g., Digital Infrastructure and Data Accessibility); see Section 3 for the empirical strategic–technical capability gap.

Synthesis and comparability: Ghazinoory et al. (2024) provide a meta-review that consolidates existing models into a multidimensional evaluation framework, improving conceptual robustness and international comparability. Their synthesis clarifies the landscape [37]; SMMM operationalizes a compatible structure as a deployable instrument with large-N municipal evidence (Baden-Württemberg roll-out) and standardized thresholds that enable routine tracking; cf. Section 2.4 validation pipeline.

Multicriteria index construction: Aragão et al. (2023) introduce a hybrid TOPSIS multicriteria approach, generating a synthetic smartness index from ISO-derived indicators [34]. This offers scoring rigor and comparability when indicator data exist, yet it presumes data availability and analytic capacity that many small municipalities lack. SMMM lowers the barrier by relying on binary items assessable in-house, thereby extending decision support to administrations with limited analytics teams.

Vendor-supported self-assessment: UrbanTide’s Smart Cities Maturity Model—co-developed with the Scottish Government and the Scottish Cities Alliance—provides a practitioner-oriented self-assessment tuned to European practice and investment road-mapping [13,38]. SMMM lands in the same usability space but adds uniform item wording, scoring transparency, and state-wide deployment at scale, producing population-class benchmarks and dimension profiles suitable for policy allocation; see Section 3 results.

Process-aware capability orientation: Ajoudanian et al. (2025) propose a capability maturity model that stresses process awareness and integration requirements for digital transformation in cities—useful for governance/process redesign and for aligning ICT projects with transformation roadmaps [35]. SMMM is complementary: it identifies where capability is missing; process-aware models inform how to fix it.

Small and medium city-tailored maturity models: Recent design-science work targets municipalities below metropolitan scale. Anschütz, Ebner, and Smolnik (2024) propose a maturity model tailored to small- and medium-sized smart cities, deriving requirements from the literature and expert interviews and evaluating the instrument in a municipal case (Bad Hersfeld) [36]. This stream highlights resource constraints and context fit; the SMMM complements it with uniform item wording (99 binary items), transparent scoring, and large-N deployment across municipality types, enabling population-class benchmarks and dimension profiles usable for policy allocation.

Put plainly: These approaches collectively enrich the field—CSR depth, benchmarking reach, stage logic, multicriteria rigor, practice-ready self-assessment, and process orientation. Yet most do not combine (i) municipality-first usability, (ii) transparent, low-burden scoring, and (iii) large-N validation with practicing administrations. That triad is where SMMM differentiates: it measures what municipalities can realistically report at scale, exposing the systemic strategic–technical capability gap that emerged in Section 3 and translating directly into prioritized action planning; see also Section 3.1, Section 3.2, Section 3.3, Section 3.4, Section 4.1 and Section 4.2.

4.4. Practical Implications and Policy Recommendations

Grounded in the comparison above (Table 3), this section distills practical guidance for administrations and state-level policymakers, translating the SMMM’s measurement backbone into capability-first interventions and differentiated support strategies.

Internal use: The SMMM functions as a low-cost diagnostic for self-assessment, peer benchmarking, and strategy alignment. For administrations, the immediate value lies in the following: (i) a common vocabulary across departments, (ii) a baseline for tracking progress, and (iii) targeted action planning on the weakest dimensions (typically Digital Infrastructure and Data Accessibility). The instrument’s simplicity reduces the cognitive and administrative load that often derails adoption [5,6,7,8,9].

Policy use: At the state or federal level, the results support differentiated support strategies as follows:

• Foundational support for small municipalities: co-funded connectivity/backbone upgrades, data stewardship programs, shared services (hosting, security operations), and training to lift baseline capacity.
• Innovation support for large municipalities: incentives for interoperability pilots, open-data ecosystems, and cross-jurisdictional standards adoption.

This tiered approach targets the binding constraints rather than subsidizing already strong dimensions, improving ROI and equity of outcomes [13,14,25,26,39].

4.5. Limitations

The study’s constraints are non-trivial. Geographic scope is limited to Baden-Württemberg, which may constrain external validity until cross-national applications accumulate. Self-assessment introduces potential social desirability and knowledge biases; mitigations included clear wording and multi-stage validation, but residual bias cannot be excluded. The binary scoring improves usability and comparability but compresses variance and may underrepresent partial progress. Finally, the cross-sectional design precludes strong causal inference about maturation pathways. A further limitation is the absence of a dedicated cybersecurity/privacy dimension in the instrument; while several items touch on risk and operations, a full dimension would better reflect contemporary governance needs and the definitional blind spot observed in the literature. A dedicated cybersecurity/privacy dimension is not included yet. This was a deliberate scoping decision to keep the instrument low-burden for non-technical respondents and comparable across municipalities: pretests indicated that security/privacy items either require specialist audit knowledge or trigger disclosure aversion, degrading reliability and response rates. Instead, the model captures security-relevant practices indirectly (e.g., through governance, data strategy, and infrastructure items), while we align the next iteration with established frameworks (e.g., ITU-T Y.4904/L.1604, ISO/IEC-based controls, TM Forum TR259) to add a compact, auditable subscale [4,14,25,32]. This omission is a known limitation, not a blind spot: it prioritizes administrative usability in the first large-N deployment and sets up a clear path for enhancement. We treat this as a priority for model evolution.

4.6. Future Research

Three avenues follow. First, longitudinal tracking to model maturation trajectories, estimate transition probabilities between levels, and identify policy levers that accelerate movement through the lower technical/data strata [6,7,40,41,42]. Second, cross-national adaptation and benchmarking to test portability and administrative contingencies, anchored in internationally recognized indicator sets and maturity frameworks [13,14,43]. Third, instrument enhancement: (i) developing a cybersecurity/privacy dimension aligned with municipal practice and existing standards [4]; (ii) experimenting with graded (ordinal) items for selected dimensions to reduce information loss [6,7]; and (iii) adding reliability/validity diagnostics for subscales once sufficient repeated measures are available, building on the model’s existing multi-stage validation pipeline (expert review, industry workshop, pilot, and large-N deployment) [11], and as detailed in Section 2.4 and Table A1 [6,7,8,9].

5. Conclusions

This paper develops and applies the Smart Municipality Maturity Model (SMMM) to a large sample of municipalities and, in doing so, documents a persistent strategic–technical capability gap—strategy and stakeholder-oriented domains are comparatively mature, while Digital Infrastructure and Data Accessibility trail, especially in small municipalities. Across size classes, the gradient in overall maturity is clear, but the wide within-class dispersion shows that resources alone do not determine outcomes; leadership, organizational learning, and data stewardship matter. Put plainly, intent outpaces capability—and that gap is where impact stalls.

Substantive contributions: Conceptually, the SMMM translates an often diffuse smart city discourse into a ten-dimensional, five-level architecture that municipalities can actually use. Methodologically, it couples low-burden measurement (99 binary items; transparent thresholds) with a multi-stage validation pipeline and large-N deployment, enabling robust cross-municipal comparisons without sacrificing diagnostic depth. Empirically, the study provides population-class benchmarks and dimension profiles that make bottlenecks legible, not anecdotal. In comparative perspective (Table 3), the SMMM complements CSR-oriented, benchmark-centric, stage-based, and multicriteria index approaches by offering a practitioner-first instrument that scales inside administrations.

Implications for policy and practice: A capability-first strategy follows directly from the evidence. For small municipalities, the binding constraints are foundational: backbone connectivity, data plumbing, basic ICT operations, and data literacy. For larger cities, the frontier shifts toward interoperability, standards adoption, and ecosystem integration. In both cases, transparent, repeated self-assessment turns maturity from a slogan into an operating discipline—supporting budgeting, portfolio reviews, and course correction. Because the instrument is simple and comparable, it also lowers the administrative burden for state-level programs that must target funds where marginal returns are highest.

Limitations and scope conditions: The results reflect Baden-Württemberg and a cross-sectional, self-assessment design with binary indicators; these choices trade granularity for scalability and comparability. They do not undermine the main finding, but they do bound its generality. One salient omission is a dedicated cybersecurity/privacy dimension. While related items exist across domains, a full dimension would better match contemporary governance needs and the blind spots visible in definitional treatments.

A further scope condition concerns sustainability, environmental quality, social well-being, mobility, and other non-digital smart city domains. Although these aspects are central to contemporary smart city research, they fall outside the operational responsibility of the IT leadership respondent group and cannot be captured reliably within a 99-item, low-burden self-assessment instrument. Including such domains would have increased response burden, reduced reliability, and produced uneven or incomplete data. For this reason, the present model focuses deliberately on digital and data-related municipal capabilities, with broader smart city dimensions positioned as complementary rather than integral to the SMMM.

Where the field should go next: Three moves would materially advance both science and practice. First, longitudinal tracking to estimate transitions across maturity levels and to identify levers that accelerate progress through the lower technical/data strata. Second, cross-national adaptation and benchmarking to test portability under different institutional regimes. Third, instrument enhancement: add a cybersecurity/privacy dimension aligned with municipal practice and relevant standards; pilot graded (ordinal) items where binary choices compress information; and attach reliability/validity diagnostics once repeated measures accrue.

Beyond these technical extensions, future research should integrate sustainability, environmental performance, social well-being, mobility, and other smart city domains that fall outside the present instrument’s digital-capability focus. Such additions would allow the SMMM to serve as a modular foundation within a broader, multi-domain smart city assessment ecosystem and link capability maturity more tightly to societal and environmental outcomes.

Answers to Research Questions:

• RQ1 (Model Development and Validation): How can a comprehensive, empirically validated Smart City Maturity Model (SMMM) be developed and tested to meet the administrative and governance requirements of German municipalities?

Answer: The study demonstrates that a municipality-first maturity model can be built and validated through a design-science pipeline combining the following: (i) an explicit architecture (five levels, ten assessment dimensions, and 99 binary items), (ii) multi-stage validation—expert review, industry workshop, pilot, and large-scale municipal deployment—and (iii) transparent scoring rules that keep respondent burden low while preserving comparability. The resulting instrument is administration-ready: it establishes a common vocabulary across departments, supports repeated self-assessment, and yields peer benchmarks at scale. Details of the artifact and validation steps are documented in Section 2 (Methods) and Table A1; the practical feasibility is evidenced by the large-N roll-out summarized in Section 3.

• RQ2 (Empirical Pattern and Capability Gap): How is digital maturity distributed across the ten SMMM dimensions in German municipalities, and what evidence substantiates a systemic capability gap between strategy/governance and foundational technical capacity (infrastructure, data management)?

Answer: Results reveal a systematic strategic–technical imbalance. Governance- and strategy-proximate dimensions (e.g., Strategy and Vision, Stakeholder Engagement) consistently score higher than foundational technical/data capacities—especially Digital Infrastructure and Data Accessibility. The pattern holds across population classes (see Figure 3 and Table 2) and is most pronounced when contrasting Urban Districts with Small Municipalities (see Figure 5). Digital Infrastructure (7.4 vs. 2.9) and Data Accessibility (7.2 vs. 2.4) show the largest separations, while Strategy and Vision remain comparatively strong in both (8.9 vs. 6.2). Together with the within-class dispersion reported in Table 1, these findings support the conclusion that intent outpaces capability and that the binding constraints are infrastructural/data foundations rather than willingness or strategy.

Implications linking back to RQ1–RQ2: Because the instrument (RQ1) exposes where progress stalls (RQ2), it enables capability-first steering: for smaller municipalities, foundational upgrades (connectivity, data plumbing, ICT operations, and data literacy); for larger cities, interoperability, standards adoption, and ecosystem integration. The same architecture supports longitudinal tracking and targeted policy design without increasing administrative load.

In sum: Cities do not lack ambition; they lack plumbing. By turning capability into something measurable, repeatable, and comparable, the SMMM gives administrations and policymakers a common language—and a map. Use it to fund the bottlenecks, not the slogans; if we close the infrastructure-and-data gap first, strategy will stop outpacing what cities can actually deliver.