2.1. UDG and data strategies

While DG was originally introduced in corporate and technical contexts (Al-Ruithe et al., Reference Al-Ruithe, Benkhelifa and Hameed2019; Jagals et al., Reference Jagals, Karger and Ahlemann2019; Nielsen, Reference Nielsen2017), the more recent adaptation of UDG in the context of the “smart city” discourse relates to activities, stakeholders, and ecosystems in urban settings, aiming at supporting urban development (Bozkurt et al., Reference Bozkurt, Rossmann and Pervez2022; Choenni et al., Reference Choenni, Bargh, Busker and Netten2022; König, Reference König2021; Paskaleva et al., Reference Paskaleva, Evans, Martin, Linjordet, Yang and Karvonen2017). While definitions vary across disciplines (Abraham et al., Reference Abraham, Schneider and vom Brocke2019; Al-Ruithe et al., Reference Al-Ruithe, Benkhelifa and Hameed2019; Bližnák et al., Reference Bližnák, Munk and Pilková2024, p. 149886; U. G. Gupta and Cannon, Reference Gupta and Cannon2020, p. 33), a “social science-informed definition” (Micheli et al., Reference Micheli, Ponti, Craglia and Berti Suman2020, p. 2) of UDG seems valid, “… understand[ing] data governance as the power relations between all the actors affected by, or having an effect on, the way data is accessed, controlled, shared and used, the various socio-technical arrangements set in place to generate value from data, and how such value is redistributed between actors …” (Micheli et al., Reference Micheli, Ponti, Craglia and Berti Suman2020, p. 3).

The definition of the interplay of data strategies, data policy, and DG by Lis et al. (Reference Lis, Gelhaar, Otto, Caballero and Piattini2023) for a corporate domain can be adopted for urban contexts where a “[data] strategy defines required capabilities for its successful execution [, its operationalization] is anchored through [data] policies” (Lis et al., Reference Lis, Gelhaar, Otto, Caballero and Piattini2023, p. 28) and results in a DG which “… defines standards …” (Lis et al., Reference Lis, Gelhaar, Otto, Caballero and Piattini2023, p. 28). Bozkurt et al. (Reference Bozkurt, Rossmann, Konanahalli and Pervez2023) highlight that “[data] governance exists in every organization but may not be formalized [.] … [Formal] data governance comprises established guidelines, practices, and clear roles and responsibilities …” (Bozkurt et al., Reference Bozkurt, Rossmann, Konanahalli and Pervez2023, p. 85658).

In recent years, government strategies and legislative frameworks for DG have been implemented across different levels of government. The European strategy for data (EC, 2020a) builds on the concept of data spaces (Cuno et al., Reference Cuno, Bruns, Tcholtchev, Lämmel and Schieferdecker2019; Schieferdecker, Reference Schieferdecker, Putnings, Neuroth and Neumann2021; Battistoni, Reference Battistoni, Bevilacqua, Balland, Kakderi and Provenzano2023) to establish common European data spaces (Weber, Reference Weber2023; EC, 2024a). DG-related goals of the European strategy for data (EC, 2020a) are translated into the European Data Governance Act (EP and EU Council, 2022) and the Data Act (EP and EU Council, 2023; EC, 2024b; EC, 2024c). With regard to the case of Germany, the Data Strategy of the Federal German Government sets the agenda for “… innovative and responsible data …” and intends to push for harmonization of state-level data laws (Bundesregierung and Bundeskanzleramt, 2021, p. 64). German states, too, have developed DG guidelines as data strategies (Schleswig-Holstein, 2024), open data strategies (Berlin, 2023; Brandenburg, 2023), or calls for federal cooperation (cf. Baden-Württemberg, 2020).

Additional laws regulate municipal data use (Helder et al., Reference Helder, Libbe, Ravin and Henningsen2023, pp. 10–11), including the GDPR (EP and EU Council, 2016), the law on re-use of public sector information (EP and EU Council, 2019; DNG, 2021), and domain-specific regulations, e.g., for geodata (INSPIRE, EP and EU Council, 2007; GeoZG, 2009). Sapienza et al. (Reference Sapienza, Palmirani, Greco, Kö, Kotsis, Tjoa and Khalil2024) discuss the European frameworks in the context of smart cities.

2.2. Municipal data strategies for UDG

Given the importance and possible contribution of UDG for common welfare in municipalities, both the EU and the German federal government promote formalization of DG practices on a regional and local level (EC, 2020b; Fuhrmann et al., Reference Fuhrmann, Böttcher and Bimesdörfer2021; Helder et al., Reference Helder, Libbe, Ravin and Henningsen2023). However, until today, there has been no established regulation for systematic and structured governance of data in municipal administrations in Germany (Helder et al., Reference Helder, Libbe, Ravin and Henningsen2023, p. 7). Key aspects of UDG are nevertheless increasingly part of international (Micozzi and Yigitcanlar, Reference Micozzi and Yigitcanlar2022) as well as German smart city strategies (Schmuck, Reference Schmuck2024). A new combined form of digitalization strategies (including partly DG aspects) and integrated urban development strategies is suggested and implemented with the integrated digital development concepts (German: Integriertes Digitales Entwicklungskonzept [IDEK]; Wittpahl and Sedlmayr, Reference Wittpahl and Sedlmayr2023) in several Bavarian piloting municipalities (e.g., Landeshauptstadt München, n.d.; Spinrath and Plessing, Reference Spinrath and Plessing2023, p. 21).

Though comprehensive research on UDG effects on small towns is lacking, existing studies indicate UDG positively influences local data practices in small municipalities, also by enhancing data literacy (Sautter et al., Reference Sautter, Henze-Sakowsky, Lindner, Schweigel, Dobrokhotova, Seick, Kirchner and Braun2023) and fostering open data publication (Rajamäe Soosaar and Nikiforova, Reference Rajamäe Soosaar and Nikiforova2025, p. 12). Implementing UDG also aligns with efforts to reduce the digital divide between larger cities and smaller municipalities (cf. Feurich et al., Reference Feurich, Kourilova, Pelucha and Kasabov2024; Löfving et al., Reference Löfving, Kamuf, Heleniak, Weck and Norlén2022).

Implementation challenges of UDG range from establishing a municipal data culture (Courmont, Reference Courmont2021) and building up data ecosystems (Liva et al., Reference Liva, Micheli, Schade, Kotsev, Gori and Codagnone2023) through acquisition of digital skills and data literacy (Sautter et al., Reference Sautter, Henze-Sakowsky, Lindner, Schweigel, Dobrokhotova, Seick, Kirchner and Braun2023) to providing sufficient financial resources for relevant infrastructure (Bauer et al., Reference Bauer, von Radecki and Ottendörfer2020, p. 19; Dembski et al., Reference Dembski, Wössner, Letzgus, Ruddat and Yamu2020, p. 3). Several contributions suggest concepts and frameworks to address shortcomings (Choenni et al., Reference Choenni, Bargh, Busker and Netten2022; Cuno et al., Reference Cuno, Bruns, Tcholtchev, Lämmel and Schieferdecker2019; de Macedo Schäfer et al., Reference de Macedo Schäfer, Schweinberg, Stenzel and Grafenstein2023; A. Gupta et al., Reference Gupta, Panagiotopoulos and Bowen2020; Lupi, Reference Lupi2019; Paskaleva et al., Reference Paskaleva, Evans, Martin, Linjordet, Yang and Karvonen2017; Schieferdecker, Reference Schieferdecker, Putnings, Neuroth and Neumann2021). Mapping the notion of UDG in cities of six EU countries, Bozkurt et al. (Reference Bozkurt, Rossmann, Konanahalli and Pervez2023) intend to lay the groundwork for a UDG reference model (Bozkurt et al., Reference Bozkurt, Rossmann, Konanahalli and Pervez2023, p. 85674). Overall, the observation that “[although] the rhetoric ‘wants’ smart cities and big data together, a significant portion of data produced within cities is still underused or not used at all …” (Liva et al., Reference Liva, Micheli, Schade, Kotsev, Gori and Codagnone2023, p. 4) seems to be valid for many European cities and especially for small towns (Milbert and Fina, Reference Milbert, Fina, Steinführer, Porsche and Sondermann2021, pp. 41–44).

Drawing on the reflections about the interplay between DG and its underlying formalizations in the form of a data strategy, we considered in this study the existence of documents like a municipal data strategy or specific chapters of larger municipal digital or smart city strategies (Helder et al., Reference Helder, Libbe, Ravin and Henningsen2023, p. 6; Schmuck, Reference Schmuck2024, pp. 77, 82–83) as an indicator for systematic local UDG practice (cf. Section 3.2 Structured online content research and analysis and Higi (Reference Higi2025), “01 Criteria Data Governance Documents Research.pdf”). In contrast to informal DG practice (Bozkurt et al., Reference Bozkurt, Rossmann, Konanahalli and Pervez2023, p. 85658), a data strategy can establish formal DG practice and underpin its intra- and inter-institutional legitimacy (Helder et al., Reference Helder, Libbe, Ravin and Henningsen2023, p. 30). In addition to this, it allows for a concerted DG approach in municipal administrations, including measures with implications on the organizational structure of the administration concerning roles and responsibilities, as well as measures to build the required culture, expertise, and data literacy (Branderhorst and Ruijer, Reference Branderhorst and Ruijer2024, p. 17; Sautter et al., Reference Sautter, Henze-Sakowsky, Lindner, Schweigel, Dobrokhotova, Seick, Kirchner and Braun2023).

2.3. Digitalization in small towns, rural areas, and districts—defining the research area

While numerous well-researched digital approaches address global challenges on a local scale in large cities (Barns, Reference Barns2018; Engin et al., Reference Engin, van Dijk, Lan, Longley, Treleaven, Batty and Penn2020; Paskaleva et al., Reference Paskaleva, Evans, Martin, Linjordet, Yang and Karvonen2017), such initiatives are less prevalent in small(er) towns (cf. e.g., Damm and Spellerberg, Reference Damm and Spellerberg2021; Dembski et al., Reference Dembski, Wössner, Letzgus, Ruddat and Yamu2020; Hosseini et al., Reference Hosseini, Frank, Fridgen and Heger2018; Ruohomaa et al., Reference Ruohomaa, Salminen and Kunttu2019; Soike and Libbe, Reference Soike and Libbe2018; Spicer et al., Reference Spicer, Goodman and Olmstead2021). Distinguishing precisely small towns from rural areas (cf. Kühne and Kamlage, Reference Kühne, Kamlage, Berr, Koegst and Kühne2024) also is challenging, as they are often considered together in funding programs and hence research projects (cf., e.g., Hosseini et al., Reference Hosseini, Frank, Fridgen and Heger2018; Viale Pereira et al., Reference Viale Pereira, Klausner, Temple, Delissen, Lampoltshammer and Priebe2023). While international definitions of small towns by population vary (Bell and Jayne, Reference Bell and Jayne2009), the German Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) defines them as urban areas with 5,000–20,000 inhabitants or with basic to medium-level central function (BBSR, n.d.).

In Germany, municipal autonomy is guaranteed by the constitution (Heinelt and Zimmermann, Reference Heinelt and Zimmermann2021, pp. 14–15). Two tiers, municipalities and rural districts (Landkreise), constitute local government outside of large cities, where districts “… represent the municipalities within them and administer a range of services and policies on behalf of these municipalities …” (Heinelt and Zimmermann, Reference Heinelt and Zimmermann2021, p. 15; cf. also Wollmann, Reference Wollmann2024a). Both local administrative levels influence digitalization policies (Wollmann, Reference Wollmann2024b) as well as urban policies (Heinelt and Zimmermann, Reference Heinelt and Zimmermann2021), and jointly perform the same tasks as large cities. Against this backdrop and recent efforts of multilevel digitalization governance (Wegrich, Reference Wegrich2021), our study focuses on (1) municipal administrations of small towns per BBSR (n.d.) definition and on (2) district administrations covering multiple towns. This also acknowledges that rural districts (Schulz, Reference Schulz2017), as well as inter-municipal cooperation, can possibly be pivotal for digitalization efforts in Germany at a regional level (Bischoff and Bode, Reference Bischoff and Bode2021) and that digitalization outside mid-sized and large towns should continue to be discussed also on a regional scale (Matern et al., Reference Matern, Schröder, Stevens, Weidner, Bisello, Vettorato, Laconte and Costa2017).

An additional level of governance is given by metropolitan regions, albeit without official administrative status. They hence represent a sometimes-disputed and nonhomogeneous organizational backdrop (Galland and Harrison, Reference Galland, Harrison, Zimmermann, Galland and Harrison2020), making them challenging to analyze comparatively. We examined formalized data governance across all 11 metropolitan regions in Germany; however, due to the complexities mentioned, we did not consider the results in depth within this article. A summary of the results at the metropolitan regional level is provided in Higi (Reference Higi2025), “02 Formalized Data Governance in all German Metropolitan Regions - Summary of Results.pdf.”

2.4. Research gap and background of RQs

While the research reviewed above underscores the need for UDG to overcome obstacles to municipal data usage and the importance of understanding UDG practices for a better theoretical reflection on urban digital innovation, the limited knowledge base regarding smaller municipal administrations reveals a persistent “theory-practice gap” (cf. Mora et al., Reference Mora, Gerli, Ardito and Messeni Petruzzelli2023, p. 16). This gap arises as digitalization frameworks and concepts developed for larger urban areas cannot be directly transferred to small towns (Anschütz et al., Reference Anschütz, Ebner and Smolnik2024). In federal governance systems such as Germany’s, this issue is even more pronounced due to the high degree of autonomy of local administrations (Marienfeldt et al., Reference Marienfeldt, Kühler, Kuhlmann and Proeller2024, p. 43). To contribute to a more robust knowledge base filling these research gaps also on a global level, our work addresses the three RQs introduced above. In the following section, we summarize the background for each of them against the backdrop of the German case.

Regarding RQ1: The call for municipal data strategies within the European and German federal frameworks seems to result in heterogeneous local DG practices and varying levels of formalization in German districts and small towns. The literature addresses UDG challenges either globally or in larger-city case studies. Recent contributions analyzed smart city strategies (including UDG) in German state capitals and cities (Schmuck, Reference Schmuck2024) and reviewed UDG literature (Bozkurt et al., Reference Bozkurt, Rossmann and Pervez2022). Nevertheless, a comprehensive analysis of the status quo of DG in German small towns, allowing for a more granular analysis of possible influencing factors, seems to be lacking.

Regarding RQ2: Formalized UDG in municipal administrations can be influenced by state-level legislative frameworks, such as freedom-of-information laws (Mueller et al., Reference Mueller, Engewald, Herr, Dragos, Kovač and Marseille2019). Decentralization in public governance can further affect public task attribution (Ebinger and Richter, Reference Ebinger and Richter2016), while state or federal funding programs could shape UDG dissemination (cf. Zabel and Kwon, Reference Zabel and Kwon2021). Discourse on spatial injustice and the urban-rural digital divide (Löfving et al., Reference Löfving, Kamuf, Heleniak, Weck and Norlén2022; Feurich et al., Reference Feurich, Kourilova, Pelucha and Kasabov2024) reveal correlations between rurality and digitalization maturity. While digital practices are assumed to foster rural and small-town development (cf., e.g., Mariotti et al., Reference Mariotti, Capdevila and Lange2023), reality is often more complex (cf., e.g., Binder and Matern, Reference Binder and Matern2020), not least because budgets constrain the introduction of DG roles like chief digital officer or data steward. Sociodemographics and the presence of innovation-driven enterprises, research institutes, and higher education institutions can also affect local municipal digitalization (Greef and Schroeder, Reference Greef and Schroeder2021). Additionally, town size possibly also impacts data availability (Milbert and Fina, Reference Milbert, Fina, Steinführer, Porsche and Sondermann2021), data literacy (Sautter et al., Reference Sautter, Henze-Sakowsky, Lindner, Schweigel, Dobrokhotova, Seick, Kirchner and Braun2023), and, hence, local UDG.

Regarding RQ3: Digitalization projects are often closely linked to fields of urban development. However, besides the aforementioned integrated digital development concepts (IDEK; Wittpahl and Sedlmayr, Reference Wittpahl and Sedlmayr2023), municipal strategy papers mostly do not consider UDG and urban development strategies as closely interwoven. Content analysis on specific fields of integrated urban development addressed in the identified UDG documents can offer more insights into the links between these two fields.

3.1. Preliminary considerations and research design validation

As the principal method, we chose structured online research (Kim and Kuljis, Reference Kim and Kuljis2010; Schweitzer, Reference Schweitzer2010; Welker and Wünsch, Reference Welker, Wünsch, Schweiger and Beck2010; Welker et al., Reference Welker, Taddicken, Schmidt and Jackob2014) for policy documents on UDG in a sample of German small towns and all rural districts. The approach was based on two assumptions: (1) The existence of a document defining rules, guidelines, or outlining typical elements of a local DG is a strong indicator of systematic UDG. (2) To promote their digital and/or data policy, town administrations have an interest in publishing documents, such as strategies, guidelines, or public presentations that should be findable online.

To validate the methodology and assess the overall research design, we discussed the underlying assumptions and reflected on the hypotheses and preliminary results in three interviews with four experts in the field of DG, digital strategies, and smart city strategies in German municipalities between February 2024 and August 2024. At the time of the interviews, the experts were affiliated with the following institutions: One expert with the German Association of Towns and Municipalities (Deutscher Städte- und Gemeindebund, DStGB), one expert with the German Institute of Urban Affairs gGmbH (Deutsches Institut für Urbanistik, Difu), and two experts with the Institute for Innovation and Technology (iit) within the VDI/VDE Innovation + Technik GmbH.

All experts interviewed confirmed assumptions (1) and (2). However, one expert pointed out that officially published strategy documents typically need to receive approval from town councils. This could lead to informal municipal practices of systematic DG that are not explicitly reflected in public strategies, following a more pragmatic approach to avoid time-consuming public debates and possible opposition. Another expert also mentioned the political character of strategy documents, which can lead to tactical maneuvers hindering publication to influence negotiations in local politics. A third expert underlined the impossibility of implementing a sustainable local DG without foundations in a strategy document.

3.2. Structured online content research and analysis

Using a customized Python script for the semiautomated entry of search strings, we conducted the structured online search using the search engine Google (https://www.google.com/, date accessed: March 25, 2024) for each sample town and for all rural districts. We performed nine different searches per administrative unit using the respective name combined with nine different keywords as search strings (cf. Higi (Reference Higi2025), “03 Procedure Systematic Online Research.pdf”). For each search string combination ([town name/district name] + [keyword_1–9]), we screened the first 10 search results of each search for documents containing data strategies or elements of DG guidelines. We then analyzed the identified documents and categorized them based on a predefined four-level gradation assessing the comprehensiveness of systematic UDG across key aspects of UDG described by Helder et al. (Reference Helder, Libbe, Ravin and Henningsen2023, pp. 15–29) and suggested guidelines by the German Smart City Dialogue (Fuhrmann et al., Reference Fuhrmann, Böttcher and Bimesdörfer2021). Definitions of the levels following the resulting criteria catalogue (cf. Higi (Reference Higi2025), “01 Criteria Data Governance Documents Research.pdf”) are briefly outlined in Table 1.

Table 1.

Definition: Levels of DG of identified policy documents

We set our focus on municipal data strategies of global character for the town. Data strategies of municipal enterprises, such as utility companies, were only considered if they were a substantial part of the municipal strategies or related guidelines. As proof of concept, this online search strategy was successfully performed for three municipalities with existing relevant documents known to the authors (cf. Higi (Reference Higi2025), “04 Protocol Online Research.pdf”).

3.3. Small town sample

We drew the sample of small towns based on the following categories. Küpper’s (Reference Küpper2016) systematic typology of German regions classifies all German districts into the four Thünen Types (1–4) given by the matrix of the two dimensions rurality and socioeconomic situation (Küpper, Reference Küpper2016, p. 17, Thünen Type 5 describes districts in nonrural areas and does not distinguish socioeconomic situations). The typology is used for this study despite some possible minor changes that could appear in a newer version currently being developed at the moment of writing (Thünen-Institut, n.d.).

Besides its aforementioned definition of small towns as municipalities with 5,000–20,000 inhabitants or towns with “at least medium-central function” (BBSR, n.d.), BBSR further distinguishes the subcategories smaller small towns (below 10,000 inhabitants) and larger small towns (BBSR, n.d.). Some towns are classified by BBSR as larger small towns, although they have less than 10,000 inhabitants.

To reflect the distribution of rurality and the socioeconomic situation of German small towns, we attributed the five Thünen Types (Küpper, Reference Küpper2016) of the respective districts to all German municipalities. The municipalities defined as “small towns” following the official definition of BBSR (n.d.; n = 2,174) are subcategorized as “smaller small town” (Kleine Kleinstadt) and “larger small town” (Größere Kleinstadt). Combining these two size categories with the five Thünen Types results in a matrix of 10 categories. We generated an initial random sample of 30% of all towns in each category, resulting in n = 653 small towns out of which n = 443 small towns were researched, representing a minimum percentage of 16.5% of each small-town category. Preliminary result analysis led to the decision to discontinue research for the remaining small towns of the sample, but extend it on the district level for all rural districts. The examined sample is evenly distributed across the federal states; nevertheless, the total number of small towns across the federal states can vary significantly due to population density and different municipal structures.

3.4. Content analysis along defined categories of thematic aspects of urban development

To perform content analysis of the identified policy papers, nine thematic categories of integrated urban development were defined, based on a comparison and synthesis of thematic key aspects in four different guidelines on the generation of an integrated urban development concept (German: Integriertes Stadtentwicklungskonzept and other similar names existing; cf. Higi (Reference Higi2025), “05 Deduction and Definition Categories Urban Development.xlsx”). The resulting categories are:

• • Housing

• • Urban design and public space

• • Mobility and technical infrastructure

• • Economy and trade

• • Culture, tourism, and leisure

• • Climate protection and climate adaptation

• • Natural areas

• • Social infrastructure and public services

• • Demography and civic engagement.

Possible values for assessing the consideration of aspects comprised by the respective categories are “yes” and “no”. We analyzed all identified documents on the town and district level using the described content analysis matrix. Figure 1 (cf. Higi (Reference Higi2025), “06 Research Design Scheme.pdf”) provides an overview of the overall research design. Research data processing, including research data quality management, is depicted in the research data processing scheme (cf. Higi (Reference Higi2025), “07 Research Data Processing Scheme.pdf”; for assessment of rater reliability: “13 Jupyter Notebook 2 Rater Reliability Data Governance Levels.zip” (resulting weighted Cohen Kappa = 0.98) and “14 Jupyter Notebook 4 Rater Reliability Thematic Content Analysis.zip” (resulting weighted Cohen Kappa = 0.99). Sampling was performed in Jupyter Notebooks (cf. Higi (Reference Higi2025), “08 Jupyter Notebook 1 Data Processing and Sampling Small Towns.zip”, “09 Jupyter Notebook 3 Data Processing Districts.zip”, and “10 Jupyter Notebook 5 Data Processing Metropolitan Regions.zip.”).

Figure 1.

Schematic overview of the research design (cf. Higi (Reference Higi2025), “06 Research Design Scheme.pdf”).