D3.3 Closing the cycle: Promoting open data users' contributions from a governance perspective

Towards a sustainable Open Data ECOsystem D3.3 Closing the cycle: Promoting open data usersʼ contributions from a governance perspective This project has received funding from the European Unionʼs Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the authorʼs view and in no way reflect the European Commissionʼs opinions. The European Commission is not responsible for any use that may be made of the information it contains. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective Project Acronym ODECO Project Title Towards a sustainable Open Data ECOsystem Grant Agreement No. 955569 Start date of Project 01-10-2021 Duration of the Project 48 months Deliverable Number D3.3 Deliverable Title Closing the cycle: Promoting open data usersʼ contribution from a governance perspective Dissemination Level Public Deliverable Leader Aalborg University (AAU) Submission Date 03-05-2024 Author Rikke Magnussen, AAU Co-author(s) Birger Larsen, AAU Davide Di Staso, TU Delft Silvia Cazacu-Bucica, KUL Ramya Chandrasekhar, CNRS María Elena López Reyes, AAU Giorgos Papageorgiou, FAROSNET S.A. Alejandra Celis Vargas, AAU Liubov Pilshchikova, TU Delft Caterina Santoro, KUL Héctor Ochoa Ortiz, UNICAM Ashraf Shaharudin, TU Delft Document history Version # Date Description (Section, page number) Author & Organisation V0.0 07-11-2023 Kick-off Rikke Magnussen, AAU V0.1 18-03-2024 First draft. First version with contributions from ESRs. Rikke Magnussen, AAU Birger Larsen, AAU Silvia Cazacu-Bucica, KUL Ramya Chandrasekhar, CNRS María Elena López Reyes, AAU Giorgos Papageorgiou, FAROSNET S.A. Alejandra Celis Vargas, AAU Liubov Pilshchikova, TU Delft Caterina Santoro, KUL Héctor Ochoa Ortiz, UNICAM Ashraf Shaharudin, TU Delft V0.1a 05-04-2024 First round of internal peer review Glenn Vancauwenberghe, KUL Anneke Zuiderwijk-van Eijk, TU Delft Charalampos Alexopoulos, UAEGEAN D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective Version # Date Description (Section, page number) Author & Organisation V0.2 11-04-2024 Second draft. Processed feedback obtained from the first round of peer review. Rikke Magnussen, AAU Birger Larsen, AAU Silvia Cazacu-Bucica, KUL Ramya Chandrasekhar, CNRS María Elena López Reyes, AAU Giorgos Papageorgiou, FAROSNET S.A. Alejandra Celis Vargas, AAU Liubov Pilshchikova, TU Delft Caterina Santoro, KUL Héctor Ochoa Ortiz, UNICAM Ashraf Shaharudin, TU Delft V0.2a 17-04-2024 Second round of internal peer review Glenn Vancauwenberghe, KUL Anneke Zuiderwijk-van Eijk, TU Delft Charalampos Alexopoulos, UAEGEAN V0.3 23-04-2024 Third draft. Integration of feedback received from second round of peer review. Rikke Magnussen, AAU Birger Larsen, AAU Silvia Cazacu-Bucica, KUL Ramya Chandrasekhar, CNRS María Elena López Reyes, AAU Giorgos Papageorgiou, FAROSNET S.A. Alejandra Celis Vargas, AAU Liubov Pilshchikova, TU Delft Caterina Santoro, KUL Héctor Ochoa Ortiz, UNICAM Ashraf Shaharudin, TU Delft V0.4 30-04-2024 Final report. Integration of feedback from supervisory board. Rikke Magnussen, AAU Birger Larsen, AAU V0.9 02-05-2024 Final report. Integration of feedback from supervisory board. Rikke Magnussen, AAU Birger Larsen, AAU V0.10 02-05-2024 Approval Bastiaan van Loenen, TU Delft V1.0 03-05-2024 Final editing Danitsja van Heusden, TU Delft D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective Table of Contents Abbreviations ..................................................................................................................................................................... 7 1 Introduction ............................................................................................................................................................... 8 1.1 Background ................................................................................................................................................... 8 1.2 Problem definition ..................................................................................................................................... 9 1.3 Role of this deliverable in the ODECO project ................................................................................ 9 1.4 Structure.......................................................................................................................................................10 2 Motivation Theory ................................................................................................................................................11 2.1 Introduction ................................................................................................................................................11 2.2 Self Determination Theory ...................................................................................................................11 2.3 Communities of Practice .......................................................................................................................11 2.4 Application of motivation and learning theories to open data studies .............................12 3 Journalist open data value motivation .........................................................................................................14 3.1 Introduction ................................................................................................................................................14 3.2 Methodology .............................................................................................................................................14 3.3 Results ...........................................................................................................................................................15 3.3.1 Literature ................................................................................................................................................15 3.3.2 Interviews ...............................................................................................................................................15 3.3.3 Action research ....................................................................................................................................15 3.3.4 Classifying the motivations .............................................................................................................16 3.4 Conclusions ................................................................................................................................................16 4 Elementary school students open data value motivation .....................................................................17 4.1 Introduction ................................................................................................................................................17 4.2 Methodology .............................................................................................................................................18 4.3 Results and analysis of actorʼs motivation .....................................................................................19 4.4 Conclusions regarding motivations of students ..........................................................................20 5 NGO open data value motivation ...................................................................................................................21 5.1 Introduction ................................................................................................................................................21 5.2 Methodology .............................................................................................................................................21 5.3 Results and analysis of actorʼs motivation .....................................................................................21 5.3.1 Intrinsic motivation ............................................................................................................................21 5.3.2 Extrinsic motivation ............................................................................................................................22 5.4 Conclusion ..................................................................................................................................................23 6 Local government users open data value motivation ............................................................................24 6.1 Introduction ................................................................................................................................................24 6.2 Methodology .............................................................................................................................................24 6.3 Results and analysis of userʼs motivations .....................................................................................25 D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 6.4 Conclusion ..................................................................................................................................................28 7 Regional/National government users open data value motivation ..................................................31 7.1 Introduction ................................................................................................................................................31 7.2 Methodology .............................................................................................................................................31 7.3 Results and analysis of userʼs motivations .....................................................................................31 7.4 Conclusions ................................................................................................................................................32 8 Commercial users open data value motivation .........................................................................................33 8.1 Introduction ................................................................................................................................................33 8.2 Methodology .............................................................................................................................................33 8.3 Results and analysis .................................................................................................................................33 8.4 Conclusion ..................................................................................................................................................34 9 Open data intermediariesʼ open data value motivation ........................................................................36 9.1 Introduction ................................................................................................................................................36 9.2 Methodology .............................................................................................................................................36 9.3 Results ...........................................................................................................................................................37 9.4 Conclusion ..................................................................................................................................................38 10 Non-expert usersʼ open data value motivations .........................................................................39 10.1 Introduction ................................................................................................................................................39 10.2 Methodology .............................................................................................................................................39 10.3 Results ...........................................................................................................................................................39 10.4 Conclusions ................................................................................................................................................40 11 Next iteration of D2.3 with focus on usersʼ motivation ............................................................41 11.1 Introduction ................................................................................................................................................41 11.2 Developing the next iteration of Action Principle 1 ...................................................................41 11.2.1 Next iteration of Action Principle 1 .............................................................................................41 11.3 Developing the next iteration of Action Principle 2 ...................................................................44 11.3.1 Next iteration of Action Principle 2 .............................................................................................44 11.4 Developing the next iteration of Action Principle 3 ...................................................................46 11.4.1 Next iteration of Action Principle 3 .............................................................................................47 11.5 Developing the next iteration of Action Principle 4 ...................................................................49 11.5.1 Next iteration of Action Principle 4 .............................................................................................49 11.6 Developing the next iteration of Action Principle 5 ...................................................................51 11.6.1 Next iteration of Action Principle 5 .............................................................................................51 11.7 Developing the next iteration of Action Principle 6 ...................................................................53 11.7.1 Next iteration of Action Principle 6 .............................................................................................53 11.8 Summary .....................................................................................................................................................56 12 Conclusion ..................................................................................................................................................57 13 References ...................................................................................................................................................64 D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective List of tables Table 1. Classification of the motivations of journalists for contributing to a circular open data ecosystem. .........................................................................................................................................................................16 Table 2. Methods and participants - school case. .............................................................................................18 Table 3. Student's motivations for delivering value back. ..............................................................................19 Table 4. Classification of the motivations of NGOs for contributing to a circular open data ecosystem. .........................................................................................................................................................................23 Table 5. Community Dimensions by Wenger (2000). .......................................................................................25 Table 6. Classification of the motivations of local government users for contributing to a circular open data ecosystem. ...................................................................................................................................................29 Table 7. Extrinsic and intrinsic motivations for open data sharing for central governments. ..........32 Table 8. Classification of the motivations of commercial users for contributing to a circular open data ecosystem. ..............................................................................................................................................................34 Table 9. Methods and participants in the intermediaries case. ....................................................................36 Table 10. Identified motivations of intermediaries. ...........................................................................................38 Table 11. Summary of the motivations of non-expert user found in the literature. ............................40 Table 12. Types of motivation of different user types ......................................................................................57 Table 13. Needs for adjustment of action principles ........................................................................................59 D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 7 Abbreviations D Deliverable ESR Early-Stage Researcher M Milestone NGO Non-Governmental Organizations NPO Non-Profit Organizations ODECO Project "Towards a sustainable Open Data ECOsystem" SMEs Small and Medium-sized Enterprises WP Work Package Nr Partner Partner short name Country Beneficiary 1 Technische Universiteit Delft TU Delft Netherlands 2 Katholieke Universiteit Leuven KUL Belgium 3 Centre National de la Recherche Scientifique CNRS France 4 Universidad de Zaragoza UNIZAR Spain 5 Panepistimio Aigaiou UAEGEAN Greece 6 Aalborg Universitet AAU Denmark 7 Università degli Studi di Camerino UNICAM Italy 8 Farosnet S.A. FAROSNET S.A. Greece Partner organisations 1 7eData 7EDATA Spain 2 Digitaal Vlaanderen DV Belgium 3 City of Copenhagen COP Denmark 4 City of Rotterdam RDAM Netherlands 5 CoC Playful Minds CoC Denmark 6 Derilinx DERI Ireland 7 ESRI ESRI Netherlands 8 Maggioli S.p.A MAG Italy 9 National Centre of Geographic Information CNIG Spain 10 Open Knowledge Belgium OKB Belgium 11 SWECO SWECO Netherlands 12 The government lab GLAB United States of America 13 Agency for Data Supply and Infrastructure ADSI Denmark 14 GFOSS Open Technologies Alliance GFOSS Greece 15 Inno3 Consulting IC France 16 Regione Marche RM Italy 17 Open Data Institute OCI United Kingdom D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 8 1 Introduction 1.1 Background The central proposition behind Open Data is that data, information, and knowledge become a shared asset in society, allowing anyone to use it to engage and participate in economic, social, political, and cultural projects (Wessels et al., 2017). Data availability enables the coalition, combination, and enhancement of data through processes and tools (Bichard & Knight, 2012) that allow the use, reuse, redistribution, and merge of available open data with other data sources (Bachtiar, Suhardi & Muhamad, 2020). These processes enable the transformation of data into facts, information, insight, interfaces, new data, or services (Davies, 2010; Susha, Grönlund, & Janssen, 2015; Purwanto, Zuiderwijk, & Janssen, 2020). How stakeholders can deliver value back to open data ecosystems is well understood in studies of open government data (see, for example, López Reyes & Magnussen, 2022). The goal of the release of open government data, as an example, is to transform how governments relate to the public by engaging citizens in using the available data (Hossain, Dwivedi, Rana, 2016). The intention is to enable citizens to transform their community or environment; or help local governments solve challenges by benefiting from their knowledge, ideas, and the ability of people to provide surveillance (Bachtiar, Suhardi & Muhamad, 2020). Different effects of this can be expected. Firstly, it can drive social effects by creating or improving solutions to public service provision and creating social value. Secondly, it can improve governance by raising transparency and accountability, increasing citizen trust, and stimulating citizen participation. And thirdly, it can lead to economic effects by driving economic development (Yuan, 2019; Safarov, Meijer, Grimmelikhuijsen, 2017). Specifically, using datasets related to public services and facilities can increase social value as they offer citizens the opportunity to enhance social life quality (Kalampokis, Hausenblas, Tarabanis, 2011). The use of open government data allows citizens to share information, participate in policing and law enforcement, analyse and monitor social issues as well as government actions, and develop social innovations. It also allows for engagement in the innovation or improvement of public services, generation of wealth through the downstream use of outputs, and, more broadly, enhancement of interactions between government and citizens to solve local problems (Hossain, Dwivedi, Rana, 2016; Safarov, Meijer, Grimmelikhuijsen, 2017). Despite the efforts to engage citizens in the use of open government data (Huijboom & Van den Broek, 2011) and the rapid advances in information and communication technology (Yuan, 2019), several studies point out that the critical problem of open government data initiatives is their underutilization (Bachtiar, Suhardi & Muhamad, 2020; Safarov, Meijer & Grimmelikhuijsen, 2017). There has been a significant focus in the literature on studying the relationship between the utilization of open government data and the social and technical conditions enabling or disabling its use (Bachtiar, Suhardi & Muhamad, 2020; Safarov, Meijer & Grimmelikhuijsen, 2017; de Azambuja, 2021; Neto, 2018). Scholars have identified various barriers and conditions that affect the use of open government data. These include data quality issues, such as metadata and readability, legislative concerns, such as policy and privacy, user-related challenges, such as lack of knowledge, skills, or interest, infrastructure barriers, such as interoperability, availability, and security, and economic challenges (Bachtiar, Suhardi & Muhamad, 2020; Safarov, Meijer & Grimmelikhuijsen, 2017; de Azambuja, 2021; Neto, 2018). More recent studies have concentrated on the barriers and conditions of using open government data by focusing on specific types and usersʼ motivations (Safarov, Meijer, Grimmelikhuijsen, 2017). For example, Purwanto et al. (Purwanto, Zuiderwijk, & Janssen, 2020) focused on the individual citizensʼ drivers and inhibitors for engaging with the use of open government data. However, to D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 9 allow more rigorous empirical research to assess if the estimated effects of open government data are measurable, there is a need to investigate the link between the types of users and the potential type of effects (Safarov, Meijer & Grimmelikhuijsen, 2017). 1.2 Problem definition From an open data ecosystem perspective, open data users should not only make use of the data for their purposes, but also deliver value back to the open data ecosystems, which can be defined as contributing to the open data ecosystem in a manner that improve the system, content or interactions e.g. by sharing the outcome of their work and analysis as new open data, or by correcting or extending the open data – thus closing the circle and contributing to creating a true circular open data ecosystem. Tasks 3.3 explores ways to sustainably establish the contribution of open data users to open data ecosystems. Task 3.3 thus addresses the following question: What motivations for delivering value back to open data ecosystems do the different open data stakeholder groups have? With this knowledge, Task 3.3 will provide the next iteration of the commons-based governance model for open data ecosystems from Deliverable 2.3 by considering the motivations to deliver value back to the open data ecosystem (see Cazacu et al., 2024). Motivations of the following stakeholders will be identified: (non-expert users, journalists, users in education, NGOs, local government users and central/regional government users, commercial users, and intermediaries. Together the identified motivations will contribute to the next iteration of the commons-based governance model for open data ecosystems from Deliverable 2.3 by considering the motivations to deliver value back to the open data ecosystem (see Cazacu et al., 2024). 1.3 Role of this deliverable in the ODECO project In ODECOʼs deliverable 3.3 (D3.3), we aim to understand the motivations of various stakeholder groups for delivering value back to open data ecosystems and to explore the effect of these contributions from a government perspective. The relation to the other deliverables in WP3 is as follows. • Deliverable 3.1. “Understanding potential contributions of open government data users to the open data ecosystem“ mapped the potential contributions of the open government data user by researching and analysing the different kinds of stakeholders and the various ways they (could) contribute to the “life” of the ecosystem by producing, consuming or producing and consuming at the same time open government data. The results of deliverable 3.1 forms a starting point for deliverable 3.2 by mapping the types of potential contributions different stakeholder may make to the open data ecosystem (see Ktistakis et al., 2023). • Deliverable 3.2 “Closing the cycle: Promoting open data users' contribution from a technical perspective” introduced the technical means that should make it easy to deliver value back to the open data ecosystem by applying the principles of circularity. This included four components: (1) designing user interfaces for open data portals where stakeholders can readily materially add value to the ecosystem, (2) researching appropriate feedback tools, (3) assessing the tools and technologies used for the analysis of different kinds of open government data and non-government dataset analysis, and (4) assessing the technical requirements of artificial and collective intelligence systems to directly interact with the open data ecosystem. The results of deliverable 3.2 shows how to support open data users in to delivering value back to the open data ecosystem from a technical perspective (see Polini et al., 2024). D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 16 organization they work for but also can solidify their personal credibility, professionalism, and recognition—attributes that are valued even outside of their current media organization. Furthermore, verifying an opinion or incident through any means (including the use of open data, though not limited to it) is considered a best practice in the profession, as the spread of incorrect or fake news can severely impact their credibility, both personally and for the organization. Therefore, the motivation to use open data for fact-checking, when possible, can provide the value of trustworthy news to the public. Journalists are also keen on publishing their datasets as open data, which can further demonstrate their skills and increase their recognition among their peers. However, this is a point that upper management may object to on several occasions. The management's main objective, as expected, is to increase the monetary value of the organization. Therefore, they differentiate between cases where some datasets or tools are to be released and potentially used by competitors (a requirement for this release is proper attribution to the organization) and cases where some assets are to be kept internal as they are considered strategic resources. 3.3.4 Classifying the motivations Using the self-determination theory (Ryan & Deci, 2000b) to examine the motivations of journalists, their motivations were classified into two groups: intrinsic and extrinsic motivation – see Table 1. Table 1. Classification of the motivations of journalists for contributing to a circular open data ecosystem. Intrinsic Motivations Extrinsic Motivations Promoting the quality of their work. Access to more open data Promoting democratic values and transparency. Credibility and recognition Financial compensation It is also important to note that management, although not strictly journalists, is more interested in extrinsic motivations such as credibility and financial compensation. Additionally, they focus on the acquisition of strategic resources for the organization that can boost their competitiveness compared to their competitors. 3.4 Conclusions In conclusion, journalists' motivation to contribute to the open data ecosystem reveals a complex interplay of intrinsic and extrinsic factors, heavily influenced by their dual role as both consumers and potential providers of open data. Journalists uphold high standards regarding the quality of their work and their traditional role as guardians of transparency and democratic values, which can be enhanced by their active involvement and contributions to the open data ecosystem. Additionally, journalists are driven to contribute to the ecosystem because its expansion and improvement provide them with access to more data, enhancing their reporting and thereby boosting their credibility, making them esteemed professionals in their field. One challenge encountered in several instances during my research is that the data collected and analysed from journalists, combined with open data sources, are considered strategic resources by their organizations and therefore they are not keen to release them into the open data ecosystem. This finding highlights the need for further research into the impact that alternative business models can have on businesses utilizing open data, especially media organizations, so they can reap benefits from the data they have collected that are not directly monetary gains. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 17 4 Elementary school students open data value motivation 4.1 Introduction Students in different educational levels from elementary school to graduate education have been at the centre of a wide range of open data initiatives (Celis Vargas et al., 2023). Overall, these open data initiatives in education look for equipping students with the essential skills needed for the current fast changing and data-driven society (Cook et al., 2018). open data learning activities have ranged from using open data in regular school subjects such as chemistry and geography (Pence et al., 2015), engage with local problems and data in undergraduate courses about open data (Palova & Vejacka, 2022), and extracurricular activities such as public hackathons (Davis & Shneyer, 2020). Students could be seen as part of the large percentage of citizens without technical backgrounds, often called non-specialists, non-data experts or lay audiences (Boyles, 2020; Concilio & Mulder, 2018). In the open data movement students have been seen as relevant actors in the long-term sustainability of open data ecosystems mainly to enlarge the percentage of citizens that can use and benefit from open data. Despite the integration of open data in schools has been defined as a key strategy for achieving inclusion and fairness in open data ecosystems (International Open Data charter, 2015), according to Celis Vargas et al. (2023) current open data initiatives in education have been focused on higher education. The fewer initiatives in basic school education have highlighted the potentials of using open data mainly as two, firstly related to the connection of classroom activities to real facts, and secondly, to increasing teacher's and student's motivation (Coughlan, 2020). Furthermore, experiments of Saddiqa et al. (2019, 2021) highlight that the use of open data from studentsʼ own municipalities increased their interest in everyday life problems and fostered discussions in the classroom which not just develops digital capabilities, but also increases their civic awareness and the authenticity of their learning activities. Research by Pellegrino & Antelmi (2023) has shown that open data initiatives at the school level primarily are focused on the use of open datasets or Data exploitation rather than on the production of them. This could be seen as a barrier for fully achieving the potential of elementary school students as contributors in open data ecosystems. In inclusive and circular open data ecosystems, students should also be seen as active contributors and active citizens. Although the research relevance of open data education is increasing, and several initiatives can be seen in different educational levels, it is still a novel perspective to see elementary school students as active contributors in open data ecosystems. Current open data systems in education are mainly exclusive and mostly linear as it was analysed in the case of Danish schools (Van Loenen et al., 2021). Current open data literacy initiatives are often top-down which respond to governmental, school or teachersʼ interest rather than to students or youngstersʼ interests and skills. This linear perspective limits the role of young pupils to in-training future actors in open data ecosystems rather than active current actors. Ongoing research is advancing on developing learning designs that support elementary school students in an active role as contributors in inclusive, circular, skill-based and user driven open data ecosystems. Previous research has identified potential value-contributions of elementary school students to open data ecosystems (Ktistakis et al., 2023). Four potential values were uncovered by identifying the potential contributions. Firstly, a potential value contribution may be in the creation of local datasets, visualizations, and data stories, to contribute with local or contextual understanding of local issues. Secondly, students may increase community knowledge as the result of training open data skills in the school community. Thirdly, students may enhance the quality of open government datasets and metadata while using them in learning activities. Finally, they may bring social value to the open data ecosystem by raising the voice of local communities and creating networks and dialogue among actors in the school ecosystem. Actors in open data ecosystems can adopt different roles as producer, users, or intermediaries in extensive interactions among stakeholders D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 18 in local and global environments. In this way, elementary school students as relevant open data actors might not just use open datasets in school learning activities but also potentially produce tangible and intangible contributions such as local open datasets, visualization, data stories, dialogue, local knowledge, among others. However, understanding their motivation for delivering value back to the open data ecosystem is key to further developing the mechanisms and tools to support them. 4.2 Methodology This study is aimed at identifying the elementary school student's motivations for delivering value back to open data ecosystems. Considering that current literature has been firstly focused on open data initiatives in higher education and secondly on students as only users of open data, exploratory qualitative research was conducted to gain knowledge about elementary school student's motivations for delivering value back to open data ecosystems. Although this study is focused on students, different research methods targeted students and teachers with the aim of gaining depth on students' insights and reducing possible biases. Methods such as observation, qualitative interviews and survey were held targeting elementary school students in the age range of 14 and 18 years old. In-depth interviews with teachers were conducted to gain knowledge about their perspectives on students' insights. The following table summarizes the methods conducted and the participants. Table 2. Methods and participants - school case. Method Participants Description Workshop and survey 39 school students aged 1516 years old in 9th grade. Workshop (open data learning activity). The first part proposed individual data exploration and the second part focused on group work on a Data story with visualizations. At the end of the workshop, students answered a brief survey. Duration: 2h Group Interview 15 school students (3 groups of 5 students) aged 15-16 years old in 9th grade Informal interviews were conducted with a group of students after the workshop. Duration: 20 min Nonparticipant observation 50 children aged 14-18 years old from different nationalities Nonparticipant observation during the co-creation workshops conducted by the partner organization CoC Playful Minds during the Children's General Assembly CGA 2022. Duration: one week In-depth interview 5 elementary school teachers Semi-structured interview. Duration: 60 minutes Sessions were recorded and transcribed for analysis. Data collected was analysed at the same time following a thematic network analysis approach (Attride-Stirling, 2001). Firstly, potential students' motivations were coded keeping the participant wording as much as possible. Secondly, categories were made to show different motivations of students to potentially deliver value back D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 19 to open data ecosystems. Finally global themes were identified to create a map of student's motivations. 4.3 Results and analysis of actorʼs motivation Seven different motivations of elementary school students were identified through the collection of qualitative research methods and a thematic network analysis. These motivations could lead students to deliver value back to open data ecosystems supported by learning designs. The following table shows the motivations, and a quote exemplifies the student's perspective. Table 3. Student's motivations for delivering value back. Motivations Quote Being proud of their work "Sharing our student's work with the outside" "They stand up a little straighter and they are prouder because it is not just presenting for Mom and Dad" Teacher Not listen but do it themselves "We learn more by doing that" "You did not have to listen to a lot but do most yourself" Student in group interview Use in real-world what they do in school "If I'm sharing it, and it could be used afterwards, students are more proud and actually more engaged to make it right because they know that it's likely to be used for something meaningful afterwards" Teacher Their ideas being heard "Children's voice as important as others" Student in CGA 2022 Engage with something that is relevant for them "Became relevant for the student because is something that the like and do in day life" "For the students in a project many times it is more interesting to have something concrete, something they can see, something that is close to them" Teacher Help the community around the school "The school could be part of the local community by creating better data" "I think local problems could be more fun because students can do something" Teacher Making the world a better place "How is this going to create a better world?" Student in CGA 2022 Pass the exams "If you work with 9th graders, they have exams, and they have to learn a topic from different angles" Teacher Motivational theory is used to gain depth on the analysis of student's motivations. According to self-determination theory (Ryan & Deci, 2000a), student's motivations could be classified in two groups, if they are intrinsic and extrinsic motivation: 1. Intrinsic motivations: it relates to the personal satisfaction of doing something. (i) Being proud of their work (ii) Not listen but do it themselves, (iii) Use in real-world what they do in school, (iv) Their ideas being heard, (v) Engage with something that is relevant for them. These motivations are personal and according to teachers could also be related to the age range of students who are in their teenage years (14-18 years old). 2. Extrinsic motivations: motivations in this group want to satisfy an external demand such as (v) Help the community around the school, this motivation is promoted by teachers and the school. It wants to position the school as a relevant actor in the local community. (vi) Making the world a better place, this motivation answers an external interest for educating responsible and active world-citizens in the 21st century. (vii) Pass the exams, this motivation responds to the expectations of the educational system. Even though the three motivations are external, according to Ryan & Deci (2000), motivations such as help the community around the school and making the world a better place could be related to an autonomous style called D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 20 identification where the motivation does not create a contrast or tension to the intrinsic motivation, instead it reinforces it. 4.4 Conclusions regarding motivations of students Several motivations of students could drive their role as active contributor in open data ecosystems. For example, their motivation of being heard could underpin a provider or intermediary educator role between local government and local communities. Nevertheless, learning designs and governance mechanisms are needed to enable the potential contributions to occur and explode the motivation of students. Limitation of the current results could be associated with the context of Danish schools where the interviews and workshops were conducted. This study is the first study addressing the research question, further research is needed to deepen and generalize on these outcomes. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 21 5 NGO open data value motivation 5.1 Introduction Non-Governmental Organisations (NGOs), also interchangeably called Non-Profit Organisations (NPOs) in this chapter, take up an intermediary role in the open data ecosystem, where they bridge the gap between open data providers and users (Gonzalez-Zapata & Heeks, 2015). NPOs are unique as intermediaries as there are specific communities of the users, they are focusing on to address a social issue (Enaholo, 2017), while also not seeking to gain any profits out of it (Salamon & Anheier, 1992). For example, an NPO may focus on making a city more accessible to people with disabilities or aim to improve governmental services for the citizens. NGOs are also well positioned to bridge the gap between public policy and its implementation through the involvement in public-private partnerships and the creation of space for the collaboration of other actors in an open data ecosystem (Mendel, 2013). Historically, NPOs pushed for data openness, developed the open data research field, and resolved the practicalities of open data use. There are many ways in which NGOs contribute to the open data ecosystem. For example, they create tools and applications or write reports to make open data accessible and understandable for the users. Moreover, they can produce additional open data to enhance the open data they use and re-share it. In this chapter, we discuss the motivations of the NGOs to bring value back to the open data ecosystem, given their organisational nature and the variety of ways they can create value. 5.2 Methodology The case studies were used to collect the data and look into the motivations of the NPOs/NGOs to bring the value they derived from the open data back into the open data ecosystem. The selection criteria for the case studies were: 1. Non-profit organisations should have different missions/focuses/aims. 2. Each case should have more than one type of open data activity being performed. 3. The cases work on different levels, i.e., municipal/regional/national. 4. The cases involve organisations and people who are willing and ready to cooperate in the research and to share information that is required to conduct this research. The two cases we have focused on are NPOs: Open Knowledge Belgium and CityLAB Berlin. Three semi-structured interviews were conducted per case, and the information from their websites was collected. For the analysis of the qualitative data, we used a deductive approach with the codes based on Ryan & Deciʼs (2000) self-determination theory, which focuses on the distinction between intrinsic and extrinsic motivations. 5.3 Results and analysis of actorʼs motivation 5.3.1 Intrinsic motivation Intrinsic motivation is coming from the interest ang enjoyment, inherent satisfaction that a person experiences when doing a task (Ryan & Deci, 2000a). For my analysis, I describe these motivational feelings as belonging to the group of people, employees, who represent and work at NPOs. There are shared motivations that bring people in such organisations to create value from open data and bring it back to the ecosystem. he work structure of NGO/NPO can have less bureaucracy and more flexibility with projects, especially in smaller organisations. The employees can pitch their own ideas and pursue projects of interest to them. Moreover, there is space for experimentation with the technical aspects of the projects. That creates space for internal motivation as employees do projects, they are passionate about. For example, one of the interviewees pointed out their “from the heart” project, which is signified by the developers coming back to the website in their spare time to update the coding behind the visualisation. During this project development, the D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 22 employees tried a new approach that was sophisticated at the time and needed to be kept updated. Thereʼs also a desire to do something about causes that are personal for the employee as well as social issues, topics they are invested in, for example, climate change and how it affects their city. 5.3.2 Extrinsic motivation Extrinsic motivation in the form of profits does not directly exist for the NGOs due to them being non-profit (Salamon & Anheier, 1992). They seek to be seen as a neutral trusted advisor rather than a private company seeking further investment or maximising profits from their projects. However, NGOs might need to have projects that are more commercially oriented as the funding is not always sufficient to maintain their projects or to start new ones. Moreover, NPO pushing for open data can create new business opportunities for private companies, which can be a motivation for NPOʼs board members. Introjected regulation can also play a part. For example, the attention and approval of others, in these cases, project supervisors or people from the open data community, motivates NPOʼs employees to pursue the project, continue with value creation, and share it back into the ecosystem with the actors whose attention it caught. One of the interviewees gave an example of a project about parking spaces in the city. The public administration does not track this data, which means that NPO is taking it upon itself to create new or merge the existing datasets on the topic to share with the users. Regulation through identification for the NPO employees comes from their desire to do what is perceived by them and others as impactful work for a political purpose, to empower people. By adding value back into the ecosystem, they can meet the needs of the group they target. They aim to work for the end goal of public good (Enaholo, 2017). That is why their projects mostly exist as open source and provide open data. They can also speak for the public and for their organisationʼs vision through, for example, lobbying the government to release high-value datasets or by showing the usefulness of open data to the public employees responsible for its provision. If employees feel that they have identified a genuine need of a community they target or a potential way for technology to address a problem, they are motivated to create and release a project (Yoon, Copeland & McNally, 2018). Integrated regulation is present when employees mention personal beliefs that make them want to contribute. They believe that open data is a foundation for democracy and transparency, which motivates them to, for example, buy and re-publish government data as open. One employee mentions a personal belief that motivates them to be part of the organisation – that there should be a levelled playing field for companiesʼ innovation through access to open data. Moreover, the personal belief that using open government data to learn from it rather than for the data to “sit on the shelf” has also been mentioned. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 23 5.4 Conclusion The motivations of NGOs to contribute open data value back into the open data ecosystem come from both intrinsic and extrinsic types of motivation as per Ryan and Deciʼs (2000a) selfdetermination theory. They are summarised in Table 4 below. Table 4. Classification of the motivations of NGOs for contributing to a circular open data ecosystem. Intrinsic Motivations Extrinsic Motivations Personal interest and enjoyment Getting funding Belonging to the group of people with shared motivations of social impact and openness Creating opportunities for other stakeholders Attention or support of the open data community Impactful political work in the eyes of other stakeholders Personal belief in openness The intrinsic motivation come from the personal interest and enjoyment of employees when it comes to creating value-producing projects using open data. As an employee has more freedom to propose and pursue their own topic of interest, they are motivated to have a variety in open data and approaches they choose towards value creation and sharing. The variety of regulations under the extrinsic motivations are also pushing NGOs to bring open data value back into the ecosystem. From creation of profit opportunities for other actors in the ecosystem to focusing on the projects that gain attention of the open data community for addressing their needs, the NGOsʼ collaboration and attention to other actors is a factor adding to the value sharing. Their desire for a politically impactful work and belief in the pursuit of the transparency and democracy is also a contributing factor to their sharing behaviour. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 24 6 Local government users open data value motivation 6.1 Introduction The publication of open data is enforced by national and European policies obliging governmental bodies to make non-private data publicly available. In that context, local governments play a pivotal role in harnessing the value of open data, acting as key players in operationalising open government initiatives on a local or municipal scale (Davies & Perini, 2016). In the broader context of public administration, local governments represent the foundational level (Goldsmith, 1992), fostering political identity, promoting economic development, providing social welfare, and governing the community (Stoker, 2011). Local government users can be any local public officer from the different governmental institutions tasked to operationalise the stateʼs goals on a local or municipal scale. Examples of their role in the open data ecosystem include collecting, augmenting, using, demanding and/or maintaining open data to meet the evolving needs of their communities. Moreover, local governments utilise open data to accomplish their public tasks, repurpose existing data for new initiatives, conduct research and analysis, and collaborate with external intermediaries to process and integrate data from various sources to develop solutions or input for policymaking (Ktistakis et al., 2023). The potential contributions of local governments in the open data ecosystem include identify local issues that data can help address, coordinating efforts to ensure access to data, offer domain expertise, enhancing data legitimacy, championing data openness, and sharing, and incorporating new data-based technologies such as predictive knowledge into decision-making activities (Ktistakis et al., 2023). This chapter explores the value motivations of local government users to contribute to the open data ecosystem, drawing insights from a multiple exploratory case study on the uses of voluntary geographical open government datasets in the Danish context. 6.2 Methodology We conducted a multiple-case study that include seven different use cases to gain insights on how local governments in Denmark utilise voluntary geographical open government repository. The voluntary geographical open government datasets are a project that resulted from a collaboration between the national institution in charge of data supply and an association of local governments in the country. The study aimed to explore the different aspects of using open government data, with a focus on the involvement of local governments on creating value through its use. We conducted seven semi-structured interviews with various stakeholders, including representatives from national ministries, government associations, non-profit organisations, and private entities. Our analysis is based on the theory of Communities of Practice by Wenger (2000). Wenger describes communities of practice as the basic building blocks of a social learning system. They are the social 'containers' of the competencies that make up such systems, and these competencies are defined by combining three dimensions: enterprise, mutuality, and repertoire. Wenger identifies three forms of participation as modes of belonging to social learning systems: engagement, imagination, and alignment, which are affected by the competence dimensions. Using these modes of belonging and the three competencies' dimensions, Wenger developed the Community Dimensions conceptual framework (Table 5), which we used to formulate our analysis. In the following section, we present the results of our analysis. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 25 Table 5. Community Dimensions by Wenger (2000). Enterprise: learning energy Mutuality: social capital Repertoire: selfawareness Engagement What are the opportunities to negotiate a joint inquiry and important questions? Do members identify gaps in their knowledge and work together to address them? What events and interactions weave the community and develop trust? Does this result in an ability to raise troubling issues during discussions? To what extent have shared experience, language, artefacts, histories, and methods accumulated over time, and with what potential for further interactions and new meanings? Imagination What visions of the potential of the community are guiding the thought leaders, inspiring participation, and defining a learning agenda? And what picture of the world serves as a context for such vision? What do people know about each other and about the meanings that participation in the community takes in their lives more broadly? Are there selfrepresentations that would allow the community to see itself in new ways? Is there a language to talk about the community in a reflective mode? Alignment Have members articulated a shared purpose? How widely do they subscribe to it? How accountable do they feel to it? And how distributed is leadership? What definitions of roles, norms, codes of behaviour, shared principles, and negotiated commitments and expectations hold the community together? What traditions, methods, standards, routines, and frameworks define the practice? Who upholds them? To what extent are they codified? How are they transmitted to new generations? 6.3 Results and analysis of userʼs motivations The case study under analysis focuses on an open government geographical data repository project that is based in Denmark. This data is voluntarily made available by the municipalities across Denmark in standard exchange formats, and it is free of charge. This makes it easily accessible to anyone who needs it. The users of this data can be considered a community of practice, as they all share an interest in the domain of opening geographical information that is essential for public administration. They are interested in making use of the data repository, which contains specific data for various purposes, such as outdoor tourism, roads and cycles, school planning, and mobility. Within this community, there are various actors, like public authorities, businesses, NGOs, and citizens, that come together, driven by their common interest in the datasets provided and the learning need associated with harnessing the potential. The motivation for their collaboration stems from shared learning objectives of effectively using the data repository for different purposes. Whether making available data on specific municipal infrastructure or using it to access meaningful information through municipal web solutions, the community converges on the repository as a valuable resource. The practice aspect of the community emphasises the need for the open government geographical data repository to be more than just a shared interest and D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 32 open government data portal and through an intensive work of adapting the FAQs of the Covid19 open data hub (Interview 1, Ireland). Feedback and suggestions were elaborated based on the e-mails received by OSI through the Covid-19 open data hub. Thousands of queries sent through the open data portal translated into a comprehensive set of answers that provided a clear guidance on relevant aspects of the data strategy (Interview 1, Ireland). Another motivation for governments to share open data is the need to satisfy the demand for open data. In France requests coming from citizens on the open government data portal, as well as on the social platforms (e.g., Twitter) were considered greatly by the governmental agency in charge of the national open data portal (Interview 1, France). Therefore, citizens played an indirect role in setting the open data collection agenda. Another motivation for data sharing is the need to fill data gaps. In the context of the Covid-19 pandemic, France heavily relied on private actors to provide data on the stock of vaccines available and vaccine appointments (Interview 2, France). To fill data gaps, the Ministry of Solidarity and Health started a collaboration with the vaccine producers to provide data on the stock and the logistics of Covid-19 vaccines (Interview 2, France). Also, the Ministry of Health and Solidarity entered a partnership with Doctolib - a private company market leader in managing medical appointments, allowing the company to host, and collect, vaccination appointment data (Bothorel et al., 2020). Finally, governments can also be motivated by the willingness to collaborate to projects initiated by other actors. In France, Etalab worked in coordination with a civil society initiative called “Open COVID19”, which built a dashboard aimed at Covid-19 data visualization (Interview 1, France). The Governmentʼs Communication and Information Service partnered with the project so that all citizens could access information through the dashboard made available on the French governmentʼs website in March 2020 (Interview 1, France). It must also be noted that some countries have mandatory feedback provision mechanisms to correct errors. For example, in the Netherlands government users of the base registries ( basisregistraties ) are by law required to report errors in the data. The data providers are obliged to research these errors, report back to the user and if necessary, fix the error. 7.4 Conclusions Governments can be motivated by both intrinsic and extrinsic motivations to contribute value to open data ecosystems as summarize in Table 7. Extrinsic motivations include legal mandates, the need to provide factual information to citizens and policymakers, and the need of filling data gaps. Intrinsic motivations encompass the willingness to collaborate and to satisfy the demand for open data. Table 7. Extrinsic and intrinsic motivations for open data sharing for central governments. Extrinsic Motivations Intrinsic Motivations Legal mandate Willingness to collaborate Legal obligation Provide factual information to citizens and policymakers Satisfy the demand for open data Fill data gaps D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 33 8 Commercial users open data value motivation 8.1 Introduction Commercial users are defined as those whose goal is to make an economic profit. This is illustrated in contrast to users who intend non-commercial use, which Creative Commons (Creative Commons, 2023) defines as “means not primarily intended for or directed towards commercial advantage or monetary compensation.” Commercial users may have different motivations to contribute to open data ecosystems, depending on their field and own necessities. In this case we are focusing on the specific example of Geospatial open data ecosystems, and in particular on the OpenStreetMap ecosystem. OpenStreetMap is a community-led (Park et al., 2020) platform, where commercial users also take a producer role as part of the broader community (Anderson et al., 2019 and OpenStreetMap Foundation, 2023). OpenStreetMap can be classified as a successful initiative where stakeholders of different types all use its data and contribute to the ecosystem e.g. collecting, maintaining, and updating data. Additionally, they utilize frameworks such as a centralized database or platform. Additionally, they utilize frameworks such as a centralized database or platform. 8.2 Methodology A qualitative semi-structured interview was conducted with commercial users, to figure out the motivation and barriers to contribution to the OpenStreetMap ecosystem. Employees in 25 companies were interviewed, including 7 big corporations and 18 SMEs (according to the European Commission (2003), SMEs are those companies with less than 250 employees). Keywords were extracted from the answers and aggregated into different motivations, which allowed the qualitative data to be measured in a quantitative way. The motivations are then classified according to the self determination theory, explained in chapter 2, into intrinsic and extrinsic motivations. 8.3 Results and analysis The interviews discovered that not all actors contribute just with data, but also with funding of OpenStreetMap-related events and/or the OpenStreetMap Foundation, contributing to the development of tools that handle OpenStreetMap data, and with community-building initiatives. It was revealed that big corporations contribute on several or all fronts, possibly because of their bigger pool of resources, both human and financial. SMEs on the other hand, prefer to concentrate their contributions on one or two fronts, depending on what is more beneficial to their business, and the personal motivations of the workers, who are usually quite familiar with OpenStreetMap on a personal level. The motivations to contribute to each of the fronts are found to be similar between big corporations and SMEs. Regarding the motivations for contributing data to the project, most interviewees of organizations who contribute data mentioned improving the data quality as the biggest motivation, as they use OpenStreetMap as a source dataset for their own projects. Other mentioned motivations were the alignment of OpenStreetMap to their own private dataset, standardization of the schema in OpenStreetMap, community building, and being grateful to the project and wanting to give back. Most of the motivations are therefore found to be extrinsic, that is to achieve an external reward (Ryan & Deci, 2000a), which in the case of OpenStreetMap is improved data, which commercial users can improve their services with. This is logical, considering commercial users' aim of maximizing economic profit. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 34 Table 8. Classification of the motivations of commercial users for contributing to a circular open data ecosystem. Intrinsic Motivations Extrinsic Motivations Alignment of OSM to their dataset Improving the data quality (as OSM is used by the organization) Community building (in a topic/domain the commercial user is interested in) Standardization of the OSM schema Being grateful and wanting to give back Some of them also found barriers in contributing data to OpenStreetMap, with the most mentioned being the license, as all sources must comply with it. Other mentioned barriers were the restrictive importing guidelines, the difficulty of integrating with their own datasets and lack of tooling for that case, lack of manpower or funding, and lack of ground truth about the produced dataset. In the case of SMEs, present barriers meant that they preferred to either direct their clients with suggested data improvements directly to OpenStreetMap so they become contributors, as they have the ground truth for their proposed changes, or to focus their resources on contributing on non-data fronts. Big corporations, meanwhile, decided to launch a new platform called Overture Maps, where they overcome some of the barriers imposed by OpenStreetMap, as well as some deficits like data heterogeneity of OpenStreetMap. In Overture Maps they can release data in a more permissive Community Data License Agreement license, together with more loose importing guidelines, adapting better to the big corporationsʼ data pipelines, as well as data in homogeneous schemes and with unique identifiers. This platform is supposed to be complementary to OpenStreetMap, being also OpenStreetMap one of the data sources of the project, and more directed to solve software developersʼ problems with OpenStreetMap data quality and standardization but has sparked critique on the OpenStreetMap community for possible affections it could have to the OpenStreetMap project, and lack of communication between the Overture Maps and OpenStreetMap communities (Zverik, 2023). About non-data contributions to the OpenStreetMap ecosystem, most mentioned community building as the main driving force, with tool improvement -including tools the commercial users utilize-, brand visibility, and achieving less push from the community towards corporate users as other motivations to contribute to non-data fronts. The non-data contributions therefore also show extrinsic motivations as the main moving force. It is relevant to mention that all the interviewed commercial organizations have employees with strong software development skills, both SMEs and big corporations. That could indicate a barrier of entry to use and contribute to OpenStreetMap from non-tech-savvy commercial users. 8.4 Conclusion The open nature of OSM as a digital data common has allowed turning commercial usersʼ selfmotivations (e.g. to improve data quality in a source the company uses) into public value (better quality and up-to-date data quality for all users). This governance mechanism has managed to create a platform where non-governmental actors feel comfortable to share their data, a role previously mostly reserved for governmental actors. However, problems can arise. Commercial users still see barriers to the contribution: legal, other stakeholdersʼ attitudes, lack of resources, and technical. This has resulted in big corporations creating the Overture Maps platform to fit their unresolved needs. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 35 OSM has to ensure its sustainability by continuing to be a welcoming platform to all stakeholder types, and work on resolving the still existing barriers, while not falling at the same time in the tragedy of the commons, maintaining a critical user base and ensuring no actor dominates the landscape. Further research could be done on how to motivate non-technologically proficient commercial users to participate in the OSM Ecosystem, to achieve a more diverse commercial stakeholder landscape, as well as how to maintain the Overture Maps and OSM communities connected. Another relevant research would be to compare the motivations of non-commercial and commercial users to contribute to the project. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 36 9 Open data intermediariesʼ open data value motivation 9.1 Introduction Open data intermediaries are instrumental in addressing barriers to value generation from open data. They are “third-party actors who provide specialised resources and capabilities to (i) enhance the supply, flow, and/or use of open data and/or (ii) strengthen the relationships among various open data stakeholders” (Shaharudin et al., 2023). Examples are developers who process and include open data in apps/software, crowdsourcing platforms that gather and publish data as open data, and organisations that transform open data into easily digestible information such as visual forms. Open data intermediaries can be in various shapes and forms, such as public organisations, companies, civil society organisations, and research organisations. Open data intermediaries carry out various tasks depending on their specialised resources and capabilities and, thus, deliver value to open data ecosystems through various contributions (as presented in Ktistakis et al. (2023). Several existing contributions of open data intermediaries as users of open government data, as captured in the literature, include developing open government data-based applications, providing advisory services for the implementation of open government data, initiating or leading engagement and interaction between open government data stakeholders, and providing open government data-based contextual materials to citizens such as articles and visualisations. In addition to their existing contributions, some potential contributions of open data intermediaries that could be explored (as suggested in Ktistakis et al. (2023) include developing open-source software with in-built pre-processed open government data, offering open government data platforms based on federated architecture especially for cross-domains interoperability, transforming open government data into specific industry standards, integrating a formal feedback mechanism on software/platform, showcasing more use cases of open government data via various means, and running open government data-based incubator programmes. 9.2 Methodology We interviewed open data intermediaries, providers, and users to explore the (potential) motivations for open data intermediaries to deliver value back to open data ecosystems. The table below summarises the method employed. These motivations may not be explicitly referred to as “motivations” by the interviewees, but they are implied based on the incentives currently in place or the benefits for open data intermediaries to contribute in certain ways in open data ecosystems. Beyond analysing how these motivations may drive open data intermediaries to carry out existing contributions, we also explore how they may be shaped to drive open data intermediaries to carry out potential contributions for the benefit of the whole ecosystem. Table 9. Methods and participants in the intermediaries case. Method Participants Description Semi-structured interviews: Questions around the business models, challenges, and expectations from open data intermediaries Interview groups: Open data intermediaries, open data providers, and open data users No. of interviews: 48 Mode: Online Period: April 2023 - August 2023 & December 2023 - February 2024 D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 37 9.3 Results Open data intermediaries can be motivated by intrinsic or extrinsic motivations in contributing value to open data ecosystems. Certain open data intermediaries may be driven by profit generation. These open data intermediaries would typically provide web/mobile applications based on open government data, contextual materials such as news content, consultancy services, or data platforms customised for specific audiences/domains. They obtain monetary returns from providing these products and services by charging end-users directly or creating an appeal to their core product that is supplemented with open government data-based features or functionalities. Some open data intermediaries may also run (civic) technology incubator programmes and invest in other open data-based companies, collecting some share of the profit as those companies grow. Some open data intermediaries may also be motivated by potential self-learning and capacitybuilding through the use of open government data. By processing open government data, they learn by doing and gain knowledge about open government data over time, which is used to improve their product to fit open government data use and to provide consultation to their customers in using open government data. As noted by one of the open data intermediaries interviewed, by using open government data themselves, they use their product “as if [they] are the customers [themselves]”. This motivation, although it does not necessarily bring monetary returns immediately, is a way for open data intermediaries to strengthen their position as the goto experts in the open data ecosystem. Open data intermediaries focusing on social goals such as media transparency, public sector accountability, and environmental monitoring may be motivated by altruism in doing good for their community and society. They may likely be non-profit organisations that contribute contextual materials and advisory services related to issues of their concern using open government data. They may also be open data advocators, showcasing the use cases of open government data, or open source advocators, developing open source software that facilitates the use of open government data, especially for organisations that cannot afford proprietary software. Certain open data intermediaries may contribute to open data ecosystems to fulfil legal obligations. For example, an open data intermediary within the public sector may develop and maintain a data platform based on federated architecture to facilitate open data publication by different agencies within the public sector that are obligated to implement open data. Legal obligations may also be considered to drive open data intermediaries to carry out tasks they may not otherwise have incentives, such as delivering pre-processed open data in open standards and licenses where possible. Some open data intermediaries may be driven by visibility enhancement, where they consider contributing to open data ecosystems, such as by showing how they use open government data in their work or disseminating open government data that they have pre-processed, may increase their visibility. By connecting and relating to the open data community, such as by presenting in open data events or by joining open data user groups, they may be able to promote their organisation and products even though they do not necessarily gain immediate benefits from using open government data. Open data intermediaries may also be incentivised to contribute to open data ecosystems through collaborations initiated by other stakeholders. For example, public agencies that provide open government data may invite certain open data intermediaries to carry out open government databased projects or initiatives, such as developing civic apps/platforms and organising hackathons or civic tech incubator programmes. These open data intermediaries may be compensated D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 38 monetarily, such as through direct payment or shares in the companies under the incubator programme, or they may be invited as sponsors, giving them visibility in return. 9.4 Conclusion In summary, the motivations for open data intermediaries to contribute value to open data ecosystems can be grouped into intrinsic and extrinsic motivations, following the selfdetermination theory, as shown in the table below. Table 10. Identified motivations of intermediaries. Intrinsic motivations Extrinsic motivations Profit generation Fulfil legal obligations Potential self-learning and capacity building Collaborations initiated by other stakeholders Altruism for their community and society Visibility enhancement D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 39 10 Non-expert usersʼ open data value motivations 10.1 Introduction In analysing the motivations of non-expert users to participate in open data ecosystems, we focus on a particular setting of participation: open data hackathons. We focus on open data hackathons because they have shifted from being tech-focused events designed around software development, to more open get-togethers welcoming the contributions of non-expert users (Endrissat & Islam, 2022). Previous research found that the main value of hackathons is in creating communities, rather than in the technical solutions produced (Endrissat & Islam, 2022). Hackathon participants not only build and test new solutions, but also test new ways of collaborating around shared ideas (Briscoe & Mulligan, 2014). Jaskiewicz et al. (2019) similarly found that open data hackathons can contribute to community capacity building, with participants developing shared mental models around common issues. These previous findings show that hackathons can contribute the goal of more inclusive open data ecosystems and is why we choose hackathons as the focus of our research. Open data hackathons are events lasting 1-3 days where the objective is to reuse open datasets to solve a challenge. The hackathon (hacking marathons) audience is shifting from an exclusively technical one, to a more balanced mix of expert and non-expert users. Hackathon priorities are not anymore to only develop impressive technical solutions, but also to develop useful solutions, which address real world challenges, hence the need for “non-expert” user participation. We define non-expert users as citizens who are not data professionals (i.e. employed as data analysts, offering commercial services which rely on open data, etc.), but who may possess domain expertise. This chapter presents a systematic literature review on the motivation of open data hackathons attendees. We apply the taxonomy by Ryan & Deci (2000a) of extrinsic and intrinsic motivations and reveal a variety of motivating factors. In the next chapter, we explain the methodology used to find, filter, and analyse the papers, we then show the results of the research, and, finally, summarize our conclusions. 10.2 Methodology We conducted a systematic literature review. We searched for the terms “open data” AND “hackathon” in the title and abstract of articles in the following databases: Scopus (46 records), Web of Science (26 records), IEE Explore (12 records). Some articles were present in multiple databases, so we filtered 57 unique records. We then removed irrelevant articles by looking at the title and abstract. We removed articles clearly unrelated to open data hackathons. For example, some papers describe open data solutions developed during a hackathon, but not the hackathon itself, and therefore do not contain information on participantsʼ motivation to attend. We removed records clearly unrelated to non-expert or citizen user participation (for example journalism hackathons, or academic hackathons). We also removed short tutorials and reviews. After the filtering based on title and abstract, we obtained 31 records. In the last step of filtering, we scanned the papersʼ full text, and removed articles that did not discuss participantsʼ motivation to attend. Only 9 items were included in the analysis, from the initial 56 unique records. For each of these unique records, we searched the text for insights on the motivations of participants and classified them according to the taxonomy by Ryan & Deci (2000a). 10.3 Results Anslow et al. (2016) performed a case study of a hackathon driven by social good organizations (SGOs) and argued that “The SGO also brings their mission and values, encouraging participants who might not be motivated by other factors (e.g., prizes, experience)” (Anslow et al., 2016, p. 617). In the case of a hackathon involving community members (older adults) working on local water quality data, Carroll & Beck (2019, p. 67) observed that “seem eager to share their D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 40 experiences and personally motivated to expand the model of their groups more generally throughout the community”. In a survey of participants of three hackathons, the top motivations were “learning and developing new skills”, “performing teamwork”, and “engaging in the resolution of civil problems” (Gama, 2017). This is convergent with the findings of Jaskiewicz et al. (2019), where the top motivations of hackathon participants were “to promote bottom-up emergence of innovations”, “to integrate my organization in an existing partnership” and “to explore the potential of open data in a certain domain”. Organizers often use prizes such as opportunities to travel abroad and compete in further events, and venture capital (Kitsios & Kamariotou, 2019). Kitsios & Kamariotou (2022, p. 11) argues that while prizes and rewards are significant motivators, other factors are ignored “such as networking, new knowledge, training, fun, and support for the creation of startups”. It is also important to note that using prizes as motivational tools will “influence both the types of participants who sign up for the contest and their ability to persevere throughout the contestʼs duration” (Kitsios & Kamariotou, 2023, p. 4). In an earlier study Kitsios & Kamariotou (2018) also noted that the support to further develop the solution further after the hackathon is a significant motivator, though this mostly applies to software developers. In an adjacent systematic review of the engagement factors for open data hackathons, Purwanto et al. (2018) found two intrinsic motivations (“fun and enjoyment” and “intellectual challenge”) as well as three extrinsic motivations (“performance expectancy/relative advantage”, “learning and developing new skills”, and “networking”). Another interesting facet of participant motivation is the type of artifact that they are invited to create. In the hackathon described by Temiz (2021, p. 7): “for each challenge category the “concepts” track is included to motivate non-technical people to participate”. This is similar to the “intellectual challenge” mentioned by (Purwanto et al., 2018). Table 11. Summary of the motivations of non-expert user found in the literature. Extrinsic Motivations Intrinsic Motivations Networking and teamwork (Carroll & Beck, 2019; Gama, 2017; Jaskiewicz et al., 2019; Kitsios & Kamariotou, 2018, 2022, 2023; Purwanto et al., 2018) Learning (Gama, 2017; Jaskiewicz et al., 2019; Kitsios & Kamariotou, 2018, 2022, 2023; Purwanto et al., 2018) Business development support (Jaskiewicz et al., 2019; Kitsios & Kamariotou, 2018, 2019, 2022, 2023) Hackathon challenge (Anslow et al., 2016; Gama, 2017; Jaskiewicz et al., 2019; Kitsios & Kamariotou, 2018; Temiz, 2021) Fun (Kitsios & Kamariotou, 2018, 2022, 2023; Purwanto et al., 2018) 10.4 Conclusions With adequate scaffolding, hackathons can welcome non-expert user participation and act as an interface to open data ecosystems. In this study we investigate attendeesʼ motivations to participate in open data hackathons, finding two extrinsic motivations (networking and teamwork, business development support) and three intrinsic ones (learning, hackathon challenge, and fun). The validity of this structured literature review is limited by the narrow set of papers reviewed, and the limited data available on participantsʼ motivations. We tried to exclude studies describing highly technical hackathons. But we also acknowledge that, in a hackathon context, it is hard to distinguish between “non-expert” and “expert” users. Open data hackathon organizers should take into consideration the motivating factors for non-expert users and design the scaffolding of the event accordingly. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 41 11 Next iteration of D2.3 with focus on usersʼ motivation 11.1 Introduction The focus in this chapter is to provide the next iteration of the commons-based governance model for open data Ecosystems from Deliverable 2.3 with focus on user motivation (see Cazacu et al., 2024). The first version of the model was generated in Task 2.3 and is based on the combination of the literature review and the empirical data obtained from the various ODECO partners and a translation of the commons-based design principles into six action principles concerning: 1. Boundary-making in relation to open data ecosystems, 2. Supporting communities of open data ecosystems, 3. Encouraging participation and shared decision-making, 4. Considering appropriate legal mechanisms, 5. Designing an ecology of interoperable projects, and 6. Ensuring sustainability of open data ecosystems. In this report, we seek to enter the next iteration of the development of the model based on the analysis of user motivation presented in the previous chapters. The sections below thus contain analysis of Action Principle 1 – 6 from the perspective of user motivation in the various research cases described in the previous chapters. Note that not all action principles are relevant to all research cases, and that some research cases (e.g. journalists) therefore do not have a substantial contribution to the next iteration of every action principle. 11.2 Developing the next iteration of Action Principle 1 Action principle 1 concerns the making of boundaries in relation to open data ecosystems. Action Principle 1: Boundary-making in relation to open data Ecosystems “To define the boundaries of an open data ecosystem, actors should be aware of the socio-technical conditions where their interactions with other actors take place. These conditions refer on one hand to social components such as historical, geographical aspects, social and cultural norms, organization norms and community affiliations, as well as practices, traditions, personal motivations and values. On the other hand, the technical aspects include soft and hard data infrastructures, interoperability practices, standards, laws and regulations. Ecosystem mapping can be done using tools from the discipline of design thinking and theoretical principles from the discipline of information visualization and communication.” (Cazacu et al., 2024, p. 38) 11.2.1 Next iteration of Action Principle 1 In several of the described research cases user motivations relate to the boundary making in action principle 1. Students have the motivation of integrating in the local context firstly by "helping the community around the school" and secondly by sharing their work and "being proud of their work". This could support the collaboration and organic development of the ecosystem. In this dynamic, students could adopt distinct roles as consumers, providers, and intermediaries of open data according to different learning objectives. Although the school can serve as a physical platform for interaction, there is a weakness in relation to the technical conditions where data interaction take place. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 48 gaps by addressing the needs of the less represented groups. It can also enable the emergence of collective responsibility that could inspire and motivate municipalities to update and maintain data. The collaborations emerging from considering a polycentric approach could also contribute to an evolving data repertoire. However, while a collaborative and decentralised approach to leadership might be valuable, the absence of an explicit leadership structure may pose challenges in relation to decision-making and coordination. This can lead to challenges on data maintenance and fragmentation. A polycentric approach to open data sharing is fundamental to correct the imbalances between resourceful actors such as regional and national governments who actively participate to the open data ecosystem and those who are left out due to various reasons. Indeed, it is important to carefully design governance mechanisms fit for guaranteeing the participation of the less privileged to the open data ecosystems. We are currently working on the design of solutions for encouraging polycentricity that encompass: (1) fostering participation of underrepresented population to open data policies design and governance; (2) understanding how the financing of open data initiatives impact disadvantaged groups; (3) evaluate how the outcome of open data programs benefit differently the various actors of open data ecosystems. The application of action principle 3 should ensure that commercial voices are heard in the governance of ODECOs. The challenge can be that when commercial users are given power in decision-making, the bigger corporations hold all the power, and the SMEs have no voice. Inside a given stakeholder group, power struggles can happen too. There should be focus on who represents the commercial users who represents the citizens. A suggestion for developing the next iteration can be to ensure mitigations for intra-stakeholder type power struggle. Open data intermediaries: The concept and application of polycentricity has been long debated, but one of the definitions of polycentricity is a “social system of many decision centres having limited and autonomous prerogatives and operating under an overarching set of rules” (Aligica & Tarko, 2012). So, it is not apparent from the description of the action principle how participation and collaborative decision making can encourage “polycentricity”. How do limited and autonomous prerogatives emerge? What is the overarching set of rules? Ultimately, it is unclear why “polycentricity” is considered one of the action principles. That being said, in terms of practicality, this action principle may be more feasible to public institutions and civil society organizations as social expectations towards them exist. On the contrary, most businesses are primarily driven by profit. For businesses to “ensure that those typically with less power […] being actively encouraged to communicate their feedback, needs and concerns”, there may need to be an extrinsic motivation (e.g., laws and regulations) requiring them to do so unless doing so is aligned with their business interests (e.g., to offer better products). It is difficult to imagine reasons for businesses to empower the less represented and disadvantaged groups “to actively contribute to the creation of strategies and plans, practices and assessments, products and services” since these are internal business decisions. Open data intermediaries may facilitate feedback between actors in the open data ecosystem, for example, by putting feedback features in their software/platform or collecting and communicating feedback on behalf of others directly to the relevant parties. They may also initiate multistakeholder collaborations that are aligned with their interests. The next iteration of the action principle should be rephrased to be less abstract and avoid using terms that may have to be further defined. For example, this action principle can instead emphasize feedback and collaboration. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 49 Non-expert users: Open data hackathons can be used as an interface to understand citizensʼ needs and concerns. The discussions and concepts developed by participants can serve as indication of the communityʼs priorities and needs. If not designed appropriately, hackathons can give the impression that complex societal problems can be resolved with simple technical solutions. It can also happen that, during an open data hackathon, participants discover the need for more or different open datasets, which are not yet available. To enable participation, institutions that support the hackathon process should be available to share more data during or after the event. Next iteration: The next iteration of action principle 3 needs to consider structures for decisionmaking and coordination. In this chapter it has been suggested to encourage polycentricity through participation and collaborative decision-making in open data ecosystem. This is to foster shared learning objectives, allowing officials to gain insights into citizensʼ needs. A polycentric approach however needs explicit leadership structures to strengthen decision-making and coordination and meet challenges on data maintenance and fragmentation. Leadership structures for creating collaborative decision-making therefore needs to be explicitly addressed in the next iteration of action principle 3. 11.5 Developing the next iteration of Action Principle 4 Action principle concerns the legal mechanisms in open data Ecosystems. Action principle 4: “To encourage the use of open licenses – including open data licenses for databases, Creative Commons licenses for content, and open source software licenses for software code and other software artefacts. Licenses have been central to the creation and continuation of knowledge, information and data commons. Where the data in question does not relate to any personal or sensitive information, broad licenses should be used that impose little to no restriction on reuse. Further, governments should, to the extent possible and subject to security concerns, procure open source infrastructures for open data technologies. Here, the open licenses also serve to instil a culture of communing.” (Cazacu et al., 2024, p. 41) 11.5.1 Next iteration of Action Principle 4 Action principle 4 does relates to user motivation in some of the research cases and less to others. NGOs are often motivated by the belief in the benefits of openness and transparency, so they promote, support, and push for appropriate legal mechanisms for open data. However, the contribution of NGOs is limited to the influence they have over other stakeholders. The best practices may not be implemented or promoted if there is insufficient power distribution between actors in the ecosystem. The action principles 4 and 5 (Considering appropriate legal mechanisms and designing an ecology of interoperable projects) are more difficult to relate to the motivations mapped in elementary school students. In general, the awareness and knowledge about open data in school is low therefore connecting to these two principles is more complex. Perhaps, these could be addressed considering school administration and educational policies. Regarding action principle four, encouraging the use of open licenses, including open data licenses for databases, in the context of local open government geographical data repositories presents several motivations for local governments. For example, local government officials engage with open data to learn about infrastructure needs, gather user preferences, and promote inclusiveness. Collaboration with businesses and other stakeholders helps improve tourism D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 50 planning, strategy, and decision-making. They also create communities that share purposes across domains, such as cycling infrastructure, leisure activities, outdoor facilities, and inclusive outdoor experiences. In those contexts, they need to integrate different types of data, share across sectors, ensure user-friendly interfaces, and promote data accessibility, which contributes to building trust within collaborative partnerships. Open licenses could be a tool to leverage those collaborations, enabling access to new ways of sharing data, such as real-time data. Open licensing could also enable the integration of open geographical data with different domains, creating a comprehensive open data ecosystem that addresses societal issues like climate change and facilitates collaboration across sectors. The challenges related to this principle could be the lack of data literacy and understanding, as some local government officials may face challenges in understanding and effectively utilising data, limiting their capacity to address data-driven initiatives within the municipalities; they may also face resource constraints to prioritise these efforts, or they may face competing priorities within the local governments. Data literacy and awareness are needed for this principle. Regional and national governments can be ‘locked-inʼ the use of commercial software due to various reasons, such as convenience, data literacy, etc. While it is important to encourage the use of open source software, the application of action principle 4 needs a strategical approach on how to effectively encourage governments to transition to open source software. In regard to application with commercial users, the principle is already applied in OpenStreetMap by using an open license (ODbL) which has a share-alike clause, and allows commercial use. The challenge of application of the principle can be that unclear licensing of databases may set a barrier to use or contribution to it. Another challenge is diverse opinions of the involved stakeholders in which license to use, and whether to include share-alike and commercial use. a suggestion to development of the principle can be that OpenStreetMap is switched from a Creative Commons license in 2012 to an Open Database License (ODbL). Creative Commons licenses are thought for artistic works more than databases. It is suggested to rephrase the action principle to add specific database licensing. Open data intermediaries: Many (if not most) open data intermediaries rely on making their products (e.g., augmented data, software, platform) under proprietary license to generate profit from their open data intermediation activities unless those open data intermediaries are sponsored by public or private funders. Therefore, while this action principle may sound ideal on paper, the challenge is, what are sustainable business models for open data intermediaries where they do not have to rely on sponsorship and simultaneously use open licenses for their products? For instance, Esri pre-processes open data from various sources and puts them in their proprietary software. They enhance the use of open data while gaining profit by selling their software. How can the software generate profit if it has to be under open license? Likewise, if the pre-processed open data is provided under an open license, and everyone else can use it without using Esriʼs software, why would Esri pre-process the data in the first place since there is no business case for it? Where possible, open data intermediaries could provide their products under open licenses if it is not affecting their business model. This action principle may negatively impact the sustainability of some open data intermediariesʼ business models and, ultimately, the sustainability of the open data ecosystem. Hence, the necessity of this action principle should be reconsidered. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 51 Non-expert users: Open data shared before or at the beginning of the hackathon event should be shared with a CC license. Commercial and institutional partners might not be willing to share their data and solutions under a CC license. Sometimes this is also true of hackathon participants who might want to develop commercial solutions. Open data hackathons need to acknowledge the need to protect commercial interests of some of the actors involved. Next iteration: The next iteration of the development of action principle 4 should address two challenges. First the next iteration needs to address challenges related to lack of data literacy and understanding of licensing, as some local government officials may face challenges in understanding and effectively utilising data, limiting their capacity to address data-driven initiatives within the municipalities. Second the future development needs to address the challenge on what types of sustainable business models there are for open data intermediaries where they do not have to rely on sponsorship and simultaneously use open licenses for their products. The action principle may negatively impact the sustainability of some open data intermediariesʼ business models and, ultimately, the sustainability of the open data ecosystem and it could therefore be necessary to reconsider the necessity of this action principle. 11.6 Developing the next iteration of Action Principle 5 Action principle 5 concerns the design of and ecology of interoperational projects. Boundaries of an (open) data ecosystem are fluid and contextual and there are overlaps between various open data Ecosystems as well as relationships between these ecosystems and collaborations between actors across different open data Ecosystems. The principle thus concerns how governance frameworks can encourage cross-collaborations and cross-interactions among different actors. Action Principle 5: “To focus on interoperability and data portability, and have a broad understanding of these concepts. In particular, efforts for interoperability should encompass technical interoperability (through, for example, semantic and syntactic interoperability of open data systems/portals and through standardised formats for (open) data, as also noted in the empirical data collected) as well as generative interoperability (through adoption of policies aimed at nurturing public spaces for decision-making in relation to open data needs and challenges). Support should be provided to regulatory measures aimed at broad interoperability and portability, through advocacy and political action.” (Cazacu et al., 2024, p. 41) 11.6.1 Next iteration of Action Principle 5 The principle on designing and ecology of inter-operational projects are highly relevant to several of the cases of user motivation but - as described above - not to others. As NGOsʼ motivation also comes from personal interest and enjoyment, if their employees are active in the open data movement, they already advocate for improved interoperability. However, the limited resources of the NGOs should be considered as to how they can be supported in their implementation and push for better interoperability. In relation to action principle 5, focusing on interoperability and data portability would facilitate more effective collaboration between government officials, businesses, NGOs, and citizens. It would allow for a seamless data exchange and joint inquiry, fostering innovation in tourism planning, strategy, and decision-making. Interoperability also facilitates the integration of diverse datasets, providing a comprehensive view to local government officials. This contributes to evidence-based decision-making, helping in areas like local government planning processes, project prioritisation, and identifying infrastructure gaps. For example, combining recreational and D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 52 transportation data creates a more complete open data ecosystem for planners. Data portability ensures that data is easily accessible and user-friendly. This strengthens social value within the community, as users can access and use data across different interfaces seamlessly. Finally, interoperability helps address knowledge gaps within collaborations of local governments with different participants on open data ecosystem, and the ability to integrate different data types and share information across sectors contributes to a more comprehensive understanding of infrastructure needs and priorities. For example, integrating data on cycling infrastructure planning and tourism can lead to a better understanding of shared purposes and collaboration. Regarding the challenges, local governments may face resource constraints in prioritising interoperability, particularly due to the challenges of data maintenance or the competing priorities within their organisations. Local governments may be reluctant to integrate interoperability practices due to the lack of standardised practices and protocols or because they follow varied methods, standards, and frameworks. It may also be difficult to face security and privacy concerns related to data portability. This principle needs to consider resources constrains and guidelines for data security and privacy. Regional and regional governments: Interoperability and data portability are two fundamental pillars of value creation through open data. Indeed, data combination allows to conduct finegrained analysis and to mitigate potential data bias. Interoperability and data portability are also at the centre of the EU agenda (e.g., EU Data Act). However, governments face budget constraints and do not always prioritize data curation and the correct implementation of interoperability standards. It is therefore important to raise awareness on the relevance of interoperability and data portability through: (1) ad hoc trainings for governmental officials; (2) simple and accessible step-by-step guides for governmental officials and citizens on interoperability; (3) designing governance strategies that take into account organizational interoperability challenges. Interoperability is not greatly applied in OpenStreetMap, causing barriers to its data use by commercial users, as shown in the interviews. OpenStreetMap data can be ambiguous in its meaning, and the same real-world scenario could be represented in various ways on the data. The challenge in the application could be an increased barrier to non-technical users to contribute data to a given ecosystem, if user-friendly tools for it donʼt exist. A suggestion to the development of the action principle could be to mention that the implementation must be simple enough to not cause future barriers to stakeholders. Open data intermediaries: What does data portability mean here? According to the OECD, data portability refers to “the ability (sometimes described as a right) of a natural or legal person to request that a data holder transfer to the person, or to a specific third party, data concerning that person in a structured, commonly used and machine-readable format on an ad-hoc or continuous basis” (OECD, 2021). It is unclear how data portability applies to open data since open data should not include personal data. Hence, more clarity on what data portability means (in practice) concerning open data is necessary. What does generative interoperability mean in practice for open data? For example, are Web Standards (https://www.w3.org/standards/) or Open Geospatial Consortium standards (https://www.ogc.org/) considered generative interoperability? Does “generative interoperability”, as a term, have a distinctive application in practice, different than other known types of interoperability [see (Interoperable Europe, n.d.)], or is it a buzzword? Open data intermediaries who have long established and transferred data in specific formats may be reluctant to adopt new interoperable formats because other stakeholders within their ecosystem may already be familiar with the proprietary formats. For instance, most Esri users are D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 53 more familiar with the shapefile format even though it is not an interoperable format. Merely as an illustration: more people are sharing word processing documents in .doc format instead of .odt, even though the former is a proprietary format, while the latter is an interoperable format. Open data intermediaries could provide data in diverse formats, including interoperable formats, where possible. Rephrase the action principle to be less abstract and avoid using terms that may have to be further defined. For example, this action principle can instead emphasize the adoption of interoperable formats/standards. Non-expert users: This action principle has limited application to open data hackathons. Data shared by partners before and during the hackathon should follow interoperability standards. Next iteration: There are several challenges that needs to be addressed in the future development of the action principle. First the principle needs to be rephrased to be less abstract and avoid using terms that may have to be further defined. For example, this action principle can instead emphasize the adoption of interoperable formats/standards. Secondly this principle also needs to consider resources, constrains and guidelines for data security and privacy. Thirdly it is important to raise awareness on the relevance of interoperability and data portability through: (1) ad hoc trainings for governmental officials; (2) simple and accessible step-by-step guides for governmental officials and citizens on interoperability; (3) designing governance strategies that take into account organizational interoperability challenges. Lastly, the future development of the principle needs to address the challenge of an increased barrier to non-technical users to contribute data to a given ecosystem, if user-friendly tools for it donʼt exist. It is suggested to mention that the implementation must be simple enough to not cause future barriers to stakeholders. 11.7 Developing the next iteration of Action Principle 6 Action principle 6 concerns ensuring sustainability of open data ecosystems to maintenance of data infrastructures. The principle speaks of both economic sustainability as well as social support to ensure the sustainability of open data ecosystems. Action Principle 6: “In terms of economic sustainability of open data Ecosystems, advocacy for availability of public funds can be accompanied with insights from economic and business models of digital commons/information commons/data commons projects, in particular from collaborative peer production. Contributions to social sustainability can be ensured through the adoption of critically situated approaches to participation from critical data studies.” (Cazacu et al., 2024, p. 42) 11.7.1 Next iteration of Action Principle 6 Economic and social sustainability is central in several of the research cases. Ecosystems economic sustainability is pivotal for journalists' use of open data. It is clear that open data have been used either by very large media organizations that have the resources to organize and run data journalism departments or by small, independent journalistic teams that rely on EU or governmental subsidies to be funded. This significant discrepancy needs to be corrected if journalists are to have a central role in the open data ecosystem. The exploration and utilization of different financial models for media organizations is needed so journalists can have the resources (money and time) to work with open data. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 54 The "motivation of making the world a better place" could also lead to the long-term sustainability of open data ecosystems involving students, schools, and local communities. Building the networks and forming communities around the use of open data to solve real problems contribute to social sustainability. NGOs are not motivated by profit, but they are motivated by the work they do and by how it can help others. Thus, the funding is of concern for them. This action principle implementation would support NGOs to be motivated to contribute back to the open data ecosystem. However, funding can create biases in the motivations of NGOs as to which projects to prioritise and how to contribute back. Thus, the action principle should be approached cautiously, and mechanisms for this issue should be considered. In relation to action principle 6, it is crucial for local governments involved in open data initiatives to advocate for economic sustainability and the availability of funds. This will encourage the development of open data ecosystems, which attract businesses, entrepreneurs, and investors who see economic potential in leveraging the available data. For example, local businesses could use local open government data for tourism planning and marketing, contributing to economic growth. Public funds should be advocated for to encourage collaboration with the private sector. Economic sustainability can ensure a stable source of funds for maintaining and expanding open data ecosystems. By securing public-private partnerships, local governments can leverage combined resources to mobilise funds to support infrastructure needs, data maintenance, and future enhancements for sustaining and expanding open data ecosystems. Resources could be allocated for technology upgrades, data quality assurance, and user-friendly interfaces. A sustainable open data ecosystem could also lead to job creation and economic impact as businesses and startups thrive on open data, contributing to employment opportunities and economic benefits for their territories. However, securing public funds for open data initiatives may be challenging, especially when local governments have limited budgets. Competing priorities for public spending could result in inadequate financial support for sustaining open data initiatives. Local governments may face challenges at demonstrating the direct economic returns of open data initiatives. Quantifying economic impact and benefits to justify public spending can be complex. Striking a balance between public and private interests in open data initiatives can be challenging and ensuring that private sector involvement aligns with public goals and benefits the entire community requires careful consideration. They may need to develop a sustainable funding model for open data ecosystems, which requires careful planning. Local governments may face challenges in creating a long-term strategy that addresses ongoing operational costs and future expansion needs, and economic sustainability efforts should ensure that open data remains accessible to a diverse set of users, including those with limited resources. Exclusionary economic models may hinder the overall inclusivity of open data ecosystem. The strategies should also consider the need to stay updated and allocate resources for technological advancements. This principle needs to consider inclusivity and accessibility in economic sustainability efforts and long-term planning capacity. Regional and regional governments: Economic sustainability of open data ecosystems is often a neglected topic of investigation. Open datasets are often shared without a thorough planning and reflection on how they can contribute (economically or societally) to the open data ecosystem. Since resources allocation is one of the competences that characterize national and regional governments, it is important to involve them and make them aware of the missed opportunities of short-sighted open data financial planning. Nonetheless, governments are often challenged by resource constraints. Therefore, collecting, sharing, and advertising successful business models D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 55 from digital commons projects is key to channel resources towards the economic sustainability of open data ecosystems. To this extent, it is also important to map investments from local, regional, and national governments in the open data ecosystems. As seen in the OpenStreetMap case study, commercial users can contribute financially to the sustainability of an open data Ecosystem, as it is also in their interest that the project is kept alive. Stakeholders can contribute too and co-produce ODECOs in several ways: data, social value and economically. All have to be sorted and a good balance of stakeholders has to be achieved for this to happen, as stakeholders may contribute in the way(s) that are in their best interest. The challenge in the application can be an unbalanced sustainability (data and social value but no economic sustain, or any of the other combinations). In the development of the action principle we suggest adding the idea of balance of the different sustainability values. The action principles defined in D2.3 are derived from the governance of data commons. As OpenStreetMap is considered a data commons, they align correctly with the commercial users' contributions to it. However, the interviews with commercial users can be used to derive a new action principle. While some are motivated by the social value they can give to open data and data commons platforms such as OpenStreetMap, commercial users are primarily motivated by their own interests. This coincides with their goal of being economically profitable. An action principle is proposed, where open data Ecosystems should be governed in such a way that personal or own stakeholder motivations (e.g. commercial users who want to improve the data in their own topic or area, because it directly benefits the data quality in their offered services) can be turned into social values and benefits for the whole community. Open data intermediaries: This action principle is read as a call for more research and development of the business models in the open data ecosystem. However, it is unclear why “digital commons/information commons/data commons projects, in particular from collaborative peer production” are singled out. Are other types of business models not sustainable? Open data intermediaries may provide support to other actors in the open data ecosystem to design and implement certain business models, for example, through incubator programs where they provide financial investments and training and networking support. The next iteration of the action principle can reconsider singling out “digital commons/information commons/data commons projects, in particular from collaborative peer production”. It may not be necessary. Non-expert users: Hackathon events typically rely on public and private funding. Private funding often implies the promotion of commercial solutions for data analysis. Public funding might require time consuming bureaucratic processes. Challenges: Alternative business models also require significant effort and time to set up and would not provide much additional benefits compared to traditional funding sources. Open data hackathons take a very small budget to organise, so it is unclear whether there is a need for alternative business models. Feedback: acknowledge the trade-offs of alternative business models, which hold potential for project that struggle to attract traditional funding, but also require significant time and investment. Next iteration: There are several challenges that needs to be addressed in this action principle. First it is mentioned that the principle needs to consider inclusivity and accessibility in economic D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 56 sustainability efforts and long-term planning capacity. Funding can create biases in the motivations of NGOs as to which projects to prioritise and how to contribute back. Thus, the action principle should be approached cautiously, and mechanisms for this issue should be considered. Secondly with commercial users the application can be an unbalanced sustainability (data and social value but no economic sustain, or any of the other combinations). In the development of the future development of the action principle it is suggested to add the idea of balance of the different sustainability values. Thirdly an action principle is proposed as a result of data from interviews with commercial users. While commercial users are motivated by the social value they can give to open data and data commons platforms, commercial users are primarily motivated by their own interests. This coincides with their goal of being economically profitable. Lastly it is suggested to pay attention to different types of business models in the future development of the action principle. It is unclear why “digital commons/information commons/data commons projects, in particular from collaborative peer production” are singled out. It should be considered if other types of business models are sustainable. 11.8 Summary In a broad perspective, the action principles from deliverable 2.3 (Cazacu et al., 2024) can be applied to many, but not all, use cases of open data users. With a more specific focus on the user motivation of any particular user group, some action principles are more important than others in each case. This chapter has explored which action principles are relevant and applicable in each case in relation to what may motivate open data users to contribute to the open data ecosystem. In chapter 12 below we summarise the motivations of different user types, and the elements in the action principles that needs to be adapted motive different types of users. D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 57 12 Conclusion Task 3.3 focused on the question: What motivations for delivering value back to open data ecosystems do the different open data stakeholder groups have? The first objective in this report has thus been to answer this research question from the perspective of different user groups. The second objective has been to provide the next iteration of the commons-based governance model for open data Ecosystems from Deliverable 2.3 (Cazacu et al., 2024) with focus on the detected user motivation. In this chapter two tables are presented as concluding answers to the two objectives: Table 12 is a summary of the motivations of different user types, and Table 13 is a summary of elements in the action principles that needs to be adapted to motive different types of users. The motivation of user groups to deliver value back to open data ecosystems has been analysed in chapter 3 – 10. A summary of this is presented Table 12 below. Table 12. Types of motivation of different user types User Types Intrinsic motivation Extrinsic motivation Communities of Practice Journalists • Promoting the quality of their work • Promoting democratic values and transparency • Access to more open data • Credibility and recognition • Financial compensation Elementary school students • Being proud of their work • Not listen but do it themselves • Use in real world what they do in school • Their ideas being heard • Engage with something that is relevant for them • Help the community around the school • Making the world a better place • Pass the exams NGO users • Creating value producing projects using open data • Freedom to propose and pursue own topic of interest • To have a variety in open data and approaches towards value creation and sharing • Creation of profit opportunities for other actors in the ecosystem • Do politically impactful work • Pursuit of the transparency and democracy D3.3 Closing the cycle: Promoting open data usersʼ contribution from a governance perspective 64 13 References Aligica, P.D., & Tarko, V. 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