By creating an autonomous, transparent, and secure decentralized system, blockchain technology enables the removal of intermediaries as trustees to be replaced by a peer-to-peer based governance system. This feature upholds important implications to redesign the public governance systems and co-production practices in a more transparent, efficient and effective way. As a first use case example of the blockchain technology in a digital co-production process, the city of Barcelona has initiated the Digital Democracy Data Commons (DDDC) project to uses the blockchain and distributed ledger technology to share a petition with participants, sign it with a mobile app and count the signatures in a cryptographically secure and completely anonymous way. Through an in-depth case study, this paper analyzes how blockchain technology can shape the coproduction processes and what possible roles can be assumed by citizens for the use of these technologies in digital coproduction. In this paper, we created a novel analytical framework to categorize possible roles imposed on coproducers in blockchain technology-based systems. Our preliminary findings suggest that the compatibility with the legal framework, the interoperability with existing institutions in data governance, and previous experiences and established communities for deliberative and participatory processes are important on the design -based digital coproduction processes.
Disruptive technologies are increasingly influencing the way coproduction provides value to society
and how coproduction processes are implemented. Governments around the globe are conceiving
of new ways to engage citizens in coproduction and collaborative efforts via disruptive technologies,
such as blockchain and distributed ledger technology (DLT). These efforts lie on the promises of
such technologies to increase trust in and security of transactions and reduce transactions cost
This paper aims to address this gap by focusing on the following research question: How can blockchain and DLT shape citizen coproduction processes? A pilot of the Digital Democracy Data Commons (DDDC) project in Barcelona, which applies DLT to engage stakeholders in a participatory process will be studied in order to answer this research question. Through this case study, the paper will outline a theoretical model to study the applications of blockchain and DLT in digital coproduction processes. The remainder of the paper is structured as followed. Section 2 discusses the background of digital coproduction and the way blockchain and DLT can shape digital coproduction processes. Section 3 presents the analytical framework, while Section 4 describes the case and methodology. Section 5 outlines key findings. Finally, Section 6 concludes the paper.
2.1.
The conception of coproduction fits in the New Public Governance paradigm that recognizes the
provision of public services as a model based on networks and inter-organizational relationships
(Bovaird & Loeffler, 2012). In this article, we draw on the concept of coproduction by
Coproduction is a relationship between a paid employee of an organization and (groups of) individual citizens that requires a direct and active contribution from these citizens to the work of the organization.
Citizens can engage in the coproduction of public services in different stages of the delivery
process, including the public service design, public service delivery and execution, and public
service monitoring. Each stage, therefore, entails different roles both from the public service
provider and the citizen
The ability to perform coproduction activities is seen to be increased by the implementation of
technological advances in the public sector
Blockchain and DLT are a class of technologies, henceforth blockchain technologies (BCT), that
create a transparent, autonomous and decentralized data governance system that gives users
confidence that archived information has not been tampered with
We expect this feature of the BCT to have important implications for the coproduction processes
in public sector, because it allows the service users and stakeholders to directly shape their service
provision without a need of intermediary or administrator to validate or channel their input. BCT
can also increase the transparency, auditability and accountability of the information provided in
the coproduction processes and thereby builds trust in the coproduction processes. For instance,
BCT provides new ways of coproduction by allowing cities to create 'voluntary data repositories' of
citizens' personal data in order to co-produce new services. Moreover, BCT, as a communication
technology, can enable peer-to-peer public service delivery
However, there are some caveats and different technological features of BCT can have different implications to the coproduction processes. First, choices of the system designers on the design of the digital platform can affect the role of the coproducers and the scope of the coproduction activities. BCT-based platforms can have permissionless/permissioned and public/private forms (Miller, 2019). The choice between permissioned and permissionless systems calls for considerations of several trade-offs and decisions to be made by policymakers and system designers. These tradeoff conditions are context-dependent and factors concerning organizational and technological capacities, provider of technology services, area of application, interoperability with other data platforms, and political preferences might determine the choice of decision-makers. Secondly, the underlying consensus protocol and the associated incentive mechanism can alter the role of the actors involved in the coproduction processes. Incentives are important to attract the validators that contribute to the system with their time and resources (e.g. computing power and/or money). The most common form of incentive mechanism is distributing tokens to the validators, which can hold either a monetary value (e.g. Bitcoin) or another form of value for the beneficiary. However, using tokens to motivate users to participate in coproduction processes may undermine the effectiveness of coproduction processes or have negative repercussions (e.g. adverse environmental impact in Proof of Work (PoW)- based systems).
Thirdly, BCT is not a monolithic technology and auxiliary technologies (e.g. tokens, zeroknowledge protocols, smart contracts, etc.) integrated in the blockchain platform can alter the way coproduction takes place. For example, through tokens and smart contracts, coproducers can enable various voting features for coproduction processes and automatically execute ex-ante agreed rules of transactions for service provision. At the same time, with zero-knowledge protocols, co-producers can preserve their anonymity without undermining the validity of transactions in the blockchain network. Depending on the applications (or decentralized applications), the scope and the underlying features of coproduction in a blockchain platform may vary.
Fourth, the way decisions are implemented in the blockchain platform can impact the legitimacy
of coproduction. In blockchain governance, there are on-chain and off-chain decision-making
mechanisms. On-chain system brings the trade-off between efficient decision-making and transition
processes and risking destabilization due to the political dissonance. Off-chain system brings the
trade-off between enhancing the political consensus in the decision-making and transition processes
undermine the stability of the system
The fifth caveat is related to the definition of the digital coproduction and concerns about to what extent different acts of engagement using BCT can be classified as coproduction or co-creation. Veiko et al. (2019) differentiate on the scope of activities associated with coproduction and cocreation based on the level of engagement where activities concerning the service production correspond to coproduction and the services at a strategic level correspond to cocreation. Moreover, they focus on whether citizens provide direct and active input. Here direct refers to whether the input provided by the citizens affects the services individually provided to them, and active refers to whether citizens take part in the design or delivery of services. Yet, this definition has certain shortcomings when applied to the user roles in a BCT-based system. The functionality of the BCT-based systems rests upon the active engagement of the users in validation of the transactions. Even if the input of the validators (be it random or selected nodes) can be rendered as active and direct according to the aforementioned definition, their involvement is arbitrary and indiscriminate to the service provided by the platform. In a way, they are passive service producers without holding an agency about the scope of the services. Therefore, it is important to identify the different roles of the participants engaging in BCT-based coproduction processes and whether they hold an agency in the services provided by the system.
The literature of digital coproduction is still at its infancy, and only a few analytical frameworks are ne framework developed by Veiko et al. (2019) provides some coarse assumptions about the impact of four categories of technology (i.e.sensing, communication, processing, and actuation) on motivation, interaction, resources and decision-making processes and the coin coproduction. However, this coarse categorization has its shortcomings when it comes to the roles of the BCT in coproduction processes. As mentioned earlier, BCT is not a monolith technology, and varying applications and different underlying protocols of blockchain platforms may change the implication of the technology concerning decisioncoproduction processes. Furthermore, it is difficult to position BCT only under one category of digital technologies. BCT can be categorized as a processing technology- as rendered by the authors
BCT allows users to control and verify the personal data and thereby it allows an on-chain and off-chain engagement at a larger scale.
A second type of analytical model available in the literature focuses on the use and ownership of
data in the coproduction processes
As a first step, we developed an analytical model to assess the user roles in BCT-based coproduction (see Figure 1). In the construction of the model, we paid attention to matching three dimensions with each other: (1) possible user roles identified by the BCT-based systems; (2) possible citizen and state roles identified by the digital co-production/co-creation systems; and (3) a versatile and evolutionary view on the role of BCT. Accordingly, we identified three stages in the use of BCT in co-production processes.
Citizen-controlled information management: BCT is used to process personal data of citizens. In this
simplest form of BCT-based coproduction, a coproducer can have the role of data provider, verifier
and data user
Citizen-controlled public service design: In this mode, applications with smart contracts and digital tokens are used to allow automated, self-enforced service decisions. In digital coproduction, citizens through the use of the tokens can vote on the priority areas and criteria in public service provision and automate the implementation through the use of smart contracts.
Citizen-controlled public service production: In the most advanced form of BCT-based co-production, we expect the convergence of BCT with other digital technologies such as artificial intelligence (AI), internet of things (IoT), or actuation technologies. In this mode, citizens can decide on the content, extent and amount of the public service production. Depending on the combination of technologies, citizens can provide tailor-made services for the specific needs of the communities or groups of people. We expect in this stage, state to have limited or no involvement in public service provision 1 According to the Digieconomist, PoW-based Bitcoin mining consumes above 70.000 terawatt-hours per year, comparable to the annual carbon footprint of Denmark and energy consumption of Austria. (https://digiconomist.net/bitcoin-energy-consumption) and instead to provide data or meta-data available in state-sources to contribute to the citizencontrolled production processes.
This framework will help us to categorize the roles imposed on the citizens in a BCT-based system. We will expand our analysis later with the barriers and drivers identified by Toots et al (2017) to understand the impact of institutional conditions on the use of open data on the BCT-based co-production processes. The findings from the case study will allow us to refine and further develop the proposed analytical framework.
This study is designed as a single exploratory case study to examine how blockchain technologies can shape citizen coproduction processes. A single exploratory case study is a useful design to gain insights about a phenomenon that are thus far understudied or not understood, and to construct new theory or generate propositions about the phenomenon (Yin, 1989). In this study, we will the whole process of coacy Data Common of the EU funded DECODE project. DDDC uses BCT to share a petition with participants, sign it with a mobile app and count the signatures in a cryptographically secure and completely anonymous way.
We will collect our empirical data from secondary sources and expert interviews. We will gather data from multiple sources of evidence (semi-structured interviews, technical reports and policy reports, government documents) to triangulate conclusions and ensure higher levels of internal validity. Furthermore, triangulation also helps to mitigate against subjectivity in the research and thus strengthens the overall research design.
Some preliminary key findings are shared below: 1) The case in Barcelona falls into the category of citizen-controlled public service design. The pilot allows the control of the personal data by the people and creating an immutable and transparent system. Each citizen of Barcelona receives a token for the use of services and functionalities provided by the platform. Through tokens, citizens can vote on the proposals for the municipality agenda on public services. The system operates through smart contracts. 2) Two possible pathways are being discussed on the governance of the DDDC. One option is building a community around the application and creating a self-governance system around the community. The second option is creating an institution for the governance. Whilst the former option refers to a more decentralized system design, where the citizens control the data structure, the second option leads to a more centralized system. 3) Several factors had been influential on the design choices with the platform and integration of -government platform: a) Need for legal framework: No personal data in the ledger is being transferred, which gives direct control to citizens, but for further applications with BCT there is a need for a DLT legal framework. b) Interoperability: Interoperability of the technology with other administrative institutions (e.g. Barcelona Data Office) is important. Further testing of the platform will focus on this dimension. c) Political leverage: Person in charge should have some political leverage. For this project, d) Building the platform around a community: Building the technology around an existing community is important for an easier transformation. e) Path dependency: Previous practices with participative democracy (e.g. 50 men movement) and digital governance (i.e. Decidim) had been influential in the prioritization of citizenempowering (or more decentralized) systems designs. Furthermore, the emerging business culture in Barcelona in adopting smart city technologies since 2015, allowed the city to cooperate with big corporations on the development of the technology. 4) There are some challenges ahead to scale up the project: a) Technology itself does not create trust: If people do not understand the technology, it is difficult to create trust only by the technical features of blockchain. Trusted parties/stakeholders can assist in building trust about the technology. b) Compatibility with existing legal/regulative framework: There are challenges with the current legal framework especially concerning the right to be forgotten and the immutability and transparency of the DLT system. It is possible that some challenges can be solved technically (e.g. zero-knowledge protocol) but some decisions may need to be centralized. Therefore, there may be a need for more complex governance systems (partially centralized, partially through DLT or other decentralized systems). The environmental impact of the PoW technology and its compatibility with the CO2 framework are other concerns and reflect on the decisions with (de)centralization of the platform.
DDDC pilot is a unique use case to understand the implications of the BCT in coproduction processes. The case shows that BCT can create a pathway for citizen-controlled coproduction processes where citizens control their own data used in service production, and collectively decide on the policy priorities. However, there are various institutional, technological, social and organizational factors that can affect the design choices of the decision-makers, and thereby the application of BCT in the coproduction processes. In the case of Barcelona, the compatibility with the legal framework, the interoperability with existing institutions in data governance, and previous experiences and established communities for deliberative and participatory processes had been system.
Further research is recommended to understand further the underlying factors affecting the choices of decision-makers toward more decentralized systems in BCT-based coproduction.
Communities to Outcomes and Pu -production: The Contribution of Users and 1138.
Bovaird, T., & Loeffler, E. (2012). From Engagement to Co-production: The Contribution of Users and Communities to Outcomes and Public Value. Voluntas, 23(4), 1119 1138.
inguishing Different Types of Coproduction: A Conceptual 435. doi: 10.1111/puar.12465.Distinguishing.
Meijer, A. J. (2016). Coproduction as a structural transformation of the public sector. International Journal of
Public Sector Management, 29(6), 596 611.
Miller, A. (2020). Permissioned and Permissionless Blockchains. In S. Shetty, C. Kamhoua and L. Njilla (Eds.)
Blockchain for Distributed Systems Security (pp.193-204). Wiley.
Mingers, J. (1996). A Comparison of Maturana's Autopoietic Social Theory and Giddens' Theory of Structuration. Systems Research, 13(4), 469-482.
Supported Cooperative Work. CSCW, 28, pp. 169 207. doi: 10.1007/s10606-018-9311-7.
Toots, M., McBride, K., Kalvet, T., Krimmer, R. (2017). Open Data as Enabler of Public Service Co-creation: Exploring the Drivers and Barriers. In: Proceedings of the 2017 International Conference for EDemocracy and Open Government (pp.102-112). Krems, Austria: IEEE Computer Society
Evrim Tan Evrim Tan is a postdoctoral researcher at KU Leuven Public Governance Institute. His research focuses on decentralization, blockchain governance, and public governance models. He is part of the academic advisory board of INATBA, and member of the Belgian representatives for European Blockchain Service Infrastructure. A. Paula Rodriguez Müller