Essay on the opportunities of taking the citizen perspective when assessing the maturity of open data platforms.
Coursework for Integrated design of ICT architectures at Delft Technical University
Open data has tremendous potential to generate citizen participation, one of three primary goals of open government efforts (Lee & Kwak, 2012). If understood properly, open data can contribute to a modern society where citizens interact continuously with their governments to collaboratively improve their environment.
Nevertheless, the success of open data is often benchmarked from the perspective of readiness of the data or service provider (Susha, Zuiderwijk, & Janssen, 2015), that is to say: from the top-down perspective of the government. In order to promote design for participation of the citizen in open data platforms, this essay proposes taking the citizen perspective when assessing open data maturity.
In the Integrated Design of I&C infrastructures course, government-to-citizen platforms were discussed in depth, of which the opportunities of open data platforms took center stage in the course literature and lectures.
However, literature on open data as prescribed in the course takes a top-down prescriptive view of open data platforms. This seems to be no exception for the field. Even studies that focus on removing barriers for participation still take a government- or technology-centric approach, for example Ojo, Porwol, Waqar, Harney, & Zeleti (2016). This is no criticism of the lecturer, as she herself has identified a gap in open data literature on the actual use of data (Susha et al., 2015).
Personal experience of the author with open data platforms in the cities of Utrecht and Amsterdam in the Netherlands have led him to take an interest in why some platforms succeed and others do not. Is the desired social impact brought on by technology, intrinsic community value, platform governance, or perhaps other external factors?
If we take it that open data platforms contribute to the overall open government goal of participation, why is participation treated as a dependent variable and not as the main value in design of open data platforms? This is the starting point for this essay.
This essay aims to answer the research question: How can open data platform maturity be assessed from the perspective of participation?
A literature review is conducted to gain an overview of the state of research into participation in open data platforms. A mapping exercise is conducted to relate stages and concepts described in three models in the literature, resulting in an initial framework of phases and concepts.
The essay is structured loosely along the lines of an academic paper, while taking the liberty of making observations throughout. First, a literature review is conducted, resulting in a framework of phases and concepts useful for understanding participation in open data platforms.
Second, I consider the cases of Amsterdam and Utrecht as examples of platforms operating on different levels of maturity. This is largely based on empirical experience, supported by grey literature on these platforms.
Third, conclusions are offered. Drawing on process management, I then formulate recommendations.
Literature review results
In this section, two definitions of open data are considered and the concept of open data is recognized in its broader context of open government.
Then, existing maturity models of open data are discussed. I argue that the underlying definitions of open data drive the differences in conceptual models of open data platforms and thus the different conceptions of open data platform maturity.
Finally I consider open data from the perspective of participation, taking stock of literature from various fields on this topic and mapping the described concepts and phases. This results in a framework that can then be applied.
Definitions of open data
As this field is still emerging, no single authoritative definition exists. Open data is understood broadly in two ways: as an artifact, and as a process (Susha et al., 2015).
Open data as an artifact puts the technical provision of the data front and center. Open Knowledge International (2017) defines open data as "any content, information or data that people are free to use, re-use and redistribute — without any legal, technological or social restriction", further listing criteria for availability and access, re-use and redistribution, and universal participation.
On the other hand, open data as a process takes a broad view of organizational means and overarching goals for the platform. An example is Solar, Concha, & Meijueiro (2012) who define open government data as "a work philosophy to empower citizens and provide them access and license to use the data generated by public entities, so that they can use, store, redistribute and integrate them with other data sources. This data opening is justified both by encouraging citizen participation, strengthening democracy, as for being an innovation driving force by enabling the creation of new companies with these data."
The distinction between artifact and process is recognized by Sieber & Johnson (2016), who articulate that "[open data as an artifact] narrows the view of open data to a commodity and provision of data as an end unto itself, as opposed to data provision as an end to improving citizen engagement, government transparency, and improving decision-making around government services."
Open data maturity
Five conceptualizations of open data platforms are described in a review of benchmarks by Susha et al. (2015). They attribute the diverging understandings of platform maturity to the different underlying definitions of open data.
Confusion around the concept of open data that is to be assessed for platform maturity is recognized by other authors as well. The view of open data as an artifact does not by itself promote participation. Releasing data to the public does not necessarily create transparency or citizen inclusion (Sieber & Johnson, 2016).
The actual use of the data in the initiatives is very hard to determine (Susha et al., 2015) and Yu and Robinson (2012, as cited in Sieber & Johnson, 2016) go as far as to suggest that "the model of open data has largely failed to advance beyond the more technocratic aspects of simple service delivery due to conflicts in motives".
Relationship to open government
In order to better understand the overarching goals in the process-oriented definition of open data, I briefly discuss the concept of open government.
The three elements of open government are participation, transparency, and collaboration (Lee & Kwak, 2012). Nevertheless, most benchmarks of open government focus mostly on elements of open data. This is likely due to the fact that the elements of participation and collaboration do not have conceptualizations that are as clear as open data is for transparency (Susha et al., 2015).
According to the authors of Open government, open data provides public value in three ways: as an infrastructure, as an enabler of transparency and innovation, and as a way to optimize the public value of existing government data (Lathrup & Rume, 2010, as cited in Solar et al., 2012).
Taking the view of open government is likely to result in using the process-oriented definition of open data, as it encompasses overarching goals of the platform in its definition of open data. Therefore I use this definition in this essay.
Open data and participation
Few researchers take participation as a dimension of analysis. I discuss three studies that aid in understanding open data from the perspective of participation.
Research by Lee and Sieber proposes levels in open government and conceptual models of open data, respectively. I map these to the levels of participation as proposed by Fischer, from the field of interaction design. An overview of the proposed relationships between these models is presented in Appendix 1. In this section I briefly discuss the methods behind the models, and my rationale for mapping them to each other.
Lee & Kwak (2012) take a broad view on open government, discussed in terms of social media integration but relevant for the field of open data as well. The authors propose five levels of open government. They argue that as governments move to the higher levels of openness, citizens become more engaged and public value of open government increases. However, greater technical and managerial complexity also increases risks (ibid.). The authors propose that the levels are to be followed sequentially, and only the best available opportunities should be selected using the Pareto principle to minimize these risks.
The closest to a maturity model for participation in open data platforms come Sieber & Johnson (2016). They offer four conceptual models of open data that are aimed for giving direction to open data platforms in the early adopter stage. Contrary to the levels of Lee & Kwak (2012), the conceptual models of Sieber & Johnson are non-exclusive and non-sequential. However, I argue that the conceptual models put forth by the authors can be mapped to these levels in order to give direction to participation in open data platforms in stages other than the early-adopter stage. Specifically, I argue that the conceptual models of Sieber and Johnson may be used to realize the aims of open government for the levels of open government of Lee and Kwak.
Participation may be further understood from the expected roles of the citizen in these levels. Fischer (2011) describes how diverging motivations lead people to participate at in different roles. These roles are incremental, but not necessarily sequential. Fischer argues for the designer to have an understanding of these roles in their systems. This is in line with the observation of Lee & Kwak (2012) that "agencies should not presume that the public will automatically come and participate if they build venues for public engagement".
Instead of attempting to build systems (e.g. open data platforms) at once, Fischer proposes designers should create flexible projects that have the potential to "change and grow" both with technical and social components (Fischer, 2011). Such flexible projects would be more suitable for involving users, or in the case of open data: the citizen.
Using the relationships between these levels and conceptual models, we have an initial framework that provides an understanding of participation of open data platforms. This can be used to answer the research question: How can open data platform maturity be assessed from the perspective of participation? With the framework in Appendix 1, I argue that the open government levels of Lee & Kwak (2012) can be realized using the conceptual models of open data of Sieber & Johnson (2016) by aiming to involve citizens with flexible projects in the roles of Fischer (2011).
The framework for understanding participation in open data platforms is illustrated using two cases. We analyze the cities of Amsterdam and Utrecht using the framework in Appendix 1.
The city of Utrecht has an open data platform, but no structural programs to involve contributors (Gemeente Utrecht, 2017). This places Utrecht at open government level 2 – data transparency.
Further openness may be realized by using open data as issue tracker, while aiming to involve contributors and decision-makers. An example of a flexible project to realize engagement of citizens in these roles might be to conduct limited policy consultations based on open government data.
The city of Amsterdam takes a "warm approach" to open data participation (CTO Office Gemeente Amsterdam, 2017) by coordinating with users and organizing activities. It proposes to align stakeholders' (including citizens') interests with their own in order to promote cooperation. This places Amsterdam at open government level 4 – open participation.
Further openness may be realized by using participatory open data and involving meta-designers to co-produce data. An example of a flexible project to realize engagement of citizens in these roles might be to invite partner organizations to assess the internal data quality of the municipality.
Successful engagement of meta-designers would bring Amsterdam to open government level 5. This may prove ambitious, as Lee & Kwak (2012) concede that in their research sample none of the initiatives has reached this level yet and so level 5 remains hypothetical for now. However, Amsterdam could prove itself to be a global frontrunner in open government by taking steps to actively involve citizens in the role of meta-designers.
Existing models of open data platforms maturity do not focus on participation, although this is one of the principal values of open government.
In a literature review, an understanding was gained of possible models for evaluating participation in open data platforms.
I argue that by understanding citizen motivation, open data platforms can be designed as flexible projects suitable for participation in appropriate roles. This in turn contributes to types of open data use that realize incrementally more open levels of government.
By mapping phases and conceptual models from literature, a framework was developed that gives an understanding of participation in open data platforms. This framework was applied to the cases of Utrecht and Amsterdam.
This essay attempts to offer insight into participation in platforms and actual use of data, but has not definitively answered the question why some platforms succeed and others fail to have social impact. I see opportunities for research that empirically assesses the output of open data initiatives, and compares this to desired impact as formulated in their respective policy objectives.
The framework in appendix 1 could be further developed into a benchmark based on the theories underlying the models of Lee, Sieber, and Fischer. Using this benchmark, output of open data initiatives can then be analyzed for a relationship with design for participation.
Once a practical understanding of participation in open government has been gained, two out of three elements as identified by Lee & Kwak (2012) are operationalized. That leaves the element of collaboration as an opportunity for future research. It could be conceptualized in terms of open data, but with other technologies. I propose taking a process-oriented view also here instead of focusing on artifacts sought.
CTO Office Gemeente Amsterdam. (2017). Open data in Amsterdam. Retrieved July 7, 2017, from http://open.datapunt.amsterdam.nl/docs/Amsterdam Open Data 2017 English.pdf
Fischer, G. (2011). Understanding, fostering, and supporting cultures of participation. Interactions, 18(3), 42–53. https://doi.org/10.1145/1962438.1962450
Gemeente Utrecht. (2017). Bestuur en organisatie: Open data. Retrieved July 6, 2017, from https://www.utrecht.nl/bestuur-en-organisatie/publicaties/open-data/
Lee, G., & Kwak, Y. H. (2012). An Open Government Maturity Model for social media-based public engagement. Government Information Quarterly, 29(4), 492–503. https://doi.org/10.1016/j.giq.2012.06.001
Ojo, A., Porwol, L., Waqar, M., Harney, O., & Zeleti, F. A. (2016). Realizing the Innovation Potentials from Open Data : Stakeholders ' Perspectives on the Desired Affordances of Open Data Environment. In 17th IFIP WG 5.5 Working Conference on Virtual Enterprises (Vol. 1, pp. 48–59). https://doi.org/10.1007/978-3-319-45390-3
Open Knowledge International. (2017). What is Open? Retrieved from http://opendatahandbook.org/guide/en/what-is-open-data/
Sieber, R., & Johnson, P. (2016). Civic open data at a crossroads : Dominant models and current challenges. Government Information Quarterly, 32(3), 308–315. https://doi.org/10.1016/j.giq.2015.05.003
Solar, M., Concha, G., & Meijueiro, L. (2012). A Model to Assess Open Government Data in Public Agencies. In H. J. Scholl, M. Janssen, M. A. Wimmer, C. E. Moe, & L. S. Flak (Eds.), Electronic Government: 11th IFIP WG 8.5 International Conference, EGOV 2012, Kristiansand, Norway, September 3-6, 2012. Proceedings (pp. 210–221). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33489-4_18
Susha, I., Zuiderwijk, A., & Janssen, M. (2015). Benchmarks for Evaluating the Progress of Open Data Adoption : Usage , Limitations , and Lessons Learned, 33(5), 613–630. https://doi.org/10.1177/0894439314560852
Appendix 1: framework for assessing maturity of participation in open data platforms
|Levels of open government with corresponding focuses (Lee & Kwak, 2012)||Conceptual models of open data (Sieber & Johnson, 2016)||Roles in ecologies of participation (Fischer, 2011)|
|Levels may be realized with open data as ↴||By aiming to involve ↴|
|1. Initial conditions Information broadcasting||(Status quo not modeled)||0. Unaware consumers|
|2. Data transparency Transparency of government and performance; Data quality||Data over the wall Government publishing of open data via an online portal||1. Consumers aware of possibilities|
|3. Open participation Public feedback, conversation, voting, ideation; Interactive communications; Crowd-sourcing||Civic issue tracker Government accepts direct feedback from citizens on limited range of issues||2. Contributors, decision makers Making contributions|
|4. Open collaboration Interagency collaboration; Open collaboration with the public; Co-creating value-added services;||Code exchange Government supports use of open data to fill needs||3. Collaborators, facilitators, organizers, curators Organizing content, mentoring|
|5. Ubiquitous engagement Increased transparency, participation, and collaboration,Ubiquitous and continuous public engagement; Integrated public engagement||Participatory open data Government-citizen||4. Meta-designers Extending the range of the environment|