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Call for Papers - Practices of “data-driven innovation” in the European public sector

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Introduction

How do policies for data-driven innovation unfold in practice, both in the public sector and among its affected publics, and what can researchers, practitioners and policymakers learn from current applications?

To inform a special issue in Data & Policy (a peer-reviewed open access journal published by Cambridge University Press), we invite papers, reflections and case studies that investigate “data-driven innovation” in public sector bodies as a practice. Through this collection of papers, we aim to explore how data-driven innovation within public institutions is understood, imagined, planned for, conducted, and assessed. With these contributions we would like to probe how data-driven innovation unfolds in the practices and perspectives of those who engage in it or are affected by it. We propose to use the notion of data-driven innovation as an investigative starting point and an actor category used in the field, aware that it is common policy vernacular with ambivalent meaning.

Data-driven innovation in the European public sector is a loose and much debated concept. Dating back to the 1990s (cf. Gray 2014), many European actors have focused on data sharing and use for various purposes, including the European Commission, the member states of the European Union, as well as urban regions, companies, policy networks, and civil society. The European Commission has developed various policies, addressing the provision of public sector information, the establishment of geospatial data infrastructures, the monitoring of the environment, and more recently data protection, data sharing and re-use in the public interest, and a more trustworthy and “human-centered” AI (see for instance European Commission, 2017, 2018, 2020, 2021). The so-called “European approach” to digital transformation promotes data sharing and use across actors, sectors and countries, while simultaneously increasing control and trust of both citizens and companies regarding their data (Craglia et al., 2020). Like many preceding European policies, this is aimed at creating value along multiple axes, including economic and social ones, by assisting policy and decision making to address pressing societal issues. To probe how such data-related policies unfold in the practices of public and social actors, the special issue purposely takes a European standpoint with the aim of examining that setting. 

By attending to data practices in particular, scholars and think tanks have shown the social consequences of innovation programs beyond their original purpose. For instance, open data standards not only become a resource to be tapped, but also have consequences for knowledge workers within government agencies (Goëta and Davies 2016), or how automated decision-making systems are not merely deployed, but shape public policy (cf. AlgorithmWatch, 2020). Other scholars, instead, examined how specific social groups understand “data innovation”, such as by looking at how “ideals”of smart cities are brought into “practice” by local administration employees (Madsen, 2018) or emphasizing the “complex set of interactions” underpinning innovative forms of data sharing at the local level based (Meijer, 2018; van Zoonen, 2020).

To build on such scholarship and to incorporate additional disciplinary perspectives, this special issue invites analytical studies on the situated practices of data-driven innovation in the European public sector. The contributions will increase understanding of enabling and challenging factors for the implementation of European data-driven innovation policies in public institutions through the analysis of actors’ perspectives and practices across contexts and sectors. 

Questions

Contributions may respond, for example, to the following questions:

  • How do those implementing data innovation programs experience or envision data-centric innovation programs?
  • What attitudes and needs do different actors express vis-à-vis data-driven innovation? In particular, what are the perspectives of public and social groups (such as citizens, governmental bodies, public administrations and civil society organisations)?
  • How similar data innovation programs are implemented in different contexts and sectors and shaped by these contexts
  • Which forms and formats are set up to experiment with data-driven innovation (for instance apps involving citizens in public research or data donations for public institutions)?
  • How are data-driven innovations translated into practices of decision-making, and to what effects? What tensions and conflicts of interest arise in relevant innovation programs? Which values are articulated and how are they negotiated?
  • Which methodologies could be adopted to assess and critically examine the role of data and innovation in the activities of the public sector?

To better understand data innovation as practice, we invite studies that develop theoretical and empirical perspectives on the data practices of the public sector. This may include, but is not restricted to, empirical studies on how policies shape and are shaped by new streams of data and the use of analytics. Studies also may discuss socio-technical perspectives on innovation programs and their roll-out and how the material and technical conditions of innovations in data access, sharing, and use are imagined and rolled out. We also accept theoretical commentaries that examine the notion of practice with regard to data-driven innovation and/or help to develop a methodology for situated or critical analysis of practices of data-driven innovation. The overall aim is to provide knowledge about what is taking place, now, in the field, concerning practices and experimentations – which could inform future policy interventions.

Submission process

As outlined in the timeline below, interested authors are asked to send extended abstracts (between 800 - 1000 words, references excluded) for feedback (deadline 30 September).

marina.micheli@ec.europa.eu
danny.laemmerhirt@uni-siegen.de

Alongside the abstract, authors should indicate the type of paper they intend to write for the full submission. Data & Policy has the following categories: 

  • Research articles based on sound empirical research. We are particularly inviting empirical contributions from sociology, organizational studies, science and technology studies, media studies, and critical data studies. (Approx 8,000 words in length).
  • Translational articles: contributions that critically examine how data driven innovation is implemented in practice in organisational settings. They may present original findings, but can be less embedded in the scholarly literature as research articles. (Approx 8,000 words in length).
  • Theoretical commentaries that examine the notion of “practice” with regard to data-driven innovation and/or help to develop a methodology for a critical analysis of practices of data-driven innovation (Approx 4,000 words in length).

At the end of August, selected authors will be invited to submit their full papers through the Data & Policy online peer-review system in time for the 30th November deadline. 

The invite will outline the full paper submission requirements, but interested authors are encouraged to familiarise themselves with the Data & Policy Instructions for Authors. Note that the journal provides LaTeX and Word templates to assist authors with the structure of papers, asks all authors to provide a Data Availability Statement with the submission and encourages, but does not require, authors to make underlying data and replication materials available via an open repository. 

Timeline

  • 21 May 2021: Call issued
  • 30 September 2021: Extended abstracts due
  • 15 October 2021: Authors receive feedback on their extended abstracts (submissions that were received by July 31st will receive feedback by August 31)
  • 11 February 2022: Submission of completed papers
  • 31 July 2022: Expected publication date of full issue (articles will be published on a rolling basis)

Guest Editors


References

Birch, K., Chiappetta, M. and Artyushina, A., (2020). The problem of innovation in technoscientific capitalism: Data rentiership and the policy implications of turning personal digital data into a private asset. Policy Studies, 41(5), 468-487 https://doi.org/10.1080/01442872.2020.1748264 

Craglia, M., Scholten, H.J., Micheli, M., Hradec, J., Calzada, I., Luitjens, S., Ponti, M. and Boter, J., (2021) Digitranscope: The governance of digitally-transformed society, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-30229-2

https://ec.europa.eu/digital-single-market/en/news/digitranscope-project-key-findings

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European Commission (2017). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. “Building A European Data Economy” COM/2017/09 final. Luxembourg: Publications Office of the European Union.

European Commission (2018). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions “Towards a common European data space” COM/2018/125 Final. Luxembourg: Publications Office of the European Union.

European Commission (2020). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions “a European Strategy for Data” COM/2020/66 Final. Luxembourg: Publications Office of the European Union.https://ec.europa.eu/info/sites/default/files/communication-european-strategy-data-19feb2020_en.pdf 

European Commission (2021). White Paper on Artificial Intelligence: a European approach to excellence and trust. COM/2020/65 Final. Luxembourg: Publications Office of the European Union. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence 

Goëta, S. and Davies, T. (2016). The Daily Shaping of State Transparency: Standards, Machine-Readability and the Configuration of Open Government Data Policies”, Science & Technology Studies, 29(4), 10-30. doi: 10.23987/sts.60221 .

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Madsen, A. K. (2018). Data in the smart city: How incongruent frames challenge the transition from ideal to practice, Big Data & Society, 5(2), 1-13, https://doi.org/10.1177/2053951718802321.

Meijer, A. (2018). Datapolis: A Public Governance Perspective on “Smart Cities,” Perspectives on Public Management and Governance, 1(3), 195–206, https://doi.org/10.1093/ppmgov...

Pfotenhauer, S. and Jasanoff, S. (2017). Traveling Imaginaries. The “practice turn” in innovation policy and the global circulation of innovation models. In: Tyfield, D.; Lave, R.; Randalls, S.; Thorpe, C. (Eds): The Routledge Handbook of the Political Economy of Science. Routledge: London.

Pinch, T, and Bijker, W. (1984). The Social Construction of Facts and Artefacts: or How the Sociology of Science and the Sociology of Technology might Benefit Each Other, Social Studies of Science, 14(1), 399-441, https://doi.org/10.1177/030631284014003004

Stilgoe, J. (2020). Who's Driving Innovation? New Technologies and the Collaborative State. Palgrave: London.

Verhulst, S. G.; Engin, Z. and Crowcroft, J. (2019). Data & Policy: A new venue to study and explore policy–data interaction. Data & Policy, 1: e1, 1–5. https://doi.org/10.1017/dap.2019.2

Wahlberg, A. (2020). Justice and Solidarity in Post-genomic, Post-personalized Times, Science as Culture, DOI: 10.1080/09505431.2020.1802710

van Zoonen, L. (2020). Data governance and citizen participation in the digital welfare state, Data & Policy, 2, https://doi.org/10.1017/dap.20...