Decision-making Support for Opening Government Data

Research output: ThesisDissertation (TU Delft)

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Abstract

Government institutions collect and produce an extraordinary number of datasets to conduct and execute their programs and agendas. Various types of datasets collected by the governments can increase transparency and accountability, improve citizen engagement, and create value-added services for the public. Through the Open Government Data (OGD) initiatives, Non-Government Organisations (NGOs), private agencies, business enablers, data analysts, researchers, civil societies, and other open data stakeholders can take advantage of disclosing the government datasets. Despite its significance, the decision-making process to disclose government datasets is given limited attention and encounters several challenges. Although numerous datasets have been published to the public, many datasets remain undisclosed. Government institutions face several challenges in deciding to open datasets. First, the governments have not systematically analysed datasets to identify the benefits and disadvantages of opening datasets. Decision-makers, policy-makers, civil servants, and administrative officers do not know how to balance the advantages and disadvantages of opening datasets. Second, various stakeholders’ backgrounds may have different objectives and interests to analyse and disclose datasets. Third, the easy understanding of possible disadvantages of opening datasets results in moving away from the potential benefits due to the risk-avoiding culture in government. Therefore, these results in keeping datasets undisclosed. Furthermore, the stakeholders’ involvement in the decision-making process to open data, such as politicians, executive boards, decision-makers, civil servants, data analysts, and societies, all play essential roles and have different objectives for opening and using the datasets. For example, some decision-makers might have the authority to publish or keep the dataset closed. Some public servants might be risk-averse, whereas others might open datasets without considering possible negative consequences. As a result, the decision-making process becomes fuzzy, and the objectives of disclosing data are not reached. The different roles and interests of the heterogonous actors in the internal government organisation might create uncertainty and delay the decision-making process. Although there are guidelines, there are no decision-making tools to help governments decide to open their datasets. On the governments’ side, the potential disadvantages might easily dominate over the advantages. It is much easier for the decision-makers to keep a dataset closed than take the disadvantages of releasing a dataset. The lack of insights and expertise in estimating the potential advantages and disadvantages of opening data can also lead to uncertainty, which might result in avoiding the disclosure of datasets. Therefore, this research aims to develop Decision-making Support for Opening Government Data (DSOD). This DSOD accommodates a systematic approach to decide to open datasets. To achieve the objective of this research, we followed the Design Science Research (DSR) approach. The DSR approach results in developing a prototype of the DSOD as a design artefact and demonstrate it to the stakeholders.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Janssen, M.F.W.H.A., Supervisor
  • Crompvoets, Joep, Supervisor, External person
Thesis sponsors
Award date22 Sep 2021
Print ISBNs978-94-6384-244-0
DOIs
Publication statusPublished - 2021

Funding

This research was funded by Indonesia Endowment Fund for Education in collaborating with the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (LPDP–BUDI LN).

Keywords

  • decision-making support
  • decision tree analysis
  • open data
  • open government data
  • stakeholders
  • advantages
  • disadvantages
  • costs
  • benefits
  • risks
  • Bayesian-belief networks
  • fuzzy multi-criteria decision making

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