Identifying the value of data analytics in the context of government supervision: Insights from the customs domain

Boriana Rukanova, Yao Hua Tan, Micha Slegt, Marcel Molenhuis, Ben van Rijnsoever, Jonathan Migeotte, Mathieu L.M. Labare, Krunoslav Plecko, Suzanne Post, More Authors

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

eCommerce, Brexit, new safety and security concerns are only a few examples of the challenges that government organisations, in particular customs administrations, face today when controlling goods crossing borders. To deal with the enormous volumes of trade customs administrations rely more and more on information technology (IT) and risk assessment, and are starting to explore the possibilities that data analytics (DA) can offer to support their supervision tasks. Driven by customs as our empirical domain, we explore the use of DA to support the supervision role of government. Although data analytics is considered to be a technological breakthrough, there is so far only a limited understanding of how governments can translate this potential into actual value and what are barriers and trade-offs that need to be overcome to lead to value realisation. The main question that we explore in this paper is: How to identify the value of DA in a government supervision context, and what are barriers and trade-offs to be considered and overcome in order to realise this value? Building on leading models from the information system (IS) literature, and by using case studies from the customs domain, we developed the Value of Data Analytics in Government Supervision (VDAGS) framework. The framework can help managers and policy-makers to gain a better understanding of the benefits and trade-offs of using DA when developing DA strategies or when embarking on new DA projects. Future research can examine the applicability of the VDAGS framework in other domains of government supervision.

Original languageEnglish
Article number101496
JournalGovernment Information Quarterly
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Capabilities
  • Collective capability
  • Data analytics
  • eCommerce
  • Framework
  • Government
  • Supervision
  • Value

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