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Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness

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  • Di Vaio, Assunta
  • Hassan, Rohail
  • Alavoine, Claude

Abstract

This study investigates the literary corpus of the role and potential of data intelligence and analytics through the lenses of artificial intelligence (AI), big data, and the human–AI interface to improve overall decision-making processes. It investigates how data intelligence and analytics improve decision-making processes in the public sector. A bibliometric analysis of a database containing 161 English-language articles published between 2017 and 2021 is performed, providing a map of the knowledge produced and disseminated in previous studies. It provides insights into key topics, citation patterns, publication activities, the status of collaborations between contributors over past studies, aggregated data intelligence, and analytics research contributions. The study provides a retrospective review of published content in the field of data intelligence and analytics. The findings indicate that field research has been concentrated mainly on emerging technologies' intelligence capabilities rather than on human–artificial intelligence in decision-making performance in the public sector. This study extends an ambidexterity theory in decision support, which enlightens how this ambidexterity can be encouraged and how it affects decision outcomes. The study emphasises the importance of the public sector adoption of data intelligence and analytics, as well as its efficiency. Furthermore, this study expands how researchers and practitioners interpret and understand data intelligence and analytics, AI, and big data for effective public sector decision-making.

Suggested Citation

  • Di Vaio, Assunta & Hassan, Rohail & Alavoine, Claude, 2022. "Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s004016252100634x
    DOI: 10.1016/j.techfore.2021.121201
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