Reducing the Effects of Information Overload on Governments Decision-Making

: Empirical Frameworks to Support Data Selection

Résumé

Decision-making is a critical activity in governments. At all levels (local, regional, national or even worldwide), public decision-makers need to make decisions every day with significant consequences for the whole society. Governments evolving in a context of volatility, uncertainty, complexity and ambiguity, it is crucial for them to decide at the right time and with the right information. To help public decision-makers in their task, several techniques and tools have been developed, among which data analysis and data visualization techniques. Decision Support Systems (DSS) and Business Intelligence (BI), for example, aim to use the data available within an organization to support people when they make decisions. However, the range of usable data readily accessible to governments is enormous and constantly increasing. This data profusion makes it really difficult for decision-makers to know what data to analyze and what data to ignore. This even leads to information overload situations where the abundance of available data becomes an hindrance rather than an help. The need is real therefore to help public decisionmakers to manage these information overload situations. The present thesis is thus at the intersection of two domains: (i) information overload in decision-making and (ii) data use in digital government. At the crossing of those two areas, the present thesis aims to equip governments with tools and methods to help them reduce the risks of information overload in digital government. More specifically, it proposes a set of user-oriented data selection tools.

Contributions are elaborated in domain (i), domain (ii) and at the intersection between both. First, in domain (i), the thesis studies the concept of information overload in the context of decision-making. More precisely, cognitive loads are studied to develop a better understanding of how information overload may lead to a lower adoption of data-based BI systems. Then, domain (ii) is also studied with a focus on the concept of data culture in the public sector. The aim here is to better understand the place of data in public decision-making. Finally, at the intersection between (i) and (ii), the thesis focuses on the development of frameworks and tools to help decision-makers of the public sector better understand the value and priority of their data. The focus is first set on Open Government Data (OGD) and a data selection criterion is proposed and instantiated to allow OGD managers to better assess the value of their data. The focus then shifts to e-participation data and a prioritization framework is developed to help policy makers better manage citizens’
ideas.

la date de réponse	24 avr. 2024
langue originale	Anglais
L'institution diplômante	Universite de Namur
Sponsors	FSR‐FNRS
Superviseur	Corentin Burnay (Promoteur), Isabelle Linden (Copromoteur), Jean-Yves Gnabo (Président), Manuel Kolp (Jury), Anthony Simonofski (Jury) & Jonathan CRUSOE (Jury)