Type

• Master Thesis Business Information Systems

Status

offered

Tutor

• Tim Brée

Abstract

Background and Motivation

Smart City studies and rankings commonly rely on composite indices that aggregate multiple indicators into overall scores. Central to these indices are scoring rules and weighting schemes, which determine how individual indicators contribute to final results. While such approaches enable comparability and simplification, they are inherently based on a variety of methodological and normative assumptions.

These assumptions - such as the relative importance of dimensions, the linearity of aggregation, or the compensability between indicators - are often implicit and rarely made transparent. As a result, Smart City rankings may produce results that are difficult to interpret, sensitive to design choices, and perceived as unfair or arbitrary by participating cities.

In addition to methodological concerns, there is increasing criticism from municipalities regarding how rankings reflect their efforts and contextual conditions. Cities may question whether scoring and weighting logics adequately capture their strategic priorities, structural constraints, or development paths. Despite this, the empirical perspective of cities on scoring fairness and transparency remains largely unexplored.

Against this background, a systematic analysis of scoring and weighting logics - combined with an empirical assessment of how these logics are perceived by cities - represents an important contribution to the methodological robustness and legitimacy of Smart City assessments.

Research Objectives

The objective of this master thesis is to analyze how different scoring and weighting logics influence the outcomes of Smart City studies and how these logics are perceived by municipalities.

The thesis aims to:

• Analyze and compare scoring and weighting models used in existing Smart City studies and rankings;

• Identify and explicate underlying methodological and normative assumptions embedded in these models;

• Develop alternative weighting scenarios and aggregation logics;

• Conduct sensitivity analyses to assess how changes in weighting and scoring affect ranking outcomes;

• Empirically explore how municipalities perceive scoring fairness, transparency, and validity, and identify common points of criticism;

• Derive implications for the design of transparent, robust, and fair scoring systems in Smart City studies

Methodology

The thesis will follow a mixed-methods research approach, combining quantitative and qualitative elements:

• A structured literature review on composite indicators, scoring models, and weighting techniques in Smart City and related fields;

• Quantitative analysis of existing scoring models, including the development of alternative weighting scenarios;

• Sensitivity and scenario analyses to assess the robustness of ranking outcomes;

• Empirical data collection through qualitative interviews or surveys with municipal representatives to capture perceptions of fairness, transparency, and critique of scoring approaches;

• Integration of quantitative and qualitative findings to inform design recommendations

The methodological design will be tailored to the scope and requirements of a master thesis

Expected Contribution

This master thesis will contribute to Smart City research by providing a systematic and empirically informed analysis of scoring and weighting logics. By making underlying assumptions explicit and incorporating the perspectives of municipalities, the thesis will support the development of more transparent, robust, and legitimate assessment and ranking approaches in Smart City studies.

Interested students are invited to send an e-mail to: tim.bree (at) uni-due.de