School of Business

A data envelopment analysis-based methodology for ranking cities and prioritizing urban criteria

Document Type

Article

Abstract

This paper proposes a methodology for ranking cities and prioritizing the criteria that influence these rankings within a multi-criteria decision-making (MCDM) framework. A composite Data Envelopment Analysis (DEA) model is developed to generate a fully ranked list of cities, ensuring robustness through data-driven and non-arbitrary weight assignments. Additionally, fixed-effects and random-effects models are employed to identify the criteria significantly impacting city rankings over time. The performance of these models is compared using the Hausman, Dickey-Fuller, and Breusch-Pagan tests. By analyzing large U.S. cities at five-year intervals from 2000 to 2020, this study determines city rankings and evaluates the influence of various quality-of-life criteria. The results show that cost of living, education, and income have the most substantial impacts on city rankings. These insights offer valuable guidance for policymakers aiming to improve urban socio-economic and environmental attributes. Furthermore, they provide businesses with critical information for strategic planning, market analysis, human resource management, and sustainable development. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Publication Title

Information Technology and Management

Publication Date

2025

ISSN

1385-951X

DOI

10.1007/s10799-025-00460-7

Keywords

city rankings, data envelopment analysis, fixed-effects model; Multiple-criteria decision making, quality of life, random-effects model

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