Improving Survey Data Interpretation: A Novel Approach to Analyze Single-Item Ordinal Responses with Non-Response Categories

Abstract

Questionnaire data plays a key role in social research, especially when evaluating public attitudes using Likert-type scales. Yet, traditional analyses often merge some ordinal categories and exclude responses such as Don’t Know, No Answer, or Refused—risking the loss of valuable information. This study introduces BS-TOSIE (Belief Structure-Based TOPSIS for Survey Item Evaluation), a novel method that preserves and integrates all response types, including ambiguous ones. By combining the Belief Structure framework with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, BS-TOSIE offers a structured approach to ranking and evaluating individual survey items measured on an ordinal scale, even in the presence of missing or incomplete data. Response distributions are transformed into a belief structure vector, enabling comparison against ideal and anti-ideal benchmarks. We demonstrate this approach using data from the Quality of Life in European Cities survey to assess perceptions of local governance in European cities. This study analyzes changes in citizen satisfaction with local public administration across five key dimensions—timeliness, procedural clarity, fairness of fees, digital accessibility, and perceived corruption—in 83 European cities between 2019 and 2023. The findings reveal persistent regional disparities, with Northern and Western European cities consistently outperforming those in Southern and Eastern Europe, although some cities in Central Europe show signs of improvement. Zurich consistently received high satisfaction scores, while other cities, such as Rome and Palermo, showed lower scores. Unlike traditional methods, our approach preserves the full spectrum of responses, yielding more nuanced and interpretable insights. The results show that BS-TOSIE enhances both the clarity and depth of survey analysis, making a methodological contribution to the evaluation of ordinal data and offering empirical insights into public perceptions of local city administration.

1. Introduction

Questionnaire data analysis plays a central role in quantitative research across disciplines like psychology, marketing, and the social sciences. In the context of the social sciences, analyzing questionnaire data is essential for understanding patterns of human behavior, social attitudes, and demographic trends [1]. Surveys provide standardized and scalable methods for collecting data from large populations, making them ideal for studying and capturing individuals’ perceptions, beliefs, and attitudes, making them especially useful for investigating complex and intangible social phenomena such as quality of life, well-being, and public opinion on various aspects of everyday life [2,3]. Surveys are also important for understanding long-term societal changes and evaluating the effectiveness of programs designed to improve people’s lives. By quantifying responses, researchers can identify correlations, test hypotheses, and draw evidence-based conclusions that support policy development [4]. Likert-scale items are commonly used to measure attitudes, perceptions, or evaluations in surveys [5,6]. For individual questions, basic descriptive statistics—such as medians, means, standard deviations, and response frequencies—are often reported. Visual tools such as bar charts or diverging stacked bar plots are typically used to effectively present response distributions.

These responses often emerge due to various cognitive, motivational, or contextual factors and may reflect uncertainty, lack of knowledge, disengagement, or discomfort with the question. The treatment of such responses requires particular attention. “Don’t Know” may indicate genuine uncertainty, low involvement with the topic, or overly complex or poorly worded questions [7]. “Refused” responses tend to occur in sensitive topics—e.g., income, politics, or personal behavior—where social desirability bias or privacy concerns lead respondents to withhold answers [8,9]. “No Answer” may result from skipped items, fatigue, or disengagement, particularly in longer surveys [10]. These responses may be excluded as missing values, retained as a separate analytical category (especially when meaningful for interpretation), or imputed using statistical methods when the data are assumed to be missing at random [11,12,13]. In most conventional analyses, such responses are either excluded or ignored altogether, leading to information loss and potential bias. While this may be convenient, such simplifications risk oversimplifying the interpretation of data, particularly when analyzing complex societal issues.

To address this gap, we propose a novel method, BS-TOSIE (Belief Structure-Based TOPSIS for Survey Item Evaluation), for the analysis of individual survey questions based on an ordinal scale that explicitly incorporates the “Don’t Know/No Answer/Refused” response category. To analyze such data, we apply the Belief Structure (BS) framework in combination with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which allows us to effectively handle the full distribution of responses by replacing it with a single aggregated value. This approach enables comparisons with both ideal and anti-ideal distributions, facilitating interpretable rankings. Moreover, it supports temporal analysis, allowing researchers to track changes in perceptions over time and compare responses across different periods.

To implement the BS-TOSIE method, we follow a structured, multi-step procedure to ensure each survey response is accurately represented. First, ordinal responses and non-responses are encoded into belief structures, capturing the certainty or ambiguity of each answer. Next, these belief structures are combined across all respondents for each survey item, reflecting the collective perception. The resulting belief structure models are then analyzed using a TOPSIS procedure adapted to operate within the belief framework, enabling the calculation of distances from both the ideal and anti-ideal solutions. Finally, individual items are ranked according to their relative proximity to these benchmarks.

We demonstrate BS-TOSIE by assessing the quality of life in European cities, specifically examining citizens’ perceptions of local public administration. Using data from the Quality of Life in European Cities survey [14,15,16], we analyze five performance dimensions: time required to resolve requests; clarity and simplicity of administrative procedures; perceived fairness of fees; ease of online access to services and information; and perceptions of local corruption. The respondent scale ranged from 1 (Strongly Disagree) through 4 (Strongly Agree), with a 99 category reserved for Don’t Know, No Answer, or Refused responses.

We compare our method with the original percentage of positive opinion approach used in the Quality of Life in European Cities reports [14,15,16], where individual responses were simplified by merging “Very Satisfied” and “Rather Satisfied” categories, and responses marked as “Don’t Know/No Answer/Refused” were excluded. We demonstrate that BS structure yields a more nuanced representation of the data, minimizing information loss and preserving the variability inherent in the original responses. The percentage of positive opinion approach offers a simplified, quick, and easily communicable overview of satisfaction, making it particularly useful for dashboards or contexts with limited analytical resources. In contrast, the BS-TOSIE approach delivers a more detailed and accurate insight, suitable for in-depth evaluations, comparative studies, and long-term monitoring.

Unlike earlier studies that applied the belief structure and TOPSIS method to analyze aggregated survey data [17], our study focuses explicitly on the evaluation of individual questionnaire items, offering a more fine-grained and interpretable perspective. This disaggregated view enables more targeted insights into specific components of public service delivery and enhances the diagnostic value of the results for local policymakers. This individual-level perspective offers important complementary insights to aggregate-level analyses [17] and helps minimize information loss inherent in traditional data aggregation.

Our empirical study is guided by the following research questions:

RQ1: 

How do European cities rank in terms of perceived satisfaction with local public administration across the five dimensions derived from individual survey items?

RQ2: 

What are the key differences in perceived satisfaction across European cities regarding these dimensions?

RQ3: 

How has satisfaction with local public administration changed between 2019 and 2023 when analyzing each dimension separately?

This study contributes to the literature by demonstrating how belief structure and TOPSIS methods can be effectively applied to evaluate individual ordinal survey questions. Our approach preserves the granularity of original responses, supports meaningful comparisons over time, and provides richer, more actionable insights for local policymakers. It serves as a methodological and empirical complement to earlier studies based on aggregated data, highlighting the potential of disaggregated ordinal analysis in survey-based social research. Summing up, this study makes several key contributions:

• It introduces a novel methodological framework for analyzing individual ordinal survey responses with uncertain or ambiguous answer categories.
• It enhances the interpretability of survey results through rankings relative to ideal and anti-ideal benchmarks.
• It demonstrates the empirical value of the approach using real-world survey data from European cities, providing practical insights for local administration evaluation.

The remainder of the paper is structured as follows: Section 2 presents the materials and methods, including a description of the survey data and empirical setting as well as the theoretical background and BS-TOSIE approach. Section 3 provides the results of their interpretation in the context of local public administration performance, and Section 4 discusses the analytical findings. Section 5 concludes the paper by summarizing the key findings and outlining directions for future research.

2. Materials and Methods

2.1. Materials

Studying the quality of local administration in cities is essential for understanding how governance influences residents’ everyday lives and the effectiveness of urban service delivery. Such research contributes directly to the achievement of Sustainable Development Goal 11 [18,19,20,21,22,23] by fostering citizen-oriented, transparent, and accountable urban governance. A key component of this effort involves survey-based studies, which provide insight into public perceptions of institutional trust, satisfaction with local services, and civic engagement. These bottom-up perspectives are invaluable for shaping data-driven urban policies and monitoring the inclusiveness and sustainability of city management practices. Several large-scale surveys support this approach, including the Flash Eurobarometer on Quality of Life in European Cities [24], conducted by the European Commission, and the European Quality of Government Index (EQI) [25] from the University of Gothenburg’s QoG Institute. In a broader international context, tools such as UN-Habitat’s Urban Monitoring Framework [26] also offer comparative data on public attitudes toward local governance.

Since its launch in 2007, the Quality of Life in European Cities survey [15] conducted by the European Commission provides detailed assessments of living conditions and public services across European cities, further enriching our understanding of how local administrations perform from a citizen’s perspective.

By gathering the experiences and opinions of respondents across European cities, the survey offers a valuable perspective on various dimensions of urban living. Respondents are asked to assess their satisfaction with various aspects of urban life, including inclusivity, social isolation, employment opportunities, safety, housing, environmental conditions, transport, cultural offerings, city services, and local administration. To date, six editions of the survey have been conducted, with the two most recent being the fifth edition based on data from 2019 [16] and the sixth from 2023 [14], which introduced a set of five questions specifically focused on the quality of local public administration. The last two editions of the Quality of Life in European Cities survey cover 83 cities across the EU, EFTA countries, the United Kingdom, the Western Balkans, and Türkiye, enabling comparative analysis across time and regions [14,16]. The fifth edition was carried out between June and September 2019, with 700 interviews conducted in each city, amounting to 58,100 respondents in total [16]. The sixth edition followed between January and April 2023, with at least 839 residents interviewed per city, resulting in 71,153 completed interviews [14].

Respondents rated five aspects of local administration by indicating their level of agreement with each statement. The question was: “To what extent do you agree or disagree with the following?”

Q1: 

I am satisfied with the amount of time it takes to get a request solved by my local public administration.

Q2: 

The procedures used by my local public administration are straightforward and easy to understand.

Q3: 

The fees charged by my local public administration are reasonable.

Q4: 

Information and services of my local public administration can be easily accessed online.

Q5: 

There is corruption in my local public administration.

The response options included: 1—Strongly Disagree, 2—Somewhat Disagree, 3—Somewhat Agree, 4—Strongly Agree, and 99—Don’t Know/No Answer/Refused. For questions Q1 to Q4, this scale is interpreted as a satisfaction scale: 1 indicates “very unsatisfied,” 2 “rather unsatisfied,” 3 “rather satisfied,” and 4 “very satisfied.” For Q5, the scale is reversed: 1 represents “very satisfied” (no corruption), while 4 indicates “very unsatisfied” (high perception of corruption).

These five items reflect dimensions widely recognized in the academic literature as essential for evaluating local public administration performance, which address critical aspects of service delivery and institutional functioning from the citizens’ perspective, offering a multidimensional view of how urban residents perceive their local authorities. Question Q1, concerning satisfaction with the time it takes to resolve a request, captures perceptions of administrative efficiency. Prompt responses to citizens’ needs are widely recognized as hallmarks of effective public service and are closely linked to overall satisfaction with local governance [27]. Question Q2, addressing the simplicity and transparency of procedures, reflects the accessibility of administrative processes. While related to broader governance concepts such as openness and participation, this question specifically targets the clarity of day-to-day institutional interactions—an aspect strongly emphasized in the literature on local government transparency [28,29]. Question Q3 focuses on the perceived fairness of fees charged by local administrations. This item is crucial for understanding residents’ evaluations of the affordability and equity of public services, with several studies linking reasonable pricing to higher satisfaction and the perceived legitimacy of local authorities [30]. Question Q4, concerning the ease of accessing information and services online, highlights the digital capacity of municipal administrations. The literature shows that developments in e-government—especially those accelerated during the COVID-19 pandemic—play a significant role in improving service delivery, reducing bureaucratic burden, and expanding civic access [31,32]. Question Q5, which asks about perceived corruption in local public administration, directly addresses concerns about institutional integrity. Unlike broader concepts such as generalized trust, this item specifically measures public suspicion of unethical or illegal practices at the local level. Corruption is consistently identified as a key factor undermining institutional legitimacy and the quality of public services [33,34,35].

Together, these five dimensions form a robust and multidimensional framework for assessing the performance of local public administration from the perspective of urban residents. The distribution of responses to questions Q1–Q5 from the 2019 and 2023 editions of the Quality of Life in European Cities survey, along with basic descriptive statistics, is presented in the Appendix (

Table A1. Distribution of responses to question Q1 in 2019 and 2023 in the Quality of Life in European Cities survey.

City	I Am Satisfied with the Amount of Time It Takes to Get a Request Solved by My Local Public Administration.
	2019					2023
	1	2	3	4	99	1	2	3	4	99
Aalborg	5.3%	11.1%	42.3%	24.7%	16.6%	8.3%	14.7%	43.9%	22.2%	11.0%
Amsterdam	15.7%	17.0%	25.7%	23.0%	18.6%	16.3%	20.2%	27.5%	21.4%	14.6%
Ankara	26.1%	14.8%	37.2%	19.0%	2.9%	21.3%	9.5%	30.4%	35.1%	3.7%
Antalya	18.8%	16.2%	38.1%	26.3%	0.7%	14.5%	14.8%	31.1%	35.9%	3.6%
Antwerpen	13.5%	12.5%	37.0%	18.7%	18.3%	13.3%	14.9%	35.7%	17.8%	18.3%
Athina	30.7%	31.2%	25.0%	12.4%	0.7%	31.2%	25.8%	27.7%	12.3%	3.1%
Barcelona	32.3%	21.8%	16.6%	28.0%	1.4%	29.6%	21.4%	20.2%	23.7%	5.0%
Belfast	14.3%	16.4%	33.1%	16.9%	19.2%	16.0%	20.5%	30.3%	13.8%	19.5%
Belgrade	43.3%	19.1%	23.6%	9.2%	4.8%	41.3%	16.0%	27.6%	10.0%	5.0%
Berlin	19.5%	38.4%	23.9%	8.7%	9.5%	20.9%	35.9%	24.7%	11.2%	7.3%
Białystok	7.8%	21.1%	49.7%	19.1%	2.3%	7.3%	23.1%	45.7%	17.8%	6.0%
Bologna	12.4%	31.8%	43.6%	8.2%	3.9%	14.5%	30.3%	39.5%	11.3%	4.4%
Bordeaux	21.5%	16.6%	28.8%	28.8%	4.3%	19.9%	22.0%	28.5%	24.4%	5.2%
Braga	9.1%	36.5%	43.8%	4.7%	5.9%	14.3%	35.9%	39.2%	3.4%	7.3%
Bratislava	13.1%	20.2%	33.1%	17.9%	15.8%	12.2%	21.2%	34.7%	16.0%	15.9%
Bruxelles	20.2%	16.1%	32.4%	30.3%	1.0%	20.0%	15.0%	32.2%	31.2%	1.6%
Bucharest	30.8%	15.0%	33.6%	12.2%	8.4%	27.1%	17.4%	32.6%	14.8%	8.1%
Budapest	17.8%	21.7%	22.0%	23.9%	14.6%	16.6%	18.4%	25.6%	19.8%	19.6%
Burgas	25.4%	18.5%	19.1%	32.8%	4.2%	22.3%	19.7%	22.9%	28.5%	6.6%
Cardiff	5.7%	18.1%	35.6%	21.6%	19.0%	8.8%	19.2%	36.6%	17.0%	18.4%
Cluj-Napoca	16.9%	11.0%	40.7%	22.1%	9.3%	14.7%	12.4%	37.7%	26.8%	8.5%
Copenhagen	12.6%	17.3%	33.2%	25.8%	11.2%	15.5%	18.4%	33.7%	22.5%	9.9%
Diyarbakir	36.0%	21.2%	15.7%	21.9%	5.3%	32.5%	21.6%	17.4%	22.6%	6.0%
Dortmund	12.8%	33.2%	36.1%	14.4%	3.6%	15.1%	33.3%	35.3%	12.8%	3.5%
Dublin	12.1%	16.3%	40.8%	18.1%	12.7%	13.3%	17.4%	41.8%	15.4%	12.1%
Essen	5.2%	31.7%	33.7%	22.9%	6.6%	9.3%	31.3%	32.9%	21.7%	4.8%
Gdańsk	12.1%	19.5%	43.1%	20.1%	5.2%	8.6%	22.2%	41.4%	21.1%	6.7%
Genève	1.4%	14.5%	46.1%	25.6%	12.4%	3.1%	13.9%	44.5%	26.2%	12.2%
Glasgow	12.7%	25.5%	21.9%	25.8%	14.2%	18.1%	24.9%	26.2%	16.6%	14.2%
Graz	9.0%	22.7%	37.2%	25.6%	5.6%	9.7%	20.0%	35.6%	25.3%	9.4%
Groningen	8.4%	10.5%	24.3%	30.8%	26.1%	8.3%	14.1%	26.5%	25.0%	26.1%
Hamburg	6.4%	23.8%	42.4%	20.5%	6.9%	7.9%	21.9%	46.6%	19.2%	4.4%
Helsinki	13.7%	23.6%	27.2%	11.9%	23.6%	13.8%	24.4%	26.2%	12.3%	23.3%
Heraklion	46.4%	18.7%	21.9%	10.4%	2.6%	43.9%	18.3%	23.6%	10.1%	4.1%
Istanbul	34.1%	12.2%	27.1%	23.8%	2.9%	31.2%	13.5%	29.1%	24.4%	1.9%
Košice	9.2%	25.4%	36.6%	20.4%	8.4%	10.8%	25.4%	37.8%	18.9%	7.1%
Kraków	11.3%	19.8%	41.3%	18.0%	9.7%	12.5%	24.2%	39.7%	14.6%	9.0%
Lefkosia	25.7%	20.5%	27.2%	25.9%	0.8%	24.3%	22.8%	26.2%	25.3%	1.4%
Leipzig	7.4%	18.2%	46.4%	16.7%	11.3%	8.9%	22.8%	43.0%	15.6%	9.7%
Liège	8.7%	22.2%	32.4%	24.6%	12.1%	9.5%	20.3%	32.1%	27.2%	11.0%
Lille	18.5%	24.9%	35.0%	17.0%	4.7%	18.5%	24.7%	38.0%	13.6%	5.3%
Lisboa	19.2%	38.2%	31.2%	2.8%	8.6%	20.6%	37.4%	30.6%	3.2%	8.3%
Ljubljana	15.3%	19.4%	39.8%	14.5%	10.9%	16.6%	20.0%	37.9%	13.7%	11.8%
London	14.4%	23.0%	32.2%	17.3%	13.0%	16.6%	24.9%	32.5%	10.8%	15.3%
Luxembourg	6.9%	19.2%	31.7%	36.4%	5.7%	6.1%	20.9%	37.6%	32.1%	3.4%
Madrid	23.1%	18.7%	28.1%	25.9%	4.2%	21.4%	25.0%	27.1%	22.1%	4.5%
Málaga	23.8%	16.5%	28.2%	22.1%	9.5%	22.6%	19.8%	27.3%	22.6%	7.7%
Malmö	15.7%	17.8%	24.0%	26.7%	15.9%	15.1%	17.5%	27.7%	19.8%	19.9%
Manchester	7.8%	16.5%	40.5%	20.7%	14.6%	12.0%	17.6%	34.2%	18.0%	18.1%
Marseille	19.2%	23.7%	26.3%	23.8%	7.0%	21.5%	24.9%	29.2%	20.3%	4.0%
Miskolc	9.8%	13.4%	25.5%	25.5%	25.7%	13.9%	16.1%	25.4%	21.2%	23.4%
Munich	11.4%	24.0%	29.7%	20.3%	14.6%	10.2%	25.1%	34.0%	19.1%	11.6%
Naples	40.3%	31.6%	18.6%	6.0%	3.6%	41.9%	28.1%	17.5%	7.9%	4.6%
Oslo	15.1%	30.1%	24.7%	4.3%	25.7%	17.5%	30.6%	23.3%	7.9%	20.7%
Ostrava	14.2%	22.1%	38.1%	9.8%	15.8%	15.0%	23.8%	35.1%	9.3%	16.8%
Oulu	12.3%	22.6%	40.2%	14.8%	10.2%	12.5%	23.1%	37.7%	14.4%	12.4%
Oviedo	26.6%	20.1%	21.0%	26.1%	6.2%	27.3%	25.4%	23.0%	19.5%	4.8%
Palermo	45.8%	37.7%	10.9%	1.9%	3.6%	45.8%	37.8%	9.4%	3.2%	3.8%
Paris	21.7%	20.6%	33.4%	21.0%	3.3%	19.6%	25.2%	29.7%	20.2%	5.3%
Piatra Neamt	12.6%	20.0%	43.1%	16.4%	7.9%	13.4%	19.6%	39.4%	20.0%	7.7%
Podgorica	33.5%	18.0%	23.1%	20.6%	4.8%	37.9%	18.7%	24.5%	12.6%	6.3%
Praha	12.7%	26.9%	29.7%	11.7%	19.0%	11.1%	27.2%	32.2%	10.2%	19.3%
Rennes	12.7%	24.1%	40.2%	21.4%	1.6%	13.7%	23.3%	39.2%	19.5%	4.3%
Reykjavík	20.3%	31.3%	15.3%	12.0%	21.1%	18.3%	32.5%	18.8%	13.2%	17.2%
Riga	21.2%	23.1%	27.7%	13.6%	14.4%	20.3%	28.3%	24.7%	12.3%	14.4%
Rome	51.4%	31.3%	14.2%	1.3%	1.8%	49.4%	32.2%	13.2%	3.7%	1.4%
Rostock	4.6%	15.3%	51.8%	13.0%	15.3%	6.4%	22.5%	48.8%	10.4%	11.9%
Rotterdam	16.2%	21.8%	25.6%	25.2%	11.1%	15.9%	25.0%	27.7%	20.7%	10.7%
Skopje	44.7%	22.8%	19.8%	11.7%	1.0%	52.2%	16.8%	18.6%	10.3%	2.1%
Sofia	27.9%	24.5%	20.0%	12.3%	15.3%	24.2%	26.0%	24.4%	13.4%	11.9%
Stockholm	15.7%	21.0%	28.8%	13.8%	20.6%	14.9%	23.3%	26.4%	13.2%	22.2%
Strasbourg	14.7%	17.5%	38.5%	25.8%	3.5%	12.6%	17.2%	39.8%	28.2%	2.2%
Tallinn	10.9%	14.2%	39.2%	14.3%	21.5%	15.6%	20.3%	31.3%	13.4%	19.4%
Tirana	38.2%	9.9%	31.5%	16.9%	3.4%	39.6%	9.9%	23.4%	22.2%	5.0%
Turin	26.2%	38.0%	26.2%	3.6%	5.9%	24.9%	37.9%	27.3%	4.5%	5.4%
Tyneside conurbation	10.6%	17.3%	32.9%	21.2%	18.0%	14.5%	20.5%	29.4%	15.8%	19.7%
Valletta	16.8%	15.1%	36.7%	17.5%	13.9%	15.7%	14.4%	38.2%	19.7%	12.0%
Verona	17.6%	35.4%	35.5%	5.6%	5.9%	16.2%	32.1%	38.1%	7.0%	6.5%
Vilnius	9.0%	30.3%	34.1%	11.1%	15.4%	12.2%	32.4%	30.7%	11.2%	13.5%
Warszawa	17.7%	21.4%	39.4%	14.1%	7.4%	13.9%	21.7%	43.9%	9.0%	11.5%
Wien	7.0%	16.2%	43.9%	23.9%	9.0%	8.4%	17.4%	42.9%	22.9%	8.4%
Zagreb	34.2%	31.2%	24.0%	7.3%	3.4%	31.5%	35.0%	19.7%	9.0%	4.8%
Zurich	1.7%	10.7%	45.6%	27.7%	14.3%	2.8%	11.7%	42.8%	27.7%	14.9%
Min	1.4%	9.9%	10.9%	1.3%	0.7%	2.8%	9.5%	9.4%	3.2%	1.4%
Max	51.4%	38.4%	51.8%	36.4%	26.1%	52.2%	37.9%	48.8%	35.9%	26.1%
Mean	18.4%	21.7%	31.9%	18.2%	9.9%	18.6%	22.6%	31.5%	17.4%	9.9%
Sd	11.1%	7.0%	8.9%	7.7%	6.7%	10.5%	6.6%	8.0%	7.3%	6.2%

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused. Source: [15].

,

Table A2. Distribution of responses to question Q2 in 2019 and 2023 in the Quality of Life in European Cities survey.

City	The Procedures Used by My Local Public Administration are Straightforward and Easy to Understand
	2019					2023
	1	2	3	4	99	1	2	3	4	99
Aalborg	11.1%	18.2%	40.0%	20.8%	9.9%	11.9%	19.5%	39.2%	21.2%	8.2%
Amsterdam	11.1%	25.6%	33.9%	23.2%	6.2%	12.6%	25.8%	33.8%	21.6%	6.2%
Ankara	18.9%	14.6%	32.4%	31.8%	2.3%	12.7%	13.9%	25.6%	45.8%	2.0%
Antalya	17.4%	3.9%	34.2%	43.8%	0.7%	14.2%	7.2%	28.4%	47.2%	2.9%
Antwerpen	13.4%	6.5%	33.2%	39.2%	7.7%	15.1%	8.1%	39.0%	33.1%	4.7%
Athina	20.8%	37.1%	30.5%	10.0%	1.5%	22.2%	33.6%	29.4%	11.6%	3.2%
Barcelona	17.7%	17.6%	22.0%	40.8%	1.9%	17.8%	23.2%	25.2%	29.3%	4.4%
Belfast	15.6%	14.6%	34.9%	28.2%	6.8%	14.7%	19.2%	34.9%	21.3%	9.9%
Belgrade	39.7%	22.1%	23.0%	12.9%	2.4%	34.9%	26.6%	21.7%	12.5%	4.3%
Berlin	19.4%	40.6%	25.7%	5.9%	8.5%	20.9%	38.3%	25.4%	8.8%	6.6%
Białystok	5.9%	28.9%	46.9%	14.3%	4.0%	8.9%	28.1%	45.5%	12.2%	5.2%
Bologna	9.7%	36.2%	42.8%	8.0%	3.3%	11.4%	32.6%	44.1%	9.2%	2.7%
Bordeaux	9.4%	26.3%	24.2%	34.5%	5.6%	7.8%	28.9%	29.6%	28.6%	5.2%
Braga	3.8%	26.5%	58.9%	7.1%	3.7%	6.3%	27.7%	54.0%	7.1%	5.0%
Bratislava	15.1%	31.4%	35.9%	10.4%	7.3%	12.6%	29.0%	37.3%	9.5%	11.6%
Bruxelles	7.6%	13.6%	36.1%	42.7%	0.0%	8.5%	15.0%	36.7%	39.3%	0.5%
Bucharest	24.9%	24.0%	30.1%	19.0%	1.9%	19.9%	23.1%	36.1%	17.5%	3.3%
Budapest	12.0%	21.7%	25.6%	31.2%	9.6%	12.7%	19.6%	29.2%	26.5%	12.0%
Burgas	17.2%	21.9%	21.3%	35.4%	4.1%	16.5%	22.7%	27.0%	29.3%	4.5%
Cardiff	5.3%	21.7%	41.1%	24.0%	7.9%	8.1%	22.5%	37.8%	22.7%	8.9%
Cluj-Napoca	9.9%	21.7%	38.3%	21.9%	8.2%	9.9%	18.7%	41.9%	23.4%	6.1%
Copenhagen	9.5%	22.8%	32.9%	25.1%	9.6%	13.1%	20.1%	36.3%	22.9%	7.6%
Diyarbakir	31.4%	18.0%	25.8%	23.6%	1.1%	27.2%	19.1%	26.4%	24.5%	2.9%
Dortmund	12.5%	40.2%	34.5%	9.1%	3.7%	13.8%	36.7%	35.9%	10.3%	3.2%
Dublin	10.7%	14.2%	41.0%	24.9%	9.2%	10.6%	18.8%	41.7%	21.2%	7.7%
Essen	15.4%	37.6%	24.9%	11.5%	10.6%	13.9%	39.1%	25.1%	14.1%	7.7%
Gdańsk	11.2%	23.2%	49.1%	12.2%	4.3%	10.2%	24.1%	46.5%	15.1%	4.0%
Genève	3.1%	16.9%	48.1%	26.5%	5.4%	4.7%	15.4%	46.4%	29.8%	3.7%
Glasgow	4.3%	27.5%	23.9%	32.0%	12.4%	8.4%	27.2%	32.6%	21.2%	10.5%
Graz	9.4%	16.1%	47.2%	23.8%	3.5%	8.8%	21.2%	42.0%	22.0%	6.0%
Groningen	6.9%	13.0%	30.0%	30.7%	19.4%	8.1%	16.2%	33.2%	25.5%	17.1%
Hamburg	11.2%	33.7%	34.0%	13.5%	7.5%	9.7%	36.1%	32.7%	16.2%	5.4%
Helsinki	16.2%	34.2%	23.6%	16.2%	9.7%	15.1%	31.4%	26.8%	14.9%	11.8%
Heraklion	29.4%	25.4%	18.0%	24.4%	2.8%	27.9%	28.0%	18.5%	22.5%	3.1%
Istanbul	25.1%	8.0%	39.2%	23.8%	3.9%	23.8%	11.6%	37.5%	24.9%	2.3%
Košice	10.1%	25.6%	37.5%	20.4%	6.4%	12.9%	24.1%	39.1%	17.9%	6.0%
Kraków	16.0%	32.0%	34.9%	13.0%	4.3%	15.9%	31.5%	34.2%	11.5%	6.9%
Lefkosia	11.0%	9.6%	36.4%	40.7%	2.3%	9.5%	19.5%	37.7%	31.7%	1.6%
Leipzig	9.2%	23.7%	47.6%	12.1%	7.4%	10.9%	31.2%	42.4%	9.4%	6.1%
Liège	8.2%	13.8%	34.9%	39.8%	3.3%	8.2%	13.3%	37.7%	39.3%	1.5%
Lille	15.2%	18.0%	37.5%	26.2%	3.1%	13.6%	17.6%	38.6%	26.7%	3.4%
Lisboa	12.6%	35.1%	46.2%	3.0%	3.1%	14.7%	32.0%	42.8%	5.6%	4.9%
Ljubljana	9.5%	22.8%	40.9%	18.8%	8.0%	12.4%	24.0%	38.9%	18.2%	6.5%
London	11.9%	22.1%	35.9%	22.6%	7.5%	13.4%	26.6%	31.0%	18.0%	10.9%
Luxembourg	3.4%	25.5%	36.4%	32.6%	2.1%	4.1%	18.1%	43.7%	33.4%	0.6%
Madrid	18.7%	19.5%	31.8%	25.6%	4.4%	21.0%	24.4%	29.0%	21.3%	4.3%
Málaga	18.9%	14.6%	32.1%	26.4%	8.0%	18.3%	21.0%	29.2%	26.2%	5.3%
Malmö	18.7%	27.8%	23.0%	20.7%	9.8%	14.9%	23.6%	27.0%	21.6%	12.9%
Manchester	6.9%	19.1%	36.9%	32.6%	4.5%	7.9%	20.4%	35.5%	24.7%	11.5%
Marseille	13.8%	25.9%	22.2%	32.7%	5.3%	13.1%	26.3%	26.8%	28.4%	5.4%
Miskolc	10.3%	16.8%	31.8%	26.4%	14.7%	10.6%	16.7%	37.2%	24.0%	11.4%
Munich	10.1%	30.0%	33.2%	18.4%	8.2%	8.9%	31.9%	36.2%	16.1%	6.8%
Naples	26.1%	36.5%	29.7%	5.7%	2.0%	26.6%	35.4%	29.7%	5.3%	3.0%
Oslo	20.6%	25.4%	25.4%	7.9%	20.7%	19.8%	26.6%	24.8%	15.1%	13.7%
Ostrava	11.1%	32.5%	33.9%	14.1%	8.5%	14.3%	29.0%	34.6%	12.2%	9.9%
Oulu	12.5%	32.9%	36.4%	10.4%	7.8%	13.8%	30.2%	36.9%	7.6%	11.5%
Oviedo	26.2%	18.2%	23.6%	27.5%	4.6%	21.7%	20.3%	29.6%	24.9%	3.4%
Palermo	28.7%	40.2%	23.6%	4.4%	3.1%	28.3%	44.3%	17.2%	5.6%	4.6%
Paris	17.2%	20.8%	25.4%	34.3%	2.3%	15.5%	22.4%	31.1%	27.1%	4.0%
Piatra Neamt	12.0%	24.0%	34.1%	22.2%	7.7%	14.0%	21.8%	34.2%	23.0%	7.0%
Podgorica	24.9%	17.8%	30.1%	22.3%	5.0%	31.1%	17.5%	30.5%	15.3%	5.5%
Praha	16.0%	27.9%	26.9%	14.7%	14.5%	16.4%	27.0%	36.3%	7.0%	13.3%
Rennes	10.9%	20.1%	38.5%	26.9%	3.5%	11.1%	21.1%	38.5%	24.8%	4.5%
Reykjavík	19.8%	29.8%	26.4%	14.6%	9.4%	16.2%	31.0%	28.5%	15.5%	8.8%
Riga	23.6%	23.5%	29.2%	19.2%	4.6%	26.6%	27.8%	24.9%	14.7%	6.1%
Rome	38.4%	32.6%	23.5%	3.6%	1.9%	33.2%	34.3%	25.3%	5.5%	1.7%
Rostock	10.6%	28.0%	43.7%	9.0%	8.7%	11.7%	37.3%	36.5%	6.7%	7.8%
Rotterdam	16.3%	17.2%	30.2%	30.9%	5.5%	15.4%	20.1%	35.2%	23.6%	5.7%
Skopje	27.3%	16.4%	36.2%	16.9%	3.2%	32.6%	16.1%	31.3%	16.3%	3.7%
Sofia	33.4%	18.6%	23.6%	13.4%	11.1%	28.6%	20.4%	26.2%	15.7%	9.2%
Stockholm	16.6%	26.1%	34.9%	10.0%	12.3%	16.3%	27.4%	33.4%	6.4%	16.4%
Strasbourg	10.5%	19.9%	41.6%	25.4%	2.6%	9.2%	20.0%	42.6%	26.5%	1.6%
Tallinn	9.4%	21.9%	40.9%	13.8%	14.1%	9.3%	24.2%	35.8%	13.0%	17.7%
Tirana	28.4%	11.2%	36.9%	21.9%	1.5%	29.0%	14.8%	30.0%	22.6%	3.6%
Turin	19.9%	41.2%	33.2%	2.8%	2.8%	24.0%	34.8%	31.9%	5.6%	3.8%
Tyneside conurbation	12.5%	13.8%	40.1%	26.7%	6.8%	12.3%	18.7%	41.0%	17.5%	10.5%
Valletta	9.1%	16.1%	31.7%	33.5%	9.6%	8.0%	15.1%	33.1%	35.5%	8.3%
Verona	15.5%	38.5%	37.4%	4.9%	3.6%	12.5%	37.1%	41.5%	3.9%	5.0%
Vilnius	6.2%	27.9%	37.6%	18.5%	9.8%	7.3%	28.7%	37.8%	16.7%	9.5%
Warszawa	18.7%	29.8%	33.4%	12.0%	6.1%	16.6%	28.9%	37.8%	9.4%	7.3%
Wien	5.8%	25.2%	42.1%	24.7%	2.1%	9.1%	23.4%	40.0%	25.3%	2.2%
Zagreb	32.1%	28.0%	24.4%	9.6%	5.9%	29.2%	30.1%	24.7%	10.0%	6.1%
Zurich	2.7%	17.9%	46.7%	25.6%	7.1%	3.7%	19.7%	43.8%	26.8%	6.1%
Min	2.7%	3.9%	18.0%	2.8%	0.0%	3.7%	7.2%	17.2%	3.9%	0.5%
Max	39.7%	41.2%	58.9%	43.8%	20.7%	34.9%	44.3%	54.0%	47.2%	17.7%
Mean	15.3%	23.7%	33.8%	21.0%	6.2%	15.3%	24.5%	34.2%	19.5%	6.5%
Sd	8.1%	8.3%	7.8%	10.3%	4.0%	7.3%	7.4%	6.8%	9.4%	3.8%

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused. Source: [15].

,

Table A3. Distribution of responses to question Q3 in 2019 and 2023 in the Quality of Life in European Cities survey.

City	The Fees Charged by My Local Public Administration Are Reasonable.
	2019					2023
	1	2	3	4	99	1	2	3	4	99
Aalborg	8.3%	16.2%	38.0%	18.8%	18.6%	8.2%	18.2%	39.1%	17.1%	17.4%
Amsterdam	16.9%	24.4%	32.3%	20.2%	6.3%	15.2%	25.8%	32.5%	20.8%	5.7%
Ankara	25.1%	17.4%	31.6%	24.6%	1.3%	18.7%	14.2%	31.7%	33.1%	2.4%
Antalya	20.2%	14.2%	38.5%	25.4%	1.7%	14.4%	15.2%	32.9%	30.9%	6.6%
Antwerpen	20.8%	16.2%	34.8%	27.1%	1.0%	19.8%	16.1%	41.9%	20.9%	1.3%
Athina	49.8%	22.5%	16.6%	9.8%	1.2%	41.1%	26.1%	20.0%	9.6%	3.2%
Barcelona	39.3%	18.9%	18.0%	22.8%	0.9%	35.5%	21.9%	20.7%	19.7%	2.2%
Belfast	10.5%	21.8%	31.6%	26.4%	9.6%	10.7%	21.4%	36.2%	19.0%	12.7%
Belgrade	38.6%	17.3%	29.3%	11.2%	3.7%	37.4%	20.3%	24.8%	12.5%	5.1%
Berlin	11.3%	18.1%	53.1%	7.1%	10.4%	13.1%	18.1%	50.0%	10.2%	8.5%
Białystok	6.2%	23.0%	60.0%	7.8%	3.0%	7.3%	23.1%	54.1%	9.9%	5.5%
Bologna	12.6%	36.1%	43.2%	6.8%	1.3%	17.0%	31.1%	41.6%	8.5%	1.8%
Bordeaux	12.4%	19.7%	32.8%	26.9%	8.1%	11.5%	22.8%	34.3%	23.9%	7.5%
Braga	4.8%	35.4%	50.4%	6.0%	3.4%	8.2%	32.7%	47.6%	6.1%	5.4%
Bratislava	6.1%	20.6%	47.1%	18.8%	7.4%	9.0%	19.7%	49.3%	15.6%	6.4%
Bruxelles	21.9%	12.5%	35.8%	26.1%	3.7%	20.9%	15.1%	34.9%	26.1%	3.0%
Bucharest	13.8%	18.3%	44.3%	19.4%	4.2%	15.7%	17.9%	42.2%	21.3%	2.9%
Budapest	17.6%	20.0%	37.5%	14.9%	10.1%	14.9%	17.5%	34.0%	20.0%	13.7%
Burgas	26.8%	20.8%	26.9%	21.1%	4.4%	26.1%	23.9%	28.0%	17.6%	4.3%
Cardiff	9.4%	20.0%	44.0%	19.7%	6.8%	9.5%	21.7%	41.4%	19.0%	8.4%
Cluj-Napoca	9.7%	15.2%	30.9%	38.7%	5.5%	10.6%	13.2%	36.1%	37.7%	2.5%
Copenhagen	4.6%	16.0%	39.4%	22.3%	17.7%	7.3%	15.9%	37.0%	25.2%	14.6%
Diyarbakir	22.5%	15.5%	34.4%	25.5%	2.0%	23.9%	15.2%	31.9%	24.3%	4.7%
Dortmund	15.2%	29.9%	38.3%	9.6%	7.0%	15.0%	31.9%	36.0%	10.5%	6.5%
Dublin	10.9%	15.5%	41.5%	22.2%	9.9%	10.0%	18.1%	42.8%	19.2%	9.9%
Essen	8.6%	23.7%	44.9%	12.5%	10.3%	10.5%	25.7%	41.8%	12.5%	9.5%
Gdańsk	12.0%	16.0%	58.2%	10.1%	3.7%	12.2%	18.6%	51.4%	12.3%	5.5%
Genève	9.7%	26.5%	37.6%	19.8%	6.5%	9.8%	23.9%	39.9%	22.2%	4.2%
Glasgow	10.9%	25.9%	22.7%	30.2%	10.3%	14.2%	25.6%	30.5%	19.0%	10.8%
Graz	5.9%	21.9%	39.3%	30.4%	2.5%	7.5%	21.2%	42.8%	24.0%	4.5%
Groningen	15.2%	25.5%	31.7%	22.5%	5.0%	13.9%	25.5%	35.1%	17.9%	7.7%
Hamburg	7.4%	22.6%	46.9%	11.0%	12.0%	10.0%	25.7%	46.7%	8.3%	9.3%
Helsinki	13.3%	16.3%	39.6%	21.3%	9.4%	12.9%	20.0%	35.9%	19.4%	11.8%
Heraklion	51.0%	20.5%	16.6%	10.0%	1.9%	47.8%	21.1%	18.2%	9.9%	3.0%
Istanbul	37.7%	5.1%	38.5%	13.2%	5.5%	38.3%	7.7%	38.1%	13.1%	2.8%
Košice	6.6%	21.8%	45.4%	22.0%	4.2%	11.1%	31.7%	39.1%	15.4%	2.8%
Kraków	13.0%	26.3%	44.9%	11.5%	4.3%	14.0%	28.6%	41.9%	9.3%	6.2%
Lefkosia	25.0%	17.4%	38.0%	15.4%	4.1%	26.5%	18.7%	36.2%	15.4%	3.3%
Leipzig	8.0%	28.8%	48.7%	6.4%	8.2%	11.4%	33.7%	43.4%	4.5%	7.0%
Liège	15.0%	21.0%	24.6%	36.7%	2.7%	14.1%	21.8%	27.3%	34.6%	2.2%
Lille	17.6%	21.0%	38.4%	16.7%	6.4%	17.7%	21.7%	35.1%	19.0%	6.4%
Lisboa	13.5%	36.6%	42.1%	3.9%	3.9%	18.3%	33.3%	37.2%	5.4%	5.8%
Ljubljana	6.8%	14.0%	51.0%	16.5%	11.8%	9.7%	18.6%	45.8%	14.6%	11.4%
London	14.6%	19.6%	39.3%	18.6%	7.9%	16.3%	24.6%	34.3%	15.4%	9.4%
Luxembourg	1.2%	19.7%	40.4%	36.0%	2.7%	2.3%	15.9%	43.4%	36.7%	1.7%
Madrid	16.3%	28.3%	22.3%	28.5%	4.6%	16.5%	30.1%	26.7%	21.4%	5.3%
Málaga	24.3%	14.3%	25.6%	26.1%	9.6%	24.5%	21.1%	28.1%	20.3%	6.0%
Malmö	12.3%	11.2%	24.7%	32.7%	19.0%	10.5%	13.5%	27.9%	28.3%	19.8%
Manchester	8.6%	18.6%	35.3%	28.9%	8.6%	11.2%	21.7%	35.1%	18.7%	13.3%
Marseille	18.5%	31.8%	21.4%	21.3%	7.0%	19.2%	30.2%	24.1%	19.9%	6.6%
Miskolc	13.6%	22.5%	34.3%	22.3%	7.2%	17.2%	21.5%	34.1%	17.8%	9.4%
Munich	5.6%	21.7%	45.3%	19.1%	8.3%	8.4%	27.3%	42.5%	14.6%	7.1%
Naples	38.1%	33.0%	23.7%	3.3%	2.0%	36.5%	31.7%	25.0%	3.7%	3.0%
Oslo	14.4%	18.6%	35.9%	13.2%	17.9%	16.5%	19.7%	32.2%	15.4%	16.2%
Ostrava	6.7%	20.0%	46.9%	20.5%	6.0%	9.0%	20.3%	44.3%	21.5%	5.0%
Oulu	8.6%	28.6%	36.8%	19.3%	6.8%	12.7%	24.8%	35.8%	18.9%	7.8%
Oviedo	29.2%	23.5%	19.7%	22.7%	4.9%	29.8%	27.4%	22.4%	16.5%	3.9%
Palermo	36.4%	40.1%	19.5%	2.5%	1.4%	33.5%	44.6%	16.0%	3.9%	1.9%
Paris	18.8%	21.3%	34.9%	18.3%	6.7%	14.7%	23.5%	33.9%	18.4%	9.5%
Piatra Neamt	13.8%	14.5%	52.2%	16.0%	3.5%	16.1%	14.4%	43.1%	22.0%	4.4%
Podgorica	16.2%	15.5%	35.6%	25.7%	7.0%	21.9%	19.4%	35.2%	17.1%	6.4%
Praha	5.4%	13.6%	52.5%	16.4%	12.1%	8.1%	15.7%	52.4%	14.2%	9.7%
Rennes	9.7%	21.8%	38.6%	26.1%	3.7%	8.6%	20.4%	39.8%	24.3%	6.9%
Reykjavík	21.5%	29.1%	29.9%	15.4%	4.1%	18.7%	31.5%	29.7%	15.1%	5.0%
Riga	39.5%	31.7%	17.3%	7.8%	3.6%	36.1%	28.4%	22.0%	7.9%	5.6%
Rome	40.8%	31.9%	24.0%	2.2%	1.2%	38.3%	35.2%	20.3%	4.3%	1.9%
Rostock	3.2%	22.2%	59.8%	10.0%	4.7%	6.2%	28.9%	50.1%	9.5%	5.4%
Rotterdam	20.4%	10.7%	30.8%	32.1%	6.0%	18.3%	15.4%	33.5%	25.6%	7.2%
Skopje	24.6%	17.7%	33.2%	22.0%	2.5%	33.2%	19.1%	30.2%	14.0%	3.5%
Sofia	29.6%	16.5%	27.1%	25.2%	1.6%	22.5%	19.3%	32.3%	23.7%	2.3%
Stockholm	3.6%	18.1%	36.1%	26.4%	15.8%	3.5%	21.0%	38.1%	20.1%	17.2%
Strasbourg	14.9%	21.0%	45.0%	14.4%	4.7%	14.5%	20.6%	46.6%	14.3%	3.9%
Tallinn	3.9%	10.8%	45.6%	17.5%	22.2%	10.8%	16.0%	38.2%	14.5%	20.5%
Tirana	35.6%	15.4%	35.8%	11.6%	1.5%	32.6%	13.6%	29.6%	20.0%	4.2%
Turin	25.2%	39.6%	31.3%	2.0%	2.0%	28.3%	36.2%	28.3%	4.6%	2.6%
Tyneside conurbation	14.1%	19.0%	40.2%	19.1%	7.6%	16.7%	23.3%	34.2%	15.0%	10.8%
Valletta	8.7%	11.2%	38.5%	23.2%	18.3%	7.4%	13.9%	34.2%	27.3%	17.3%
Verona	12.9%	26.5%	51.1%	5.7%	3.7%	16.4%	29.4%	45.1%	6.0%	3.1%
Vilnius	12.7%	25.9%	39.5%	10.1%	11.8%	14.6%	28.3%	36.7%	10.2%	10.2%
Warszawa	13.1%	30.4%	42.0%	9.9%	4.6%	13.9%	30.1%	42.3%	7.0%	6.8%
Wien	5.9%	20.7%	44.7%	25.9%	2.7%	11.2%	22.7%	39.9%	23.3%	3.0%
Zagreb	37.4%	21.4%	25.9%	11.8%	3.4%	33.8%	25.1%	26.8%	11.1%	3.3%
Zurich	1.9%	16.9%	51.1%	26.0%	4.1%	2.4%	17.1%	51.4%	25.9%	3.2%
Min	1.2%	5.1%	16.6%	2.0%	0.9%	2.3%	7.7%	16.0%	3.7%	1.3%
Max	51.0%	40.1%	60.0%	38.7%	22.2%	47.8%	44.6%	54.1%	37.7%	20.5%
Mean	16.8%	21.3%	37.0%	18.4%	6.5%	17.3%	22.7%	36.1%	17.2%	6.8%
Sd	11.3%	6.8%	10.1%	8.5%	4.7%	9.8%	6.5%	8.4%	7.6%	4.4%

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused. Source: [15].

,

Table A4. Distribution of responses to question Q4 in 2019 and 2023 in the Quality of Life in European Cities survey.

City	Information and Services of My Local Public Administration Can Be Easily Accessed Online.
	2019					2023
	1	2	3	4	99	1	2	3	4	99
Aalborg	2.7%	7.4%	29.2%	55.4%	5.3%	4.1%	8.8%	38.7%	42.0%	6.4%
Amsterdam	5.2%	7.5%	33.2%	47.3%	6.7%	7.2%	10.0%	37.6%	39.8%	5.5%
Ankara	9.1%	15.1%	32.2%	40.3%	3.3%	9.3%	11.8%	25.2%	49.1%	4.6%
Antalya	6.1%	7.2%	29.6%	52.0%	5.0%	6.1%	7.8%	31.6%	48.2%	6.3%
Antwerpen	19.8%	16.0%	30.4%	22.2%	11.6%	16.4%	17.4%	35.5%	20.4%	10.3%
Athina	15.3%	19.9%	30.2%	22.4%	12.2%	15.5%	20.3%	30.9%	21.6%	11.7%
Barcelona	19.4%	10.8%	24.5%	42.3%	3.0%	17.5%	13.4%	30.8%	34.9%	3.4%
Belfast	7.3%	8.3%	26.5%	42.5%	15.4%	6.3%	13.3%	34.1%	33.0%	13.4%
Belgrade	13.5%	14.4%	37.1%	22.4%	12.4%	15.6%	17.0%	31.2%	23.7%	12.5%
Berlin	7.6%	19.3%	38.4%	17.0%	17.8%	9.0%	21.6%	35.6%	18.9%	14.9%
Białystok	5.8%	6.9%	55.7%	20.7%	10.9%	5.5%	9.5%	54.0%	22.4%	8.6%
Bologna	6.3%	12.9%	50.8%	23.6%	6.4%	7.5%	14.5%	48.9%	23.9%	5.3%
Bordeaux	6.5%	15.1%	37.4%	35.0%	5.9%	8.0%	16.0%	42.0%	29.8%	4.2%
Braga	3.2%	21.8%	58.5%	9.2%	7.3%	6.1%	22.3%	59.5%	7.6%	4.5%
Bratislava	7.5%	17.3%	40.6%	26.1%	8.5%	7.2%	15.8%	42.4%	23.7%	10.9%
Bruxelles	9.4%	15.4%	29.1%	41.0%	5.2%	9.8%	15.3%	31.3%	39.4%	4.3%
Bucharest	13.6%	14.4%	28.0%	27.1%	16.9%	13.2%	15.6%	33.3%	24.1%	13.8%
Budapest	6.1%	7.0%	30.0%	37.8%	19.2%	7.0%	9.4%	32.5%	34.0%	17.2%
Burgas	8.4%	5.7%	28.0%	52.2%	5.6%	8.8%	9.4%	30.3%	47.2%	4.3%
Cardiff	5.5%	8.4%	36.4%	39.0%	10.7%	5.6%	12.1%	38.4%	34.7%	9.2%
Cluj-Napoca	4.9%	13.0%	32.2%	30.8%	19.2%	4.2%	13.0%	32.0%	38.0%	12.9%
Copenhagen	0.7%	9.3%	42.1%	41.2%	6.8%	3.3%	10.0%	44.0%	36.1%	6.5%
Diyarbakir	23.3%	16.1%	31.9%	25.6%	3.2%	20.8%	16.1%	28.3%	29.4%	5.4%
Dortmund	4.2%	20.5%	44.5%	20.9%	9.9%	5.1%	25.6%	40.1%	22.4%	6.9%
Dublin	4.4%	8.8%	34.5%	43.2%	9.1%	5.9%	11.1%	40.7%	35.3%	7.0%
Essen	6.7%	14.5%	33.2%	32.6%	13.0%	8.5%	15.5%	36.7%	28.6%	10.8%
Gdańsk	3.4%	12.1%	48.2%	29.9%	6.3%	4.2%	14.6%	48.8%	28.0%	4.4%
Genève	6.3%	16.9%	33.3%	35.1%	8.4%	7.9%	17.7%	32.3%	34.2%	7.9%
Glasgow	5.5%	11.7%	28.5%	38.3%	15.9%	4.4%	15.8%	40.8%	27.6%	11.4%
Graz	3.2%	7.5%	31.0%	52.1%	6.1%	4.6%	9.2%	36.5%	43.2%	6.5%
Groningen	2.0%	6.0%	30.4%	52.3%	9.3%	3.2%	9.7%	35.3%	44.2%	7.5%
Hamburg	2.3%	17.3%	47.5%	23.8%	9.0%	2.5%	17.9%	52.0%	20.9%	6.7%
Helsinki	4.4%	17.4%	33.0%	39.3%	5.9%	5.0%	17.1%	40.3%	31.2%	6.4%
Heraklion	7.8%	11.5%	31.2%	34.4%	15.2%	8.0%	11.6%	32.8%	33.9%	13.7%
Istanbul	16.1%	11.4%	27.9%	40.3%	4.4%	14.0%	13.2%	31.6%	38.3%	2.9%
Košice	4.4%	12.8%	39.0%	34.2%	9.6%	5.7%	15.0%	41.9%	29.8%	7.6%
Kraków	6.8%	9.3%	49.0%	25.6%	9.4%	6.9%	11.7%	51.5%	21.9%	8.0%
Lefkosia	4.4%	9.0%	29.6%	44.0%	12.9%	5.3%	11.5%	31.8%	39.9%	11.6%
Leipzig	1.6%	14.0%	48.4%	15.0%	20.9%	3.6%	14.9%	50.9%	13.2%	17.3%
Liège	9.4%	13.4%	25.0%	35.6%	16.6%	10.6%	14.9%	25.1%	32.0%	17.4%
Lille	8.7%	19.2%	30.2%	31.3%	10.6%	10.9%	15.6%	37.7%	28.1%	7.7%
Lisboa	3.6%	22.6%	57.5%	7.2%	9.1%	6.5%	22.5%	56.5%	7.5%	7.0%
Ljubljana	4.8%	12.7%	34.1%	35.8%	12.6%	7.2%	14.4%	39.4%	29.3%	9.7%
London	5.5%	8.9%	29.4%	47.0%	9.1%	7.1%	14.8%	41.1%	29.2%	7.8%
Luxembourg	1.3%	13.4%	44.2%	39.7%	1.5%	2.7%	13.1%	44.1%	37.8%	2.3%
Madrid	9.2%	17.2%	24.4%	38.7%	10.5%	13.0%	19.5%	29.2%	31.6%	6.7%
Málaga	11.0%	11.9%	33.0%	33.0%	11.1%	11.1%	15.8%	34.7%	30.5%	8.0%
Malmö	3.8%	13.5%	31.2%	43.6%	7.9%	6.4%	11.1%	35.4%	34.5%	12.6%
Manchester	5.1%	6.4%	38.3%	37.0%	13.2%	5.3%	11.5%	40.7%	29.8%	12.7%
Marseille	17.5%	18.6%	21.0%	35.9%	7.0%	14.3%	19.3%	26.8%	33.5%	6.0%
Miskolc	2.5%	5.9%	30.6%	40.0%	21.0%	5.4%	8.7%	31.8%	32.7%	21.5%
Munich	7.4%	8.2%	43.1%	30.0%	11.2%	9.3%	9.7%	42.2%	31.1%	7.7%
Naples	21.0%	20.1%	40.6%	9.6%	8.8%	18.9%	22.6%	37.8%	13.0%	7.7%
Oslo	5.0%	17.3%	37.1%	26.9%	13.8%	6.8%	16.9%	40.4%	25.1%	11.0%
Ostrava	4.0%	8.3%	40.0%	41.0%	6.8%	6.1%	9.6%	42.0%	36.5%	5.7%
Oulu	2.6%	21.4%	37.7%	31.1%	7.2%	5.6%	17.5%	43.4%	26.8%	6.7%
Oviedo	17.3%	12.8%	25.4%	34.1%	10.4%	16.3%	18.1%	31.8%	29.5%	4.3%
Palermo	15.8%	29.2%	42.2%	8.8%	4.0%	16.6%	31.2%	37.8%	10.1%	4.3%
Paris	10.9%	14.7%	26.1%	44.8%	3.4%	11.0%	15.3%	32.9%	36.4%	4.4%
Piatra Neamt	6.9%	12.5%	27.1%	26.9%	26.6%	6.6%	15.1%	27.1%	25.7%	25.5%
Podgorica	12.0%	19.0%	27.2%	35.4%	6.3%	12.3%	20.1%	29.4%	30.5%	7.7%
Praha	3.9%	11.0%	39.5%	32.2%	13.3%	5.8%	13.3%	44.5%	26.1%	10.3%
Rennes	5.7%	14.4%	35.1%	39.6%	5.2%	6.7%	14.1%	39.0%	35.2%	5.1%
Reykjavík	5.7%	15.3%	36.2%	28.1%	14.7%	9.0%	15.1%	38.5%	26.0%	11.4%
Riga	13.1%	13.7%	27.5%	35.9%	9.8%	12.4%	15.0%	33.7%	29.5%	9.4%
Rome	17.9%	22.3%	45.9%	6.3%	7.6%	18.3%	20.4%	47.5%	8.2%	5.6%
Rostock	3.0%	14.5%	49.2%	20.1%	13.2%	6.2%	18.4%	47.5%	17.4%	10.5%
Rotterdam	4.9%	13.7%	32.9%	39.3%	9.2%	7.1%	14.6%	38.7%	32.0%	7.6%
Skopje	14.6%	18.2%	28.2%	30.2%	8.9%	20.3%	15.6%	28.7%	27.5%	7.9%
Sofia	11.4%	13.3%	23.9%	36.9%	14.4%	13.5%	14.8%	23.3%	36.3%	12.1%
Stockholm	2.8%	14.6%	38.5%	29.0%	15.1%	6.1%	16.0%	37.5%	24.5%	15.9%
Strasbourg	11.6%	12.9%	36.5%	30.5%	8.4%	9.3%	14.1%	36.9%	30.8%	8.9%
Tallinn	4.3%	8.4%	45.2%	34.1%	8.0%	4.9%	10.0%	44.9%	33.1%	7.1%
Tirana	13.6%	9.3%	43.4%	28.1%	5.5%	17.2%	12.4%	33.8%	32.3%	4.3%
Turin	8.2%	21.7%	40.6%	15.1%	14.3%	10.8%	20.3%	42.0%	13.2%	13.7%
Tyneside conurbation	6.5%	11.2%	24.2%	37.8%	20.4%	5.3%	11.4%	36.4%	30.5%	16.4%
Valletta	5.1%	9.0%	34.9%	37.3%	13.6%	6.1%	9.4%	36.1%	37.0%	11.4%
Verona	4.8%	18.7%	50.9%	14.6%	11.0%	5.5%	16.8%	51.6%	17.3%	8.7%
Vilnius	5.6%	19.5%	32.6%	35.3%	7.0%	5.2%	18.6%	36.0%	33.8%	6.4%
Warszawa	4.6%	14.4%	51.1%	26.9%	3.0%	7.2%	15.4%	53.1%	20.8%	3.6%
Wien	1.2%	10.8%	42.1%	39.8%	6.2%	3.9%	12.2%	45.1%	35.0%	3.9%
Zagreb	11.3%	10.8%	34.8%	20.9%	22.3%	15.4%	15.7%	34.3%	18.4%	16.3%
Zurich	0.5%	7.8%	33.2%	46.9%	11.6%	0.7%	8.5%	36.0%	45.1%	9.7%
Min	0.5%	5.7%	21.0%	6.3%	1.5%	0.7%	7.8%	23.3%	7.5%	2.3%
Max	23.3%	29.2%	58.5%	55.4%	26.6%	20.8%	31.2%	59.5%	49.1%	25.5%
Mean	7.7%	13.5%	35.8%	32.7%	10.3%	8.6%	14.8%	38.1%	29.5%	9.0%
Sd	5.1%	4.8%	8.4%	10.9%	5.0%	4.5%	4.2%	7.5%	9.0%	4.4%

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused. Source: [15].

and

Table A5. Distribution of responses to question Q5 in 2019 and 2023 in the Quality of Life in European Cities survey.

City	There Is Corruption in My Local Public Administration.
	2019					2023
	1	2	3	4	99	1	2	3	4	99
Aalborg	61.6%	17.3%	13.3%	3.3%	4.5%	48.2%	19.1%	15.2%	9.4%	8.1%
Amsterdam	31.1%	13.6%	11.4%	16.7%	27.2%	30.6%	11.6%	18.1%	15.5%	24.1%
Ankara	24.6%	24.6%	20.4%	21.8%	8.6%	34.0%	19.8%	18.7%	17.2%	10.3%
Antalya	31.4%	20.4%	15.1%	18.6%	14.5%	32.0%	20.1%	14.2%	16.8%	17.0%
Antwerpen	30.5%	16.3%	18.5%	7.9%	26.7%	23.4%	18.5%	24.8%	10.3%	22.9%
Athina	6.5%	10.9%	22.4%	36.0%	24.2%	6.7%	12.8%	25.0%	33.1%	22.4%
Barcelona	21.4%	26.7%	21.6%	21.8%	8.4%	18.8%	22.0%	24.4%	22.6%	12.2%
Belfast	21.6%	20.3%	21.4%	15.7%	21.1%	16.8%	20.5%	23.8%	14.7%	24.3%
Belgrade	4.6%	4.2%	26.9%	48.0%	16.3%	4.8%	6.0%	20.6%	54.7%	13.9%
Berlin	6.8%	22.4%	31.0%	6.1%	33.6%	9.2%	20.3%	28.3%	10.1%	32.1%
Białystok	7.9%	36.4%	20.5%	9.7%	25.6%	11.4%	31.3%	21.5%	8.7%	27.1%
Bologna	11.1%	37.4%	26.7%	11.9%	13.0%	9.6%	32.3%	24.0%	16.7%	17.4%
Bordeaux	22.9%	25.5%	13.5%	13.0%	25.2%	21.9%	26.7%	14.8%	12.8%	23.8%
Braga	2.1%	26.0%	45.1%	8.3%	18.4%	4.1%	16.9%	47.7%	11.5%	19.8%
Bratislava	4.4%	18.7%	23.8%	26.0%	27.1%	5.8%	19.9%	24.1%	21.9%	28.3%
Bruxelles	27.2%	22.6%	14.6%	13.8%	21.8%	21.9%	24.2%	14.8%	14.4%	24.6%
Bucharest	3.7%	8.2%	22.3%	41.6%	24.3%	5.3%	8.2%	24.8%	39.1%	22.6%
Budapest	22.1%	8.3%	19.7%	17.7%	32.2%	21.2%	10.7%	19.2%	15.9%	33.0%
Burgas	10.5%	20.0%	18.8%	34.7%	16.0%	9.8%	19.1%	21.8%	33.9%	15.3%
Cardiff	27.3%	30.1%	16.0%	3.6%	23.1%	23.1%	25.6%	18.9%	7.1%	25.3%
Cluj-Napoca	7.2%	5.9%	38.6%	19.4%	28.9%	9.0%	7.8%	33.7%	21.1%	28.5%
Copenhagen	59.1%	19.7%	11.6%	5.1%	4.4%	51.7%	22.8%	10.9%	7.3%	7.3%
Diyarbakir	19.0%	23.8%	34.0%	16.7%	6.5%	18.0%	17.9%	22.0%	30.1%	12.0%
Dortmund	10.9%	23.2%	31.9%	7.7%	26.3%	12.5%	25.6%	31.4%	7.5%	23.0%
Dublin	25.1%	23.2%	19.2%	14.6%	17.8%	20.5%	22.4%	23.4%	16.3%	17.4%
Essen	13.3%	24.9%	19.0%	4.6%	38.2%	14.7%	23.5%	20.0%	6.0%	35.7%
Gdańsk	12.6%	25.3%	23.9%	11.6%	26.6%	11.8%	25.6%	23.1%	11.7%	27.7%
Genève	14.7%	23.7%	21.8%	16.6%	23.2%	16.3%	25.4%	20.9%	16.5%	20.8%
Glasgow	29.0%	20.2%	18.4%	14.6%	17.8%	21.3%	19.0%	23.4%	12.7%	23.5%
Graz	27.9%	31.2%	15.6%	6.9%	18.3%	22.8%	28.3%	20.0%	10.2%	18.7%
Groningen	43.1%	16.8%	9.5%	7.2%	23.3%	37.3%	17.5%	10.5%	9.0%	25.6%
Hamburg	14.5%	27.3%	16.8%	8.3%	33.1%	11.8%	28.4%	19.7%	10.6%	29.6%
Helsinki	33.8%	26.0%	21.0%	13.1%	6.1%	27.9%	25.5%	22.5%	13.8%	10.2%
Heraklion	18.8%	12.7%	20.9%	41.9%	5.8%	16.6%	12.3%	22.0%	41.4%	7.8%
Istanbul	18.8%	13.6%	20.5%	35.8%	11.3%	26.0%	15.5%	20.7%	26.1%	11.7%
Košice	8.6%	14.2%	27.7%	20.4%	29.1%	10.5%	14.7%	28.3%	18.6%	27.9%
Kraków	13.1%	23.8%	21.4%	8.5%	33.3%	14.2%	21.0%	22.6%	10.5%	31.8%
Lefkosia	16.7%	19.4%	26.2%	33.1%	4.5%	15.8%	20.2%	26.4%	33.5%	4.1%
Leipzig	9.6%	28.2%	13.3%	7.0%	41.9%	10.2%	29.0%	15.2%	6.4%	39.2%
Liège	25.1%	14.5%	13.1%	18.1%	29.2%	13.8%	21.7%	16.1%	17.0%	31.3%
Lille	23.6%	19.6%	15.4%	10.1%	31.3%	21.6%	20.9%	14.8%	12.7%	29.9%
Lisboa	3.2%	20.6%	46.3%	11.0%	19.0%	5.1%	19.3%	39.1%	15.9%	20.7%
Ljubljana	8.3%	15.2%	33.9%	22.4%	20.2%	7.4%	13.5%	36.1%	23.9%	19.0%
London	31.7%	20.3%	9.4%	16.5%	22.2%	18.8%	20.6%	19.5%	14.9%	26.3%
Luxembourg	33.8%	27.3%	14.2%	16.8%	7.8%	30.8%	28.3%	16.0%	16.5%	8.5%
Madrid	24.6%	16.6%	14.3%	25.5%	19.0%	19.3%	18.7%	19.5%	24.3%	18.3%
Málaga	21.5%	19.2%	21.8%	19.9%	17.6%	19.3%	18.5%	25.0%	19.6%	17.5%
Malmö	41.7%	25.5%	17.2%	8.0%	7.6%	32.8%	24.5%	18.9%	9.6%	14.2%
Manchester	33.3%	20.4%	16.7%	22.1%	7.6%	22.3%	21.2%	20.4%	14.6%	21.4%
Marseille	10.4%	14.3%	23.8%	21.9%	29.5%	9.2%	16.1%	24.4%	24.1%	26.2%
Miskolc	17.5%	9.6%	22.5%	21.4%	29.0%	17.3%	12.7%	22.5%	19.2%	28.3%
Munich	25.1%	23.7%	15.7%	3.4%	32.1%	18.8%	28.2%	15.7%	8.1%	29.3%
Naples	8.2%	16.8%	31.8%	28.9%	14.2%	8.8%	15.1%	33.1%	28.4%	14.5%
Oslo	32.6%	22.2%	18.2%	9.6%	17.4%	28.9%	20.3%	19.6%	11.2%	20.0%
Ostrava	10.4%	14.4%	25.2%	28.2%	21.8%	11.0%	14.1%	23.6%	25.9%	25.5%
Oulu	34.4%	26.1%	17.1%	11.5%	10.9%	28.6%	23.6%	19.5%	13.5%	14.8%
Oviedo	19.6%	18.3%	19.9%	27.3%	14.9%	17.3%	18.6%	23.8%	26.4%	13.9%
Palermo	6.2%	14.3%	41.6%	30.4%	7.5%	8.1%	11.7%	43.2%	28.1%	8.8%
Paris	26.1%	18.4%	18.6%	11.9%	25.0%	22.9%	19.9%	17.8%	11.1%	28.2%
Piatra Neamt	6.6%	11.3%	27.9%	28.3%	25.9%	7.6%	12.6%	29.1%	27.2%	23.5%
Podgorica	6.1%	8.5%	23.0%	44.0%	18.4%	6.8%	7.7%	23.3%	43.4%	18.8%
Praha	6.5%	17.6%	21.6%	25.5%	28.8%	3.7%	19.2%	24.6%	23.4%	29.1%
Rennes	27.5%	34.9%	13.1%	3.3%	21.2%	24.7%	34.1%	13.1%	5.4%	22.7%
Reykjavík	13.3%	22.0%	30.6%	26.4%	7.7%	14.0%	20.8%	32.9%	24.7%	7.5%
Riga	5.6%	15.0%	22.8%	45.3%	11.4%	6.2%	15.2%	26.6%	38.0%	14.0%
Rome	4.5%	11.1%	39.9%	37.3%	7.2%	7.8%	10.1%	39.3%	34.9%	7.9%
Rostock	14.5%	30.4%	18.0%	5.3%	31.7%	16.3%	29.7%	17.2%	6.4%	30.4%
Rotterdam	26.2%	15.8%	7.0%	15.4%	35.7%	24.0%	19.6%	11.3%	13.1%	32.0%
Skopje	5.9%	4.8%	20.5%	55.9%	12.9%	7.8%	6.2%	17.2%	58.7%	10.0%
Sofia	8.0%	9.5%	23.3%	27.0%	32.2%	5.8%	11.7%	25.2%	30.5%	26.8%
Stockholm	33.2%	20.7%	16.3%	6.2%	23.7%	27.1%	19.1%	17.1%	8.0%	28.8%
Strasbourg	24.3%	24.7%	14.9%	10.3%	25.9%	25.2%	26.4%	16.6%	8.6%	23.2%
Tallinn	4.4%	23.8%	33.2%	23.1%	15.5%	5.2%	17.4%	35.2%	23.8%	18.4%
Tirana	7.2%	14.8%	16.4%	59.2%	2.4%	NA	NA	NA	NA	NA
Turin	6.4%	29.9%	36.9%	12.3%	14.5%	9.7%	25.5%	33.1%	13.7%	18.0%
Tyneside conurbation	21.8%	27.0%	16.3%	10.7%	24.2%	17.9%	22.6%	19.3%	11.6%	28.6%
Valletta	27.1%	21.8%	13.6%	4.9%	32.6%	26.7%	20.4%	13.9%	7.7%	31.2%
Verona	3.8%	24.4%	48.5%	11.1%	12.3%	6.7%	23.6%	38.8%	14.1%	16.8%
Vilnius	8.4%	27.5%	26.4%	15.9%	21.8%	8.9%	26.6%	28.6%	16.4%	19.4%
Warszawa	7.9%	28.4%	21.5%	11.6%	30.6%	10.9%	26.1%	20.9%	14.0%	28.1%
Wien	21.7%	39.9%	17.7%	4.9%	15.9%	16.7%	32.4%	22.7%	10.9%	17.3%
Zagreb	2.9%	6.3%	29.1%	46.2%	15.4%	3.4%	7.3%	29.9%	43.8%	15.6%
Zurich	33.2%	34.1%	14.6%	2.6%	15.7%	31.2%	36.3%	13.5%	3.6%	15.5%
Min	2.1%	4.2%	7.0%	2.6%	2.4%	3.4%	6.0%	10.5%	3.6%	4.1%
Max	61.6%	39.9%	48.5%	59.2%	41.9%	51.7%	36.3%	47.7%	58.7%	39.2%
Mean	18.5%	20.4%	22.0%	18.9%	20.2%	17.1%	20.1%	22.9%	18.8%	21.1%
Sd	12.4%	7.6%	8.5%	12.9%	9.2%	9.9%	6.7%	7.4%	11.3%	7.8%

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused. NA: Not available Source: [15].

).

In response to the question regarding satisfaction with the amount of time it takes to get a request solved by local public administration (Q1), opinions remained relatively stable between 2019 and 2023, though some shifts across satisfaction levels were observed (Figure 1). On average, the share of very unsatisfied respondents (Strongly Disagree) slightly increased from 18.4% in 2019 to 18.6% in 2023, while those who were rather unsatisfied (Somewhat Disagree) reported a small increase from 21.7% to 22.6%. At the same time, the share of rather satisfied respondents (Somewhat Agree) declined marginally from 31.9% to 31.5%, and the very satisfied group (Strongly Agree) also saw a slight drop from 18.2% to 17.4%. In both 2019 and 2023, 9.9% of respondents selected “Don’t know/No answer/Refuses”.

In 2019 (see Table A1), city-level variation showed that the percentage of very unsatisfied respondents ranged from 1.4% in Genève to 51.4% in Rome, while for the rather unsatisfied, values extended from 9.9% in Tirana to 38.4% in Berlin. The share of rather satisfied respondents varied from 10.9% in Palermo to 51.8% in Rostock, and for the very satisfied, from 1.3% in Rome to 36.4% in Luxembourg. Responses in the “Don’t know/No answer/Refused” category ranged between 0.7% in Antalya and Athina and 26.1% in Groningen. By 2023 (see Table A1), the pattern had slightly shifted: the share of very unsatisfied respondents ranged from 2.8% in Zurich to 52.2% in Skopje, while the rather unsatisfied group ranged from 9.5% in Ankara to 37.9% in Turin. Among those rather satisfied, the range was between 9.4% in Palermo and 48.8% in Rostock, while very satisfied responses varied from 3.2% in Palermo to 35.9% in Antalya. The “Don’t know/No answer/Refused” category ranged from 1.4% in Lefkosia to 26.1% in Groningen.

On average, citizen satisfaction from time to resolve requests by local administration remained mostly unchanged between 2019 and 2023. Slight decreases in both “very unsatisfied” and “very satisfied” responses indicate a subtle polarization. However, the data also reveal substantial variation between cities, suggesting that local conditions continue to play a significant role in shaping public opinion.

Regarding perceptions of how clear and understandable local administrative procedures are (Q2), public opinion remained relatively stable between 2019 and 2023, with only minor changes observed in satisfaction levels (Figure 2). On average, the share of very unsatisfied respondents stayed unchanged at 15.3%, while those who were rather unsatisfied increased slightly from 23.7% to 24.5%. At the same time, the proportion of rather satisfied respondents rose marginally from 33.8% to 34.2%, whereas the very satisfied group saw a small decline from 21.0% to 19.5%. The share of respondents in the “Don’t know/No answer/Refused” category remained low, with a slight increase from 6.2% to 6.5%. City-level variation was also evident: in 2019 (see Table A2), the share of very unsatisfied respondents ranged from 2.7% (Zurich) to 39.7% (Belgrade), while rather unsatisfied responses spanned 3.9% (Antalya) to 41.2% (Turin); for the rather satisfied, the range was 18.0% (Heraklion) to 58.9% (Braga), and for the very satisfied, 2.8% (Turin) to 43.8% (Antalya), the “Don’t know” responses ranged from 0.0% (Bruxelles) to 20.7% (Oslo). By 2023 (see Table A2), the very unsatisfied group ranged from 3.7% (Zurich) to 34.9% (Belgrade), the rather unsatisfied from 7.2% (Antalya) to 44.3% (Palermo); the rather satisfied from 17.2% (Palermo) to 54.0% (Braga), and very satisfied from 3.9% (Verona) to 47.2% (Antalya), while “Don’t know” responses ranged from 0.5% (Bruxelles) to 17.7% (Tallinn).

Overall, the data show only slight changes in average satisfaction between 2019 and 2023, with perceptions of administrative procedures remaining generally stable. However, a small increase in ‘rather unsatisfied’ responses and a decrease in “very satisfied“ ones may indicate mild frustration with clarity or bureaucracy. Notably, considerable differences between cities highlight the importance of local context in shaping public perceptions.

When evaluating the fairness of fees imposed by local public administrations (Q3), citizen opinion between 2019 and 2023 showed general stability, with only modest variations across satisfaction levels (Figure 3). On average, the share of very unsatisfied respondents increased slightly from 16.8% in 2019 to 17.3% in 2023, while those who were rather unsatisfied also rose modestly from 21.3% to 22.7%. Meanwhile, the proportion of rather satisfied respondents decreased slightly from 37.0% to 36.1%, and the share of those very satisfied declined from 18.4% to 17.2%. The “Don’t know/No answer/Refused” group remained relatively stable, increasing only marginally from 6.5% to 6.8%. City data showed a wide range of variation: in 2019 (see Table A3), the very unsatisfied category ranged from 1.2% (Luxembourg) to 51.0% (Heraklion), rather unsatisfied from 5.1% (Istanbul) to 40.1% (Palermo), rather satisfied from 16.6% (Athina) to 60.0% (Białystok), and very satisfied from 2.0% (Turin) to 38.7% (Cluj-Napoca); the “Don’t know” responses varied between 0.9% (Barcelona) and 22.2% (Tallinn). In 2023 (see Table A3), the very unsatisfied share ranged from 2.3% (Luxembourg) to 47.8% (Heraklion), rather unsatisfied from 7.7% (Istanbul) to 44.6% (Palermo), rather satisfied from 16.0% (Palermo) to 54.1% (Białystok), very satisfied from 3.7% (Naples) to 37.7% (Cluj-Napoca), and “Don’t know” from 1.3% (Antwerpen) to 20.5% (Tallinn). In summary, while average responses remained broadly consistent, the data reveal continued and significant differences in perception across cities, underlining the importance of local conditions in shaping views on the fairness of administrative fees. On average, opinions on local administrative fees were also steady. Slight increases in dissatisfaction and slight drops in satisfaction point to minimal overall change, though the range of responses by city remained wide, indicating local disparities.

When it comes to accessing local public services and information online, on average, citizen perceptions between 2019 and 2023 remained broadly stable (Figure 4). The average share of respondents who were very satisfied slightly declined from 32.7% to 29.5%, while those who were rather satisfied increased from 35.8% to 38.1%. At the same time, dissatisfaction rose slightly, with very unsatisfied responses increasing from 7.7% to 8.6% and rather unsatisfied from 13.5% to 14.8%. The share of respondents unsure or unwilling to answer decreased modestly from 10.3% to 9.0%. In terms of variation across cities, in 2019 (see Table A4), the share of very unsatisfied respondents ranged from 0.5% (Zurich) to 23.3% (Diyarbakir), while for the rather unsatisfied, the range was 5.7% (Burgas) to 29.2% (Palermo). For the rather satisfied category, responses varied from 21.0% (Marseille) to 58.5% (Braga), and for the very satisfied, from 6.3% (Rome) to 55.4% (Aalborg). The “Don’t know/No answer/Refused” category ranged from 1.5% (Luxembourg) to 26.6% (Piatra Neamt). By 2023 (see Table A4), the share of very unsatisfied respondents ranged from 0.7% (Zurich) to 20.8% (Diyarbakir), rather unsatisfied from 7.8% (Antalya) to 31.2% (Palermo), rather satisfied from 23.3% (Sofia) to 59.5% (Braga), and very satisfied from 7.5% (Lisboa) to 49.1% (Ankara). Uncertainty responses varied between 2.3% (Luxembourg) and 25.5% (Piatra Neamt). Overall, on average, results suggest a relatively steady view of online accessibility, though with a small uptick in both satisfaction and dissatisfaction at the margins, indicating a slight polarization in citizen opinion.

In response to the statement “There is corruption in my local public administration” (Q5), it is important to note that higher agreement indicates a more negative perception, suggesting greater concern about corruption, while disagreement implies a more positive assessment, i.e., a belief that corruption is not present. Between 2019 and 2023, citizen opinion on this issue remained fairly steady, with only minor shifts in the average responses (Figure 5). On average, the share of respondents who strongly disagreed (meaning they firmly believe there is no corruption) declined slightly from 18.5% in 2019 to 17.1% in 2023, and those who somewhat disagreed remained nearly unchanged, moving from 20.4% to 20.1%. On the other hand, somewhat agreeing (i.e., suggesting some perception of corruption) rose slightly from 22.0% to 22.9%, while strong agreement (a strong belief that corruption exists) remained almost stable, from 18.9% to 18.8%. The share of respondents in the “Don’t know/No answer/Refused” category increased slightly from 20.2% to 21.1%, indicating a slight rise in uncertainty or reluctance to answer.

In 2019 (see Table A5), the percentage of strongly disagreeing respondents ranged from 2.1% (Braga) to 61.6% (Aalborg) and somewhat disagreeing from 4.2% (Belgrade) to 39.9% (Wien). Those who somewhat agreed ranged between 7.0% (Rotterdam) and 48.5% (Verona), while strongly agreed responses ranged from 2.6% (Zurich) to 59.2% (Tirana). The “Don’t know” category ranged from 2.4% (Tirana) to 41.9% (Leipzig). By 2023 (see Table A5), these ranges had shifted slightly: strongly disagree responses varied from 3.4% (Zagreb) to 51.7% (Copenhagen), somewhat disagree from 6.0% (Belgrade) to 36.3% (Zurich), somewhat agree from 10.5% (Groningen) to 47.7% (Braga), and strongly agree from 3.6% (Zurich) to 58.7% (Skopje). The “Don’t know” category ranged from 4.1% (Lefkosia) to 39.2% (Leipzig). Overall, the data suggest that perceptions of corruption on average in local public administrations have remained relatively stable over the years, with a slight increase in the proportion of respondents perceiving some level of corruption. However, the wide variation across cities highlights differing local experiences and trust levels in public institutions.

Between 2019 and 2023, average public opinion across all five dimensions of local public administration remained largely stable, with only modest shifts in response distributions, mainly slight increases in dissatisfaction and uncertainty. For each question, city-level variation remained significant, suggesting that local context plays a crucial role in shaping public perceptions. In particular, both satisfaction and dissatisfaction levels varied widely between cities in each wave, highlighting persistent disparities in citizen experience. While no major trends changed, subtle signs of growing frustration, especially around clarity and perceived corruption, point to uneven experiences and the continued relevance of local governance quality for citizen trust.

2.2. Methods

To assess respondents’ evaluations of individual survey items, we propose a novel method—the Belief Structure and TOPSIS Approach for Individual Survey Item Ranking (BS-TOSIE)—which integrates the BS model with the TOPSIS technique. This approach enables the structured aggregation and ranking of ordinal survey responses, while also accounting for ambiguity and non-responses.

The BS model was developed in papers [36,37,38]. Belief structure-based TOPSIS (B-TOPSIS) enhances traditional TOPSIS by integrating BS to better handle uncertainty in group decision-making [39]. Burns and Roszkowska [40] explored this method through the lens of Sociological Game Theory, linking belief components to decision-making behavior. It was further developed using Fuzzy Belief Structures (FBS) to represent imprecise expert evaluations with fuzzy numbers [41,42]. Belief structure-based TOPSIS has been applied in diverse areas, such as failure risk analysis [42], urban transport delay assessment [43], and hybrid efficiency evaluation methods [44], quality of life [17], proving its adaptability across complex Multi-Criteria Decision Making (MCDM) contexts.

The proposed BS-TOSIE approach consists of several systematic steps that guide the evaluation and ranking process.

Step 1: Data Collection.

Responses to individual survey items are collected and categorized according to an ordinal scale, while non-responses are also recorded as a distinct category. We assume a set of objects $O=\{O_1,O_2,\dots O_m\}$ is assessed by respondents using an ordinal evaluation scale with $N$ categories $H_1,H_2,\dots H_N$, where $H_{\mathrm{k}}$ is more preferred than $H_{\mathrm{k}+1}$. An additional category captures “Don’t Know/No Answer/Refused” responses. In our analysis, the objects are cities, which are evaluated by their residents based on specific questionnaire items using this scale.

Step 2: Construct the Belief Structure Models.

Respondents’ evaluations are encoded into belief distributions for each survey item, capturing the collective perception and associated uncertainty. Each object $O_i$ is represented using a Belief Structure model [17,39]:

$$BS\left(O_i\right)=\left\{\left(H_k; {\beta }_{ik}\right); k=1,\dots ,N\right\}$$

(1)

This model can be written as a vector:

$$S_i=\left[{\beta }_{i1},{\beta }_{i2},\dots {\beta }_{iN}\right].$$

(2)

where ${\beta }_{ik}={\displaystyle \frac{n_{ik}}{n_i}}$ is the belief degree—i.e., the proportion of respondents who selected evaluation grade $H_k$, $n_i$ is the number of respondents who evaluated $i–$th object. The degree of ignorance ${\beta }_i=1-{\sum }_{k=1}^N{\beta }_{ik}$ captures the share of non-responses for $i–$th object $(i=\mathrm{1,2},\dots m)$.

Step 3: Normalize the Belief Structure Models.

In cases where data are incomplete, normalization ensures the consistency of belief distributions. To account for uncertainty, the center of gravity method is used [17,39,41]. The Normalized Belief Structure Model is defined as:

$$BSC\left(O_i\right)=\left\{\left(H_k,{\beta }_{ik}+{\displaystyle \frac{{\beta }_i}{N}}\right):k=1,\dots ,N\right\}.$$

(3)

This guarantees that:

$${\sum }_{k=1}^N{(\beta }_{ik}+{\displaystyle \frac{{\beta }_i}{N}})={\sum }_{k=1}^N{ \beta }_{ik}+N{\displaystyle \frac{{\beta }_i}{N}}=1.$$

(4)

Consequently, the normalized vector can be written as:

$${\overline{S}}_i=\left[{\beta }_{i1}+{\displaystyle \frac{{\beta }_i}{N}},{\beta }_{i2}+{\displaystyle \frac{{\beta }_i}{N}},\dots ,{\beta }_{iN}+{\displaystyle \frac{{\beta }_i}{N}}\right].$$

(5)

Step 4. Determine the Utility Function and Similarity Matrix.

The assessment grades are then translated into their respective utility values and calculate the similarity between them to measure their closeness. Each evaluation grade $H_k$ is assigned a utility score $U\left(H_i\right)\in \left[\mathrm{0,1}\right]$ with $U\left(H_1\right)=1$, for the most preferred grade and $U\left(H_N\right)=0$, for the least. The scores are strictly decreasing:

$$U\left(H_{k+1}\right)<U\left(H_k\right) \mathrm{for} k=\mathrm{1,2},\dots ,N-1.$$

(6)

This utility function quantifies the satisfaction level associated with each evaluation grade, providing a numerical representation of how desirable or acceptable a given grade is from the decision-maker’s perspective. The assignment of utility values can follow a symmetric or asymmetric pattern, depending on the decision-maker’s preferences and the specific nature of the decision problem. In some situations, equal differences between grades may be assumed (symmetric), whereas in others, certain grades may be considered more significant than others (asymmetric). The utility function should be chosen to reflect these considerations accordingly.

The similarity between grades $H_i$ and $H_j$ is calculated as [39]:

$${\tilde{s}}_{ij}\left(H_i,H_j\right)=1-\left|U\left(H_i\right)-U\left(H_j\right)\right|.$$

(7)

These values form the similarity matrix [39]:

$$\tilde{S}=\left[{\tilde{s}}_{ij}\right],$$

(8)

capturing the degree of closeness between evaluation categories.

Step 5: Identify Ideal and Negative Ideal Belief Solutions.

The Positive Ideal Belief Solution (PIBS) represents the best possible evaluation outcome:

$$A^{+}=\left[1,\dots ,0\right]$$

(9)

meaning that 100% of respondents assign the highest evaluation grade $H_1$ to the object, indicating unanimous top preference.

Conversely, the Negative Ideal Belief Solution (NIBS) represents the worst-case scenario:

$$A^{-}=\left[0,\dots ,1\right].$$

(10)

where 100% of respondents assign the lowest evaluation grade $H_{\mathrm{N}}$, indicating unanimous bottom preference.

In summary, the vectors $A^{+}$ and $A^{-}$ correspond to belief structures where the entire belief mass is assigned to the highest and lowest evaluation grades, respectively, thus clearly representing the best and worst possible evaluation outcomes.

Step 6: Compute Separation Measures.

To assess how close each object is to the ideal and worst cases, we compute separation measures using a belief-based distance metric [39], where the comparison between two BS models is transformed into the distance measure between two vectors.

The distance from PIBS for object $O_{\mathrm{i}}$ is calculated as:

$$D_i^{+}=d_{BS}\left( {\overline{S}}_i,A^{+}\right)={\left({\displaystyle \frac{1}{2}}\left( {\overline{S}}_i-A^{+}\right)\tilde{S}{\left( {\overline{S}}_i-A^{+}\right)}^T\right)}^{{\displaystyle \frac{1}{2}}}.$$

(11)

Similarly, the distance from NIBS for the object $O_{\mathrm{i}}$ is calculated as:

$$D_i^{-}=d_{BS}\left( {\overline{S}}_i,A^{-}\right)={\left({\displaystyle \frac{1}{2}}\left( {\overline{S}}_i-A^{-}\right)\tilde{S}{\left( {\overline{S}}_i-A^{-}\right)}^T\right)}^{{\displaystyle \frac{1}{2}}}.$$

(12)

Step 7: Calculate Relative Closeness.

The relative closeness of the object $O_i$ to the ideal solution is given by:

$$T_i={\displaystyle \frac{D_i^{-}}{D_i^{-}+D_i^{+}}}$$

(13)

where $D_i^{-}$− represents the separation measure of $O_i$ from the NIBS, and $D_i^{+}$ represents the separation measure of $O_i$ from the PIBS, with $i=\mathrm{1,2},\dots ,m$.

A higher $T_i$ indicates that object $O_i$ is closer to the ideal solution and thus represents a more favorable outcome.

Step 8: Rank the objects.

Based on the values of $T_i$, we rank the object from most to least favorable. The object with the highest relative closeness $T_i$ is considered the best-performing in terms of perceived satisfaction among respondents.

The diagram below (Figure 6) illustrates the key steps of the BS-TOSIE method, outlining the entire process from data encoding to final ranking.

3. Results

In this section, we apply the BS-TOSIE method to rank cities based on residents’ satisfaction with local public administration. Drawing on survey data from the 2019 and 2023 editions of the Quality of Life in European Cities survey [15], we examine five key dimensions of satisfaction: Q1—time taken to resolve requests; Q2—clarity of procedures; Q3—reasonableness of fees; Q4—accessibility of online services; and Q5—perception of corruption. The five indicators TQ1–TQ5, derived through the BS-TOSIE approach for each item calculated by Equation (13), enable us to track and compare public opinion on local administration across cities and over time.

The BS-TOSIE method is implemented through a step-by-step procedure described in Section 2.1., as outlined below:

Step 1: Data Collection.

Responses to individual items (Q1–Q5) are categorized according to an ordinal satisfaction scale. For questions Q1–Q4, the scale ranges from very satisfied ($H_1$) to very unsatisfied ($H_4$), corresponding to values 4 to 1 from the ordinal scale. For question Q5, the scale is reversed (from 1 to 4). Additionally, non-responses—including “Don’t know,” “No answer,” and “Refused”—are recorded as a separate category 99 and correspond to a degree of ignorance. The analysis includes 83 cities as evaluation objects. Due to the lack of data in 2023 regarding responses to question Q5, the TQ5 indicator for Tirana were omitted.

Step 2: Construct the Belief Structure Models.

Respondents’ evaluations are transformed into belief distributions for each city, item, and year (see Table A1). For example, for Aalborg in 2019 (Q1), the belief structure is: BS = {(very satisfied, 0.247), (rather satisfied, 0.423), (rather unsatisfied, 0.111), (very unsatisfied, 0.053)}, with the degree of ignorance 1 − (0.247 + 0.423 + 0.111 + 0.053) = 0.166. This is represented as a belief vector: S1 = [0.247, 0.423, 0.111, 0.053]. Similarly, for Aalborg in 2019 and question Q5 (see Table A5) the vector is S2 = [0.616, 0.173, 0.133, 0.033], with degree of ignorance 0.045.

Step 3: Normalize the Belief Structure Models.

All belief vectors obtained in Step 2 are normalized using Formula (5). For example, the normalized vector for Aalborg (Q1, 2019) is: $\overline{S}1$ = [0.289, 0.464, 0.153, 0.095]. Consequently, the normalized vector for Aalborg (Q5, 2019) becomes $\overline{S}2$ = [0.627, 0.184, 0.144, 0.044].

Step 4: Determine the Utility Function and Similarity Matrix.

Each satisfaction level is assigned a utility value (Formula (6)). We adopt the utility function [17,39] as follows: $U\left(H_1\right)=1, U\left(H_2\right)=0.7, U\left(H_3\right)=0.4,U\left(H_4\right)=0.$ These value, by using Formula (7), form the basis for constructing the similarity matrix $\tilde{S}$, which quantifies the perceived closeness between satisfaction levels using the formula as follows:

$$\tilde{S}=\left[\begin{array}{cccc}1& 0.7& 0.4& 0\\ 0.7& 1& 0.7& 0.3\\ 0.4& 0.7& 1& 0.6\\ 0& 0.3& 0.6& 1\end{array}\right].$$

Step 5: Identify Ideal and Negative Ideal Belief Solutions.

The Positive Ideal Belief Solution (PIBS) represents full satisfaction:$A^{+}=\left[1,\dots ,0\right]$, while the Negative Ideal Belief Solution (NIBS) represents full dissatisfaction: $A^{-}$ = [0, 0, 0, 1]. Full satisfaction refers to a situation in which all respondents (100%) select the rating category corresponding to “very satisfied”, while full dissatisfaction occurs when all (100%) respondents choose the category “very dissatisfied”.

Step 6: Compute Separation Measures.

Distances from each city’s belief vector to PIBS and NIBS are computed using belief-based distance measures (Formulas (11) and (12)). For example, for Aalborg (Q1, 2019), the distance to PIBS is 0.417 and to NIBS is 0.723. For Aalborg (Q5, 2019), the respective distances are 0.231 and 0.825.

Step 7: Calculate Relative Closeness.

Relative closeness to the ideal solution is calculated using the standard TOPSIS closeness coefficient (Formula (13)). For Aalborg in 2019, the values are 0.634 for Q1 and 0.781 for Q5. The results for all cities, questionnaire items, and years are presented in

Table 1. Assessment of satisfaction with local administration in European cities using TQ1–TQ5, 2019.

City	BS-TOSIE Approach					Rank
	TQ1	TQ2	TQ3	TQ4	TQ5	Q1	Q2	Q3	Q4	Q5
Aalborg	0.634	0.588	0.590	0.770	0.781	4	33	26	1	1
Amsterdam	0.563	0.581	0.546	0.728	0.586	36	36	50	7	21
Ankara	0.521	0.598	0.537	0.673	0.538	59	27	57	29	38
Antalya	0.580	0.673	0.574	0.745	0.583	23	4	34	4	24
Antwerpen	0.574	0.665	0.571	0.550	0.614	29	5	36	80	15
Athina	0.441	0.477	0.332	0.565	0.377	75	74	82	78	75
Barcelona	0.491	0.627	0.441	0.639	0.527	64	13	74	51	43
Belfast	0.554	0.599	0.599	0.695	0.545	39	26	20	17	37
Belgrade	0.377	0.405	0.419	0.586	0.299	78	82	76	73	81
Berlin	0.468	0.460	0.549	0.583	0.519	69	76	48	75	47
Białystok	0.598	0.575	0.570	0.631	0.537	13	40	37	56	39
Bologna	0.525	0.529	0.515	0.630	0.535	56	53	65	58	40
Bordeaux	0.567	0.623	0.598	0.663	0.579	31	14	23	35	27
Braga	0.521	0.570	0.547	0.584	0.487	60	42	49	74	54
Bratislava	0.559	0.514	0.601	0.623	0.428	37	60	18	62	68
Bruxelles	0.585	0.693	0.569	0.672	0.589	19	1	39	30	20
Bucharest	0.468	0.506	0.576	0.596	0.336	70	66	30	70	78
Budapest	0.547	0.607	0.534	0.682	0.524	42	22	58	23	45
Burgas	0.552	0.598	0.505	0.739	0.417	41	28	68	6	71
Cardiff	0.606	0.617	0.591	0.692	0.635	7	15	25	18	8
Cluj-Napoca	0.580	0.590	0.662	0.650	0.437	24	32	2	43	65
Copenhagen	0.596	0.598	0.619	0.717	0.768	14	29	9	9	2
Diyarbakir	0.447	0.496	0.558	0.551	0.521	74	71	46	79	46
Dortmund	0.535	0.509	0.515	0.613	0.528	49	64	64	65	42
Dublin	0.577	0.611	0.599	0.712	0.571	26	19	21	11	31
Essen	0.594	0.496	0.564	0.650	0.566	16	70	43	44	32
Gdańsk	0.583	0.558	0.564	0.663	0.530	21	47	42	34	41
Genève	0.648	0.645	0.576	0.659	0.518	3	8	31	36	49
Glasgow	0.572	0.629	0.598	0.679	0.585	30	12	22	27	22
Graz	0.606	0.615	0.639	0.755	0.627	8	16	4	3	10
Groningen	0.634	0.641	0.561	0.761	0.687	5	10	45	2	3
Hamburg	0.598	0.535	0.566	0.636	0.561	12	51	40	52	33
Helsinki	0.522	0.512	0.583	0.682	0.615	58	61	29	24	14
Heraklion	0.364	0.488	0.328	0.657	0.400	80	73	83	38	73
Istanbul	0.492	0.555	0.458	0.647	0.432	63	48	72	46	67
Košice	0.580	0.578	0.608	0.671	0.459	22	38	13	31	60
Kraków	0.575	0.518	0.542	0.640	0.546	28	59	54	49	36
Lefkosia	0.531	0.679	0.510	0.712	0.442	51	2	67	10	63
Leipzig	0.591	0.563	0.543	0.610	0.552	18	45	51	66	34
Liège	0.599	0.678	0.618	0.647	0.552	11	3	10	45	35
Lille	0.531	0.591	0.542	0.628	0.584	52	31	53	60	23
Lisboa	0.460	0.507	0.500	0.574	0.473	71	65	70	76	57
Ljubljana	0.546	0.581	0.601	0.673	0.445	44	37	17	28	61
London	0.547	0.583	0.563	0.722	0.600	43	34	44	8	18
Luxembourg	0.657	0.648	0.679	0.704	0.611	2	6	1	16	17
Madrid	0.545	0.567	0.566	0.656	0.517	45	43	41	39	50
Málaga	0.529	0.575	0.542	0.640	0.525	53	41	52	50	44
Malmö	0.576	0.525	0.626	0.707	0.674	27	56	7	15	4
Manchester	0.602	0.646	0.624	0.689	0.573	10	7	8	19	30
Marseille	0.544	0.596	0.522	0.603	0.462	46	30	63	68	58
Miskolc	0.603	0.607	0.572	0.707	0.487	9	23	35	13	53
Munich	0.566	0.561	0.601	0.653	0.621	33	46	19	40	13
Naples	0.364	0.441	0.373	0.505	0.420	81	79	78	83	70
Oslo	0.479	0.474	0.541	0.632	0.623	66	75	55	55	12
Ostrava	0.527	0.539	0.606	0.707	0.432	54	49	16	14	66
Oulu	0.555	0.523	0.575	0.652	0.629	38	57	33	42	9
Oviedo	0.519	0.532	0.487	0.610	0.484	61	52	71	67	55
Palermo	0.311	0.414	0.369	0.515	0.398	82	81	80	81	74
Paris	0.536	0.600	0.538	0.681	0.581	48	24	56	25	26
Piatra Neamt	0.566	0.576	0.575	0.623	0.411	34	39	32	63	72
Podgorica	0.469	0.526	0.586	0.630	0.333	68	55	28	57	79
Praha	0.525	0.518	0.607	0.668	0.438	57	58	15	33	64
Rennes	0.577	0.609	0.608	0.684	0.644	25	20	14	22	6
Reykjavík	0.470	0.499	0.501	0.636	0.459	67	69	69	53	59
Riga	0.499	0.512	0.372	0.636	0.336	62	62	79	54	77
Rome	0.290	0.373	0.358	0.510	0.357	83	83	81	82	76
Rostock	0.595	0.538	0.587	0.625	0.575	15	50	27	61	29
Rotterdam	0.565	0.600	0.597	0.686	0.578	35	25	24	20	28
Skopje	0.370	0.504	0.530	0.600	0.267	79	67	60	69	83
Sofia	0.451	0.441	0.514	0.644	0.422	73	78	66	48	69
Stockholm	0.526	0.511	0.633	0.653	0.640	55	63	5	41	7
Strasbourg	0.594	0.607	0.551	0.628	0.593	17	21	47	59	19
Tallinn	0.567	0.564	0.614	0.680	0.442	32	44	11	26	62
Tirana	0.454	0.526	0.447	0.622	0.275	72	54	73	64	82
Turin	0.427	0.458	0.432	0.573	0.498	76	77	75	77	51
Tyneside conurbation	0.584	0.612	0.569	0.671	0.582	20	18	38	32	25
Valletta	0.553	0.642	0.613	0.685	0.624	40	9	12	21	11
Verona	0.483	0.489	0.528	0.595	0.477	65	72	61	71	56
Vilnius	0.538	0.582	0.532	0.658	0.497	47	35	59	37	52
Warszawa	0.532	0.504	0.528	0.646	0.519	50	68	62	47	48
Wien	0.620	0.615	0.626	0.708	0.613	6	17	6	12	16
Zagreb	0.406	0.426	0.417	0.591	0.305	77	80	77	72	80
Zurich	0.660	0.641	0.648	0.741	0.667	1	11	3	5	5
min	0.290	0.373	0.328	0.505	0.267
max	0.660	0.693	0.679	0.770	0.781
mean	0.533	0.559	0.547	0.651	0.518
st dev	0.075	0.068	0.075	0.055	0.105

for the 2019 year and

Table 2. Assessment of satisfaction with local administration in European cities using TQ1–TQ5, 2023.

City	BS-TOSIE Approach					Rank
	TQ1	TQ2	TQ3	TQ4	TQ5	Q1	Q2	Q3	Q4	Q5
Aalborg	0.611	0.584	0.583	0.710	0.695	6	25	20	7	2
Amsterdam	0.553	0.569	0.554	0.688	0.577	33	35	38	11	21
Ankara	0.607	0.680	0.605	0.711	0.595	9	2	12	6	13
Antalya	0.632	0.692	0.613	0.728	0.594	4	1	8	2	14
Antwerpen	0.567	0.635	0.559	0.562	0.569	27	9	37	77	24
Athina	0.447	0.479	0.378	0.561	0.394	74	72	80	78	74
Barcelona	0.492	0.572	0.448	0.618	0.503	63	32	74	57	49
Belfast	0.527	0.569	0.574	0.656	0.528	49	36	25	25	42
Belgrade	0.399	0.420	0.419	0.573	0.271	78	81	77	73	81
Berlin	0.474	0.465	0.551	0.579	0.512	70	77	44	71	46
Białystok	0.589	0.557	0.568	0.634	0.547	16	43	29	43	33
Bologna	0.525	0.532	0.507	0.624	0.507	50	51	65	50	48
Bordeaux	0.549	0.606	0.587	0.639	0.575	36	14	18	40	22
Braga	0.493	0.557	0.536	0.570	0.468	62	44	52	74	57
Bratislava	0.556	0.524	0.582	0.618	0.454	31	55	21	56	61
Bruxelles	0.591	0.674	0.568	0.665	0.565	15	4	30	21	26
Bucharest	0.490	0.530	0.575	0.589	0.352	64	52	24	70	78
Budapest	0.545	0.591	0.564	0.662	0.532	39	23	33	23	39
Burgas	0.551	0.581	0.492	0.711	0.414	34	28	68	5	71
Cardiff	0.576	0.599	0.583	0.670	0.596	17	20	19	18	12
Cluj-Napoca	0.603	0.603	0.662	0.683	0.442	12	16	2	12	64
Copenhagen	0.570	0.582	0.619	0.688	0.730	22	26	5	10	1
Diyarbakir	0.469	0.518	0.545	0.574	0.462	71	58	47	72	60
Dortmund	0.520	0.513	0.514	0.606	0.538	56	59	61	65	37
Dublin	0.561	0.592	0.588	0.674	0.539	30	22	16	16	36
Essen	0.574	0.511	0.551	0.629	0.563	18	63	42	46	27
Gdańsk	0.594	0.569	0.562	0.650	0.528	14	37	34	30	41
Genève	0.645	0.653	0.591	0.648	0.528	2	7	15	34	40
Glasgow	0.519	0.580	0.549	0.641	0.553	57	29	45	37	32
Graz	0.604	0.599	0.612	0.712	0.584	11	19	9	4	17
Groningen	0.607	0.613	0.552	0.719	0.653	10	12	40	3	4
Hamburg	0.595	0.546	0.543	0.627	0.540	13	49	49	47	35
Helsinki	0.521	0.519	0.569	0.652	0.584	54	57	28	29	16
Heraklion	0.378	0.485	0.347	0.655	0.392	79	71	83	26	75
Istanbul	0.510	0.558	0.451	0.648	0.512	60	41	73	36	47
Košice	0.570	0.562	0.551	0.648	0.474	21	40	43	35	56
Kraków	0.551	0.512	0.524	0.624	0.537	35	60	56	51	38
Lefkosia	0.530	0.633	0.499	0.689	0.437	45	10	67	9	66
Leipzig	0.573	0.534	0.514	0.598	0.555	19	50	62	67	30
Liège	0.609	0.678	0.615	0.625	0.516	8	3	6	49	44
Lille	0.522	0.601	0.545	0.619	0.567	52	18	46	54	25
Lisboa	0.455	0.507	0.484	0.565	0.463	72	66	70	76	59
Ljubljana	0.535	0.565	0.574	0.640	0.431	41	39	26	39	67
London	0.512	0.548	0.533	0.641	0.540	59	47	54	38	34
Luxembourg	0.645	0.664	0.686	0.693	0.598	3	5	1	8	10
Madrid	0.528	0.530	0.540	0.612	0.498	48	53	51	63	52
Málaga	0.532	0.566	0.513	0.625	0.513	43	38	63	48	45
Malmö	0.555	0.555	0.614	0.665	0.626	32	45	7	20	5
Manchester	0.568	0.608	0.570	0.653	0.556	26	13	27	28	29
Marseille	0.523	0.586	0.519	0.612	0.450	51	24	59	61	62
Miskolc	0.564	0.601	0.541	0.662	0.500	29	17	50	22	51
Munich	0.570	0.557	0.566	0.648	0.582	23	42	31	33	18
Naples	0.362	0.438	0.384	0.520	0.421	80	80	79	81	70
Oslo	0.479	0.502	0.535	0.622	0.597	68	68	53	52	11
Ostrava	0.517	0.524	0.599	0.681	0.446	58	54	13	13	63
Oulu	0.549	0.511	0.562	0.634	0.590	37	62	35	44	15
Oviedo	0.486	0.547	0.459	0.594	0.476	67	48	71	69	55
Palermo	0.313	0.407	0.378	0.509	0.409	82	82	81	83	72
Paris	0.531	0.581	0.551	0.649	0.574	44	27	41	32	23
Piatra Neamt	0.573	0.574	0.581	0.615	0.421	20	31	22	59	69
Podgorica	0.418	0.471	0.523	0.609	0.336	76	74	57	64	79
Praha	0.528	0.501	0.587	0.637	0.438	46	69	17	41	65
Rennes	0.566	0.598	0.607	0.666	0.625	28	21	10	19	6
Reykjavík	0.487	0.519	0.509	0.618	0.465	66	56	64	55	58
Riga	0.488	0.470	0.397	0.615	0.372	65	76	78	58	77
Rome	0.304	0.404	0.369	0.519	0.377	83	83	82	82	76
Rostock	0.569	0.509	0.561	0.598	0.578	24	65	36	68	20
Rotterdam	0.546	0.575	0.575	0.650	0.578	38	30	23	31	19
Skopje	0.329	0.471	0.455	0.569	0.262	81	75	72	75	82
Sofia	0.477	0.474	0.544	0.630	0.400	69	73	48	45	73
Stockholm	0.521	0.496	0.607	0.621	0.605	53	70	11	53	8
Strasbourg	0.612	0.617	0.554	0.637	0.603	5	11	39	42	9
Tallinn	0.528	0.553	0.565	0.672	0.431	47	46	32	17	68
Tirana	0.455	0.512	0.486	0.612	NA	73	61	69	62	NA
Turin	0.437	0.453	0.426	0.559	0.502	75	78	76	79	50
Tyneside conurbation	0.540	0.571	0.531	0.653	0.553	40	34	55	27	31
Valletta	0.568	0.657	0.627	0.680	0.610	25	6	4	14	7
Verona	0.500	0.503	0.507	0.605	0.482	61	67	66	66	54
Vilnius	0.520	0.571	0.519	0.656	0.494	55	33	58	24	53
Warszawa	0.533	0.509	0.516	0.615	0.518	42	64	60	60	43
Wien	0.609	0.606	0.591	0.680	0.559	7	15	14	15	28
Zagreb	0.414	0.440	0.430	0.558	0.320	77	79	75	80	80
Zurich	0.654	0.638	0.646	0.732	0.659	1	8	3	1	3
min	0.304	0.404	0.347	0.509	0.262
max	0.654	0.692	0.686	0.732	0.730
mean	0.528	0.553	0.538	0.634	0.511
st dev	0.072	0.063	0.068	0.047	0.090

Note: NA—data not available.

for the 2023 year. Higher closeness values indicate greater satisfaction.

Step 8: Rank the objects.

Finally, cities are ranked according to their relative closeness scores across all five dimensions of local administration satisfaction. For each dimension (Q1–Q5), both the satisfaction levels (TQ1–TQ5), measured using the BS-TOSIE approach, and the rankings of European cities are presented for the years 2019 and 2023. These results are shown in Table 1 and Table 2, respectively.

Descriptive statistics for the values of TQ1–TQ5 for 2019 and 2023 are presented in Table 1 and Table 2 and box plots in Figure 7.

The analysis addresses the three research questions (RQ1–RQ3) presented in the introduction. It is conducted separately for each of the five dimensions of satisfaction with local administration in European cities, evaluated through questions Q1–Q5 using the corresponding measures TQ1–TQ5.

The satisfaction indicator TQ1 reflects how citizens perceive the efficiency of local administrations in resolving requests. In 2019 (Table 1), scores ranged from 0.290 in Rome to 0.660 in Zurich, with an average of 0.533 and a standard deviation of 0.075. Zurich (0.660), Luxembourg (0.657), Geneva (0.648), Aalborg (0.634), and Groningen (0.634) ranked highest, reflecting strong institutional capacity in Western and Northern Europe. In contrast, Rome (0.290), Palermo (0.311), Naples (0.364), Heraklion (0.364), and Skopje (0.370) showed the lowest satisfaction, highlighting persistent issues such as bureaucracy and delays. A clear North–South divide was evident, with higher satisfaction in Northern and Western cities.

By 2023 (Table 2), the average dropped slightly to 0.528 and a standard deviation of 0.072, indicating stable perceptions with a minor decline. Zurich (0.654), Genève (0.645), Luxembourg (0.645), and Antalya (0.632) remained top performers, while Rome (0.304), Palermo (0.313), Skopje (0.329), and Naples (0.362) stayed among the lowest. However, cities like Ankara and Antalya improved notably, suggesting successful reforms.

Overall, the average change of −0.005 reflects a minor drop in satisfaction. While top cities maintained their position, gains in parts of Turkey and Eastern Europe contrast with slight declines in some UK cities (e.g., Glasgow, Cardiff, Manchester) and Northern Europe (e.g., Copenhagen), possibly linked to rising expectations or service strain. Despite ongoing regional disparities, improvements in several cities suggest the potential for wider progress.

The satisfaction indicator TQ2 reflects citizens’ satisfaction with how straightforward and easy to understand the procedures used by local public administrations are. In 2019 (Table 1), TQ2 ranged from a low of Rome (0.373) to a high of Brussels (0.693), with average satisfaction at 0.559 and a standard deviation of 0.068. In 2019, satisfaction with the clarity of administrative procedures varied widely. Cities such as Brussels (0.693), Lefkosia (0.679), Liège (0.678), Antalya (0.673), and Antwerpen (0.665) reported the highest levels of agreement, suggesting well-designed and accessible administrative systems. In contrast, cities like Rome (0.373), Belgrade (0.405), Palermo (0.414), and Zagreb (0.426), recorded low satisfaction, indicating administrative complexity and limited citizen engagement.

By 2023 (Table 2), the average satisfaction with procedures decreased to 0.553, with a standard deviation of 0.063, indicating a slight drop in satisfaction with the clarity of local public administration procedures. By 2023, the overall landscape showed continuity with notable regional shifts. Antalya (0.692), Ankara (0.680), Liège (0.678), and Brussels (0.674) remained among the top performers. Ankara showed the most improvement of 0.082, whereas Podgorica experienced a decline of −0.055.

The satisfaction indicator TQ3 reflects citizens’ agreement with the reasonableness of the fees charged by their local public administration. In 2019 (Table 1), TQ3 ranged from a low of Heraklion (0.328) to a high of Luxembourg (0.679), with average satisfaction at 0.547 and a standard deviation of 0.075. Satisfaction varied considerably across cities. The highest scores were observed in Luxembourg (0.679), Cluj-Napoca (0.662), and Zurich (0.648) indicating widespread satisfaction with fee fairness in well-funded, efficient systems. Cities such as Graz (0.639), Stockholm (0.633), and Vien (0.626) also performed well. In contrast, cities like Heraklion (0.328), Athina (0.332), Rome (0.358), Palermo (0.369). Riga (0.372) and Naples (0.373) ranked low, suggesting concerns over cost transparency and fairness. Central and Eastern Europe showed mixed results: Cluj-Napoca (0.662) and Košice (0.608) performed strongly, while Belgrade (0.419), Zagreb (0.417), and Sofia (0.514) lagged.

By 2023 (Table 2), the average satisfaction decreased slightly to 0.538, with a standard deviation of 0.068, reflecting a slight increase in the perception of unreasonable fees in some cities. In 2023, cities such as Luxembourg (0.686), Cluj-Napoca (0.662), Zurich (0.646) Valletta (0.627) continued to rank highly. Notably, Ankara showed a significant improvement of 0.068. Elsewhere, changes were more modest. Aalborg saw a small decline from 0.590 to 0.583, and Bratislava dropped slightly from 0.601 to 0.582. Rome, Palermo, and Athina remained among the lowest-scoring cities, though Athina improved modestly from 0.332 to 0.378, while Skopjee had a decline of −0.075.

The average difference of −0.027 indicates a moderate decrease in satisfaction regarding the reasonableness of fees over the four years. While average satisfaction with administrative fees slightly declined between 2019 and 2023, Northern and Western European cities continued to perform well, benefiting from transparent governance. Southeastern Europe showed some positive movement, particularly in Turkish cities. Eastern Europe exhibited mixed dynamics, with leaders like Cluj-Napoca and Tallinn maintaining high scores, while others stagnated. The overall picture remains marked by persistent regional disparities, though gradual progress is evident in some areas.

The satisfaction indicator TQ4 reflects citizens’ satisfaction with the accessibility of information and services provided by local public administrations online. In 2019 (Table 1), TQ4 ranged from a low of Naples (0.505) to a high of Aalborg (0.770), with an average satisfaction score of 0.651 and a standard deviation of 0.055, suggesting a moderate variation in access to services online across European cities. Top performers in 2019 included Aalborg (0.770), Groningen (0.761), Graz (0.755), Antalya (0.745), and Zurich (0.741) indicating robust digital infrastructure and user-friendly platforms. Scandinavian cities, such as Copenhagen (0.717) and Malmö (0.707), also scored highly in terms of accessibility. In contrast, cities like Naples (0.505), Rome (0.510), and Palermo (0.515) lagged significantly, showing challenges in providing effective digital services. The overall average satisfaction remained relatively stable at 0.634 by 2023, with a standard deviation of 0.047. Top-performing cities in 2023 (Table 2) included Zurich (0.732), Antalya (0.728), Groningen (0.719), Graz (0.712), Ankara (0.711), Burgas (0.711), and Aalborg (0.710). Cities showing improvements from 2019 to 2023 included Ankara (from 0.673 to 0.711) and Cluj-Napoca (from 0.650 to 0.683). On the other hand, London (from 0.722 to 0.641) and Miskolc (from 0.707 to 0.662) experienced slight declines. The average difference of −0.017 indicates a moderate decline in satisfaction regarding online access to services from 2019 to 2023.

The satisfaction indicator TQ5 addresses the perception of corruption, where higher scores indicate a lower perceived level of corruption and greater satisfaction with local public administration. In 2019 (Table 1), TQ5 ranged from a low of Skopje (0.267) to a high of Aalborg (0.781), with an average satisfaction score of 0.518 and a standard deviation of 0.105. In 2019, cities such as Skopje (0.267), Tirana (0.265), Belgrade (0.299), and Zagreb (0.305), had the lowest scores, reflecting high levels of perceived corruption. In contrast, cities like Aalborg (0.781), Copenhagen (0.768), Groningen (0.687), Malmö (0.674), and Zurich (0.667) scored highest, indicating strong public trust and perceived integrity in administration. In 2023 (Table 2), the average satisfaction decreased slightly to 0.511, with a standard deviation of 0.090, indicating that the perception of corruption across the cities remained relatively unchanged, despite small fluctuations. In 2023, this pattern remained broadly consistent, with Copenhagen (0.730), Aalborg (0.695), and Zurich (0.659) continuing to lead, although their scores declined slightly—Copenhagen by 0.038 and Aalborg by 0.086—suggesting a marginal increase in perceived corruption. Istanbul saw high improvements in their scores of 0.08.

In summary, perceptions of corruption remained relatively stable across Europe. Northern European cities maintained leadership in public trust, while cities in Southern and Eastern Europe continued to struggle, albeit with modest improvements.

The overall analysis of changes between 2019 and 2023 reveals a stable, though regionally uneven, trajectory of satisfaction with local public administration across Europe. The Pearson correlation coefficients between the TQ measures for 2019 and 2023 are as follows: 0.958 for TQ1, 0.945 for TQ2, 0.950 for TQ3, 0.946 for TQ4, and 0.974 for TQ5. Northern and Western European cities continue to lead in most indicators, with Zurich, consistently ranking among the top performers, while Rome and Palermo are among the bottom. Several cities in Turkey and Eastern Europe have demonstrated notable progress, highlighting that reforms can lead to positive outcomes. In contrast, many cities in Southeastern Europe show persistent underperformance, particularly in transparency, procedural clarity, and digital services. Broader systemic challenges remain across Southern and Southeastern Europe, underscoring the need for sustained investment and reform.

4. Discussion

In this chapter, we compare the results obtained using the BS-TOSIE method, presented earlier in Section 3, with the methodology used in the Reports on the Quality of Life in European Cities [14,16] to assess satisfaction with local administration in each domain. The percentage of respondents with a positive opinion (i.e., “rather satisfied” and “very satisfied”) is aggregated, with the percentages calculated based on all respondents, excluding those who selected “don’t know” or did not answer. The measures denoted as PQ1 to PQ5, allow us to rank cities according to satisfaction levels for each item separately. The results of the PQ1–PQ5 measure calculations are presented in

Table 3. Evaluation of satisfaction from the local administration in European cities based on the PQ1–PQ5 for the 2019 year.

City	PQ Measure					Rank
	PQ1	PQ2	PQ3	PQ4	PQ5	Q1	Q2	Q3	Q4	Q5
Aalborg	80.3%	67.5%	69.8%	89.3%	82.6%	3	22	23	4	1
Amsterdam	59.8%	60.9%	55.9%	86.3%	61.4%	38	44	60	10	29
Ankara	57.9%	65.7%	56.9%	75.0%	53.8%	40	23	57	47	39
Antalya	64.8%	78.6%	65.0%	85.9%	60.6%	24	4	33	12	30
Antwerpen	68.2%	78.4%	62.5%	59.5%	63.9%	14	5	40	79	23
Athina	37.7%	41.1%	26.7%	59.9%	22.9%	72	75	80	78	74
Barcelona	45.2%	64.0%	41.2%	68.9%	52.5%	67	32	75	68	41
Belfast	61.9%	67.6%	64.3%	81.6%	53.0%	31	21	35	26	40
Belgrade	34.4%	36.8%	42.0%	68.0%	10.5%	76	78	74	70	83
Berlin	36.0%	34.4%	67.2%	67.3%	44.1%	74	81	27	71	52
Białystok	70.4%	63.7%	69.9%	85.7%	59.5%	11	33	22	13	32
Bologna	54.0%	52.5%	50.7%	79.5%	55.7%	51	54	67	34	37
Bordeaux	60.1%	62.2%	65.0%	76.9%	64.6%	37	39	32	42	21
Braga	51.5%	68.5%	58.3%	73.0%	34.5%	59	17	52	57	60
Bratislava	60.5%	49.9%	71.2%	72.9%	31.7%	35	63	14	59	66
Bruxelles	63.3%	78.9%	64.3%	73.8%	63.7%	27	1	34	52	24
Bucharest	50.0%	50.1%	66.5%	66.4%	15.6%	64	61	29	73	80
Budapest	53.7%	62.8%	58.2%	83.8%	44.9%	53	38	53	20	50
Burgas	54.1%	59.2%	50.2%	85.0%	36.3%	50	47	68	16	58
Cardiff	70.6%	70.8%	68.4%	84.4%	74.5%	10	14	24	18	6
Cluj-Napoca	69.2%	65.6%	73.6%	77.9%	18.5%	12	25	9	39	77
Copenhagen	66.3%	64.2%	74.9%	89.3%	82.5%	21	30	7	4	2
Diyarbakir	39.6%	50.0%	61.2%	59.3%	45.8%	69	62	43	80	49
Dortmund	52.3%	45.2%	51.6%	72.5%	46.3%	58	68	66	61	47
Dublin	67.5%	72.6%	70.7%	85.5%	58.8%	17	11	17	14	34
Essen	60.6%	40.7%	64.0%	75.6%	61.9%	34	76	37	45	28
Gdańsk	66.7%	64.1%	70.9%	83.4%	51.5%	19	31	16	22	43
Genève	81.8%	78.9%	61.3%	74.7%	50.0%	2	1	42	48	45
Glasgow	55.6%	63.7%	59.0%	79.5%	59.8%	47	33	49	34	31
Graz	66.5%	73.6%	71.5%	88.5%	72.4%	20	9	13	6	10
Groningen	74.6%	75.4%	57.1%	91.2%	78.2%	5	8	55	1	5
Hamburg	67.5%	51.4%	65.9%	78.5%	62.5%	16	56	31	38	27
Helsinki	51.2%	44.1%	67.3%	76.9%	63.7%	61	70	25	42	24
Heraklion	33.2%	43.6%	27.1%	77.3%	33.4%	77	72	79	41	62
Istanbul	52.3%	65.6%	54.7%	71.3%	36.5%	57	25	62	63	57
Košice	62.2%	61.8%	70.3%	80.9%	32.2%	30	41	19	28	64
Kraków	65.6%	49.9%	59.0%	82.3%	55.3%	23	63	50	25	38
Lefkosia	53.5%	78.9%	55.7%	84.5%	37.9%	55	1	61	17	56
Leipzig	71.1%	64.4%	60.0%	80.2%	65.1%	9	29	46	30	20
Liège	64.8%	77.2%	63.0%	72.7%	55.9%	25	7	38	60	36
Lille	54.5%	65.7%	58.8%	68.8%	62.9%	49	23	51	69	26
Lisboa	37.2%	50.8%	47.9%	71.2%	29.4%	73	58	70	64	68
Ljubljana	61.0%	64.9%	76.5%	80.0%	29.4%	33	27	5	31	68
London	57.0%	63.3%	62.9%	84.1%	66.7%	43	37	39	19	14
Luxembourg	72.2%	70.5%	78.5%	85.1%	66.4%	7	15	3	15	15
Madrid	56.4%	60.1%	53.3%	70.5%	50.8%	45	45	64	66	44
Málaga	55.5%	63.6%	57.2%	74.3%	49.4%	48	35	54	49	46
Malmö	60.3%	48.5%	71.0%	81.2%	72.7%	36	66	15	27	8
Manchester	71.6%	72.7%	70.2%	86.8%	58.1%	8	10	20	8	35
Marseille	53.8%	58.0%	45.9%	61.2%	35.1%	52	48	72	77	59
Miskolc	68.7%	68.2%	61.0%	89.4%	38.2%	13	19	44	3	54
Munich	58.6%	56.3%	70.2%	82.4%	71.8%	39	50	21	24	11
Naples	25.5%	36.1%	27.5%	55.0%	29.2%	81	79	78	82	70
Oslo	39.1%	42.0%	59.8%	74.2%	66.3%	70	73	47	50	16
Ostrava	56.8%	52.4%	71.7%	86.8%	31.7%	44	55	12	8	66
Oulu	61.2%	50.7%	60.1%	74.1%	67.9%	32	59	45	51	13
Oviedo	50.3%	53.5%	44.6%	66.4%	44.6%	62	53	73	73	51
Palermo	13.3%	28.9%	22.4%	53.1%	22.2%	83	82	83	83	76
Paris	56.3%	61.2%	57.0%	73.5%	59.3%	46	42	56	55	33
Piatra Neamt	64.5%	61.0%	70.7%	73.6%	24.2%	26	43	18	53	72
Podgorica	45.9%	55.1%	65.9%	66.9%	17.9%	66	51	30	72	78
Praha	51.2%	48.6%	78.3%	82.8%	33.8%	60	65	4	23	61
Rennes	62.6%	67.8%	67.3%	78.8%	79.2%	29	20	26	37	4
Reykjavík	34.6%	45.2%	47.3%	75.3%	38.2%	75	68	71	46	54
Riga	48.3%	50.7%	26.1%	70.3%	23.2%	65	59	82	67	73
Rome	15.8%	27.6%	26.4%	56.5%	16.8%	82	83	81	81	79
Rostock	76.5%	57.7%	73.3%	79.9%	65.8%	4	49	10	32	18
Rotterdam	57.2%	64.6%	66.9%	79.6%	65.3%	42	28	28	33	19
Skopje	31.8%	54.8%	56.6%	64.0%	12.4%	79	52	58	76	81
Sofia	38.2%	41.6%	53.1%	71.1%	25.8%	71	74	65	65	71
Stockholm	53.7%	51.3%	74.2%	79.5%	70.5%	54	57	8	34	12
Strasbourg	66.7%	68.8%	62.3%	73.2%	66.0%	18	16	41	56	17
Tallinn	68.1%	63.6%	81.1%	86.2%	33.4%	15	35	1	11	62
Tirana	50.2%	59.8%	48.2%	75.7%	22.5%	63	46	69	44	75
Turin	31.8%	37.1%	33.9%	65.1%	42.4%	80	77	77	75	53
Tyneside conurbation	65.9%	71.8%	64.2%	77.8%	64.4%	22	13	36	40	22
Valletta	63.0%	72.1%	75.6%	83.6%	72.5%	28	12	6	21	9
Verona	43.7%	43.9%	59.0%	73.6%	32.1%	68	71	48	53	65
Vilnius	53.5%	62.2%	56.3%	73.0%	45.9%	56	39	59	57	48
Warszawa	57.8%	48.4%	54.4%	80.4%	52.3%	41	67	63	29	42
Wien	74.5%	68.3%	72.6%	87.3%	73.1%	6	18	11	7	7
Zagreb	32.4%	36.1%	39.1%	71.6%	10.9%	78	79	76	62	82
Zurich	85.6%	77.9%	80.4%	90.6%	79.7%	1	6	2	2	3
min	13.3%	27.6%	22.40%	53.1%	10.5%
max	85.6%	78.9%	81.11%	91.2%	82.6%
mean	55.9%	58.4%	59.56%	76.4%	49.1%
st dev	14.2%	12.6%	13.45%	8.7%	19.1%

for the year 2019 and in

Table 4. Evaluation of satisfaction from the local administration in European cities based on the PQ1–PQ5 for the 2023 year.

City	PQ Measure					Rank
	PQ1	PQ2	PQ3	PQ4	PQ5	Q1	Q2	Q3	Q4	Q5
Aalborg	74.2%	65.9%	68.0%	86.2%	73.2%	3	22	17	2	5
Amsterdam	57.3%	59.0%	56.5%	81.8%	55.6%	37	40	50	14	31
Ankara	68.0%	72.9%	66.4%	77.9%	60.0%	11	9	20	30	23
Antalya	69.5%	77.9%	68.3%	85.1%	62.7%	8	3	14	6	18
Antwerpen	65.5%	75.7%	63.6%	62.4%	54.4%	19	6	28	77	33
Athina	41.2%	42.4%	30.6%	59.4%	25.2%	70	74	79	80	72
Barcelona	46.3%	57.0%	41.3%	68.0%	46.5%	64	44	73	65	47
Belfast	54.7%	62.3%	63.3%	77.4%	49.2%	43	30	31	33	44
Belgrade	39.6%	35.7%	39.3%	62.7%	12.5%	71	81	75	76	82
Berlin	38.7%	36.7%	65.9%	64.1%	43.5%	74	79	23	72	51
Białystok	67.6%	60.9%	67.8%	83.6%	58.6%	12	34	18	9	28
Bologna	53.1%	54.8%	51.0%	76.8%	50.8%	49	51	65	36	42
Bordeaux	55.8%	61.3%	62.9%	75.0%	63.8%	40	32	33	44	16
Braga	45.9%	64.3%	56.7%	70.3%	26.2%	65	24	49	60	70
Bratislava	60.3%	52.9%	69.3%	74.2%	35.9%	29	53	10	47	59
Bruxelles	64.5%	76.4%	62.8%	73.8%	61.2%	22	5	34	48	21
Bucharest	51.6%	55.5%	65.4%	66.6%	17.4%	53	48	24	68	79
Budapest	56.5%	63.2%	62.5%	80.2%	47.6%	38	28	36	22	45
Burgas	55.0%	59.0%	47.7%	80.9%	34.1%	42	40	68	17	62
Cardiff	65.6%	66.5%	65.9%	80.6%	65.2%	18	20	22	19	11
Cluj-Napoca	70.5%	69.5%	75.6%	80.4%	23.5%	6	13	3	20	75
Copenhagen	62.4%	64.1%	72.8%	85.7%	80.4%	25	26	6	4	1
Diyarbakir	42.5%	52.4%	59.0%	61.0%	40.8%	69	55	42	78	55
Dortmund	49.8%	47.8%	49.8%	67.1%	49.4%	59	66	67	67	43
Dublin	65.0%	68.1%	68.8%	81.6%	51.9%	20	15	13	15	37
Essen	57.4%	42.6%	60.0%	73.1%	59.5%	36	73	40	51	25
Gdańsk	66.9%	64.2%	67.4%	80.3%	51.7%	15	25	19	21	39
Genève	80.5%	79.1%	64.8%	72.3%	52.7%	2	1	26	55	35
Glasgow	49.8%	60.2%	55.4%	77.2%	52.7%	59	37	53	35	35
Graz	67.2%	68.1%	70.0%	85.2%	62.8%	13	15	9	5	17
Groningen	69.7%	70.7%	57.4%	86.0%	73.7%	7	10	45	3	4
Hamburg	68.9%	51.6%	60.6%	78.1%	57.1%	10	57	39	28	29
Helsinki	50.2%	47.3%	62.7%	76.4%	59.5%	58	69	35	39	25
Heraklion	35.1%	42.3%	29.0%	77.3%	31.3%	77	75	81	34	65
Istanbul	54.5%	63.8%	52.7%	72.0%	47.1%	44	27	60	56	46
Košice	61.0%	60.6%	56.1%	77.5%	34.9%	27	35	51	32	60
Kraków	59.7%	49.1%	54.5%	79.8%	51.5%	32	63	56	25	40
Lefkosia	52.2%	70.5%	53.4%	81.1%	37.5%	52	11	57	16	57
Leipzig	64.9%	55.2%	51.5%	77.6%	64.4%	21	49	64	31	14
Liège	66.6%	78.2%	63.3%	69.1%	51.8%	16	2	30	62	38
Lille	54.5%	67.7%	57.9%	71.3%	60.7%	44	18	43	57	22
Lisboa	36.8%	50.9%	45.2%	68.8%	30.8%	76	58	72	64	66
Ljubljana	58.5%	61.1%	68.1%	76.1%	25.8%	35	33	15	41	71
London	51.1%	55.1%	54.8%	76.3%	53.4%	56	50	55	40	34
Luxembourg	72.1%	77.6%	81.5%	83.8%	64.6%	4	4	1	8	13
Madrid	51.5%	52.6%	50.9%	65.2%	46.4%	55	54	66	69	48
Málaga	54.1%	58.5%	51.5%	70.8%	45.9%	47	42	63	59	49
Malmö	59.3%	55.8%	70.1%	79.9%	66.8%	34	47	8	24	8
Manchester	63.8%	68.0%	62.0%	80.8%	55.5%	24	17	37	18	32
Marseille	51.6%	58.4%	47.1%	64.2%	34.3%	53	43	70	71	61
Miskolc	60.8%	69.1%	57.3%	82.1%	41.8%	28	14	46	13	53
Munich	60.1%	56.2%	61.5%	79.4%	66.4%	30	46	38	26	9
Naples	26.6%	36.1%	29.6%	55.1%	28.0%	81	80	80	82	67
Oslo	39.4%	46.2%	56.8%	73.5%	61.6%	73	71	48	50	19
Ostrava	53.4%	52.0%	69.2%	83.3%	33.6%	48	56	11	10	63
Oulu	59.4%	50.3%	59.3%	75.3%	61.3%	33	60	41	43	20
Oviedo	44.7%	56.5%	40.5%	64.1%	41.7%	66	45	74	72	54
Palermo	13.1%	23.9%	20.3%	50.0%	21.7%	83	83	83	83	76
Paris	52.7%	60.6%	57.8%	72.5%	59.7%	50	35	44	53	24
Piatra Neamt	64.3%	61.5%	68.1%	70.9%	26.5%	23	31	16	58	69
Podgorica	39.6%	48.6%	55.9%	64.9%	17.8%	71	64	52	70	78
Praha	52.6%	49.9%	73.7%	78.7%	32.3%	51	61	5	27	64
Rennes	61.4%	66.3%	68.8%	78.1%	76.1%	26	21	12	28	3
Reykjavík	38.7%	48.2%	47.1%	72.8%	37.7%	74	65	69	52	56
Riga	43.2%	42.1%	31.6%	69.7%	24.8%	67	76	78	61	73
Rome	17.1%	31.3%	25.1%	59.0%	19.4%	82	82	82	81	77
Rostock	67.2%	46.9%	62.9%	72.5%	66.0%	13	70	32	53	10
Rotterdam	54.2%	62.4%	63.7%	76.5%	64.2%	46	29	27	38	15
Skopje	29.5%	49.4%	45.8%	61.0%	15.6%	80	62	71	78	80
Sofia	43.0%	46.0%	57.3%	67.8%	23.9%	68	72	47	66	74
Stockholm	50.9%	47.7%	70.4%	73.8%	64.9%	57	68	7	48	12
Strasbourg	69.5%	70.2%	63.4%	74.3%	67.1%	8	12	29	46	7
Tallinn	55.4%	59.3%	66.3%	84.0%	27.6%	41	39	21	7	68
Tirana	47.9%	54.6%	51.8%	69.0%		63	52	62	63
Turin	33.6%	38.9%	33.8%	64.0%	43.0%	78	77	77	74	52
Tyneside conurbation	56.4%	65.3%	55.2%	80.0%	56.7%	39	23	54	23	30
Valletta	65.8%	74.8%	74.3%	82.5%	68.6%	17	8	4	12	6
Verona	48.3%	47.8%	52.7%	75.5%	36.4%	62	66	59	42	58
Vilnius	48.4%	60.2%	52.2%	74.5%	44.1%	61	37	61	45	50
Warszawa	59.8%	50.9%	52.8%	76.6%	51.4%	31	58	58	37	41
Wien	71.8%	66.8%	65.1%	83.3%	59.4%	5	19	25	10	27
Zagreb	30.2%	37.0%	39.2%	62.9%	12.7%	79	78	76	75	81
Zurich	82.9%	75.1%	79.9%	89.8%	79.9%	1	7	2	1	2
min	13.1%	23.9%	20.28%	50.0%	12.5%
max	82.9%	79.1%	81.53%	89.8%	80.4%
mean	54.5%	57.4%	57.34%	74.2%	47.5%
st dev	13.1%	11.8%	12.46%	7.9%	17.0%

Note: NA—data not available.

for the year 2023. Due to the lack of data in 2023 regarding responses to question Q5, the PQ5 indicator for Tirana was omitted.

Descriptive statistics for the values of PQ1–PQ5 for 2019 and 2023 are presented in Table 3 and Table 4, and box plots in Figure 8.

In 2019 (Table 3), satisfaction with the time taken to resolve requests (PQ1) ranged from a low of 13.3% in Palermo to a high of 85.6% in Zurich. The average satisfaction was 55.9%, with a standard deviation of 14.2%, showing significant variation across cities. Cities like Zurich (85.6%), Geneve (81.8%), and Aalborg (80.3%), reported high satisfaction, while Palermo (13.3%), Naples (25.5%), and Turinn (31.8%) struggled with slower responses. By 2023 (Table 4), Zurich (82.9%) remained at the top, while Palermo (13.1%) again ranked at the bottom. The average increased slightly to 54.5%, with a standard deviation of 13.1%, suggesting some improvement but continued disparities. Top cities in 2023 again included Zurich (82.9%), Geneve (80.5%), and Aalborg (74.2%) while Palermo (13.1%), Rome (17.1%), and Naples (26.6%) remained among the worst. An average decline of −1.4 percentage points (p.p) indicates a slight reduction in administrative responsiveness, with Northern and Western cities still leading and those in Southern Europe trailing.

In 2019 (Table 3), satisfaction with the clarity of procedures (PQ2) ranged from 15.8% in Rome to 78.9% in Lefkosia, with an average of 58.4% and a standard deviation of 12.6%. The best cities were Lefkosia (78.9%), Geneve (78.9%), and Bruxelles (78.9%), while the worst performers included Rome (15.8%), Palermo (28.8%), Naples (36.1%). In 2023 (Table 4), Geneve led first with 79.1%, and Palermo remained last at 23.9%. In 2023, the top-performing cities included Geneva (79.1%), Liège (78.2%), Antalya (77.9%), and Luxembourg (77.6%), whereas Palermo (13.1%), Rome (31.3%), Belgrade (35.7%), and Naples (36.1%) ranked among the lowest The average satisfaction declined to o 57.4%, with a slightly reduced standard deviation of 11.8%, indicating marginal overall improvement. The average decrease of −1 p.p reflects some overall regress in the clarity of procedures across the cities. Some cities, particularly in Southern and Eastern Europe, still faced difficulties in simplifying their processes, also regional disparities remain significant.

In 2019 (Table 3), satisfaction with the reasonableness of public service fees (PQ3) ranged from just 22.4% in Palermo to 81.1% in Tallinn, with an average of 59.6% and a standard deviation of 13.45%, reflecting strong disparities across cities. Leading cities included Tallinn (81.1%), Zurich (80.4%), Luxembourg (78.5%), and Praha (78.3%), while the lowest ratings came from Palermo (22.4%), Riga (26.1%), Rome (26.6%), and Athina (26.7%).

By 2023 (Table 4), Luxembourg (81.5%) and Zurich (79.9%) remained at the top, but the average dropped slightly to 57.34%, with a standard deviation of 12.46%, suggesting a mild convergence in perceptions but not a clear improvement. The overall change of −2.22 p.p signals stagnation or even decline.

In 2019 (Table 3), satisfaction with online access to public services (PQ4) ranged from 53.1% in Palermo to 91.2% in Groningen, with an average of 76.4% and a standard deviation of 8.7%. The best cities were Groningen (91.2%), Zurich (90.6%), Micolic (89.4%), and Copenhagen (89.3%). The lowest scores came from Palermo (53.1%), Naples (55%), and Rome (56.6%). By 2023 (Table 4), little had changed at the top or bottom: Zurich (89.8%) led first, and Palermo (50.0%) remained last. The average decreased slightly to 74.2%, with a small drop in deviation to 7.9% showing a modest regression in digital transformation, with persistent regional gaps.

In 2019 (Table 3), satisfaction with the perception of corruption (Q5) ranged from a low of 10.5% in Belgrade to a high of 82.6% in Aalborg, with an average of 49.1% and a high standard deviation of 19.1%. The best cities were Aalborg (82.6%), Copenhagen (82.5%), and Zurich (80.4%), and the worst included Belgrade (10.5%), Zagreb (10.9%), and Skopje (12.4%). In 2023 (Table 4), top rankings held: Copenhagen (80.4%), Zurich (79.9%), Rennes (76.1%), Belgrade (12.5%), Zagreb (12.7%), and Skopje (15.6%), and stayed at the bottom. The average rose slightly to 47.5%, with a standard deviation of 17.0% reflecting again regression. Corruption remains a serious issue in Southern and Eastern Europe, despite high-performing cities maintaining solid scores.

The comparison between the two aggregate indicators, PQ and TQ, reveals key methodological differences in how citizen satisfaction with local public administrations is measured. While both indicators aim to capture citizen perception, TQ offers a more refined and informative measure. PQ aggregates only the share of positive responses (“rather satisfied” and “very satisfied”), ignoring the intensity of opinion as well as negative and neutral responses. As a result, different distributions of responses can yield identical PQ values, potentially masking dissatisfaction or differences in satisfaction strength. TQ, in contrast, incorporates all response categories and reflects response intensity. This provides a more nuanced view of public perception by capturing the full spectrum of opinion. A crucial methodological distinction between the two measures, PQ and TQ, lies in how they treat missing or undefined responses—specifically, those categorized as 99, representing “Don’t Know,” “No Answer,” or “Refused” answers. In the PQ method, these responses are completely excluded from the calculation. PQ is derived as the percentage of respondents selecting “rather satisfied” or “very satisfied” among only those who provided a valid ordinal answer. This approach assumes that non-responses offer no informational value. However, by excluding them, the method can inadvertently overestimate satisfaction levels—particularly in cities with high rates of non-response—since it only considers respondents who expressed a clear opinion. In contrast, the TQ method includes these ambiguous responses by redistributing them proportionally across the full range of satisfaction categories (1 to 4). This approach assumes that even non-committal answers reflect an underlying distribution of satisfaction and thus contribute meaningfully to the overall result. By doing so, TQ avoids the bias introduced by excluding uncertain respondents, offering a more cautious and balanced estimate. This distinction becomes especially significant in contexts with large shares of category 99 responses, as it determines whether these inputs are integrated into the overall picture or ignored.

To illustrate the implications of these differing methodologies,

Table 5. Comparison of TQ1 and PQ1 for selected cities.

City	1	2	3	4	99	PQ1	TQ1
Helsinki	13.70%	23.60%	27.20%	11.90%	23.60%	51.20%	0.512
Praha	12.70%	26.90%	29.70%	11.70%	19.00%	51.20%	0.525
Dortmund	12.80%	33.20%	36.10%	14.40%	3.60%	52.30%	0.535
Istanbul	34.10%	12.20%	27.10%	23.80%	2.90%	52.30%	0.492
Lefkosia	25.70%	20.50%	27.20%	25.90%	0.80%	53.50%	0.531
Vilnius	9.00%	30.30%	34.10%	11.10%	15.40%	53.50%	0.538
Budapest	17.80%	21.70%	22.00%	23.90%	14.60%	53.70%	0.547
Stockholm	15.70%	21.00%	28.80%	13.80%	20.60%	53.70%	0.526
Heraklion	46.40%	18.70%	21.90%	10.40%	2.60%	33.20%	0.364
Naples	40.30%	31.60%	18.60%	6.00%	3.60%	25.50%	0.364
Berlin	19.50%	38.40%	23.90%	8.70%	9.50%	36.00%	0.468
Bucharest	30.80%	15.00%	33.60%	12.20%	8.40%	50.00%	0.468
Ankara	26.10%	14.80%	37.20%	19.00%	2.90%	57.90%	0.521
Braga	9.10%	36.50%	43.80%	4.70%	5.90%	51.50%	0.521
Bologna	12.40%	31.80%	43.60%	8.20%	3.90%	54.00%	0.525
Praha	12.70%	26.90%	29.70%	11.70%	19.00%	51.20%	0.525

Note. 1—Strongly Disagree; 2—Somewhat Disagree; 3—Somewhat Agree; 4—Strongly Agree; 99—Don’t Know/No Answer/Refused.

presents examples of cities with identical PQ1 scores but diverging TQ1 scores—and vice versa—highlighting how the two indicators can tell different stories depending on how satisfaction is measured.

We begin by examining cities that report the same PQ1 scores but show differences in their TQ1 values. These examples demonstrate how TQ can capture meaningful nuances that PQ overlooks:

Helsinki and Prague: Both cities report a PQ1 of 51.2%, indicating a similar proportion of satisfied residents. However, Helsinki’s TQ1 is slightly lower (0.512 vs. 0.525), reflecting its higher share of non-responses (23.6% compared to 19.0%). This suggests greater uncertainty among Helsinki residents, which is captured by TQ but not PQ.

Dortmund and Istanbul: While both cities share a PQ1 of 52.3%, their TQ1 scores diverge significantly: 0.535 for Dortmund and 0.492 for Istanbul. The difference is not driven by uncertainty—since Istanbul has a low non-response rate (2.9%)—but by a more polarized satisfaction pattern, with stronger responses at both ends of the scale. TQ reflects this imbalance, whereas PQ does not.

Lefkosia and Vilnius: Both cities show a PQ1 of 53.5%, yet Lefkosia’s TQ1 is slightly lower (0.531 vs. 0.538). The difference stems from Lefkosia’s more polarized responses, whereas Vilnius exhibits a more moderate and balanced distribution of satisfaction. Again, TQ identifies differences that PQ misses.

Budapest and Stockholm: Although both report a PQ1 of 53.7%, Budapest’s TQ1 (0.547) is significantly higher than Stockholm’s (0.526). This gap is largely due to Stockholm’s higher non-response rate (20.6% vs. 14.6%), which, when redistributed in the TQ calculation, lowers its score. This indicates that satisfaction in Budapest is more widespread and less uncertain—something only TQ captures.

Conversely, there are cases where cities have identical TQ1 scores but varying PQ1 values. These examples reveal how TQ can identify underlying similarities that PQ conceals:

Heraklion and Naples: Both cities have a TQ1 of 0.364, but Heraklion has a higher PQ1 (33.2%) than Naples (25.5%). Despite the lower satisfaction share in Naples, the distribution of responses across the spectrum results in a comparable TQ1, indicating a similar overall balance of satisfaction.

Berlin and Bucharest: With a shared TQ1 of 0.468, Berlin reports a PQ1 of 36.0%, while Bucharest stands at 50.0%. The explanation lies in Bucharest’s more polarized responses compared to Berlin’s even spread. TQ reflects that the overall satisfaction balance between the two cities is more alike than PQ suggests.

Ankara and Braga: Both cities report a TQ1 of 0.521, yet Ankara’s PQ1 is higher at 57.9% compared to Braga’s 51.5%. Ankara’s responses are more concentrated in strong agreement, while Braga’s are more moderate. TQ equalizes these differences, showing that the overall balance of satisfaction is similar.

Prague and Bologna: Despite a TQ1 of 0.525 in both cities, Bologna has a slightly higher PQ1 (54.0%) than Prague (51.2%). Bologna’s satisfaction responses are more strongly skewed toward agreement, whereas Prague’s are more evenly distributed. TQ reflects this similarity in overall balance, despite PQ’s divergence.

The city-by-city comparisons underscore the value of TQ1 as a more nuanced and comprehensive measure of public satisfaction. While PQ1 offers a simple and intuitive metric of how many people are satisfied, it can overlook critical factors such as satisfaction intensity, distributional balance, and response uncertainty. This is particularly problematic in cities with high non-response rates or sharply divided opinions. By incorporating all responses—including those typically dismissed as “uninformative”—and reflecting their satisfaction distribution, TQ avoids the distortions inherent in PQ. It avoids the upward bias that can arise when non-responses are excluded and accounts for differences in response behavior that can meaningfully shape perception outcomes. It reveals hidden differences masked by similar satisfaction shares, highlights underlying similarities in seemingly divergent cities, and supports fairer comparisons across diverse urban contexts.

Table 6. Comparison of PQ and TQ satisfaction measures.

Criterion	PQ Measure	TQ Measure
Response Range	Includes only “rather satisfied” and “very satisfied” responses	Includes all response categories, including “Don’t Know/No Answer/Refused”
Opinion Intensity	Ignores the intensity and distribution of responses	Captures opinion intensity through utility scores and full distribution
Treatment of Missing Data	Completely excludes non-responses (category 99)	Redistributes non-responses proportionally across all response categories
Accuracy of Insight	May overestimate satisfaction, especially with high non-response rates	Provides a more balanced and representative picture of satisfaction
Computational Complexity	Simple to compute and easy to understand	More complex; requires belief structure modeling and similarity-based distance measures
Use Case	Suitable for quick comparisons, dashboards, summaries, or limited-resource contexts	Ideal for diagnostic analysis, cross-city comparisons, and longitudinal monitoring
Potential Drawbacks	Masks nuances and may falsely suggest high satisfaction levels	Complexity may hinder understanding among non-expert audiences
Advantages	Easy to compute, implement, and communicate; provides a fast overview	Methodologically robust, fair, and informative; offers richer insights for analysts and decision-makers
Main Recommendation	Use when simplicity, speed, and broad communication are priorities	Use when precision, fairness, and analytical depth are key goals

provides a structured comparison between the PQ and TQ satisfaction measures. It highlights key differences in methodology, interpretation, and use cases.

In summary, TQ stands out as a more robust, equitable, and methodologically sound tool for measuring public satisfaction. Its capacity to incorporate uncertainty, reflect satisfaction intensity and enable fairer cross-city comparisons makes it particularly valuable for policy evaluation, service benchmarking, and longitudinal monitoring. In complex and diverse urban contexts, TQ provides a deeper and more reliable foundation for understanding citizen perspectives. It should be preferred in policy evaluation, service benchmarking, and longitudinal analysis.

It is worth noting that, despite methodological differences, both the PQ1–PQ5 and TQ1–TQ5 indicators provide highly consistent results when compared pairwise. Pearson correlation coefficients for 2019 reveal strong alignment between the two measures PQ and TQ for each pair: 0.959 for Q1, 0.957 for Q2, 0.948 for Q3, 0.883 for Q4, and 0.961 for Q5. In the same way, Spearman rank correlation coefficients indicate a strong relationship between PQ and TQ for each question, with values of 0.955 for Q1, 0.933 for Q2, 0.930 for Q3, 0.847 for Q4, and 0.974 for Q5.

A similar pattern is observed in 2023, with correlations of 0.964 for Q1, 0.967 for Q2, 0.965 for Q3, 0.897 for Q4, and 0.964 for Q5. In the same way, Spearman rank correlation coefficients indicate a strong relationship between PQ and TQ for each question, with values of 0.973 for Q1, 0.962 for Q2, 0.954 for Q3, 0.884 for Q4, and 0.977 for Q5.

Rankings of top and low-performing cities are also largely consistent across both sets of indicators. Western and Northern Europe maintain high satisfaction levels, particularly in digital accessibility and low corruption perceptions. Central and Eastern Europe show notable improvements in online services and procedural clarity. In contrast, the Balkans continue to face significant challenges across most dimensions of administrative quality.

5. Conclusions

This study introduced BS-TOSIE, a novel methodological framework designed to improve the analysis of ordinal survey data by preserving the full distribution of responses—including ambiguous or uncertain ones such as “Don’t Know,” “No Answer,” and “Refused.” By integrating the Belief Structure framework with the TOPSIS method, BS-TOSIE offers a way to compare individual survey items relative to ideal and anti-ideal benchmarks. Empirical testing using data from the Quality of Life in European Cities survey demonstrated that the method is both stable and interpretable, yielding rankings highly correlated with traditional measures. Findings reveal moderate satisfaction with local administration across European cities, with slight declines from 2019 to 2023. Northern and Western European cities consistently scored highest, while cities in the Balkans and parts of Eastern Europe lagged, especially on corruption and responsiveness.

BS-TOSIE offers several methodological strengths. It provides a transparent approach to handling uncertain responses, enhances comparability across cities and time, and preserves valuable data that is often lost through traditional preprocessing techniques. The framework is adaptable to various types of ordinal survey data and can be applied to different areas. Overall, BS-TOSIE represents a significant contribution to ordinal data analysis, combining theoretical rigor with real-world relevance.

Nonetheless, certain limitations should be acknowledged. The current implementation assumes a uniform interpretation of ordinal scales across individuals and cultures, which may not always be valid. It also applies a fixed model for ambiguous responses, potentially underrepresenting the nuanced psychological or situational reasons behind such answers. Additionally, while informative, the approach is computationally demanding and may present accessibility challenges for non-technical users.

Future research could explore several extensions. One direction is to incorporate respondent-specific or probabilistic utility structures to better capture individual variation in scale interpretation. The use of Fuzzy Belief Structures [41] could enhance the modeling of graded uncertainty, while data-driven methods could improve the assignment of ambiguity across categories. Incorporating contextual information—such as demographics, response time, or prior experience—may further refine belief assignments and shed light on patterns of non-response.

Another promising research direction involves extending the BS-TOSIE framework to construct aggregate indices across multiple evaluation criteria. This would allow for direct comparison with other multi-criteria decision-making methods, such as B-TOPSIS [17], approaches based on intuitionistic fuzzy sets [45,46,47,48], or interval-intuitionistic fuzzy sets [49]. Broader application and testing in diverse real-world scenarios will be essential to fully evaluate the robustness and practical applicability of the BS-TOSIE methodology.