Quality of life in Swiss cities

Suíças, Qualidade de Vida em Cidades

doi:10.22167/2675-6528-202602714

Article

September 30, 2026

Quality of life in Swiss cities

Quality of Life in Swiss Cities

Beatriz Oyadomari de Sousa; Adriana Camargo De Brito

DOI: 10.22167/2675-6528-202602714

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The growth of urban areas intensified the need to evaluate quality of life as a tool to analyze population well-being. Given the limitations of global rankings that do not make their data available, a study used official and open databases to ensure transparency and replicability. The objective was to develop a ranking to classify five Swiss cities (Zurich, Bern, Basel, Lausanne, and Geneva), based on 2023 data. Variables were selected from the OECD’s well-being dimensions, adapted to the Swiss context. After data processing and normalization, Multi-Criteria Decision-Making methods (WSM, TOPSIS, and VIKOR) were applied, combined with Shannon Entropy to define variable weights, minimizing interpretive biases. Spearman correlation showed strong convergence between the methods, demonstrating the consistency and robustness of the evaluation. The results of the three methods were synthesized into a global ranking, which positioned Zurich in first place, followed by Basel, Bern, Geneva, and Lausanne. The research established a replicable and well-founded model, offering support for urban management.

Keywords: Swiss cities; City statistics; Urban quality of life; Ranking; Multicriteria decision making.

1. Introduction

Urban areas represent the main centers of economic, social, and political development of a country. However, the growing population concentration in cities intensifies challenges associated with urban development (Răducan et al., 2025). Given this scenario, the assessment of quality of life emerges as an essential instrument for understanding and measuring the well-being of the population (Ríos-Vásquez & de la Fuente-Mella, 2023).

The World Health Organization [WHO] defines quality of life as an individual’s perception of their position in life, influenced by cultural context, value systems, expectations, and personal goals (Răducan et al., 2025). This perception can vary across cultures and over time, aligning with social priorities (MacLean & Salama, 2019). Academic literature indicates that quality of life is composed of two pillars: subjective well-being, which refers to personal satisfaction, and objective well-being, which encompasses concrete indicators, such as socioeconomic ones (Das, 2008; Patil & Sharma, 2022).

In Switzerland, recent data from the study “Stadt-Land-Monitor” (Hermann et al., 2025) indicate that, although the majority of Swiss people positively assess their quality of life, the country’s growth generates critical challenges. Concerns about housing shortages and traffic are examples of urban tensions that affect citizens’ well-being. While this data reflects the subjective pillar, this study focuses on the analysis of the objective pillar of quality of life, using concrete and statistical indicators.

The measurement of these aspects often occurs through evaluation indices that classify cities in dimensions such as quality of life, urban mobility, and socioeconomic development (Tischer & Polette, 2019). However, global rankings like Mercer (2024) and IMD (2025) present limitations for a replicable scientific analysis, as their raw data are not open. Furthermore, many of these rankings are based on subjective weights or opinion surveys, which can introduce a global bias that ignores local cultural contexts (MacLean & Salama, 2019). Although the IMD index addresses well-being, its main focus lies on technological infrastructure and digital perception, which differs from the multidimensional approach to quality of life (De Jong et al., 2015; Răducan et al., 2025).

Given these limitations, the present research adopts an approach that prioritizes transparency, verifiability, and replicability. The selection of reference indicators and cities (Tischer & Polette, 2019) is addressed through the use of official and open databases. The choice of indicators is based on the quality of life model of the Swiss Federal Statistical Office (FSO, 2025), which, although based on the well-being dimensions of the Organization for Economic Co-operation and Development [OECD], has been expanded to include specific aspects of the Swiss urban reality, such as mobility, infrastructure, and leisure. To support the statistical analysis, the “City Statistics” database (FSO, 2024) is used, which integrates the European Eurostat project, ensuring the standardization and quality of Swiss official statistics (FSO, 2026a). The selection of cities for study, including Zurich, Bern, Basel, Lausanne, and Geneva, is justified by their presence in international rankings and their demographic representativeness, concentrating half of the Swiss urban population (Swiss Confederation, 2023).

The need for a model based on official and objective statistics, capable of measuring well-being in a multidimensional way and mitigating interpretative biases, justifies the relevance of this study. Thus, the objective of this work is to develop an open ranking for the main Swiss cities, based on the official database “City Statistics” (FSO, 2026b), with the purpose of measuring well-being in a multidimensional way.

2. Material and Methods

The present research adopted a quantitative approach, of an exploratory and descriptive nature, structured as a multiple case study. The objective was to carry out a comparative analysis of urban quality of life, focusing on the elaboration of a ranking for five Swiss cities: Zurich, Bern, Basel, Lausanne, and Geneva. The evaluation was based on official indicators, extracted from open databases, to ensure the objectivity and replicability of the well-being measurement.

The data used were obtained from the “City Statistics” database (FSO, 2026b), maintained by the Swiss Federal Statistical Office (FSO). The collection took place via the interactive platform “Swiss Stats Explorer” (FSO, 2026c), where filters were applied to select the cities, the period, and the variables of interest. The resulting tables were exported in Excel format (.xlsx) for subsequent processing. All data were open and anonymized, in compliance with Swiss data protection legislation (Swiss Confederation, 2022).

The selection of indicators was based on the FSO quality of life model (2025), which, although grounded in the well-being dimensions of the Organization for Economic Co-operation and Development [OECD], was expanded to include specific axes of Swiss urban reality, such as mobility, infrastructure, and leisure. The original model contemplated 11 dimensions, to which two dimensions of economic and demographic context were added. Initially, 30 indicators were selected, broken down into 56 variables.

In the selection process of indicators, within the “Culture and Leisure” dimension, the scope of the original indicator for demand for museums and theaters was expanded to include the demand for cinemas. This decision aimed to ensure the internal coherence of the dimension, as other indicators already encompassed cinema in their metrics, and the availability of specific data allowed for a refinement of cultural measurement.

The data processing was carried out using the Python programming language, in the “Google Colab Research” environment, with the aid of the pandas and numpy libraries. Initially, the database was loaded in Excel format, followed by a structural cleaning that included the removal of headers, non-informative footers, and the exclusion of rows and columns with missing data. Indicators without coverage for the year 2023 were discarded, resulting in a final set of 45 variables.

Among the relevant exclusions, the Infrastructure and Services dimension was removed due to the absence of data for the selected period. The “Average taxable income (in CHF)” indicator was also discarded, aligning with the perspective that income, while necessary, is not sufficient to measure well-being in isolation (Goerlich & Reig, 2021). Variables with gaps, such as noise pollution and green areas, and “Theater visits” (due to the absence of records for Geneva), were also suppressed.

Additionally, the variable names were standardized, including the translation from German and English with the aid of artificial intelligence tools, followed by manual review. The feasibility of dimensional reduction was investigated through Principal Component Analysis (PCA), with a Pearson correlation analysis. However, the Bartlett’s Test of Sphericity indicated the infeasibility of PCA, as the reduced sample dimension (five cities) was insufficient to guarantee the statistical robustness required for variable reduction.

Due to the infeasibility of PCA, the methodological strategy was directed towards Multicriteria Decision Making (MCDM), based on the approach proposed by Ye et al. (2022). This methodology integrated the Shannon Entropy Method for weight assignment with three MCDM algorithms, aiming for an evaluation of urban quality of life based on objective and mathematically robust criteria.

For the determination of the weights of the 45 variables, the Shannon Entropy Method was adopted. This technique defines the importance of each indicator based on its ability to provide information to the system, measured by the dispersion of the raw data (Ye et al., 2022). The calculation of the entropy (ej) for each indicator j and the final weight (wj) of each variable were performed according to the mathematical formulations described in the original work.

The 45 variables were classified according to their functional polarity in relation to urban well-being, defining benefit variables (value 1) and cost variables (value -1), according to Ye et al. (2022). Nationality variables were excluded to avoid interpretive biases. After this categorization, the data were subjected to Min-Max linear normalization, which uses these polarities to convert variables of different units into a common scale between 0 and 1, following the specific equations for benefit and cost variables presented in the original study.

Statistical processing and multicriteria modeling were based on the approach proposed by Ye et al. (2022). For this purpose, a triple aggregation strategy was adopted using the Weighted Sum Method (WSM), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and VIKOR (VlseKriterijumska Optimizacija i Kompromisno Resenje) methods. The application of these algorithms was carried out using the pymcdm library (Kizielewicz et al., 2023). The final score for WSM, the proximity coefficient for TOPSIS, and the final index for VIKOR were calculated using the respective mathematical formulations detailed in the original work.

To verify the correlation between the rankings generated by the methods, Spearman’s Correlation was calculated, which is suitable for ordinal data. The consolidation of the global ranking was performed by the arithmetic mean of the classifications obtained in the three algorithms (WSM, TOPSIS, and VIKOR), a procedure justified by the strong convergence shown between the methods, ensuring a more robust and balanced final result.

During the preparation of this work, artificial intelligence tools (ChatGPT, Gemini, and DeepL) were used to assist in the translation of terms and texts originally presented in German and English, as well as in the standardization of city names and indicators. Additionally, the tools provided methodological support in data organization and the application of exploratory statistical techniques. The analytical procedure was defined, executed, and validated by the author, who assumes full responsibility for the final content.

3. Results and Discussion

The initial stage of the research focused on consolidating and processing the dataset, resulting in a final “dataset” composed of forty-three variables. This process involved the exclusion of specific dimensions and indicators, such as ‘Infrastructure and Services’ and ‘Average Taxable Income’, due to the absence of consistent data or inadequacy for isolated well-being measurement, as argued by Goerlich and Reig (2021). The removal of nationality indicators was also carried out, aiming to mitigate potential interpretive biases and ensure that the ranking was based on objective quality of life criteria, aligned with the study’s scope. This standardization was crucial to ensure comparability between the five Swiss cities analyzed, eliminating distortions arising from gaps in official data or sociopolitical criteria.

The verification of the feasibility of dimensional reduction, through Principal Component Analysis (PCA), revealed that the approach was not statistically robust for the sample in question. Although the Pearson correlation heatmap indicated significant associations between several indicators, especially in the dimensions of labor market and education, the Bartlett’s Test of Sphericity did not validate the adequacy of the matrix for PCA. The test result, with a Chi-Square statistic of negative infinity and a P-value of one, accompanied by a zero determinant error warning, confirmed the singularity of the matrix. This phenomenon occurred because the number of variables (fifty-six initially) exceeded the limited number of observations (five cities), making the extraction of stable components unfeasible and justifying the methodological transition to Multi-Criteria Decision Making (MCDM).

Shannon Entropy Weighting

With the decision matrix properly structured, the Shannon Entropy Method was applied to objectively determine the weights of the forty-three variables, according to the approach of Ye et al. (2022). This technique, which differs from methods that use subjective weights, assigns greater importance to indicators that show greater dispersion in the raw data, reflecting their ability to discriminate performance among cities. The calculated percentage weights revealed that the indicator ‘Economically active people’ obtained the highest weighting, with approximately 4.83%, followed by ‘Percentage of employed in the secondary economic sector’, with about 4.56%. These results indicate that the dynamism of the labor market and the local productive structure are the factors that most distinguish the Swiss cities evaluated.

Other indicators with significant weights included ‘Public transport displacements’, with approximately 4.29%, and educational levels, such as ‘Population aged twenty-five to sixty-four years with secondary school level II as the highest level of education’ (about 4.07%) and ‘Students in higher education institutions’ (approximately 4.06%). The high weighting of these factors underscores that the main differences in urban quality of life among Swiss metropolises lie in educational opportunities and the efficiency of mobility systems. The Shannon Entropy method, by identifying these indicators with greater variability, established them as the criteria with the highest discriminatory power for constructing the ranking.

In contrast, variables such as ‘Percentage of employees in the tertiary sector of the economy’ and ‘Motorized individual transport displacements’ showed the lowest weights, with approximately 1.07% and 1.11%, respectively. This low weighting suggests that these dimensions are more homogeneous among the analyzed Swiss cities, possessing reduced discriminatory power for the formation of the final ranking. The objective distribution of weights, determined by Shannon Entropy, is fundamental to the model’s reliability, as it ensures that the final index prioritizes nuances where cities indeed exhibit significant contrasts, offering practical support for urban decision-making by directing investments to areas with greater potential for differential impact.

Grouping and Analysis of Weights by Conceptual Dimension

The analysis of the indicator weights was deepened through grouping into the conceptual dimensions that structure the quality of life model of the Swiss Federal Statistical Office (FSO, 2025). Although the original reference model predicted thirteen dimensions, the ‘Infrastructure and Services’ dimension was discontinued due to the absence of consolidated and homogeneous secondary data for the analyzed period. The consolidation of weights by dimension, obtained by the algebraic sum of the percentages of the variables in each axis, allowed for an integrated view of the behavior of the main thematic areas.

The ‘Economic Context’ dimension assumed the greatest relevance in the model, concentrating 24.62% of the total weight with nine indicators, resulting in an average weight of 2.74% per indicator. This finding reinforces that the greatest statistical differences between the studied cities are found in the composition of the labor market and local productive forces. Following this, ‘Culture and Leisure’ (14.52% with six indicators and an average weight of 2.42%) and ‘Mobility’ (13.58% with seven indicators and an average weight of 1.94%) stood out, together accounting for more than 28% of the captured variation, evidencing the importance of these axes for urban quality of life.

The ‘Education’ dimension, despite having a cumulative weight of 9.62%, registered the highest average weight per indicator (3.21%), with three variables, followed by ‘Income and Jobs’ (9% with three indicators and an average weight of 3.00%). This highlights the high discriminatory power of variables related to qualifications and professional opportunities. In contrast, ‘Housing Conditions’ (4.10% with three indicators and an average weight of 1.37%) and ‘Environmental Quality’ (1.19% with one indicator and an average weight of 1.19%) obtained the lowest cumulative weights. This lower weighting in Shannon Entropy does not imply irrelevance, but rather low dispersion in the raw data, indicating that housing services and environmental parameters are relatively homogeneous and standardized among Swiss metropolises, which limits their differentiation power in the ranking.

Aggregation of results

The application of different multicriteria decision-making (MCDM) methods, WSM, TOPSIS, and VIKOR, along with Shannon Entropy for objective weight determination, allowed for a comparative analysis of the urban performance of Swiss cities from various analytical perspectives, according to the methodology of Ye et al. (2022). Each method offers a distinct logic for ranking, revealing nuances about the strengths and weaknesses of each urban center.

Using the WSM (Weighted Sum Method), which sums the weighted scores of the indicators, Zurich consolidated its position in first place with a score of 0.266136. Basel came in second with 0.200780, and Bern came in third with 0.200178. Geneva occupied the fourth position with 0.171219, and Lausanne the fifth with 0.161687. The proximity of the scores between Bern and Basel suggests that both have quantitatively very similar indicator structures in the overall accumulation, indicating a fairly balanced overall average performance between them.

The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution), which evaluates the proximity of cities to a positive ideal solution and a negative ideal solution, showed an inversion in the intermediate positions. Zurich maintained the first position with a proximity coefficient (Ci) of 0.574402. Bern maintained the second position with Ci of 0.401586, surpassing Basel, which remained in third place with Ci of 0.398582. Geneva and Lausanne maintained the fourth and fifth positions, with Ci of 0.313113 and 0.268851, respectively. Bern’s better performance in this method indicates greater efficiency in moving away from critical indicators and approaching urban excellence values in a more balanced way.

Finally, the VIKOR method (VlseKriterijumska Optimizacija | Kompromisno Resenje), which seeks a compromise solution and is more rigorous with cities that present bottlenecks in specific indicators, also ranked Zurich first, with a Qi index of 0.081459. Basel regained second place with a Qi of 0.319007, while Geneva reached third place with a Qi of 0.424167. Bern fell to fourth place with a Qi of 0.688925, and Lausanne remained last with a Qi of 1.000000. Bern’s regret index (Ri) (0.047899) revealed vulnerabilities in isolated indicators that penalized its final ranking, while Geneva demonstrated a more resilient performance from the compromise perspective, surpassing Bern in this specific methodology.

The synthesis of the results obtained by the three methods demonstrated a remarkable consistency at the extremes of the ranking. Zurich maintained the first position in all evaluations, while Lausanne remained in the last. The intermediate positions, however, showed variations, with Bern oscillating between second and fourth place, Basel between second and third place, and Geneva fluctuating between third and fourth position. This variation in intermediate positions between the TOPSIS and VIKOR methods indicates that the cities exhibit distinct behaviors according to the logic of the applied algorithm, confirming that multicriteria analysis is capable of capturing varied nuances of urban quality of life that a simple ranking would not reveal.

Correlation between the methods

To verify the agreement between the rankings generated by the WSM, TOPSIS, and VIKOR methods, Spearman’s correlation was calculated. The results revealed coefficients of 0.9 between WSM and TOPSIS, and also between WSM and VIKOR, indicating a strong central tendency in the classification. The relationship between TOPSIS and VIKOR showed a slightly lower index of 0.7. These values confirm that, although there is substantial convergence between the rankings, the results are not identical, which ratifies that the methods evaluate urban performance from different perspectives.

The correlation of 0.7 between TOPSIS and VIKOR, in particular, demonstrates that the hierarchy of cities is sensitive to the different calculation logics of each algorithm. This variation reinforces the importance of employing a multicriteria approach to capture data nuances, as suggested by Ye et al. (2022). The joint use of these methods allows for a more complete understanding of the cities’ performance, mitigating the influence of distortions that could arise from the application of a single evaluation model.

Consolidated global ranking

With the consistency of the methods confirmed by Spearman’s correlation, the consolidated global ranking was elaborated. For this purpose, the arithmetic mean of the classifications obtained in the three algorithms (WSM, TOPSIS, and VIKOR) was adopted, a procedure justified by the strong convergence evidenced between the methods. The union of the different views allowed for a more robust final result, which reduces the influence of distortions specific to a single algorithm and offers a balanced view of urban performance.

The consolidation of the rankings confirmed the model’s stability at its extremes. Zurich maintained absolute leadership, consistently being the city with the best urban performance across all methodologies, with an average ranking of one. Lausanne, in turn, consistently occupied the last position, with an average ranking of five. The global ranking defined the intermediate positions as follows: Basel consolidated itself in second place with an average of 2.333333, followed by Bern in third with an average of three. Geneva stabilized in fourth position with an average of 3.666667.

Bern, despite achieving an excellent result with the TOPSIS method, ranked third in the overall average, influenced by specific indicators that penalized its score in the VIKOR method. Geneva, in turn, stabilized in fourth position, completing the hierarchy of the main Swiss cities analyzed. However, an important methodological limitation is recognized, related to the size of the sample universe, composed of only five cities. The application of Shannon Entropy in reduced samples requires interpretive caution, as the measurement of entropy and statistical variability is sensitive to the range of data with few observations. In scenarios like this, punctual variations in a single city can amplify the weight of a certain indicator or smooth out discrepancies that would behave differently in a larger sample set. Therefore, the weights and rankings obtained strictly reflect the context and specific variability of these five major Swiss metropolises within the analyzed time frame.

In summary, the research established a replicable and well-founded analysis model for assessing the quality of urban life in Swiss cities. The integration of multi-criteria methods, with objective weighting by Shannon Entropy, allowed for mapping the areas of concern and the competitive advantages of each city, delivering a diagnosis based on statistical evidence and the real variability of indicators. The results confirmed Zurich as a consistent leader and Lausanne as the last placed, while the intermediate positions revealed important nuances about the performance of Basel, Bern, and Geneva, responding to the objective of developing a transparent and robust ranking for the main Swiss cities.

4. Conclusion

This study was dedicated to developing an open ranking for the main Swiss cities, using the official “City Statistics” database from 2023, with the purpose of measuring well-being multidimensionally. It was found that, after data consolidation and processing, a set of forty-three variables was established for the analysis. Objective weighting by Shannon Entropy revealed that the dynamism of the labor market, with indicators such as ‘Economically active persons’ and ‘Percentage of employed in the secondary economic sector’, and education, with ‘Population aged twenty-five to sixty-four with upper secondary education’ and ‘Students in higher education institutions’, were the factors that most distinguished the cities. The application of Multi-Criteria Decision-Making methods (WSM, TOPSIS, and VIKOR) demonstrated strong convergence in the rankings, as evidenced by Spearman correlation. The consolidated global ranking positioned Zurich in first place, followed by Basel, Bern, Geneva, and Lausanne, establishing a replicable and transparent model that offers an evidence-based statistical diagnosis for urban management.

However, an important methodological limitation is recognized concerning the sample size, composed of only five cities. The application of Shannon Entropy in reduced samples requires interpretative caution, as the measurement of entropy and statistical variability is sensitive to the amplitude of data with few observations, potentially amplifying the weight of a certain indicator or smoothing discrepancies. The weights and rankings obtained strictly reflect the context and specific variability of these five major Swiss metropolises within the analyzed time frame. For future work, it is suggested to explore weighted consolidation techniques for multicriteria methods, where the weights of each algorithm are derived from the degree of agreement between the rankings, and to expand the sample universe to include other Swiss cities, in order to improve the robustness and generalization of the model.

Bibliographic References

Das, D. (2008). Urban quality of life: A case study of Guwahati. Social indicators research 88(2): 297-310.

De Jong, M.; Joss, S.; Schraven, D.; Zhan, C.; Weijnen, M. 2015. Sustainable-smart-resilient-low carbon-eco-knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. Journal of Cleaner production 109(2015): 25-38.

Federal Statistical Office [FSO]. 2024. City Statistics – Quality of life in the cities: A brief overview. Neuchâtel: FSO. Disponível em: <https://www.bfs.admin.ch/asset/en/30206410> . Acesso em: 15 jan. 2026.

Federal Statistical Office [FSO]. 2025. Quality of Life Indicators. Disponível em: <https://www.bfs.admin.ch/bfs/en/home/statistics/cross-sectional-topics/city-statistics/indicators-quality-life.html>. Acesso em 19 set. 2025.

Federal Statistical Office [FSO]. 2026a. Quality commitment. Disponível em: <https://www.bfs.admin.ch/bfs/en/home/fso/quality-commitment.html> Acesso em 19 fev. 2026.

Federal Statistical Office [FSO]. 2026b. City Statistics: Ausgewählte Variablen nach Städten und Agglomerationen 2020. Swiss Stats Explorer. Disponível em: <https://dv.stats.swiss/?chartId=b4b7ed7a-9937-4e29-ac80-0f8696eb7c26>. Acesso em: 27 jan. 2026.

Federal Statistical Office [FSO]. 2026c. Swiss Stats Explorer. Disponível em <https://www.bfs.admin.ch/bfs/en/home/services/research/swiss-stats-explorer.html>. Acesso em 04 jan. 2026.

Hermann, M.; Rohner, N.; Stückelberger, S. 2025. Stadt-Land-Monitor 2025: Studienbericht. Zurique: Sotomo. Disponível em:<https://sotomo.ch/site/wp-content/uploads/2025/11/StadtLandMonitor2025_DE.pdf> . Acesso em: 5 fev. 2026.

Institute for Management Development [IMD]. 2025. IMD Smart City Index 2025 Report. Disponível em: <https://imd.widen.net/s/psdrsvpbk7/imd_smart_city_2025_report>. Acesso em 19 set. 2025.

MacLean, L.; Salama, A. M. 2019. Towards a context specific and multidimensional quality of urban life model. Open House International 44(1): 25-33.

Mercer. 2024. Quality of Living City Ranking. Disponível em: <https://www.mercer.com/insights/total-rewards/talent-mobility-insights/quality-of-living-city-ranking/>. Acesso em 19 set. 2025.

Patil & Sharma, 2022 [Referência completa não encontrada no documento original]

Ríos-Vásquez, G.; de la Fuente-Mella, H. 2023. Mathematical Analysis and Modeling of the Factors That Determine the Quality of Life in the City Councils of Chile. Mathematics 11(5): 1218.

Răducan, R.; Lozsa, J., Vîrga, D.; Matichescu, M. 2025. New Integrative Model of the Quality of Urban Life: A Systematic Review. Social Indicators Research 179: 895–921.

Swiss Confederation. 20

Swiss Confederation. 2023. Urban and spatial development in Switzerland. Disponível em: <https://www.aboutswitzerland.eda.admin.ch/en/urban-and-spatial-development>. Acesso em 19 set. 2025.

Tischer, V.; Polette, M. 2019. Sistema de avaliação de cidades de referência em transportes e mobilidade urbana sustentável. Cadernos Metrópole 21(45): 481–509.

Article originating from the Final Course Project of the Specialization in Data Science and Analytics of the MBA USP/Esalq

To learn more about the course, click here and access the MBX Academy platform

Most recent

Economy, Investments And Banking

October 23, 2026

Financial Education, Behaviors and Biases in Financial Decision Making

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

Personal financial management represents a growing challenge, influenced by cognitive biases that affect decision-making, even in individuals with high knowledge. The present study analyzed the relationship between the level of financial education and the occurrence of behavioral biases in the financial decision-making of adults residing in Brazil. An applied, quantitative, and descriptive-correlational field survey was conducted by administering a structured questionnaire to 163 respondents. The instrument included sociodemographic variables, financial knowledge and habits, and hypothetical situations to identify cognitive biases. The results indicated that higher levels of financial education were associated with better financial control, saving, and investment practices, with significant correlations with income and schooling. However, the presence of behavioral biases (anchoring, loss aversion, scarcity, optimism/negativity, present bias, status quo, overconfidence, and mental accounting) persisted even among individuals with higher schooling and income, with no statistically significant association with the analyzed sociodemographic variables. It was concluded that financial education, although essential for financial organization and control, was not sufficient to neutralize the influence of cognitive biases. The incorporation of behavioral approaches into financial education programs is suggested.

Keywords: Financial behavior; Behavioral economics; Personal finance; Cognitive heuristics; Cognitive biases.

October 22, 2026

Challenges and Possibilities of the First School Year of Children with ASD for Teaching Work

The inclusion of children with Autism Spectrum Disorder (ASD) in Early Childhood Education presents significant challenges, given the difficulty of adapting to new environments and the intensity of stimuli. The objective was to analyze the teaching performance in Early Childhood Education in the initial schooling process of children with ASD. A case study was conducted, using narrative interview as a data production technique. The results revealed that the teaching performance was characterized by mediation and affection practices, with the flexibility of routine and the use of playful resources as essential strategies for the child’s development and sensory organization. The importance of the school support professional for the success of the pedagogical work was also highlighted. The child’s integral development was promoted through intentional teaching actions that prioritized environmental adaptation, strengthening of bonds, and attention to the specific needs of ASD. As a contribution, a guide of pedagogical strategies aimed at welcoming children with ASD in public daycare centers was developed.

Keywords: Early Childhood Education; School Inclusion; Teacher Narratives; Inclusive Pedagogical Practices; Autism Spectrum Disorder.

October 22, 2026

Consumer Journey in the Decision to Purchase Infoproducts in the Digital Environment

The expansion of e-commerce and technological transformation have driven the need to understand consumer behavior regarding infoproducts, whose perceived experience has become crucial for professional development. The study aimed to analyze the factors that influenced the purchase decision of infoproducts in the digital environment, considering the stages of the consumer journey. An applied, quantitative, and descriptive research was conducted through a field survey with 100 consumers from Serra Gaúcha. Data collection occurred via a structured questionnaire, using non-probabilistic sampling. The results revealed a qualified audience with strong “mobile-first” behavior. It was found that the availability of detailed information and social approval were the main attraction triggers in the pre-purchase phase. In the transactional and subsequent stages, the minimization of operational friction, the agility of immediate access, and system security proved crucial for conversion and satisfaction, although gaps in human support efficiency were identified. Retention was based on the trust generated by positive past experiences, overcoming the price factor. It was concluded that the infoproduct consumer journey operates as an integrated system, requiring alignment between technological infrastructure, content curation, and support humanization to ensure engagement.

Keywords: E-commerce; Consumer behavior; Consumer experience; Loyalty; Virtual business.

October 22, 2026

Psychosocial Risks and Mental Health at Work in a Banking Institution

Mental health in the workplace is a topic of growing relevance, impacting professional development and employee well-being. A study was conducted to identify aspects and situations that can influence absenteeism levels in a banking institution. The research involved 108 participants and used a structured questionnaire, based on the short version of COPSOQ II by Gonçalves (2021), which contained 45 closed Likert scale questions and two open-ended questions. Data were collected via an online platform and analyzed with a descriptive and quantitative approach. The results revealed a scenario that indicated the need for adjustments in the distribution of activities and in managers’ perception of behaviors and signs of disorders. Important evidence was identified in the participants’ open-ended responses. It was concluded that the study contributed to promoting the perception of a safe environment for mental health in the organization.

Keywords: Absences due to disorders; Professional development; Operational leadership; Medical leave; Mental health.

October 22, 2026

Digital Observatory of the Amazon: Data Science Applied to the Analysis of Social, Economic, Environmental, and Health Indicators

The state of Amazonas faced historical challenges in consolidating the Unified Health System (SUS), stemming from geographical barriers, socioeconomic inequalities, and structural limitations. This study described the development and implementation of a health information geoportal, designed to support monitoring, evaluation, and planning in the Amazonian context. A descriptive case study approach was adopted, based on documentary analysis and secondary data, covering the mapping of functionalities, technical infrastructure, and validation steps of the tool. The Portal was developed with open technologies and integrated multiple national databases, offering interactive dashboards, thematic maps, and a digital document repository. Among the results, highlighted were the georeferenced visualization of key indicators, the possibility of temporal comparative analyses, and the use of the platform in reviewing health plans and redirecting strategic actions. The portal can represent an advance in digital governance and evidence-based management, with potential for replication in other highly complex territories, provided it is accompanied by investments in technological infrastructure, professional training, and inter-institutional coordination.

Keywords: Amazon; Health Evaluation; Digital Health; Health Information Systems.

October 22, 2026

Continuous Learning as a Factor of Organizational Growth in the Perception of Collaborators

The impact of continuous learning and knowledge management on organizational growth was studied in a context of globalization and constant changes. The study’s objective was to analyze employees’ perception of learning culture and its importance for personal and professional development. Data were obtained through an anonymous survey, with a quantitative approach, using a form with “survey” type questions applied to 87 professionals from the technology, health, education, commerce, and services sectors. The results indicated that employees had a positive perception of the contribution of continuous learning to personal and organizational growth, recognizing its benefits for the development of new skills, innovation, and competitiveness. However, it was observed that the incentive for internal knowledge exchange and the offering of practical training still presented moderate levels of agreement. It was concluded that continuous learning significantly contributes to individual and organizational development, suggesting that companies should develop strategic policies for learning focused on new skills, aiming for greater competitive advantage and innovation.

Keywords: Continuous learning; Self-development; Organizational growth; Knowledge Management; Innovation.

October 22, 2026

Statistical Regression in Microgrids for Operational Performance Estimation: Campusgrid Case

A multiple statistical regression model was proposed to estimate the operational performance of UNICAMP’s CampusGrid microgrid, based on internal electrical quantities. The microgrid integrates photovoltaic generation, batteries, and a gas generator. Energy management system data were collected at five-minute intervals over one month. The Operational Reliability Index, defined as the ratio between supplied and demanded energy, was established as the dependent variable. The model was adjusted for the islanded operation mode, evaluating the impact of demand, state of charge, solar generation, and supplied energy. The results revealed high explanatory power, indicating that operational reliability depends on the interaction between supply and demand. It was observed that the nighttime period concentrated the highest rate of partial supply, attributed to the reduction in solar generation. It was concluded that multiple regression offers an interpretable and useful framework for microgrid monitoring and control strategy support.

Keywords: Battery storage; Photovoltaic generation; Reliability Index; Islanded operation; Energy Management System.

October 22, 2026

Forecast of Aggregate Default in the Brazilian Financial System by Sarimax Model with Macroeconomic Variables

Aggregate non-payment in the Brazilian financial system is a crucial indicator of credit risk, the prediction of which is regulated by CMN Resolutions n.° 4.557/2017 and 4.966/2021 for the prospective measurement of expected losses. Aggregate non-payment was modeled and predicted using the SARIMAX model, projecting its trajectory until December 2026 under optimistic, base, and pessimistic scenarios. 164 monthly observations (July/2012 to February/2026) and macroeconomic variables such as unemployment, Selic, IPCA, IBC-Br, and household debt were used. The specification with contemporaneous variables was selected based on information criteria and coefficient stability, and residual diagnostics confirmed statistical adequacy, with a MAPE of 3.12%. Historical non-payment ranged between 4.33% and 7.64%. The estimation identified unemployment (β = 0.2534; p < 0.001) and Selic (β = 0.2128; p < 0.001) as the main positive determinants, with strong temporal persistence (AR(1) = 0.8965). Selic showed increasing correlation with non-payment (r = 0.62 at lag 12), while unemployment demonstrated a contemporaneous effect (r = 0.33). Projections indicated non-payment between 6.30% and 7.98% in December 2026, with a range of 1.68 percentage points, highlighting direct implications for provisioning in financial institutions.

Keywords: Unemployment; Predictive modeling; Monetary policy; Loss provisioning; Time series.

October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]