1. The impact of ESG on business performance : anǂempirical analysis of NASDAQ–NYSE-Listed companiesAljaž Herman, Žan Oplotnik, Timotej Jagrič, 2025, izvirni znanstveni članek Opis: This study investigates the relationship between ESG ratings and a firm’s financial performance, focusing on Return on Assets (ROA) and Return on Equity (ROE). Using a combination of stepwise linear regression and feedforward neural networks (FFNN), we assess both the linear and nonlinear effects of ESG on financial performance. The regression models identify ESG as a significant, positively correlated factor in explaining financial performance, alongside firm demographics, sector affiliation, and financial indicators. Neural networks reveal nonlinear dynamics, particularly for ROA, suggesting threshold effects in the ESG–performance relationship. Sensitivity analysis confirms that ESG’s influence strengthens at higher values. Our findings highlight that ESG is not only statistically relevant but also interacts with firm characteristics in complex ways. These results contribute to the ongoing discourse on sustainable finance by showing that ESG can be a meaningful driver of financial outcomes, especially when modeled through nonlinear approaches.
Ključne besede: ESG, financial performance, ROA, ROE, regression, neural network
Objavljeno v DKUM: 06.11.2025; Ogledov: 0; Prenosov: 7
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2. Developing an alternative calculation method for the smart readiness indicator based on genetic programming and linear regressionMitja Beras, Miran Brezočnik, Uroš Župerl, Miha Kovačič, 2025, izvirni znanstveni članek Opis: The European Union is planning to introduce a new tool for evaluating smart solutions in buildings—the Smart Readiness Indicator (SRI). As 54 energy efficiency categories must be evaluated, the triage process can be long and time-intensive. Altogether, 228 data points (or inputs) about the smartness of the buildings are required to complete the evaluation. The present paper proposes an alternative calculation method based on genetic programming (GP) for the calculation of Domains and linear regression (LR) for the calculation of Impact Factors and the total SRI score of the building. This novel calculation requires 20% (Domain ventilation and dynamic building envelope) to 75% (Domain cooling) fewer inputs than the original methodology. The present study evaluated 223 case study buildings, and 7 genetic programming models and 8 linear regression models were generated based on the results. The generated results are precise; the relative deviation from the experimental data for Domain scores (modelled with GP) ranged from 0.9% to 2.9%. The R2 for the LR models was 0.75 for most models (with two exceptions, with one with a value of 0.57 and the other with a value of 0.98). The developed method is scalable and could be used for preliminary and portfolio-level screening at early-stage assessments.
Ključne besede: SRI, modelling, genetic programming, linear regression, energy efficient buildings, smart buildings, optimisation
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 2
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3. A comprehensive survey on integration of machine learning with secure blockchain-based applicationsChahna Meka, Keerthi Reddy Palakollu, Maria Azees, Arun Sekar Rajasekaran, Ashok Kumar Das, Marko Hölbl, 2025, izvirni znanstveni članek Opis: Machine learning (ML) is a critical technology that provides pervasive intelligence for the Internet of Things (IoT), enabling smart decision and automation. Meanwhile, Blockchain has emerged as a reliable, secure, decentralized and distributed network with applications in a variety of sectors like healthcare, insurance, finance, banking, and business. The integration of blockchain and ML may further enhance security, optimize data processing and ensure intelligent automation. The linkage of blockchain technology with ML aims to safeguard the privacy of ML models by executing blockchain transparency functions. However, maintaining the integrity of ML models and optimizing blockchain process are challenging. The integration in this work aims to solve challenges like security vulnerability, scalability and computational efficiency. Integration enables automation through smart contracts, enabling secure decision making while preserving data integrity and supporting auditing tasks. Moreover, the security benefits of blockchain networks result from anomaly detection technologies enabled by ML that detects fraudulent activities while defending blockchain networks from security threats. This work presents an organized approach to examine contemporary blockchain-ML research developments, analysis of applications based on the integration of blockchain and ML, technical aspects of Integration and its case studies. Finally, integration with respect to industry focus, followed by open challenges and research problems in ML-based blockchain technology, future directions and emerging trends are discussed in this survey.
Ključne besede: machine learning, blockchain, artificial intelligence, security, privacy, regression
Objavljeno v DKUM: 05.09.2025; Ogledov: 0; Prenosov: 2
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4. Tourism-driven economies and income disparity : insights from a regression discontinuity designZdravko Šergo, Jasmina Gržinić, Anita Ilak-Peršurić, 2024, izvirni znanstveni članek Opis: This paper aims to answer two questions: (1) Does overtourism incline countries to favorise the capital asset holder instead of workers in income distribution? (2) Does income inequality come hand in hand with overtourism? Employing Kaldor's theory of income distribution, this study adapts its theoretical framework to assess countries with a specialization in tourism. The analysis encompasses data from 115 developed countries over the period of 2000 to 2019. A Regression Discontinuity Design (RDD) methodology is utilized for the empirical investigation, categorizing key variables based on the exposure of a country's unit to a tourism-focused developmental strategy. The study reveals that adopting overtourism as a developmental strategy has led to increased inequality, characterized by a rising capital share and a declining labor share over time. Evidence supporting these findings is presented through both parametric and non-parametric Regression Discontinuity Design (RDD) analyses. Robustness checks and placebo tests corroborate these results.
Ključne besede: overtourism, developmental strategy, income distribution, regression discontinuity design, economic inequality, RDD
Objavljeno v DKUM: 13.08.2025; Ogledov: 0; Prenosov: 8
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5. Inflation, inflation uncertainty and economic growth in Tunisia : nonlinear modelling frameworkThouraya Dammak Boujelbene, Khoutem Ben Jedidia, 2025, izvirni znanstveni članek Opis: Inflation uncertainty is a critical factor influencing not only the market mechanisms but also the economic activity efficiency. In this paper, we investigated the relationship between inflation and growth to capture the impact of inflation uncertainty in Tunisia. The study relied on a dataset covering the period 1984.01-2018.08 and was characterized by a nonlinear specification. We used Hansen’s (2001) Threshold Regression (TR) analysis to determine one threshold effect of inflation on growth while explaining the role of inflation uncertainty in the whole process. This study concluded that an optimal inflation rate does exist. Under this rate, a little rise in inflation may enhance economic growth, allowing an adverse impact of inflation uncertainty. Above the critical threshold of 3%, it was revealed that inflation and inflation uncertainty play opposite roles: while the former harms growth, the latter benefits it. Thus, we cannot sustain the Friedman-Ball hypothesis for the two regimes. To the best of the authors’ knowledge, this is the first study that aimed to investigate the simultaneous effects of inflation and inflation uncertainty on growth in Tunisia using a non-linear methodology. This study aims to fulfil the knowledge gap of such studies for developing countries.
Ključne besede: threshold regression model, inflation, inflation uncertainty, economic growth, Tunisia
Objavljeno v DKUM: 01.08.2025; Ogledov: 0; Prenosov: 4
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6. A new logistic regression approach for the identification of factors affecting the partition of costs and risk in the international tradeMarjan Sternad, Dejan Dragan, 2024, izvirni znanstveni članek Opis: Background: The selection of the right Incoterms is crucial for minimising risks and costs in international trade. This paper aims to develop a model that identifies the key factors influencing the selection of Incoterms. The main contribution of the research is the creation of a new statistical modelling process that effectively identifies the variables impacting trade costs and risk. The study uses import and export data from non-EU countries in the context of a Slovenian case study. Methods: A novel model selection mechanism is developed, combining the logistic regression (logit) modelling with Monte Carlo simulations to identify influential factors in Incoterms selection. This mechanism incorporates heuristic techniques, which guide a sequential process of gradually searching through logit model candidates to determine the bestfit model for both import and export scenarios. Results: The application of the new logit modelling procedure reveals that the delivery location is the most significant factor affecting Incoterms selection. Additionally, the value of goods and the type of transport (containerised vs. noncontainerised) also have a considerable influence. For imports, the mass of goods is found to be a significant factor as well. Conclusions: The research results offer valuable insights for companies formulating their international business strategies. By selecting the appropriate Incoterm, companies can reduce transportation risks and costs. Managing costs and risks is especially important for higher-value goods. The research finds that, for lower-value goods, sellers often take on associated costs and risks. This pattern is particularly notable with imports, where sellers are more likely to assume responsibility for lighter-weight goods.
Ključne besede: international trade, Incoterms, Incoterms selection, logit regression, Monte Carlo simulation
Objavljeno v DKUM: 22.07.2025; Ogledov: 0; Prenosov: 2
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7. Sustainable business practices and the role of digital technologies : a cross-regional analysisSamuel Plečko, Barbara Bradač Hojnik, 2024, izvirni znanstveni članek Opis: This study examines the relationship between digital transformation and sustainable
practices within enterprises against the backdrop of global transformative forces framed within
the holistic paradigm of systems thinking. It examines the extent to which digital advances either
facilitate or impede the sustainable development of companies, while also considering the systemic
impact of demographic variables (such as gender, age, education), national income levels, and
geographical regions on business sustainability. Using data from the Global Entrepreneurship Monitor
(GEM), which encompasses 26,790 entrepreneurs in 47 countries, this research uses multinomial
regression to assess how these factors influence companies’ commitment to social and environmental
goals. A key finding is that the strategic use of digital technologies in sales processes significantly
increases the likelihood that entrepreneurs will integrate social and environmental considerations
into their decision-making. Notably, this conscientious approach to business is most prevalent among
entrepreneurs in Latin America and the Caribbean. Our findings underscore the central role of digital
technologies in driving sustainable business transformation while also highlighting the significant
influence of regional socio-environmental contexts on business sustainability orientations.
Ključne besede: digital transformation, sustainable transformation, digitainability, demographic factors, country income, geographical regions, multinomial regression, Cramer’s V, enterprises
Objavljeno v DKUM: 10.07.2025; Ogledov: 0; Prenosov: 3
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8. Exogenous variables and their influence on domestic credit provision in Nigeria : evidence from quantile regressionInnocent Chile Nzeh, 2024, izvirni znanstveni članek Opis: This article analyses the work-related factors that impact the occurrence of presenteeism in Croatia during the COVID-19 pandemic. The main objective is to examine the connection between key variables and the prevalence of presenteeism among employees in Croatia. The study used logistic regression analysis to examine data from the 2021 European Working Condition Telephone Survey (EWCTS), encompassing a sample of 491 employees from Croatia. The study investigated work-related factors such as job demands, working hours, supervisory responsibilities, telework, and cooperation with coworkers. The analysis also takes into account demographic control variables, including age, gender, and educational level. The findings suggest that there is a significant relationship between work stressors and presenteeism. Employees who lack good collaboration with colleagues are more prone to attending work even when they are unwell. Age was also determined to be a significant factor. The study indicates that work-related factors have an impact on presenteeism in Croatia. To mitigate the adverse effects of presenteeism, firms should adopt strategies to enhance working conditions and foster a conducive work atmosphere. The results offer valuable perspectives for future research and practical strategies to reduce presenteeism and improve employee wellbeing.
Ključne besede: domestic credit, monetary policy, interest rate, exchange rate, quantile regression
Objavljeno v DKUM: 02.06.2025; Ogledov: 0; Prenosov: 2
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9. Modeling of tensile test results for low alloy steels by linear regression and genetic programming taking into account the non-metallic inclusionsMiha Kovačič, Uroš Župerl, 2022, izvirni znanstveni članek Opis: Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main
motive for this study was to study the influences of non-metallic inclusions on mechanical properties
obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data
(472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final
user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical
composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium
and globular oxides), determined based on ASTM E45, several models for tensile strength, yield
strength, percentage elongation, and percentage reduction area were obtained using linear regression
and genetic programming. Based on modeling results in the period from January 2022 to April 2022,
five successively cast batches of 30MnVS6 were produced with a statistically significant reduction
of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile
strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength
increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary
to emphasize that there were no statistically significant changes in other monitored parameters.
Ključne besede: mechanical properties, tensile test, tensile strength, yield strength, percentage elongation, percentage reduction area, low alloy steel, modeling, linear regression, genetic programming, industrial study, steel making, optimization
Objavljeno v DKUM: 24.03.2025; Ogledov: 0; Prenosov: 7
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10. International energy security risk index - analysis of the methodological settingsIztok Podbregar, Goran Šimić, Mirjana Radovanović, Sanja Filipović, Polona Šprajc, 2020, izvirni znanstveni članek Opis: The main objective of this paper is to analyze model settings of the International Energy Security Risk Index developed by the U.S. Chamber of Commerce. The study was performed using stepwise regression, principal component analysis, and Promax oblique rotation. The conclusion of the regression analysis shows that Crude Oil Price and Global Coal Reserves are sufficient to explain 90% of the variance of the Index. However, if a model that explains 100% of the variance of the Index is chosen and other variables are added, Global Coal Reserves loses importance due to the presence of other parameters in which it is contained. Regardless of the chosen model of analysis, it is evident that there is room for revising the Index and removing variables that do not contribute to its precision. The research showed that the main disadvantage of the variables that make up the Index rests with the fact that the variables are of different degrees of generality, that is, one parameter is contained in other parameters (unclear which other). The research covers data for 25 countries over a 26-year period, with the first year of the research being 1980 and the last 2016 (the latest available report).
Ključne besede: international energy security risk index, analysis, stepwise regression, principal component analysis, Promax oblique rotation
Objavljeno v DKUM: 04.02.2025; Ogledov: 0; Prenosov: 24
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