Business Systems Research : International journal of the Society for Advancing Innovation and Research in Economy
Business Systems Research (BSR) is a scientific journal focusing on research findings in economics and business systems. Additionally, BSR considers research that combines business and economics with other fields of scientific enquiry, e.g. information systems, mathematics and social sciences. BSR examines a wide variety of business decisions, processes and activities within the actual business setting, as well as within the systems approach framework. Why submit? Our policy is to give feedback to the authors as fast as possible. After receiving the paper that is prepared according to the Author instructions, the editorial board will inform the author on the appropriateness of the paper to the journal within two weeks. The first review will be delivered as fast as possible, but not later than two months. Reviews that we provide are thorough and give author clear directions on how to improve the paper. Journal is indexed in Scopus, Web of Science (ESCI-WoS), and is indexed at Portal of Croatian Scientific and Professional Journals. Currently, it is indexed as Q3 for Economics, Econometrics and Finance in Scopus.
Society for Advancing Innovation and Research in Economy "Irenet"
Copyright (c) 2025 Business Systems Research : International journal of the Society for Advancing Innovation and Research in Economy https://creativecommons.org/licenses/by-nc/4.0
Measuring Social Impact and Enhancing Workforce Integration in Croatia’s Hotel Industry: Challenges, Opportunities, and Strategic Perspectives
Background: Croatia’s hotel industry faces persistent labour shortages and a rising reliance on foreign workers, underscoring the importance of structured workplace integration and measurable social outcomes. Objective: This paper proposes a sector-specific framework for social impact and integration aligned with EU sustainability directives and leading Environmental, Social, and Governance (ESG) standards. Approach: Drawing on literature and insights from three Croatian hotel leaders, we synthesise integration practices and map them to relevant indicators. Results: We identify actionable metrics and governance levers that improve employee satisfaction, retention, and community cohesion. Implications: The framework supports transparent ESG reporting and practical workforce strategies for sustainable tourism growth. In addition, the study makes a conceptual contribution by linking workforce integration practices to ESG reporting requirements, and a practical contribution by offering a structured indicator set tailored to the hospitality sector. This positions the paper as a conceptual, case-informed research article with both academic and managerial relevance.
Designing a Digital Business Model for Internal Audit Functions: A Framework and Case Study from the Financial Sector
Background: The internal audit function is a strategic infrastructural function of a company, organisation, or institution. Its digital transformation process must align with the organisation-wide digital business transformation process. The main determinants distinguishing successful from unsuccessful companies and internal audit functions are their overall business models. Objectives: The main aim of this paper is to propose and design a unique digital business model canvas for the internal audit function and test its usefulness in practice. It also aims to investigate the current state and characteristics of internal audit functions’ business models and their innovations in the financial sector. Methods/Approach: A combination of a comprehensive literature review and qualitative empirical research is applied to achieve the defined objectives. Results: A unique canvas for designing the internal audit function’s business model is proposed. It summarises, on one page, how the function creates, delivers, and captures value for its stakeholders. This theoretically developed and empirically validated model is suitable for developing, describing, analysing, and synthesising customer segments, value propositions, channels, customer relationships, strategic direction, key resources, key activities, key partnerships, and operational direction. Conclusions: Empirical investigation of the current state and characteristics of internal audit functions’ business model innovations and their compliance with global digital transformation trends shows that, in the Croatian financial sector, internal audit functions digitally innovate some elements but do not yet holistically transform all aspects. Thus, their digital business models differ in digital maturity, though digital transformation processes within the internal audit function align with those at the institutional level.
From Responsibility to Resilience: The Role of Corporate Social Responsibility Committees in the Stability of MENA Banks
Background: Bank stability is fundamental for sustaining economic growth, fostering investment, and maintaining confidence in the financial system. In recent years, the rise of Corporate Social Responsibility (CSR) and the creation of CSR committees (CSRCs) have transformed banks’ governance models toward more ethical, transparent, and accountable practices. Objectives: This study examines how CSR committees influence bank stability in the Middle East and North Africa (MENA) region, with a comparative analysis between Gulf Cooperation Council (GCC) and non-GCC countries. Methods/Approach: Using an unbalanced panel of 40 commercial banks from 2010 to 2022, the study employs the System Generalised Method of Moments (SGMM) estimator to address endogeneity, account for dynamic panel bias, and control for unobserved heterogeneity, thereby producing robust and reliable estimates. Results: Empirical findings reveal that the presence of CSR committees significantly enhances bank stability in the overall sample. However, this positive association is statistically significant only for GCC banks, not for non-GCC counterparts. Conclusions: These results highlight that institutional quality, regulatory strength, and governance culture determine how CSR governance mechanisms translate into financial stability. The study contributes to understanding the role of CSR governance in promoting sustainable banking in emerging economies.
AI-Driven Personalisation and Customer Engagement in Social Commerce: Evidence from Kosovo
Background: The study aims to examine the role of artificial intelligence-oriented personalisation in customer interaction of social commerce platforms in the developing market, such as Kosovo. Objectives: A quantitative design was used, and 312 active users were sampled, with surveys offered to all demographic segments. Methods/Approach: The customer's behaviour was conceptualised using the Technology Acceptance Model, the Stimulus-Organism-Response theory, and the Uses and Gratifications Theory. Results: The findings reveal a strong, positive correlation between AI-based personalisation and engagement. AI-based personalisation has significantly improved satisfaction, loyalty, and purchase intent. Age and education were ranked among the most critical moderators, and gender differences were not substantial. Conclusions: The study is informative for both theory and practice, as it provides insights into strategies for maximising customer contact through prudent personalisation under new market conditions.
Early Identification of At-Risk Students in Online Education: A Deep Learning Approach to Predictive Modelling
Background: Predicting student performance in online learning is difficult due to class imbalance and limited model interpretability. At-risk students are fewer than high performers, biasing predictions, and methods like SMOTE fail to preserve temporal patterns. Although black-box models are accurate, they lack transparency for actionable insights. Objectives: This study proposes a deep learning framework combining LSTM networks and attention mechanisms to address these issues using the OULAD dataset. LSTMs capture temporal dependencies, while attention improves interpretability by emphasising key features. Advanced resampling mitigates class imbalance for robust at-risk student detection. Methods/Approach: The methodology applies the KDD framework to process data, uncover patterns, and build models that predict student success risk, ensuring efficient data handling, robust modelling, and actionable insights to improve retention. Results: The BiLSTM-RNN achieved the best performance, effectively capturing temporal dependencies and attaining the highest accuracy, precision, recall, and F1-score. Conclusions: The findings support more effective and targeted interventions in online education, offering valuable insights for research and practice.
Stakeholder Perceptions of the Organic Foods Industry in Sri Lanka
Background: The organic food market in Sri Lanka has been growing steadily, driven by increasing consumer awareness of health and environmental sustainability. However, the market faces several challenges, particularly related to supply and affordability. Objectives: This study aims to identify the perceptions of four stakeholder groups regarding the key factors influencing the development of the organic food market in Sri Lanka, with particular emphasis on demand, supply, marketing, and related factors. Methods/Approach: This research adopted an inductive approach; qualitative surveys and thematic analysis of both primary and secondary data sources were used to analyse market conditions and the perceptions of four stakeholder groups in the organic food sector in Sri Lanka. Results: Findings indicate a rising trend in consumer demand, especially among younger urban populations. However, the market is constrained by high production costs, inconsistent supply chains, and limited consumer trust. Conclusions: To promote the growth of the organic food market, the study recommends increased subsidies for organic farming, enhanced government certification programs, and more substantial marketing efforts to raise awareness and build consumer confidence.
Business Model Innovation: Impact of Entrepreneurial Competencies to New Value Proposition
Background: Rapid changes and dynamic markets significantly impact the way businesses operate. Many companies fail to adapt and innovate their business models, which jeopardises their sustainability. Managers, as key decision-makers, play a pivotal role in the innovation process, whereby their entrepreneurial competencies directly influence various dimensions of business model innovation. Objectives: The main aim of this study is to examine the impact of entrepreneurial competencies on the new value proposition dimension of business model innovation (BMI), exploring competencies such as opportunity recognition, analytical thinking, innovativeness, tenacity, and passion for work. It investigates how these competencies contribute to developing new offerings and new channels, attracting new customers and markets, and building new customer relations. Methods/Approach: The study is conducted on a sample of 267 managers of medium and large companies across various industries in Bosnia and Herzegovina. The data were analysed using Structural Equation Modelling (SEM). Results: The results show that different entrepreneurial competencies have a significant impact on various aspects of a new value proposition. Conclusions: The study contributes to a deeper understanding of the influence of different entrepreneurial competencies on new value proposition dimensions within BMI. Managers who effectively utilise these competencies can enhance their companies' value propositions, thereby increasing competitiveness and business success.
Mapping Competitive Dynamics Research: A Bibliometric Analysis (2000–2024)
Background: Competitive dynamics has been a central theme in business, management, and finance research, addressing how firms interact strategically to gain and sustain competitive advantage. Over the past 25 years, this field has evolved significantly, incorporating theoretical and empirical advancements to analyse competitive actions and responses. Objectives: This paper aims to provide a comprehensive review of the literature on competitive dynamics published between 2000 and 2024, focusing on key contributions, trends, and gaps in the fields of business, management, and finance. Methods/Approach: A systematic literature review was conducted using peer-reviewed articles from leading academic databases, applying predefined inclusion criteria to identify relevant studies. The review is structured to highlight thematic developments and methodological approaches. Results: The findings reveal significant growth in the study of competitive actions and responses, with an increasing emphasis on technology-driven competition and dynamic capabilities. Key trends include the integration of big data analytics, sustainability strategies, and cross-industry rivalry. Conclusions: This review synthesises critical insights into competitive dynamics, providing a roadmap for future research and practical implications for strategic management in dynamic markets.
Real-Time Shipment Tracking through IT-Enabled Systems and Electronic Logistics Marketplaces
Background: This case study illustrates the journey of a medium-sized transport firm in improving cross-organizational processes within a demand-driven supply chain through automated information exchange, connected IT-enabled systems, and the Electronic Logistics Marketplace (ELM). Objectives: Both parties (carrier and manufacturer-shipper) experienced positive changes reflected through a decrease in loading and unloading waiting times, better organization of processes in the client’s warehouse, less workforce needed to handle loading and unloading, decreased workload for dispatchers in the Transport Department, better management of the COVID-19 restrictions, etc. Methods/Approach: This research uses a case study approach. Within a single case study, evidence is gathered from multiple internal and external sources. A thorough case study protocol was also developed to ensure a transparent chain of evidence. Results: This case study illustrates the critical role of IT-enabled information systems and ELMs in connecting cross-organizational processes to enable real-time shipment tracking. The study highlights the optimization of invoicing and document handling processes. The study also emphasizes the increased flexibility that logistics companies can achieve when equipped with real-time data. Finally, the study advocates greater investment in integrating demand-driven supply chain processes with available IT infrastructure. Conclusions: This in-depth case study underscores the critical roles of process transformation and the strategic deployment of IT-enabled information in facilitating enhanced collaboration with business partners in a demand-driven supply chain.
Volunteering Practices During the COVID-19 Pandemic: A Case Study Perspective
Background: The COVID-19 pandemic brought significant changes in all aspects of society, including the work of non-profit organisations. Many restrictions reshaped the economy and everyday life, causing serious threats to the work of both for-profit and non-profit sectors. However, the crisis also created an opportunity for innovation. As an important part of civil society, associations had to find new ways to support their beneficiaries and maintain volunteer activities. Methods/Approach: This paper used a quantitative survey of volunteers from the Krijesnica Association. Data analysis was conducted to assess changes in volunteer activities and to identify the main challenges volunteers faced during the COVID-19 pandemic. Results: The research results showed that the COVID-19 pandemic led to the proposal and implementation of new forms of volunteering, such as digital volunteering. Significant restrictions on physical contact encouraged the modification of existing forms of volunteering, which enabled the continued provision of assistance to beneficiaries. Conclusions: Understanding the changes in volunteer activities and the challenges volunteers face is crucial for developing strategies to support civil society organisations and ensure their sustainability in the future. The Krijesnica Association successfully adapted to the pandemic's challenges and continued to support its beneficiaries. The development of new forms of volunteering and the challenges volunteers face are important lessons for the future and an incentive for further research
Tourism on a Budget: Retrofitting IoT Solutions in Existing Apartment Accommodation
Background: The concept of applying smart devices known as the Internet of Things (IoT) is widespread in many industries, including tourism. However, a gap has been observed regarding retrofitting possibilities in existing facilities. Objectives: This paper explores feasible scenarios for implementing contemporary technological innovations in existing tourist capacities at an affordable price. Methods/Approach: To achieve the objectives, a conceptual model is first developed that provides an overview of the possible solutions for retrofitting IoT into an existing apartment. Then, a case study analyses several practical procedures suitable for smaller private renters to accomplish the optimal effects of the selected implementation measures. Results: The results show that even with small investments, consumers of tourist accommodation services will recognise the advantages of the comfort of a smart home. In contrast, service providers have the potential to reduce costs. The benefits for owners of multiple facilities at dispersed locations are particularly highlighted. Conclusions: Based on the advantages that the proposed change could bring to the providers of tourist services, various options are available for achieving the optimal combination of solutions - the maximisation of modernisation effects while minimising costs.
The Food Safety Level in the European Union Underpinned by the Performance of the Private Companies
Background: Monitoring food safety in the supply chain has been a priority of European consumer protection policies. In this context, over the last two decades, there has been a significant increase in notifications to the RASFF by private companies. Objectives: This study provides new insights by demonstrating that implementing private standards, such as GlobalGAP, is associated not only with increased notifications but also with reduced food safety issues when certifications are widespread. Approach: Thus, the effect of improvements in companies' actions, specifically the concrete application of the Global GAP protocol, on the number of notifications reported to the RASFF has been quantified through a multivariate panel-data analysis across 18 European countries. Results: The results highlight that while private standards improve detection, they also reduce risks, and that producer, trading company, and retail business management is indeed highly relevant to the effectiveness of the RASFF, as well as the fact that enterprises have become a leading actor in maintaining food safety. Conclusions: These findings underscore the importance of integrating private initiatives into public food safety policies. Specifically, the European Administration should incentivize the adoption of certification and enhance public-private collaboration to maximize food safety outcomes.
Machiavellianism and Aggression as Predictors of Emotional Manipulation
Background: Manipulation is the deliberate attempt by individuals to influence the actions of others. Machiavellianism is associated with a variety of undesirable behaviors including emotional manipulativeness, exploitation of others, high aggression, and low morality. Objectives: The aim of this study was to investigate the associations between Machiavellianism, proactive and reactive aggression and emotional manipulation, and to test whether Machiavellianism added incrementally to the aggression in predicting emotional manipulation, poor emotional skills and concealment of emotions. Methods/Approach: The Emotional Manipulation Scale, Short Dark Triad and the Reactive-Proactive Aggression Questionnaire were applied to a sample of 332 individuals. Data was collected online from an convenient sample of the general population. Results: The findings showed that Machiavellianism positively predicted emotional manipulation, poor emotional skills and concealment of emotions. Proactive aggression positively predicted emotional manipulation and concealment of emotions while reactive aggression positively predicted emotional manipulation. The results showed that Machiavellianism did add incrementally to reactive and proactive aggression in accounting for variance in emotional manipulation, poor emotional skills, and concealment of emotions. Conclusions: The results generally indicate a greater importance of Machiavellianism than aggression in explaining emotional manipulation and provide additional insight into the complex relationships between individual factors and manipulative behavior. Keywords: Machiavellianism, leadership, aggression, emotional manipulation
Ethical Perceptions of Foreign Product Purchases Among Generation Z: The Impact of Patriotism, Ethnocentrism, and Worldmindedness
Background: Understanding consumer levels of ethnocentrism, patriotism, and worldmindedness is crucial for marketers, as these factors influence consumer preferences, purchasing decisions, brand loyalty, and ultimately the domestic economy. Objectives: This paper aims to examine patriotic and ethnocentric tendencies, assess worldmindedness, and explore the interrelations among these constructs within Generation Z in Slovakia. Methods/Approach: Primary data were collected via a structured questionnaire among 301 Slovak Generation Z respondents. Data analysis included descriptive statistics, Pearson’s correlation with confidence intervals, and network analysis of 50 items across three validated instruments (CETSCALE, pseudopatriotism scale, and the Worldmindedness Scale). Reliability was assessed using McDonald’s ω, Cronbach’s α, and item-rest correlation. Results: The findings reveal low levels of ethnocentrism and patriotism, but relatively high worldmindedness within the cohort. Conclusions: These results suggest a growing openness of Generation Z to global values and foreign products, posing a challenge for domestic producers but creating potential for foreign exporters. As this cohort becomes a key economic force, businesses and policymakers must adapt to their global outlook when designing marketing and economic strategies.
Industry 4.0, Business Process Management, and Lean Six Sigma for Achieving Business Excellence through Operational Excellence
Background: Asset management organizations have integrated new technologies before, but this trend is expected to reach its peak in the coming years. Investors using technical instruments buy financial products from asset management companies (AMCs). Thus, AMC is working diligently to enhance its operations and processes, enabling investors to have seamless transaction experiences across its various platforms through simplified procedures and efficient management. Objectives: This study examines the performance drivers of AMCs, more specifically, how fundamental components contribute to business excellence by improving firm procedures and operations and creating new investor experiences. Methods/Approach: The report uses data from 387 asset management professionals from 10 businesses. In India, 10 prominent AMCs provided 387 valid replies to a work survey. The analysis was conducted using PLS-SEM. Results: The findings indicate that Industry 4.0, business process management, and Lean Six Sigma facilitate operational excellence and are key drivers of achieving business excellence in asset management firms in India. Conclusions: Organizations can achieve business excellence by applying concepts from Industry 4.0, business process management, Lean Six Sigma, and operational excellence. This is among the few empirical studies examining the enablers of business excellence in Indian AMCs.
Agricultural Land-Use Classification on Satellite Data Using Machine Learning
Background: The utilization of satellite images has become increasingly popular for detecting land usage, with a focus on agricultural land classification in recent years due to the significant decline in the number of bees. Objectives: This paper seeks to address these challenges by applying several machine learning algorithms on multi-spectral satellite data from Sentinel-2 to derive accurate land classification models. Methods/Approach: Specifically, we use five bands: Red, Green, Blue, NIR, and NDVI to build three models, namely Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Results: Our results show that on collected satellite data, the CNN model outperforms the other algorithms, with an accuracy score of 0.82, F1-score of 0.72, and AUC score of 0.94, followed by the RF and LSTM models. Conclusions: This highlights the importance of utilizing advanced machine learning techniques, particularly CNNs, in accurately classifying agricultural land use changes.
Impact of Personality and Psychopathy on Deviant Workplace Behaviour: Systemic Approach
Objectives: The main aim of this study was to examine the effect of the HEXACO domains and triarchic psychopathy dimensions on interpersonal and organisational deviant workplace behaviour. Methods/Approach: The Workplace Deviance Measure, HEXACO-PI-R and Triarchic Psychopathy Measure were applied to a sample of 429 workers recruited from employees of several IT organisations and education staff working in higher education. Results: Results showed that honesty-humility and agreeableness significantly predicted both interpersonal and organisational workplace deviance, and agreeableness showed significant effects on interpersonal workplace deviance. Openness to experience was unrelated to both interpersonal and organisational workplace deviance, but extraversion positively predicted organisational deviance. All triarchic dimensions (boldness, meanness, and disinhibition) showed significant effects on both interpersonal and organisational workplace deviance. Conclusions: Findings indicate that honesty-humility, agreeableness, and conscientiousness significantly enhance the probability of developing positive relationships with co-workers. Triarchic psychopathy features demonstrate considerable maladaptive potential, consequently presenting a significant risk factor for the development of positive workplace interactions.
Exploring Analytic Hierarchy Process (AHP) Applications in Banking: A Bibliometric Study
Background: A bibliometric analysis was conducted to investigate the prevalence of the Analytic Hierarchy Process (AHP) in academic studies, revealing it to be one of the most widely used methods in multi-criteria decision-making. The AHP structures complex decisions in a hierarchy, facilitating the evaluation of multiple criteria and alternatives. This approach is particularly valuable in situations where quantitative methods are not always applicable or sufficient. Objectives: The main aim of this study is to assess the extent of research development on this topic at global and national levels using various performance indicators. Methods/Approach: The WoS database was used to obtain the required data. The search revealed 286 research publications on "AHP and banking", of which 79 were analysed to illustrate the potential applications of AHP and hybrid methods. Results: The analysis provided a qualitative assessment of bibliometric indicators, highlighting existing trends and future research directions for the (r)evolution of academic progress. According to the selected fields of analysis, a gradual increase in the number of citations, published papers, and research on vertical and horizontal topics within the banking sector was observed. Conclusions: The main contribution of this paper is the identification of new research foci and the filling of unresolved gaps in the academic literature. The evidence suggests that the effectiveness of the AHP method has stimulated the development of related research. Future papers should further elaborate on the proposed research areas in collaboration with other disciplines to create a robust research programme and foster innovations.
Vol 15 No 2 (2024) Special Issue in Novel Solutions and Novel Approaches in Operational Research
Special Issue in Novel Solutions and Novel Approaches in Operational Research
Copyright (c) 2024 Editor-In-Chief https://creativecommons.org/licenses/by-nc/4.0
Editorial for the Special Issue: "Novel Solutions and Novel Approaches in Operational Research"
This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.
Copyright (c) 2024 Business Systems Research : International journal of the Society for Advancing Innovation and Research in Economy https://creativecommons.org/licenses/by-nc/4.0
Full Issue of Vol. 15 No. 1 (2024): A Systems View across Technology & Economics
Does Gender Matter in Audit? Evidence on Earnings Management and Audit Delay from Croatia
Background: There is an expanding body of research on the relationship between auditor gender and earnings management, based on the assumption that female auditors possess specific innate characteristics relevant to external audit quality. Objectives: To further existing knowledge, this study examines the moderating effect of audit delay and the difference between post-audit and pre-audit financial reporting quality as direct measures of audit quality and provides insight into opportunities for women in the Croatian audit market. Methods/Approach: This analysis used panel regression to estimate the research model and the modified Jones model as a measure of discretionary accruals. Results: In addition to highlighting Croatia as the country with the highest female audit partner participation in audit engagements, the analysis revealed that earnings management decreases following an audit conducted by male audit partners. The beneficial impact of female audit partners is documented after the moderating effect of audit delay is included, and during the first year of the COVID-19 pandemic. Conclusions: The findings suggest that female audit partners may utilise audit delay more effectively, enhancing oversight during periods of financial distress. At the same time, earnings management is generally reduced in audits conducted by male audit partners.
Insights into Kaizen Culture in Project Management Development Teams
Background: This paper examines factors influencing kaizen adoption in project management teams, emphasizing agile methods, employee engagement, organizational culture, and effective communication. Objectives: The aim of the research was to explore LS Retail employees' experiences with kaizen implementation in project management, assessing whether a kaizen culture exists and how agile practices support its adoption. Methods/Approach: A qualitative methodology was employed, involving semi-open interviews with 12 employees from three development teams at LS Retail. Results: The findings indicate that LS Retail's strong agile tradition has facilitated growing acceptance and support for kaizen. Implementation, however, varies significantly between teams. Notable challenges persist, particularly regarding communication flow, knowledge sharing, and kaizen training, especially within teams less experienced with kaizen methods. Addressing these challenges is essential to achieve a more consistent and effective kaizen implementation throughout the organization. Conclusions: The findings provide valuable guidance for organizations implementing kaizen in project management. Key success factors include fostering a supportive and flexible environment, continuous employee education and empowerment, and ensuring effective communication for consistent and successful kaizen adoption.
Estimating Short-term Default Probabilities Conditional to Economic Conditions: Applications of Regularisation Approach and Economic Adjustment Coefficients
Background: Corporate bonds are crucial for corporations as they provide a flexible and often less costly alternative to equity financing. However, rising corporate debt levels, along with rating downgrades and economic uncertainty, can cause corporations to face financial distress, exacerbating the probability of default. Objectives: The purpose of this paper is to estimate bond default probabilities conditional on fluctuations in economic growth over short-term frequencies using inputs from rating transitions. Methods/Approach: The estimation is based on a Markov chain framework and the incorporation of economic growth by utilizing specifications of the economic adjustment coefficient. Further, quasi-optimisation of the roots matrix is utilized to extend the model within a quarterly domain. Results: Economic growth (proxied by GDP) carries little informational content on the future default probabilities. Non-investment grade ratings depict higher default probability, while investment-grade ratings yield default propensity of less than 1.1% in the next quarters and exhibit higher distance between default probabilities by tenor points and neighbouring states as the time horizon lengthens. Conclusions: First, practitioners can measure forward-looking bond exposure across different tenure buckets using the estimation approach developed in this study. Second, by considering historical fluctuations in the economic cycle as an additional factor for estimating future default probability, this study informs financial market regulators by providing entities with an alternative reference point to their in-house generated models, helping them meet regulatory requirements.
Using CNNs for Photovoltaic Panel Defect Detection via Infrared Thermography to Support Industry 4.0
Background: This study demonstrates how convolutional neural networks (CNNs), supported by open-source software and guided by corporate social responsibility (CSR), can enhance photovoltaic (PV) panel maintenance. Connecting industrial informatics with sustainable practices underscores the potential for more efficient and responsible energy systems within Industry 4.0. The rapid expansion of solar power necessitates effective maintenance and inspection of PV panels to ensure optimal performance and longevity. CNNs have emerged as potent tools for detecting defects in PV panels through infrared thermography (IRT). Objectives: The review aims to evaluate CNNs' effectiveness in detecting PV panel defects, align their capabilities with the IEC TS 62446-3:2017 standard, and assess their economic benefits. Methods/Approach: A systematic review of literature focused on studies using CNNs and IRT for PV panel defect detection. The analysis compared performance metrics, economic benefits, and alignment with industry standards. Results: CNN models demonstrated high accuracy in defect detection, with most achieving above 90%. Integrating UAVs for image acquisition significantly reduced inspection times and costs. Conclusions: CNNs are highly effective in detecting PV panel defects, offering substantial economic benefits and potential for industry-wide standardisation. Further research is needed to enhance model robustness across diverse conditions and PV technologies.
School-to-Work Transition in the Youth Labor Market in Central and Eastern Europe: A Cluster Analysis Approach
Background: This study analyzes education, training, and the youth labor market in central and eastern Europe. Objectives: This study aims to evaluate similarities and differences in youth labor markets among eleven central and eastern European countries from 2008 to 2021. It specifically examines three aspects: wage ratios, early departure from education or training, and the share of the population not in employment, education, or training. Methods/Approach: This study applies hierarchical clustering and multidimensional scaling to panel data. The complete-link method organizes countries into clusters. This study combines three-dimensional Cartesian projections and two-dimensional projections based on multidimensional scaling with dendrograms and heatmaps, to graphically illustrate the "school-to-work" transition across this region. Results: Clustering highlights the Visegrád countries, the Baltics, and the Balkans as zones with internally homogeneous yet externally heterogeneous challenges for the youth generation. As the outliers in each of these regions, Poland, Estonia, and Bulgaria support clustering solutions that deviate from conventional understandings of central and eastern Europe. Conclusions: Historical and geographical ties continue to define this region’s youth labor markets across political and economic dimensions. Clustering analysis identifies triumphs and struggles in policymaking in some of the poorest and most politically challenging member-states of the European Union.
Default Prediction in the Finance Industry Based on Ensemble Learning: Combining Machine Learning and Deep Learning
Background: Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives: This study aims to enhance default prediction by employing advanced ensemble learning techniques in machine learning and deep learning. Methods/Approach: Instead of relying on transformation, fine-tuning, or single algorithm models, this research focuses on combining multiple models using voting and stacking techniques, particularly highlighting a stacking model combining Light Gradient Boosting Machine (LGBM) and Artificial Neural Networks (ANN). Results: The ensemble learning methods, especially the LGBM-LSTM and XGB-LSTM stacking models, showed higher precision in identifying borrowers who defaulted, while the LGBM-LSTM and XGB-LSTM voting models excelled in recall and achieved an F1-score 0.1% higher. Both the stacking and voting models attained AUC values close to 90%, indicating strong overall classification performance. Conclusions: The findings not only contribute to the fields of lending and peer-to-peer financial operations but also offer crucial insights that aid financial organizations in making well-informed decisions regarding loan processing and management.
Influencing E-commerce Performance in European Transition Economies: An Urgent Call for Digital Marketing
Background: This study investigates the influence of digital marketing techniques on the performance of e-commerce businesses in the transition economy of Albania. Objectives: While no comparable research has yet been carried out in the Balkan region so far, the goal is to offer both empirical insights and practical value for businesses to enhance strategy frameworks of digital marketing. Methods/Approach: Using a representative sample, the research employs hierarchical regression with control variables to test hypotheses, which advances knowledge in the field by introducing a new framework that has not been previously employed. Results: The empirical data of the representative sample show that different digital marketing techniques combinations positively impact e-commerce performance. Conclusions: The findings not only contribute to the advancement of knowledge in the field but also offers a practical framework for e-commerce businesses in the transition economies at large and Albania in particular to optimize their digital marketing strategies and enhance their performance.
Scaling Up Organic Agricultural Enterprises: An Empirical Study of the Role of Associations of Micro Producers
Background: Development of organic agricultural entrepreneurship often requires “scaling up” from a multitude of individual, largely disconnected micro organic enterprises with haphazard achievements, toward more integrated units, operating more systematically and allowing for replication of their achievements on a larger territorial and temporal scale. Objectives: The current states of activity, sectoral and inter-sectoral cooperation of Croatian associations of micro organic producers are assessed with a view to instigate and facilitate scaling up processes. Methods/Approach: The inquiry is based on thematic analysis of qualitative data collected through semi-structured interviews with representatives of Croatian associations of micro organic producers. Results: Lessening of tension between farmers’ strivings for autonomy and their need for mutual cooperation, trust in interpersonal relationships and organizational structures, and integration through value-based supply chains (VBSCs) are identified as important aspects of scaling-up. Conclusions: Scaling-up processes are distinctly path-dependent and, although examples of earlier working practices are welcome, they can seldom be carbon copied in different developmental contexts. Conceptualization of scaling up as intensification of relational interdependencies among various organizational units at several levels of aggregation underlines the need for strengthening the role of associations of micro producers as intermediaries between family farms and other, more complex, inter-sectoral organizational forms.