Search Results (263)

The work presents a comprehensive analysis and costing of sugar beet cultivation in 2020–2022, for individual farms of the Lublin region. About 120 farms were analyzed. Based on this analysis, the criteria for a model farm were determined and adopted for the calculation of sugar beet production costs. ARIMA process modeling was performed, based on which forecasts were determined for several selected parameters. Customs tariffs introduced by the USA have a drastic impact on the economy. The effects of the COVID19 pandemic may also have a significant impact on the current market situation. Forecasting in the current geopolitical situation is very difficult because of the lack of stationarity of parameters. The financial result obtained by growers is mainly influenced by indirect costs absorbing 61.31% of total costs in 2020. In 2021 and 2022, indirect costs were 61.16% and 59.61% of production income, respectively. Among this group of costs, the largest share is accounted for by the costs of sowing services, sugar beet harvesting, and soil liming amounting from 14.27% to 15.92%. During the analyzed period, sugar beet cultivation remained profitable, with a production profitability index of 1.31 in 2020 and 2021, and 1.10 in 2022. The unit cost of production increased every year. In 2020, it was 14.27% and in 2021, it increased to 15.19%. The unit cost of production in 2022 was the highest, at 23.41%. Sugar beet cultivation is one of the profitable activities in agricultural production, but it is characterized by high production costs, which increased during the years analyzed (2020 to 2022), topping out at 90.87% of total revenue. The information and data presented in this study will be used in the development of a farmer-oriented application and will support the creation of an expert system for sugar beet growers. Cost forecasting will enable farmers to plan their production more effectively. Full article

Anxiety is a significant mental health concern in universities worldwide. This study examines the structure of anxiety symptoms and their relationship with contextual stressors among pre-service English teachers. The sample included 269 students enrolled in a Teaching English as a Foreign Language program at a public university in Manabí, Ecuador. Data were collected using the Zung Self-Rating Anxiety Scale and a custom-designed questionnaire identifying anxiety triggers. Results showed that while most students reported normal or mild anxiety levels, a considerable portion exhibited moderate to severe symptoms. Cluster analysis revealed three emotional profiles, with the high-anxiety group strongly associated with stressors such as economic hardship and job insecurity. Academic pressure and financial instability emerged as the strongest predictors of anxiety. These findings highlight the urgent need to develop and evaluate targeted psycho-educational strategies to prevent and reduce anxiety within teacher training programs in higher education. Full article

This study explores the relationship between customer satisfaction and loyalty in mobile banking, emphasizing the moderating role of Technology Readiness. As mobile banking becomes increasingly central to financial service delivery, understanding the nuanced drivers of customer loyalty is essential for strategic growth. Drawing from the Technology Readiness Index, this study examines how four dimensions, optimism, innovativeness, discomfort, and insecurity, moderate the satisfaction–loyalty linkage. Data were collected via a structured survey from 258 mobile banking users in the United States, analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that optimism and innovativeness positively moderate this relationship, while discomfort and insecurity act as negative moderators. Practically, this research introduces a segmented approach to mobile banking service design, underscoring the need for differentiated strategies that address varying levels of user readiness. Theoretically, this study addresses a gap in mobile banking literature by shifting the focus from adoption to sustained usage and satisfaction-based loyalty, enriching the discourse on customer behavior in digital finance. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

This study explores the link between brand image and Net Promoter Score (NPS) in Iceland’s banking sector using data from three survey waves (2021, 2023, and 2025). While NPS is widely used to track customer loyalty, its relationship with brand image, especially in financial services, remains unclear. Drawing on repeated cross-sectional data (n = 1504), we examine how trust, corporate social responsibility, customer satisfaction, and perceived corruption relate to NPS across three major banks. Results show a consistently strong positive correlation (r > 0.5), with Arion Bank customers showing the highest association (r = 0.68). This suggests that customers with a positive image of the bank are far more likely to recommend it. The findings offer both theoretical and practical value: they reinforce the role of brand image in driving customer advocacy and support a more contextualized use of NPS in brand strategy and customer experience management. Full article

This study explores the transformative role of financial technology (fintech) in advancing sustainability, financial inclusion, and customer engagement in Jordan’s banking sector. Utilizing a quantitative descriptive survey design, data were collected from 400 participants—comprising 300 bank customers and 100 banking professionals—through a structured bilingual questionnaire distributed via digital platforms. The study aims to evaluate how fintech innovations align with sustainable finance practices, extend banking access to underserved populations, and influence customer satisfaction. The results reveal strong evidence of fintech’s positive impact across all three domains. Regression analysis confirmed a statistically significant relationship between fintech innovation and the adoption of sustainable finance practices (β = 0.6498, p < 0.001), explaining 42.2% of the variance in sustainability outcomes. Similarly, fintech adoption was found to significantly improve financial inclusion among underserved populations (β = 0.6842, p < 0.001), accounting for 46.85% of the variance in access to services. One-way ANOVA analysis further showed that increased fintech integration significantly enhances customer engagement, with mean satisfaction scores rising progressively with higher fintech usage levels (F = 24.49, p < 0.001). The study underscores that fintech is a critical enabler of ethical banking transformation in Jordan, promoting ESG objectives, reducing financial access disparities, and strengthening customer loyalty. The findings confirm that fintech significantly contributes to sustainable, inclusive, and customer-centric banking practices. These insights support the notion that fintech adoption not only redefines banking operations but also charts a sustainable and socially responsible future for the Jordanian financial sector. Full article

The surge in digital transformation within financial technology has catalyzed the development of super-apps—comprehensive mobile applications designed to serve a multitude of a customer’s daily needs on a single platform. Despite their widespread use, there is a dearth of research regarding customer adoption in the banking industry. Employing the integrated Information Systems (IS) success model, this study delves into how the characteristics of mobile banking super-apps influence user adoption intentions, taking into account various levels of service integration, in South Korea. The results reveal that factors such as interactivity, service diversity, process completeness, and technological service innovation positively affect the perceived ease of use. However, only service diversity and process completeness significantly influence perceived usefulness. Furthermore, distinct relationships between constructs are observed among different user groups based on their preferred service integration levels. This research can help banks formulate app management strategies and identify the optimal levels of service integration for their mobile banking super-apps. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

The fintech sector is entering the maturity phase of the life cycle, in which customer brand engagement (CBE) is connecting companies and customers. The aim of this paper is to investigate the driving forces behind CBE in the fintech sector and its contribution to loyalty intentions. In the framework of service-dominant logic, a quantitative approach on 239 fintech users was undertaken to propose and validate a conceptual model of customer brand engagement in fintech, using Partial Least Square–Structural Equation Modeling. The results showed that the perception of personalization, the perception of interaction between customers and brands, and the perception of benefits contribute to CBE in fintech, and the latter is positively associated with loyalty intentions. This research contributes to the literature of brand engagement by offering an integrative framework to analyze CBE in the fintech sector. Full article

This study investigates inventory management and pricing techniques in a two-tier supply chain where newborn items are grown, slaughtered, and transported to retailers for consumer sale. This study assesses how certain carbon regulations can enhance or hinder profitability for suppliers and retailers, demonstrating the interdependence of their financial performance in connection to environmental regulations. A mathematical model considers demand as impacted by unit weight, selling price, and storage duration, with consumption patterns as a power function of these variables. This paper examines demand dynamics and proposes a solution for optimizing crucial factors such as the number of newborn items, the retailer’s selling price, and operating cycle time to increase profitability while maintaining excellent customer service. Full article

For a country to be able to sustain a policy of increasing the use of renewable energy sources to supply electricity, it must be able to continue to provide a reliable electricity supply service to its customers. Typically, electricity reliability is maintained by thermal electricity generation. To substitute solar PV for thermal electricity generation to a significant degree, it is imperative to determine the least-cost complementary technologies that will provide system reliability. In many parts of Africa and Asia, potential sites for seasonal storage dams are available or have been built. In the case studied here, maintaining service reliability by expanding the capacity of the generation plant of a seasonal storage dam in all scenarios is less costly than providing service reliability by a thermal alternative. However, maintaining service reliability while expanding generation by solar PV is in all cases costly. The levelized financial cost of the incremental energy supplied when a reliable service is maintained is between 30% and 89% greater than the levelized cost of a standalone solar PV plant. For the same set of scenarios, the range of the economic levelized cost is 28% to 85% greater with reliability than the standalone solar PV field without reliability. Given the circumstances of the electricity market, the least-cost technology to maintain a reliable service may be specific to the market. The analysis also shows that when the economic opportunity cost of funds increases from 2% to 11.5%, the levelized cost of renewable electricity generation systems doubles. Hence, if the developed countries of the world want low-income countries to maintain policies to reduce the use of fossil fuels to generate electricity, capital subsidies to low-income countries that are facing high economic opportunity costs of funds are likely to be necessary. Full article

The automotive industry, especially the after-sales service segment, faces significant challenges due to economic changes and market dynamics. In this context, the optimization of service processes becomes essential to increase the performance and profitability of organizations in the industry. However, there is a lack of research that specifically and in detail explores how to model service processes to improve performance in this sector. Most studies focus on general aspects of quality management or process optimization without addressing the particularities of after-sales services in the automotive industry. This paper aims to identify and analyze how to model service processes in an automotive repair service provider organization to increase performance and ensure customer satisfaction. This research was conducted using data from service activity reports and participatory direct observation within an automotive repair service provider organization. Statistical analysis of key performance indicators, such as productivity, efficiency, and customer satisfaction, was performed. This study identified several critical success factors and proposed concrete measures for shaping service processes, including optimizing resource allocation and customer communication, improving customer intake and communication, ensuring technical competence and procedural compliance, and improving the process of handing over and collecting feedback. The implementation of these measures can lead to increased efficiency, customer satisfaction, and, by extension, the financial performance of automotive repair organizations. Full article

The banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study examines the relationship between customer attrition and account balance using decision trees (DT), random forests (RF), and gradient-boosting machines (GBM). This research utilises a customer churn dataset and applies synthetic oversampling to balance class distribution during the preprocessing of financial variables. Account balance service is the primary factor in predicting customer churn, as it yields more accurate predictions compared to traditional subjective assessment methods. The tested model set achieved its highest predictive performance by applying boosting methods. The evaluation of research data highlights the critical role of financial indicators in shaping effective customer retention strategies. By leveraging machine learning intelligence, banks can make more informed decisions, attract new clients, and mitigate churn risk, ultimately enhancing long-term financial results. Full article

Road freight transport is the key driver of the European economy and society; thus, distortion of its operation would have negative influence on growth and well-being. For that reason, implementation of European policies, including transport decarbonization, should be comprehensively evaluated from an environmental, social and economic perspective. In that case, introduction of electric trucks will create a mutual impact on the market and on haulage companies. The main research problem is to assess the future impact of decarbonization on the international road freight transport market structure on the supply side and the competitiveness of companies operating there. Today, a number of small and medium companies, to a great extent from Eastern Europe, render transportation services, creating a competitive structure with high flexibility, accessibility and low prices. Shifting towards electric trucks, with significantly higher upfront costs, will redefine the market structure, eliminating the small carriers and activating horizontal integration. The key objective of this research is to identify the main factors and challenges related to electric truck implementation and define crucial areas of its impact on future market structure. The research shows that the improvement of environmental performance requires low- or zero-emission trucks, where the battery technology is a leading solution. Thus, fleet renewal needs additional financial support from the public side. Different measures are available in European countries, so the level of support is not equal from a competitiveness perspective. Battery truck selling, as well as sustainable strategies, refer mostly to huge transport companies. On the other hand, the case of Polish truckers shows that the economic viability of SMEs is poor; thus, the introduction of BET would be beyond its reach. The research findings could be treated as recommendations for market regulators (EC), where the tempo of implementation, as well as availability of public support programs, should be rethinking. As a result, the costs of the transition will be covered by citizens, as customers, in the prices of products and transport service, or as taxpayers, in public support programs, mainly consumed by large market stakeholders. Full article

In the context of rapid advancements in financial technologies and the evolving demand of the digital economy, this study explores the transformative impact of FinTech and artificial intelligence (AI) on the banking sector, with a particular focus on payment systems. By examining innovative financial instruments and AI-driven solutions, this research investigates how these technologies enhance efficiency, security, and customer experience in banking operations. This study evaluates the integration of AI in payment systems, including its role in predictive analytics, fraud detection, and personalization, while aligning with global trends in digital transformation and sustainability. Adopting an interdisciplinary approach, this analysis highlights scalable and resilient strategies that address emerging challenges in the financial ecosystem. The findings provide a comprehensive framework for leveraging AI and FinTech to drive the evolution of banking services, supporting the transition toward a more innovative, digitalized, and sustainable financial future. Full article