Browse
Recent Submissions
- ItemA resilient model for trade volume forecasting under economic uncertainty: Addressing challenges in the global supply chain(Technická Univerzita v Liberci, ) Zhou, Guanglan; Wu, Ziyi; Ekonomická fakultaIn recent years, the escalating economic uncertainty arising from Sino-US trade frictions has made the accurate forecasting of trade volume a crucial yet challenging task, with wide-ranging implications for the stability of the global supply chain. Precise trade forecasts are essential for supporting strategic decision-making and ensuring resilience across industries that rely on international trade. To address this challenge, this study introduces an innovative predictive model, the principal component analysis-simulated annealing-backpropagation neural network (PCA-SA-BPNN), specifically developed to enhance forecasting accuracy within this volatile economic landscape. The model utilizes principal component analysis (PCA) to reduce the dimensionality of extensive datasets collected from search engines, simplifying the data while retaining critical information. Simultaneously, simulated annealing (SA) is applied to optimize the backpropagation neural network (BPNN), effectively addressing the local optimization challenges often impair traditional backpropagation neural network models, which can hinder prediction accuracy. The effectiveness of the PCA-SA-BPNN model is demonstrated through comprehensive comparative experiments, demonstrating its superior performance compared to other models, including principal component analysis-adaptive differential evolution-backpropagation neural network (PCA-ADE-BPNN) and principal component analysis-backpropagation neural network (PCA-BPNN) models, as well as standalone XGBoost and BPNN models. The PCA-SA-BPNN model achieves notably lower mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values, with an R2 approaching 1, underscoring its superior predictive performance. This research thus offers valuable insights into how combining dimensionality reduction, optimization techniques, and neural network networks can significantly enhance trade volume forecasting amidst economic uncertainties. Furthermore, it provides valuable insights into the interplay between predictive accuracy, model efficiency, and resilient decision-making within global supply chain management, contributing to both theoretical advancements and practical applications in the field.
- ItemA bibliometric overview of job demands-resources theory literature(Technická Univerzita v Liberci, ) Sengullendi, M. Fatih; Kurt, Enes; Ekonomická fakultaThis study aims to provide a comprehensive overview of the literature on job demands and resources (JD-R) theory and uses bibliometric analysis. A total of 688 articles and 204 journals published in the Web of Science database are included in the analysis. The study first provides an overview based on bibliometric indicators such as publication trends, influential journals, and prominent authors. Then, the thematic structure and relationship networks of the JD-R literature are examined using advanced bibliometric methods such as co-word analysis, citation, and co-citation analysis. Through these methods, the development of the field of JD-R theory is mapped, key concepts are identified, and critical studies, journals, authors, and subtopics are revealed. According to the results of the study, two significant findings were obtained: i) the literature on JD-R theory focuses predominantly on core concepts such as burnout, work engagement, job demands, and job resources; and ii) JD-R theory is increasingly being integrated with broader topics, proactive behaviors, organizational dynamics, and work-life balance. This research has not been found to have a comprehensive bibliometric analysis study conducted on the basic framework of JD-R. This study examines scientific research conducted with JD-R using bibliometric analysis methods. It presents important findings regarding the past, current status, and methods of developing the theory in business and management disciplines.
- ItemAge management implementation in the workplace: Trends, contributing factors, and implications for organizational performance(Technická Univerzita v Liberci, ) Seberini, Andrea; Kascakova, Alena; Tokovska, Miroslava; Solcova, Jana; Ekonomická fakultaThis study investigates age management implementation and its impact on employment patterns in Slovak organizations between 2021–2024. The research examines organizational responses to workforce aging challenges in a post-transition economy through a mixed-methods approach. Quantitative workforce analysis of Statistical Office data (n = 2,503) was combined with qualitative organizational assessment through key informant interviews (n = 6) to analyze employment trends across age cohorts and evaluate organizational responses. Findings reveal significant increases in older worker participation, with the 50–64 age group showing a 5.8% increase in employment rates (66.8% to 72.6%). Qualitative analysis identified six critical dimensions of successful age management implementation: technology adaptation, workplace flexibility, bidirectional knowledge transfer, professional development, health-conscious adaptations, and career continuation support. Organizations implementing comprehensive age management strategies demonstrated improved workforce retention among older employees. The findings provide empirical evidence supporting Slovakia’s active aging initiatives while highlighting challenges in gender equity and technology adaptation. This work aligns with the journal’s focus on contemporary workforce development challenges in transitional economies and contributes valuable insights for both practitioners and policy makers seeking to address the demographic shift in labor markets.
- ItemCase studies of subjective data dimensions in business intelligence based on literature(Technická Univerzita v Liberci, ) Antlova, Klara; Zelenka, Martin; Ekonomická fakultaData quality is widely recognized as a decisive factor for the success of business intelligence systems, as it directly influences the reliability of insights, the effectiveness of decision-making, and the level of trust placed in analytical outcomes. Traditional approaches have emphasized technical aspects such as accuracy, completeness, and consistency. Recently, attention has shifted toward subjective, user-related dimensions of data quality, influenced by perception, trust, and understanding. This study responds to this development by defining and categorizing subjective dimensions of data quality and identifying the organizational and technical conditions affecting user perception and trust in business intelligence environments. A mixed-methods approach was employed, combining a structured literature review with five case studies conducted in financial and non-financial organizations. Data from the case studies were gathered through semi-structured interviews with practitioners responsible for designing and managing data solutions. The findings revealed four distinct categories of subjective data quality (data access, usability, processing, and evaluation), which together capture the ways in which users assess the relevance, interpretability, and value of data. Six critical success factors were identified as essential in shaping these perceptions: data governance, metadata management, knowledge and competence development, organizational culture, technological infrastructure, and stakeholder relationships. From these insights, five best practices were derived that support the enhancement of subjective data quality, such as developing business glossaries, comprehensive metadata catalogues, and transparent documentation of data lineage. The study concludes that subjective data quality is co-produced by technological infrastructures and human factors, and it proposes a multi-layered model that integrates these dimensions to guide the design of business intelligence systems that foster trust, understanding, and greater decision-making value.
- ItemCrises precautions: Analysing changes in consumer behaviour – A Romanian food retailer’s perspective(Technická Univerzita v Liberci, ) Stanca, Liana; Campian, Veronica; Dinu, Vasile; Dabija, Dan-Cristian; Ekonomická fakultaThe food retail industry has experienced significant transformations during recent crises, including the pandemic, energy crisis, and armed conflicts. These events generated substantial socio-economic challenges for both companies and consumers, directly influencing purchasing behaviour and relational dynamics with brands and products. For instance, mobility restrictions during the pandemic forced consumers to reconsider how they purchased food and non-food items. Concurrently, armed conflicts and the energy crisis severely disrupted supply chains and store operations, requiring retailers to adapt quickly to meet shifting consumer needs and shortages. Many consumers rapidly abandoned previous shopping routines for online purchasing, mandating retailers to rapidly develop strategic responses to these changes. To explore the consequences of recent crises on food retail in Romania, an emerging market, and to show how retailers managed to adapt and implement new strategies based on the lessons learned, in 2025, the authors conducted in-depth interviews with food retail representatives. The aim was to identify transformations in consumer behaviour during the crises. Interview data were processed and analysed, combining text analytics (word frequency, associations, sentiment analysis, clustering) with thematic coding. Respondents consistently emphasised changes in consumer experience, increased adoption of online channels, precautionary behaviours such as stockpiling, and periods of panic buying. The findings provide an integrated perspective on how food retailers perceived and managed changes in consumer behaviour during the crises. Rather than establishing statistical causal effects, the research highlights consistent thematic patterns and emerging themes that link the crises situations to shifts in consumption and food retail strategies. The paper thus contributes to ongoing discussions on consumer behaviour dynamics under uncertainty, extending the methodological use of cluster analysis in food retail contexts, and adding interpretive insights in line with the dynamic capabilities theory.
- «
- 1 (current)
- 2
- 3
- »