Search Results (5,206)

There are numerous ways in which generative artificial intelligence (GAI) can be applied in the teaching and learning process. This paper presents one application in the Business Decision Analysis (BDA) course. BDA is considered as the most challenging course in the Graduate Study Program in Economic Entrepreneurship at the University of Zagreb Faculty of Organisation and Informatics; consequently, the teachers continuously analyse possibilities to make the course more attractive for students. The innovative teaching activity at BDA was implemented as a betting shop during the first colloquium (which accounts for 50% of the overall grade). In the activity, GAI analysed learning management system (LMS) data of students’ results (attendance, self-assessment test results, logs in the system) of the initial (pre-course) test, as well as their results of the pub quiz (activity organised a week before the colloquium as a preparatory activity). GAI analysed all the data and predicted the number of points each student will achieve. Additionally, GAI calculated the risk index, average growth (among self-assessment tests) and learning consistency for each student. Finally, GAI created a message for each student that explained what went well in their learning activity, what could be improved, and included a motivational note for the test. The rule was: if a student achieved a higher result than the GAI predicted, the teacher would buy a chocolate for that student. More than 60% percent of students achieved a higher score than was predicted. Surprisingly, exceeding the expected result was not in correlation with the risk indices determined by the GAI. Cluster analysis identified four student profiles consistent with the correlation results, showing weak overall agreement between the predicted and achieved scores, except in the male subgroup, while higher predicted scores were associated with higher average growth and lower risk indices. Qualitative analysis of the GAI application in teaching yielded positive comments, as students perceived the activity as helpful, motivating, and engaging, and would have liked more similar activities. Full article

The relevance of this study is driven by the need to develop new mechanisms and tools aimed at improving the technological, resource, and economic efficiencies of industrial businesses while minimizing their negative environmental impacts and enhancing their environmental performances. Although such approaches as the theory of constraints, the concept of sustainable development, and principles of Best Available Techniques have garnered attention individually, their combined, interdisciplinary application to the streamlining of business processes in industry has not yet been fully explored. The purpose of this study is to demonstrate the advisability of managing business processes based on the principles of resource efficiency enhancement by preventing irrational resource consumption, production losses, pollution and waste in the context of the Sustainable Development Goals. This article analyzes the current state of research business process optimization for a greener future. The proposed methodological approach is based on ranking business processes according to their levels of resource efficiency. Business process engineering and an evaluation of its outcomes in terms of resource efficiency were conducted using a case study of a building materials manufacturer in Northwest Russia. Various business management scenarios were developed to improve resource efficiency through process engineering initiatives. The findings of this study can inform the development of strategic approaches for building materials manufacturers as they transition toward sustainable development. Full article

In municipal wastewater treatment plants (WWTPs), anaerobic digestion of municipal mixed sludge (MMS) often yields low energy recovery and operational instability due to imbalances between primary and secondary sludges. Anaerobic co-digestion (AcoD) with readily biodegradable wastes, such as fruit and vegetable waste (FVW), can enhance process stability and biogas production. Life cycle assessment (LCA) methodology is used in this study to evaluate the environmental performance of implementing AcoD of MMS and FVW in a municipal WWTP, compared with a business-as-usual scenario combining mono-digestion of MMS and incineration of FVW. The LCA was modelled in openLCA 2.5 using the ecoinvent 3.9.1 database (cut-off allocation approach), and impacts were assessed with the ReCiPe 2016 Midpoint (H) method, focusing on climate change, terrestrial acidification, fossil fuel depletion, and marine eutrophication. Results indicate that AcoD reduces impacts across all environmental categories, mainly due to higher biogas yields that increase on-site electricity generation and decrease reliance on grid electricity. Improved total solids removal also lowers digestate production and composting-related burdens. Electricity consumption remains the main hotspot in both scenarios, highlighting the importance of energy efficiency and electricity mix. Sensitivity analysis on methane content (61–65% v/v) confirms the robustness of AcoD’s environmental benefits. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

This study aims to automate the generation of execution plans for university projects by transforming BPMN-based process models into hierarchical planning representations that can be executed by HTN planners. Effective implementation of university extension projects requires explicit management of objectives, dependencies, and operational constraints, yet this process is often carried out manually and without formal planning support. To address this problem, the paper proposes PlanProjU, a web-based platform that captures project knowledge through BPMN and translates it into HDDL domain and problem files for execution with SHOP2 and PyHOP. The system was evaluated through real university project cases and a comparative analysis of alternative generated plans. The results show that BPMN-based project knowledge can be operationalized into executable hierarchical planning structures and that different planners may produce distinct plan alternatives depending on project characteristics. The originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning the originality of the study lies in the design of a traceable BPMN-to-HDDL workflow for university project planning, implemented in an integrated platform that connects business process modeling with HTN automated planning in a domain that has received limited attention in prior research. In this sense, the proposal serves both as an innovative research contribution and as a practical alternative for structuring implementation decisions in institutional settings. Full article

Environmental sustainability has emerged as a strategic requirement of those organizations that want to remain competitive in the long run, but most companies continue to adopt green human resource management (GHRM) practices and organizational change initiatives individually, thus restraining their potential transformation. This paper constructs and confirms a combined approach of how the fit between GHRM practices and adaptive change management processes results in high performance in sustainable innovation. In this study, 83 organizations from both the manufacturing and service sectors were selected using a purposive sampling method, to ensure diversity across developed and developing countries and varying levels of GHRM integration (low, moderate, and high). The sample was chosen to represent a broad spectrum of sustainability maturity levels, allowing for a comprehensive analysis of how GHRM practices influence green product, process, and business model innovation. This selection, alongside 30 peer-reviewed studies published between 2020 and 2025, underpins the conceptual framework used to activate change preparedness and link GHRM dimensions with innovation outcomes. I demonstrate that organizations with a high GHRM–change management fit have much higher levels of innovation performance—both in terms of the number of green product innovations (485%) and more sustainable performance improvement (90.5 on average)—than low-integration organizations. Findings also reveal that leadership commitment, employee engagement, organizational learning, and systemic reinforcement are key mediating processes that enhance the effect of GHRM activities. Temporal trajectory analysis demonstrates that integrated organizations go through deployment, consolidation, and optimization phases, as well as increasing returns to performance, with an accelerating trend of 36 months. This paper is important in management research as it fills in gaps in the literature, providing an explanation of how human resource practices facilitate organizational change at the system level. In practice, this study offers evidence-based recommendations to managers who want to establish sustainability-oriented innovation capability by implementing a coordinated GHRM and adaptive change management approach. Full article

This study aims to systematically review how artificial intelligence (AI) is being adopted by small and medium-sized enterprises (SMEs) in Latin America to improve resilience and support sustainable practices in uncertain business environments. Based on the PRISMA protocol, fourteen peer-reviewed studies published between 2020 and 2025 were analyzed. The results identify significant barriers such as lack of adoption, including insufficient technological infrastructure, limited specialized talent, and budgetary constraints. Despite these obstacles, AI is increasingly used to automate processes and enhance predictive decision-making. While most applications remain concentrated in logistics, manufacturing, and telecommunications, their potential to improve efficiency and competitiveness is evident. The review also identifies the absence of robust regulatory frameworks as a critical limitation, particularly regarding ethical issues such as data privacy and algorithmic transparency. Although these aspects are not extensively covered in the reviewed literature, they represent important gaps for future research. In conclusion, the responsible adoption of AI in SMEs can contribute to business performance and sustainability goals, provided that appropriate public policies and sectoral strategies are implemented. Full article

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners. Full article

This study examines Millennial business students’ expectations of organizational career systems (OCS) to inform the design of work environments that attract and retain Millennial employees. It explores preferred OCS features, the role of personal value orientations (PVO), and potential cross-national differences. Data were collected through a cross-national survey of 284 business students in Canada and Germany. Variance analyses and group comparisons were used to assess differences in OCS expectations, and ordinary least squares regression examined the influence of PVO on preferences for four OCS features: internal recruitment, recognition of group contributions, formal promotion processes, and tenure-based advancement. The results show that Millennial business students favor OCS that emphasize recognition of group contributions and transparent, formal procedures, while placing less importance on internal recruitment and tenure-based advancement. PVO significantly predict these preferences: self-transcendence values are positively associated with preferences for formal procedures, whereas conservation values relate positively to tenure-based advancement. Canadian respondents exhibit slightly stronger preferences for formal procedures, group recognition, and tenure than German respondents, although overall cross-national differences remain modest. The study’s reliance on a convenience sample and self-reported data limits generalizability, highlighting the need for more diverse samples and qualitative approaches. By linking career system expectations to underlying personal values rather than generational labels, this study provides theoretical insight and practical guidance for designing fair and transparent OCS aligned with the career expectations of Millennial respondents. Full article

The increasing digitalization of industrial ecosystems under the Industrial Revolution 4.0 has intensified the demand for fast, reliable, and inclusive broadband connectivity. The expansion of 5G technology led by data-driven services addresses the growing demand for high-capacity, low-latency communication through Fixed Wireless Access (FWA) as a cost-effective broadband solution. FWA is a wireless broadband access technology that provides high-speed connectivity to fixed locations using 5G New Radio (NR) infrastructure instead of physical fiber networks, while reducing deployment time and infrastructure investment. This review examines the technical challenges, economic business implications, and comparative performance of 5G FWA relative to other broadband technologies. It also examines the implementation of Enhanced Telecom Operations Map (eTOM) in several telecommunication network functions. The analysis indicates that successful 5G FWA implementation requires not only technical optimization, but also the adaption of standardized, scalable, and AI-driven network management practices. Emphasis is placed on the role of the eTOM as a structured framework for aligning technical, operational, and organizational processes in FWA deployment. This review highlights how eTOM can support readiness assessment, process harmonization, and lifecycle management to ensure consistent and efficient service delivery. This study provides a comprehensive reference for researchers and industry stakeholders in developing sustainable and future-ready 5G FWA networks. Full article

This study examines female microenterprise entrepreneurship in the city of Juliaca, Peru, as a response to structural conditions of poverty, informality, and limited inclusion in public policies. The research aims to understand and interpret the dynamics of women-led entrepreneurship and its relationship with sustainable local socioeconomic development. A qualitative methodological approach based on an interpretive phenomenological design was adopted. Data was collected through in-depth interviews, direct observation, and document analysis with sixteen microentrepreneurs selected through purposive and snowball sampling. The findings reveal that intrinsic motivations (resilience, leadership, and self-fulfillment) and extrinsic motivations (economic independence, access to financing, and education) are key factors in the entrepreneurial process. In addition, entrepreneurial social capital, expressed through family, community, and institutional networks, plays a strategic role in the sustainability of businesses. The results also show that women entrepreneurs actively and significantly contribute to sustainable local socioeconomic development by strengthening local development ecosystems, generating employment, and promoting socially, fiscally, and ethically responsible practices. Despite their role as agents of change and transformation, women entrepreneurs continue to face structural barriers, highlighting the need for public policies with territorial and gender-sensitive approaches to strengthen their impact and sustainability. Full article

Background: This study examines long-term trends in intra-EU trade among the Visegrad Group (V4) countries from 2004 to 2023, focusing on changes in export specialisation and comparative advantages in the context of trade resilience and sustainability. Methods: Trade performance is analysed at both the aggregate level and across SITC product groups, using Eurostat data. The analysis applies export and import dynamics, trade balance, export–import coverage ratio, trade balance index, and the symmetric revealed comparative advantage index. Results: The findings show significant heterogeneity in specialisation and competitiveness across the V4 countries. Poland reveals competitive advantages and trade stability in agri-food products. After European Union (EU) accession, comparative advantages and export specialisation emerged mainly in manufacturing and selected medium- and high-processed goods (SITC6–8), especially in Czechia and Hungary, and increasingly in Poland. Poland and Czechia shifted most clearly towards higher value-added products, Hungary followed a mixed pattern, while Slovakia remained narrowly focused on the automotive sector. Export competitiveness is closely linked to the business cycle, with upturns strengthening advantages and downturns causing only temporary weakening. Conclusions: The V4 intra-EU trade exhibits structural resilience, as key competitive positions persist and recover after economic shocks. Only Slovakia’s highly concentrated specialisation may entail risks for sustainable growth. Full article

The mandatory introduction of Romania’s national e-invoicing system, ANAF e-Factura, in January 2024 has reshaped B2B document exchange in the retail sector, but suppliers still operate in parallel with two proprietary electronic data interchange (EDI) platforms, EDINET and DocProcess, which increases integration complexity. This paper presents the architecture, implementation, and evaluation of a custom Laravel-based B2B platform developed to automate commercial workflows across these three channels. The system supports XML purchase order ingestion and normalization, product identifier resolution, unified order persistence, platform-specific invoice XML generation, and ANAF SPV submission via SmartBill and Oblio REST APIs. A comparative analysis of real production XML documents showed full field-level overlap across 21 invoice data dimensions, with the main differences between systems related to entity identification schemes rather than business information content. During 2025, the platform processed 1247 EDI purchase orders and achieved an 87.30% fully automated processing rate, reaching 94.60% by year-end through progressive product catalog enrichment. The results indicate that ANAF e-Factura is technically capable of covering the core invoice exchange function currently duplicated by proprietary EDI platforms, while their coexistence continues to impose additional integration effort and slows SME digital transformation, particularly for small and medium-sized suppliers. Full article

Ports play a central role in global trade and act as key hubs for both maritime and land transport. Free ports, characterized by special customs regimes and fiscal advantages, represent a distinctive segment of this landscape. Despite their relevance, the literature on port gate management and on free ports has developed disconnected research streams, leaving the operational implications of special customs regimes largely unexplored. This study addresses this gap by investigating how gate procedures in free ports can be managed more efficiently, using the Port of Trieste as a case study. The analysis combines Business Process Model and Notation (BPMN) with discrete event simulation: BPMN served as the logical foundation for capturing the procedural complexity of free port gate operations, while simulation provided the quantitative framework for scenario evaluation. The model was calibrated on real gate access data and validated against observed vehicle volumes. Nine scenarios were evaluated, covering managerial, technological, infrastructural, and disruption-related interventions. The results show that no single measure produces significant improvements across all performance indicators and the integrated approaches consistently outperform standalone measures. Infrastructure interventions, while more costly, prove particularly valuable in improving port resilience under severe disruption conditions. Full article

The PLASTRON (Reuse of plastic from the sea using additive manufacturing as a strategy for the challenges of tourism supply chains and business resilience (Italian acronym: riuso della PLAstica dal mare usando la manifattura additiva come Strategia per le sfide delle filiere del TuRismO e la resilieNza delle imprese)) project aims to enhance the sustainability of coastal communities by improving plastic waste management and fostering the transition to efficient circular economy models, aligned with the European Green Deal. A Franco-Italian multidisciplinary team is testing low-investment local initiatives for collecting plastics from coasts, ports, and the sea. The project develops protocols to integrate waste into the recycling chain and create value-added goods through additive manufacturing. Special focus is given to degraded marine litter and mixed plastics, exploring their reuse via blending with other materials and natural additives. The focus was on the characterisation of two material blends, polyolefin mix (MPO) and Polyethylene terephthalate (PET), both with plastic marine litter. The processability of the MPO blend was comparable to that of commercial recycled MPO. The differences observed between 3D printing and injection moulding for the MPO derived from marine litter were negligible, unlike those found in the commercial MPO. The PET, modified with 0.8% chain extender additive, exhibited performance equivalent to—or in some cases even superior to—that of virgin commercial PET. However, 3D printing processing induced a certain brittleness in the material. Full article