Search Results (1,193)

The growing incorporation of environmental, social, and governance (ESG) considerations into global financial systems has significantly influenced investment decision-making. Previous studies have mainly concentrated on ESG performance and their associated implications for businesses and have failed to examine the role of ESG risks in shaping barriers to cross-border investment. In this regard, this paper attempts to analyze the effects of ESG risks on foreign direct investment (FDI) inflows based on an unbalanced panel dataset for up to 250 countries spanning the years 2000 to 2024, coupled with cross-sectional data for 2020. This study uses a two-dimensional approach, whereby structural ESG risks are evaluated using panel FMOLS regression, while ESG risk exposures are assessed using cross-sectional models. This research also considers moderating factors such as economic development, industrial composition, and innovation capabilities. Based on the use of the national-level ESG risk, it is evident that ESG risks considerably reduce inward foreign direct investment. Full article

The growing complexity of enterprise resource planning (ERP) systems necessitates intelligent approaches for dynamically identifying and evaluating key performance indicators (KPIs) that accurately reflect organizational performance. This paper proposes a multi-agent architecture for dynamic KPI management over Oracle E-Business Suite (EBS). The core design combines a dynamic multi-agent analytics layer, an extendable dedicated EBS KPI Model Context Protocol (MCP) server layer, and a data layer. The dynamic multi-agent analytics layer defines a set of independent large language model (LLM) agents, each responsible for a specific task determined by the business requirements of a particular company. The EBS KPI MCP server layer defines the tools required to access and transform Oracle EBS data and exposes them to the AI agents in the upper layer. Above these layers is the user layer, where the user actively participates in the process through a human-in-the-loop approach. Based on this general architecture, we proposed and implemented, as a proof of concept (PoC), a multi-agent system for dynamic business KPI selection, evaluation, and quantification, in which three distinct agents for KPI selection, KPI quantification, and KPI forecasting were instantiated within the multi-agent analytics layer. This demonstrates the practical applicability of the proposed general architecture. The study contributes to intelligent business analytics by showing how coordinated LLM agents can automate KPI lifecycle activities within ERP ecosystems, enabling adaptive, data-driven performance management aligned with evolving organizational needs. Full article

Sentiment analysis has become increasingly important across various domains, particularly in business intelligence, where it is crucial for improving the performance of companies by identifying the sentiments and emotions expressed in customer feedback on products and services. Despite its growing relevance, sentiment analysis still faces several challenges, including class imbalance in datasets, limitations in feature extraction techniques, and the selection of appropriate classification models. Effectively addressing these challenges requires the integration of robust representation methods, reliable data balancing strategies, and efficient classification frameworks. In this study, we propose a novel sentiment analysis approach that combines SBERT for contextual feature extraction, WGAN-based synthetic data generation for addressing class imbalance, and a soft voting ensemble classifier for improved prediction. The proposed approach is evaluated on five datasets, including two English datasets and three Arabic datasets, in order to assess its performance in a multilingual setting. We compare the effectiveness of the proposed model with several baseline machine learning classifiers, as well as with commonly used data balancing techniques such as the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic (ADASYN). The evaluation is conducted using multiple performance metrics, including accuracy, precision, recall, F1-score, MCC, ROC–AUC and training and inference time, along with different validation strategies including fixed train–test splits and k-fold cross-validation. The experimental results demonstrate the effectiveness and stability of the proposed approach. In particular, they highlight the importance of capturing sentence-level contextual representations and generating realistic synthetic samples to address class imbalance. Full article

Background: Artificial intelligence (AI) has shown promise in breast ultrasound image analysis, but most evidence still comes from single-dataset studies. Clinical translation requires evaluation under heterogeneous acquisition and curation conditions. This study presents a patient-leakage-aware, reproducible benchmark for breast ultrasound AI under dataset shift, with emphasis on external generalization, calibration, and confidence-related behavior. Methods: A reproducible benchmark framework was developed using patient-level splitting, internal testing, pairwise cross-dataset evaluation, whole-image and region-of-interest (ROI) input strategies, calibration analysis, targeted ROI-margin sensitivity analysis, representative explainable AI visualization, and an auxiliary lesion-versus-normal confidence-based analysis. Four public breast ultrasound datasets (BUSI, BUS-UCLM, BUS-BRA, and BrEaST) were harmonized for a primary benign-versus-malignant lesion classification task. Normal images were excluded from the primary endpoint and used only in auxiliary analyses when sufficient numbers were available. Results: Cross-dataset testing was weaker on average than internal testing, with mean raw AUROC decreasing from 0.801 to 0.719 and mean balanced accuracy from 0.723 to 0.635. ROI input improved external performance, especially for the vision transformer, increasing mean external AUROC from 0.666 to 0.805 and mean external balanced accuracy from 0.594 to 0.713 relative to whole-image input. Temperature scaling improved calibration-related metrics, reducing mean external expected calibration error from 0.180 to 0.150 and mean external negative log-likelihood from 0.848 to 0.682. Conclusions: This study establishes a reproducible benchmark for evaluating breast ultrasound AI under dataset shift, with explicit attention to patient-level leakage control, external validity, and reliability of predicted probabilities. Full article

European football has become a massive business. Keeping football clubs financially viable depends on accurate player valuations, which underpin balancing incoming and outgoing transfers, contract negotiations, and other expenses. Players’ market values are generally available on public platforms. Still, clubs and analysts increasingly rely on data-driven approaches to enable consistent valuation across leagues, to assess the main drivers of players’ market value, and to early identify the most promising players. This study attempts to predict and interpret football players’ market value in five major European football leagues (England, Spain, Italy, Germany, and France) using match-derived performance statistics and players’ general information. The dataset analyzed comprises about 14,000 player–season observations available through the worldfootballR package, which aggregates data from FBref and Transfermarkt. Five regression algorithms were evaluated within a unified machine learning framework. Model performance was assessed on a test set using RMSE and R² metrics. Results show that non-linear machine learning models outperform the linear ones. Gradient boosting and neural networks recorded the best predictive performance. Model interpretation techniques reveal playing-time exposure and player age as the main determinants of predicted market value, highlighting the importance of match involvement and career stage in the valuation of football players. Full article

Worldwide, breast cancer is the leading cause of death in women. This emphasizes the significance of an accurate breast cancer detection system. This study presents a unified framework for segmentation of breast cancer using multimodal imaging, such as histopathology, MRI, mammogram, and ultrasound. This framework integrates the CNN with Transformer modules and has three core technical innovations. First, features are extracted using an encoder–decoder design. The encoder has Residual Blocks with a base channel of 32, following feature extraction, which are progressively mapped and downsampled into four stages (32 → 64 → 128 → 256) of channels. The spatial channel is reduced using MaxPool2d operations from 256 × 256 to 128 × 128, 64 × 64, 32 × 32, and 16 × 16. After further convolutional refinement, a Transformer encoder is used on the 16 × 16 feature maps in the bottleneck. The Transformer comprises four encoders with multi-head self-attention (eight heads) and a 4.0 MLP ratio, enabling the model to capture local and global contextual dependencies at the lowest resolution. The proposed framework is trained with a learning rate of 1 × 10⁻⁴, up to 50 epochs with early stopping (patience = 12), using a combined Dice and binary cross-entropy loss that balances pixel-wise accuracy and overlap-based learning. Gradient clipping with a maximum norm of 5.0 is used to ensure training stability; ReduceLROnPlateau (factor = 0.5, patience = 5) is used to dynamically adjust the learning rate; and early stopping is used to prevent overfitting. To improve generalization and enhance robustness to data variability, data augmentation techniques such as random horizontal and vertical flips, intensity variations, and small rotations (±15°) are applied. Incremental learning was implemented in this study as a warm-start fine-tuning strategy, where the model was initialized based on learned weights from a previously trained model instead of training from scratch. This is done by loading saved checkpoints of the best-performing model and continuing training on a new dataset. The performance of the proposed framework is evaluated on four publicly available datasets and one local dataset, such as BUS-UCLM, BUSI, BreastDM, TNBC NucleiSegmentation, and BCSD-2024. The impressive results are achieved with Dice scores of 0.974 on ULCM, 0.975 on BUSI, 0.971 on BreastDM, 0.904 on TNBC nuclei segmentation, and 0.982 on BCSD-2024. The proposed model consistently performed better than classical U-Net models. Full article

Artificial intelligence (AI) has transformed how we engage with visual data, particularly within the context of enterprises. Multi-modal codification systems enable the creation of semantic connections between text and visual data using AI models. This opens new markets for businesses by enabling visual search engines, recommendation systems, and automatic tagging of visual data. However, implementing these systems presents significant technical challenges. The typical workflow involves encoding images using an AI model, converting these representations into semantic vectors, and inserting them into databases optimized for fast searches. This not only affects technical efficiency but also impacts the ability of companies to scale these systems to a commercial level. This paper presents a comprehensive comparative analysis of communication architectures for large-scale image encoding systems, evaluating gRPC, RabbitMQ, serverless Lambda, and SageMaker approaches across performance and resource efficiency dimensions. Through controlled experiments processing up to 18,000 images using the SigLIP model, we establish clear performance–architecture relationships that inform system design decisions for visual content-based search applications. Full article

Predictive business process monitoring (PBPM) plays an important role in intelligent workflow management by enabling organizations to anticipate future process behavior and support operational decisions. However, many existing approaches represent execution traces primarily as linear prefixes, thereby limiting their capacity to explicitly capture the control-flow semantics of non-sequential processes. To address this limitation, this paper proposes KG-MTPM, a knowledge-graph-enhanced multi-task framework that integrates process-model-level structural knowledge with prefix-level runtime dynamics in a unified predictive architecture. In particular, control-flow relations are organized as a process knowledge graph so that non-linear execution dependencies can be explicitly represented during prediction. Based on the integrated representation, the model jointly predicts the next-activity, next-activity time, and remaining-time of an ongoing case. Experiments on three real-world event log datasets demonstrate that KG-MTPM achieves the best overall performance among the evaluated baselines, with a marked advantage in time-related prediction tasks. Relative to the best-performing baseline, KG-MTPM improves next-activity prediction accuracy from 0.84 to 0.85, while reducing the mean absolute error (MAE) of next-activity time prediction from 0.81 to 0.25 and that of remaining-time prediction from 0.98 to 0.47. Ablation results confirm the contributions of both the structure-aware representation and the multi-task learning scheme. Overall, the findings suggest that explicit modeling of process structure is beneficial for predictive monitoring in business processes with complex execution behavior. Full article

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

In fixed-view interaction scenarios of unmanned restaurants, face detection models face two core bottlenecks: the mismatch between training data distribution and real deployment scenarios, and the misalignment between model feature capacity allocation and business priority. To address these problems, this paper takes YOLOv8n (You Only Look Once version 8n) as the baseline, proposes a unified Scale-Aligned Capacity Allocation (SACA) theoretical framework, and constructs an end-to-end Scale Distribution Reconstruction Network (SDRNet) for lightweight face detection. First, we define the SACA loss with KL (Kullback-Leibler) divergence as the core optimization objective, which mathematically characterizes the matching degree between model capacity allocation and real scene face scale distribution. Second, a two-stage scene-aware scale distribution reconstruction strategy is designed based on the SACA framework, which derives the core face scale interval of the unmanned restaurant scene through a monocular imaging model, and constructs a scene-adaptive training dataset based on the public WIDER FACE benchmark, which is highly consistent with the real scale distribution of unmanned restaurant scenarios. Third, three scale-aligned lightweight modules, including LFEM (Lightweight Feature Extraction Module), LDown (Feature Segmentation and Sparse Optimization Module), and MSCH (Multi-Feature Shared Convolution Module), are proposed to realize the priority allocation of model capacity to core interaction scales, achieving collaborative optimization of data distribution and model structure. Fourth, a 2 × 2 controlled experiment is designed to separate the independent contributions of the data strategy and architectural improvements, and the robustness of the proposed model is verified on the standard WIDER FACE benchmark. Finally, a scale-specific validation mechanism is established to conduct fine-grained evaluation of the model’s detection performance on faces of different scales, avoiding the overall indicator masking the accuracy fluctuation of core scenarios. Experimental results show that the parameters of the proposed model are reduced to 1.76 M (a decrease of 41%), and the computational complexity is reduced to 5.5 GFLOPs (Giga Floating-point Operations Per Second) (a decrease of 32%). The mAP@0.5 (mean Average Precision) of the core medium-scale face reaches 0.684, with the performance loss controlled within 2% compared with the baseline. On the standard WIDER FACE benchmark, the model maintains competitive detection accuracy under extreme lightweight compression, which fully verifies its robustness. On the NVIDIA Jetson Orin NX embedded platform, the inference frame rate of TensorRT-FP16 reaches 79.9 FPS (Frames Per Second), which fully meets the real-time deployment requirements of resource-constrained unmanned restaurant scenarios. Full article

Economic challenges are a latent reality in emerging economies such as Peru, and the growth capacity of entrepreneurs depends largely on certain factors, such as education and financial inclusion. To delve deeper into these factors, this study aims to analyze the association between micro-business performance, education, and financial inclusion, as well as to evaluate the mediating role of financial inclusion in the association between financial education and micro-business performance. The study was of an explanatory design. The research focused on owners, business owners, general managers, and other administrators of micro-businesses who could provide information on the performance of the companies. The results showed a statistically significant positive association between micro-business performance, education, and financial inclusion. It was also proven that financial inclusion is positively associated with micro-business performance, and it was also proven that financial inclusion has a mediating role in the association between financial education and micro-business performance. While these relationships are meaningful, the moderate explanatory power of the model (R² = 0.370–0.488) suggests that financial education and financial inclusion are important but partial contributors to business outcomes in this context. In conclusion, entrepreneurs with stronger financial knowledge appear to be better positioned to navigate business challenges and leverage financial systems, which may contribute to improved micro-business performance indicators. Full article

The transition from sustainable human resource management (S-HRM) toward common good human resource management (CG-HRM) has introduced new research challenges for the academic community. This study examines the nexus among CG-HRM, employee resilience, and sustainable corporate performance, as assessed through the triple-bottom-line framework. First, the study evaluates the relationship between CG-HRM and sustainable corporate performance, followed by an examination of whether employee resilience moderates these relationships. The study also examines the moderated mediation effect of employee resilience via social performance on the relationship between CG-HRM and both economic and environmental performance. The research was conducted with 370 respondents from companies in the Republic of Serbia, using the PLS-SEM methodology. The results suggest that CG-HRM is directly related to environmental and economic performance, but not to social performance. Moreover, social performance showed a positive relationship with environmental and economic performance. The results also suggest that CG-HRM is indirectly associated with environmental and economic performance through the mediating effect of social performance, with employee resilience moderating the first segment of these indirect relationships. Thus, the study enhances the understanding of the mechanisms connecting CG-HRM, employee resilience, and sustainable corporate performance. Furthermore, the findings provide useful implications for managers seeking to integrate sustainability into HRM systems and improve sustainable business outcomes. Full article

AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the Czech Republic (n = 1250), we compare a regularized linear baseline (Elastic Net) with nonlinear approaches (Decision Tree and Random Forest) under a consistent out-of-sample evaluation framework, and we examine the best-performing model using permutation importance and PDP/ICE tools. Random Forest achieves the strongest out-of-sample predictive performance and reduces absolute errors relative to Elastic Net for most test observations, although diagnostics also reveal a small tail of extreme errors. Across model families, ai_iot_advantage_share emerges as the most stable predictor of reported AI/IoT success. Nonlinear diagnostics indicate a threshold-like transition in predicted success around the mid-range of advantage attribution and a saturation pattern at higher values. Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success. The results position value realization as the most informative predictive signal in the dataset and provide an interpretable basis for enterprise-level screening and managerial reflection rather than causal inference. Full article

Digital transformation and transparency in the seed supply chain are cornerstones of national food security and sustainable agricultural development. Existing agricultural traceability systems suffer from elevated storage overhead and performance degradation with massive seed data processing and fail to iterate quality supervision standards without disrupting continuous business operation. To address these problems, this study proposes a dual-optimization architecture-based traceability system for seed supply chains. An edge-assisted Merkle-tree dimension-reduction aggregation protocol is introduced to compress seed logistics scanning data before blockchain submission. Instead of storing each circulation record as an independent on-chain state update, the proposed scheme anchors one fixed-size Merkle root for each aggregated batch, reducing the per-batch on-chain payload to a constant size and lowering the overall on-chain anchoring burden from record-level growth to batch-level growth. Furthermore, it adopts a decoupled regulatory architecture based on the Strategy Pattern for the separation of traceability state storage and compliance inspection logic, enabling uninterrupted rule switching under the tested upgrade scenario via on-chain hash pointer adjustment. Rigorous statistical evaluation of the experimental results indicates that the system stably processes seed circulation records at a peak effective throughput of 1952.4 transactions per second. Under high-frequency concurrency, the 95th percentile (P95) latency remains controlled under 0.28 s. The average physical on-chain storage for 100,000 circulation records was reduced to 0.52 MB, and deploying a new quality inspection rule takes an average of only 2.2 s, with limited computational resource overhead. Full article

The New Zealand construction industry, while central to national infrastructure and economic development, continues to grapple with persistent performance challenges rooted in weak strategic governance and fragmented decision-making processes. This study examines the relationship between strategic decision-making and organisational performance within the New Zealand construction sector, addressing a gap that construction management scholarship has largely left unattended. The study draws on survey data from construction professionals across diverse organisational sizes, project types, and regions in New Zealand, employing Partial Least Squares Structural Equation Modelling (PLS-SEM) as its analytical approach. The analysis identifies four significant predictors of construction business performance: strategic decision formulation, strategic decision implementation practices, strategic decision evaluation, and financial strength. Workforce capabilities, by contrast, did not demonstrate a statistically significant relationship with performance outcomes. This nuanced finding challenges prevailing assumptions about the primacy of human capital in construction performance models. The structural model achieved strong explanatory power, confirming the robustness of the proposed framework. These findings offer theoretically coherent, empirically supported insights into strategic performance determinants among mid-sized construction organisations in New Zealand. The voluntary sampling design and modest sample size of 102 respondents define the inferential boundaries of these conclusions. Full article