This study develops an integrated model to explain e-commerce adoption among agricultural producers by combining the Technology–Organisation–Environment (TOE) framework with social identity theory (SIT). Drawing on cross-sectional data from 585 farmers in Shanxi Province, China, and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the research assesses both the direct and mediating effects of technological readiness, organisational barriers, and environmental enablers. Results reveal that social identity acts as a critical mediator, transforming indirect contextual drivers into adoption behaviour. Notably, factors such as complexity and digital environmental change influence adoption exclusively through social identity rather than direct paths. These findings advance existing literature by embedding sociopsychological mechanisms into digital adoption models, and offer practical guidance for promoting inclusive e-commerce development in rural and agricultural contexts, particularly in developing regions like rural China.

As the global tourism industry becomes increasingly shaped by digital transformation, the strategic role of Information and Communication Technologies (ICTs) has gained particular significance, especially in emerging economies. Albania, with its growing appeal as a tourist destination and its economic reliance on tourism, presents a compelling context in which to examine the digital maturity of tourism enterprises. This study investigates the adoption of ICT tools—particularly websites and e-marketing practices—among Albanian tourism businesses and assesses the impact of managerial attitudes and digital infrastructure on perceived business performance. Grounded in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the research employs a quantitative survey methodology, collecting responses from 208 enterprises across five key tourism regions. The findings reveal that 81% of these enterprises maintain an active website, with adoption rates significantly higher in coastal areas such as Durrës and Sarandë. Hierarchical regression analysis demonstrates that a favorable perception of ICTs is positively associated with ICT-based marketing strategies (r = 0.243, p < 0.01) and reported profit growth (R² = 0.291). Although high initial website development costs are acknowledged as a barrier, they are also correlated with long-term profitability, reflecting a growing recognition of ICT as a strategic asset rather than a cost burden. The results support the dual theoretical lens: TAM explains the behavioral inclination toward digital tools, while RBV underscores their value as inimitable resources for sustained competitive advantage. The study highlights the need for targeted government policies, including digital upskilling programs, infrastructure investment, and support for small enterprises lagging in digital readiness. Future research could expand this inquiry through longitudinal and cross-country analyses, exploring how emerging technologies—such as AI-driven personalization, immersive media, and data analytics—reshape destination competitiveness in the digital age.

Handwritten digit classification represents a foundational task in computer vision and has been widely adopted in applications ranging from Optical Character Recognition (OCR) to biometric authentication. Despite the availability of large benchmark datasets, the development of models that achieve both high accuracy and computational efficiency remains a central challenge. In this study, the performance of three representative machine learning paradigms—Chi-Squared Automatic Interaction Detection (CHAID), Generative Adversarial Networks (GANs), and Feedforward Deep Neural Networks (FFDNNs)—was systematically evaluated on the Modified National Institute of Standards and Technology (MNIST) dataset. The assessment was conducted with a focus on classification accuracy, computational efficiency, and interpretability. Experimental results demonstrated that deep learning approaches substantially outperformed traditional Decision Tree (DT) methods. GANs and FFDNNs achieved classification accuracies of approximately 97%, indicating strong robustness and generalization capability for handwritten digit recognition tasks. In contrast, CHAID achieved only 29.61% accuracy, highlighting the limited suitability of DT models for high-dimensional image data. It was further observed that, despite the computational demand of adversarial training, GANs required less time per epoch than FFDNNs when executed on modern GPU architectures, thereby underscoring their potential scalability. These findings reinforce the importance of model selection in practical deployment, particularly where accuracy, computational efficiency, and interpretability must be jointly considered. The study contributes to the ongoing discourse on the role of artificial intelligence (AI) in pattern recognition by providing a comparative analysis of classical machine learning and deep learning approaches, thereby offering guidance for the development of reliable and efficient digit recognition systems suitable for real-world applications.

The integration of edge computing for real-time data processing in autonomous systems has been identified as a promising solution to mitigate the performance bottlenecks and latency associated with traditional cloud-based models. Autonomous systems, including vehicles, drones, and robotics, rely heavily on quick data analysis to make timely decisions. However, cloud computing, with its inherent data transmission delays, hinders the responsiveness and efficiency of these systems. To address these challenges, edge computing is proposed as a means to process data locally, at the point of use, thus enabling faster decision-making processes and reducing data transfer overhead. This approach leverages distributed machine learning for decision-making and dynamic resource allocation to balance computational tasks between edge and cloud resources. Through extensive experimentation, it has been demonstrated that the edge computing paradigm can reduce latency by up to 65%, offering substantial improvements in both energy efficiency and data processing speed when compared to traditional cloud-based methods. Furthermore, the proposed system outperforms existing alternatives in terms of computational speed, reliability, and energy consumption. The introduction of an Edge Computing Decision Model (ECDM) and a Dynamic Resource Allocation Algorithm (DRAA) is shown to optimize system performance by balancing computational demands between local edge nodes and remote cloud servers. These innovations enable autonomous systems to function more effectively and efficiently, even in resource-constrained environments. This study highlights the importance of integrating edge computing into autonomous system architectures to meet the growing demand for low-latency, high-performance applications. The potential of edge computing to significantly enhance the reliability and operational capacity of autonomous systems has been established, paving the way for more reliable and scalable solutions in real-time environments.