International Engineering and Technology Institute

The widespread adoption of AI-powered customer service systems has transformed how users engage with digital platforms. However, understanding how users form satisfaction and trust toward these systems remains a major challenge, especially when emotional responses and platform-level design factors interact in complex ways. To address this, the authors propose a Multi-Level Structural Equation Modeling (ML-SEM) framework that captures both individual-level perceptions (e.g., flow, trust, empathic accuracy) and organization-level design variables (e.g., interface quality, anthropomorphic and empathic features). The model simultaneously estimates direct and mediated effects, including cross-level pathways linking chatbot design to satisfaction. Using two large-scale, multi-domain datasets (Bitext and Twitter), they evaluate ML-SEM against six recent baseline models. Results show that ML-SEM outperforms all baselines, achieving the lowest RMSE (0.243) and highest adjusted R2 (0.691) in satisfaction prediction, with statistically significant improvements (p < 0.01).

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

This study examines the impact of generative artificial intelligence (GAI) on Chinese enterprises. 320 participants completed electronic surveys, revealing a positive relationship between GAI and corporate performance. Managerial relationships were found to play a crucial role, mediating the influence of GAI on performance. Additionally, the speed of technological change in the industry was identified as a moderator, highlighting the dynamic nature of GAI's impact on managerial relationships and firm performance. These findings underscore the strategic importance of GAI in cultivating managerial relationships and improving enterprise performance, deepening our understanding of the integration of advanced AI technologies and management practices in the Chinese market. Practical implications are offered for decision-makers in technology-intensive industries, providing valuable insights for leveraging GAI to gain a competitive edge and achieve sustainable growth in a rapidly evolving technological landscape.

La finalidad del artículo es la de evaluar el proceso de crecimiento histórico de la capacidad receptiva hotelera; medir la producción y la renta generada por el sector; presentar los aspectos básicos del empleo en la actividad; definir las características de la demanda hotelera; estimar el valor del capital y los problemas financieros existentes.

Wood logs need to be measured for size when passing through customs to verify their quantity and volume. Due to the large number of wood logs needs through customs, a fast and accurate measurement method is required. The traditional log measurement methods are inefficient, have significant errors in determining the long and short diameters of the wood, and are difficult to achieve fast measurements in complex wood stacking environments. We use a Mask R-CNN instance segmentation model to detect the contour of the wood log and employ a binocular stereo camera to measure the log diameter. A rotation search algorithm centered on the wood contour is proposed to find long and short diameters and to optimal log size according to the Chinese standard. The experiments show that the Mask R-CNN we trained obtains 0.796 average precision and 0.943 IOUmask, and the recognition rate of wood log ends reaches 98.2%. The average error of the short diameter of the measurement results is 5.7 mm, the average error of the long diameter is 7.19 mm, and the average error of the diameter of the wood is 5.3 mm.