This study critically examines the relationship between Total Quality Management (TQM) and Service Quality (SQ) within Dubai’s housing sector, with a specific focus on the moderating influence of blockchain technology (BT) in this relationship. Employing a quantitative approach grounded in a deductive research strategy and positivist epistemology, data were gathered from a sample of industry professionals and subjected to rigorous analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the deliberate deployment of TQM methodologies leads to significant improvements in SQ metrics, and the catalytic role of BT further enhances these service quality improvements. The study highlights the transformative potential of BT in recalibrating conventional paradigms of service delivery within the housing sector. Specifically, the analysis reveals that BT plays a pivotal moderating role in the relationship between TQM practices and SQ outcomes, thereby enriching our comprehension of the intricate interplay between these constructs. The study concludes by furnishing nuanced insights into the multifaceted dynamics shaping SQ within the housing sector, while also delineating avenues for future inquiry.

This study aims to determine the level of satisfaction of business actors with halal certification services by the Halal Product Assurance Organizing Agency (BPJPH), the only Indonesian government-owned agency for halal certification. This study uses a mixed method (quantitative-qualitative), with data collected using questionnaires involving 2367 respondents. The overall quality of certification services was evaluated using key dimensions from the perspective of the Service Quality Model (SERVQUAL), such as (1) certification requirements, (2) information and procedures, (3) completion time, (4) costs/tariffs, (5) service products, (6) competencies of executors, (7) executor behavior, (8) complaint handling, and (9) suggestions and inputs. Data were analyzed using descriptive analysis and the analysis of the weighted average of each dimension of satisfaction with the quality of public services. This study revealed that the overall satisfaction level of business actors was 84.86 (0–100). Among the nine indicators measured, eight fall within the “good” category (above 80.19); one indicator, i.e., the processing time of halal certification, was rated unsatisfactory (76.45); and none was classified as “very good.” The service gap between business actors’ expectations and BPJPH’s service delivery indicates the need to improve halal certification services. These include improvement in completion time, the executive’s behavior, costs, infrastructure, and information and procedures to streamline the certification process. The application of the SERVQUAL model in assessing halal certification standards in this study highlights the specific dimensions of service quality and the performance gaps, suggesting the need for continuous improvement to meet customer expectations effectively. This study examines halal certification services from BPJPH based on inputs from a large sample of Indonesian companies.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The process management variable and the service quality variable date most prominently from the beginning of the last century, and therefore, in organizations from different parts of the world, whose search was to contribute effectively to administrative tasks, facing the challenges of constant changes and evaluations. In Peru, both variables were implemented since 2018, by technical standards, in order to contribute and improve public institutional work. Thus, the objective was to know the most outstanding characteristics of process management and service quality, using studies from different entities at the ecumenical level and revealing their main benefits of application and contribution. Furthermore, based on the systematic and methodical review of scientific articles from databases indexed to multiple journals, which are registered and organized in databases such as WOS and SCOPUS, thus theorizing their authors and perspectives. For this study, the documentary analysis technique and the data collection guide were considered as an instrument; in accordance with the PRISMA method. Finally, it is concluded that process management are methods available in an organization to provide effective results using resources efficiently, with dimensions of analysis, monitoring, and process improvements, contributing to organizational and strategic productivity; Likewise, the quality of the service is user satisfaction when judging the value of some service, dimensioning, analyzing needs, as well as evaluating, supervising and improving the service, fulfilling needs with knowledge of their expectations.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.