In the rapidly evolving landscape of digital marketing, the influence of social media on consumer behavior has become a focal point of scholarly inquiry. This study delves into the intricate dynamics between social media interaction and the quality of relationships in the context of s-commerce, examining how these interactions impact customer loyalty and purchase intentions. It is imperative to note that while the study does explore the mediating role, it is not the primary focus. The core objective revolves around understanding the nuanced relationships between social media interaction and relationship quality. This clarification ensures a precise delineation of the research scope and objectives. Furthermore, it is worth emphasizing that while the study delves into customer loyalty, this aspect is not explicitly reflected in the title. However, the examination of loyalty remains an integral component of the research, providing a holistic view of customer behavior in the digital marketplace. By addressing the interplay between social media engagement and relationship quality, this study aims to provide valuable insights for businesses navigating the complexities of s-commerce. Through this research, we seek to illuminate the pivotal role of social media interactions in shaping customer-company relationships, thus offering actionable insights for practitioners and enriching the academic discourse in the field of digital marketing.

China’s graduate quality management system is designed to ensure that students possess the necessary skills, knowledge, and competencies for future success. This system is rooted in China’s ambitious educational reforms aimed at cultivating a highly skilled workforce to drive economic growth and innovation. Effective graduate quality management significantly impacts employment levels, training models, and national policy formulation. This study investigates the quality management approaches of 56 vocational institutions in Yunnan Province using a 5-level questionnaire and a quantitative research methodology. A sample of 556 individuals was selected through stratified random sampling. Exploratory factor analysis identified five primary components of the quality management model: College graduate quality (mean = 4.56, SD = 0.49), teaching quality (mean = 4.39, SD = 0.42), hardware environment (mean = 4.38, SD = 0.44), social support (mean = 4.37, SD = 0.42), and job satisfaction (mean = 4.38, SD = 0.42). College graduate quality and teaching quality were the most influential factors, while hardware environment, social support, and job satisfaction had lesser impacts.

This study aimed to measure the impact of implementing mechanisms of accounting data governance, represented by International Accounting Standards, internal auditing, external auditing, audit committees, disclosure and transparency, and performance evaluation, on the quality of financial reporting data for the commercial banks listed on the Amman Stock Exchange, totaling (15) banks. To achieve the objectives of this study, a descriptive-analytical approach was adopted by developing a questionnaire to collect the primary data measuring the study variables. The questionnaire was distributed to employees in the financial and control departments of these banks, with a total of (375) respondents from the total study population of (733) individuals. Appropriate statistical methods were used to analyze the data, test hypotheses, and the results of this study revealed a strong positive impact of five variables of accounting data governance mechanisms on achieving the quality of financial reporting data. These variables are ranked from highest to lowest in terms of the strength of impact and correlation with the quality of financial reports: disclosure and transparency, external auditing, International Accounting Standards, internal auditing, and audit committees. However, there was no impact of the performance evaluation governance variable on achieving the quality of financial reporting data. These results call on the management of commercial banks in the study to commit to the objective implementation of the requirements of accounting data governance mechanisms as stipulated by international professional assemblies.

The food and beverage sector played a big part in contributing to the economic growth in Malaysia hence there was a major increase in the numbers of restaurants opening up for businesses. This study therefore examines factors with the aims of ensuring a sustainable development in full-service restaurants in West Malaysia. The results of this study have made a substantial contribution to restaurant owner’s’ comprehension of the fundamental components that underlie customer satisfaction and loyalty. By examining the moderating effect of the customer’s gender in full-service restaurants in West Malaysia, the objective of this study was to ascertain the relationships between the three variables (quality of the food served at the restaurant, service quality, and environment), as well as the degree to which each attribute was able to relate to diner satisfaction. The underpinning theory for this study was the Theory of Planned Behavior (TPB). Quantitative methods according to descriptive research and convenience sample strategy were utilized in this cross-sectional study. Questionnaires were distributed to 264 respondents through various online platforms such as WhatsApp, Telegram, Facebook, and email. Data collection was evaluated using the Statistical Program for Social Sciences (SPSS) version 27. In order to examine the connection between the three factors and diner’s satisfaction, various tests such as the multiple regression analysis, One-way ANOVA and Beta Coefficient test were carried out. The findings gave current restaurant owners and potential restaurant owners an overview of the different attributes influencing diner’s satisfaction at full-service restaurants in West Malaysia and also the extent of the moderating effect of diner’s gender had on each attribute. The outcome of this paper is expected to provide a sustainable growth in this industry.

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.