Currently, school sustainability is the focus of attention of all parties, including education quality management experts, which is related to schools' weaknesses in using big data and artificial intelligence (AI). The aim of this research is to analyze the role of using big data and AI in improving the quality of education quality management in Indonesia and its impact on school sustainability. The research design uses a quantitative approach, especially correlational, verification or hypothesis testing based on empirical data in the field. The research population was all teachers, principals and high school supervisors in Palangkaraya City, Central Kalimanan, totaling around 5,423 people. The sample size used the Hair formula and obtained a sample size of 178 people. Data was collected using a questionnaire which was distributed to selected samples using a Google form. Primary data was analyzed using SMART PLS. The results shows that the use of big data had an impact on the quality education management, it also has an impact on school sustainability, while artificial intelligence had an impact on the quality education management, it also had an impact on school sustainability, In addition, quality education management had an impact on school sustainability and it mediated the relationship between the use of big data and school sustainability, and finally, quality education management mediated the relationship between artificial intelligence and school sustainability.

The future of the Internet lies in the utilization of contemporary virtual technology, the metaverse is a virtual ecosystem for communication and interaction. This study aims to determine how the metaverse can enhance customer awareness, interest, engagement and experience. The study population consists of customers of retail clothing stores. A questionnaire was developed to collect study data and distribute it electronically. The number of respondents reached (77) customers. Using path coefficients, the results indicate that metaverse can enhance customer awareness, interest, engagement and experience.

This study seeks to analyze the impact of digital marketing, social media, and digital transformation on the development of digital leadership abilities and the resulting enhancement of employee performance. The research examines the complex connections between digital elements and their influence on organizational dynamics, using a case study done at the Amman Stock Exchange as a basis for analysis. The inquiry explores the impact of digital marketing strategies on leadership skills, the use of social media platforms to promote digital leadership, and the transforming influence of digital initiatives on employee performance. In addition, the study evaluates the interaction of these characteristics and their combined impact on organizational success. Evidence suggests that digital methods have a complex and wide-ranging effect on leadership and performance, including factors such as adaptation, communication, and strategic decision-making. The study highlights the capacity of companies, specifically within the framework of the Amman Stock Exchange, to utilize digital tools for the purpose of developing leadership skills and improving performance. The findings obtained from this study provide important insights for enhancing digital ecosystems in comparable organizational contexts, highlighting the importance of a thorough comprehension and strategic integration of digital components in order to attain long-lasting success.

The aim of this study was to evaluate the impact of social media, big data, and data mining on the development of organizational behavior within the telecommunications industry in Jordan. The main objective of this study was to investigate the effects of technological components on the alteration of organizational behavior in the communications sector of Jordan. To accomplish this objective, a thorough empirical investigation was undertaken, encompassing the collecting of data from key stakeholders within the telecommunications sector in Jordan. A sample size of 412 participants, encompassing people from diverse roles within the communications sector, was chosen for the purpose of this study. The participants' replies and perspectives were gathered via the administration of surveys and conducting interviews, resulting in a comprehensive data set suitable for analysis. This study investigated the intricate relationship between the utilization of social media, the application of big data analytics, and the implementation of data mining techniques in influencing the dynamics of organizational behavior. The study's results underscored the substantial impact that social media platforms have on communication patterns and collaboration within telecommunication firms. Furthermore, the utilization of big data analysis has emerged as a significant catalyst for the enhancement of informed decision-making processes, exerting influence on diverse facets of organizational behavior, including strategic planning, employee engagement, and customer interactions. Data mining techniques have been identified as having a crucial function in extracting significant patterns and trends from extensive datasets, hence helping to the improvement of organizational learning and adaptation. The research findings indicated that the incorporation of social media, big data, and data mining technologies had a beneficial effect on the development of organizational behavior within the telecommunications industry in Jordan. The findings underscore the importance for enterprises to proactively utilize these technologies to cultivate a work environment that is characterized by increased agility, responsiveness, and collaboration. This study provides significant contributions to the subject of organizational behavior by examining the impact of social media, big data, and data mining within the specific context of the telecommunication sector in Jordan. The research sheds light on the transformative consequences of these technological advancements. The consequences of these findings have broad relevance for organizational leaders, politicians, and researchers, serving as a basis for further investigations in the dynamic realm of technology-driven organizational behavior.

Technology has significantly transformed knowledge, education, and access to information by introducing online learning platforms, interactive games, and virtual reality simulations in traditional classrooms, creating a dynamic, engaging, and inclusive learning environment. The ChatGBT project (a pre-developed transformer for training) is a remarkable achievement in artificial intelligence technology. It allows students tailored and efficient learning experiences by providing individual feedback and explanations. ChatGPT e-learning platform has been extensively studied for its adoption and acceptance, but there is a significant gap in research on its acceptability and use, highlighting the need for further exploration. The goal of this work is to bridge this disparity by introducing a comprehensive model that includes three basic elements: performance expectation, expected effort, and social impact. A total of 241 graduate students were surveyed and their data were analyzed using structural equation modeling techniques. The results indicate that “expectation of performance and expected effort” have the greatest impact and importance in determining students’ intentions to use learning platforms via ChatGPT, while social influence does not play an important role. This study enhances the current body of knowledge related to artificial intelligence and environmental sustainability, and provides important insights for professionals, policymakers, and producers of artificial intelligence products. These observations may provide guidance for creating and implementing artificial intelligence technologies to match consumers’ needs and preferences more effectively, while also taking into account broader environmental conditions.

Stock prices of different companies frequently display similar temporal fluctuations because of common influencing factors. Accurate prediction of stock prices is of utmost importance for investors in determining their investment strategies. Utilizing multivariate forecasting, which involves analyzing multiple time series, has been shown to be highly effective and efficient when applied to stocks that exhibit similar temporal patterns. It is possible to model the relationship between shares by using a shared temporal model approach. Nevertheless, it is important to note that not all stocks selected for prediction demonstrate a strong correlation; certain stocks may deviate from expected patterns. Therefore, the direct implementation of a comprehensive shared temporal component model is not universally applicable. This study presents a new method called the Semi-Shared Temporal Model, which focuses on the correlation structure among variables that have similar patterns, while also modeling all stocks simultaneously. This methodology is applied to the three leading stocks of 2023: Amazon (AMZN), Alphabet (GOOG), and MercadoLibre (MELI). Based on monthly data collected from January 2010 to December 2023, the study forecasts the stock prices for the months of January to December 2024. The analysis findings suggest that the temporal patterns of AMZN and GOOG shares are highly similar, which supports the idea of modeling them together with shared temporality. Three forecasting methods are utilized: univariate models, full shared temporal models, and semi-shared temporal models. The analysis determines that the semi-shared temporal model approach produces the most precise forecasting outcomes, with a Mean Absolute Percentage Error (MAPE) of 17.97%, surpassing both univariate and full shared temporal models. The forecast for 2024 indicates a favorable trajectory for all three stocks.

Technological advancements in Indonesia have been on the rise, and the advent of technology has brought about significant changes in various aspects of society. The integration of technology into human life has greatly enhanced various activities, making information exchange and communication more accessible. As we observe the continuous evolution of technology, it has created new business prospects for companies to harness the power of the internet in providing online shopping services that connect consumers, service providers, and intermediary traders. E-commerce has emerged as a vital platform supporting commercial transactions via the internet. Prior to making a purchase, consumers frequently turn to social media for product reviews, where electronic word of mouth enables users to share their product-buying experiences. This study seeks to investigate the impact of user-generated information through electronic word of mouth on purchase intentions within the e-commerce landscape of Indonesia, utilizing the Information Acceptance Model (IACM). Employing a causal descriptive research approach, this study targeted e-commerce users in Indonesia, with 155 respondents participating. Data collection was carried out through Google Forms, and the collected data was analyzed using SmartPLS 3. Hypothetical testing included t-tests, p-tests, and path coefficient assessments. The study's findings revealed that variables such as information quantity, information needs, attitudes toward information, information usability, and information adoption all play a significant role in influencing purchase intentions, while information quality and information credibility had no discernible impact on purchase intentions.

The welfare of people has always piqued our interest, and it remains the primary goal of nations around the world in their development endeavors. To effectively drive development efforts, it is critical to understand the diverse welfare features that exist in different locations. Thus, the purpose of this statistical analysis is to classify Indonesian provinces based on a comprehensive set of People's Welfare Indicators, which includes Population Density (PD), Percentage of Poor Population (PPP), Life Expectancy Rate (LER), and Average Years of Schooling (AYS). The methodology used in this study is Hierarchical Cluster Analysis, which employs five distinctive techniques: Single Linkage, Average Linkage, Complete Linkage, Ward's Linkage, and the Centroid Method. The data for this study was obtained from reliable secondary sources, notably the official website of the Central Bureau of Statistics (BPS), and it provides insights on Indonesia's welfare picture in 2021. The average linkage approach shows as the most suitable of the five hierarchical cluster analysis methods used, with the closest cophenetic correlation to 1. The analysis reveals three distinctive clusters within the Indonesian context. Cluster 1 demonstrates a tendency toward low PWI (People's Welfare Index) status, while Cluster 2 exhibits a notably high PWI status. Cluster 3 occupies an intermediate position, characterized by moderate PWI status. These findings not only give useful classification but also act as an important reference point for the Indonesian government. They provide an in-depth insight into each province's distinct welfare features, supporting smart resource allocation and prioritizing aid distribution in regions of highest need. As a result, this research is an essential resource for creating equitable and effective policies and methods to improve people's well-being throughout Indonesia.

The present research endeavours to explore the efficacy of gamified pedagogy in the realm of early childhood education within the context of Jordan, while simultaneously examining its intricate relationship with parental engagement. The examination of data uncovers a discernibly elevated degree of student involvement in the realm of gamified education, thereby suggesting the effectiveness of employing gamified methodologies in captivating the attention and interest of youthful scholars. Moreover, the presence of a moderate degree of parental involvement implies the possibility of proactive parental participation in the scholastic odyssey. The empirical evidence consistently demonstrates a robust and affirmative association between parental involvement and student engagement, underscoring the crucial and influential position that parents occupy in augmenting student motivation and active involvement in gamified educational endeavors. The present study serves as a valuable contribution towards enhancing our comprehension of the intricate relationship between gamified education and parental involvement. Its findings hold significant implications for educators, policymakers, and parents alike, as they strive to maximize the effectiveness of early childhood education.

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%.