In terms of investment, social and governance (ESG) issues consider various non-financial aspects of business performance. This includes the impact of the company's operations on the environment, society, and the quality of corporate governance. ESG factors have received significant attention from the investment community, along with increasing awareness of the importance of sustainability in investment decision making. Investors are increasingly realizing that taking ESG factors into account when making investment decisions can provide long-term benefits, both from an environmental and financial perspective. This study's objective is to investigate how ESG issues affect investment choices by using the mediation of investment goals as a variable in the relationship. This study included quantitative methodology and a survey questionnaire. Researchers gather and use analytical methods to study quantitative data. Simple random selection was used to perform the questionnaire survey of Indonesian stock market users, including individuals and businesses. There are 371 samples total that may be examined for this investigation. Software called SmartPLS 3.0 was used for the study's analysis. According to the study's findings, corporate governance, social responsibility, and the environment all have varied effects on investment choices. Environmental considerations have a major impact on investing objectives but little to no impact on investment choices. Social considerations have a favorable and considerable impact on investment decisions, but they have little impact on investment aims. Investment goals and choices are significantly impacted by corporate governance variables. According to this study, investment objectives play a part in mediating the relationship between environmental, social, and corporate governance (ESG) concerns and investment choices. Investment objectives operate as a mediator between environmental and corporate governance considerations, which both have an impact on investment choices. The impact of social considerations on investment decisions is not, however, moderated by investment objectives.

The objective of this investigation is to analyze the correlation among students' readiness for mobile learning, regulation of emotions, nomophobia, cyberloafing via smartphones, and addiction to smartphones while attending classes amidst the COVID-19 pandemic. Current research introduces a theoretical framework that outlines the factors influencing cyberloafing within the m-learning setting. The study involved a total of 719 participants. The structural equation modelling technique was utilized to evaluate a study's framework. The study's results suggest a significant association between the factors of m-learning readiness, emotion regulation, nomophobia, smartphone cyberloafing, and smartphone addiction among learners. The current study also introduces a conceptual framework for this entrepreneurial shift that outlines the factors influencing cyberloafing within the m-learning setting. The discourse pertains to the ramifications for both students and institutions of higher learning.

This study aims to analyze the effect of transformational leadership (TL) on organizational citizenship behavior (OCB), the relationship between TL and performance, TL and IWB, Leader member exchange (LMX) and OCB, LMX and performance, LMX and innovative work behavior ( IWB), organizational commitment (OC) and OCB, OC and performance, OC and IWB, digital transformation (DT) and OCB, DT and performance, DT and IWB, QWL and OCB, QWL and performance, QWL and IWB, OCB and performance, and the relationship between IWB and performance. The method of this research is quantitative and the population in this study were 341 private universities while the number of samples used in this study were 181 private universities. The sampling technique in this study used multistage random sampling. In this study the study used a seven-point Likert scale. The study uses SmartPLS software as a data processing tool. Validity testing is applied to all question items in each variable and there are several stages of testing that will be carried out, namely through convergent validity testing, average variance extracted (AVE) testing, and discriminant validity testing. There is a positive and significant relationship between TL and OCB, a positive relationship between TL and the performance of private universities. There is also a positive and significant relationship between TL and IWB, a positive and significant relationship between LMX and OCB. However, there is no significant relationship between LMX and university performance. There is a positive and significant relationship between LMX and IWB, a positive and significant relationship between OC and OCB, a positive and significant relationship between OC and university performance, and a positive and significant relationship between OC and IWB. There is also a positive and significant relationship between DT and OCB, an insignificant relationship between DT and university performance. This means that DT cannot directly affect performance. There is an insignificant relationship between DT and IWB, a positive and significant relationship between QWL and OCB. There is a non-significant relationship between QWL and university performance. There is a positive and significant relationship between QWL and IWB as well as between OCB and university performance and also between IWB and university performance.

Designing sustainable and scalable educational systems is a challenge. Artificial Intelligence (AI) offers promising solutions to enhance the effectiveness and sustainability of blended learning systems. This research paper focuses on the integration of the Chat Generative Pre-trained Transformer (ChatGPT), with a blended learning system. The objectives of this study are to investigate the potential of AI techniques in enhancing the sustainability of educational systems, explore the use of ChatGPT to personalize the learning experience and improve engagement, and propose a model for sustainable learning that incorporates AI. The study aims to contribute to the body of knowledge on AI applications for sustainable education, identify best practices for integrating AI in education, and provide insights for policymakers and educators on the benefits of AI in education delivery. The study emphasizes the significance of AI in sustainable education by addressing personalized learning and educational accessibility. By automating administrative tasks and optimizing content delivery, AI can enhance educational accessibility and promote inclusive and equitable education. The study’s findings highlight the potential benefits of integrating AI chatbots like ChatGPT into education. Such benefits include promoting student engagement, motivation, and self-directed learning through immediate feedback and assistance. The research provides valuable guidance for educators, policymakers, and instructional designers who seek to effectively leverage AI technology in education. In conclusion, the study recommends directions for future research in order to maximize the benefits of integrating ChatGPT into learning systems. Positive results have been observed, including improved learning outcomes, enhanced student engagement, and personalized learning experiences. Through advancing the utilization of AI tools like ChatGPT, blended learning systems can be made more sustainable, efficient, and accessible for learners worldwide.

Technological developments are increasingly sophisticated, so micro, small and medium enterprises (MSMEs) must maintain their business through digital markets. The main problem faced by MSMEs in Aceh province is the lack of use of social media as a medium for promoting or selling products online. Thus, this study analyzes the MSMEs' product marketing model, determines the factors that influence MSME labor productivity, and determines MSMEs' resilience strategies in entering the digital market. The research location is Aceh Province which consists of 23 districts/cities. The population in this study were all MSME actors in Aceh Province who were spread across districts/cities, using a purposive random sampling technique. The samples in the study were related agencies and MSMEs actors in Aceh Province, which are spread across 13 regencies/cities, namely Banda Aceh, Sabang, Lhokseumawe, Subulussalam, Langsa, Aceh Tamiang, East Aceh, North Aceh, Central Aceh, West Aceh, Aceh Singkil, Aceh Besar and Aceh Jaya. The results of the study show that (1) the marketing model that is used effectively is the marketing mix, namely the marketing mix, (2) the productivity of MSME workers is influenced by the level of education, age, work experience, gender and expertise or skills possessed by the workforce, (3) The MSMEs resilience strategy is dealing with the digital market can be pursued through government policies by providing training or assistance to business actors to increase product innovation and increase promotion or product sales online through various types of social media, such as Instagram, Facebook, WhatsApp, and market places other.

The Android operating system is the most used mobile operating system in the world, and it is one of the most popular operating systems for different kinds of devices from smartwatches, IoT, and TVs to mobiles and cockpits in cars. Security is the main challenge to any operating system. Android malware attacks and vulnerabilities are known as emerging risks for mobile devices. The development of Android malware has been observed to be at an accelerated speed. Most Android security breaches permitted by permission misuse are amongst the most critical and prevalent issues threatening Android OS security. This research performs several studies on malware and non-malware applications to provide a recently updated dataset. The goal of proposed models is to find a combination of noise-cleaning algorithms, features selection techniques, and classification algorithms that are noise-tolerant and can achieve high accuracy results in detecting new Android malware. The results from the empirical experiments show that the proposed models are able to detect Android malware with an accuracy that reaches 87%, despite the noise in the dataset. We also find that the best classification results are achieved using the RF algorithm. This work can be extended in many ways by applying higher noise ratios and running more classifiers and optimizers.

The major goal of this research is to examine how vlogging on YouTube affects the communication skills of Jordan teenagers with language and speech disorders. Fifty people, ranging in age from 13 to 18, participated in an eight-week vlogging intervention program designed to improve their communication skills. The results of the pre- and post-tests showed a statistically significant improvement in communicative competence, with the mean difference between the two measures being 10.5. Vlogging has a significant effect as an intervention because of its remarkable ability to actively engage participants in the creation of relevant content, the facilitation of authentic real-life communication experiences, the encouragement of creative and self-expressive expression, and the provision of individualized therapeutic approaches. Adolescents with speech and language disabilities have unique obstacles in communicating, and this study gives insight on the possible use of novel tools, such as vlogging, to address these issues.

With the hope of improving students' social interactions who have been diagnosed with Autism Spectrum Disorder (ASD) in Saudi Arabia, the current study set out to examine the effectiveness of peer-mediated therapies delivered through the ubiquitous social media platform Snapchat. Thirty kids with ASD and 15 generally developing peers participated in the study. Alterations in social communication difficulties and observable social interactions were evaluated before and after the intervention to determine its efficacy. The results of the research revealed significant improvements in both self-reported difficulties with social communication and the quality of participants' interpersonal relationships. These findings demonstrate the groundbreaking methodology's deep applicability and widespread cross-cultural significance. Individuals with ASD have shown to benefit greatly from individualized, technology-based therapy to improve their social interactions, as shown by this study.

The aim of this research was to evaluate the impact of decentralization and electronic participatory budgeting on managerial performance. Additionally, the study sought to investigate the management accounting system's role in strengthening the effect of decentralization and electronic participatory budgeting on managerial performance. This quantitative research involved 30 regional units, using primary data collected through questionnaires of 108 fund management officials. The findings of this study demonstrated that decentralization and electronic participatory budgeting both have a positive and significant effect on managerial performance. Furthermore, the management accounting system was found to act as a moderator between decentralization and performance, but not between electronic participatory budgeting and performance.

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts.