The Internet of Things (IoT) is an innovative technology that has the potential to help public hospitals better meet the demands of hospitalization. However, only a small portion of the research looked at patients' behavioural intentions (BI) to utilise IoT healthcare devices (IoTHD). This study intends to investigate the variables that influence the BI's use of IoTHD. The research suggests that the BI may be explained by factors of UTATU. The patients of public hospitals make up the population. A questionnaire was used to obtain the data using convenience sampling. Participants in this research totalled 161. Smart Partial Least Square results demonstrated that social influence (SI) has an impact on performance expectancy (PE). Technological complexity (TC) and playfulness (PP) had an impact on effort expectancy (EE). Additionally, the BI to adopt IoTHD was impacted by PE, EE, perceived security (PS), and perceived privacy (PV). The impact of PE and EE on BI to use IoTHD was not moderated by the facilitating conditions (FC). In order to improve patients' perceptions of IoTHD usage in public health organisations, simple process and more positive word of mouth is required.

This research studies the effects of the religiosity on financial technology (fintech) adoption. The study examines religiosity as part of the Technology Acceptance Model (TAM) dimensions for the adoption of mobile payment technology. We explore the role of religiosity in TAM and recommend several policies for related organizations. The study uses professional sample calculation from 113 traditional markets under Perumda Pasar Jaya as a business entity whose capital is wholly or mostly owned by the regional government through regional assets of DKI Jakarta Province, Indonesia, which use mobile payment technology. We obtained 363 respondents from June 2020 to June 2021, coinciding with the Covid-19 pandemic. Hypothesis testing was done employing SmartPLS 3.2.9 software and questionnaires. The study also adapts previous studies to ensure the questionnaires are relevant to the research objects. The research result show that religiosity explained the formation of TAM by small businesses in traditional markets under Perumda Pasar Jaya Management. Religiosity and the adoption of mobile payment technology determined whether a user used fintech or not. As the research period was limited to June 2020 - June 2021, including field research in the traditional markets, newer TAM mobile payment technology development and other TAM mobile payment-based research were not included. This research offers a new TAM development model using religiosity for mobile payment adoption in traditional markets.

Phishing is an attack by cybercriminals to obtain sensitive information such as account IDs, usernames, and passwords through the use of the anonymous structure of the Internet. Although software companies are launching new anti-phishing tools that use blacklists, heuristics, visual methods, and machine learning-based methods, these products cannot prevent all phishing attacks. This research offers an opportunity to increase accuracy in the detection of phishing sites. This study develops a model using machine learning algorithms, specifically the decision tree and the random forest, due to their outperforming the rest of the classifiers and being accredited by researchers in this field to achieve the highest accuracy. The study is based on two phases: the first phase is to measure the accuracy of classifiers on the dataset in the usual way before and after feature selection. The second phase uses the class decomposition approach and measures the accuracy of classifiers in the dataset before feature selection and after feature selection to detect phishing sites. The class decomposition approach is a technique to improve the performance of classifiers by distributing each class into clusters and renaming the examples of each cluster with a new class. This provides a specific metric that more accurately predicts the level of phishing. Testing on a dataset containing 11,055 instances, 4,898 phishing, and 6,157 legitimate, each instance has 30 features. It achieved the highest accuracy in the first phase through the random forest algorithm by 96.9% before feature selection, and after feature selection, it was by 97.1%. In the second phase, the highest accuracy of both the decision tree and random forest classifiers was achieved by 100% with the two and four classes after feature selection. While before feature selection, the random forest algorithm achieved 100% with only the two classes.

The rapid development of e-commerce in Indonesia makes the competition in this business increasingly fierce. This study aims to determine and analyze the effect of e-service quality, e-word of mouth (e-WOM), customer trust on customer satisfaction on e-commerce customers in Indonesia, then the study aims to determine and analyze the effect of e-service quality, e-word of mouth (e-WOM), customer trust and customer satisfaction on the repurchase intention of e-commerce customers in Indonesia. The study also aims to determine and analyze the mediating role of customer satisfaction on the relationship between e-service quality, e-word of mouth (e-WOM), and customer trust in repurchase intentions. The research is quantitative by distributing questionnaires to respondents; the sample collection method is purposive sampling. The number of samples used was 344 e-commerce consumers from Shopee, Tokopedia, Lazada, and Bukalapak throughout Indonesia. Data processing is applied by using the SmartPLS 3 Structural Equation Modelling (SEM) method. The results of this study indicate that there was a positive and significant effect of e-service quality on customer satisfaction, there was a positive and significant effect of e-WOM on customer satisfaction, customer trust had a positive and significant impact on customer satisfaction, e-service quality had no significant effect on purchase intention, e-WOM had a positive and significant effect on repurchase intention, customer trust had no significant effect on repurchase intention, e-service quality had a positive and significant effect on repurchase intention through customer satisfaction, e-WOM had a positive and significant effect on repurchase intention through customer satisfaction, customers trust had a positive and significant impact on repurchase intention through customer satisfaction.

Can artificial intelligence networks promote extremism awareness through social intelligence and emotional intelligence? This research contributes to this question in the context of Saudi Arabia. This study defines a model of a cooperative process through an artificial intelligence network, based on knowledge exchange, to generate a high level of extremism awareness and social intelligence. Four main variables were adopted, developed, defined, and measured: artificial intelligence networks, social intelligence, emotional intelligence, and extremism awareness. We fixed attributes for contextualized interactions through a network platform, between professionals and non-professionals, against extremism. The application of artificial intelligence in such platforms lets members share reliable information to combat extremism more effectively. The findings demonstrate that network centrality, network scale, relationship strengths, relationship stability, and reciprocity developed through artificial intelligence networks stimulate extremism awareness by developing social awareness. Emotional intelligence also seems to be important. It moderates the link between platform users and extremism awareness. It facilitates situational and contextual awareness to define appropriate behavior.

Reliable data transfer protocols are algorithmic techniques that guarantee the safe and secure transport of data through networks that could experience data loss or corruption. The performance of the systems and some accounting information systems (AIS) will be negatively impacted if the real-time data is not sent. The main factors that affect the performance of computer networks are the number of users, the hardware and software and the bandwidth. The computer network performance will play a role in the performance of the banks as it is an important component of the bank infrastructure. With the advancement of information technology, network technology, and computer technology, computers have been utilized to aid AIS operations, and AIS has become an unavoidable trend of development. Therefore, the purpose of this study was to investigate Data analysis in computer networks to improve AIS performance in Jordanian banks. A questionnaire was used to obtain the information. Jordanian banks account for the bulk of the participants in the survey. A total of 115 people took part in the study. According to the conclusions of this study, communication technology networks have a statistically significant impact on the growth of Jordanian banks' improved AIS performance.

This study was designed to analyze the factors influencing the utilization of an E-Learning system in training repatriated workers in Vietnam's urban regions in the new context. According to research findings, professional qualifications and education have a significant influence on the income and employment of repatriated workers in urban regions. E-Learning systems are employed as an effective channel to transfer information and skills to workers in urban areas to fulfill the improvement of professional certifications and professional abilities of workers. The analysis results also show that several factors have a significant impact on the usage of the E-Learning online training system for employees in Vietnam's urban regions, including the factor representing the ease of use of the E-learning system, Easy access to E-Learning system scale has the highest influence score with coefficient 0.932, the factor Learners feel useful, the scale of saving time getting to the study location has an influence coefficient of 0.965, and the element reflecting the joy of learning, the scale of getting more highly rated experiences have the greatest influence with a coefficient of 0.942. Data for the study were obtained from 188 repatriated laborers in urban regions of Vietnam. The multivariate regression analysis method and factor analysis were utilized to analyze the data in the study with the help of SPSS 20.0 software.

This research involves developing a model that investigates the impact of perceived values on continuance intention to use Facebook. It also examines the impact of Facebook continuance intention on addiction and the mediating role of security concerns on the relationship between them. A structural equation modeling technique was used to analyse data obtained based on a convenience sample from 296 Facebook users in Jordan. Results showed an acceptable fit of the model. The influence of perceived values on continuance intention was confirmed based on findings. However, no similar significant effect was found for trust. A significant effect of continuance intention on Facebook addiction was found but not for security concerns. The contrasting findings represent a challenge and a possible opportunity for future research and practitioners. The research discusses different theoretical and professional contributions and outlines the implications arising from the research model.

The primary goal of this research is to examine the influence of the Internet of Things on Pharmaceutical Inventory Management in Jordan. The employees of the pharmaceutical companies in Jordan represent the population of the study. Accordingly, the study instrument was distributed to an appropriate sample of 620 employees of pharmaceutical companies in Jordan; the questionnaires used in the analysis were 432. Structural equation modeling (SEM) was used to test the study hypotheses. The study results indicated that all dimensions of the Internet of Things had an influence on inventory management. The greatest influence was for cloud computing. Based to the study findings, pharmaceutical companies in Jordan should provide high-quality Internet coverage in their offices and stores, as well as to invest in Internet of things technologies that allow the company to remotely monitor and control the physical components of the stores, attract employees with knowledge of modern Internet technologies, and provide the necessary training and development for their employees.

This study aims to evaluate the roles of Gender Equity and Social Inclusion (GESI) and institutions, as drivers of green entrepreneurship and sustainable development goals (SDGs). A systematic and holistic integrated approach was used in encouraging the developmental processes, with both primary and secondary data qualitatively and quantitatively utilized. Mixed methods were also used through two phases, namely exploratory and explanatory design. The results showed that the role of GESI and community institutions encouraged the improvement of green entrepreneurship, whose role was one of the win-win solutions in mitigating the impact of global climate change and encouraging the achievement of the SDGs. Based on the limitations, the awareness of every individual was globally required on the importance of entrepreneurial trends. These results are expected to increase the knowledge and understanding of green entrepreneurship importance, as an alternative to contemporary global business.