The paper aimed to examine the impact of corporate governance on the financial performance of commercial banks in Jordan. The variables used to measure corporate governance were the board of directors' size, independent members of the board of directors, and the number of audit committee members, while those used to measure financial performance were return on investment, return on equity and earnings per share. The study used a quantitative approach based on the data of 12 commercial banks in Jordan during the period 2005-2022. The panel data were analyzed using the EViews software based on the ordinary least squares time series technique. The paper found the effect of all corporate governance variables on both return on investment and return on equity. However, it indicated that the board of directors' size and the independent members of the board of directors had an impact on earnings per share. This study highlighted corporate governance variables in one of the significant sectors of developing economies. Moreover, it recommended the need to review the principles used in selecting members of the audit committee for commercial banks in Jordan, due to their importance in developing long-term financial performance.

This paper examines the health operations flexibility dimensions in the United Arab Emirate in the healthcare sector by employing Structural Equation Modeling (SEM) approaches. The study also attempts to determine the numbers for the operational flexibility dimensions which will help the researchers in future find healthcare operational flexibility dimensions valid and reliable. A model consisting of two constructs of operations flexibility structures: external flexibility and internal robustness is examined to measure health operations flexibility elements in service sectors. Respondents are the health leaders (managers, middle manager, top manager and others) who were working in health service sectors in the United Arab Emirate. The underlying constructs of operations flexibility are empirically verified and validated through Reliability Analysis Procedure, Exploratory Factor Analysis (EFA), First and Second Confirmatory Factor Analysis, and Construct Validity Procedures, Structural Equation Modeling (SEM) was employed to test the model, drawing on a sample of 250. The findings revealed that the model of the UAE health service sector consists of two latent's operations flexibility dimensions namely external flexibility and internal robustness, each dimension consisting of four items. Further research should be considered to validate these findings in the other firms. The two dimensions of health operations flexibility represent a valid instrument to measure the operations flexibility in the services sector in the United Arab Emirate. This research is important for one to understand the main topics of health operations flexibility in the health services sector.

The aim of this research is to examine the impact of supply chain management through social media on competitiveness of the private hospitals in Jordan. The population of the study includes managers in various administrative disciplines. Therefore, the complete census method was used in this research to collect the primary data, where the total responses were 438 responses were used for analysis. The questionnaire was used as a basic instrument in the current research, as it was designed electronically through Google Forms and sent to the research sample via email. AMOS software was used to test the research hypotheses. The results showed that all dimensions of supply chain management through social media had a positive impact on competitiveness; the greatest effect was for strategic relations with suppliers. Based on the study results; managers at the examined companies should enhance their usage of social media with suppliers, according to the report, in order to support strategic connections with them.

The increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.