An Evaluation of the Performance of Higher Educational Institutions using Data Envelopment Analysis: An Empirical Study on Algerian Higher Educational Institutions

The aim of this research paper is to clarify to evaluate the performance of the Algerian institutions of higher education using data envelopment analysis method based on the concept of benchmarking Five indicators of inputs as well as outputs that reflect three dimensions of teaching learning and scientific research were used were used total number of students enrolled in graduation total number of students enrolled in post-graduation permanent professors graduated students and scientific publications The findings of data envelopment analysis pointed out that there is a significant variation in the performance of the Algerian institutions of higher education in favor of the academic years It was highlighted that inefficient internal processes or poor conditions surrounding these processes were the main causes of the weak performanc

Using the Linear Discriminant Analysis Method to Classify Types of Bowels and Esophageal cancer in Jordan

The research aims at achieving the best linear model to distinguish between two types of Bowels and Esophageal cancer in Jordan, using the methodofdiscriminant analysis, the SPSS program was used to analyze the data. The study concluded a number of results, the most prominent of which were: the variables sex (x1), weight (x3), and Platelets Count P.C (x8) which have a significant impact in constructing the discriminatory function. The probability of correct classification of a disease belonging to the first group was equal to (62.8%) and to the second group was equal to (77%). The probability of misclassification in the first group, was equal to (37.2%), and for the second group was (23%), the overall correct classification ratio (71.6%) and the false classification ratio (28.4%),the probability of correct classification of a disease belonging to the first group was equal to (66.4%) and the second group was equal to (77.6%). It was noted that the discriminant analysis method was able to identify the most important independent variables in the diagnosis of both types of Bowel and Esophageal cancer

The effect of digital marketing capabilities on organizational ambidexterity of the information technology sector

The aim of the study was to examine the impact of digital marketing capabilities on organizational ambidexterity by focusing on the Information Technology Sector in UAE. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of managers at different levels via email. This study was conducted structural equation modeling (SEM) to test the hypotheses, which represents a contemporary statistical technique for testing and estimating the relationship between factors and variables. The results showed that the highest impact on organizational ambidexterity was for strategic approach and data content infrastructure, followed by integrating customers with employees, and finally the lowest impact belonged to the process of improving performance. Based on the study findings, the researcher hopes that the decision-makers and managers define all tasks, roles and work procedures in companies through digital marketing systems to improve their organizational ambidexterity and enhance their performance

Forecasting the Number of Traffic Accidents in Jordan using the Poisson Regression Model

The study aims at forecasting the number of traffic accidents in Jordan for the year 2022, based on the monthly data related to traffic accidents for the period (2017–2021) using the Poisson regression model. SPSS version 26 and Minitab version 19 data analysis programs were used to analyze the collected data. The study concluded that the use of the Poisson regression model is very appropriate to forecast the number of traffic accidents during the next period of time. The Poisson regression method is a useful method for estimating and forecasting. The researchers recommend adoption of this technique in related studies, conducting more extensive studies on the Poisson regression model, and reconsidering the current legislation and the penalties related to traffic accidents

The role of digital marketing, CSR policy and green marketing in brand development

Corporate Social Responsibility (CSR) policy, digital marketing and green marketing are considered as some of the most emerging topics. However, the major problem is associated with the lack of CSR policies, development and adaptation of green marketing in the companies operating in manufacturing companies in the UK. In this manner, this study aimed to determine the role of digital marketing, CSR policies and green marketing in brand development. Concerning this, the case of UK’s manufacturing companies was considered which can help the manufacturing companies operating in the UK to make the development of brand more effective, as the consumers would perceive the brand which complies with the environmental laws. To attain the aim, the researchers utilized a quantitative method of data collection where a close-ended survey questionnaire was utilized. The data was collected from the concerned participants working in the manufacturing sector of the UK and the sample size considered for the analysis was based on 404 participants. The analysis was conducted using Structural Equation Modeling (SEM) on Smart PLS. The analysis revealed that the overall impact of green marketing, CSR policy and digital marketing was statistically significant on the brand development of UK’s manufacturing companies. Considering this, it has been recommended to the manufacturing companies in the UK to focus on environmental disclosure, green innovation, green alliance and promotional activity for the purpose of ensuring brand development. However, this study is limited to the geographical bounds of the UK; therefore, it has a certain room for future research

The effect of big data on financial reporting quality

The current manuscript aimed to explain the impact of big data on the financial reporting quality of the industrial sector in Jordan. To achieve the manuscript goals and validate hypotheses, a field study was conducted by distributing a questionnaire to 325 financial managers in industrial companies listed on the Amman Stock Exchange during a specific period. Gathered data were analyzed using structural equation modeling (SEM). The manuscript concluded that the big data dimensions, including variety, volume, and velocity, had a positive impact on financial reporting quality. Therefore, a set of recommendations were provided to managers of the industrial companies in Jordan to put in place an extensive data governance system to as-sure data quality, security, privacy, and compliance. To ensure the integrity and dependability of financial reporting, define data ownership, create data quality standards, and develop processes for data access, use, and preservation

The adoption of big data analytics in Jordanian SMEs: An extended technology organization environment framework with diffusion of innovation and perceived usefulness

While many small and medium enterprises (SMEs)recognize the benefits of Big Data Analytics (BDA) for digital transformation, they face challenges in implementing this technology, highlighting the need for more research on its adoption by SMEs. The objective of this study is to amalgamate the Technology Organization Environment (TOE) framework with the Diffusion of Innovation (DOI) theory, aiming to dissect the factors that sway BDA adoption in Jordanian SMEs. Additionally, the study delves into how perceived usefulness impacts this adoption process. Utilizing structural equation modeling, the study examined data from 388 managers in Jordan. The study validates all its hypotheses, revealing that variables like relative advantage, compatibility, complexity, top management support, competitive pressure, and security influence perceived usefulness, which subsequently has a positive impact on BDA adoption. This research presents a range of theoretical and practical insights

Nursing Perspectives on the Association between Human Capital Development and the Work Engagement: A Cross-Sectional Study

This study examined the impact of human capital development on the nurses’ work engagement. A questionnaire was adapted and distributed to a nursing convenience sample in different types of hospitals. The sample size was 286 male and female nurses who completed it. Structural Equation Modelling (SEM) was used to test the research hypothesis. Results revealed that human capital development had a direct impact on the nursing work engagement. Nursing work engagement give managers ability to improve the work environment, increase the work professional and institutional loyalty. Supporting the human capital development tools is very important for any healthcare organization

Artificial intelligence in Jordanian education: Assessing acceptance via perceived cybersecurity, novelty value, and perceived trust

The growing significance of Artificial Intelligence (AI) across different fields highlights the essential role of user acceptance, as the success of this technology largely depends on its adoption and practical use by individuals. This research aims to examine how perceived cybersecurity, novelty value, and perceived trust affect students' willingness to accept AI in educational settings. The study's theoretical basis is the AI Device Use Acceptance (AIDUA) model. Using structural equation modeling, the study tested hypothesized relationships using data from 526 students at Jordanian universities. The results showed that social influence is positively associated with performance expectancy, while perceived cybersecurity is positively related to both performance and effort expectancy. Novelty value is positively associated with performance expectancy but a negative one with effort expectancy. Additionally, effort and performance expectancy significantly influence perceived trust and the willingness to accept AI. Moreover, perceived trust has a notable positive effect on the willingness to accept AI in education. These findings provide valuable guidance for the creation and improvement of AI-driven educational systems in universities, contributing to the broader understanding of AI technology acceptance in the educational field

Determinants of behavioral intention to use big data analytics (BDA) on the information and communication technologies (ICT) SMEs in Jordan

Big Data Analytics (BDA) provides an important resource for businesses seeking to enhance their performance and gain a competitive advantage, although not all organizations are adopting BDA techniques, and small and medium-sized enterprises (SMEs) in Jordan have been slow in this regard, despite being key players in any healthy economy, and the fact that BDA adoption can be facilitated by using the Technology Acceptance Model (TAM). The purpose of this study is to investigate the drivers of behavioral intention among managerial-level employees in Jordanian ICT SMEs to adopt BDA through a quantitative correlational research approach. The TAM questionnaire was used to gather data from 271 online survey participants in Jordan using Google Forms. The target group included management level staff working in small and medium-sized ICT firms (SMEs). Confirmatory factor analysis (CFA) was used to evaluate the research instrument's reliability and validity, and structural equation modeling (SEM) was utilized to test the study's hypotheses. The findings revealed that perceived usefulness, perceived ease of use, and perceived “privacy and security” significantly influenced managerial-level employees' behavioral intention to use BDA in their organizations. The research findings also supported the application of TAM, and the results of the investigation indicated that managerial-level employees would be willing to use BDA techniques providing they were perceived to be useful, user-effortless, and posed little concern about privacy and security. Overall, the current study's results demonstrate that the suggested model had good predictive power, 51% of the variance in behavioral intention, and was therefore capable of predicting managers' intentions to use BDA