Green management is fundamentally referred to the environmental practices and the implementation of the appropriate eco-friendly activities, which improve the level of people’s awareness about the environmental protection. Such green management practices have been investigated namely, redesigning of operation and production processes, innovation and R&D functions, use of technology to save energy and reduce waste, environmental awareness of stakeholders through seminars and training, investment in equipment and technology, and improvement in products or services towards improving firm efficiency in Oil and Gas industry in Jordan. The design of this study has a quantitative approach. Three hypotheses have been developed through literature review and tested using descriptive statistical analysis performed by SPSS. The number of population was 250 employees from different departments that include HR, environment, operation, maintenance, and marketing, a total of 208 completed questionnaires were analyzed as a final sample. The results show a moderate implementation of Green management practices, redesigning of operation and production processes indicated the strongest influence, while innovation and R&D functions indicated the weakest influence. Statistical positive association also was indicated between green management practices and firm efficiency. This study contributes to support the literature of Green management and environment protection that is little in developing countries like Jordan. And it urges firm management to develop effective green innovation culture to improve the implementation level of Green management which may in turn create a high level of environmental efficiency outcome in the medium and long run. Also, it’s very significant for firm management to invest more money in green innovation and R&D functions in order to improve the level of efficiency/ performance.

The purpose of this study is to explore the relationship between green human resource management (HRM) practices, including Green recruitment and selection, Green training and development, and Green rewards, and environmental performance in Jordanian health service organization. The research hypotheses are tested by means of a questionnaire survey carried out among health service organizations in Jordan between April and May 2018. Our design choice to focus on a single sector was because of our intention to diminish the confounding effects of non- controllable factors in our research study, such as legislative, culture, and economical contexts. The results show a moderate implementation of Green HRM in Jordanian hospitals, the strongest correlation was with recruitment and selection while the weakest correlation was with training and development. Statistical positive association also was indicated between the three HRM practices and environmental performance.

Web based and/or existing e-commerce have changed the traditional view of CRM to the eCRM. Recent studies on eCRM has investigated success factors of e-CRM in different contexts and fields. This paper aims to investigate the effect of customer satisfaction, customer trust and commitment, customer profitability on eCRM in Saudi Arab. This study was designed quantitative cross-sectional questionnaire survey. This quantitative study used questionnaire survey design and 319 respondents were taken into account for analysis procedure. Structural Equation Modelling was applied and SmartPLS tools was used to test the hypotheses. Customer satisfaction, customer profitability, trust and commitment have shown to have positive significant effects on eCRM. Empirical evidence of this study confirmed that from the customer point of view customers satisfaction, customer profitability, customer trust and commitment were the strong predictors of successful eCRM.

The purpose of this research is to investigate the impact of total JIT practices (JIT purchasing, JIT operations, and JIT selling) on operational performance (quality, delivery, and cost) of local fast-food restaurants in Jordan. The study is quantitative and covers 43 out of 142 local fast-food restaurants in Amman. Data were collected by questionnaires from 101 managers and supervisors. After confirming normality, validity, reliability, and relationships between variables, multiple regressions were conducted to test the hypothesis. Results indicate that there are strong relationships among total JIT sub-variables, very strong relationships among operational performance dimensions, and a very strong relationship between JIT and operational performance. Finally, results show that total JIT practices have a positive significant effect on the operational performance of Jordanian local fast-food restaurants.

The purpose of this study is to investigate the moderating role of digital marketing strategies on innovation through Artificial Intelligence (AI) mediating impact in Small and Medium Enterprises within Saudi Arabia. Advanced analytics tools analyzed data from KSA SMEs to establish the role of AI, customer behavior, and experiences in product and process innovation. Artificial intelligence boosts product and process innovation with unconventional customer knowledge. Integrating AI combined with digital marketing to improve decisions and efficiency, to increase understanding of customer dynamics for sustaining growth and promoting collaborations. Using AI-empowered digital marketing strengthens Saudi SMEs advancement by promptly reacting to market motions. Sustainability practices attract the environmentally conscious consumer. This research provides actionable insights for SMEs who want to employ digital marketing and AI strategies and contribute to building an economic environment in Saudi Arabia.

The main goal of this research is to study and investigate the impact of data management and analysis tools on improving organizational behavior within three IT service firms in Jordan. This research focuses on choosing three data management and analysis tools: Database analysis tool, data processing tool, and big data processing tool and how these tools could positively influence enhancing employee performance and organizational behavior. It accomplishes this within the distinct framework of three Jordanian IT service firms. The research supposed that combining big data analysis, integrated data processing, and database analysis is necessary to enhance organizational behavior and employee performance, as indicated by the findings. Furthermore, the research highlights the potential for IT service firms in Jordan to benefit from emerging database analysis technologies, promote the use of integrated data processing techniques, and leverage big data analysis for their own benefit. This can enhance corporate behavior and employee performance. Data was distributed and collected from three Jordanian IT service firms, and all collected data was analyzed using AMOS. The study's findings offer valuable recommendations for enhancing the operational efficiency of IT service firms in similar business environments, emphasizing the crucial importance of comprehending and purposefully employing database-related technologies for sustained prosperity.

This study examines the extent of adoption of electronic payment methods in Jordanian banks and compares them with traditional alternatives. Focusing on banking customer behavior, we conducted a comprehensive survey among banking customers to uncover insights on trust, perceived benefits, financial literacy, ease of use, security and cost considerations. By analyzing the data collected using the questionnaire study tool, we reveal the motivations behind customers’ electronic payment preferences. By highlighting these factors, our research contributes to the understanding of the evolving payment landscape and provides actionable insights to enhance the adoption of electronic payment in Jordan’s financial system, in line with global technological trends. The results of the study revealed that all study variables had a significant impact (trust, ease of use, perceived security, and cost of service) on the use of electronic payment methods. In addition, gender, number of years of dealing with the bank, and type of electronic payment used more than once by customers in the banking sector did not have a significant impact.

Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.

Despite certain advancements, the incorporation of artificial intelligence in universities is still inadequate. The requirement for students will continue for a while, although the development of artificial intelligence-based chatbots in schools has limited the role of students. The research aimed to assess the willingness of Jordanian learners in higher education to use artificial intelligence-powered chatbots for instructional purposes. The present research suggests nine hypotheses derived from the UTAUT2 model to assess students' desire to use artificial intelligence-based chatbots in learning. The pupils' information was gathered and examined using PLS-SEM. The research results showed that nine hypotheses were confirmed. The outcomes indicate that learners are interested in adopting artificial intelligence-based chatbots into their studies. The research's findings will supply administrators at higher education with valuable insights into the effectiveness of artificial intelligence-based chatbots in learning. Moreover, the findings will help developers of artificial intelligence-based chatbots, higher learning administrators, and legislators execute artificial intelligence-based chatbots that fulfil modern educational requirements.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.