The study aimed at bridging or reducing the knowledge gap between digital marketing dimensions and customer’s happiness, by diagnosing both the level of digital marketing adoption and the level of customer’s happiness achievement, determining the nature of the relationship between digital marketing and customer’s happiness, and recognizing the level of effect and contribution of digital marketing dimensions in achieving customer’s happiness. This is based on the main idea that digital marketing activities are the main basis for achieving customer’s happiness, when designing digital services. Opinions of (360) customers in Amman five-star hotels were surveyed and viewed to achieve this direction, by designing and distributing a questionnaire. The most important results of the study showed that all digital marketing dimensions positively affect customer’s happiness, as well, the level of using dimensions of both digital marketing, and achieving customer happiness is lower than the required level, by five-star hotels in Amman. The most important recommendations were using of distinguished digital channels tools to respond to attract customers, such as the use of advertisements that accustom customers to use of products or services based on virtual reality or through the applications that use live streaming marketing system (photos or video) in order to bring customers close to what services the hotel offers. Using creative, and effective methods in communicating with customers through human emotions to create delicious marketing for customers, which makes them engaged with the offers they receive, whether through e-mail or SMS. Interest in providing real and effective content to digitally marketed service, in order not to create a gap between hotel and its customers as a result of the mis-match between what is advertised and what is actually provided. Determining success indica-tors of a hotel digital marketing campaigns by knowing customer’s feelings and their happiness (i.e. through customers’ positive feedback).

In the era of IoT gaining traction, attacks on IoT-enabled devices are the order of the day that emanates the need for more protected IoT networks. IoT's key feature deals with massive amounts of data sensed by numerous heterogeneous IoT devices. Numerous machine learning techniques are used to collect data from different types of sensors on the objects and transform them into information relevant to the application. Furthermore, business and data analytics algorithms help in event prediction based on observed behavior and information. Routing information securely over the internet with limited resources in IoT applications is a key problem. The study proposes a model for detecting network anomalies in IoT devices to enhance the security of the devices. The study employed the IoT Botnet dataset, and K-fold cross-validation tests were used for validating the values of evaluation metrics. The average values of Accuracy, Precision, Recall, and F Score was 97.4.

The increasing popularity of smartphones has led to the rise of e-wallets, which allow users to store their payment information on their devices and complete financial transactions conveniently and securely. This research extended the unified theory of acceptance and use of technology (UTAUT2) model to investigate the main factors influencing users’ intention to accept e-wallets in Jordan, where little research focused on such apps. Using a quantitative method, a sample of 181 users was utilized, and an instrument of 32 items was used. Findings revealed that price value, hedonic motivations, social influence, performance expectancy and perceived trust are major predictors of users' intention to use e-wallets apps. In contrast, facilitating conditions and effort expectancy are not significant toward the adoption process. Finally, conclusions and future work are presented in the last section of the study.

This study aimed to demonstrate the impact of big data analytics techniques on reducing information asymmetry in industrial companies listed on the Amman Stock Exchange from the point of view of workers in Jordanian financial intermediation companies. Two approaches have been adopted to achieve the target of this research. The first approach is the analytical descriptive approach through a survey to collect primary data that measures the elements of the independent variable related to big data analytics techniques (Volume, Velocity, Variety, and Veracity). The second approach is an applied approach that measures the dependent variable of information asymmetry based on the financial statements of industrial companies listed on Amman Stock Exchange for the period (2015-2021). The statistical program (SPSS) has been used to analyze data and test the hypotheses through multiple regression testing. Based on the results of the statistical analysis of the data and the opinions of the research community, it was found that the huge volume of big data has become difficult to process using traditional data processing applications. Furthermore, there is a statistically significant relationship between big data analytics techniques and the reduction of information asymmetry from the point of view of employees in intermediation firms in Jordan. Consequently, it is necessary for those in charge of the industrial companies listed on the Amman Stock Exchange to develop modern techniques capable of analyzing big data with high efficiency. It can also assist in providing target groups including investors, stakeholders, and other beneficiaries with reliable and efficient data required to make rational decisions, as well as to reduce the risks of information asymmetry.

The use of data mining to predict early employment readiness of students is gaining importance due to the expansion of data production in various industries. This study aims to address the employability issue in Middle Eastern nations by utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) data mining technology. The experimental investigation used data from tracer studies conducted by three Jordanian universities, consisting of 22 parameters. Results showed that despite achieving an accuracy of 94% for the graduate dataset, ANFIS exhibited high complexity due to the large number of attributes used. The study has implications for selecting relevant variables and investigating multiple aspects. Data mining has various applications, including classification, clustering, regression, association rule development, and outlier analysis. As data production continues to expand, this study provides insights into the potential use of ANFIS in predicting early employment readiness of students in Middle Eastern nations.

Big data has become much more widely used in recent years, particularly in the banking sector. Banks have begun to use big data to enhance customer experience, optimize operations, and create novel products and services. This study investigates the effect of big data governance on financial technology in Jordanian commercial banks. The study focuses on the four dimensions of big data governance: strategy and planning, ethics and social responsibility, data quality and management, and infrastructure and architecture. Additionally, the study considers the mediating role of organizational culture in the relationship between big data governance and financial technology. Primary data were collected from 250 IT employees working in Jordanian commercial banks, and the data were analyzed using PLS-SEM. The study findings indicate that big data governance has a significant positive effect on financial technology in Jordanian commercial banks. Additionally, it has been discovered that organizational culture partially mediates the relationship between big data governance and financial technology, emphasizing the significance of creating a culture that encourages the efficient use of data in fintech. In the context of Jordanian commercial banks, this study presents empirical evidence for the connection between big data governance and financial technology. The results indicate that big data governance measures should be applied while taking organizational culture into account as a potential mediator of the effects on financial technology. This study offers guidance on how to successfully integrate big data governance policies in commercial banks to advance financial technology for bank managers and policymakers.

Repurchasing in the future reflects the success of a business that can build customer loyalty, which happens because the company can meet the needs and expectations of consumers. The dynamics of changes in consumer behavior in choosing products encourage companies to be able to provide satisfying products. Satisfied consumers will feel a bond with the product. Purpose of the study is to examine Self-Brand Connection (SBC) as a mediating variable between customer satisfaction on repurchase intention and the role of love mark in moderating SBC for intention to repurchase. The sample used in the study was 210 and analysed quantitatively via SmartPLS3. The study found that customer satisfaction affects repurchase intentions. Customer satisfaction positively affects self-brand connection. The SBC affects repurchase intention. Love for culinary brands cannot moderate self-brand connection on repurchase intention. Brand respect can moderate SBC on repurchase intention.

This study aims to find out how much the ways that blockchain technology helps in different parts of the supply chain affect how SMEs use blockchain technology. Moreover, the research examines the correlation between blockchain use and overall performance. The data was obtained from the SMEs' owners and managers, who have the authority to implement blockchain technology. Two follow-up phone calls yielded 145 usable replies from 500 prospective respondents, for a response rate of 29%, which was analysed employing structural equation modelling (SEM). The results demonstrated that the contributions of blockchain adoption to task technology fit, task characteristics, technology characteristics, operational supply chain transparency, and supply chain agility influenced the acceptance intentions of SME managers and that there is a significant positive correlation between blockchain adoption and overall performance. The results aid vendors and policymakers in formulating strategies and effective plans to accelerate blockchain adoption by SMEs. Furthermore, the findings provide owners/managers of SMEs with reason to believe that blockchain technology might be a source of competitive advantage.

The present research examines the debated relationship between quality management and innovation using a multidimensional quality management perspective. The quality performance that is supposed to result from the adoption of quality management is investigated further as a possible mediator between quality management and innovation in the higher education sector. The data needed to test the hypotheses was gathered by sending a survey via the internet to the faculty members at universities in the United Arab Emirates. Applying the approach of structural equation modelling with partial least squares, the hypothesised associations between 175 respondents are evaluated. According to the findings, implementing rigorous quality management has a direct as well as indirect impact on innovation performance via its impact on quality performance. The impacts of soft quality management on hard quality management have indirect consequences on innovation performance. The association between rigorous quality management and innovation performance is moderated in part by quality performance. This study provides one of the initial studies to apply the multidimensional method of quality management in higher education and has the potential to assist directors in better comprehending the interdependencies between soft and hard quality practices.

This study has examined factors, such as technology and employee influence on artificial intelligence (AI) adoption of e-innovative projects in the United Arab Emirates. The present study revealed the success or failure of e-innovation adoption in the public sector of the UAE and hinted at potential e-innovative projects to consider essential factors before adopting it. The study's sample covered the government sector, and the data collection method was a survey questionnaire with a sample size of 1037 responses made up of government employees. This paper was mainly built upon the diffusion of innovation and technology acceptance theories. The analysis findings showed that technology (an external factor) significantly and positively contributed to adopting AI e-innovation technology. Further analysis revealed that employee (internal factor) proxies directly influenced the adoption of AI e-innovation technology. Overall, internal and external factors contributed to adopting e-innovation technology in the United Arab Emirates. For future directions, additional factors related to the market should be considered to explore their contribution.