In this digital age, fraudulent practices are among the most challenging that organizations must be aware of due to the increasing use of online transactions. This also applies to the banking sector whose business has become more complex with the recent developments in information and communication technology, which has changed the nature of bank fraud requiring advanced prevention measures. From this perspective, this paper aims to determine how cybersecurity affects fraud prevention for Jordanian commercial banks. A five-dimensional NIST cybersecurity framework was used. The research data was collected from 173 information technology managers in commercial banks listed on the Amman Stock Exchange. Structural equation modeling (SEM) was applied to investigate research hypotheses. The results of the research demonstrated the significant impact of cybersecurity in fraud prevention, especially detect function which had the largest impact among the dimensions of cybersecurity. Therefore, a set of recommendations were formulated for policymakers in Jordanian commercial banks, the most important of which is the adoption of multi-factor authentication (MFA) approaches for customer accounts, employee access, and biometric systems that add an additional layer of protection and make access to sensitive information to unauthorized individuals more difficult.

Recent technological advancements are endless and have had a profound influence on everyone in every part of life throughout the preceding decades. Artificial intelligence is one such invention that has the potential to change the world. Now, artificial intelligence is being used in almost all commercial operations. Hence, this research attempted to investigate the impact of artificial intelligence dimensions, including natural language processing, machine learning, expert systems, and computer vision on the financial decisions of pharmaceutical companies in Jordan. A cross-sectional approach was used through a comprehensive survey to collect research data from 148 accountants and financial managers in pharmaceutical companies listed on the Amman Stock Exchange with a response rate of 81.3%. The research hypotheses were examined using structural equation modeling of the collected quantitative data. The results indicated that the dimensions of artificial intelligence positively impact financial decisions. Accordingly, companies should spend on building strong artificial intelligence infrastructure and skills. Access to modern artificial intelligence technology, data analysis tools and cloud computing resources are also essential to rationalizing financial decision-making. Besides, Jordan's pharmaceutical sector can overcome these limitations and realize the full potential of artificial intelligence in financial decision-making by solving data privacy issues, encouraging ethical AI re-search, investing in artificial intelligence expertise, and enhancing collaboration.

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.

Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.

Mobile e-learning within the education domain is now an integral part in education technology. However, there has been little discussion about the benefits and the challenges of the use of M-learning in the higher education system in KSA. Interviews were the primary instruments used for data collection in identifying the enabling and deterring the quality attributes of mobile e-learning use in the context in Saudi Arabia higher education. The research results revealed that learning content/information quality, system quality, service quality and instructional quality were the primary antecedents of Blackboard as a Learning Management System (LMS) in general and within the mobile devices, and this situation can enhance user’s attitude towards the use of E-learning. Data were analyzed and results were discussed. Recommendations were made based on the findings.

This paper examines the potential determinants associated with a firm’s decision to use a corporate website. The probability of web-based corporate reporting adoption was measured by using a dichotomous variable, where one is given if the firm has a website and zero otherwise. Based on a sample of 1217 US listed firms, it was found that 950 firms have a website and 267 do not. Those firms with a website are larger, more profitable and have a larger board size with more female directors when compared with firms without websites. In addition, the results of the regression analysis revealed that firm size, profitability, leverage, board size and the percentage of female directors in the boardroom have a significant positive impact on the probability of a firm having a website. However, firm age has a significant negative impact on the probability of web-based corporate reporting adoption. A weakness in the previous literature has been the neglect of firms without an online presence, which implies a potential selection bias. Consequently, this research contributes to the international accounting literature by expanding our understanding in relation to the probability of firms adopting web-based corporate reporting and the economic consequences of them doing so through reducing asymmetric information, which acts as an incentive to encourage investment.

The international strategic alliance is an inevitable solution for making competitive advantage and reducing the risk in today’s business environment. Partner selection is an important part in success of partnerships, and meanwhile it is a complicated decision because of various dimensions of the problem and inherent conflicts of stockholders. The purpose of this paper is to provide a practical approach to the problem of partner selection in international strategic alliances, which fulfills the gap between theories of inter-organizational relationships and quantitative models. Thus, a novel Robust Fuzzy Possibilistic AHP approach is proposed for combining the benefits of two complementary theories of inter-organizational relationships named, (1) Resource-based view, and (2) Transaction-cost theory and considering Fit theory as the perquisite of alliance success. The Robust Fuzzy Possibilistic AHP approach is a novel development of Interval-AHP technique employing robust formulation; aimed at handling the ambiguity of the problem and let the use of intervals as pairwise judgments. The proposed approach was compared with existing approaches, and the results show that it provides the best quality solutions in terms of minimum error degree. Moreover, the framework implemented in a case study and its applicability were discussed.

This paper presents an investigation into the flexural behaviour of basalt FRP reinforced concrete beams through experimental and analytical methods. To achieve the research objectives, four concrete beams reinforced with steel and four identical concrete beams reinforced with BFRP bars were tested under four-point bending. The main parameters examined under the tests are the type of concrete (lightweight foam glass concrete and normal concrete) and the type of longitudinal reinforcement bars (BFRP and steel). Test results are presented in terms of failure modes; deformation crack pattern and the ultimate moment of resistance are presented. The experimental results are analysed and compared to predictive models proposed by ACI 440.1R, 2006 and BS EN 1992, Eurocode 2, for deformations and ultimate flexural capacities of the steel and BFRP reinforced concrete beams. The experimental results indicated that the flexural capacity decreased for the beams reinforced with BFRP bars compared to that of a corresponding beam reinforced with steel bars. Both types of beams failed in the modes predicted. The prediction models underestimated the flexural capacity of BFRP reinforced concrete beams. The increase in foam glass aggregate content was observed to reduce the cracking load by almost 10 - 40% and 25 - 50% for steel and BFRP reinforced concrete beams, respectively. The flexural capacities of BFRP reinforced beams were underestimated by using equations stipulated in ACI 440.1R and Eurocode 2 codes of practice.

In-plane strength and constitutive properties of composite materials is known as crucial problems. Although, several studies have been made for obtaining the in-plane mode II properties, the common test fixtures are blind in confrontation of shear zone. Furthermore, toughening mechanisms and consequently mode II fracture toughness cannot be evaluated, precisely. Also, proposing the convenient configuration of shear test fixture increase the accuracy of evaluation for the shear-zone energy dissipation, especially in orthotropic materials. Hence in the present research, a novel shear fixture configuration is proposed based on Iosipesque structural modification. Hereof, some arbitrary composite materials (e.g. graphite/epoxy and wood) were studies on the basis of DOE and E-691 ASTM. Furthermore, as the numerical method for indicating the comparison of the uniform and non-uniform shear stress distribution in the modified and common shear test fixture, a statistical procedure is used based on ASTM standard in addition to FEM analysis. The obtained results reveal that applying major amendments through the new scheme of the shear test fixture, would provide a remarkable precision and a reasonable estimation of the shear strength in comparison with the previous Iosipesque experiments.

The purpose of the study is to present a digital marketing data analytics model to analyze campaign efficacy and inform strategy based on website performance, social media metrics, email marketing performance, customer data for targeting and personalization, and customer journey analysis. This model defines campaign success criteria for strategy. A statistical analysis approach was used to analyze the data for the research. Data was gathered through a survey. This study analyzes demographic parameters descriptively using the structural equation model (SEM). From comprehensive surveys, 125 digital media and 115 online shop subjects responded. Sampled were 240 people. According to the findings, social media data, customer journey research, successful advertising, and informed approaches are highly correlated. Compared to the previous study, website performance evaluation does not match the marketing plan's success. The model's results can be used by any company that communicates with clients online.