The purpose of this study is to identify the key macroeconomic variables that affected stock price fluctuations in Amman Stock Exchange during the period 1980-2018. Using Augmented Dickey-fuller (ADF) test, it was found that the variables did not have the same degree of integration. According to Breusch-Pagan-Godfrey test, the residuals violated the constant variance assumption under Ordinary Least Square (OLS) model. Therefore, the study employed Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methodology to analyze the model after taking the first difference of natural logarithm for all variables to be stationary at the same level and to show the fluctuation in the variables. It was found that fluctuations in portfolio investment and in industrial production index are statically significant to lead fluctuations in the stock price index in Amman Stock Exchange and they follow the same direction, whereas fluctuations in real effective exchange rate, real interest rate, and Brent crude oil prices were statically significant to lead fluctuations in the stock price index but in the opposite direction.

Engaging customer is the burning issue for companies especially the service sector, either online or offline. Minimizing the customer disengagement is the same like reducing dissatisfaction or churn. Customer disengagement may be caused by many factors, ad skepticism is one of them; ad skepticism has two main antecedents personality variable and consumption/influencing varia-bles. This research explores the relationship of ad skepticism with customer disengagement through personality variables which are cynicism, reactance and self-esteem. The unit of analysis is the telecom and banking industry of Pakistan which is foreseeing an era of virtual currency and both are customer oriented industries. Only offline disengagement is researched and data is collected from the Business centers of telecom and banking branches dealing with virtual curren-cy in Pakistan. Hypothetical model is given after digging the relevant literature; model is tested through confirmatory factor analysis and structural equation modeling. Eight hypotheses were purposed from the connections of model, all hypotheses are accepted except the cynicism posi-tive effect on social ad skepticism. This can be due to commonality of social and charity in Paki-stani society, Muslims consider charity as a pious act and they do not think for cynic behavior in charity or social related works. The results manifest that customers in telecom industry are hav-ing ad skepticism and that is becoming the cause of their disengagement. Further, social ad skep-ticism has more impact on the customer disengagement than the general ad skepticism. While the reactance has more effect on general ad skepticism than other antecedents and cynicism has the lowest impact on social ad skepticism than other antecedents.

The study examines the technological factors influencing the adoption of artificial intelligence (AI) technology. In addition, this study examines the mediating role of accounting automation on AI adoption in a small and medium-sized enterprise (SMEs) context. The owners and managers of SMEs were surveyed online using a convenience sampling technique. The proposed model was tested using SEM. The findings confirmed the relationships between the predictive variables and AI adoption. The results showed that accounting automation partially mediated the relationship between predictive variables and the adoption of AI. The results contribute to the TOE model by incorporating accounting automation into the TOE framework as a mediating variable. The study also contributed to the literature by including new variables in the model, such as saving time and efficiency-improving.

Inventory control of deteriorating items constitutes a large part of the world’s economy and covers various goods including any commodity, which loses its worth over time because of deterioration and/or obsolescence. Vendor managed inventory (VMI), which is a win-win strategy for both suppliers and buyers gains better results than traditional supply chain. In this research, we study an economic order quantity (EOQ) with shortage in form of partial backorder under VMI policy. The model is concerned with multi-item subject to multi-constraint including storage space, time period and budget constraints. Two metaheuristic algorithms, namely Simulated Annealing and Tabu Search, are used to find a near optimal solution for the proposed fuzzy nonlinear integer-programming problem with the objective of minimizing the total cost of the supply chain. Furthermore, the sensitivity analysis of the metaheuristic parameters is performed and five numerical examples containing different numbers of items are conducted in order to evaluate the performance of the algorithms.

A molecular mechanism of the [3+2] cycloaddition has been explored using various DFT theoretical levels. It was found that the reaction proceeds via transition states with different synchronicity, but no intervention of the theoretical possible zwitterionic intermediates. Additionally, regioselectivity of the cycloaddition process has been analysed using vibrational analysis of localised TSs

This paper empirically tried to analyze the effects of education on the gender wage gap in Indonesia and to examine whether globalization could alter such a relationship. We modified a decomposition method for the panel dataset that corresponds to the National Socio-Economic Survey from 1996 to 2016. Overall, we found that schooling could significantly influence the salary discrepancy. We also proved that globalization did matter in explaining the relationship between the educational attainment and the salary gap. Here, it could widen the difference in the remuneration. Based on the results, strengthening the gender-responsive budget at the national government level might be one of the solutions. Basically, it actually started with a new awareness of the budget that was not genderneutral. Thus, the positioning of women in education and decision-making process is very crucial for the future of development.
This paper has been retracted due to conflict of interest

In this study, we incorporate trade credit policy into a joint marketing and pricing problem in which demand rate depends on the length of the credit period provided by the retailer for her customers, marketing expenditure, and selling price. The trade credit policy adopted here is a delayed payment policy in partial form in which the customers must pay a percent of the total purchasing cost at the time of placing an order and can pay the remaining amount later. Shortages are allowed and partially backordered. The main objective of this study is to determine the optimal credit period, marketing expenditure, selling price, and variables of inventory control simultaneously in order to maximize retailer’s total profit. For solving the proposed problem, first an approximation method is applied to simplify the profit function and transform the problem into a constrained Signomial Geometric Programming (SGP) problem, then a global optimization approach is used for solving the model. Finally, a numerical example and sensitivity analysis of the important parameters are conducted to show the effectiveness of proposed approach.

This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.

Risk monitoring and control is often poorly implemented in construction projects because of a failure to monitor and manage identified risks. Construction companies experience significant losses due to project managers' lack of project risk monitoring and control in construction projects. Most studies have concentrated on risk identification, risk assessment, and risk analysis processes while neglecting crucial risk management processes of risk control, risk monitoring, and risk response. The lack of research on these three crucial processes highlights a gap in the literature concerning how these processes can increase the delivery of successful projects. The purpose of this study was to examine whether the utilization of project risk monitoring and control practices was related to project success in construction projects in the United States. An electronic survey instrument was used to collect data from a sample of 50 construction project managers in the Dallas-Fort Worth area in the state of Texas, in the United States. Spearman rho correlation analysis was used to examine the relationship between project risk monitoring and control practices and project success. The results of this study indicated that all project risk monitoring and control practices, including risk reassessment, risk audits, contingency reserves analysis, and risk status meetings, were significantly and positively related to project success in construction projects. One of the recommendations presented in this study was that future research should conduct the same study in developing countries to see if the study’s findings remain the same and generalizable. The study concluded that construction organizations should regularly consider the importance and usage of project risk monitoring and control practices and apply them to improve the success rate of a project.

Detecting how a cutout affects the critical buckling load in circular cylindrical shell is a serious issue for the design of the shells used in marine structures, aerospace and automobile applications. In this paper, buckling behavior of aluminum cylindrical shell with rectangular cutout, subjected to axial pressure was studied by means of finite element simulations. The effects of geometric parameters (R, t, a, b and L) on the first buckling mode capacity of the shell were studied. The effects of these factors and their interaction effects were investigated by combined numerical and statistical analysis. The results show that R/b, t and Rt/b were the main effective factors of critical buckling load. Based on the statistical analysis, a model for prediction of the critical buckling load was obtained with an accuracy equal to 95% (R2, R2 (pred) and R2 (adj)). This equation could be used to predict the critical buckling load of an isotropic circular shell with a rectangular cutout.