Advances in Science and Technology Vol. 129

Abstract: This study estimates the relative efficiency of twelve colleges of the University of Sharjah from 2014 to 2019. The methodological approach we employed ensures the homogeneity of the colleges under review. We used an output-oriented smoothed bootstrap data envelopment analysis (DEA) model for the efficiency assessment while assuming that variable returns-to-scale prevail. Output orientation facilitates target-setting rather than cost-cutting, which is supported by input-oriented DEA models. Our analysis indicated an improvement in the efficiency of the University of Sharjah during the period under review. Also, the College of Communication, Engineering, and Law are the most influential benchmarks for the remaining colleges. However, the Colleges of Arts, Humanities & Social Sciences and Business Administration present a definite improvement. Increasing the postgraduate student recruitment is more useful for further efficiency improvement of the University of Sharjah than expanding undergraduate student recruitment.

Abstract: When employees consciously suppress important information, suggestions or concerns from their managers, negative implications for organizational performance can emerge. Some studies suggested that employees often choose to remain silent when faced with the choice of whether or not to raise an issue. Therefore, the main objective of this research is to examine the factors that impact employee voice behavior (VB). The research theorizes that empowering leadership and Leader-Member Exchange (LMX) significantly and positively impacts employee voice behavior in UAE public sector (N=146). Moreover, this study broadens the previous research on the empowering leadership, LMX and employee voice relationship by introducing employee psychological empowerment as a mediator. The data was gathered using the online survey. The results of the statistical analysis using structural equation modeling with Smart-Partial Least Squares (PLS).3 showed that empowering leadership directly and indirectly (through psychological empowerment) impact on employee voice behavior. Surprisingly, the results presented no significant relationships between LMX and voice behavior. However, the relationships only exist through the psychological empowerment (fully mediate). Implications of the study model for management or human resource management as well as for future research are discussed. Keywords: Empowering leadership, Leader-member exchange, psychological empowerment, employee voice behavior

Abstract: Aim - This study aims to reveal the effect of religiosity and brand image on the behavioral intention with trust and satisfaction as mediating variables at the Larazeta halal restaurant. Design/Method/Approach - Data were collected through a questionnaire with a total sample of 100 customers of the Larazeta halal restaurant. The respondents were customers of the Larazeta halal restaurant who had visited the restaurant that has many branches in Indonesia at least once. The sampling technique used is convenience sampling. This study used a quantitative approach with the Structural Equation Modelling-Partial Least Squares Analysis. The exogenous variables in this study include religiosity and brand image, the mediating variables consisted of trust and satisfaction, while the endogenous variable was the behavioral intention. Results - The results indicate that the variables of religiosity and brand image had a significant effect on the behavioral intention, trust, and satisfaction variables of Larazeta halal restaurant customers. Practical Implications – This study provides an understanding of how religiosity, brand image, trust, and satisfaction can influence the behavioral intention of halal food consumers. Originality– There is a little research investigating the relationship between religiosity and marketing in Islam in Indonesia with unique demographic conditions. Research gaps are found in the previous studies, namely the broad scope of research so that they were not effective in explaining areas that have unique demographic characteristics. Therefore, for the first time, the present study aims to analyze specifically the relationship of religiosity and brand image on behavioral intention, with trust and satisfaction as mediating variables in discussing halal food as a healthy lifestyle in Indonesia. Keywords : Healthy Lifestyle, Religiosity, Brand Image, Trust, Satisfaction, Behavioral Intention, Halal Food

Abstract: Purpose - The current study integrated number of research fields to develop and test a model on the determinants of employees’ happiness and creativity. Hypothesizing that quality of work life, perceived training intensity and job security affect employees’ happiness and creativity in in the United Arab Emirates (UAE) Public sector. Design - The study uses survey data from 120 employees from public sector companies in United Arab Emirates (UAE). Based on an extensive literature review, eight hypotheses were formulated and explored. These were tested through multiple regression analysis using smart PLS Partial Least Squares. Findings – Work life balance, perceived training intensity and job security showed no significant relationship. However, the relationships only exist through the feeling of happiness. Research limitation – The sample is from a single sector (public) in a single country. Future research would benefit from examining the above relationships in privet sector in the UAE. It could also explore the validity of these relationships in the public sector of other countries in the Middle East and Gulf regions. Originality/value –few studies have adequately examined their determinants particularly in UAE. Although research examining the employee creativity in public sector is limited, it is clear that public sector stands to gain from creative employees because employee creativity and innovation will contribute to the attainment of organizational goals. KEY WORDS-Employee creativity, Employee happiness, Job security, Training, Work-life balance, Human resources, UAE, Public sector Paper Type –Research paper

Abstract: To confirm the safety of Nuclear Power Plants (NPP) several regulatory safety inspections based on defense in depth criteria were conducted. However, once the plant is put into service the regulatory safety inspection must be focused on whether to minimize the risk of accident using defense-in-depth concept and risk insight obtained from probabilistic safety analysis. The regulatory oversight of accident prevention can be strengthened by requiring that safety improvements be considered through the use of deterministic and probabilistic approaches (such as probabilistic risk assessments (PRA). Hence, the incorporation of DID concept and risk insight into deterministic-based safety inspection has not been well studied so far since the regulatory safety inspection was developed depending on each country’s specific regulation. The aim of this study is to propose a methodology using APR1400 Large LOCA as a case study to develop Safety Inspection Methodology using AIMS-PSA as an analysis platform to develop Event tree and fault trees and analyze how to secure the success path and how to block the failure path in a specific event tree. This study will help to improve accident anticipation, high reliability, and risk assessments to maintain the operability of all safety systems and to ensure the long-term ability to mitigate any extreme accident scenarios

Abstract: The presence of road traffic accidents is subjected to various contributing factors including drowsy driving. The occurrence of drowsy driving has been a major cause of road accidents globally. Therefore, this study aims to analyze demographic, socio-economic, daily habits and drowsy-related characteristics associated with fatigued or drowsy driving in the United Arab Emirates (UAE). The data were gathered upon a questionnaire-based survey among a sample size of 525 drivers in the UAE. Inputs were given weights upon consulting experts in the field of transportation. Data were analyzed using artificial neural networks (ANN). Daily habits significantly affect the driver’s risk to experience fatigued driving. Socio-economic, drowsy-related, and demographic characteristics followed sequentially. Time of day to experience drowsy driving has the largest importance. Moreover, daily habits such as driving durations, distance driven, and sleeping hours demanded the importance of drowsy driving risk next. Socio-economic characteristic such as the average monthly income was the least significant. Prevalence of sleep-related accidents in the UAE is a fact, where drivers are less concerned about fatigue driving than other traffic safety issues. Raising awareness of drowsy driving among society is a need since people tend to see other factors to be riskier than drowsy driving. The results highlight the need to counteract drowsy driving with treatments on-road and more education to the public.

Abstract: Road crashes are one of the leading causes of death and injuries in many countries around the world, which leads to enormous losses in terms of health, social, and economic aspects. Researchers are using different tools to locate, assess, and treat hazard spots in the road network. this paper aims to investigate and understand the important variables that contribute to road crashes using different crash frequency modeling techniques. Negative Binomial and Poisson regression models were used to identify the most significant variables that increase the crash frequency on the roads. The study was conducted on data obtained from the Highway Safety Information System database for a 5-years crash period in the state of North Carolina. The results of these models showed that for the Poisson model, the p-value was significant for the segment length, AADT, speed limit, right shoulder width, and median width while the left shoulder width and number of lanes weren’t significant. The coefficient estimate B sign could be used to indicate the type of contribution to the independent variable, all the dependent variables were positive signs except for speed and median width. Therefore, the increase in speed limit will decree the number of crashes. In contrast to the Poisson model, the negative binomial model showed significance only in three variables segment length, AADT, and the speed limit, the rest are not significant based on p-value, similar to Poisson the coefficient estimate B sign for the speed was negative. As expected, the increase of exposure increases the likelihood of being in a crash, therefore countermeasures are urgently needed to manage the speed, improve traffic operation, and enhance traffic safety.

Abstract: Road accidents are a major world economic and social problem, as shown by the report of loss of lives and properties in many countries worldwide. Reporting indicated the number of fatalities from road accidents per year of about 1.35 million and 50 million injuries was recorded or an average of 3000 deaths/day and 30,000 injuries/ day. Furthermore, its consequences have an impact on economic and social conditions in terms of health care costs of injuries and disabilities. The objectives of this paper are to implement four modeling techniques, Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to predict accident severity and compare the performance of these models in terms of their prediction accuracy. More than 117,000 accident records with over 32 variables were retrieved from London. The results showed that the nonlinear SVM model outperformed other techniques in terms of performance with an accuracy of 78.32%. On the other hand, the linear SVM was the worst overall model with an accuracy of 69.27%. In terms of training time, a considerable difference was found between two groups of models: Logistic Regression, Naïve Bayes on one hand, and SVM and ANN on the other group. The former required a shorter training time (less than 10 min for each model), while the latter had training times between 20 to 70 min per model. Overall, the nonlinear SVM seems to perform the best in terms of accuracy, while Naïve Bayes is the best for fast prediction. This result can be beneficial for researchers and practitioners to predict accident severity levels and suggest improvements to traffic safety.

Abstract: This paper focuses on investigating public perception in the United Arab Emirates (UAE) towards the implementation of High-Occupancy Toll (HOT) lanes in major freeways. HOT lanes provide the UAE government with significant potential to enhance the transportation network through decline in motorway accidents, procuring additional revenues, decreasing the overall sector costs, as well as lessening the carbon footprint ensuing from this sector. However, the primary challenge encountered during the implementation of HOT lanes in the UAE is public perception and Willingness to Pay (WTP). A questionnaire-based survey was developed and circulated among the public in the UAE to deduce the public’s attitude towards the utilization of HOT lanes. The survey intended to capture the socio-economic, demographic, and commute-related characteristics of respondents, as well as their current knowledge of HOT lanes. The survey data were collected and processed to identify the features of the obtained sample. Comparative statistical and advanced numerical analyses, in the form of Linear Regression (LR) and Artificial Neural Networks (ANN) were conducted to model the relationships between different characteristics and the public’s WTP. Additionally, the significance of the factors affecting the WTP were ranked using Bayesian Networks. The results showed that monthly income was the most significant factor affecting public WTP followed by age, frequency of trips, employment status, peak hour traffic, and emirate of residence. Prediction equations generated from ANN and LR, utilizing the most significant factors, indicated that ANN had significantly higher accuracy and lower MSE compared to linear regression. Overall, this study could significantly help decision-makers for future transportation systems improvement.