THE INFLUENCE OF HUMAN RESOURCES MANAGEMENT ON JOB EMBEDDEDNESS AND VOLUNTARY TURNOVER INTENTION: A CASE STUDY OF THE CONSTRUCTIONS INDUSTRY IN SAUDI ARABIA

This thesis investigated the effect of six antecedent HRM (Human resource Management) practices on intention to remain of Saudi national employees in the Saudi Arabia construction industry based on a job embeddedness model that includes testing of the mediating impact of on-job embeddedness. 374 Saudi national construction employees from two Saudi construction organisations completed the questionnaires, measuring their intention to stay in their present organisation of employment based on their perceived notion of the HRM practices such as perceived employee selection, perceived employees training, perceived employee’s compensation, perceived employee participation, perceived job security and perceived supervisor support in their respective organisation. Path analysis was used to examine the hypothesised relationships in the model. The results of the primary research that was conducted supported all the hypotheses, though on-job embeddedness only partially mediated the relationship between the investigated antecedent HRM practices and intention to remain. Additionally, though positive correlation was established between off-the-job embeddedness and intention to remain. The study findings indicated that there was positive moderately significant relationship between on-job embeddedness and the six antecedent HRM variables of perceived employee selection, perceived employees training, perceived employee’s compensation, perceived employee participation, perceived job security and perceived supervisor support. Furthermore, the results suggest that consistent and targeted use of HRM practices by organisations can increase employee intention to remain, which in turn will reduce turnover intentions. In terms of off-the-job embeddedness, the study results showed that community embeddedness accounted for 4.4% of intention to remain and that improvement in community embeddedness can lead to up to 36.5% increase in intention to remain. Accordingly, the study has valuable implications for managing the turnover intentions of Saudi national employees in the Saudi Arabia construction industry, including specific HRM strategies can be used to deepen on-the-job embeddedness of Saudi national employees and HRM strategies for increasing the value of the fit, Link and sacrifice that would have to be made by employees that leave the organisations. Hence, contributing to HRM literature in the construction industry and to research on the applicability of SHRM models in cross-cultural, non-Western contexts

Design and modeling of a PV system for a house in Saudi Arabia

Consumption of electricity in the Middle East is quite high due to the high cooling demands during summer time in each home. However, Saudi Arabia has high solar energy resources that could be used to meet all home energy needs. In this Thesis, a solar energy system is designed using BEopt and Homer software. BEopt was used to build a thermal model for an actual house in Qassim, Saudi Arabia to stimulate the hourly kilowatt electrical consumption for mainly cooling purposes. Mathematical equations have been used to calculate the necessary photovoltaic and Battery size. The collection of data and BEopt results are used by Homer software to design various options for a PV system. Results indicate that an 18.85 kW PV system, 52 batteries 200 Ahr each and a 10kw inverter can meet all house energy needs. This study presents a dynamic modeling of a photovoltaic (PV) system for a residential application using Simulink. The PV system designed here consists of 56, 325W, 24 V PV modules, 52, 200Ahr, 12V batteries, a maximum power point tracking (MPPT) charge controller and a 10 kW inverter to power a house. This Thesis proposes a boost converter; (MPPT) to be applied to the system to obtain a maximum output power of the PV system. Additionally, varying weather curves data were implemented in the design to simulate potential conditions, namely solar radiation and temperature. A step-up transformer is used to achieve the house required voltage. The simulation results prove that such a PV system would work smoothly without grid connection at a location such as Qassim, Saudi Arabia. The research aims to design the installation process of a PV system of a typical Saudi house. HelioScope Software is a fundamental tool to evaluate the PV system installation. Moreover, many installation factors have been investigated such as; wiring material, cables specs, shadow effect and protection devices

Evaluation of Phosphorus Use Efficiency in Winter Wheat Varieties and Using Optical Sensors to Predict the Maize Population (Zea Mays L.)

This dissertation includes two topics; 1) evaluation of P use efficiency in winter wheat varieties. 2) using optical sensors to predict the maize population. The objective of the first topic was to (i) simulate a soil with insoluble forms of phosphorus and to evaluate seventeen winter wheat varieties for P uptake and utilization efficiency based on the total mass of P present in the soil; (ii) screen seven wheat varieties for P use efficiency in filed experiments (iii) predict the possibility of using the normalized-difference vegetative index (NDVI) for determining P limiting conditions. For P use efficiency, several studies were conducted in the growth chamber and filed experiments. The results of greenhouse studies suggested that Gallagher and Endurance were the most efficient varieties for both P uptake efficiency and P utilization efficiency. Also, The Ok11755W was the most P utilization efficient while OK10430-2 was the most P uptake efficient. Otherwise, the Ruby Lee was among the less efficient varieties either to uptake or utilize P under greenhouse conditions. Under experimental field conditions, Duster and Endurance were found to be more efficient in extracting or utilizing more P while Ruby Lee was found to be less efficient. NDVI was also possible to estimate the P deficiency due to a strong correlation between the NDVI and yield prediction under low P conditions; this observation may be used as an indicator for P recommendation. The objective of the second topic was to identify whether there is a correlation between normalized difference vegetation index (NDVI), the Coefficient of variation (CV), and the maize population. For using optical sensors, data was collected from 76 plots located at the Agronomy Research Station (EFAW) near Stillwater, OK, and the Lake Carl Blackwell Research Station (LCB) near Bray, OK. Finding and conclusion of this study suggested that since NDVI and CV were correlated to plant population, the growth stage V4 would be the appropriate stage to predict the plant population and biomass estimation, which could be useful for producers for making replant decisions and precise estimations of replanting rates.Soil Scienc

Inverse Correlation between Stress and Adaptive Coping in Medical Students

BACKGROUND: Medical students in their academic years are generally under stress but very few studies revealed the relationship between the stress and how the students manage to adapt these stressful conditions. AIM: The aim of the study was to investigate the levels of stress and their adaptive coping in the 1st 3 years medical students and also to determine the factors associated with adaptive coping strategies. METHODS: This is a descriptive cross-sectional study conducted on 441 medical students of Qassim University from September-October 2019. First 3 years medical students were randomly selected and their stress levels or adaptive coping strategies were determined by general health questionnaire (GHQ-12) and strategies coping mechanisms (SCM), respectively. The 5-points Likert scale was used for scoring and the data obtained were further validated by DASS and Brief COPE scales. RESULTS: Out of 441 medical students, 39.2% agreed to participate. The data showed that the level of stress among students was highest during their 1st year academic blocks, followed by 2nd and 3rd year students. Interesting, the adaptive coping among them was found highest during the academic blocks of 3rd year students, followed by the 2nd and 1st year students. Importantly, female students showed better adaptation against stress. Students living with their parents avoided stress in better ways as compared to those who were living alone. CONCLUSION: This is the first study that shows an inverse correlation between the stress and adaptive coping in medical students of Qassim University. The data concluded that adaptation of stress in the 3rd-year students was the highest followed by 2nd and 1st year medical students. Moreover, female students adapted well against stress and students living alone showed worse adaptation of stress

Acute Liver Failure and the Neurological Complications: Theoretical Review

This study aimed at theoretically reviewing the Acute Liver Failure (ALF) and the Neurological Complications by reviewing the related studies in this area. As the problem of this study lies in exploring the neurological complications caused by Acute Liver Failure, and defining the causes of Acute Liver Failure, besides Diagnosing of Acute Liver Failure and the treatment processes of Acute Liver Failure. And the study concluded that the management of acute liver failure addresses the individual pathophysiological processes that occur in this condition. It improves chances of survival in patients awaiting liver transplantation and dramatically reduces the risk of death from neurological complications

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

An Accident Detection and Classification System Using Internet of Things and Machine Learning towards Smart City

Daily traffic accidents increase annually, causing a significant number of death and disability cases. Most of fatalities occur because of the late response to these emergency cases. The time after the traumatic injury is called the golden hour, where providing essential medical and surgical aid at that time increases the probability of saving human lives by one-third an average. Thus, the focus of this paper was to develop a system based on IoT for accident detection and classification. The system detects and classifies vehicle accidents based on severity level and reports the essential information about the accident to emergency services providers. The system consists of a microcontroller, GPS, and a group of sensors to determine different physical parameters related to vehicle motion. In addition, different types of machine learning classifiers were examined with the developed system to determine the most accurate classifier for the system. The classifiers are the Gaussian Mixture Model (GMM), Naive-Bayes Tree (NB), Decision Tree (DT), and Classification and Regression Trees (CART). The implementation of the system showed that GMM and CART models were better in terms of precision and recall. It was also shown that the severity of accidents depends mainly on the g-force value and fire occurrence