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Home > Using Job-Demands-Resources Model to Predict Job Performance Among It Employees in Pakistan With Mediating Role of Job Burnout, Work Engagement and Employees? Commitment and Moderating Role of Job Experience

Using Job-Demands-Resources Model to Predict Job Performance Among It Employees in Pakistan With Mediating Role of Job Burnout, Work Engagement and Employees? Commitment and Moderating Role of Job Experience

Thesis Info

Access Option

External Link

Author

Mehwish Javaid

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2018

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=254

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676721006566

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In the present study, Job Demands-Resources (JD-R) Model was used as an underlying model to study the development of Job Burnout, Work Engagement and their respective antecedents i.e., Job Demands and Job Resources in Information Technology sector of Pakistan. Using a cross-sectional survey design, the researcher tested the fundamental assumptions of JD-R Model along with some new pathways where Job Demands and Job Resources directly and/or indirectly influenced Job Burnout and Work Engagement of Information Technology employees. The researcher also examined the direct impact of Job Demands and Resources on Employees? commitment and Job Performance. Moreover, the study was also conducted to explore the direct and/or indirect effect of Job Burnout and Engagement on commitment and performance of IT employees. This study further investigated the moderating role of Employees? experience in managing burnout, improving work engagement and overall performance of IT workforce. Total 260 usable responses were collected from 130 Public Sector and 130 Private Sector Information Technology employees in Pakistan. The researcher used a data mining software - SMART PLS3 to test direct, indirect, and interactive relationships among various constructs of the study. Pakistan suffered from exhaustion and disengagement/cynicism due to Job Burnout which caused a reduction in their commitment towards their work and their organization. It was also found that there was an inverse relationship between burnout and job performance of IT employees in Pakistan. Interestingly, this study highlighted that IT specific job demands (Time pressure and Work Load) lead to burnout among IT employees but didn?t have had a direct negative impact on Employees commitment and performance outcomes. The present study confirmed that IT employees in Pakistan are provided with Job resources like Self-efficacy and Work Control helped to reduce the negative impact of Time Pressure and Workload on their motivation and performance outcomes. Moreover, these resources have had a positive impact on Employees? energy and dedication towards their work. Further, it was observed that while IT related Job demands didn?t have a direct impact on Employees Commitment but through Engagement Job demands can be used to improve Employees? commitment. In the similar vein, the study proved that engagement in meaningful activities indirectly diminishes the negative impact of deadlines pressure and workload on the Job performance of IT employees. Self-efficacy and Work Control have reported having a weaker positive impact on Employees commitment but including Employee Engagement as a mediator fortifies the positive impact of Job resources on Employees? commitment. However, work engagement didn?t improve the insignificant positive impact of Job resources on Job Performance of IT employees. This study tested both Continuance Commitment (CC) and Affective Commitment (AC) of IT employees. The results showed that IT employees in Pakistan, in general, have longer job tenure than organization tenure. Hence, it is implied that they are committed to their organizations due to need rather than desire. It was also noted that individual performance (IRB) of IT employees was better than overall organizational performance outcomes (OCB). Consequently, employees? commitment plays no role in improving job performance of employees. Moreover, this study provided no evidence for mediating role Employees? Commitment to Job Demands relationship with Job performance and Job Resources relationship with Job performance. This study reported interesting results regarding the moderating role of Experience. This study highlighted that experience neither play moderating role in reducing the negative impact of burnout on job performance nor it improves individual and organizational performance using Job resources. Partial Least Square-Multigroup Analysis (PLS-MGA) was used to test the similarities and /or difference in the impact of Job-related demands and Resources on motivation, commitment and performance outcomes. It was found that Job Demands and Job resources of both sectors were different from each other. Similarly, the impact of Burnout on Employees commitment and the influence of Job Resources and Work Engagement on improving Job Performance of IT employees working in both Public and Private sector organizations of Pakistan sectors were also found to be significantly different from each other. The findings of this research have had practical implications for Information Technology managers to formulate sustainable policies that reduce job burnout, encourage engagement, boost effective organizational commitment through retention of experienced employees and produce a productive and valuable workforce that contribute to improve organizational performance.
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جسٹس مولاناسید کرامت حسین

جسٹس مولانا سیّد کرامت حسین
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A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis

Automated vertebrae analysis from medical images plays an important role in computer aided diagnosis (CAD). It provides an initial and early identification of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation and classification are important but difficult tasks in medical imaging due to low contrasts in image, noise and high topological shape variations in radiological scans. It becomes even more challenging when dealing with various deformities and pathologies present in the vertebral scans like osteoporotic vertebral fractures. In this work, we want to address the challenging problem of vertebral image analysis for vertebra segmentation and classification. In the past, various traditional imagery techniques were employed to address these problems. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular in solving various medical image analysis problems due to their robustness and accuracy. In this research, we present a solution of vertebrae segmentation and classification problem with the help of deep learning approach. We present a novel combination of traditional region based level-set with deep learning framework in order to extract the shape of vertebral bones accurately; which would be able to handle the deformities in the vertebral bones precisely and efficiently. After vertebrae segmentation, we further extend the work to abnormal vertebrae classification with the help of deep learning approach. This novel framework would be able to help the medical doctors and radiologists with better visualization of vertebral bones and providing the initial automated classification of vertebrae to be normal or abnormal. The proposed method of vertebrae segmentation was successfully tested on different datasets with various fields of views. The first dataset comprises of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view, collected from a local hospital. The second dataset comprises 73 CT scans of cervical vertebrae in sagittal view, also collected from a local hospital. The third dataset comprises 20 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view collected from spine segmentation challenge CSI 2014. The forth dataset comprises 25 CT scans of lumbar vertebrae in sagittal view collected from spine segmentation challenge CSI 2016. Lastly, we have utilized the same locally collected set of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae and extracted in axial view to perform the segmentation task.For classification purpose, we have utilized the locally collected set of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae as these include osteoporotic vertebral fractures in it. The details of these datasets have been presented in respective sections. We have achieved promising results on our proposed techniques. The evaluation of the segmentation performance on the datasets with various machines and field of views helped us to ensure the robustness of our proposed method. On validation sets of these datasets, we have achieved an average dice score of around 95% for vertebrae segmentation; and accuracy of above 80% for the vertebrae classification. The detailed results have been presented in the results section. These results reveal that our proposed techniques are competitive over the other state of the arts in terms of accuracy, efficiency, flexibility and time.