The British University in Dubai (BUiD) Digital Repository

Welcome to BSpace, the online institutional repository of the British University in Dubai. BSpace provides access to the Dissertations, Thesis, Research projects, Faculty publications and archives of BUiD.

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Recent Submissions

Exploring Nursing Education Stakeholders’ Perceptions of Students with Disabilities Inclusion in Nursing Education Programs in the United Arab Emirates: Issues and Challenges
(The British University in Dubai (BUiD), 2023-01) DARWISH, AMANI ABDUL RAZZAK; Professor Emaan Gaad
The purpose of the study was to explore nursing education stakeholders’ perceptions about inclusion of students with disabilities in nursing education programs in the UAE and the barriers and facilitators to their inclusion. A sequential exploratory mixed-methods design was used to conduct this study. Data was collected using unstructured interviews, -semi-structured interviews, and questionnaires. Thematic analysis of the interviews with 7 nursing education stakeholders revealed the following barriers to the inclusion of nursing students with disabilities: 1) Nature of Disability 2) Knowledge of Nursing Faculty 3) Attitudes 4) Communication 5) Resources 6) Nursing Program Requirements 7) Admission and Support Policies 8) Disability Outreach Activities. Semi-structured interviews with 14 nursing education stakeholders showed eight themes related to the facilitators of the inclusion of nursing students with disabilities in nursing education programs: 1) Disability Laws and Policies 2) Disability Awareness 3) Establish Early Detection of Cases 4) Education and Training Programs 5) Creative Access 6) Attitudes 7) Communication to Meet Needs 8) Collaboration with Potential Employers. 284 nursing education students and 29 nursing education faculty members from health science academic institutions in Abu Dhabi, Ajman, Al-Ain, and Al Dhafra in the UAE completed the questionnaires. The descriptive and inferential statistical analysis of the responses of nursing education faculty members and nursing education students was performed using SPSS software. The findings showed that the educators and students had concerns regarding inclusion of students with disabilities. However, implementing facilitators and overcoming barriers can enhance the accessibility of nursing students with disabilities to nursing education programs. The findings also showed significant differences in nursing students' and faculty's perceptions toward inclusion concerning their interactions with students with disabilities and having completed a course about individuals with disabilities. This research fills a knowledge gap related to disability inclusivity in nursing education in the UAE. Understanding the perceptions of nursing education stakeholders towards the inclusion of students with disabilities in nursing education may help in reducing negative attitudes and discriminatory practices and may assist in improving the general understanding of how to increase the participation of students with disabilities in nursing education and the access to care for underrepresented groups. Moreover, the findings of the will support nursing educators in meeting the needs of students with disabilities from legal, ethical and individualistic perspectives. Furthermore, findings will aid nursing education faculty members, nursing education administrators, disability services, and clinical practice partners to determine and provide reasonable accommodations that promote success of nursing students with disabilities in theory and clinical settings, Finally, findings will aid in the revision and subsequent development of policies and guidelines related to educating and supporting students with disabilities in nursing education programs in the UAE.
Analysis of Synergy of Information Technology and Engineering Departments in a Telecommunication Company in United Arab Emirates and Middle East
(The British University in Dubai (BUiD), 2023-10) ALMUHIRI, RASHID; Professor Alaa Ameer
The following research is an investigation of the dynamic interplay between Information Technology (IT) and Engineering departments of a well-known telecommunication company from the United Arab Emirates and the Middle East. The main focus of this study is on increasing organizational efficiency, employee motivation, and cost-effectiveness, and thus the study delves into the consequences of integrating these departments. The objective of this study is to reveal the links between the synergy between IT and Engineering departments and important organizational factors, comprising of approval processes, employee motivation, and outdated interfaces costs. It aims to deliver actionable insights for telecommunication companies targeting optimization of their operations. Quantitative research approach was adopted for the study. The findings support that merging of the two departments significantly enhances efficiency of approval processes, streamlines workflows, reduces delays, and improves overall coordination. Positive link between departmental merger and heightened employee motivation is observed. It furthermore reveals mitigation of costs linked with outdated interfaces through proactive technology adoption. The study can help guide organizations to gain a competitive edge, optimize operations, and enhance customer satisfaction. This study informs a path that leads to organizational efficiency, competitiveness, and strategic integration.
Sentiment Analysis of the Emirati Dialect text using Ensemble Stacking Deep Learning Models
(The British University in Dubai (BUiD), 2023-03) AL SHAMSI, ARWA AHMED; Professor Sherief Abdallah
The study of thoughts, feelings, judgments, values, attitudes, and emotions regarding goods, services, organizations, persons, tasks, occasions, titles, and their attributes is known as sentiment analysis and it involves a polarity classification task for recognizing positive, negative, or neutral text to quantify what individuals believe using textual qualitative data. The rise of social media platforms provided an excellent source for sentiment analysis data. People use these platforms for various reasons, ranging from sharing their opinions and thoughts to gaining knowledge. Twitter, Instagram, and Facebook are examples of social media platforms. As more users join social media platforms, the amount of data that is generated online continues to grow at an accelerating pace. Most of the previous research that studied sentiment analysis for the Arabic language focused on Modern Standard Arabic and Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine Dialects. However, to our knowledge, no study involved employing deep learning models to conduct sentiment analysis on the Emirati dialect texts. Dialects are the informal form of a language. Each country of the Arab world has its own Dialect, and each dialect may have several sub-dialects. The main objective of this study is to develop a deep learning model that outperforms the state-of-the-art for Sentiment Analysis of the Emirati Dialect. Toward this objective, I first conducted a systematic review to identify the research gaps in the existing literature and investigate the available constructed resources for Arabic dialects and the used approaches for sentiment analysis of Arabic dialects. The systematic review focused on empirical research on the subject of Sentiment Analysis of the Arabic Dialect that was released between January 2015 and January 2021. Through the analysis, I found, with the exception of a few articles that investigated Saudi, Levantine, Jordanian, Algerian, Tunisian, and Egyptian dialects, researchers rarely specified the dialect type in their papers; instead, it was mostly mentioned (MSA and Arabic Dialects). Emirati Dialect has not been explored for Sentiment Analysis purposes. The sizes of most datasets of previous research were between 10,000 and 50,000. Moreover, the Twitter platform was the most popular online platform for constructing Arabic datasets. Most of the studies evaluated basic Machine Learning approaches for Sentiment Analysis of Arabic Dialects. My research aims to fill these identified gaps as I detail below. Since Instagram is one of the most popular social media platforms in UAE, I constructed a dataset of the Emirati dialect from the Instagram platform. My dataset consists of 216,000 posts, of which 70,000 posts were manually annotated by three human annotators. Each post is annotated into (Positive/ Negative/Neutral), and it is further annotated into (Emirati Dialect/ Arabic Dialect/ MSA). In order for the dataset to be used as a benchmark, the inter-annotator agreement (IAA) was measured using Fleiss's Kappa coefficient. The findings reveal that the overall Fleiss Kappa coefficient is = 0.93, indicating an almost-perfect agreement amongst the three annotators. Once the dataset was constructed and validated, I then conducted a performance evaluation and comparison of various basic Machine Learning algorithms, Deep Learning models, and stacking deep learning models on different datasets of Sentiment Analysis of Arabic Dialects. For the basic machine learning algorithms, LR, NB, SVM, RF, DT, MLP, AdaBoost, GBoost, and an ensemble model of machine learning classifiers were used. For deep-learning models, CNN, Bi-LSTM, Bi-GRU, as well as Hybrid deep-learning models were used for Sentiment Analysis. In order to improve performance further, I have proposed three ensemble-stacking deep-learning models with meta-learner layers of classifiers. The first stacking deep learning model combined 2 of the used deep learning models that produced the best results in terms of accuracy, the second stacking deep learning model combined 4 of the used deep learning models that produced the best results in terms of accuracy, and the final stacking deep-learning model combined all the trained deep learning models in this research. The proposed ensemble stacking model was evaluated using three datasets: the ESAAD Emirati Sentiment Analysis Annotated Dataset (which is one of this thesis contributions), and two other benchmark datasets (A Twitter-based Benchmark Arabic Sentiment Analysis Dataset ASAD and Arabic Company Reviews dataset). Experimental results show that my proposed ensemble stacking model outperformed existing deep learning models and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the benchmark ASAD, and 96.65% for the Arabic Company Reviews Dataset.
Financial development and Inclusion of UAE
(2023-05-15) Latifa Abdulla Mahmoud
Financial inclusion refers to the state where people and enterprises can access financial products and services that are helpful and affordable, meet their needs, are delivered sustainably, and contribute to a country's economic growth. The first step towards achieving higher financial inclusion is access to transaction-based accounts, which allow for the receipt and storage of cash (Riabi, 2019). Both theoretical and empirical economic literature indicates that various factors, such as interest rates, play a crucial role in determining the saving behaviour of borrowers. Evidence for the critical role financial-based development plays in economic-based growth can be found in empirical and theoretical research. Mwangi (2021) has contributed substantially to the debate among scholars about the relationship between growth rates and institutions with a financial foundation. Since it was unclear until the early 1900s how financial development and economic growth were related, this topic attracted much attention. In many instances, liberalising banking regulations might have been bad for economic expansion. Recent research, however, points to a constructive role for financial-based development and the link between growth and foreign direct investment (FDI).
Reducing Delays in the Construction of Infrastructure Projects: A Case Study of Applying Agile Methodology Best Practices in Railway Project in the UAE
(The British University in Dubai (BUiD), 2023-06) ALNAQBI, NOUF MOHAMMED; Dr Gul Jokhio Ahmed
The infrastructure project sector in The United Arab Emirates is considered one of the most advanced and developed in the region. Infrastructure projects have a massive impact on the country's economic development and growth. Usually, these projects are costly, take many years to construct, and effects millions of people. The successful completion of infrastructure projects is essential, as it can improve economic growth and quality of life in the UAE. However, 50% of the construction projects in the UAE face delay due to the complex and dynamic environment of these projects. Infrastructure projects require a more fixable framework than the Traditional Project Management framework commonly utilized in this sector. This research paper explores the Agile Project Management (APM) framework practices and tools to identify the best APM practices that can be adopted in a Traditional infrastructure project in the United Arab Emirates to develop a Hybrid project management framework. Also, the factors causing the delay and the Agile enables projects can implement to help the adoption of APM practices in infrastructure projects. All of this is explored using a survey conducted on a Railway project in the UAE. The survey finding shows that all the proposed APM practices can assist in reducing the time overrun, and a Hybrid framework is developed according to the research findings; this framework focuses on applying the agile practice in the execution phase, where most of the delay factors occur, and applying traditional project management practices in the initial phases of the project is more effective in establishing a well-detailed project schedule. Using the hybrid framework in infrastructure projects can help improve performance, communication, and adaptability to changing requirements. The developed Hybrid Framework model is suggested for infrastructure projects in the United Arab Emirates.