BSpace

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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Now showing 1 - 7 of 7

Recent Submissions

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Individual Determinants Influencing the Acceptance and Use of Mobile Health Applications for Pandemic Management in the UAE
(The British University in Dubai (BUiD), 2023-12) ALSHEHHI, SAEED ALI SAEED ZAID; Dr Sulafa Badi
Pandemics are among the crises that are challenging to manage and contain because of their unique characteristics, such as high speed of spread, lack of medicine and development of mysterious side effects. In order to efficiently manage and control pandemics, modern technologies are used by governments and health authorities, such as mobile health applications (m-health) applications installed on smartphones that are used by everyone. At the beginning of the COVID-19 pandemic, the UAE realised the severity of the pandemic and the need for technology utilisation to overcome this dilemma; thus, the government developed m-health applications such as the Al Hosn app and the Covid19 DXB smart app to manage, monitor and control the pandemic. The main goal of these applications was to be used by the population in order to manage the pandemic efficiently and contain the disease. It is important to explore what factors determine if the users will use such m-health applications for pandemic management because if users are concerned about their medical security and privacy or if they have any fears regarding using the application, then it might have a negative impact on their intention to use the application. The main aim of this research is to investigate the factors that influence the user adoption of mobile applications for pandemic management and to extract the individual determinants that influence the adoption of the mobile application for pandemic crisis management in the UAE. The study is underpinned by innovation adoption theory and utilises an extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study extends the UTAUT model by testing the significance of the two additional constructs of perceived privacy risk and perceived security risk, as well as testing the moderating role of the fear of social isolation, social discrimination, and technology optimism. The research is underpinned by the positivism research philosophy and employs a deductive research approach. A quantitative questionnaire survey was distributed among the UAE public and a total of 384 complete responses were returned and analysed using SPSS version 25 and SmartPLS 3. Structural Equation Modeling (SEM) analysis was conducted to analyse and evaluate the linkages among the study’s constructs. SEM is a multivariate analysis method which is employed to evaluate structural relationships. The findings highlighted individual determinants such as perceived performance expectancy perceived effort expectancy, social influence, perceived privacy risk, and perceived security risk have a positive impact on the behavioural intention of the users and their adoption of m-health applications in the UAE. The findings also demonstrated a positive mediating effect of behavioural intention between the determinants and the use behaviour and the moderating effect of the fear of social isolation, social discrimination, and technology optimism on the relationship between the determinants and behavioural intention. The research theoretically contributes to an extended UTAUT framework by confirming the significance of the constructs of perceived privacy risk and perceived security risk and testing the moderating role of the fear of social isolation, social discrimination, and technology optimism and their effect on the behavioural intention of users of m-health applications for pandemic management. The research practically contributes to practice by developing recommendations for how m-health applications should be designed and managed to motivate individuals to use them while undergoing a pandemic.
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Big Data Governance and Innovation Performance: The Mediating Role of Big Data Analytics Capabilities, and Organisational Agility
(The British University in Dubai (BUiD), 2024-02) AL KAMZARI, MARYAM ALI; Dr Farzana Asad Mir
Big data governance has become a top consideration in Information Technology and business management due to exponential data growth and its various applications. However, despite the efforts of researchers and practitioners to examine its value, it is still unclear whether and how it drives firm’s innovation performance. To fill this gap, this study draws on the resource-based view, the dynamic capabilities view, and recent literature on big data governance and big data analytics capabilities (BDACs) to examine the relationship between big data governance and innovation performance, while focusing on the mediating roles of BDACs and organisational agility in this relationship. To test the hypotheses presented in the study’s conceptual framework, a partial least squares- structural equation modelling approach was used and the questionnaire responses from 152 enterprises from various industries in the Gulf Cooperation Council (GCC) countries were analysed. The study’s main findings are that BDACs fully mediate the big data governance relationships with innovation performance and organisational agility. Evidence of significant serial mediation by BDACs and organisational agility between big data governance and innovation performance was also found. The study highlights the importance of BDACs and organisational agility in enacting the relationship between big data governance and innovation performance. For GCC firms, the ability of management to develop and deploy an appropriate combination of essential resources depends on their resources and capabilities (big data governance, BDACs, and organisational agility), leading towards the improvement of firm innovation performance.
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Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM)
(The British University in Dubai (BUiD), 2024-02) AL SHOUBAKI, HAMZA YOUNIS; Professor Sherief Abdallah
Arabic poetry with its beauty, deep cultural importance and linguistic features, has always been a subject of attraction for scholars and readers. It attracted numerous researchers and writers to analyze and extract deep poetic features from various poems. As the literature review shows, there are numerous successful attempts to identify these traits and characteristics such as categorizing the used poetry metric and identifying the poets behind these poems. In our research, we introduce a comprehensive approach to Arabic poetry text classification using deep learning techniques. We have used an almost one-million record dataset of Arabic poetry verses extracted from a poetry encyclopedia. These verses are labeled with different nine poets and cover both classical and modern poetic styles. Due to the complexity of Arabic poetry such as the excessive use of metaphors, figurative language, unlimited imagination, and the diversity of styles from one poet to another and from one poem to another, we tackle these challenges by careful employment of preprocessing steps, feature engineering and selection. We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. We have decided to employ LSTSM and Bi-LSTM as our main baseline models. The reason behind selecting such models is observing a concentration on RNN (Recurrent Neural Network) and its variants when it comes to text classification. LSTM has proven its capability for sequential data analysis in many different languages. Our reported results have shown promising classification accuracy with an average of 92.35%. This sheds some light on the feasibility of automating the classification of a morphologically complex language text (Arabic). Bi-LSTM has slightly outperformed the classic LSTM in normal situation with average accuracy of 92.15% and 92.56% for LSTM and Bi-LSTM respectively. We discuss what would be the impact of our research findings on Arabic literature in particularly Arabic poetry. We also address the challenges associated with this study.
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Critically Describing the Effectiveness of Antibullying Interventions Used at Schools
(SpringerLink, 2024) Salameh, Nahida
Bullying at schools has been among the public concerns that prevailed as it causes many negative consequences, including poor academic performance, poor physical and mental health, in addition to suicidal or criminal acts. Purpose- To critically describe the effectiveness of the antibullying interventions at schools. Methodology- A descriptive qualitative study that was conducted utilizing a critical review of literature. The study has utilized the available electronic database. References were mainly chosen among the ones published in the last five years; special consideration was given to studies conducted in UAE. Findings- Several programs were developed and implemented to manage bullying. Interventions entailed constructing strict anti-bullying school policies, and utilized various involvement levels; being whole school, or one group such as parents, teachers or students. Many interventions were rooted in sociocultural theories to enhance resilience and positive behaviour development among students. Antibullying interventions in the UAE were mostly targeting awareness with compromised measurement of effectiveness. Implications- Identifying the best antibullying interventions may contribute to increasing awareness among stakeholders, and thus facilitate decisions that may inform the policy making pertinent to managing bullying at schools in the UAE. Originality/ value- Limited number of studies were conducted in the UAE.
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The Impact of Job Satisfaction on Teachers’ Performance in the UAE
(SpringerLink, 2024) Salameh, Nahida; Benkohila, Nora
Job satisfaction greatly affects job performance. This is of a prodigious value in education as the teachers’ performance impacts the quality of education and thus the Human Development Index (HDI) of the nation. Purpose: to investigate the effect of job satisfaction on the performance of teachers in the United Arab Emirates (UAE) utilizing a scale that was used in business management previously. Methodology: The research used a quantitative empirical method, whereby the impact of the independent variable (teachers’ satisfaction) was measured on the dependent variable (teachers’ performance). The data used was primary data collected via a survey distributed to a convenient sample. Respondents included 112 teachers working in UAE. SPSS application was used for data analysis. Reliability, factor analysis and construct validity were tested, and shown adequate sample and reliable tool. Regression model was applied, in order to test the two developed hypotheses. Findings: revealed that job satisfaction is associated with job performance among teachers. Implications: Results may help decision makers to enhance the teachers’ satisfaction in order to improve teachers’ performance.