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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Recent Submissions

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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.
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Highlighting the Impacts of Parents’ Beliefs on Students’ Education
(SpringerLink, 2024) Alahmad, Alaaeddin; Hussin, Mohamad; Sapar, Ahmad Arifin
Purpose- To highlight the impacts of parents’ beliefs on students’ education due to the dearth of such studies in the field of education. Methodology- A sequential mixed-method approach was utilised, where quantitative data were collected and analysed, and then triangulated with qualitative data. The study employed a questionnaire and semi-structured interviews completed by 51 and nine parents, respectively. Convenience sampling was used. Findings- The obtained data showed that parents’ beliefs play a key role in students’ education, as these beliefs are associated with parents’ expectations regarding their children. Moreover, parents’ beliefs function as a guide for their children’s efforts and endeavours. Implications- This study recommends that increasing parental awareness regarding their role in their children’s education would be an effective strategy that would provide students with an enhanced learning environment. Originality/value- This study is considered of great value as it urges policy makers and leaders to increase investment in the parents’ role in students’ education.