A conceptual framework for facial recognition integration in ADNOC refining's physical security
High-risk sectors, such as the oil and gas industry, demand optimal infrastructure to safeguard their valuable assets, given their substantial contribution to national economies. In recent years, the oil and gas industry in the United Arab Emirates (UAE) has experienced significant growth, solidifyi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2024
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28260/1/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf http://eprints.utem.edu.my/id/eprint/28260/2/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my-utem-ep.28260 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Teknikal Malaysia Melaka |
| collection |
UTeM Repository |
| language |
English English |
| advisor |
Zamri, Ruzaidi |
| description |
High-risk sectors, such as the oil and gas industry, demand optimal infrastructure to safeguard their valuable assets, given their substantial contribution to national economies. In recent years, the oil and gas industry in the United Arab Emirates (UAE) has experienced significant growth, solidifying its position as one of the top 10 oil producers globally. Security is an indispensable aspect of any business enterprise, regardless of size or industry, as it protects assets from various physical security threats, including unauthorized access, theft, espionage, vandalism, and other disruptive incidents that impede operations. Organizations have recognized the importance of enhancing their physical security systems, driven by technological advancements and external factors such as the Coronavirus Disease (COVID-19) pandemic. Multiple studies have suggested that integrating Facial Recognition Technology (FRT) with the existing physical security culture can create a holistic security system capable of effectively responding to physical security threats. Consequently, the main objective of this research was to investigate whether integrating FRT into the physical security measures of one of the Abu Dhabi National Oil Company (ADNOC) subsidiaries’, ADNOC Refining, a prominent company in the UAE, would enhance its overall performance. The study also aimed to explore the relationship between physical security culture and performance, the efficiency of FRT, its integration with physical security, and the impact of external factors and physical security threats on physical security performance. A comprehensive review of physical security frameworks and models was conducted using deductive and inductive reasoning to accomplish these objectives, which provided meaningful information about key factors and concepts. Based on this knowledge, a conceptual framework was developed to test the research hypotheses. The study employed a quantitative research approach using a survey questionnaire to collect essential participant information. The sample size of 371 was determined using a simple random sampling method to ensure the validity and reliability of the research results. Inferential statistics were then conducted using SmartPLS software version 3.3.9, utilizing Structural Equation Modeling (SEM) to identify significant relationships between the research hypotheses and the conceptual framework. The study’s results revealed a positive and statistically significant relationship between physical security culture and performance, FRT efficiency and its integration within the physical security system, physical security threats and performance, and external factors and performance. Furthermore, a positive and statistically significant relationship was observed between FRT integration and physical security performance. These findings provide valuable insights for organizations operating in the UAE oil and gas industry, enabling them to make informed decisions regarding investments in security technology. This study makes a notable contribution to the under-researched FRT integration with physical security in the oil and gas industry, particularly in the Middle East and North Africa (MENA) region. Its findings offer a foundation for further advancements in security practices within this sector, benefiting industry stakeholders and the broader community. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Al Zaabi, Saeed Hasan Salem |
| spellingShingle |
Al Zaabi, Saeed Hasan Salem A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| author_facet |
Al Zaabi, Saeed Hasan Salem |
| author_sort |
Al Zaabi, Saeed Hasan Salem |
| title |
A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| title_short |
A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| title_full |
A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| title_fullStr |
A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| title_full_unstemmed |
A conceptual framework for facial recognition integration in ADNOC refining's physical security |
| title_sort |
conceptual framework for facial recognition integration in adnoc refining's physical security |
| granting_institution |
Universiti Teknikal Malaysia Melaka |
| granting_department |
Institute of Technology Management and Entrepreneurship |
| publishDate |
2024 |
| url |
http://eprints.utem.edu.my/id/eprint/28260/1/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf http://eprints.utem.edu.my/id/eprint/28260/2/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf |
| _version_ |
1818612058753597440 |
| spelling |
my-utem-ep.282602024-12-13T09:45:17Z A conceptual framework for facial recognition integration in ADNOC refining's physical security 2024 Al Zaabi, Saeed Hasan Salem High-risk sectors, such as the oil and gas industry, demand optimal infrastructure to safeguard their valuable assets, given their substantial contribution to national economies. In recent years, the oil and gas industry in the United Arab Emirates (UAE) has experienced significant growth, solidifying its position as one of the top 10 oil producers globally. Security is an indispensable aspect of any business enterprise, regardless of size or industry, as it protects assets from various physical security threats, including unauthorized access, theft, espionage, vandalism, and other disruptive incidents that impede operations. Organizations have recognized the importance of enhancing their physical security systems, driven by technological advancements and external factors such as the Coronavirus Disease (COVID-19) pandemic. Multiple studies have suggested that integrating Facial Recognition Technology (FRT) with the existing physical security culture can create a holistic security system capable of effectively responding to physical security threats. Consequently, the main objective of this research was to investigate whether integrating FRT into the physical security measures of one of the Abu Dhabi National Oil Company (ADNOC) subsidiaries’, ADNOC Refining, a prominent company in the UAE, would enhance its overall performance. The study also aimed to explore the relationship between physical security culture and performance, the efficiency of FRT, its integration with physical security, and the impact of external factors and physical security threats on physical security performance. A comprehensive review of physical security frameworks and models was conducted using deductive and inductive reasoning to accomplish these objectives, which provided meaningful information about key factors and concepts. Based on this knowledge, a conceptual framework was developed to test the research hypotheses. The study employed a quantitative research approach using a survey questionnaire to collect essential participant information. The sample size of 371 was determined using a simple random sampling method to ensure the validity and reliability of the research results. Inferential statistics were then conducted using SmartPLS software version 3.3.9, utilizing Structural Equation Modeling (SEM) to identify significant relationships between the research hypotheses and the conceptual framework. The study’s results revealed a positive and statistically significant relationship between physical security culture and performance, FRT efficiency and its integration within the physical security system, physical security threats and performance, and external factors and performance. Furthermore, a positive and statistically significant relationship was observed between FRT integration and physical security performance. These findings provide valuable insights for organizations operating in the UAE oil and gas industry, enabling them to make informed decisions regarding investments in security technology. This study makes a notable contribution to the under-researched FRT integration with physical security in the oil and gas industry, particularly in the Middle East and North Africa (MENA) region. Its findings offer a foundation for further advancements in security practices within this sector, benefiting industry stakeholders and the broader community. 2024 Thesis http://eprints.utem.edu.my/id/eprint/28260/ http://eprints.utem.edu.my/id/eprint/28260/1/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf text en public http://eprints.utem.edu.my/id/eprint/28260/2/A%20conceptual%20framework%20for%20facial%20recognition%20integration%20in%20ADNOC%20refining%27s%20physical%20security.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124162 phd doctoral Universiti Teknikal Malaysia Melaka Institute of Technology Management and Entrepreneurship Zamri, Ruzaidi |