AI technology factors mediated via intention to use in UAE petroleum companies case study

Alblooshi, Surour Mohammed Surour Hamada (2024) AI technology factors mediated via intention to use in UAE petroleum companies case study. Masters thesis, Universiti Teknikal Malaysia Melaka.

	Text (24 Pages) AI technology factors mediated via intention to use in UAE petroleum companies case study.pdf - Submitted Version Download (413kB)
	Text (Full Text) AI technology factors mediated via intention to use in UAE petroleum companies case study.pdf - Submitted Version Restricted to Registered users only Download (2MB)

Abstract

Artificial Intelligence (AI) represents a transformative force globally, with its computational prowess, data accessibility, and revolutionary algorithms. While the United Arab Emirates (UAE) has set a national AI strategy 2031 as a testament to AI's transcendent potential, the actual adoption of AI within the UAE, like in many governments, remains at a nascent stage, necessitating a comprehensive exploration of the underlying complexities and obstacles. This research undertakes a thorough investigation into the determinants shaping the adoption of AI technologies within the United Arab Emirates' oil and gas sector. By integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study constructs a robust theoretical framework for scrutinizing AI technology adoption in a distinct industry context. Specifically, this research focuses on discerning the pivotal factors influencing AI adoption within the Abu Dhabi National Oil Company (ADNOC). These factors encompass perceived ease of use, perceived usefulness, technology attitude towards AI, perceived knowledge in AI, and behavioral intention to use AI. The research encompasses a sample of 500 practitioners and employees operating within ADNOC's information systems and technology department. For quantitative analysis, data collected from the respondents are analyzed using widely recognized statistical tools, namely the Statistical Package for the Social Sciences (SPSS) version 29 and Smart-Pls 3.3.9. A total of 329 valid questionnaires serve as the basis for statistical data analysis. The results reveal that behavioral intention to use AI partially mediates the relationship between each respective independent variable (perceived ease of use, perceived usefulness, technology attitude towards AI, and perceived knowledge in AI) and AI adoption within ADNOC. This mediation signifies that behavioral intention plays a discernible role in influencing the impact of these independent variables on AI adoption, although the relationships are not entirely mediated by behavioral intention. Furthermore, this research enriches the domain of technology adoption models by presenting a holistic framework that integrates TAM and UTAUT. This integration furnishes a more comprehensive understanding of user behavior and acceptance of AI technologies in the UAE oil and gas sector. Moreover, the study validates the conceptual model for this specific industry, ensuring its alignment with the sector's unique needs and challenges, thereby providing a relevant framework for AI technology adoption. Practically, this research offers valuable insights to organizations operating in the UAE oil and gas sector. By identifying key factors such as perceived ease of use, perceived usefulness, technology attitude towards AI, and perceived knowledge in AI, the study assists industry stakeholders in making well-informed decisions regarding the adoption and implementation of AI technologies. Furthermore, the research lays the groundwork for future investigations in cooperative banking performance analysis, both within the UAE and globally, thus contributing to advancements in the field of strategic quality planning. Based on the findings, it is recommended that organizations in the UAE oil and gas sector prioritize enhancing the perceived ease of use and usefulness of AI technologies to stimulate their adoption. Additionally, industry stakeholders should invest in employee training and knowledge-building programs to cultivate a more positive attitude towards AI. Future research should broaden its scope to encompass comparative studies across industries and explore the enduring impact of AI adoption on business performance. In conclusion, this research provides a comprehensive comprehension of AI technology adoption in the UAE oil and gas sector, bridging theoretical, practical, and methodological aspects. It offers a valuable roadmap for organizations and researchers, propelling further progress in the dynamic field of technology adoption.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Artificial Intelligence (AI), Oil and gas sector, Technology Acceptance Model (TAM)
Divisions:	Library > Tesis > FPTT
Depositing User:	MUHAMAD HAFEEZ ZAINUDIN
Date Deposited:	16 Dec 2024 08:32
Last Modified:	16 Dec 2024 08:32
URI:	http://eprints.utem.edu.my/id/eprint/28310
Statistic Details:	View Download Statistic

Actions (login required)

View Item