{"id":30,"date":"2026-09-20T16:03:36","date_gmt":"2026-09-20T08:03:36","guid":{"rendered":"https:\/\/leebanfatt.com\/?page_id=30"},"modified":"2026-09-20T16:03:36","modified_gmt":"2026-09-20T08:03:36","slug":"journal-1-phd-research-summary","status":"publish","type":"page","link":"https:\/\/leebanfatt.com\/?page_id=30","title":{"rendered":"Journal 1 \u2014 PhD Research Summary"},"content":{"rendered":"\n

Artificial Intelligence Adoption and Decision-Making Efficiency: Evidence from Modern Businesses<\/strong><\/h3>\n\n\n\n

This journal article presents a concise academic summary of doctoral research examining the impact of Artificial Intelligence (AI) adoption on decision-making efficiency and organizational performance in modern businesses. The study focuses on decision speed, decision accuracy and decision consistency, while examining the role of decision-making efficiency in linking AI adoption to organizational performance.<\/p>\n\n\n\n

Publication Overview<\/h2>\n\n\n\n

This research examines the relationship between Artificial Intelligence (AI) adoption, decision-making efficiency and organizational performance in modern businesses. Particular attention is given to the effectiveness of AI in supporting managerial decision-making, including decision speed, decision accuracy and decision consistency.<\/p>\n\n\n\n

The study further examines the role of decision-making efficiency in the relationship between AI adoption and organizational performance, providing a framework for understanding how the adoption of AI may contribute to more effective organizational decision-making.<\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

Artificial intelligence (AI) is increasingly embedded in organizational processes, yet evidence remains fragmented regarding the mechanisms through which AI adoption improves managerial decisions and organizational outcomes. This study examines the influence of AI adoption on decision speed, decision accuracy, decision consistency, and organizational performance, while evaluating the mediating role of decision-making efficiency. A positivist, deductive, and quantitative research design was employed. Data were obtained through a structured questionnaire administered to managers, executives, department heads, and business analysts working in AI-adopting organizations across manufacturing, banking, healthcare, retail, logistics, telecommunications, and information technology. Of 350 questionnaires distributed, 320 valid responses were analyzed. Descriptive statistics, reliability and validity assessment, correlation analysis, regression analysis, and structural equation modeling were used to evaluate the proposed relationships. The thesis results indicate that AI adoption is positively associated with decision speed, accuracy, and consistency. Decision-making efficiency also demonstrates a positive relationship with organizational performance and is reported to mediate the AI adoption-performance relationship. The findings suggest that AI creates organizational value not merely through technological deployment but through its integration into managerial routines, data infrastructures, and human decision processes. The study contributes an integrated model linking AI adoption, decision-making efficiency, and organizational performance. It also highlights the importance of data quality, employee capability, human oversight, explain ability, and responsible AI governance. The manuscript concludes that organizations should treat AI as a complementary decision capability rather than a substitute for managerial judgment.<\/p>\n\n\n\n

Keywords:<\/strong> Artificial intelligence adoption; decision-making efficiency; decision speed; decision accuracy; organizational performance; human-AI collaboration; structural equation modeling<\/p>\n\n\n\n

Research Focus<\/h2>\n\n\n\n

This study examines the influence of Artificial Intelligence (AI) adoption on decision-making efficiency and organizational performance in modern businesses. The research focuses on three dimensions of decision-making efficiency: decision speed, decision accuracy and decision consistency. It also evaluates the relationship between decision-making efficiency and organizational performance, including its mediating role in the relationship between AI adoption and organizational performance.<\/p>\n\n\n\n

AI Adoption<\/strong> \u2014 examining the adoption and integration of AI within organizational processes and decision routines. <\/p>\n\n\n\n

Decision Speed<\/strong> \u2014 examining whether AI adoption is associated with faster decision-making. <\/p>\n\n\n\n

Decision Accuracy<\/strong> \u2014 examining whether AI adoption is associated with more accurate decisions. <\/p>\n\n\n\n

Decision Consistency<\/strong> \u2014 examining whether AI adoption is associated with greater consistency in decision-making. <\/p>\n\n\n\n

Organizational Performance<\/strong> \u2014 examining the relationship between AI adoption, decision-making efficiency and organizational performance.<\/p>\n\n\n\n

Research Methodology<\/h2>\n\n\n\n

The study adopted a positivist, deductive and quantitative research design. Data were collected through a structured questionnaire administered to managers, executives, department heads and business analysts working in AI-adopting organizations across manufacturing, banking, healthcare, retail, logistics, telecommunications and information technology. A total of 350 questionnaires were distributed, of which 320 valid responses were analyzed. The study employed descriptive statistics, reliability and validity assessment, correlation analysis, regression analysis and structural equation modeling to evaluate the proposed relationships.<\/p>\n\n\n\n

Key Findings<\/h2>\n\n\n\n

The findings indicate that AI adoption is positively associated with decision speed, decision accuracy and decision consistency. Decision-making efficiency also demonstrates a positive relationship with organizational performance and is reported to mediate the relationship between AI adoption and organizational performance. The findings suggest that AI creates organizational value not merely through technological deployment, but through its integration into managerial routines, data infrastructures and human decision processes.<\/p>\n\n\n\n

The study also highlights the importance of data quality, employee capability, human oversight, explainability and responsible AI governance in supporting effective AI adoption.<\/p>\n\n\n\n

Research Contribution<\/h2>\n\n\n\n

The study contributes an integrated model linking AI adoption, decision-making efficiency and organizational performance. It also highlights the importance of data quality, employee capability, human oversight, explain ability and responsible AI governance in supporting effective AI adoption. The research further suggests that organizations should treat AI as a complementary decision capability rather than a substitute for managerial judgment.<\/p>\n\n\n\n

Author & Publication Information<\/h2>\n\n\n\n
Information<\/th>Details<\/th><\/tr><\/thead>
Author<\/strong><\/td>LEE BAN FATT<\/td><\/tr>
Research<\/strong><\/td>PhD Research, University of Malaya<\/td><\/tr>
Article<\/strong><\/td>Artificial Intelligence Adoption and Decision-Making Efficiency: Evidence from Modern Businesses<\/td><\/tr>
Research Area<\/strong><\/td>Artificial Intelligence \u2022 Decision-Making Efficiency \u2022 Organizational Performance<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Journal Article<\/h2>\n\n\n\n

The full journal article is available for reading and reference.<\/p>\n\n\n\n

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Artificial Intelligence Adoption and Decision-Making Efficiency: Evidence from Modern Businesses This journal article presents a concise academic summary of doctoral research examining the impact of Artificial Intelligence (AI) adoption on decision-making efficiency and organizational performance in modern businesses. The study focuses on decision speed, decision accuracy and decision consistency, while examining the role of decision-making […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-30","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/pages\/30","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/leebanfatt.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=30"}],"version-history":[{"count":7,"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/pages\/30\/revisions"}],"predecessor-version":[{"id":38,"href":"https:\/\/leebanfatt.com\/index.php?rest_route=\/wp\/v2\/pages\/30\/revisions\/38"}],"wp:attachment":[{"href":"https:\/\/leebanfatt.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}