Artificial intelligence capability and organizational performance: unraveling the mediating mechanisms of decision-making processes

组织绩效 决策质量 调解 知识管理 组织学习 结构方程建模 计算机科学 组织承诺 质量(理念) 动态决策 实证研究 组织研究 管理科学 心理学 人工智能 社会心理学 机器学习 工程类 社会学 数学 社会科学 团队效能 哲学 统计 认识论
作者
Suheil Neiroukh,Okechukwu Lawrence Emeagwali,Hasan Yousef Aljuhmani
出处
期刊:Management Decision [Emerald Publishing Limited]
被引量:7
标识
DOI:10.1108/md-10-2023-1946
摘要

Purpose This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality. Design/methodology/approach Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination. Findings The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed. Originality/value This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.
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