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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.

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.

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.

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.

Although prior researchers have consistently established a significant relationship between different strategic stances and organizational performances across different research contexts, the mechanisms underlying this link remain unclear. This study attempts to fill this gap in the literature by testing the mediating effect of the use of strategic performance measurement systems (SPMS) on the relationship between strategic stances (prospector, defender, and reactor) and organizational performance in the public sector.

This research is based on data collected by surveying 224 managers at public organizations in the Turkish Republic of Northern Cyprus (TRNC) and conducts an analysis using structural equation modeling (SEM).

The study findings show that prospector strategy is positively associated with organizational performance through the use of SPMS. The reactor strategy was negatively related to organizational performance through the use of SPMS. The defender strategy shows mixed results in terms of its effect on the use of SPMS and organizational performance.

The results obtained here provide strong evidence of the vitality of the use of SPMS for efficiency and effectiveness as a mediator between prospector strategy and organizational performance. To extend this position, future researchers could incorporate other contingent variables, such as structural autonomy, or use experimental design methods during economic austerity in the aftermath of the coronavirus disease 2019 (COVID-19) global pandemic.

This study represents an attempt to address public administration literature' general calls for grounded research that spells out to practitioners how different strategic stances are likely to affect the use of SPMS to achieve organizational performance levels in the public sector. The present study extends the public administration literature by examining the unexplored linkage of the use of SPMS through which strategic stances influence organizational performance in major public sector organizations.