Publication | Closed Access
Artificial intelligence-driven decision making and firm performance: a quantitative approach
20
Citations
74
References
2024
Year
Artificial IntelligenceFirm PerformanceBusiness IntelligenceAi AdoptionData-driven InnovationBusiness AnalyticsManagementManagerial CapabilityAi CapabilitiesDecision TheoryQuantitative ManagementResource-based ViewStrategyStrategic ManagementApplied Artificial IntelligenceDynamic CapabilityReal-time Decision-makingBusinessBusiness StrategyKnowledge ManagementData-driven Decision-makingIntelligent Decision MakingTechnologyDecision ScienceBig Data
Purpose The purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making. Design/methodology/approach We surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data. Findings Results indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making. Originality/value These findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.
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