In this data-driven era, AI-driven economic models have emerged as a possible prescription in the sports management domain. Scholars have noted that artificial intelligence is transforming the decisionmaking process, performance analytics, and the financial sustainability, strategic planning, and operational efficiency of sports organizations across the global sports industry. The paper attempts to move forward research in AI-driven economic models for sports management from theoretical, empirical, and computational contexts to emerging stakeholder partnership frameworks that address current industry challenges. In proposing such a framework, the authors aim to develop Analytical Hierarchy Process (AHP) and regression-based framework that is particularly suited for the iterative evaluation, optimization, and validation of stakeholder partnership in the different decision-support models such as resource allocation strategies and revenue forecasting systems. Additionally, the AHP-based framework is used to organize a hierarchical assessment of stakeholder partnerships to identify some best practices related to specific economic and managerial decisions. This approach then furthers the examination of the strategic and financial implications related to the use of AI-driven models in terms of investment decisions, performance analytics, and stakeholder engagement. In order to practicallly approve feasibility of the framework, A closing case finally examines the application of the framework in a prominent sports club (i.e., a leading football club in Uzbekistan) in context of the AI-driven economic model - stakeholder partnership nexus. Its effect is to increase the economic viability of sports enterprises operating using the AI-driven decision-making framework based on the aforementioned methodological insights.
Akhmatov et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: