Abstract:
The emergence of artificial intelligence in recent decades has provided the possibility for a fundamental review of how performance evaluation systems are designed and implemented. Artificial intelligence, particularly the subfields of machine learning (ML) and natural language processing (NLP), is capable of analyzing massive and heterogeneous data in structured and unstructured formats and revealing complex relationships that remain hidden from traditional methods. This research was conducted with the aim of investigating the impact of artificial intelligence on managers' performance evaluation systems within the framework of ESG indices. The research method was quantitative, descriptive-analytical, and applied, and the required data were collected from companies listed on the Tehran Stock Exchange during the period 1399 to 1403. Artificial intelligence indices, the quality of the managers' evaluation system, and ESG dimensions were extracted using content analysis of reports and natural language processing. Data were analyzed using panel data methods and structural equation modeling (SEM) in Stata and Python software. The findings showed that artificial intelligence has a positive and significant impact on ESG reporting, and this impact flows primarily through the improvement of the quality of the managers' performance evaluation system as an intervening variable. These findings are enlightening, both from a theoretical and practical perspective, for the design of next-generation evaluation systems in the era of sustainability.
Machine summary:
(Eccles, Ioannou, & Serafeim, 2014) From the perspective of modern accounting, executive performance evaluation is no longer limited to traditional financial metrics such as profitability or return on capital, but has increasingly expanded to include measurable non-financial ESG dimensions (Gillan, Koch, & Starks, 2021).
Specifically, this study seeks to answer how the application of artificial intelligence models can reduce the limitations of traditional evaluation methods, such as subjectivity, reporting delays, and the inability to combine qualitative and quantitative data in evaluating executive performance in ESG domains.
Ultimately, this research addresses the answer to this key question: How can artificial intelligence transform executive performance evaluation systems within the framework of ESG indicators, and what are the implications for transparent reporting, managerial competence, and stakeholder trust?
In this regard, the present research is based on the theoretical hypothesis that artificial intelligence can align executive performance evaluation systems with the long-term sustainability goals of the organization by improving the measurability, transparency, and dynamism of ESG indicators.
Its main objective is to test the structural relationships between artificial intelligence (as a facilitating technology), executive performance evaluation systems (as an institutional mechanism), and ESG indicators (as sustainability metrics).
Conclusion This research was conducted with the aim of examining the "impact of artificial intelligence on executive performance evaluation systems within the framework of ESG indices" and, in a quantitative and applied manner using data from 132 companies listed on the Tehran Stock Exchange during the period 1399 to 1403, tested the structural relationships between technology, institution, and sustainability.
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