Improving the Food and Agriculture Sector Tehran Stock Exchange with using Artificial Intelligence: A Comprehensive Mixed-Methods Analysis

Subject Areas : Agriculture Marketing and Commercialization

Ramin Zeraatgari ¹ , Hamid Mir ² , Reza Sotoudeh ³

1 - Department of Accounting, Sistan and Baluchestan University, Zahedan, Iran,
2 - Department of accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran,
3 - Department of Accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran

Received: 2020-12-22 Accepted : 2021-05-02 Published : 2021-06-30

Keywords: Artificial Intelligence, Agricultural Financial Performance, Technology Implementation, Tehran Stock Exchange, Mixed-Methods Research, Organizational Digital Maturity,

Abstract :

This study investigates the complex relationship between artificial intelligence (AI) implementation and financial performance within agricultural sectors listed on the Tehran Stock Exchange (TSE). Utilizing a sequential exploratory mixed-methods research design, we conducted a two-phase investigation incorporating both qualitative depth and quantitative breadth. The qualitative phase comprised in-depth interviews with 24 domain experts, analyzed through systematic thematic coding, revealing four distinct dimensions of AI implementation: Predictive Trading Systems, Supply Chain Optimization, Risk Assessment Mechanisms, and Market Intelligence Integration. Subsequently, the quantitative phase leveraged survey data from 385 stakeholders across institutional investment, agricultural management, and individual trading domains. Structural Equation Modeling (SEM) validated our proposed framework with exceptional fit indices (χ²/df=2.16, RMSEA=0.055, CFI=0.94, GFI=0.92). Advanced econometric analyses, including hierarchical multiple regression and multivariate time-series modeling, demonstrated that Predictive Trading Systems (β=0.41, p<0.001) and Market Intelligence Integration (β=0.37, p<0.001) exerted the strongest influence on performance outcomes. MANOVA results revealed significant heterogeneity in AI adoption patterns across agricultural subsectors (Wilks’ λ=0.78, p<0.001), with agro-technology firms demonstrating significantly higher implementation levels than traditional farming operations. Longitudinal analysis of 42 agricultural companies over a three-year period indicated that high AI-implementing organizations outperformed their low-implementing counterparts by 23.7% in annualized returns (t=4.82, p<0.001) with substantially reduced volatility (F=8.73, p<0.001). GARCH modeling further demonstrated lower volatility persistence in high-implementing firms (α+β=0.78) compared to low-implementing counterparts (α+β=0.92). Moderation analysis revealed organizational digital maturity as a critical contingency factor (β=0.21, p<0.01), with high-maturity firms extracting substantially greater performance benefits from AI implementations. This research contributes a theoretically grounded, empirically validated framework elucidating the mechanisms through which AI technologies transform agricultural financial performance, offering strategic guidance for executives, investors, and policymakers in emerging market contexts

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