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Article Type: Original Research

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 -
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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Full-Text:

Mir et al; Improving the Food and Agriculture Sector Tehran Stock Exchange ….

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

Hamid Mir 1, Ramin Zaraatgari 2*, Reza Sotoudeh 3

1PhD Student in Department of accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran

2 Department of Accounting, Sistan and Baluchestan University, Zahedan, Iran, https://orcid.org/0000-0002-8793-8098, Email: Zeraatgari.r@gmail.com

3 Department of Accounting, Zahedan Branch, Islamic Azad University, Zahedan, Iran.

Abstract

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

Introduction

The strategic integration of artificial intelligence (AI) technologies into financial markets represents one of the most consequential technological disruptions of the early 21st century. Within emerging market contexts, particularly in economies with significant agricultural sectors, this technological evolution holds transformative potential (Rahmani & Jafari, 2019). The agricultural sector in Iran presents a distinctive analytical context, characterized by complex intersections of climate variability, geopolitical constraints, water resource limitations, and domestic market dynamics that differentiate it substantively from comparable sectors in other emerging economies. AI implementation offers multidimensional pathways to address these contextual challenges through enhanced predictive capabilities, operational efficiencies, and strategic decision-making frameworks (Hosseini & Karimi, 2020).

Iran’s agricultural sector constitutes approximately 10% of the nation’s GDP while employing nearly 18% of the workforce according to official economic indicators (Statistical Center of Iran, 2020). Within the Tehran Stock Exchange (TSE), agricultural entities represent approximately 8% of total market capitalization, encompassing diverse subsectors including agro-processing, fertilizer production, irrigation systems, agricultural machinery, and food distribution networks (Tehran Stock Exchange, 2020). Despite this economic significance, agricultural companies have historically demonstrated systematic underperformance relative to broader market indices, with average returns 6.8% below comparative benchmarks over the preceding decade (Mohammadi & Tehrani, 2019).

The integration of AI technologies into agricultural financial markets represents a potentially paradigmatic shift in operational and strategic capabilities. Global evidence suggests that AI applications in agricultural contexts have demonstrated significant potential for productivity enhancement and operational efficiency gains (Liakos et al., 2018). Within financial market contexts, AI-driven trading and investment strategies have demonstrated performance advantages of 12-17% relative to traditional methodological approaches across various international market conditions (Chen & Lee, 2020).

Despite these promising indicators, empirical research examining the specific intersection of AI implementation and agricultural sector performance within the Iranian stock market remains notably underdeveloped. Existing literature has primarily focused on either AI applications in agricultural operations from a technical perspective (Ahmadi et al., 2019) or on general technological adoption patterns in Iran’s financial markets (Hosseini & Karimi, 2020). Leaving a significant analytical gap regarding the specific performance impacts of AI on agricultural stock performance.

The research objectives of this study are multifaceted:

1. To develop a comprehensive taxonomic framework identifying key dimensions of AI implementation within Iranian agricultural stock sectors

2. To quantify the differential impacts of various AI technologies on the financial performance metrics of agricultural companies

3. To analyze heterogeneity in AI adoption patterns and performance impacts across distinct agricultural subsectors

4. To establish causal relationships between AI implementation intensity and stock market performance metrics using advanced econometric techniques

5. To construct and empirically validate a theoretical framework explaining the mechanisms through which AI enhances agricultural stock performance

6. To identify and measure contingency factors that moderate the relationship between AI implementation and market performance

This research offers substantive contributions to both theoretical understanding and practical application. For scholarly discourse, we advance theoretical models of technology implementation in emerging market contexts and provide empirical validation of resource-based perspectives on technological advantage. For practitioners, we offer evidence-based guidance for agricultural executives, investment professionals, and regulatory policymakers seeking to leverage AI technologies for competitive advantage within Iran’s evolving agricultural financial ecosystem.

Theoretical Framework and Literature Review

Theoretical Foundations

Our investigation is anchored in three complementary theoretical perspectives that collectively provide a robust foundation for understanding the complex relationship between AI implementation and agricultural stock performance.

Resource-Based View (RBV): (Barney, 1991) seminal articulation of the Resource-Based View posits that sustainable competitive advantage derives from organizational resources and capabilities that are valuable, rare, imperfectly imitable, and non-substitutable. Within this theoretical framework, AI technologies and implementation capabilities represent potentially strategic resources that can confer differential advantage to agricultural firms. The RBV provides a theoretical explanation for why AI implementations might generate heterogeneous performance outcomes across organizations with varying capabilities and complementary resources (Fardmanesh et al., 2020).

Efficient Market Hypothesis (EMH): (Fama, 1970) Efficient Market Hypothesis proposes that financial markets rapidly incorporate all available information into asset prices, theoretically eliminating opportunities for systematic outperformance. However, subsequent theoretical refinements by (Grossman & Stiglitz, 1980) acknowledge that information acquisition and processing advantages can yield above-market returns, particularly in contexts with information asymmetries. This theoretical perspective informs our understanding of how AI-enabled information processing capabilities might create performance advantages in agricultural stock markets characterized by complex information environments.

Dynamic Capabilities Theory: The dynamic capabilities framework articulated by (Teece et al., 1997) emphasizes organizations’ abilities to integrate, build, and reconfigure internal and external competencies in response to rapidly changing environments. This theoretical lens is particularly salient for understanding how agricultural companies develop capabilities for effectively implementing and leveraging AI technologies in dynamic market contexts. The theory suggests that organizations with superior dynamic capabilities will more effectively sense opportunities for AI application, seize those opportunities through implementation, and transform their operations and strategies accordingly.

The synthesis of these theoretical perspectives guides our empirical investigation, providing conceptual explanations for how and why AI implementation might enhance agricultural stock performance through multiple causal mechanisms (HajiAbedi et al., 2020).

Artificial Intelligence in Agricultural Contexts

Research on AI applications in agricultural domains has evolved significantly over the past decade. (Wolfert et al., 2017) provided a comprehensive framework for understanding big data applications in smart farming, identifying critical challenges including data ownership, privacy concerns, and security considerations. Their analysis emphasized that successful AI implementation requires integration of data across the entire agricultural value chain, from primary production to consumer interfaces.

Building on this foundation, (Liakos et al., 2018) developed a taxonomic classification of AI applications in agriculture, categorizing implementations across crop management, livestock monitoring, water resource optimization, and soil management systems. This classification has significantly informed subsequent research on agricultural AI applications. Machine learning algorithms have demonstrated particular efficacy in yield prediction, achieving accuracy rates between 85-93% across diverse crop varieties and geographical contexts (Balducci et al., 2018).

Computer vision systems for crop disease detection represent another significant application domain, with (Wang et al., 2019) documenting reduced diagnosis times of 76% alongside accuracy improvements of 22% compared to traditional diagnostic methods. The economic impact of AI implementation in agriculture has been substantial, with (Joshi et al., 2019) reporting productivity improvements of 15-32% and cost reductions of 8-24% across various agricultural operations.

In the specific context of Iran, (Ahmadi et al., 2019) conducted a comprehensive assessment of machine learning applications in agricultural systems, identifying both significant potential and substantial implementation challenges. Their research highlighted context-specific factors affecting AI adoption, including infrastructural limitations, data quality concerns, and technical capacity constraints within the Iranian agricultural sector.

Artificial Intelligence in Financial Markets

The integration of AI methodologies into financial markets has fundamentally transformed multiple domains including trading strategies, risk assessment frameworks, portfolio management techniques, and market analysis approaches. The theoretical foundation for understanding information processing in financial markets was established by (Fama, 1970) Efficient Market Hypothesis, which has subsequently been refined and challenged by AI-enabled information processing capabilities. (Grossman & Stiglitz, 1980) important theoretical modifications acknowledged that information acquisition and processing advantages can yield above-market returns, a perspective particularly relevant to AI applications in market contexts.

(Bahrammirzaee, 2010) conducted a systematic comparative analysis of artificial intelligence implementations in financial services, concluding that AI methodologies generally outperform traditional statistical approaches across domains including credit evaluation, portfolio management, and financial forecasting. The author emphasized that hybrid systems integrating multiple AI techniques typically achieve superior performance compared to single-methodology implementations.

In the Iranian context, (Fallahpour et al., 2016) examined the application of artificial neural networks for stock price prediction on the Tehran Stock Exchange. Their analysis reported prediction accuracy of 74.28% using multilayer perceptron architectures, demonstrating the potential of AI methodologies within the specific context of the Iranian market. (Kumar et al., 2019) subsequently identified five primary categories of AI application in stock markets: predictive analytics, algorithmic trading, sentiment analysis, risk assessment, and portfolio optimization.

Machine learning algorithms for stock price prediction have demonstrated variable accuracy rates between 63-87% depending on market conditions and time horizons (Jiang & Song, 2019). Deep learning architectures have shown particular promise in capturing complex non-linear relationships in financial data. (Chen & Patel, 2020) empirically demonstrated that Long Short-Term Memory (LSTM) neural networks outperformed traditional ARIMA models by 23% in forecasting accuracy across multiple market indices.

Natural Language Processing (NLP) applications analyzing news sentiment have improved trading strategy returns by 7-12% compared to price-based strategies alone (Zhang et al., 2020).

Iranian Agricultural Sector in Financial Markets

The Iranian agricultural sector presents distinctive characteristics that significantly influence its stock market performance dynamics. (Mohamadi & Zibaei, 2018) provided a comprehensive analysis of structural issues affecting Iran’s agricultural sector in their book “Agricultural Economics in Iran: Challenges and Opportunities.” They identified fragmented landholdings, technological adoption constraints, and capital access limitations as significant structural factors that have historically restricted innovation within the sector.

(Razavi & Mohammadi, 2018) documented pronounced seasonal patterns in agricultural stock performance, with price movements closely correlated with harvest cycles and international commodity price fluctuations. Their analysis demonstrated stronger seasonality effects in agricultural stocks compared to other market sectors. Building on this work, (Hosseini & Rahimi, 2019) identified four distinctive factors affecting agricultural companies on the Tehran Stock Exchange: elevated vulnerability to climate fluctuations, extensive government intervention through subsidy mechanisms and price controls, international sanctions affecting input costs and export opportunities, and water resource constraints.

(Pourzamani & Naderi, 2018) conducted an empirical investigation into factors affecting stock returns for agricultural companies on the TSE between 2010-2017. Their multivariate analysis identified significant correlations between financial ratios, operational efficiency metrics, and stock performance indicators, suggesting potential pathways for technological enhancement. (Mohammadi & Tehrani, 2019) provided a longitudinal performance analysis of major sectors in the Tehran Stock Exchange over a 10-year period, documenting the historical underperformance of agricultural companies relative to broader market benchmarks.

Technological adoption patterns within Iran’s agricultural companies have demonstrated significant heterogeneity. (Jafari et al., 2020) surveyed 112 agricultural firms and found that while 78% had implemented basic digital technologies, only 23% had deployed advanced analytics or AI systems. Their analysis identified implementation barriers including limited access to international technology partnerships, infrastructure constraints, and specialized skill deficiencies.

The Convergence of AI, Agriculture, and Financial Markets

Research specifically addressing the intersection of artificial intelligence, agricultural operations, and financial market performance remains limited, particularly within emerging market contexts. (Teece, 1986) foundational work on profiting from technological innovation provides an important theoretical framework for understanding how agricultural companies capture value from AI investments. This perspective emphasizes the importance of complementary assets and appropriability regimes in determining returns from technological innovation.

(Saeedi & Mahmoudi, 2018) investigated the relationship between technology investment and stock returns across multiple sectors in Iran, finding that technology expenditure had a significant positive correlation with stock performance in the agricultural sector (r=0.36, p<0.01). However, their analysis did not specifically differentiate AI technologies from other technological investments. (Goldsmith & Silva, 2018) argued in their book “Financial Technology and Agricultural Markets” that digital technologies including AI would fundamentally transform agricultural financing and risk management globally, with particularly pronounced effects in emerging markets with underdeveloped financial infrastructures.

(Rahman & Siddiqi, 2019) analyzed AI adoption in agricultural companies across five emerging markets and documented significant correlations between implementation intensity and reduced stock price volatility (r=-0.38, p<0.01), as well as improved analyst forecast accuracy (r=0.42, p<0.001). In the specific Iranian context, (Tehrani et al., 2020) conducted detailed case analyses of three agricultural technology companies that implemented AI systems, documenting substantial operational improvements (22% reduction in waste, 17% increase in yield) and enhanced financial performance (18% increase in gross margins) within two years of implementation. However, their investigation did not systematically examine broader stock market implications.

Research Hypotheses

Based on the theoretical foundations and empirical evidence reviewed above, we formulate the following hypotheses to guide our investigation:

H1: Agricultural companies with higher levels of AI implementation will demonstrate superior stock market performance compared to those with lower implementation levels.

H2: The relationship between AI implementation and stock performance will be moderated by organizational factors, specifically:

H3: Different dimensions of AI implementation will demonstrate differential impacts on stock performance metrics:

H4: AI implementation levels will differ significantly across agricultural subsectors, with technology-intensive subsectors demonstrating higher implementation levels.

H5: The relationship between AI implementation and stock performance will demonstrate temporal precedence, with implementation changes preceding performance changes rather than the reverse relationship.

These hypotheses guide our methodological approach and analytical strategy, as detailed in the following section.

Methods and Materials

Research Design

This study employed a sequential exploratory mixed-methods research design, conducted in two complementary phases: an initial qualitative investigation followed by a comprehensive quantitative analysis. This methodological approach was selected to achieve both depth of understanding regarding the complex relationships between AI implementation and agricultural stock performance, while also providing robust quantitative evidence regarding the magnitude and significance of these relationships. The sequential design enabled qualitative findings to inform the development of quantitative instruments and analytical approaches, thereby enhancing both validity and explanatory power.

This approach aligns with methodological recommendations from mixed-methods scholars (Braun & Clarke, 2006; Creswell & Clark, 2018) for investigating complex phenomena where both exploratory understanding and confirmatory testing are required.

Qualitative Phase

Sampling Strategy and Participant Selection

For the qualitative phase, we employed purposive expert sampling to identify and recruit 24 domain specialists with extensive knowledge of AI applications in agricultural sectors and Iranian financial markets. The participant cohort was deliberately diversified to capture multiple stakeholder perspectives:

· 7 senior executives from agricultural companies listed on TSE

· 6 financial analysts specializing in agricultural sectors

· 5 AI and technology specialists with agricultural implementation experience

· 6 policymakers and regulators from relevant governmental agencies

Selection criteria included a minimum of 8 years of professional experience in relevant domains and direct involvement with either AI implementation or agricultural stock analysis. This sampling strategy ensured the capture of diverse perspectives while maintaining focused domain expertise. (Table 1) provides detailed characteristics of the qualitative participant sample.

Table 1. Characteristics of Qualitative Study Participants (N=24)

Participant Category	Count	Average Experience (Years)	Educational Background	Organizational Distribution
Agricultural Executives	7	15.3	Agricultural Science (3) Business Administration (2) Engineering (2)	Large-cap (3) Mid-cap (3) Small-cap (1)
Financial Analysts	6	11.8	Finance (4) Economics (2)	Investment Banks (3) Research Firms (2) Independent (1)
AI/Technology Specialists	5	9.6	Computer Science (3) Data Science (1) Agricultural Engineering (1)	Technology Providers (3) In-house Corporate (2)
Policymakers/Regulators	6	17.2	Public Administration (2) Economics (2) Law (1) Agricultural Policy (1)	Securities Commission (2) Ministry of Agriculture (2) Central Bank (1) Statistical Center (1)

Data Collection Procedures

Qualitative data collection was conducted between September 2019 and January 2020 through two complementary approaches:

1. Semi-structured interviews

2. Focus group discussions

These sessions were designed to explore emergent themes from individual interviews and identify areas of consensus and divergence across stakeholder groups.

All interviews and focus groups were audio-recorded with participant consent, professionally transcribed, and translated where necessary for analysis. Comprehensive field notes documented non-verbal cues and contextual factors throughout the data collection process.

Data Analysis Approach

Qualitative data analysis employed thematic analysis following (Braun & Clarke, 2006) six-phase methodological approach:

1. Data familiarization

2. Initial coding

3. Theme identification

4. Theme review

5. Theme definition

6. Report production

To enhance analytical rigor and trustworthiness, several validation strategies were employed:

· Member checking

· Peer debriefing

· Audit trail

· Researcher triangulation

· Negative case analysis

Quantitative Phase

Target Population and Sampling Frame

The target population for the quantitative phase comprised three distinct stakeholder groups with direct involvement in the agricultural sectors of the Tehran Stock Exchange:

1. Institutional investors managing portfolios that include agricultural stocks (fund managers, investment analysts, portfolio managers)

2. Agricultural company executives and managers from companies listed on the TSE

3. Individual traders with substantial agricultural stock holdings (defined as >10% of portfolio in agricultural sectors)

The sampling frame was constructed from multiple sources to ensure comprehensive coverage:

· For institutional investors: Tehran Securities and Exchange Organization (SEO) database of licensed institutional investors (N=187)

· For company executives: TSE directory of listed agricultural companies (N=53 companies)

· For individual traders: Brokerage firm client databases from five major Iranian securities firms (after obtaining appropriate permissions) (N=approximately 2,400 eligible traders)

Sampling Procedure and Sample Size Determination

We employed stratified random sampling with proportional allocation to ensure adequate representation across stakeholder groups. Sample size determination utilized Cochran’s formula:

n = (Z²pq/e²) / [1 + (Z²pq/e²N)]

Where:

· Z = 1.96 (95% confidence level)

· p = 0.5 (maximum variance assumption)

· q = 0.5

· e = 0.05 (5% margin of error)

· N = total population size for each stratum

This calculation yielded required sample sizes of 142 institutional investors, 127 agricultural company managers, and 116 individual traders, for a total target sample of 385 participants.

Potential participants were randomly selected from each stratum and contacted via email and telephone. Multiple follow-up attempts were made to maximize response rates. The final achieved sample consisted of 301 complete responses (response rate: 78.2%), distributed as follows:

· 112 institutional investors (78.9% response rate)

· 102 agricultural company managers (80.3% response rate)

· 87 individual traders (75.0% response rate)

Non-response bias was assessed by comparing early and late respondents on key demographic and response variables, with no significant differences detected (all p > 0.05), suggesting the absence of substantial non-response bias.

Instrument Development and Validation

Based on qualitative findings, we developed a 47-item survey instrument to measure the identified dimensions of AI implementation and stock performance. The instrument underwent a rigorous four-stage validation process:

1. Content validity

2. Face validity

3. Construct validity

4. Reliability assessment

The final instrument comprised four sections:

· Demographic and organizational information (7 items)

· AI implementation dimensions (24 items across four subscales)

· Performance outcomes (12 items across three domains)

· Moderating factors (8 items across two domains)

(Table 2) presents the psychometric properties of the key survey instrument scales.

Table 2. Psychometric Properties of Survey Instrument Scales

Scale/Subscale	Items	Cronbach’s α	Test-Retest ICC	Mean	SD	Example Item
AI Implementation Dimensions
Predictive Trading Systems	6	0.91	0.87	3.27	0.84	“Our company employs machine learning algorithms to predict agricultural stock price movements”
Supply Chain Optimization	6	0.89	0.84	3.06	0.78	“We use AI systems to optimize inventory management across our agricultural supply chain”
Risk Assessment Mechanisms	6	0.87	0.81	3.18	0.82	“Our risk assessment incorporates AI analysis of climate patterns and their potential impacts”
Market Intelligence Integration	6	0.88	0.83	2.94	0.76	“Our decision-making processes integrate AI-generated competitive intelligence”
Performance Metrics
Financial Returns	4	0.86	0.89	3.31	0.89	“Our stock has outperformed agricultural sector averages over the past year”
Market Volatility	4	0.84	0.85	3.04	0.77	“Our stock demonstrates lower price volatility compared to sector peers”
Market Valuation	4	0.89	0.88	3.18	0.81	“Market analysts positively value our technological implementations in their valuations”
Moderating Factors
Digital Maturity	4	0.93	0.86	3.22	0.92	“Our organization has established digital capabilities across all major operations”
Organizational Characteristics	4	0.85	0.82	3.47	0.71	“Our organizational culture actively supports technological innovation”

Note: ICC = Intraclass Correlation Coefficient for test-retest reliability assessment

Secondary Data Collection

To complement primary survey data and provide objective performance metrics, we collected comprehensive secondary data from multiple sources:

· Financial statements of 42 agricultural companies listed on TSE (2017-2020)

· Daily and monthly stock price data from the Tehran Stock Exchange database

· AI implementation reports and technology investment disclosures from annual reports and regulatory filings

· Market analyst reports on agricultural sectors from major Iranian investment banks

· Patent filings and intellectual property registrations related to AI in agricultural domains

· Corporate governance reports and board compositions to assess technology orientation

This secondary data enabled triangulation with survey responses and facilitated objective assessment of stock performance metrics. Companies were classified into high, medium, and low AI implementers based on documented technology investments, with classification validated through multiple independent raters to ensure reliability (inter-rater reliability: Cohen’s κ = 0.84).

Analytical Strategy

Quantitative data analysis employed a sophisticated multi-stage approach utilizing SPSS 26.0 and AMOS 24.0 software packages:

1. Preliminary analyses:

2. Measurement model assessment:

3. Structural model testing:

4. Advanced regression analyses:

5. Group difference testing:

6. Time-series analyses:

7. Advanced causal inference techniques:

For all hypothesis testing, significance levels were established at p<0.05, with specific p-values reported for all analyses. Effect sizes were calculated and reported for all significant findings to facilitate interpretation of practical significance. Statistical power analyses confirmed adequate power (>0.80) for detecting medium effect sizes at the established alpha level.

Results

Qualitative Findings

Thematic analysis of interview and focus group data revealed four primary dimensions of AI implementation in agricultural stocks in Iran, each with several associated sub-themes.

Predictive Trading Systems

This dimension encompasses AI applications specifically designed for stock trading and investment decisions related to agricultural securities. The implementation of these systems represents a direct application of AI to financial market activities rather than agricultural operations. As articulated by one experienced agricultural investment analyst:

“Machine learning models have transformed how we evaluate agricultural stocks by identifying patterns that would be impossible for human analysts to detect, especially given the unique seasonal cycles and climate dependencies of these companies.” (Participant 8, Stock Market Analyst)

The qualitative analysis identified five distinct sub-themes within this dimension:

1. Technical Analysis Enhancement

2. Fundamental Analysis Automation

3. Sentiment Analysis Integration

4. Alternative Data Processing

5. Algorithmic Trading Execution

Supply Chain Optimization

This dimension focuses on AI applications that enhance operational efficiency throughout the agricultural value chain, ultimately improving company fundamentals and financial performance. These implementations focus on operational aspects rather than market-facing activities. One executive detailed the financial impacts of such systems:

“Our implementation of AI for supply chain

Optimization reduced inventory costs by 18% while improving product availability by 12%. These operational improvements translated directly to improved financial metrics that analysts recognize in their valuations.” (Participant 3, Agricultural Company Executive)

Four sub-themes emerged within this dimension:

1. Inventory Management Intelligence

2. Logistics Network Optimization

3. Supplier Selection Systems

4. Production Planning Automation

Risk Assessment Mechanisms

This dimension encompasses AI systems specifically designed to evaluate and mitigate risks unique to agricultural sectors. Given the distinctive risk profile of agricultural operations, these systems address sector-specific vulnerabilities:

“Agricultural companies face distinctive risks – weather patterns, water access, and pest outbreaks – that traditional risk models poorly capture. Our AI systems integrate satellite imagery, historical climate data, and crop-specific vulnerabilities to provide a much more accurate risk profile for investors.” (Participant 14, AI Specialist)

The analysis identified four primary sub-themes:

1. Climate Risk Modeling

2. Market Volatility Prediction

3. Regulatory Compliance Assessment

4. Biosecurity Threat Detection

Market Intelligence Integration

This dimension focuses on AI’s role in synthesizing market information and competitive intelligence to support strategic decision-making. These implementations enhance the quality and speed of strategic decisions:

“What differentiates the top-performing agricultural stocks is their capacity to rapidly integrate market intelligence from diverse sources – international commodity trends, local harvest conditions, consumer preference shifts. AI gives them a significant advantage in this integration process.” (Participant 22, Policymaker)

Quantitative Findings

Descriptive Statistics

The quantitative sample encompassed 301 respondents across three stakeholder categories. (Table 3) presents detailed demographic characteristics of the sample.

Table 3. Demographic Characteristics of Survey Respondents (N=301)

Characteristic	Category	Frequency	Percentage
Stakeholder Type	Institutional Investor	112	37.2%
	Company Manager	102	33.9%
	Individual Trader	87	28.9%
Years of Experience	<5 years	63	20.9%
	5-10 years	118	39.2%
	11-15 years	76	25.2%
	>15 years	44	14.6%
Educational Background	Business/Finance	143	47.5%
	Agricultural Sciences	72	23.9%
	Computer Science/IT	53	17.6%
	Other	33	11.0%

The survey measured perceptions of AI implementation across different agricultural subsectors. (Table 4) presents these implementation levels, demonstrating substantial variation across subsectors.

Table 4. Perceived AI Implementation Levels by Agricultural Subsector

Agricultural Subsector	Mean Implementation Score (1-5 scale)	Standard Deviation	95% Confidence Interval	Coefficient of Variation
Agro-technology	3.87	0.58	[3.76, 3.98]	15.0%
Fertilizer Production	3.42	0.67	[3.29, 3.55]	19.6%
Food Processing	3.18	0.71	[3.04, 3.32]	22.3%
Agricultural Machinery	2.95	0.62	[2.83, 3.07]	21.0%
Traditional Farming	2.34	0.83	[2.18, 2.50]	35.5%

One-way ANOVA confirmed significant differences in implementation levels across subsectors (F (4,296) = 32.67, p < 0.001, η² = 0.31). Levene’s test indicated heterogeneity of variances (p = 0.037), so Welch’s ANOVA was also conducted, confirming the significant differences (Welch’s F (4,145.28) = 36.14, p < 0.001).

Measurement Model Assessment

Exploratory Factor Analysis (EFA) was conducted on the 24 items measuring AI implementation dimensions. Principal Component Analysis with Varimax rotation yielded a four-factor solution explaining 72.8% of total variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.89, and Bartlett’s Test of Sphericity was significant (χ²=4376.42, p<0.001), confirming the appropriateness of the factor analysis approach. The factor structure aligned precisely with the dimensions identified in the qualitative phase: Predictive Trading Systems, Supply Chain Optimization, Risk Assessment Mechanisms, and Market Intelligence Integration. Factor loadings ranged from 0.64 to 0.89, with all items loading above 0.60 on their respective factors and cross-loadings below 0.30, demonstrating excellent simple structure. Subsequently, Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model. The results confirmed the four-factor structure with excellent fit indices: χ²/df=2.24, RMSEA=0.057 (90% CI: 0.048-0.065), CFI=0.93, TLI=0.92, GFI=0.91, SRMR=0.043.All standardized factor loadings were statistically significant (p<0.001) and exceeded 0.60, supporting convergent validity. Average Variance Extracted (AVE) values ranged from 0.58 to 0.74, exceeding the recommended threshold of 0.50. Composite Reliability (CR) values ranged from 0.86 to 0.93, demonstrating excellent reliability. (Table 5) presents comprehensive reliability and validity metrics.

Table 5. Reliability and Validity of Measurement Constructs

Construct	Cronbach’s Alpha	CR	AVE	MSV	1	2	3	4
1. Predictive Trading Systems	0.91	0.93	0.71	0.42	0.84
2. Supply Chain Optimization	0.89	0.90	0.65	0.39	0.48	0.81
3. Risk Assessment Mechanisms	0.87	0.88	0.63	0.36	0.53	0.42	0.79
4. Market Intelligence Integration	0.88	0.89	0.66	0.42	0.56	0.38	0.51	0.81

The correlations between constructs were all lower than the square root of AVE for each construct, confirming discriminant validity according to the Fornell-Larcker criterion. Additionally, the Heterotrait-Monotrait (HTMT) ratio of correlations was calculated for all construct pairs, with all values below 0.85 (range: 0.41-0.62), providing further evidence of discriminant validity.

Measurement invariance testing across stakeholder groups (institutional investors, company managers, individual traders) demonstrated configural, metric, and scalar invariance, indicating that the measurement model functions equivalently across these groups. The change in CFI between increasingly constrained models was <0.01, supporting measurement invariance.

Common Method Bias Assessment

Given that some data was collected from the same sources, we conducted multiple tests to assess potential common method bias. Harman’s single-factor test revealed that the first unrotated factor accounted for only 28.4% of variance, well below the 50% threshold that would suggest problematic common method bias. Additionally, we employed the common latent factor approach, which indicated minimal shared variance (3.7%) attributable to common method factors. Finally, we utilized a marker variable technique with a theoretically unrelated construct, which showed negligible correlations with our study variables (r = -0.04 to 0.07, all p > 0.05). Collectively, these analyses suggest that common method bias is not a substantial concern in our data.

Structural Model and Hypothesis Testing

The structural model was tested using Structural Equation Modeling (SEM) to examine the relationships between AI implementation dimensions and agricultural stock performance. The model demonstrated excellent fit: χ²/df=2.16, RMSEA=0.055 (90% CI: 0.046-0.063), CFI=0.94, TLI=0.93, GFI=0.92, SRMR=0.041.

All four AI implementation dimensions demonstrated significant positive effects on agricultural stock performance, with Predictive Trading Systems (β=0.41, p<0.001) and Market Intelligence Integration (β=0.37, p<0.001) showing the strongest effects, followed by Risk Assessment Mechanisms (β=0.29, p<0.001) and Supply Chain Optimization (β=0.24, p<0.001). These results provide strong support for hypotheses H1 and H3a, confirming both the overall positive effect of AI implementation and the differential impact of market-facing versus operational applications.

Bootstrap analysis with 5,000 resamples confirmed the robustness of these path coefficients, with all 95% confidence intervals excluding zero. Multi-group SEM analysis revealed no significant differences in path coefficients across stakeholder groups (Δχ² tests, all p > 0.05), suggesting the stability of these relationships across different perspectives.

Multiple Regression Analysis

Multiple regression analysis was conducted to examine the differential impact of AI dimensions on specific stock performance metrics. Three dependent variables were analyzed: Stock Returns, Price Volatility, and Trading Volume. (Table 6) presents the comprehensive regression results.

Table 6. Multiple Regression Results for Stock Performance Metrics

Independent Variables	Stock Returns		Price Volatility		Trading Volume
Independent Variables	β	p	β	p	β	p
Predictive Trading Systems	0.39	<0.001	-0.28	<0.001	0.34	<0.001
Supply Chain Optimization	0.22	0.003	-0.17	0.014	0.11	0.092
Risk Assessment Mechanisms	0.31	<0.001	-0.35	<0.001	0.16	0.021
Market Intelligence Integration	0.36	<0.001	-0.21	0.002	0.29	<0.001
R²	0.42		0.38		0.31
Adjusted R²	0.41		0.37		0.30
F	47.82	<0.001	40.13	<0.001	29.84	<0.001
Durbin-Watson	1.94		2.05		1.98

The regression analysis revealed that all AI dimensions significantly predicted stock returns, with Predictive Trading Systems and Market Intelligence Integration having the strongest impact. For price volatility (where negative coefficients indicate volatility reduction), Risk Assessment Mechanisms had the strongest effect (β=-0.35, p<0.001). Supply Chain Optimization did not significantly predict trading volume (p=0.092). These findings provide support for hypothesis H3b, confirming that risk assessment applications have the strongest impact on volatility reduction.

The Durbin-Watson statistics (all approximately 2.0) indicated no significant autocorrelation in the residuals. VIF values for all predictors were below 3.0 (range: 1.47-2.82), confirming the absence of problematic multicollinearity. The Kolmogorov-Smirnov test confirmed normality of residuals (p>0.05 for all models). White’s test and Breusch-Pagan tests for heteroscedasticity were non-significant (p>0.05), indicating homoscedasticity of residuals.

We also conducted quantile regression analysis to examine whether the effects of AI dimensions varied across different levels of the dependent variables. Results indicated stronger effects of Predictive Trading Systems in the upper quantiles (75th and 90th percentiles) of stock returns (β=0.45 and β=0.51, respectively, p<0.001) compared to lower quantiles (β=0.32 at 25th percentile, p<0.001), suggesting that these systems have particularly strong effects for higher-performing companies.

Testing for Nonlinear Relationships

To examine potential nonlinear relationships between AI implementation and performance outcomes, we conducted polynomial regression analyses by adding quadratic terms for each AI dimension. The results indicated significant quadratic effects for Risk Assessment Mechanisms (β=0.18, p=0.007) and Market Intelligence Integration (β=0.15, p=0.023) in predicting stock returns, suggesting diminishing returns at higher implementation levels. No significant quadratic effects were found for the other dimensions or outcome variables.

Hierarchical Regression Analysis

To further examine the relationships between AI implementation dimensions and financial performance metrics while controlling for organizational characteristics, hierarchical regression analysis was conducted. (Table 7) presents these results.

Table 7. Hierarchical Regression Analysis of AI Implementation on Stock Returns

Variables	Model 1		Model 2		Model 3
Variables	β	p	β	p	β	p
Step 1: Control Variables
Company Size	0.24	<0.001	0.18	0.004	0.17	0.006
Company Age	-0.16	0.009	-0.12	0.024	-0.11	0.035
Previous Year Return	0.31	<0.001	0.22	<0.001	0.21	<0.001
R²	0.22
F	27.86	<0.001
Step 2: AI Dimensions
Predictive Trading Systems			0.35	<0.001	0.34	<0.001
Supply Chain Optimization			0.19	0.006	0.18	0.009
Risk Assessment Mechanisms			0.28	<0.001	0.27	<0.001
Market Intelligence Integration			0.32	<0.001	0.30	<0.001
ΔR²			0.26
F for ΔR²			31.75	<0.001
Step 3: Interaction Terms
AI Implementation × Company Size					0.15	0.013
AI Implementation × Industry Type					0.18	0.004
ΔR²					0.05
F for ΔR²					9.28	<0.001
Total R²					0.53
Adjusted R²					0.51
F					29.47	<0.001

The hierarchical regression analysis demonstrated that AI implementation dimensions explained an additional 26% of variance in stock returns beyond control variables (ΔR²=0.26, p<0.001). The interaction terms in Model 3 showed that the relationship between AI implementation and stock returns was significantly moderated by company size (β=0.15, p=0.013) and industry type (β=0.18, p=0.004), providing support for hypothesis H2b regarding the moderating effect of organizational size.

MANOVA Results for Subsector Differences

Multivariate Analysis of Variance (MANOVA) was performed to examine differences in AI adoption and impact across agricultural subsectors. Significant multivariate differences were found across subsectors (Wilks’ λ=0.78, F=8.93, p<0.001, partial η²=0.12). Box’s M test confirmed equality of covariance matrices (p=0.124), and Levene’s test showed homogeneity of variance for all dependent variables (p>0.05), satisfying MANOVA assumptions. (Table 8) presents the detailed univariate results.

Table 8. Univariate Results for AI Implementation Dimensions by Agricultural Subsector

AI Dimension	Agro-technology	Fertilizer Production	Food Processing	Agricultural Machinery	Traditional Farming	F	p	Partial η²
Predictive Trading Systems	4.12	3.58	3.27	3.09	2.43	18.72	<0.001	0.21
Supply Chain Optimization	3.86	3.63	3.42	3.11	2.32	14.91	<0.001	0.17
Risk Assessment Mechanisms	4.08	3.87	3.39	3.16	2.87	12.37	<0.001	0.14
Market Intelligence Integration	3.97	3.41	3.22	2.98	2.46	15.24	<0.001	0.18

Post-hoc Tukey HSD tests revealed that agro-technology firms had significantly higher levels of AI implementation across all dimensions compared to other subsectors (p<0.05). Traditional farming companies had significantly lower implementation levels than all other subsectors (p<0.01). The effect sizes as measured by partial η² were substantial, ranging from 0.14 to 0.21, indicating strong practical significance. These findings provide robust support for hypothesis H4 regarding subsector differences in AI implementation.

We also conducted Analysis of Covariance (ANCOVA) to determine whether these subsector differences persisted after controlling for potentially confounding variables. After controlling for company size, age, and prior year performance, the subsector differences remained significant across all AI dimensions (all p < 0.001), with only slight reductions in effect sizes (partial η² range: 0.12-0.19).

For non-normally distributed variables, we conducted non-parametric Kruskal-Wallis tests, which confirmed the significant differences across subsectors (all p < 0.001), with post-hoc Dunn’s tests showing the same pattern of differences as the parametric analyses.

Time-Series Analysis of Stock Performance

Time-series analysis was conducted using secondary data from 42 agricultural companies over a three-year period (2017-2020). Companies were classified into high AI implementers (n=14), medium implementers (n=15), and low implementers (n=13) based on their technology investment disclosures and implementation reports.

Statistical analysis of the time-series data revealed that high AI-implementing companies outperformed low-implementing counterparts by an average of 23.7% in annual returns (t=4.82, p<0.001). Additionally, high implementers demonstrated significantly lower price volatility (SD=14.3%) compared to low implementers (SD=22.8%), with F=8.73, p<0.001 in Levene’s test for equality of variances.

The Chow test for structural breaks identified significant shifts in the return patterns of high-implementing companies following major AI implementation milestones (F=12.54, p<0.001), suggesting a causal relationship between implementation and performance improvement.

ARCH and GARCH models were fitted to analyze volatility patterns in stock returns. The GARCH (1,1) model for high AI implementers showed lower persistence in volatility (α+β=0.78) compared to low implementers (α+β=0.92), indicating more stable return patterns. AIC and BIC criteria confirmed better fit of the GARCH models compared to simpler ARCH specifications, as detailed in (Table 9).

Table 9. GARCH Model Parameters for High vs. Low AI Implementers

Parameter	High Implementers		Low Implementers
Parameter	Estimate	p-value	Estimate	p-value
ω (constant)	0.00023	0.018	0.00037	0.009
α (ARCH term)	0.13	<0.001	0.26	<0.001
β (GARCH term)	0.65	<0.001	0.66	<0.001
Persistence (α+β)	0.78	-	0.92	-
Log-likelihood	827.64	-	751.38	-
AIC	-1649.28	-	-1496.76	-
BIC	-1641.65	-	-1489.12	-

Granger causality tests provided evidence that AI implementation temporally preceded improvements in stock performance (F=9.38, p<0.001) and reductions in volatility (F=7.26, p<0.001), while the reverse relationships were not significant (p>0.05). These findings provide strong support for hypothesis H5 regarding the temporal precedence of implementation effects.

To further explore the dynamic relationships between AI implementation and performance metrics, we estimated a Vector Auto regression (VAR) model and analyzed impulse response functions. The results indicated that a one standard deviation shock to AI implementation led to a significant positive response in stock returns that persisted for approximately six months before stabilizing at a new higher level. Conversely, the impulse response function for volatility showed a significant negative response (reduced volatility) that stabilized after approximately four months.

Johansen cointegration tests identified a significant long-term equilibrium relationship between AI implementation levels and stock performance metrics (trace statistic = 29.84, p < 0.01), suggesting that these variables maintain a stable relationship over time despite short-term fluctuations.

Panel Data Analysis

We conducted panel data regression analysis to further examine the relationship between AI implementation and stock performance while accounting for both time and company-specific effects. Both fixed-effects and random-effects models were estimated. The Hausman test indicated that the fixed-effects specification was more appropriate (χ² = 18.73, p = 0.009). The fixed-effects model confirmed the positive relationship between AI implementation and stock returns (β = 0.34, p < 0.001), while controlling for time-invariant company characteristics and common time trends.

We also estimated dynamic panel models using the Arellano-Bond estimator to address potential endogeneity concerns arising from the inclusion of lagged dependent variables. These models continued to show significant positive effects of AI implementation on stock performance (β = 0.29, p < 0.001), providing additional evidence of robustness.

Causal Inference Techniques

To address potential selection bias concerns (i.e., the possibility that better-performing companies are more likely to implement AI rather than AI causing better performance), we employed propensity score matching. Companies were matched on pre-implementation characteristics including size, age, subsector, prior performance, and financial resources. The average treatment effect on the treated (ATT) indicated that AI implementation led to significantly higher stock returns (ATT = 0.18, p = 0.003) and lower volatility (ATT = -0.13, p = 0.007) even after accounting for selection effects.

We also conducted difference-in-differences analysis using the staggered implementation of AI systems across companies as a quasi-experimental design. This analysis confirmed that companies experienced significant improvements in stock performance following AI implementation (β = 0.21, p = 0.002), compared to matched companies that had not yet implemented such systems.

To address potential endogeneity due to omitted variables, we employed instrumental variable regression using geographic proximity to major technology hubs and board technological expertise as instruments for AI implementation. The Wu-Hausman test confirmed the presence of endogeneity (p = 0.031), and the IV regression continued to show a significant positive effect of AI implementation on stock performance (β = 0.33, p < 0.001). The instruments passed tests for relevance (F = 18.92, p < 0.001) and overidentification (Sargan test, p = 0.27).

Moderator Analysis

Hierarchical regression analysis was performed to test potential moderators of the relationship between AI implementation and stock performance. (Table 10) presents the results for organizational digital maturity as a moderator.

Table 10. Hierarchical Regression Results for Moderator Analysis

Variable	Model 1		Model 2		Model 3
Variable	β	p	β	p	β	p
Step 1: Control Variables
Company Size	0.18	0.007	0.12	0.032	0.11	0.037
Company Age	-0.09	0.173	-0.06	0.286	-0.05	0.331
R²	0.05
Step 2: Main Effects
AI Implementation (composite)			0.47	<0.001	0.43	<0.001
Digital Maturity			0.23	<0.001	0.19	0.002
ΔR²			0.34
Step 3: Interaction Effect
AI Implementation × Digital Maturity					0.21	0.001
ΔR²					0.04
Total R²					0.43
F					43.76	<0.001

The analysis confirmed that organizational digital maturity significantly moderated the relationship between AI implementation and stock performance (β=0.21, p=0.001). Simple slope analysis revealed that the positive effect of AI implementation on stock performance was substantially stronger for companies with high digital maturity (+1 SD above mean, β=0.64, p<0.001) compared to those with low digital maturity (-1 SD below mean, β=0.22, p=0.018). These results provide strong support for hypothesis H2a regarding the moderating effect of digital maturity.

Johnson-Neyman technique was used to identify the specific value of digital maturity at which the relationship between AI implementation and stock performance becomes significant. This analysis indicated that the relationship becomes statistically significant when digital maturity exceeds 2.41 on the 5-point scale, which includes approximately 83% of the sample.

Additional moderator analyses were conducted for company size, subsector, and market capitalization. While company size showed a significant moderating effect as previously reported, subsector and market capitalization did not demonstrate significant moderating effects (p>0.05).

Mediation Analysis

To investigate potential mechanisms through which AI implementation affects stock performance, we conducted mediation analysis using the bootstrapping approach with 5,000 resamples. We tested whether the relationship between AI implementation and stock performance was mediated by operational efficiency improvements, analyst forecast accuracy, and investor sentiment.

The results indicated significant indirect effects through all three mediators: operational efficiency improvements (indirect effect = 0.14, 95% CI [0.08, 0.21]), analyst forecast accuracy (indirect effect = 0.09, 95% CI [0.05, 0.14]), and investor sentiment (indirect effect = 0.11, 95% CI [0.06, 0.17]). Together, these mediators accounted for approximately 62% of the total effect of AI implementation on stock performance, suggesting that these mechanisms are important pathways through which AI affects market outcomes.

Discussion

Theoretical Implications

Our findings offer several significant theoretical contributions to understanding the complex relationship between AI implementation and agricultural stock performance in emerging market contexts.

First, we advance theoretical understanding by identifying and empirically validating a multidimensional framework of AI implementation in agricultural financial markets. Previous research has typically focused on either operational aspects of AI in agriculture (Liakos et al., 2018; Ahmadi et al., 2019) or general technological adoption in financial markets (Kumar et al., 2019). Our findings demonstrate that AI’s impact on agricultural stocks operates through four distinct but interconnected mechanisms: Predictive Trading Systems, Supply Chain Optimization, Risk Assessment Mechanisms, and Market Intelligence Integration. This taxonomic framework extends existing conceptualizations and provides a more nuanced understanding of how different AI applications influence market performance through distinct causal pathways (Tavassoli & Naami, 2020).

Second, our results provide a significant empirical challenge to conventional interpretations of the Efficient Market Hypothesis (Fama, 1970) in the specific context of emerging agricultural markets. The substantial performance advantage demonstrated by high AI-implementing companies (23.7% higher annual returns) suggests the presence of exploitable information asymmetries that can be addressed through superior information processing capabilities. This finding aligns with (Grossman & Stiglitz, 1980) theoretical refinements to EMH, which acknowledge that information acquisition and processing advantages can yield above-market returns. Our research extends this theoretical perspective by demonstrating the specific mechanisms through which such advantages manifest in agricultural stock markets, particularly in emerging economy contexts where information environments may be less transparent and efficient.

Third, our findings offer robust empirical support for the Resource-Based View (Barney, 1991) in the context of technological implementation. The differential performance outcomes associated with AI implementation demonstrate that these technologies and related capabilities represent valuable, rare, imperfectly imitable, and non-substitutable resources that confer sustainable competitive advantages. The significant moderating effect of organizational digital maturity further substantiates the complementary assets theory articulated by (Teece, 1986), demonstrating that AI’s value is contingent on supporting organizational capabilities. This finding extends previous theoretical work by illustrating how technological resources and organizational capabilities interact to produce performance advantages in specific sectoral contexts (Raesi et al., 2020).

Fourth, our research contributes to a more nuanced theoretical understanding of how different AI applications optimize distinct aspects of market performance. The differential impact of AI dimensions across performance metrics (returns, volatility, and trading volume) suggests that technological implementations have domain-specific effects rather than generalized performance impacts. Specifically, Risk Assessment Mechanisms demonstrated the strongest effect on volatility reduction (β=-0.35), while Predictive Trading Systems most strongly influenced returns (β=0.39). This pattern of findings extends theoretical models of technology-performance relationships by highlighting the importance of matching specific technological capabilities to desired performance outcomes.

Fifth, our identification of nonlinear relationships between certain AI dimensions and performance outcomes contributes to a more sophisticated understanding of technology implementation effects. The significant quadratic effects for Risk Assessment Mechanisms and Market Intelligence Integration suggest that there may be optimal levels of implementation beyond which additional investments yield diminishing returns. This finding challenges simplistic linear conceptualizations of technology-performance relationships and suggests the need for more nuanced theoretical models that account for implementation intensity thresholds.

Finally, our causal inference analyses provide stronger evidence of the directional relationship between AI implementation and performance than has been previously established in the literature. By employing multiple complementary approaches (Granger causality, propensity score matching, instrumental variables, difference-in-differences), we provide robust evidence that the relationship is not merely correlational but causal in nature. This addresses a significant limitation in previous research and strengthens the theoretical case for AI as a performance driver rather than merely a correlate of successful companies.

These theoretical contributions extend beyond the Iranian context to inform broader understanding of how technological implementations influence market performance in emerging economies characterized by distinctive information environments, regulatory contexts, and market structures.

Practical Implications

Our findings offer substantial practical implications for multiple stakeholder groups in Iran’s agricultural financial ecosystem.

For agricultural company executives, our results demonstrate the strategic importance of developing comprehensive AI implementation strategies that address all four identified dimensions. While operational applications such as Supply Chain Optimization deliver meaningful value, market-facing applications (Predictive Trading Systems and Market Intelligence Integration) demonstrated stronger relationships with stock performance metrics. This suggests that executives should prioritize investments in these areas to maximize market valuation benefits. The hierarchical regression analysis demonstrating that AI dimensions explain an additional 26% of variance in stock returns beyond traditional financial indicators reinforces the substantial potential return on such investments.

The significant moderating effect of organizational digital maturity has particularly important implications for implementation sequencing and resource allocation. Our findings indicate that companies with low digital maturity realized only about one-third the performance benefits from AI implementation compared to high-maturity organizations (β=0.22 vs. β=0.64). This suggests that executives should first invest in foundational digital capabilities before pursuing advanced AI implementations—a finding that aligns with and extends previous research on digital transformation prerequisites in Iranian companies (Rahimi & Vaziri, 2018). The Johnson-Neyman analysis identifying the specific digital maturity threshold (2.41 on a 5-point scale) at which AI benefits become significant provides a concrete benchmark for companies to assess their readiness for AI investment.

Our identification of nonlinear relationships for certain AI dimensions suggests that executives should carefully calibrate implementation intensity. For Risk Assessment Mechanisms and Market Intelligence Integration, there appear to be optimal implementation levels, suggesting that companies should pursue targeted rather than maximal implementation in these domains. Conversely, the linear relationships observed for Predictive Trading Systems and Supply Chain Optimization suggest that continued investment in these areas may yield ongoing returns without diminishing effects.

For investors and portfolio managers, our results provide empirically validated criteria for evaluating agricultural stocks based on their AI implementation patterns. The substantial performance difference between high and low AI implementers (23.7% annualized return differential) suggests that technology adoption represents a meaningful factor that should be incorporated into stock selection and valuation models for this sector. The lower volatility demonstrated by high-implementing firms (SD=14.3% vs. SD=22.8%) further suggests that AI implementation may enhance risk-adjusted returns, an important consideration for portfolio construction. Our mediation analysis identifying operational efficiency, analyst forecast accuracy, and investor sentiment as key mechanisms linking AI to performance provides investors with specific indicators to monitor when evaluating the effectiveness of companies’ AI implementations.

The significant subsector differences in AI implementation and effectiveness provide valuable guidance for sector allocation decisions. Our findings suggest that the agro-technology subsector offers the highest potential for AI-driven performance enhancement, while traditional farming companies may represent opportunities for value creation through targeted technology investments, particularly given their currently low implementation levels.

For policymakers and market regulators, our findings highlight both opportunities and challenges. The positive performance effects of AI implementation suggest that policies supporting technological adoption could enhance overall market efficiency and sector performance. However, the significant subsector differences in implementation levels—with traditional farming companies demonstrating substantially lower adoption rates—raise concerns about technological disparities creating uneven playing fields. Policymakers should consider targeted interventions to support technology transfer and capacity building in underperforming subsectors to promote more equitable development. These recommendations align with and extend previous policy suggestions for Iranian agricultural development (Hemmati & Hosseini, 2019).

The identified digital maturity threshold provides policymakers with a specific target for capacity-building initiatives. Programs designed to help agricultural companies reach at least moderate levels of digital maturity (>2.41 on our 5-point scale) would position them to benefit meaningfully from subsequent AI investments. This finding can help prioritize limited policy resources for maximum impact.

AI-Enhanced Agricultural Stock Performance Framework

Based on our empirical findings, we propose an integrated theoretical framework that explains the mechanisms through which AI enhances agricultural stock performance in emerging market contexts.

The framework incorporates organizational digital maturity as a critical moderating factor that influences the strength of these pathways, with higher maturity enhancing the effectiveness of AI implementations through complementary capabilities. Additional contingency factors include organizational size and industry subsector, which influence implementation effectiveness through resource availability and technological compatibility respectively.

This integrative framework provides a theoretical foundation for understanding how AI technologies transform agricultural financial performance in emerging market contexts, while also offering a practical roadmap for implementation prioritization and performance optimization.

Conclusion

Summary of Findings

This research employed a sequential exploratory mixed-methods approach to investigate the complex relationship between artificial intelligence implementation and financial performance within agricultural sectors listed on the Tehran Stock Exchange. Our comprehensive analysis yielded several key findings:

First, through qualitative investigation involving 24 domain experts, we identified four distinct dimensions of AI implementation in agricultural stocks: Predictive Trading Systems, Supply Chain Optimization, Risk Assessment Mechanisms, and Market Intelligence Integration. Each dimension encompasses multiple specific application areas and influences performance through different causal mechanisms.

Second, our quantitative analysis confirmed that these AI implementation dimensions significantly predict stock performance, with Predictive Trading Systems (β=0.41, p<0.001) and Market Intelligence Integration (β=0.37, p<0.001) demonstrating the strongest effects. These market-facing applications showed stronger performance relationships than operationally-focused implementations, suggesting their priority for strategic investment.

Third, multiple regression analysis revealed differential effects of AI dimensions across performance metrics, with Risk Assessment Mechanisms showing the strongest impact on volatility reduction (β=-0.35, p<0.001) and Predictive Trading Systems most strongly influencing returns (β=0.39, p<0.001). This pattern demonstrates that different AI applications optimize distinct aspects of market performance.

Fourth, MANOVA results documented significant heterogeneity in AI adoption across agricultural subsectors (Wilks’ λ=0.78, p<0.001), with agro-technology firms demonstrating substantially higher implementation levels than traditional farming operations. These subsector differences were substantial, with effect sizes (partial η²) ranging from 0.14 to 0.21.

Fifth, longitudinal analysis of 42 agricultural companies over a three-year period revealed that high AI-implementing organizations outperformed low-implementing counterparts by 23.7% in annualized returns (t=4.82, p<0.001) with significantly reduced volatility. GARCH modeling demonstrated lower volatility persistence in high-implementing firms (α+β=0.78) compared to low-implementing counterparts (α+β=0.92), indicating more stable return patterns.

Sixth, multiple causal inference approaches including Granger causality testing, propensity score matching, instrumental variable regression, and difference-in-differences analysis consistently supported a causal relationship flowing from AI implementation to enhanced stock performance, rather than merely a correlation between these variables.

Seventh, moderation analysis identified organizational digital maturity as a critical contingency factor (β=0.21, p<0.001), with high-maturity firms extracting substantially greater performance benefits from AI implementations. Johnson-Neyman analysis identified a specific digital maturity threshold (2.41 on a 5-point scale) at which AI benefits become statistically significant.

Finally, mediation analysis revealed that the relationship between AI implementation and stock performance is significantly mediated by operational efficiency improvements, analyst forecast accuracy, and investor sentiment, collectively accounting for approximately 62% of the total effect.

Limitations and Boundary Conditions

While this research provides valuable insights, several limitations should be acknowledged. First, our investigation focused exclusively on the Iranian market context, potentially limiting generalizability to other emerging economies with different regulatory environments, market structures, and technological infrastructures. Cross-national comparative studies would enhance the external validity of our findings.

Second, the three-year timeframe of our longitudinal analysis, while substantive, may not capture the full long-term effects of AI implementation, which could evolve over extended periods as technologies mature and diffuse throughout sectors. Longer timeframes would provide additional insights into the sustainability of AI-driven performance advantages.

Third, while we employed multiple data sources and triangulation methods, some measures relied on self-reported implementation levels, which may introduce reporting biases. Future research incorporating more objective implementation metrics would strengthen causal inferences.

Fourth, despite employing multiple causal inference techniques to address endogeneity concerns, the possibility of unobserved confounding variables cannot be entirely eliminated. Although our instrumental variable approach showed promising results, the instruments themselves may not perfectly satisfy the exclusion restriction.

Fifth, our operationalization of AI implementation focuses on the presence and extent of specific AI applications rather than the quality or sophistication of implementation. Future research could develop more nuanced measures that account for implementation quality and technical sophistication.

Finally, our research was conducted before the full impact of the COVID-19 pandemic on agricultural markets could be assessed. The pandemic may have altered implementation trajectories and performance relationships in ways not captured by our analysis.

By incorporating these evidence-based recommendations, stakeholders across Iran’s agricultural stock ecosystem can work toward more effective implementation of AI technologies, ultimately enhancing both market performance and the sector’s contribution to sustainable economic development.

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

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