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Stronger human–AI interaction—notably ethical governance and innovation—is strongly associated with higher leadership effectiveness and organizational sustainability in Colombian firms; however, reliance on cross-sectional self-reports and poor model fit means these large correlations should not be interpreted as causal evidence.

The Association Between Human–AI Interaction and Leadership: A Structural Equation Modeling Analysis in Colombian Organizations
Rodrigo Arturo Zarate-Torres, C. Fabiola Rey-Sarmiento, Julio Cesar Acosta-Prado, Alvaro Moncada-Niño · July 27, 2026 · Technologies
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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A cross-sectional survey of 170 Colombian organizational respondents finds strong positive associations between Human–AI Interaction—especially ethical governance and innovation—and leadership effectiveness and organizational sustainability, but the design and measurement limitations preclude causal claims.

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Artificial Intelligence (AI) has evolved from a tool for automation into a strategic component of organizational decision-making. However, the extent to which the dimensions of Human–AI Interaction are associated with leadership remains underexplored, particularly in emerging economies. This study examines how interaction quality, productivity enhancement, user experience, organizational impact, ethical governance, and innovation are associated with Leadership Effectiveness and Organizational Sustainability in Colombian organizations. We applied Structural Equation Modeling (SEM) to data collected from 170 participants using a purpose-built 30-item instrument designed to measure eight dimensions of the human–AI relationship through a five-point Likert scale. Six dimensions assessed Human–AI Interaction (Interaction Quality, Productivity and Efficiency, User Experience and Acceptance, Organizational Impact, Ethical Governance, and Innovation and Transformation), while two dimensions assessed leadership (Leadership Effectiveness and Organizational Sustainability). Estimation used maximum likelihood (ML/FIML) as the primary method, with robust ML (MLR) and an item-level WLSMV estimator as sensitivity checks. Correlation analysis (Pearson, with Spearman as a robustness check) revealed consistent positive and significant associations among all construct indicators. Confirmatory Factor Analysis (CFA) confirmed convergent validity, with innovation (λ=0.88) emerging as the highest-loading dimension. The structural model demonstrated a significant association between Human–AI Interaction and leadership (two-parcel model: β=0.95, R2=0.91). Under a more conservative six-item leadership specification, the association attenuates to β=0.87 (R2=0.75), which we treat as the substantive estimate. Common-method-variance diagnostics (Harman’s first factor =48.8%; a common latent factor accounting for ≈41% of variance) and an elevated RMSEA (= 0.13) signal the need for expanded measurement models and longitudinal designs to address causality. The findings suggest that ethical governance and innovation-oriented AI interaction are primary correlates of leadership effectiveness in digital organizations operating in emerging economies.

Summary

Main Finding

Human–AI Interaction is strongly and positively associated with Leadership Effectiveness and Organizational Sustainability in Colombian organizations. Ethical governance and innovation-oriented AI interaction are the strongest correlates of leadership outcomes, though cross-sectional, self-reported data and model-fit issues limit causal claims.

Key Points

  • Sample: 170 participants from Colombian organizations; data collected via a purpose-built 30-item instrument on a 5-point Likert scale.
  • Measured constructs:
    • Six Human–AI Interaction dimensions: Interaction Quality; Productivity & Efficiency; User Experience & Acceptance; Organizational Impact; Ethical Governance; Innovation & Transformation.
    • Two leadership dimensions: Leadership Effectiveness; Organizational Sustainability.
  • Correlations: Pearson (and Spearman robustness) show consistent positive, significant associations among all construct indicators.
  • CFA: Confirmed convergent validity. Innovation had the highest factor loading (λ = 0.88).
  • Structural model results:
    • Two-parcel leadership model: β = 0.95, R2 = 0.91.
    • Conservative six-item leadership model (treated as substantive estimate): β = 0.87, R2 = 0.75.
  • Diagnostics & limitations:
    • Common-method variance: Harman’s first factor = 48.8%; common latent factor ≈ 41% of variance — indicates appreciable shared-method bias.
    • Model fit concern: RMSEA = 0.13 (elevated), suggesting misfit and need for richer measurement/modeling.
    • Estimation: primary ML/FIML; robustness checks with MLR and item-level WLSMV.
  • Interpretation: strong associations but not definitive evidence of causality due to cross-sectional design, self-report measures, and model-fit issues.

Data & Methods

  • Instrument: 30 items mapping eight latent dimensions (6 human–AI; 2 leadership), 5-point Likert responses.
  • Sample: n = 170 participants across Colombian organizations (emerging-economy context).
  • Analytic approach:
    • Correlation analyses (Pearson; Spearman robustness).
    • Confirmatory Factor Analysis for construct validation.
    • Structural Equation Modeling (SEM) to estimate the association between Human–AI Interaction and leadership outcomes.
    • Estimators: maximum likelihood (ML/FIML) primary; robust ML (MLR) and WLSMV at item-level as sensitivity checks.
    • Parceling: models estimated both as a two-parcel leadership specification and a six-item leadership specification.
  • Key statistical outputs reported: λ (factor loadings, innovation = 0.88), path coefficients (β), explained variance (R2), Harman’s first-factor percentage, common latent-factor variance share, RMSEA.

Implications for AI Economics

  • Organizational returns to AI investment: Strong association between Human–AI Interaction and leadership suggests that AI that improves governance and innovation capacity may yield high organizational value through improved leadership and sustainability—important for cost-benefit considerations of AI adoption in emerging economies.
  • Human capital and complementary investments: Findings imply that investments in ethical governance, user acceptance, and innovation capabilities (training, processes, governance structures) are likely complements to AI technology to realize leadership and sustainability gains.
  • Policy and regulation: Ethical governance emerges as a primary correlate of leadership outcomes; regulators and policymakers in emerging economies should prioritize governance frameworks that enable trustworthy AI to maximize organizational and social returns.
  • Measurement & evaluation: Economic analyses of AI should avoid relying solely on cross-sectional self-report data. To improve inference on productivity and leadership impacts, use multi-source data (behavioral, objective performance metrics), larger samples, and longitudinal or experimental designs to estimate causal effects and elasticities.
  • Research directions: Estimate heterogeneous returns across sectors, firm sizes, and adoption stages; quantify how improvements in specific AI-interaction dimensions translate into productivity, turnover, and financial performance; model macro-level impacts of leadership-enabled AI adoption on labor markets and growth in emerging economies.
  • Caution for decision-makers: The reported large associations are conditional on cross-sectional, self-reported measurement and imperfect model fit (RMSEA = 0.13; high common-method variance). Treat the β ≈ 0.87 association as a substantive correlate, not proof of causation.

Suggested next steps for researchers and economists: - Conduct longitudinal and multi-source studies linking Human–AI Interaction indicators to objective organizational outcomes. - Use experimental or quasi-experimental designs to identify causal effects of AI governance and innovation interventions on leadership and firm performance. - Translate latent-effect estimates into economic metrics (productivity gains, ROI, wage/productivity elasticities) to inform investment and policy choices.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional, self-reported survey data with substantial common-method variance and elevated model misfit (RMSEA = 0.13) prevent credible causal inference; results show strong associations but do not establish causality. Methods Rigormedium — The authors used appropriate psychometric and modeling tools (CFA, SEM, multiple estimators, robustness checks) and reported diagnostics, but the study is limited by small sample size (n=170) for a multi-factor SEM, parceling choices, high common-method bias, and poor model fit, which together weaken inferential reliability. Samplen = 170 participants from Colombian organizations (emerging-economy context), responding to a purpose-built 30-item instrument on 5-point Likert scales measuring six Human–AI Interaction dimensions and two leadership dimensions. Themeshuman_ai_collab org_design GeneralizabilitySingle-country sample (Colombia) limits transferability to other national or cultural contexts, Relatively small sample (n=170) reduces power and stability of SEM estimates, Non-random / unspecified sampling frame — likely not representative of Colombian firms or sectors, Cross-sectional, self-report measures prone to common-method bias and social desirability, No objective organizational performance measures; leadership and sustainability are self-reported latent constructs, Sector, firm-size, and role heterogeneity not reported/controlled, limiting subgroup generalization

Claims (9)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Human–AI Interaction is strongly and positively associated with Leadership Effectiveness and Organizational Sustainability in Colombian organizations. Organizational Efficiency positive Leadership Effectiveness and Organizational Sustainability
Reading fidelity high
Study strength medium
n=170
0.3
Ethical governance and innovation-oriented AI interaction are the strongest correlates of leadership outcomes. Governance And Regulation positive Leadership Effectiveness and Organizational Sustainability
Reading fidelity high
Study strength medium
n=170
0.3
Pearson correlations, with Spearman robustness checks, showed consistent positive and significant associations among all construct indicators. Organizational Efficiency positive Associations among Human–AI Interaction and leadership construct indicators
Reading fidelity high
Study strength medium
n=170
0.3
The confirmatory factor analysis supported convergent validity, and the Innovation dimension had the highest factor loading at λ = 0.88. Innovation Output positive Construct validity and factor loading of the Innovation dimension
Reading fidelity high
Study strength medium
n=170
λ = 0.88
0.3
In the two-parcel leadership structural model, the estimated association was β = 0.95 and the model explained R2 = 0.91 of the leadership outcome variance. Organizational Efficiency positive Leadership outcome, combining Leadership Effectiveness and Organizational Sustainability in a two-parcel specification
Reading fidelity high
Study strength low
n=170
β = 0.95; R2 = 0.91
0.15
In the conservative six-item leadership model, the estimated association was β = 0.87 and the model explained R2 = 0.75 of the leadership outcome variance. Organizational Efficiency positive Leadership outcome under the six-item leadership specification
Reading fidelity high
Study strength low
n=170
β = 0.87; R2 = 0.75
0.15
The study found appreciable common-method variance: Harman’s first factor accounted for 48.8% of variance and the common latent factor accounted for approximately 41% of variance. Ai Safety And Ethics negative Common-method variance in the survey measurements
Reading fidelity high
Study strength medium
n=170
48.8% of variance; approximately 41% of variance
0.3
The structural model exhibited elevated RMSEA of 0.13, indicating model misfit and a need for richer measurement or modeling. Other negative Structural equation model fit
Reading fidelity high
Study strength medium
n=170
RMSEA = 0.13
0.3
The large estimated associations should not be interpreted as causal effects because the study used cross-sectional, self-reported data and had model-fit concerns. Organizational Efficiency mixed Causal interpretation of the association between Human–AI Interaction and leadership outcomes
Reading fidelity high
Study strength high
n=170
0.5

Notes