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Firms reporting stronger AI-driven analytics also report better managerial decisions and higher organizational performance, and decision quality explains a substantial share of the association — though the cross-sectional survey cannot establish causality.

AI-DRIVEN BUSINESS ANALYTICS FOR IMPROVING MANAGERIAL DECISION-MAKING AND ORGANIZATIONAL PERFORMANCE
Lakshmi Vasanthi Jampani · September 10, 2026 · World Journal of Advanced Research and Reviews
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A cross-sectional survey of 250 professionals finds that stronger AI-driven business analytics is positively associated with managerial decision-making effectiveness and organizational performance, with managerial decision-making partially mediating the relationship.

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The rapid adoption of Artificial Intelligence (AI) and business analytics is transforming organizational decision-making by enabling managers to utilize large volumes of data for timely and informed business decisions. This study examines the role of AI-driven business analytics in improving managerial decision-making and organizational performance. The study proposes an integrated framework in which AI-driven business analytics capability influences organizational performance through enhanced managerial decision-making effectiveness. The framework considers the ability of AI-enabled analytics to provide predictive insights, identify business patterns, support risk assessment, and improve the quality and speed of managerial decisions. A quantitative research approach is proposed, using a structured questionnaire to collect data from managers and executives working in organizations that utilize AI and business analytics. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the proposed relationships and mediation effects. The study is expected to demonstrate that effective utilization of AI-driven analytics can strengthen managerial decision-making and contribute to improved organizational outcomes. The study contributes to the emerging literature on AI-enabled management by linking analytical capabilities, managerial decision-making, and organizational performance within a unified framework. The findings are expected to provide practical guidance for organizations seeking to develop data-driven and AI-enabled decision-making capabilities.

Summary

Main Finding

AI-driven business analytics positively affects organizational performance both directly and indirectly by substantially improving managerial decision-making. In an illustrative PLS-SEM analysis of 250 respondents, AI-enabled analytics strongly predicts decision-making effectiveness (β = 0.717) which in turn predicts organizational performance (β = 0.548); managerial decision-making partially mediates the AI → performance link (indirect β = 0.393, direct β = 0.312, total β = 0.705). The model explains 51.4% of variance in managerial decision-making and 67.2% of variance in organizational performance.

Key Points

  • Research question: How does AI-driven business analytics influence managerial decision-making and, through it, organizational performance?
  • Theoretical frame: Resource-Based View (RBV) — AI and analytics capabilities treated as strategic resources whose value is realized via managerial processes.
  • Hypotheses tested:
    • H1: AI-driven analytics → managerial decision-making (supported; β = 0.717, p < 0.001).
    • H2: Managerial decision-making → organizational performance (supported; β = 0.548, p < 0.001).
    • H3: AI-driven analytics → organizational performance (supported; β = 0.312, p < 0.001).
    • H4: Managerial decision-making mediates AI → performance (partial mediation supported; indirect β = 0.393, p < 0.001).
  • Measurement quality: strong reliability and validity (indicator loadings > 0.80; Cronbach’s α 0.903–0.921; CR 0.928–0.941; AVE 0.721–0.763). Discriminant validity via HTMT: 0.681–0.747 (< 0.85).
  • Sample and context: illustrative cross-sectional survey of 250 managers/analysts/IT professionals with AI/analytics experience (43.2% > 5 years experience); purposive sampling.
  • Main interpretation: AI analytics creates value both directly (e.g., process gains, forecasting) and by enabling better, faster, more confident managerial choices—managerial decision-making is an important conversion mechanism.

Data & Methods

  • Design: Quantitative, cross-sectional survey; purposive sampling targeting professionals with exposure to AI/business analytics.
  • Sample: 250 valid responses; mixed roles (managers, business analysts, IT professionals); demographic and experience breakdowns reported.
  • Constructs and measures:
    • AI-Driven Business Analytics: predictive analytics, data processing, analytical insights, AI-enabled information support (5-point Likert).
    • Managerial Decision-Making: decision quality, speed, analytical information use, decision confidence (5-point Likert).
    • Organizational Performance: operational efficiency, productivity, innovation, overall business performance (5-point Likert).
  • Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS; measurement model (loadings, Cronbach’s α, rho_A, CR, AVE, HTMT) and structural model (path coefficients, R², f², bootstrapping with recommended 10,000 subsamples). Mediation evaluated via direct/indirect/total effects and bootstrapped CIs.
  • Key quantitative outputs:
    • R² (Managerial Decision-Making) = 0.514; R² (Organizational Performance) = 0.672.
    • H1 β = 0.717 (t = 15.284, p < .001, f² = 1.059).
    • H2 β = 0.548 (t = 9.672, p < .001, f² = 0.418).
    • H3 β = 0.312 (t = 5.486, p < .001, f² = 0.136).
    • Indirect (AIBA → MDM → OP) β = 0.393 (t = 8.216, p < .001).
  • Ethics: voluntary participation, confidentiality assured.
  • Limitations noted by author: cross-sectional design, purposive sampling, self-reported performance measures; results presented as illustrative dataset.

Implications for AI Economics

Practical and theoretical implications relevant to economists studying AI adoption, productivity, and firm performance:

  • Mechanism clarity: The paper quantifies managerial decision-making as a key channel through which AI analytics translates into firm-level performance gains. Economic models of AI-driven productivity should explicitly include managerial quality / decision-making effectiveness as a mediator rather than assuming a direct technology→productivity link only.
  • Magnitude guidance: Estimated coefficients (strong effect on manager behavior, moderate direct effect on performance) can help calibrate structural or macro models where AI capability raises firm-level TFP partly via improved managerial choices and partly via direct process improvements.
  • Complementarities and investment priorities: Findings support the complementarity story—returns to AI investments depend on complementary human and organizational capabilities (training, managerial processes, data governance). Policy and firm-level cost–benefit analyses should account for investments in managerial training and organizational change as part of AI adoption costs.
  • Heterogeneity and distributional considerations: Because value is realized through managerial application, benefits of AI may concentrate in firms with stronger managerial capital, potentially increasing between-firm inequality in productivity. Empirical work should test cross-firm heterogeneity (size, industry, managerial skill) and labor-market effects (re-skilling demand).
  • Measurement for empirical work: The constructs and validated scales provide survey instruments for firm-level studies. However, economists seeking causal inference should combine such subjective measures with objective performance indicators (accounting data, output, profitability) and exploit longitudinal or quasi-experimental designs.
  • Policy design: Policies aiming to accelerate AI returns (subsidies, tax incentives) may be more effective if paired with programs that build managerial analytics capability and data governance standards.
  • Research extensions for economics:
    • Use longitudinal or difference-in-differences designs to identify causal impacts of AI analytics on performance via managerial channels.
    • Incorporate costs of developing AI capability and managerial complements to estimate net returns and payback horizons.
    • Model diffusion dynamics where managerial competence influences adoption thresholds and spillovers across industries.
    • Assess labor-market and distributional externalities (skill-biased adoption, shifting wage premiums) as managerial use amplifies or dampens automation effects.

Cautions for interpretation - Cross-sectional, self-reported, purposive sample limits causal claims and generalizability. Results are robust as an illustrative analysis, but economists should treat coefficients as suggestive priors to be tested with objective, longitudinal data and quasi-experimental identification.

Reference Jampani, L. V. (2026). AI-Driven Business Analytics for Improving Managerial Decision-Making and Organizational Performance. World Journal of Advanced Research and Reviews, 31(03), 371–381. DOI: https://doi.org/10.30574/wjarr.2026.31.3.2319

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on a single-source, cross-sectional, purposive online survey with self-reported measures and no causal identification strategy; although statistical metrics (reliability, AVE, HTMT, bootstrapped path estimates) are reported and associations are strong, risks of selection bias, common-method variance, omitted variable bias, and reverse causality substantially weaken causal claims and external validity. Methods Rigormedium — The analysis follows accepted PLS-SEM practice (reporting indicator loadings, reliability, AVE, HTMT, R², bootstrapping), but study design choices reduce rigor: purposive sampling, cross-sectional single-respondent data, no controls or robustness/endogeneity checks, and limited description of questionnaire items and sampling frame. SampleIllustrative online sample of 250 valid responses collected via purposive sampling from professionals (managers/executives 38.4%, business analysts 20.4%, IT professionals 24.8%, other professionals 16.4%) with varying AI/analytics experience (43.2% >5 years); demographic breakdown by age and gender provided; geographic location and firm-size/sector sampling frame not specified. Themeshuman_ai_collab productivity org_design IdentificationCross-sectional survey using purposive (non-probability) sampling and PLS-SEM to estimate associations and mediation; identification relies on theoretical assumptions and observed covariation (no experimental or quasi-experimental strategy, no exogenous variation or instrumental variables). GeneralizabilityNon-probability purposive sampling via professional networks limits representativeness, Single-source, self-reported measures (managerial perceptions) create risk of common-method bias, Cross-sectional design prevents credible causal inference (reverse causality/omitted confounders), Geographic and industry coverage not specified, limiting transferability to other contexts or national settings, Sample size modest and may not capture heterogeneity by firm size, sector, or country

Claims (7)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI-driven business analytics has a positive and significant association with managerial decision-making effectiveness. Decision Quality positive Managerial decision-making effectiveness, measured through decision quality, decision speed, analytical information use, and decision confidence.
Reading fidelity high
Study strength low
n=250
β = 0.717
0.15
AI-driven business analytics explains 51.4% of the variance in managerial decision-making. Decision Quality positive Variance in managerial decision-making effectiveness.
Reading fidelity high
Study strength low
n=250
R² = 0.514 (51.4% of variance)
0.15
Managerial decision-making effectiveness has a positive and significant association with organizational performance. Organizational Efficiency positive Organizational performance, assessed through operational efficiency, productivity, innovation, and overall business performance.
Reading fidelity high
Study strength low
n=250
β = 0.548; f² = 0.418
0.15
AI-driven business analytics has a positive and significant direct association with organizational performance. Organizational Efficiency positive Organizational performance, including operational efficiency, productivity, innovation, and overall business performance.
Reading fidelity high
Study strength low
n=250
β = 0.312; f² = 0.136
0.15
AI-driven business analytics and managerial decision-making jointly explain 67.2% of the variance in organizational performance. Organizational Efficiency positive Variance in organizational performance.
Reading fidelity high
Study strength low
n=250
R² = 0.672 (67.2% of variance)
0.15
Managerial decision-making partially mediates the relationship between AI-driven business analytics and organizational performance. Organizational Efficiency positive Organizational performance through the indirect pathway of managerial decision-making.
Reading fidelity high
Study strength low
n=250
Indirect effect β = 0.393; direct effect β = 0.312
0.15
The total association between AI-driven business analytics and organizational performance is positive and significant. Organizational Efficiency positive Overall organizational performance associated with AI-driven business analytics through direct and managerial-decision-mediated pathways.
Reading fidelity high
Study strength low
n=250
Total effect β = 0.705
0.15

Notes