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Managerial-accounting AI adoption in Jordanian industrial firms is linked to better decision-making and higher reported financial performance, driven by perceived usefulness and ease of use; however, the evidence is observational and relies on self-reported outcomes.

Artificial intelligence acceptance in managerial accounting and its strategic impact on financial performance: Evidence from industrial firms in emerging markets
Huthaifa Al-Hazaima, Mohammad Barakat, Mahmoud Mahmoud, Suhaib Bani Kinana · July 29, 2026 · Corporate & Business Strategy Review
semantic_scholar correlational low evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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In a cross-sectional survey of managerial accountants at Jordanian listed industrial firms, perceived ease of use and usefulness of AI predict behavioral intention and actual use, and reported actual use is associated with better decision quality and higher reported financial performance.

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The rapid integration of artificial intelligence (AI) into organizational processes has fundamentally altered the landscape of managerial accounting, yet empirical evidence on its behavioral adoption and financial consequences in emerging industrial markets remains limited (Vărzaru, 2022; Secinaro et al., 2024). This study examines AI acceptance in managerial accounting and assesses its strategic impact on the financial performance (FP) of industrial firms listed on the Amman Stock Exchange (ASE) in Jordan. The technology acceptance model (TAM) serves as the theoretical lens through which perceived usefulness (PU), perceived ease of use (PEU), behavioral intention (BI), and actual use (AU) are examined. A quantitative research design was adopted, with data collected from 228 managerial accountants across listed industrial firms. Partial least squares structural equation modeling (PLS-SEM) was employed to test the hypothesized relationships. The results confirm that PEU and PU positively influence BI, which in turn drives AU of AI systems. Furthermore, the AU of AI significantly enhances decision-making (DM) quality, which subsequently improves FP. These findings depict AI as a strategic enabler in managerial accounting, with important implications for organizations in emerging markets seeking to leverage AI use for sustainable competitive advantage.

Summary

Main Finding

The study finds that in industrial firms listed on the Amman Stock Exchange, perceived ease of use (PEU) and perceived usefulness (PU) of AI drive behavioral intention (BI) to adopt AI; BI leads to actual use (AU); AU improves decision-making (DM) quality; and improved DM translates into better financial performance (FP). AI thus functions as a strategic enabler in managerial accounting for these emerging-market firms.

Key Points

  • Theoretical lens: Technology Acceptance Model (TAM) — PU, PEU, BI, AU.
  • Behavioral pathway confirmed: PEU → PU → BI → AU.
  • Performance pathway confirmed: AU → better DM → higher FP.
  • Sample: 228 managerial accountants from industrial firms listed on the Amman Stock Exchange (Jordan).
  • Method: Quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM).
  • Contribution: Provides empirical evidence linking AI acceptance in managerial accounting to firm financial outcomes in an emerging-market context.
  • Literature gap addressed: Extends limited empirical work on behavioral adoption and financial consequences of AI in emerging industrial markets (citing Vărzaru, 2022; Secinaro et al., 2024).

Data & Methods

  • Population/sample: Managerial accountants employed by ASE-listed industrial firms; N = 228 survey responses.
  • Measures: TAM constructs (perceived usefulness, perceived ease of use, behavioral intention, actual use), decision-making quality, and financial performance (self-reported/firm-level proxy not detailed in summary).
  • Analytical approach: Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypothesized direct and mediated relationships.
  • Inferential outcomes: Statistically significant positive paths along the TAM adoption chain and from AU → DM → FP (exact effect sizes and p-values not provided in the summary).

Implications for AI Economics

  • Adoption-to-productivity linkage: Empirical support that managerial-accounting AI adoption can improve firm-level financial outcomes through enhanced decision quality — relevant for models linking technology adoption to productivity and profitability in emerging markets.
  • Policy and investment: Findings suggest policymakers and investors in emerging economies should support AI diffusion (training, infrastructure, digital skills) in managerial functions to realize financial gains.
  • Managerial strategy: Firms should prioritize both perceived usefulness (show clear business value) and ease of use (training, user-friendly interfaces) to raise BI and actual uptake of AI tools.
  • Research directions: Need for longitudinal designs to establish causality, use of objective financial metrics, exploration of firm- and market-level moderators (firm size, governance, competition), and sectoral comparisons to generalize results across emerging-market contexts.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on a single cross-sectional self-report survey (N=228) with PLS-SEM associations; no temporal ordering, randomization, or plausibly exogenous variation to establish causality, and financial performance appears to be self-reported or a proxy, increasing risk of common-method bias and endogeneity. Methods Rigormedium — Use of established TAM constructs and PLS-SEM is appropriate for latent-variable modeling and exploratory mediation testing, and sample size is modestly adequate, but the design lacks longitudinal or exogenous variation, objective outcome measures, and clear controls for confounders and common-method bias. SampleCross-sectional survey of 228 managerial accountants employed by industrial firms listed on the Amman Stock Exchange (Jordan); measures include TAM constructs (perceived usefulness, perceived ease of use, behavioral intention, actual use), decision-making quality, and financial performance (summary does not specify whether financial performance is objective firm-level data or self-reported proxy). Themesadoption productivity human_ai_collab IdentificationCross-sectional survey analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) to test TAM-derived mediation paths (PEU → PU → BI → AU and AU → DM → FP); no experimental or quasi-experimental identification, no instruments, and causal claims rely on theoretical ordering rather than exogenous variation. GeneralizabilitySingle-country study (Jordan) — may not generalize to other national contexts, Only industrial firms listed on Amman Stock Exchange — excludes unlisted, smaller, and non-industrial firms, Sample limited to managerial accountants — findings may not apply to other occupational groups or broader firm functions, Cross-sectional, self-reported measures — susceptible to common-method bias and reverse causality, Modest sample size (N=228) limits ability to examine heterogeneous effects (e.g., by firm size or governance)

Claims (5)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Perceived ease of use (PEU) and perceived usefulness (PU) of AI positively influence managerial accountants' behavioral intention (BI) to adopt AI. Adoption Rate positive Behavioral intention to adopt AI
Reading fidelity high
Study strength medium
n=228
0.3
Behavioral intention to adopt AI positively predicts actual AI use among managerial accountants in ASE-listed industrial firms. Adoption Rate positive Actual use of AI
Reading fidelity high
Study strength medium
n=228
0.3
Actual AI use is positively associated with the quality of managerial decision-making. Decision Quality positive Decision-making quality
Reading fidelity high
Study strength medium
n=228
0.3
Higher decision-making quality is positively associated with better financial performance in the industrial firms studied. Firm Productivity positive Firm financial performance
Reading fidelity high
Study strength low
n=228
0.15
The study reports an indirect performance pathway in which actual AI use improves financial performance through improved decision-making quality. Firm Productivity positive Firm financial performance mediated by decision-making quality
Reading fidelity high
Study strength low
n=228
0.15

Notes