AI-based accounting and better financial reporting lift SMEs' cost information quality and speed managerial decisions; but gains depend on data quality and managerial interpretation rather than technology alone.

AI-Based Accounting Systems, Financial Reporting Quality, and Decision-Making Efficiency in Thai SMEs

Patcharavadee Thongprim, Phichayapha Tulacharatkul, Titaporn Sincharoonsak · December 21, 2025 · Journal of Cultural Analysis and Social Change

openalex correlational low evidence 7/10 relevance Full text usable extracted full text DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation: August 24, 2026

Patcharavadee Thongprim provider ID
Phichayapha Tulacharatkul, Titaporn Sincharoonsak provider ID

Semantic Scholar

Latest observation: August 24, 2026

Patcharavadee Thongprim provider ID
Phichayapha Tulacharatkul, Titaporn Sincharoonsak provider ID

In a survey of Thai SMEs, adoption of AI-based accounting systems and higher financial reporting quality are positively associated with cost information quality, which in turn mediates improvements in managerial decision-making efficiency.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

2 cumulative citations

OpenAlex · Observed August 24, 2026

View corpus context

1 cumulative citations

Semantic Scholar · Observed August 24, 2026

View corpus context

In the digital economy, artificial intelligence (AI) has emerged as a transformative force in the accounting profession, reshaping how organizations collect, process, and analyze financial data. This study investigates the influence of AI-based accounting systems (AIA) and financial reporting quality (FRQ) on decision-making efficiency (DME), with cost information quality (CIQ) serving as a mediating variable, among small and medium-sized enterprises (SMEs) in Thailand. Anchored in the Technology–Organization–Environment (TOE) Framework, Innovation Diffusion Theory (IDT), Decision Usefulness Theory (DUT), and Resource-Based View (RBV), the study employs a mixed-method research design combining qualitative and quantitative approaches. Qualitative data were obtained from in-depth interviews with 15 SME accounting managers, revealing that AI integration enhances data accuracy, automates repetitive accounting processes, and accelerates reporting timelines. Quantitative data were collected through 400 structured questionnaires; of which 372 were valid responses analyzed using Structural Equation Modeling (SEM) with AMOS version 24.0. The results demonstrate that both FRQ and AIA have significant positive effects on CIQ and DME. Moreover, CIQ exhibits a strong mediating effect, reinforcing that reliable cost information serves as a critical mechanism linking technology adoption and information quality to managerial decision performance. The findings suggest that SMEs with higher levels of AI adoption and superior reporting quality can achieve improved cost accuracy, faster decision cycles, and stronger managerial confidence. This underscores that technological advancement alone is insufficient unless supported by high-quality financial data and skilled managerial interpretation. The study contributes theoretically by integrating technological and informational perspectives into a unified framework for decision efficiency and provides practical insights for SME managers, policymakers, and technology developers seeking to enhance accounting digitalization in Thailand. Overall, this research confirms that the synergy between AI-based accounting systems and financial reporting quality significantly enhances decision-making efficiency through the improvement of cost information quality, thereby strengthening the competitive capability and sustainability of Thai SMEs in the digital era.

Summary

Main Finding

AI-based accounting systems (AIA) and high financial reporting quality (FRQ) both significantly improve decision-making efficiency (DME) in Thai SMEs, primarily by raising cost information quality (CIQ). CIQ is a strong mediator: AIA and FRQ increase CIQ, and improved CIQ in turn substantially increases DME. All hypothesized direct effects were supported.

Key Points

Study design: mixed-methods — 15 in-depth interviews + a survey of 400 SMEs (372 valid responses).
Main quantitative results (SEM, AMOS 24): model fit acceptable (χ²/df ≈ 2.0; CFI ≈ 0.95; TLI ≈ 0.94; RMSEA ≈ 0.05).
Path estimates (standardized):
- FRQ → CIQ: 0.42 (t ≈ 7.16), supported.
- AIA → CIQ: 0.38 (t ≈ 6.75), supported.
- CIQ → DME: 0.54 (t ≈ 8.91), supported.
- FRQ → DME (direct): 0.25 (t ≈ 4.12), supported.
- AIA → DME (direct): 0.19 (t ≈ 3.68), supported.
- Indirect (via CIQ) FRQ/AIA → DME: indirect effect ≈ 0.29; Sobel z = 5.47 (significant).
Qualitative themes: automation reduces errors and closing time; real-time dashboards improve timeliness; integration challenges (legacy systems, skills, upfront cost); improved managerial confidence for pricing, forecasting, cash-flow.
Measurement reliability: Cronbach’s α > 0.85 for all constructs; means indicate generally high FRQ, AIA, CIQ, and DME among respondents.

Data & Methods

Theoretical grounding: TOE + Innovation Diffusion Theory (technology adoption), Decision Usefulness Theory (information value), Management Accounting Theory (role of cost data), and Resource-Based View (information systems as strategic resources).
Qualitative: thematic analysis of 15 accounting managers/finance officers in manufacturing and services.
Quantitative:
- Sampling: purposive sample of SME accounting/finance staff; targeted n=400; valid n=372 (93% response rate).
- Instrument: structured questionnaire (5-point Likert) covering FRQ, AIA, CIQ, DME; content-validated and pilot-tested.
- Analysis: descriptive stats, reliability (Cronbach’s α), CFA, correlation, and SEM (AMOS 24). Fit indices and convergent validity reported.
Context: Thai SMEs across manufacturing and services; ~67% partial AI adoption, 33% full integration.

Implications for AI Economics

Complementarities matter: Returns to AI adoption depend strongly on data and reporting quality. Investments in AI without concurrent improvements in financial reporting and managerial capacity yield weaker gains.
Productivity and decision value: AI-enabled automation and real-time analytics raise the quality and timeliness of cost information, which translates into measurable gains in managerial decision efficiency (pricing, inventory, forecasting). This supports models where AI raises firm-level productivity through informational improvements rather than automation alone.
Adoption barriers and heterogeneity: Upfront costs, legacy-system integration, and skills shortages are short- to medium-term frictions. These create heterogeneity in adoption and benefits across SMEs, implying uneven productivity gains that could widen within-sector performance dispersion.
Policy and public goods role: Targeted subsidies, training programs, and standards for financial reporting/data interoperability can increase the social return on AI investments by lowering adoption costs and improving data quality externalities.
Labor and organizational implications: AI reduces time on repetitive accounting tasks and increases managerial reliance on analytics; the complementarity implies reallocation of labor toward interpretation, control, and strategic tasks rather than direct displacement alone.
Measurement and research: Economic evaluations of AI should incorporate mediating information-quality channels (e.g., CIQ) when estimating returns to adoption. Future empirical work should quantify productivity/product-market outcomes (profits, growth) and investigate thresholds of data/reporting quality required for positive ROI.
Market structure and competition: As CIQ amplifies the value of AI, firms with superior reporting systems may achieve persistent competitive advantages, suggesting potential for increasing returns and concentration unless smaller firms get support to upgrade complementary assets.

Optional brief recommendation (for policymakers / SME managers): - Prioritize integrated investments: pair AI tooling with data governance and staff training. - Promote interoperability and reporting standards to reduce integration friction. - Provide targeted support (grants, technical assistance) for SMEs to capture the AI–CIQ–DME benefits.

Reference: Thongprim, P., Tulacharatkul, P., & Sincharoonsak, T. (2025). AI-Based Accounting Systems, Financial Reporting Quality, and Decision-Making Efficiency in Thai SMEs. Journal of Cultural Analysis and Social Change, 10(4), 3326–3334.

Assessment

Paper Typecorrelational Evidence Strengthlow — The study establishes associations using cross-sectional survey data and SEM but lacks experimental or quasi-experimental sources of exogenous variation, making causal claims vulnerable to reverse causality, omitted variable bias, and common-method bias despite theoretical framing and qualitative support. Methods Rigormedium — Strengths include a mixed-methods approach, a reasonably sized survey sample (n=372), use of SEM for mediation testing, and grounding in established theories; limitations include cross-sectional self-reported data, unclear sampling frame/representativeness, potential measurement and common-method biases, and no robust identification strategy for causality. SampleQualitative: 15 in-depth interviews with accounting managers of Thai SMEs; Quantitative: 400 structured questionnaires distributed to SMEs in Thailand with 372 valid responses used for SEM analysis (AMOS v24.0); unit of analysis is SMEs/managers; sectoral coverage, sampling frame, and timing not specified. Themesproductivity adoption human_ai_collab IdentificationCross-sectional mixed-methods design: qualitative interviews (n=15) for contextualization and a cross-sectional survey of SMEs (372 valid responses) analyzed with Structural Equation Modeling (SEM) to estimate direct and mediated associations between AI-based accounting adoption (AIA), financial reporting quality (FRQ), cost information quality (CIQ), and decision-making efficiency (DME); identification relies on theoretical model assumptions and statistical mediation (SEM) rather than exogenous variation, instruments, or longitudinal/panel identification. GeneralizabilityLimited to SMEs in Thailand — may not generalize to large firms or other countries, Cross-sectional, self-reported survey limits temporal and causal generalizability, Potential sampling or response bias (sampling frame not fully described), Industry heterogeneity not detailed — results may vary by sector or firm complexity, Findings pertain to accounting/decision contexts and may not extend to other firm functions

Claims (15)

Claim	Direction	Outcome	Confidence & Evidence	Details
Qualitative interviews with SME accounting managers revealed that AI integration enhances data accuracy. Output Quality	positive	data accuracy	Reading fidelity high Study strength medium	n=15 0.3
Qualitative interviews indicated AI automates repetitive accounting processes in SMEs. Organizational Efficiency	positive	automation of repetitive accounting processes	Reading fidelity high Study strength medium	n=15 0.3
Qualitative evidence showed AI integration accelerates reporting timelines for SMEs. Task Completion Time	positive	reporting timelines / speed of reporting	Reading fidelity high Study strength medium	n=15 0.3
The study collected 400 structured questionnaires, of which 372 valid responses were analyzed using Structural Equation Modeling (SEM) with AMOS version 24.0. Other	null_result	sample size and analysis method	Reading fidelity high Study strength high	n=372 0.5
Financial reporting quality (FRQ) has a significant positive effect on cost information quality (CIQ). Output Quality	positive	cost information quality (CIQ)	Reading fidelity high Study strength medium	n=372 0.3
Financial reporting quality (FRQ) has a significant positive effect on decision-making efficiency (DME). Decision Quality	positive	decision-making efficiency (DME)	Reading fidelity high Study strength medium	n=372 0.3
AI-based accounting systems (AIA) have a significant positive effect on cost information quality (CIQ). Output Quality	positive	cost information quality (CIQ)	Reading fidelity high Study strength medium	n=372 0.3
AI-based accounting systems (AIA) have a significant positive effect on decision-making efficiency (DME). Decision Quality	positive	decision-making efficiency (DME)	Reading fidelity high Study strength medium	n=372 0.3
Cost information quality (CIQ) exhibits a strong mediating effect, linking financial reporting quality (FRQ) to decision-making efficiency (DME). Decision Quality	positive	mediating effect of CIQ on FRQ -> DME	Reading fidelity high Study strength medium	n=372 0.3
Cost information quality (CIQ) exhibits a strong mediating effect, linking AI-based accounting systems (AIA) to decision-making efficiency (DME). Decision Quality	positive	mediating effect of CIQ on AIA -> DME	Reading fidelity high Study strength medium	n=372 0.3
SMEs with higher levels of AI adoption can achieve improved cost accuracy. Output Quality	positive	cost accuracy	Reading fidelity medium Study strength medium	n=372 0.18
SMEs with higher AI adoption and superior financial reporting quality achieve faster decision cycles. Task Completion Time	positive	speed of decision cycles	Reading fidelity medium Study strength medium	n=372 0.18
SMEs with higher AI adoption and superior reporting quality have stronger managerial confidence in decisions. Worker Satisfaction	positive	managerial confidence	Reading fidelity medium Study strength medium	n=372 0.18
Technological advancement alone is insufficient to improve managerial decision performance unless supported by high-quality financial data and skilled managerial interpretation. Decision Quality	mixed	conditional effect of technology on decision performance (dependent on data quality and skills)	Reading fidelity high Study strength medium	n=372 0.3
The study contributes theoretically by integrating technological (AIA) and informational (FRQ/CIQ) perspectives into a unified framework for decision efficiency in SMEs. Other	positive	theoretical integration / contribution	Reading fidelity high Study strength speculative	not reported 0.05