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View corpus contextA survey of 120 Indonesian finance leaders finds human-centered AI tools are associated with improved financial decisions and stronger sustainability outcomes. The evidence is correlational—based on self-reports and cross-sectional PLS-SEM—so causality remains unproven.
Human Centered Artificial Intelligence Adoption and Financial Decision Making Quality for Sustainable Corporate Performance
openalex
correlational
low evidence
7/10 relevance
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In a survey of 120 Indonesian finance executives, higher reported adoption of human-centered AI in financial systems is associated with better financial decision-making quality, which in turn is linked to higher sustainable corporate performance.
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The increasing adoption of artificial intelligence (AI) in financial management requires a human-centered approach to enhance decision quality and ensure sustainable corporate performance. This study examines the effect of Human-Centered Artificial Intelligence Adoption on Financial Decision Making Quality and its implications for Sustainable Corporate Performance. A quantitative cross-sectional survey was conducted involving 120 financial managers and executives from Indonesian companies using AI-based financial systems. Data were collected through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Human-Centered AI Adoption significantly improves Financial Decision Making Quality, which subsequently enhances Sustainable Corporate Performance. These findings highlight the strategic role of human-centered AI in supporting high-quality financial decisions and promoting long-term organizational sustainability.
Summary
Main Finding
Human-centered AI adoption in corporate financial systems significantly improves Financial Decision Making Quality, and this improved decision quality in turn enhances Sustainable Corporate Performance.
Key Points
- Independent variable: Human-Centered AI Adoption (use of AI designed around human needs, oversight, and collaboration).
- Mediator/outcome pathway: Adoption → higher Financial Decision Making Quality → greater Sustainable Corporate Performance.
- Empirical context: 120 financial managers and executives at Indonesian firms using AI-based financial systems.
- Statistical evidence: Results reported as significant using Partial Least Squares Structural Equation Modeling (PLS-SEM).
- Practical emphasis: Human-centered design is positioned as a strategic enabler for higher-quality financial decisions and long-term organizational sustainability.
Data & Methods
- Design: Quantitative, cross-sectional survey.
- Sample: 120 financial managers and executives from Indonesian companies that use AI-enabled financial systems.
- Data collection: Structured questionnaires (self-reported measures of AI adoption, decision quality, and sustainability-related performance).
- Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) to test relationships and indirect effects.
- Methodological notes: Cross-sectional, survey-based design limits causal inference and may be subject to common-method bias and limited generalizability beyond the sampled firms/country.
Implications for AI Economics
- Theoretical: Provides empirical support for human-AI complementarity in firm decision processes; human-centered design can be modeled as an input that raises the productivity (quality) of managerial decision-making and thereby firm-level sustainable performance.
- Firm strategy: Managers should prioritize AI systems that augment human judgment (transparency, explainability, usability, and human oversight) to translate AI adoption into measurable sustainability gains.
- Policy/regulation: Regulators and standards bodies can encourage or require human-centered features in AI deployments (accountability, explainability) to improve financial governance and long-term firm stability.
- Measurement and valuation: Corporate valuation models and productivity analyses should account for qualitative improvements in decision quality from human-centered AI, not just cost or automation gains.
- Research directions: Test causal mechanisms with longitudinal or experimental designs; expand samples across countries and industries; link self-reported decision quality to objective financial and sustainability metrics; quantify effect sizes for incorporation into economic models of firm performance.
Assessment
Paper Typecorrelational
Evidence Strengthlow — Findings rely on a small (N=120) cross-sectional convenience sample with self-reported measures and PLS-SEM associations; common-method bias, selection into AI use, and reverse causality cannot be ruled out, so causal claims are weak.
Methods Rigorlow — PLS-SEM is appropriate for modeling latent constructs in small samples, but the design lacks longitudinal or experimental identification, uses subjective self-reports, and appears not to address key threats (endogeneity, omitted variables, selection); measurement and robustness details are not provided.
SampleCross-sectional structured questionnaire of 120 financial managers and executives at Indonesian firms that use AI-enabled financial systems; self-reported measures of human-centered AI adoption, financial decision-making quality, and sustainable corporate performance.
Themeshuman_ai_collab org_design
IdentificationCross-sectional survey associations estimated via Partial Least Squares Structural Equation Modeling (PLS-SEM) testing direct and indirect (mediation) paths between self-reported human-centered AI adoption, financial decision-making quality, and sustainable corporate performance; no exogenous variation, randomization, or longitudinal identification is used.
GeneralizabilitySingle-country sample (Indonesia) limits applicability to other institutional contexts, Small sample size (N=120) restricts representativeness and statistical power, Sample restricted to firms already using AI-enabled financial systems (selection bias), Self-reported outcomes susceptible to common-method and social-desirability bias, Cross-sectional design prevents causal generalization over time, Industry and firm-size heterogeneity not specified, limiting sectoral generalizability
Claims (4)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| Human-Centered AI Adoption significantly improves Financial Decision Making Quality. Decision Quality | positive | Financial Decision Making Quality |
Reading fidelity
high
Study strength
medium
|
n=120
|
| Higher Financial Decision Making Quality enhances Sustainable Corporate Performance. Firm Productivity | positive | Sustainable Corporate Performance |
Reading fidelity
high
Study strength
medium
|
n=120
|
| Human-Centered AI Adoption has a positive indirect relationship with Sustainable Corporate Performance through Financial Decision Making Quality. Firm Productivity | positive | Sustainable Corporate Performance via Financial Decision Making Quality |
Reading fidelity
high
Study strength
medium
|
n=120
|
| The study's cross-sectional, self-reported survey design limits causal inference and may be subject to common-method bias and limited generalizability beyond the sampled firms and Indonesia. Other | negative | Causal interpretability and external generalizability of the reported relationships |
Reading fidelity
high
Study strength
high
|
n=120
|