AI-related competitive pressure correlates with illustrators' earnings on a Twitter artist community but accounts for just 7% of income differences; reputation, differentiation and adaptability remain the dominant drivers of financial sustainability.

Main Finding

The study finds that perceived AI-driven competitive pressure has a statistically significant but modest effect on illustrators' revenue performance on platform X: regression coefficient b = 0.330 (p < 0.05), with AI explaining R² = 7.4% of variance in self-reported revenue performance. The authors interpret AI as a measurable financial risk factor that raises price pressure, market transparency, and revenue volatility while noting most income variation remains explained by other strategic factors (e.g., product differentiation, reputation, adaptability).

Key Points

• Research question: Does development/penetration of generative AI (operationalized as perceived competitive pressure) affect illustrators’ revenue performance on platform X (Twitter)?
• Sample: 385 illustrators from the “Artist’s Base” community on X; selected via simple random sampling (Slovin’s formula, 5% margin).
• Demographics: predominantly female (89.9%), skewed young (83.6% under 26), most had 1–10 years tenure; 100% reported AI awareness.
• Measurement:
  • Independent variable (AI): 18 Likert items measuring perceived competitive pressure, client preference shifts, price transparency attributable to generative AI.
  • Dependent variable (Revenue Performance): 18 Likert items on perceived income level, income stability, and revenue sustainability.
• Main quantitative result: simple linear regression Y = α + β·AI + ε produced β = 0.330 (p < 0.05), R² = 0.074.
• Interpretation: AI increases financial risk for illustrators (downward price pressure, greater volatility), but effect size is modest — most revenue differences are due to other factors.
• Limitations noted by authors (and implicit): single-path model (AI only), reliance on self-reported/perceptual measures, cross-sectional design, single platform/community in an Indonesian context.

Data & Methods

• Design: Quantitative, explanatory, cross‑sectional survey.
• Population: ~10,000 active Artist’s Base accounts on platform X → sample n = 385.
• Sampling: Simple random sampling using Slovin’s formula.
• Instrument:
  • Structured online questionnaire using 4-point Likert scale (1–4).
  • Items adapted from prior work and vetted by two experts.
  • Non‑participant observation of platform activity for contextualization.
• Psychometrics and pretests:
  • Validity checked via Pearson product‑moment correlations (r-table = 0.100 for n = 385).
  • Reliability via Cronbach’s alpha (threshold ≥ 0.60).
  • Normality of residuals checked with Kolmogorov–Smirnov (p > 0.05).
• Analysis:
  • IBM SPSS used.
  • Hypothesis test: simple linear regression with t-test for coefficient significance at 5%.
  • Outcome: AI → Revenue Performance: β = 0.330, p < 0.05, R² = 7.4%.

Implications for AI Economics

• Conceptual: The paper reframes AI not only as a technical innovation but as a quantifiable financial determinant and external risk factor that alters market supply, pricing, and revenue volatility in platformized creative labor markets.
• Empirical implications:
  • Small but significant R² suggests heterogeneity in AI effects — AI increases competitive pressure but other variables (skill, brand/reputation, AI complementarity, client segments) explain most income variation.
  • Perception-based measures capture market sentiment; objective-revenue analyses are still needed to quantify real income effects.
• Policy and practice:
  • For policy: consider income‑smoothing mechanisms, training/subsidies for AI upskilling, platform rules on disclosure/labeling of AI-generated content, and protections for creative freelancers.
  • For platforms and creators: invest in differentiation, build digital reputation, adopt AI as complement (hybrid workflows), and use scenario-based revenue forecasting that includes AI-driven supply shocks.
• Research directions for AI economics:
  • Use multi-variable and longitudinal designs to separate immediate competitive effects from adaptation and complementarities over time.
  • Link perceived AI pressure to objective earnings data and platform-level metrics (order volume, price changes, supply growth).
  • Investigate heterogeneity (skill level, AI-tool adoption, genre/complexity of work) and cross‑platform comparisons.
  • Model AI as a supply-shock increasing effective supply elasticity and price transparency, and study effects on price dispersion and income variance.
• Accounting implications:
  • Firms and micro-entrepreneurs should incorporate AI-related risk into revenue forecasting, sensitivity analyses, and scenario planning; accountants should treat AI exposure as a factor in financial risk disclosures for platform-based creative activities.

Source: Niu, F. A. L., Hadis, S., Jan, R. H., & Hasan, J. (2026). The influence of artificial intelligence on revenue performance: Evidence from platform-based illustrators. International Journal of Accounting and Finance in Asia Pacific, 9(2), 338–356. DOI: https://doi.org/10.32535/ijafap.v9i2.4440