Modeling NFT pricing through aesthetic features: an explainable SEM-neural network framework

Purpose This study aims to examine how core visual aesthetics, namely structural complexity (SC), texture uniformity (TU), and color vibrancy and composition (CVC), shape NFT pricing. It addresses a key research gap in developing valuation models that are both theory-driven and interpretable. To that end, the study explores (1) how these visual features influence perceived value and (2) whether a hybrid modeling framework combining structural equation modeling (SEM), physics-informed neural networks (PINNs) and explainable AI (XAI) can effectively capture and validate these relationships. Design/methodology/approach A four-phase, theory-driven and interpretable modeling framework is employed. Phase 0 uses convolutional neural networks (CNNs) to establish the predictive value of raw visual features. Phase 1 extracts interpretable visual attributes using classical computer vision techniques, grouped under color, texture, composition and general categories. In Phase 2, SEM identifies the latent constructs SC, TU and CVC and tests their influence on NFT prices. Phase 3 introduces a PINN constrained by SEM-derived directional hypotheses and validated using XAI tools including SHAP, permutation importance, PDP and ICE. Findings Findings from both SEM and PINN-XAI analyses converge on key results. SC significantly increases NFT prices (β = 0.116, p < 0.001), highlighting consumer preference for visually intricate designs. In contrast, TU and CVC reduce this effect, acting as negative moderators (TU × SC: β = −0.408, p = 0.002; CVC × SC: β = −0.517, p = 0.02). The PINN model embeds these theoretical constraints while maintaining predictive accuracy and interpretability. XAI techniques consistently show that SC drives price increases, but its effect diminishes when TU or CVC is high, suggesting that overly smooth textures or intense color schemes visually dilute complexity and reduce perceived value. Research limitations/implications This study contributes to NFT valuation theory by identifying how SC drives pricing and how TU and CVC moderate this effect. It also demonstrates how a hybrid SEM and PINN-XAI framework can align interpretability with predictive modeling. However, the analysis is based on static NFTs from early 2021 and excludes contextual factors like creator reputation or platform-specific trends, which may limit generalizability. Future work should extend this approach to dynamic NFTs, varied marketplaces and evolving visual norms. Practical implications The study offers actionable insights for NFT creators, platform managers and brand strategists. Visually intricate NFTs (high SC) tend to command higher prices, but this effect weakens with overly smooth textures or saturated colors. Designers should therefore avoid excessive uniformity or vibrancy to preserve the pricing premium from complexity. Platform managers can improve curation and discovery by spotlighting SC-rich designs. Brand strategists may tailor visual styles, whether minimalistic or vivid, to optimize value perception without undermining visual intricacy. These findings guide more effective aesthetic, branding and curation strategies in NFT marketplaces. Social implications The study's findings suggest several socially impactful policy interventions to enhance fairness and transparency in NFT marketplaces. Platforms and industry bodies could introduce best-practice guidelines on visual valuation to aid creators and inform consumers. Third-party aesthetic audits based on validated perceptual metrics (e.g. SC, TU and CVC) can help certify quality and reduce asymmetry between artists and buyers. In addition, a lightweight visual feature footprint label, similar to energy-efficiency tags, could guide consumer interpretation and promote better design standards. These self-regulatory tools can foster trust, mitigate speculation and support a more transparent and accountable digital art ecosystem. Originality/value This research makes three distinct contributions. Theoretically, it identifies and validates aesthetic constructs that explain NFT pricing beyond conventional traits like rarity. Methodologically, it pioneers a hybrid PINN-XAI architecture that embeds theory-driven constraints into a predictive model while offering full interpretability. Practically, it offers creators, collectors and marketplace platforms actionable insights into how design features affect perceived value and market performance. The study sets a precedent for transparent and theoretically aligned valuation in digital markets. The PINN-XAI validation script is available at https://t.ly/xsodr.