Algorithmic pricing can both bolster and erode market legitimacy: moderate algorithmic use improves perceived fairness by stabilizing references and adding procedural objectivity, but beyond a critical intensity personalization, volatility and opacity provoke exploitative attributions and legitimacy loss.

Main Finding

Algorithmic pricing intensity has an inverted‑U relationship with the normative legitimacy of consumer fairness norms: low-to-moderate algorithmic use tends to strengthen perceived fairness (via procedural objectivity and adaptive reference‑price updating), but beyond a critical intensity it undermines legitimacy through volatility, granular personalization, and causal opacity.

Key Points

• Central claim: Fairness should be treated as a dynamic normative constraint that responds nonlinearly to how intensively pricing is algorithmically driven.
• Proposed curvilinear dynamics: an initial positive effect of algorithmic intensity on fairness legitimacy up to a threshold, followed by a negative effect at high intensity.
• Mechanisms that increase legitimacy at low-to-moderate intensity:
  • Procedural objectivity: algorithms make pricing appear consistent and rule‑based.
  • Adaptive reference updating: consumers revise price expectations in light of systematic algorithmic behavior, stabilizing norms.
• Mechanisms that erode legitimacy at high intensity:
  • Price volatility: rapid, frequent changes destabilize reference anchors.
  • Granular personalization: hyper‑tailored prices create perceptions of unfair discrimination.
  • Causal opacity: difficulty attributing causes of price differences fuels exploitative attributions and legitimacy loss.
• The analysis reframes algorithmic optimization as bounded by normative and institutional considerations, not purely technical performance.

Data & Methods

• Type of contribution: conceptual/theoretical framework and synthesis (no original empirical dataset reported).
• Intellectual inputs: draws on justice theory, reference‑price literature, attribution psychology, and institutional legitimacy scholarship to derive mechanisms and predicted curvilinear relationship.
• Formalization: proposes an inverted‑U functional relationship between algorithmic pricing intensity and normative legitimacy; recommends operationalizing intensity and legitimacy for empirical testing.
• Suggested empirical approaches implied by the paper (not implemented in the article): lab experiments manipulating algorithmic intensity and transparency; field or platform data linking pricing dynamics to consumer perceptions; longitudinal designs to capture reference‑price evolution; regression specifications including quadratic terms and interaction effects to detect nonlinearity.

Implications for AI Economics

• Modeling: incorporate normative constraints and nonlinearity (inverted‑U) into models of algorithmic pricing and platform strategy; account for reputation/trust externalities that affect demand over time.
• Firm strategy: optimal algorithmic intensity may be interior (not maximal)—firms should weigh short‑run profit gains from aggressive personalization vs. long‑run legitimacy and demand erosion.
• Policy and governance: motivates interventions around transparency, limits on personalization granularity, stabilization mechanisms (e.g., caps on reprice frequency), and institutional legitimacy metrics.
• Measurement and empirical research: calls for operational measures of algorithmic intensity, consumer reference‑price dynamics, perceived fairness, and attribution; encourages causal tests of the proposed curvilinear relation.
• Welfare and market design: suggests that purely efficiency‑driven algorithmic optimization can harm normative foundations of markets, implying tradeoffs between allocative efficiency and durable market legitimacy.