Algorithmic Pricing Intensity and the Curvilinear Reconfiguration of Consumer Fairness Norms - Uus Muhamad Husni Tamyiz (Manexia: Journal of Business, Management, and Creative Economy, 2026)
Algorithmic pricing is widely framed as a technological instrument for efficiency and revenue optimization. Yet as pricing decisions become increasingly embedded within autonomous computational systems, their implications extend beyond performance outcomes to the normative foundations of market exchange. This article develops a conceptual framework explaining how algorithmic pricing intensity reshapes consumer fairness norms through curvilinear dynamics. Drawing on justice theory, reference price stability, attribution processes, and institutional legitimacy, the analysis proposes that algorithmic pricing intensity exhibits an inverted-U relationship with normative legitimacy. At low to moderate levels, algorithmic systems enhance procedural objectivity and enable adaptive updating of reference expectations, thereby strengthening fairness norms. Beyond a critical threshold, however, heightened volatility, granular personalization, and causal opacity destabilize reference anchors and intensify exploitative attributions, resulting in legitimacy erosion. By reframing fairness as a dynamic normative constraint rather than a static perception, the article contributes to research on digital market governance and strategic legitimacy, highlighting the bounded nature of algorithmic optimization in competitive digital environments