Fairness Under Algorithmic Opacity: A Configurational Model of Price Evaluation in Digital Platform Markets

Abstract

The present study aimed to develop a qualitative configurational model explaining how consumers evaluate price fairness under conditions of algorithmic opacity in digital platform markets. This study was conducted using a qualitative exploratory design with a configurational approach. The research population consisted of digital platform users in Tehran who had prior experience with algorithmically mediated pricing systems in online marketplaces, ride-hailing services, food delivery applications, and e-commerce platforms. Twenty-eight participants were selected through purposive sampling with maximum variation in age, gender, occupation, and platform usage patterns. Data were collected through semi-structured in-depth interviews and scenario-based discussions focused on experiences of dynamic pricing, personalized pricing, transparency, trust, and fairness perceptions in digital markets. Interviews lasted between 45 and 75 minutes and were audio-recorded and transcribed verbatim. Data analysis was performed using thematic and configurational analysis through open, axial, and selective coding procedures with the assistance of MAXQDA software. Credibility and trustworthiness were enhanced through participant validation, peer review, memo writing, and audit trail documentation. The findings revealed that fairness evaluation in digital platform markets is a multidimensional and configurational process rather than a simple reaction to price magnitude. Perceived transparency emerged as the strongest factor associated with positive fairness judgments, while algorithmic opacity intensified distrust, emotional dissatisfaction, and perceptions of manipulation. Participants generally accepted dynamic pricing when contextual justifications such as demand pressure, urgency, or service convenience were perceived as legitimate. Comparative assessment across platforms and users played a major role in fairness interpretation, especially under conditions of informational uncertainty. Personalized pricing practices generated strong perceptions of discrimination when users believed that behavioral or personal data influenced prices without procedural explanation. Prior trust in the platform partially moderated negative reactions toward algorithmic pricing, although repeated unexplained inconsistencies weakened platform credibility. The final configurational model demonstrated that fairness judgments emerge through the interaction of transparency cues, trust mechanisms, contextual necessity, emotional responses, comparison processes, and perceived consumer vulnerability. The study concluded that fairness in digital platform markets is socially and cognitively constructed through users’ interpretations of opaque algorithmic systems rather than solely through objective price outcomes. Algorithmic opacity transforms price evaluation into an interpretive process shaped by procedural transparency, platform accountability, trust, and contextual reasoning. Digital platforms that fail to provide understandable explanations for pricing mechanisms risk increasing perceptions of manipulation, discrimination, and procedural injustice. The findings suggest that sustainable digital marketplace governance requires explainable and transparent pricing systems capable of maintaining both technological efficiency and consumer trust.