Objective: Algorithmic pricing uses extensive data and intelligent algorithms to adjust service prices according to market conditions and demand. Despite its economic benefits, users’ perceptions of fairness are important because price evaluations involve not only the final amount but also transparency, the rationale for price changes, equality, and trust. This study explores consumers’ lived experiences of perceived fairness in algorithmic pricing among Snapp users.

Method: This qualitative study employed a phenomenological approach. Participants were 15 Snapp users with experience using the platform and encountering price changes. They were selected purposively through maximum variation sampling. Data were collected through in-depth, semi-structured interviews lasting 45–70 minutes. Following informed consent, interviews were recorded and transcribed. Data analysis involved repeated reading of transcripts, identification of meaning units, initial coding, and organization of codes into higher-level conceptual categories. Research quality was assessed based on the criteria of credibility, transferability, dependability, and confirmability proposed by Lincoln and Guba.

Findings: Five main themes emerged: pricing transparency and explainability, justice and equality, platform trust and accountability, privacy and user-experience context, and consequences of perceived fairness or unfairness. Users considered prices more acceptable when the reasons for changes, such as increased demand, traffic, or specific circumstances, were understandable. In contrast, sudden and unexplained changes generated ambiguity and distrust. Participants distinguished between price changes consistent with actual conditions and discriminatory price differences, and regarded accountability, access to explanations, and complaint mechanisms as important to perceived fairness. Concerns about personal data use and previous experiences also influenced users’ perceptions of pricing.

Conclusion: The lived experience of fairness in algorithmic pricing depends less on price level or stability than on the understandability of pricing logic, justifiability of changes, equal treatment, and access to explanations and complaint mechanisms. Variable pricing is therefore not necessarily perceived as unfair. Transparency, accountability, equal treatment, and responsible use of user data can strengthen trust and acceptance of algorithmic pricing.