Data fairness-the new social challenge of the data economy: what shapes social media users' perception of data fairness?

Background: Data fairness has become an increasingly important issue in the digital economy. Although social media platforms extensively collect and utilize personal data, limited research has examined how users perceive data fairness and the factors shaping such perceptions. Methods: Drawing on the Theory of Planned Behavior and Privacy Calculus Theory, a research model was developed incorporating algorithmic awareness, algorithmic attitude, data transparency, perceived control, privacy self-efficacy, and privacy concerns. Survey data from 589 WeChat users were analyzed using partial least squares structural equation modeling. Results: The results showed that algorithmic awareness positively influenced algorithmic attitude, which subsequently enhanced perceived data fairness. Data collection transparency and data processing transparency positively affected perceived control, whereas data use transparency had no significant effect. Perceived control and privacy self-efficacy positively influenced perceived data fairness. Privacy self-efficacy also reduced privacy concerns, which negatively affected perceived data fairness. Conclusion: This study advances the emerging literature on data fairness by identifying the key factors shaping users' perceptions of data fairness in social media. The findings provide new insights into the psychological mechanisms underlying fairness perceptions and offer practical implications for developing more transparent and user-centered data governance practices.