Algorithmic transparency (AT) plays a key role in the gig economy, where platforms rely on algorithms for decision-making. While AT is intended to build fairness and trust, it can sometimes increase worker resistance. Using the elaboration likelihood model (ELM), this study explores when and why transparency may backfire and examines factors such as worker involvement, algorithmic familiarity, and information quality. A field survey of 480 gig workers will test the model, offering insights to help organizations implement AT effectively while minimizing resistance.