This study explores the application of signaling theory in content monetization within e-mentoring platforms, specifically focusing on the expected impact of signal types and consistency on perceived mentor quality. We investigate how various signals (e.g., qualifications, experience, reviews) associated with mentors' service quality can reduce information asymmetry and enhance content monetization. An experiment employing a factorial survey design will be conducted to manipulate attribute levels representing signal quality. Additionally, we expect to employ Fuzzy-set Qualitative Comparative Analysis (fsQCA) to assess signal consistency, a method that allows for the examination of signal combinations and their effects on service quality. Our research aims to provide insights into effective signaling strategies for mentors and platforms to reduce information asymmetry and enhance the e-mentoring experience, thereby facilitating content monetization.