Becoming a professional influencer on LinkedIn: Privacy calculus drivers of self-disclosure

Abstract

This study examines the factors that influence users’ self-disclosure on LinkedIn through the lens of privacy calculus theory, considering perceived benefits, trust, and privacy concerns. A survey of 370 active users in Chile was conducted, and a structural model was assessed using partial least squares structural equation modeling. The results show that self-disclosure is primarily driven by functional and symbolic benefits, particularly learning/information exchange and professional self-presentation. Trust in the platform positively affects the willingness to disclose and reduces privacy concerns. In contrast, trust in other members, privacy concerns, and benefits related to professional networking and career development did not have significant effects. These findings suggest that in Latin American professional contexts, self-disclosure is more aligned with visibility and positioning oriented strategies than with explicit job search intentions. This study offers theoretical implications by extending privacy calculus to professional networking platforms and practical implications for platform design and recruitment strategies that rely more on reputational signals than on active job-seeking behaviors.