<p>The use of mobile applications as a means of facilitating whistleblowing is becoming increasingly prevalent, allowing employees, suppliers, and collaborators to report instances of misconduct. This study examines the factors that influence an employee’s intention to use whistleblowing mobile applications in both public and private organizations in Ethiopia. The researchers expanded the Unified Theory of Acceptance and Use of Technology (UTAUT) model by incorporating four new elements: trust, perceived privacy risk, perceived security risk, and information quality. Data were collected from 652 users of a smartphone whistleblowing application in Ethiopia. The study employed a dual-stage analytical approach, first utilizing deep learning-based partial least square-structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods. Structural equation modeling (SEM) was used to identify the key factors influencing consumer acceptance of government-provided mobile whistleblowing services. In the second stage, a neural network model was employed to validate the SEM results and assess the relative importance of the determinants of acceptability for government-based mobile whistleblowing services. The deep learning-based two-step PLS-SEM and ANN analysis revealed that the most significant factors influencing user intention to use mobile whistleblowing applications are privacy risk, performance expectancy, and trust. This study contributes to the existing theoretical knowledge on whistleblowing services and offers practical implications for decision-makers involved in the development and deployment of mobile whistleblowing services in Ethiopia.</p>