The Adoption of Whistleblowing Mobile Applications in Whistleblowing Services: Deep Learning Based SEM-Neural Network Approach

Whistleblowing Mobile Applications are becoming a popular delivery method for whistleblowing services, allowing employees, suppliers, and collaborators to send reports of wrongdoing and alleged. This study examines the factors influencing the employee’s behavioral intention to use whistleblowing mobile applications in Ethiopian public and private organizations.This study included four new elements to the Unified Theory of Acceptance and Use of Technology (UTAUT) model: trust, perceived privacy risk, perceived security risk, and information quality. Data was gathered from 689 users of the smartphone application from Ethiopia. This study performs dual-stageanalysis by applying deep learning-based Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) methods. Structural equation modeling (SEM) was used to identify essential characteristics influencing consumers' approval of government mobile whistleblowing services. A neural network model was employed in the second stage to confirm SEM results and evaluate the relative relevance of determinants of government mobile whistleblowing services acceptability. The deep learning-based two-step PLS-SEM & ANN results revealed that perceived privacy risk, performance expectancy and trust are the most important factors influencing user intention to use mobile whistleblowing application. The findings of this study have contributed theoretically to previous research on whistleblowing services and have practical implications for decision-makers involved in the development and deployment of mobile whistleblowing services in Ethiopia. Keywords: whistleblowing mobile application, whistleblowing, UTAUT, Perceived Privacy Risk Perceived Security Risk, Trust, Information quality