Privacy-Centered Design Principles for Employee-Determined Data Collection and Use in Personalized Assistance Systems

A personalized assistance system can provide personalized, real-time assistance tailored to the individual employee. It is dynamically adaptable to continuously collect, individual knowledge levels. To enable personalized assistance and improve efficiency in the workplace, personal data from the employee who uses the wearable is needed. However, the issues of data protection and data security raise concerns among employees, which can negatively influence the acceptance of personalized assistance systems. In this paper, we develop eight design principles for employee-determined data collection and use in personalized assistance systems in order to provide a framework to counteract adoption barriers due to privacy concerns. Thereby, we concentrate on the central approaches of privacy control, privacy by design and default. Our study contributes to the theoretical discussion of current developments regarding personalized assistance in the workplace. By presenting design principles, this research also demonstrates practical relevance by providing a framework for software designers.