Acceptance, Preferences, and Privacy Concerns of University Students Regarding Digital Recommender Systems for Psychosocial Support Services: An Exploratory Cross-Sectional Online Survey

Abstract

This study aims to investigate university students’ acceptance, preferences, and privacy concerns regarding digital health recommender systems designed to facilitate access to psychosocial support services in higher education. Despite the availability of psychosocial support services at universities, these resources often remain underutilized. Digital recommender systems may help students identify suitable support services by providing personalized recommendations based on individual needs and preferences. The study will employ an exploratory, non-interventional, cross-sectional online survey targeting students aged 18 years or older who are currently enrolled at universities in Germany. Data will be collected anonymously using the Unipark survey platform. A minimum sample size of 250 participants is targeted. The research focuses on four main areas: (1) perceived usefulness, acceptance, and trustworthiness of digital recommender systems; (2) privacy concerns and willingness to share personal information for personalized recommendations; (3) preferences regarding different recommendation strategies; and (4) attitudes toward generative artificial intelligence and AI-based chatbots in the context of psychosocial support. The questionnaire is informed by established theoretical frameworks, including the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Internet Users’ Information Privacy Concerns (IUIPC). The findings are expected to provide insights into user-centered requirements for developing transparent, trustworthy, and privacy-sensitive recommender systems for university mental health promotion. This study is conducted as a preliminary investigation within the broader research initiative “ConnectedHealth@University.”