Recently, digital interventions have proliferated and show promising results in preventing and treating common mental health disorders, such as depression, in different settings (e.g., workplaces). Digital interventions may have advantages over face-to-face interventions (e.g., more accessible; easily customisable; real-time monitoring). However, despite efforts made by healthcare systems worldwide (e.g., apps on prescription in Germany), actual adoption is still rather low in many countries. It is essential to understand innovation acceptance in order to tailor digital interventions and to measure user technology acceptance. In this way, determinants can be identified to derive strategies to promote acceptance. Technology acceptance has been studied extensively, resulting in the development of various theoretical models (e.g., Technology Acceptance Model-TAM; Unified Theory of Acceptance and Use of Technology-UTAUT, UTAUT2). Besides several methodological strengths, technology acceptance models also have various limitations, which makes it difficult to investigate causality or to generalize findings across different contexts, populations, and cultures.