User experience (UX) refers to the emotional, aesthetic, and functional experience of a product or service as perceived by the user (Pettersson et al., 2018).

Users react to aesthetic and hedonic experiences rather emotionally and subjectively - both positive and negative (Bhandari et al., 2019). However, explicit research methods such as surveys and interviews are limited in reliably gathering and analyzing emotional data, e.g., due to social desirability bias or instructor bias (Blair et al., 2020; Ekman, 2009). This leads to uncertainty for UX researchers, marketing managers, entrepreneurs, and designers about which methods to apply for testing the aesthetic and hedonic aspects of their prototypes, products, services, and communication (Law et al., 2014; Tawfik et al., 2022).

To address this need, a toolbox for Digital Empathy (DE) containing six methods for collecting and analyzing implicit, explicit, and behavioral reactions within UX is developed, namely Facial Coding, Eye Tracking, Qualitative Interview, Think Aloud Protocol, Log File Analysis, Survey/Questionnaire. Digital Empathy is understood as an approach to empathizing with users’ feelings and thoughts when interacting with digital technologies and designing user experiences of highly functional, aesthetic, and hedonic quality (Klug & Hahn, 2021). DE is becoming a must-have in marketing practice as UX research moves towards frameworks that incorporate artificial empathy since users expect digital technologies to be tailored to their current cognitive and affective conditions (Liu-Thompkins et al., 2022).

This dissertation project will triangulate methods from the developed DE toolbox within established conceptual frameworks from marketing research to yield insights about the added value of these UX research methods combined to better understand and predict user behavior.