Unlocking Trust in Digital Product Passports : An fsQCA and Multi-Linear Regression Approach

Abstract

This thesis investigates the factors influencing trust in Digital Product Passports (DPPs), in terms of the information they provide and the systems themselves. Also, the relation between trust in the DPP information, the DPP system, and behavioral intention to use the DPP was tested. A survey was conducted among potential future users of DPPs within an organizational context, focusing on multiple trust precursors related to DPP design requirements. The data was analyzed using a fuzzy-set Qualitative Comparative Analysis to identify factors and their combinations that lead to high or low trust in DPP information and systems, and using multiple linear regression to test the relation between the trust factors and behavioral intention. Key findings include unique location identifiers, data calculation transparency, and third-party certification as core conditions for trust in DPP information, and secure authentication mechanisms for data editing as a single predictor for high trust in DPP systems. However, the study also found that trust alone may not be sufficient to ensure the use of DPPs in decision making processes related to circularity and sustainability. The research contributes to the limited academic literature on DPPs by advancing the understanding of trust in DPPs and similar initiatives. It provides validated survey measures for analyzing trust precursors within information systems and demonstrates the potential of the fsQCA method for understanding complex constructs like trust. The study acknowledges limitations such as the limited literature on DPPs, constraints in data collection, and potential bias in the fsQCA method. Future research could extend this study by testing specific propositions, exploring more or different trust precursors, performing longitudinal studies, and the needs of organizations to see the DPP as more than just a legal requirement.