This paper extends a well-established credence goods model to digital platforms and disintermediation. Users seek the assistance of a digital platform to find the right expert. However, they are not aware of which service type they need; this is why they consult the expert in the first place. Asymmetric information allows experts to prioritize their profit over the users’ needs, as users cannot, even after consumption, detect potential mistreatment. Digital platforms can help users find the right expert and learn about their required service level, mitigating this inefficiency. However, some users with minor quality requirements may then systematically bypass the platform to avoid transaction fees. To adress the problem of disintermediation, the platform could explore a subscription-based revenue model. However, a transaction-based revenue model potentially increases the number of users on the platform under information asymmetry. This could result in an improved matching algorithm and stronger network effects.

Online review system providers often use review templates to guide
reviewers in sharing their product experiences in online consumer reviews. However,
there is a huge number of reviews that cover similar product aspects and
provide repetitive and less diverse information. To address this issue, we propose
to use review templates that dynamically adapt to existing product information
in reviews to guide reviewers towards sharing product experiences that have not
or only rarely been shared before. Employing the design science research paradigm,
we derive design requirements and develop design principles for the design
of dynamic review templates. Based on an instantiated dynamic review template
following these principles, we show that they successfully guide reviewers towards
providing more diverse product information in reviews while maintaining
high reviewer engagement. We contribute to research and practice by providing
prescriptive design knowledge on dynamic review templates.