Resumo

Some evidence exists that feedbacks tend to be more biased in collaborative services (such as Uber or Airbnb), than in traditional ones. This represents a major a problem, since interactions in these services are often very personal and as more users are entering the platforms. We develop a theoretical framework and put forward propositions as to why and under which conditions this bias occurs. We propose that the higher bias in collaborative services (vs traditional ones) is due to its more interpersonal and prosocial nature, mainly as a result of anticipation of guilt and rapport.

The main goal of this paper is to shed light on social aspects possibly behind the higher feedback bias in collaborative services (compared to traditional ones). To achieve this goal, a theoretical background was developed, followed by a few propositions that may warrant further investigation in the future. Through this study we aim to answer the following question: “which prosocial aspects are possibly behind feedback bias in collaborative services?”

We develop a theoretical framework by exploring the theory on collaborative and traditional service motivations, prosocial behavior, feedback mechanisms, anticipation of guilt and rapport.

Based on previous theory, it seems to be possible that, due to collaborative services prosocial nature, users knowing the power of their feedback to harm others, might feel anticipation of guilt and avoid leaving negative feedbacks (or any feedback at all) to other users. Also, the lack of face-to-face interaction with another user could mean bad -or lack of- rapport (connected and pleasant interaction), leading to less biased feedbacks in situations where no contact with the host occurs. Furthermore, it is possible that the lack of rapport leads to less anticipated guilt.

The importance of feedbacks in collaborative services is clear: it is crucial for users to have access to unbiased feedbacks to assert trustworthiness and avoid risks or unpleasant situations. It is pivotal to resolve the issue of feedback bias before more changes and new possibilities of interaction are added to the platforms. Exploring when and why this bias in feedback occurs may lead to more reliable feedback mechanisms.

The literature review for this study was conducted using Scopus, Web of Science and Google Scholar databases between the years of 2019 and 2022. The criteria for literature selection included seminal works, books, highly cited studies and papers published in prestigious journals and conferences.