Choosing a Physician on Social Media: Comments and Ratings of Users are More Important than the Qualification of a Physician

an article by Guillermo Carbonell (University of Duisburg-Essen, Germany) and Matthias Brand (University of Duisburg-Essen, Germany; Erwin L Hahn Institute for Magnetic Resonance Imaging, Essen, German) published in International Journal of Human–Computer Interaction Volume 34 Issue 2 (2018)

Abstract

Social media provides users with descriptions, comments, and ratings from others to facilitate decision-making. In this study, we want to assess how users make decisions with the help of the tools provided by social media.

In order to do so, we simulate a physician rating website in which participants had to choose one physician among four options in a task of 20 trials. For this, we used a choice-based conjoint design, which allowed us to experimentally observe which features of social media have more impact on the decisions of the participants.

Furthermore, personal characteristics of the participants, such as executive functions, cognitive styles, and personality traits, were measured.

We found that the subjective features of social media, such as the comments and ratings provided by other users, have a greater impact on the decision of the participants when compared with the objective characteristics such as experience or specialty.

Regarding the personal characteristics, we found that the executive functions cognitive flexibility and categorization were higher for those participants who preferred objective features (e.g. availability, specialty, experience of the physician) than for those participants who preferred subjective features.

The results of the current study help understand how users make decisions with social media tools. We also stress the importance of the comments and ratings of users in decision making on social media.

Furthermore, we suggest considering these results in the fields of recommender systems and information retrieval, in order to improve the human-computer interaction in platforms that use recommendations as an important part of the decision-making process of users.

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