Suggestion pattern on online social networks: Between intensity, effectiveness and user's satisfaction

Abstract

Forms of influence are widely applied in social networks in order to encourage users to take actions that are favourable to these technologies providers. In our prior work, we proposed a set of influence patterns that are applied in social networks (suggestion pattern, reminder pattern, reward pattern, interaction pattern and social influence pattern) which influence users progressively over time in order to shape their behaviours and to persuade them to stay as long as possible. Nevertheless, the guidance or recommendations for applying these patterns for developers have not yet been defined. This research will focus on the first suggestion pattern and describes an experiment designed to examine whether excessive/intense application of suggestions (which may adversely affect user time) is also more effective and more satisfying from a user perspective. We used two video sharing applications (YouTube and YouTube Focus); the first contains excessive/intense suggestions against the second that contains limited suggestions. Our finding shows that limited suggestions are more effective than excessive/intense suggestions and are as satisfactory as excessive/intense suggestions. We believe that these results will promote favourable outcomes when applying the suggestion pattern, (1) for users: by helping them understand the nature of influence techniques and to empower them to be proactive in creating an environment that is more favourable for them, and that helps them to achieve their goals without getting distracted; (2) for designers: by providing them with insights on the optimal and effective method of using patterns of influence notably, suggestion pattern.