Publication Date

Fall 2015

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Human Factors/Ergonomics

First Advisor

Daniel Rosenberg


HCI, Human-Computer Interaction, Persuasive Design


This study involved 40 participants from the community of varying ages and genders filling out various versions of a social network registration that utilized no persuasive mechanics, a praise persuasive mechanic, a social-pressure persuasive mechanic, and both mechanics combined in an effort to determine the effectiveness of each by measuring the amount of data supplied during registration, as well as self-reported scores on a persuasiveness scale. Attitudes towards risk as well as gender were factors also considered. The results were not statistically significant with the exception of the final, self-reported, most-persuasive design. Participants felt overall that no-mechanics was the most persuasive. Possible effects, causes, and implications for future research are discussed.