Does a Smile Matter if the Person Is Not Real?: The Effect of a Smile and Stock Photos on Persona Perceptions

ABSTRACT

We analyze the effect of using smiling/non-smiling and stock photo/non-stock photo pictures in persona profiles on four key persona perceptions, including credibility, likability, similarity, and willingness to use. For this, we collect data from an experiment with 2,400 participants using a 16-item survey instrument and multiple persona profile treatments of which half have a smiling photo/stock photo and half do not. The results from structural equation modeling, supplemented by a qualitative analysis, show that a smile enhances the perceived similarity with the persona, similar personas are more liked, and that likability increases the willingness to use a persona. In contrast, the use of stock photos decreases the perceived similarity with the persona as well as persona credibility, both of which are significant predictors to a willingness to use a persona. These professionally crafted stock-photos seem to diminish the sense of identification with the persona. The above effects are consistent across the tested ages, genders, and races of the persona picture, although the effect sizes tend to be small. The results suggest that persona creators should use smiling pictures of real people to evoke positive perceptions toward the personas. In addition to presenting quantitative evidence on the predictors of willingness to use a persona, our research has implications for the design of persona profiles, showing that the picture choice influences individuals’ persona perceptions even when the other persona information is identical.

Notes

1. Note that throughout the manuscript, we italicize the concept of persona perception, in order to make it more visually distinct for the reader, relative to person perception. Conceptually, the difference is that person perceptions are targeted to real people, whereas persona perceptions are targeted to personas, i.e., fictitious people that represent a certain customer or user segment.

2. https://www.upwork.com.

3. B is the standardized regression coefficient. It is similar to the unstandardized regression coefficient β, except it measures shifts in standard deviations rather than absolute values. It is more often used in structural equation modeling since it always direct comparison of the relative intensity of the effect.

Additional information

Notes on contributors

Joni Salminen

Joni Salminen is a postdoctoral researcher at Qatar Computing Research Institute, Hamad Bin Khalifa University, and Turku School of Economics at the University of Turku. His current research focuses on multiple aspects of automatic persona generation, including computational challenges, information design, and value creation with personas.

Soon-Gyo Jung

Soon-gyo Jung received B.E. degree in computer software from the Kwangwoon University, and M.S. degree in electrical and computer engineering from the Sungkyunkwan University, Suwon, Korea. He works at Qatar Computing Research Institute developing a system that generates personas automatically from online analytics and social media platforms.

João M. Santos

João M. Santos is a researcher based at the Lisbon Universitary Institute (ISCTE-IUL) in Portugal. João collaborates regularly with other higher education institutions such as Instituto Superior Técnico (IST) in Portugal and the University of Hong Kong, and provides consultancy for governments, trans-national organizations, and private businesses.

Bernard J. Jansen

Bernard J. Jansen is a Principal Scientist in the Social Computing group of the Qatar Computing Research Institute, and a professor with the College of Science and Engineering, Hamad bin Khalifa University, and an adjunct professor with the College of Information Sciences and Technology at The Pennsylvania State University.