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Just the Faces: Exploring the Effects of Facial Features in Print Advertising

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  • Li Xiao

    (School of Management, Fudan University, Shanghai, People's Republic of China 200433)

  • Min Ding

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802; and School of Management, Fudan University, Shanghai, People's Republic of China 200433)

Abstract

Human faces are used extensively in print advertisements. In prior literature, researchers have studied spokespersons in general, but few have studied faces explicitly. This paper aims to answer three questions that are important to both researchers and practitioners: (1) Do faces affect how a viewer reacts to an advertisement on the metrics that advertisers care about? (2) If faces do have an effect, is it large enough to warrant careful selection of faces when constructing print advertisements? (3) If faces do have an effect and the effect is large, what facial features elicit such differential reactions on these metrics, and are such reactions different across individuals and/or product categories? Relying on the eigenface method, a holistic approach widely used in the computer science field for face recognition, we conducted an empirical study to answer these three questions. The results show that different faces do have an effect on people's attitude toward the advertisement, attitude toward the brand, and purchase intention and that the effect is nontrivial. Multiple segments were identified and substantial differences were found among people's reactions to the faces in the ads across those segments. We also found that the effect of faces interacts with product categories and is mediated by various facial traits such as attractiveness, trustworthiness, and competence. Implications and directions for future research are discussed.

Suggested Citation

  • Li Xiao & Min Ding, 2014. "Just the Faces: Exploring the Effects of Facial Features in Print Advertising," Marketing Science, INFORMS, vol. 33(3), pages 338-352, May.
    • Handle: RePEc:inm:ormksc:v:33:y:2014:i:3:p:338-352
    DOI: 10.1287/mksc.2013.0837
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    References listed on IDEAS

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    8. Chen, Yunqing & Wyer, Robert S., 2020. "The effects of endorsers' facial expressions on status perceptions and purchase intentions," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 371-385.
    9. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    10. Mengyue Wang & Xin Li & Patrick Y. K. Chau, 2021. "Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context," Information Systems Frontiers, Springer, vol. 23(3), pages 607-626, June.
    11. Shasha Lu & Li Xiao & Min Ding, 2016. "A Video-Based Automated Recommender (VAR) System for Garments," Marketing Science, INFORMS, vol. 35(3), pages 484-510, May.
    12. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    13. Zecong Ma & Sergio Palacios, 2021. "Image-mining: exploring the impact of video content on the success of crowdfunding," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 265-285, December.
    14. Matia Torbarina & Lara Jelenc & Ivana Brkljačić, 2020. "Increasing Consumer Engagement on Social Networks: Social Media Influencer’s Followers ‘Like’ to See a Face in a Post," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 32(SI), pages 67-81.
    15. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.

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