Research and Insights

March 24-27, 2022
St. Petersburg, Florida

Facebook Ad Engagement in the Russian Active Measures Campaign of 2016

 Authors: Mirela Silva, Luiz Giovanini, Juliana Fernandes, Daniela Oliveira, Catia S. Silva

Abstract: This paper examines 3,517 Facebook ads created by Russia’s Internet Research Agency (IRA) between June 2015 and August 2017 in its active measures disinformation campaign targeting the 2016 U.S. general election. We aimed to unearth the relationship between ad engagement (as measured by ad clicks) and 41 features related to ads’ metadata, sociolinguistic structures, and sentiment. Our analysis was three-fold: (i) understand the relationship between engagement and features; (ii) find the most relevant feature subsets to predict engagement via feature selection; and (iii) find the semantic topics that best characterize the dataset via topic modeling. We found that ad expenditure, text size, ad lifetime, and sentiment were the top features predicting users’ engagement to the ads. Additionally, positive sentiment ads were more engaging than negative ads, and sociolinguistic features (e.g., use of religion-relevant words) were identified as highly important in the makeup of an engaging ad. Linear SVM and Logistic Regression classifiers achieved the highest mean F-scores (93.6% for both models), determining that the optimal feature subset contains 12 and 6 features, respectively. Finally, we corroborate the findings of previous research that the IRA specifically targeted Americans on divisive ad topics (e.g., LGBT rights, African American reparations).

Friend or Foe? A Mixed Method Analysis on YouTube Users’ Replies to Top Comments of Femvertising

Authors: Huan Chen and Yang Feng 

Abstract: Given its popularity and user base, YouTube has become an important marketing communication tool for marketers and advertisers. Comments and replies on YouTube are not only a way for users to evaluate the content and express their feelings and opinions, but also an important reference for marketers and advertisers to gain in-depth understandings of consumers’ attitudes and behaviors. By using a mixed method research design, the study investigates replies to top comments under Always “Like a Girl” YouTube femvertising video to gauge consumers’ responses regarding femvertising as well as relationships among commenters. Based on the findings, a conceptual framework of viewer response to woke advertising in an anonymous and uncivil environment is constructed. The study has both theoretical and practical implications.

How do Femvertisements EmpowHER? The Role of Motivation in Female Empowerment Through Femvertising

Authors: Sophia Mueller, Yu-Hao Lee, Benjamin Johnson

Abstract: Femvertisements are a brand’s answer to taking a stance on gender issues. This form of advertising empowers women through a rejection of stereotypical female portrayals and utilizes pro-female messages. In short, their purpose is to empower women. However, little research has been conducted on if and how femvertisements empower women. This study is rooted in self-determination and cognitive evaluation theory and argues that empowerment is fulfilled through the motivational factors of autonomy and competence. A 2 (femvertisement vs. traditional advertisement) x 4 (within-subject: ads) experimental design was employed to test the research question and hypotheses. It was found that femvertisements generally produce more positive attitudes than do traditional advertisements. However, empowerment through the femvertisement produces surprising results: the more empowered women felt through the femvertisement, the more negatively they responded to the advertisement and the brand. Implications for advertising practitioners are outlined.

Influencer Marketing and Social Commerce: Exploring the Role of Influencer Communities in Predicting Usage Intent

Authors: Hyehyun Julia Kim, Sylvia Chan-Olmsted

Abstract: This study investigates three influencer-related relational angles, influencer community, individual consumer, and influencer follower relationship, as they relate to commitment toward the influencer in facilitating usage intent. In addition, platform factors derived from technology acceptance model, namely perceived usefulness, and enjoyment are also explored. Using a survey, the study findings indicated that perceived enjoyment, interactivity, social support, identification, usefulness, and enjoyment are significant predictors of usage intent. Most significant finding and unique contribution to existing research is the effect of influencer community factors, identification, social support, and interactivity on usage intent which was successfully mediated by commitment toward the influencer.

In Smartness We Trust: How Consumers Experience and Balance Smart Device Personalization and Privacy Concerns

Authors: Sylvia Chan-Olmsted, Huan Chen and Hyehyun Julia Kim

Abstract: Drawing on personalization-privacy paradox (PPP) and guided by Means-End theory, this study explores how consumers balance their concerns for privacy and benefits of smart home device personalization and the role that trust plays in the process. More specifically, the study investigates how perceptions of smart device personalization and privacy concerns are shaped by consumers’ motivations and experiences, and the role of trust in the deliberation process.

Revealing Context-Dependent Consumer Sentiments via Machine Learning: A Case Study of Always #LikeAGirl Campaign

Authors: Yang Feng and Huan Chen

Abstract: With practitioners and scholars increasingly using artificial intelligence to analyze consumer sentiments toward a social media-based campaign, we compared various traditional supervised machine learning algorithms with two proprietary models from Amazon and Google in terms of their performances in classifying user comments into a sentiment category. In particular, we adopted Always’ #LikeAGirl Campaign as a case and analyzed the sentiments of 19,198 YouTube comments on the campaign using different machine learning algorithms. Results indicate that the two proprietary models from Amazon and Google performed better than traditional supervised machine learning algorithms. Then, we used unsupervised machine learning to reveal the hidden topics from positive and negative consumer comments. The study has important methodological implications for advertising scholars and practitioners.

Understanding Ageism in Advertising: In-Depth Interviews with Advertising Practitioners

Authors: Kasey Windels and Sophie Mueller

Abstract: Advertising’s ageism problem has been addressed in the trade press, but few academic studies have sought to understand it from practitioners’ perspectives. Through in-depth interviews with a purposive sample of 22 advertising professionals, this study seeks to broadly understand the reasons and reasoning behind ageism in the industry today. Findings detail who experiences ageism, when ageism is prevalent, why ageism occurs, the strategies older individuals have adopted to combat ageism, where workers go when they are forced from full-time agency employment, and structural changes the industry can adopt. Person-environment fit theory is used to understand the mechanisms at play and more fully examine how ageism has impacted workers and the industry.

Panel: How to be an Effective Reviewer: A Work from the Best Reviewer Award Winners

Participants: Cynthia Morton, Chen Lou, Jameson Hayes, Kasey Windels and Laura Bright

Description: A panel of the top reviewers from advertising journals discussing their approach to reviewing manuscripts for journals.

You Can’t Shop with Us: Plus-size Female Consumer Perceptions of the Online Retail Shopping Environment for Plus-size Clothing

 Authors: Summer Shelton, Amanda Bradshaw, Matthew Cretul, Debbie Treise

Abstract:  Plus-size women represent a large consumer segment that has grown in popularity with the fashion industry, retailers, and advertisers. Despite advancements in clothing availability for plus-size women, the shopping experience for plus-size women (compared with that of straight-size women) often still falls short, leaving plus-size women feeling like a second-class, minority group despite the fact that the majority of women in the U.S. are considered plus-size. This study assessed the websites of N = 68 popular plus- and straight-size retailers in an attempt to answer the question: how do U.S. based, value and mid-market online clothing retailers position their plus-size female clothing sections? Findings revealed that the majority of retailers completely separated out the plus-size section from the straight-size section and that the language used to describe plus-size clothing was body focused (versus clothing focused for straight-size clothing sections). Theoretical and practical implications for marketers, advertisers, and retailers are discussed.