Research

The Long and Short of It: Consumers Lose Track of Time While Watching Short-Form Videos

with Tomomichi Amano and Lucy Shen, Reject & Resubmit at Journal of Marketing Research. 

Social media users have been spending increasingly more time watching short-form videos over the last few years. The authors demonstrate one explanation for this increased consumption: when watching short-form videos, consumers are more prone to losing track of time and spend more time than they anticipate. Across seven studies, viewers who successively watch a series of short-form video clips underestimate total time spent watching videos, compared to viewers who viewed the same video content and length presented as one long-form video. The authors show that this effect, termed the time underestimation effect, occurs because shorter videos naturally induce more viewer action to continue video consumption, i.e., clicking or swiping to the next video. These content-unrelated actions divert viewer attention from the actual video content, leading to lower perceptions of time spent on watching videos. Content characteristics moderate the time underestimation effect. These findings suggest that consumers’ time spent on short-form videos may diverge from their engagement with the content, which has implications for how platforms and advertisers measure consumer interest.

Calculated Complaints: Understanding Strategic Mentions of Discrimination in Customer Service

with Grant Donnelly. 

Online discourse related to discrimination, including complaints about firm actions, has surged in recent years. While consumer complaints about discriminatory behavior by firms are often rooted in reality, they may at times contain distortions from the truth (e.g., false attributions, exaggerations) regarding experiences of differential treatment based on their membership in certain social categories. Combining evidence from Twitter with five incentive-compatible, online experiments, I investigate consumer motivations behind mentioning discrimination in their complaints within the context of airline customer service. I find that consumers perceive mentions of discrimination to be effective in eliciting a firm’s response, and this view is confirmed by Twitter data on major U.S. airlines: tweets mentioning discrimination-related words elicit faster responses from airlines. This is because consumers consider complaints mentioning discrimination (e.g., “I've been discriminated against”) as particularly damaging to the firm’s reputation. As a result, in settings where firms are more concerned about their reputation (e.g., public channels, corresponding track records), consumers are more inclined to strategically mention discrimination in their complaints, even when it is ambiguous whether discrimination actually occurred.

Does Understanding Why Always Matter? The Case for Prediction-Focused Consumer Research

with Reed Orchinik, Santiago Pardo Sanchez and David G. Rand.

Consumer research has long prioritized understanding the psychological mechanisms underlying behavior, but when might knowing what predicts consumer choices matter more than understanding why those choices occur? We examine this question in the context of environmentally friendly products, where complex psychological mechanisms are well-established but may be less actionable than simple predictive variables. Using two survey experiments (total N = 2,089), we demonstrate substantial heterogeneity in purchasing intentions when identical pro-environmental digital products are labeled as “green” versus “smart.” Environmental concern serves as an important moderator, with participants low in environmental concern showing substantial aversion to green branding. However, we find that political party affiliation—a readily observable demographic variable—provides equally effective market segmentation, correlating highly with environmental concern while capturing additional variance in green branding efficacy. Through simulations of multiple targeting schemes, we show that political party-based targeting performs as well as sophisticated causal machine learning approaches that rely on unobservable preferences and psychological tendencies, increasing purchasing intentions by 4%. This suggests that in polarized environments, simple predictive approaches may be as valuable as mechanistic understanding for practical targeting purposes. Our findings highlight the importance of considering when prediction matters as much as mechanism in consumer research, with implications extending beyond environmental products to any domain where consumer preferences align with easily observable characteristics.

Robo-Journalism: A Tool to Reducing Selective Exposure to Partisan News

with Adam Waytz and Michael I. Norton.

The political divide in the United States has worsened in recent years, segregating the behavior of liberals and conservatives and contributing to selective consumption of news media that aligns with one’s ideological leanings. However, consuming politically biased news can influence political behavior and decisions, exacerbating the already raging political divide. Our research examines a novel technology—robot journalism—as a potential means to break partisans from seeking ideologically biased content. We report five preregistered studies demonstrating that both Democrats and Republicans perceive news written by a robot columnist to be unbiased because they perceive that the robot columnist is capable of aggregation. Furthermore, we find that they choose to read robot-generated news (even over news written by an ideological ingroup member) when incentivized to consider the news objectively. We suggest that robot journalism can reduce selective exposure to news and may reduce perceptions of media bias as well.