Consequences of Brands Staying Silent in the Wake of Sociopolitical Injustice (with Nooshin Warren, University of Arizona and Yashoda Bhagwat, Texas Christian University)
May, 2022: Awarded the The Data Institute for Societal Challenges (DISC) grant for this project.
Managers are reluctant to take sides on hot-button sociopolitical debates, either to stay apolitical, or to avoid alienating opposing stakeholders and risk averse investors. However, nearly 60% of Americans believe it is unacceptable for firms to remain silent on sociopolitical issues. This disconnect between managers and customers challenges conventional wisdom, which dictates that by remaining silent, firms, can “opt out of the conversation” and avoid negative financial consequences. However, in the wake of a sociopolitical injustice, silence may communicate information about the firm’s values and still elicit backlash. For example, consumers were critical of Coca-Cola for not speaking against a voting bill in Georgia perceived to be restrictive. The authors address this conflict by answering the questions: (1) When do consumers notice a firm’s silence on contentious sociopolitical issues? (2) How do consumers react to silent firms in terms of their emotions? and (3) What are the financial consequences of staying silent in terms of brand equity, future sales and firm value? The authors first scrape the Twitter verse to extract the online chatter about a brand’s silence towards a sociopolitical issue. Then they use text analysis and machine learning to categorize consumer sentiment towards the silence. Finally, they investigate the downstream effect of consumer sentiment towards silence on brand and firm value. The findings will provide unique insights to managers trying to navigate an increasingly charged political climate by helping them understand the consequences of choosing not to engage in activism.
Quantifying the creativity of digital platform posts based on temporal evolution of content similarities (with Ruyu Chen, Stanford and Jeremy Yang, Harvard University)
This project proposes an algorithm to quantify the creativity of posts on digital platforms. We use the temporal evolution of content similarities to assign a creativity score for each post in a network, where a post is considered to be creative if it appears as atypical among all prior and contemporaneous posts and becomes typical among all subsequent posts. Leveraging a large-scale Instagram post dataset that includes 1,601,074 Instagram posts by38,113 Instagram influencers with 2,273,578 brand-mentions published between 2013 and2019, we empirically test the algorithm based on deep learning image embeddings. We also propose a few research topics on how creativity affects the performance of influencers (such as sales, engagement, growth, etc.) and the factors that may influence the creativity of creators (such as the network position and network cascading, etc.) for future research.
Social Media and Demand Sensing for Innovative Products/Services: The Case of Movie Box Office Revenue (with Mei Li, University of Oklahoma and Naveen Kumar, University of Oklahoma)
Network based recommender systems for sponsored advertising on Instagram (with Bindan Zhang, Northwestern University and Jeremy Yang, Harvard University