Under Revision

Curate to Engage: The Role of Reposting in Shaping Audience Engagement Among Content Creators (with Mina Ameri, University of Pittsburgh). Revising for 3rd Round at Journal of Marketing Research. Article here – SSRN Link

Content creators on social media face an ongoing challenge: maintaining audience attention through continuous content production. As a result, many creators rely on reposting, sharing others’ content without commentary, to fill gaps between original posts. Yet, because engagement on reposts accrues to the original author rather than the curator, does reposting benefit the curator’s subsequent original content? This study uses data from over five million posts by 731 creators over two years on Twitter. Because reposting decisions are sequential and endogenous, the authors develop a Markov principal strata model that leverages Latent Sequential Ignorability to identify causal effects. Findings show reposting positively affects engagement with subsequent original posts: a single repost increases engagement by 16% and 10% for creators with 100,000 and 1 million followers, respectively. However, as repost chains lengthen, marginal benefits diminish, eventually turning negative, particularly for smaller creators, reflecting risk of brand dilution. Engagement depends not only on how much creators repost but on how those reposts are distributed: short chains interspersed with original posts generate substantially higher total engagement than concentrating reposts in a single block. These findings offer guidance for creators balancing curation and creation and highlight opportunities for platforms to support effective posting patterns.

The Impact of Minority Representation in Sponsored Content and the Moderating Role of Brand Characteristics (with Keran Zhao, Penn State; Amy Pei, Northeastern University; Xinhui Zhan and Heshan Sun, University of Oklahoma), To be submitted to Journal of Management Information Systems. Article here – SSRN link

Firms increasingly hire social media influencers to facilitate direct peer-to-peer communication with users. Despite the rapid growth of the influencer economy, research on minority representation among influencers remains limited, with findings that are often mixed and sometimes conflicting. This study develops a research model that explains how minority representation affects user engagement with firms on social media in non-linear ways. The model incorporates key mediators—users’ perceived organizational legitimacy, trust in the firm, and perceived tokenism of the firm’s DEI efforts—and a moderator, the firm’s prior DEI commitment. To test this model, two empirical studies were conducted using data collected before and after the significant political shift following the 2024 presidential election. Study 1 analyzes pre-election Twitter data, while Study 2 employs a post-election experiment to examine the underlying mechanisms. The results demonstrate that minority representation among influencers exerts a U-shaped effect on user engagement, with the effect moderated by firms’ prior DEI commitments. Furthermore, this relationship is mediated positively through users’ perceptions of organizational legitimacy and trust, and negatively through perceived tokenism of the firm’s DEI efforts. These findings offer both theoretical contributions and practical insights for firms’ representation strategies.

Transforming Marketing Research with Generative AI: Opportunities, Limitations, and Ethical Implications (with Amirhossein Pezeshgi, University of Oklahoma), Invited book chapter, Encyclopedia of AI in Marketing, Springer Nature. Article here – SSRN link

The rapid advancement of Generative Artificial Intelligence (GenAI) is transforming marketing research by automating and enhancing routine marketing tasks. In this paper, we explore how GenAI is impacting marketing research practices across four areas: a) Synthetic Respondents for Market Research, b) Automated Content Generation, c) AI-Powered Customer Service, and d) B2B Marketing Automation. For each of these areas, we conduct a thorough and systematic review of existing literature to evaluate the opportunities and limitations of GenAI in marketing, including its accuracy, cost-efficiency, and ethical challenges. Overall, our findings show that while GenAI enhances scalability and innovation, its effectiveness hinges on a hybrid approach, combining responsible human guidance with ethical AI use and continued research to ensure trust and transparency. Based on our analysis, we recommend best practices such as validating synthetic data with real-world samples, ensuring human oversight in content creation, training customer service models on context-rich datasets, and aligning B2B automation with relationship-building strategies.