Under Revision

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

Creating regular content to maintain audience engagement is a demanding task for creators on digital platforms. This paper investigates reposting as a content curation strategy, where creators share existing content by others on social media without adding personal commentary. Using a dataset of 800 creators and their posts over two years on Twitter, we assess the impact of reposting on engagement. Our findings reveal a nuanced, non-linear relationship between reposting and engagement, moderated by creator indegree. Smaller creators experience diminishing returns from frequent reposting, while larger creators maintain engagement benefits with longer repost sequences. Content variety also moderates these effects: greater variety may benefit smaller creators if used in moderation, but may backfire for larger creators whose audiences expect thematic consistency. Additionally, reposting effectiveness depends on distribution patterns over time. Consecutive reposting boosts engagement for the next original post but plateaus after a point. In contrast, short, segmented repost chains interspersed with original posts yield higher cumulative engagement. This research highlights both the benefits and limitations of reposting strategies, offering insights into when and how reposting may lead to diminishing returns.

The Impact of Minority Representation in Sponsored Content and the Moderating Role of Brand Characteristics (with Keran Zhao, Penn State; Amy Pei, Northeastern University; Heshan Sun, University of Oklahoma), 2nd Round Review at Information Systems Research. 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), Under Review. 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.