About

Jui Ramaprasad is an Associate Professor of Information Systems at the Robert H. Smith School of Business at the University of Maryland. She holds a PhD in Management, Information Systems from the Paul Merage School of Business at the University of California, Irvine, and a B.S. from the University of Southern California. Prior to her current position, she was an Associate Professor of Information Systems at the Desautels Faculty of Management at McGill University in Montreal, QC. Her research examines interactions on online platforms and their impact from multiple perspectives. Specifically, she studies the influence of platform features and social influence—measured by social media activity, social buzz, and social network characteristics—on user participation, interaction, consumption, and payment in online music and online dating platforms. More recently, her work has focused on the nefarious aspects of engagement in online communities, exploring the interplay between online incivility and engagement. She has presented her research at various information systems conferences and industry events, and her work has been published in prominent journals such as Information Systems Research, Management Information Systems Quarterly, and Management Science. She has served as a conference chair for multiple events, including the Conference on Information Systems and Technology (CIST) and the Workshop on Information Systems and Economics (WISE), and has been actively involved in conference program committees. Additionally, she reviews extensively for top IS journals and has served as an associate editor for Information Systems Research and Management Science.

Research topics

• Computer Science
• Sociology
• Business
• Political Science
• Economics
• Law
• Development economics
• World Wide Web
• Marketing
• Finance
• Microeconomics
• Transport engineering
• Demography
• Engineering
• Medicine
• Geography
• Demographic economics
• Immunology

Selected publications

• What's in a Name? More Than a Rose: Social Discoverability and Familiarity in Online Matching

  Journal of the Association for Information Systems · 2025-12-14

  articleSenior author

  This study investigates how social discoverability—defined by the presence of a digital footprint and the commonness of a name—shapes user behavior on online matching platforms. Using a field experiment with synthetic profiles, we find that users are less likely to accept requests from highly discoverable profiles, particularly those with unique names, despite lower search costs. Instead, users display a strong preference for name familiarity, suggesting that the halo effect outweighs informational benefits provided by verifiable digital traces. Moreover, profiles with a visible digital footprint are significantly more likely to be rejected, indicating that external visibility may trigger skepticism rather than trust. These findings challenge dominant assumptions in the literature on signaling and information asymmetry and highlight the importance of perceived congruence and cognitive heuristics in trust-intensive environments. The results offer design implications for digital platforms seeking to balance visibility, authenticity, and user trust in identity-sensitive decision-making contexts.

  Publisher

• Popularity Feedback and Adaptation Strategies in Online Dating: A Social Comparison Perspective

  MIS Quarterly · 2025-05-29 · 3 citations

  articleSenior author

  Digital platforms are increasingly employing informational nudges to motivate user participation. This paper examines the provision of popularity information as a feedback mechanism and its impact on users’ adaptation strategies. Leveraging ego utility theory and self-determination theory, we hypothesize that comparative popularity information—information that facilitates social comparison—will trigger different reactions based on gender and popularity level. In collaboration with an online dating service provider, we designed and conducted two randomized field experiments in which we provided popularity feedback to platform users and investigated their post-feedback behavioral changes in two adaptation strategies: the selectiveness in choosing potential partners (i.e., selectivity calibration) and the frequency of their online profile modifications (i.e., self-marketing). In the first experiment, where we revealed information about their popularity relative to other users, we found that those who received low-popularity feedback significantly increased self-marketing efforts and lowered their selectivity, but the opposite was observed in individuals who received high-popularity feedback. We also found that men readily made adaptations to their selectivity calibration and self-marketing, whereas women’s behaviors were more persistent as they exhibited little strategic change. We then conducted a second experiment in which we revealed absolute popularity instead of comparative popularity and observed no significant changes in adaptation strategies. Comparing the outcomes of the two experiments, we argue that it is the social comparison information associated with comparative popularity that drives user behavioral changes.

  PublisherDOI

• Labeling in Their Shoes: Improving Text Annotation with Cognitive Empathy Priming

  2025-10-29

  articleSenior author
  PublisherDOI

• Labeling in Their Shoes: Improving Text Annotation with Cognitive Empathy Priming (Extended Abstract)

  Proceedings of the AAAI/ACM Conference on AI Ethics and Society · 2025-10-15

  articleOpen accessSenior author

  Human-annotated labels are crucial for training and evaluating machine learning models especially in domains requiring human judgment. However, crowdsourced labelers frequently struggle to reach consensus and differ from expert consensus. Through studies in sexist content identification, we demonstrate how this systematic misalignment persists even with large numbers of annotators and significantly impacts model performance. To address this challenge, we introduce cognitive empathy priming (CEP)—a scalable psychological intervention that enhances annotators' ability to recognize perspectives different from their own. Our results show that CEP substantially improves label quality: empathy-primed labelers demonstrate around 10-20% higher alignment with expert consensus compared to standard crowdsourcing methods, while inter-rater consistency also improves dramatically. These improvements translate directly to model performance, with Large Language Models trained on empathy-primed labels showing approximately 16% higher agreement with expert-determined labels compared to those trained on control group labels. Our sensitivity analyses confirm these results remain robust even when accounting for potential expert biases. This research provides organizations with an cost-effective solution to enhance AI training data quality, particularly in subjective domains like content moderation and bias detection.

  PublisherOA PDFDOI

• The Impact of AI Chatbot Usage on Collective Problem Solving

  Open Science Framework · 2025-11-10

  otherOpen access

  This study investigates how the integration structure (shared vs. separate AI use) and continuity (continuous vs. intermittent access) of AI chatbot support affects how human teams reason, coordinate, and integrate distributed information.

  PublisherDOI

• Programming Tasks Impact Responses to Moral Dilemmas for Novice Programmers

  Information Systems Research · 2025-10-15

  article

  The rapid diffusion of programming skills across education and industry may impact how individuals consider moral dilemmas. Across a series of experiments, we show that performing even simple programming tasks shifts novice programmers’ evaluation of the classic trolley problem toward utilitarian responses. After solving a programming problem, respondents are more willing to sacrifice one life to save many. This effect arises because programming induces a deliberative, rule-based cognitive style. However, the effect diminishes with greater programming experience and can be mitigated through interventions, such as time delays or moral nudges. These findings highlight that organizations training employees in coding should be aware that programming tasks may temporarily alter moral reasoning, potentially influencing judgments in ethically charged contexts (e.g., product design, risk management, or AI development). Incorporating reflective cooling-off periods or explicit ethical reinforcement may reduce bias toward utilitarian reasoning. As programming becomes a baseline skill across the workforce, its cognitive spillovers could shape societal attitudes toward contested moral dilemmas. These include ethical trade-offs in settings such as autonomous vehicles and artificial intelligence systems.

  PublisherDOI

• How Do LLMs Impact Human-Provided Mental Healthcare Services? A Study of a Mental Health Forum

  Academy of Management Proceedings · 2025-07-01

  article

  Mental healthcare has become a concern globally, and the challenges of providing and accessing appropriate care are only magnified by a dearth of available providers. Large Language Models (LLMs) are anticipated to hold transformative potential for numerous industries including healthcare. LLMs demonstrate superior performance in medical expertise, communication skill, as well as emotional and social intelligence, and are expected to alleviate the problem of workforce undersupply in mental healthcare. However, the question of how human-provided services will evolve in response to the emergence of LLMs remains critical but unanswered. We focus on an online mental health forum wherein mental healthcare counselors offer support to seekers through question-and-answer (Q&A). In early 2023, an LLM-powered chatbot was integrated into the forum to automatically leave responses to seekers. We investigate changes in the engagement behavior and the nature of support from human counselors—both of which are essential for seekers’ mental well-being and counseling success. We observe human counselors’ disengagement and their downgrading support in the forum. Concerningly, the disengagement became more pronounced in tougher cases, such as seekers expressing suicidal ideation. Furthermore, the observed effects extended to services that were not directly exposed to the chatbot, indicating a spillover effect. Overall, our study provides important implications for AI integration in mental healthcare, offering actionable insights for multiple stakeholders.

  PublisherDOI

• Dancing to the #Challenge: The Effect of TikTok on Closing the Artist Gender Gap in the Music Industry

  MIS Quarterly · 2025-07-03 · 3 citations

  article

  This study investigates how “Hashtag Dance Challenges” (HDCs), a phenomenon popularized on the short-video platform TikTok, are instrumental in helping music artists gain traction in the digital music marketplace. HDCs represent an appealing combination of music and dance, designed to engage users and achieve virality, thereby benefiting artists whose music is featured. This research focuses on how HDCs contribute to the success of women artists, as compared to men, in an industry known for its diversity but challenged by gender inclusivity. We apply role congruity theory to posit that women artists are in a better position to derive benefits from being featured on HDCs, relative to male artists, particularly in cases of gender concordance—when both the creator and the artist are women. We measure the benefits of HDCs using daily changes in the artist’s followership on Spotify, a leading music streaming service, and test our hypotheses using song and artist-level data collected from Spotify and TikTok. We found that artists featured in a new HDC achieve a significant increase in followership on Spotify, relative to similar artists not featured in an HDC. Further, we observed that women creators drive this effect, enhancing the daily growth of Spotify followers by approximately 3% more for women artists, underscoring the value of gender concordance. Our findings shed light on the role of short videos, especially through the vehicle of HDCs, in advancing women artists, while also promoting inclusivity within the digital music industry.

  PublisherDOI

• Authenticity in the Age of AI Music: The Effect of GPT4 on Digital Music Consumption

  Academy of Management Proceedings · 2025-07-01

  articleSenior author

  This study investigates the impact of perceived authenticity on digital listenership for artists following the release of GPT-4. By utilizing online streaming data and live events data collected from various sources, we identify the effect of artists’ authenticity on their online streaming after GPT4’s release following a staggered Diff-in-Diff design. Our findings show that, on average, artists who demonstrated authenticity post-GPT4 experienced a 6.34% increase in digital streaming compared to those who did not. However, when these authentic artists were affiliated with major labels, the effect was reduced to 5.45%. In contrast, authentic artists affiliated with independent labels experienced a significantly larger increase of 8.90%. These findings underscore the growing importance of authenticity in an AI-influenced music industry, where consumers increasingly value genuine artist connections. Our study contributes to the literature on authenticity in the digital music landscape and offers practical insights for music industry stakeholders when facing the disruption of generative AI.

  PublisherDOI

• Gender Gating? Addressing the Impact of Congestion on the User Experience for Women in Online Matching Platforms

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Ravi Bapna

  12 shared

• Akhmed Umyarov

  12 shared

• Isabelle Vedel

  McGill University

  7 shared

• Galit Shmueli

  5 shared

• Liette Lapointe

  5 shared

• Geneviève Bassellier

  McGill University

  5 shared

• Gordon Burtch

  Boston University

  4 shared

• Sanjeev Dewan

  University of California, Irvine

  4 shared

Awards & honors

• Best Conference Paper Award at the Conference on Information…