I am Justin, a PhD candidate in Information Systems at the Warrington College of Business, University of Florida. I am currently on the academic job market, seeking IS faculty positions where I can contribute to cutting-edge research at the intersection of technology and society.
My work employs rigorous econometric methods, machine learning, and field experiments to understand the complex relationships between technology and societal well-being. My research investigates how the introduction and regulation of emerging technologies influence human behavior, business outcomes, and social welfare. Specifically, I examine how Health IT reshapes healthcare delivery and patient outcomes. My work highlights the unintended consequences and profound social implications of technological disruption.
Education
2021-2026
PhD in Information Systems
University of Florida, Gainesville, FL
GPA: 3.96/4.0 | Advisor: Dr. Liangfei Qiu
2019-2021
Master of Data Science
Brown University, Providence, RI
2015-2019
Bachelor of Mathematics & Economics (Double Major)
Lafayette College, Easton, PA
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For a comprehensive view of my academic background, publications, and achievements
My primary research examines the unintended consequences and inequality implications of emerging technologies. I investigate how platforms and AI-driven services, such as Generative AI, robotaxis, ride-hailing, live streaming, and online gambling, impact human behavior, economic outcomes, and social welfare.
Health IT
My research also investigates how digital technologies are transforming the healthcare ecosystem. I examine the impact of innovations like telemedicine and online health platforms on the behavior of both patients and physicians, and the subsequent effects on healthcare delivery and outcomes.
Working Papers
Topic 1: Societal & Economic Impact of Emerging Technology
Pleasure or Peril? The Rise of Online Sports Gambling and Its Mental Health Impact
Jiayuan Tian, Guohou Shan, Michael Rivera, Liangfei Qiu, Md Mahmudul Hasan
Abstract: The rapid development and deployment of robotaxis mark a transformative shift in urban mobility, offering a novel transportation mode with far-reaching implications for traffic systems, public safety, and human behavior. Although robotaxis are believed to be safer than human drivers, it remains unknown how they may affect human driver behavior. Under the framework of social learning theory, human drivers may adopt safer driving behaviors by observing the lawful and consistent patterns of robotaxis. Conversely, in line with risk compensation theory, the perceived low-risk environment due to robotaxis' conservative and predictable nature may prompt them to engage in more aggressive driving. Our study addresses this ambiguity by exploiting a natural experiment and investigating the impact of robotaxis on human traffic violations, leveraging a unique panel dataset from Phoenix. Our findings highlight that following the presence of robotaxis, human drivers reduce traffic violations by approximately 19% on average, reinforcing the social learning effect. We further examine the heterogeneous impacts, revealing a pronounced learning effect in contexts with elevated attention to robotaxis and a diminished effect among elderly drivers. Our study contributes to the Information Systems literature by uncovering the positive social externalities of an emerging but underexplored application of artificial intelligence.
Substitute or Supplement? Generative AI and News Consumption
Jiayuan Tian, Zixi Lei, Wen Wen, Liangfei Qiu
Working Paper
Topic 2: Health IT
Primed for Growth or Fade: An Empirical Investigation of the Impact of Telemedicine Adoption on Healthcare Channel Migration Behavior
Peer Power: How Physician Peer Endorsements Shape Patient Choices on Online Healthcare Platforms
Lun Li, Jiayuan Tian, Qiuju Yin, Liangfei Qiu, Zhijun Yan
Information Systems Research Under Major Revision
Does Digital Mental Healthcare Service Access Affect Care Outcomes? Evidence from a Natural Experiment
Guohou Shan, Jiayuan Tian (Co-first author), Michael Rivera, Liangfei Qiu
Working Paper
Abstract: Depression and anxiety are common mental health conditions that demand appropriate treatment and care. Digital mental healthcare platforms have emerged as a recent solution to address this issue. However, it remains unclear how access to mental healthcare services over digital platforms can impact care outcomes. To explore this, we leverage a natural experiment and examine the impact of the introduction of Done, a digital mental healthcare platform, on care outcomes in the United States. Using a difference-in-differences (DID) estimation on more than ten years of county-level data, we find that the introduction of digital mental healthcare platforms can significantly reduce average mentally unhealthy days by 1.71% and the percentage of adult smokers by 34.6%. Moreover, our results suggest that this impact is more pronounced in counties with higher percentages of disadvantaged groups and internet penetration, confirming the theoretical mechanisms of accessibility and availability. Our study has both theoretical and practical implications for digital platform owners, managers, and county policymakers, providing insights into how digital platforms can be used to enhance mental healthcare outcomes.
Angel or Devil : Telehealth Across Borderlines and Healthcare Outcomes
Jiayuan Tian, Guohou Shan, Liangfei Qiu
Working Paper
Abstract: Telehealth adoption is becoming common in the healthcare industry, aiming to improve the equitable allocation of healthcare resources across different counties and states. However, it could be a double-edged sword. On the one hand, the adoption of telehealth could benefit undeveloped areas where patients could access more resources from developed areas through telehealth. On the other hand, the adoption of telehealth could drain healthcare resources in developed areas, worsening the quality of life. Aiming to uncover the effect of telehealth adoption, we leverage a Quasi-natural experiment, states' entry into the Interstate Medical Licensure Compact, with the difference-in-difference model by analyzing ten years of data across the counties in the United States. Our results reveal that joining interstate telehealth compacts does not necessarily improve the quality of life. In contrast, the adoption of telehealth increases patients' physically unhealthy days. Also, we found that the rural rate moderates the main effect. Specifically, telehealth adoption benefits the rural area more than the urban area. Our findings have important managerial implications for policymakers.
An Empirical Study of the Feedback Source on Real-time Doctor Feedback in a Crisis
Jiayuan Tian, Guohou Shan, Michael Rivera, Liangfei Qiu
Working Paper
Abstract: Real-time feedback can improve clinical outcomes in healthcare environments like hospitals and foster a more engaged, resilient workforce. Within hospitals, the exchange of real-time feedback between physicians and residents provides valuable insights into workplace behavioral competencies. A public health crisis may differentially affect the feedback-writing behaviors of physicians and residents toward their colleagues. Crises can intensify incidents against caregivers, including physicians and residents, highlighting the heightened risks they face. This study explores how physicians and residents may differ in their generation of performance feedback during a crisis. Furthermore, we assess whether the feedback writers' network embeddedness moderates the effect, given its role in shaping feedback behaviors. Leveraging a natural experiment, we conduct difference-in-difference models on proprietary data. We found that physicians are nicer during a public health crisis in providing their real-time feedback, and the network embeddedness further enhances the impact. Our research has important theoretical and managerial implications.
Selected Conference Presentations (2023-2025)
CHITA 2025 (Austin, TX), POMS 2025 (Atlanta, GA), INFORMS 2025 (Atlanta, GA) - "Pleasure or Peril? The Rise of Online Sports Gambling and Its Mental Health Impact"
CIST 2024 (Seattle, WA) - "How Does Ride-Hailing Congestion Pricing Shape Digital Access and Transportation Equity?"
AMCIS 2024 (Salt Lake City, UT) - "Angel or Devil: Telehealth Across Borderlines and Healthcare Outcomes"
AMCIS 2024 (Salt Lake City, UT) - "An Empirical Study of the Feedback Source on Real-time Doctor Feedback in a Crisis"
POMS 2024 (Minneapolis, MN) - "Primed for Growth or Fade: Telemedicine Adoption and Healthcare Channel Migration"
POMS 2024 (Minneapolis, MN), INFORMS 2023 (Phoenix, AZ) - "Does Digital Mental Healthcare Service Access Affect Care Outcomes ? Evidence from a Natural Experiment"
Machine Learning: Predictive modeling, data analytics, deep learning
Field Experiments: Large-scale randomized controlled trials in digital environments
Programming Skills: Python, R, SQL, Stata, Matlab, Latex, HTML
Teaching Experience & Philosophy
I am passionate about helping students connect theoretical knowledge with practical application in information systems and data analytics. In my classroom, I bring concepts to life by presenting material in a clear, organized way and using relevant, real-world examples to inspire curiosity. I cultivate a hands-on, interactive environment where students learn by doing. My commitment to their success extends beyond the classroom, as I make it a priority to be an accessible and supportive resource.
Teaching operations analysis to 49 undergraduate students | Student Evaluation: 4.6/5.0
Selected Student Comment
"Professor Tian is a very passionate
instructor. You can tell he cares a lot about his students and came to class each day with a positive
demeanor which made learning even more enjoyable. He was always very clear about his course
expectations, his lectures were methodical, logical, and well thought out, and he was very
receptive to the midterm evaluation suggestions. This was my one of my last classes to take here
at UF, as a senior, and Professor Tian is one of my favorite instructors I've had."
Courses I'm Prepared to Teach
Information Systems
Introduction to Information Systems
Database Management
Digital Innovation & Entrepreneurship
Health Information Systems
Analytics & Methods
Data Analytics for Business
AI Applications in Business
Business Statistics
Research Methods in IS
Honors & Services
Recognition & Services
2025
CHITA Doctoral Consortium
Selected Participant
2024
Inaugural INFORMS ISS Doctoral Consortium
Volunteer and Selected Participant
2024
POMS Doctoral Consortium
Selected Participant
Journals & Conferences Referee
Production and Operations Management
Conference on Information Systems and Technology (CIST)
International Conference on Information Systems (ICIS)
Workshop on Information Technologies and Systems (WITS)
Americas Conference on Information Systems (AMCIS)
Hawaii International Conference on System Sciences (HICSS)
Conference Session Chair
POMS 2025, Healthcare Analytics Track
CHITA 2025
Membership
The Institute for Operations Research and the Management Sciences (INFORMS)
The Production and Operations Management Society (POMS)
