Mason School of Business · William & Mary
Understanding behavior.
Developing judgment.
I study the unintended consequences of information systems, and teach business students to turn evidence into informed decisions.
Raymond A. Mason School of Business
01 / Research
The human side of information systems.
My research examines the unintended consequences of information systems, from choices on online platforms to the information people share and the networks they build.
All publications and working papers →Selected studies
01Organization Science · 2026FT50Who gains when colleagues move together?
Colleagues that Work Together Move Together: How Co-Moving Among Newcomers Affects Individual and Team Performance
The study compares newcomers who move to a new team together with colleagues from their previous team (co-movers) to those who move alone (solo movers).
- Approach
- Archival NBA data and qualitative interviews
- What the study shows
- Co-movers outperform solo movers, and the advantage persists. Teams with co-movers perform worse than teams with solo movers, with partial evidence that the gap narrows over time.
02MIS Quarterly · 2022FT50How do social cues influence choice?
Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?
The study examines how positive social cues address users’ concerns about sponsored results in consumer-to-consumer e-commerce.
- Approach
- Three laboratory experiments
- What the study shows
- Product-related and seller-related social cues have different effects on cognitive, behavioral, and affective avoidance of sponsored search results.
03Information Systems Research · 2022FT50Why do we share unverified information?
Seems Legit: An Investigation of the Assessing and Sharing of Unverifiable Messages on Online Social Networks
The research distinguishes information leaks from rumors and examines both observed sharing behavior and the psychological processes behind it.
- Approach
- Twitter data and a controlled experiment
- What the study shows
- Unverifiable messages are reshared when people see them as useful or novel. Plausibility works together with vividness and sender credibility in shaping that assessment.
02 / Teaching
Data literacy for better business judgment.
I use real business problems to help students with different technical backgrounds turn data into decisions.
Teaching approach and courses →Teaching with AI · BUAD 466
Developing judgment with AI.
In Developing Business Intelligence, students learn to read, test, and improve AI-generated data models and queries.
- Think independentlyIdentify the entities, relationships, and business rules before seeing the AI version.
- Inspect the AI draftCheck for missing entities, unique identifiers, and relationships that misrepresent the scenario.
- Explain the judgmentUse the original requirements to explain what to keep, what to change, and why.
Do enough to judge. Judge enough to decide.
03 / Service
Editorial service and recognition.
Associate editor at three journals, including MIS Quarterly and the Journal of the Association for Information Systems.
Editorial roles
- Associate EditorMIS Quarterly2026 –
- Associate EditorJournal of the Association for Information Systems2026 –
- Associate EditorDecision Support Systems2025 –
- Editorial BoardAIS Transactions on Human-Computer Interaction2025 –
Recognition
- Best Reviewer of the YearJournal of AIS2024
- Best Reviewer AwardAIS THCI2023
- Dean's Research FellowWilliam & Mary2021–2025
- Best Accepted PaperAcademy of Management2017
- New publication in Organization Science — Colleagues that Work Together Move Together: How Co-Moving Among Newcomers Affects Individual and Team Performance
- Appointed Associate Editor — MIS Quarterly
- Appointed Associate Editor — Journal of Association for Information Systems
- New publication in JAIS — Virtual Reality, Mental Models, and Mindful Decision-Making
- Best Reviewer of the Year (2024) — Journal of Association for Information Systems
- AI Initiatives Grant awarded — Raymond A. Mason School of Business, 2024