Academic Job Market Candidate

• Position Sought: Tenure-Track Assistant Professor in Management Information Systems
• Research Areas: Human–AI Interaction, Hyper-realistic AI Agent, AI Governance
• Availability: I will be attending CIST, INFORMS, WISE, and WITS and welcome the opportunity to meet with search committees.

A Short Bio:

Meixian (Mei) Wang is a Ph.D. candidate in Information Systems Management at the Fox School of Business, Temple University. Before joining Temple, she earned her master’s degree in Business Analytics and Project Management from the University of Connecticut. Prior to her academic career, Mei accumulated six years of industry experience in the retail industry, working with global brands such as Uniqlo and Adidas in China.

Her research lies at the intersection of intelligent systems, digital platforms, and behavioral economics, with a particular focus on prosocial behavior and AI-human interaction in consumer-facing environments. She explores how technologies like hyper-realistic AI agents and platform-integrated donation tools reshape user engagement, trust, and monetization on digital platforms.

Using a combination of large-scale field experiments, quasi-experimental designs, and econometric analysis, Mei investigates the psychological and economic consequences of human-AI collaboration in digital commerce and the creator economy. She has taught and assisted in courses on data and web analytics and is committed to inclusive, student-centered pedagogy.

Dissertation:

Title: Three Essays on Prosocial Behavior and the AI Agent Disclosure Dilemma in the Digital Economy

• Committee: Sunil Wattal, Jaehwuen Jung, Konstantin Bauman.
• Proposal Defense: July 2025
• PhD Expected: May 2026

Research Topics:

• Social & Economic Impact of Artificial Intelligence
• Hyper-realistic AI Agent
• Human-AI collaboration
• Creator economy and Influencer marketing

Research Methodologies:

• Field and Lab Experiments
• Econometrics and Causal Inference
• Machine Learning

Working Papers

• Meixian Wang, Jaehwuen Jung, Ravi Bapna “The Impact of Realism and AI Disclosure on Virtual Influencer Effectiveness: A Large Field Experiment” Invited for 2nd round review at Information Systems Research – Job Market Paper.

  Conference presentations: PlatStrat2024, CIST 2024, INFORMS 2025

• Meixian Wang, Keran Zhao, Jason Bennett Thatcher. “The Impact of Superstar Exits on Live Streaming E-Commerce Platforms.” Invited for 2nd round review at Journal of the Association for Information Systems.

  Conference presentations: WITS 2022 (Best paper nomination).

• Meixian Wang, Keran Zhao, Sunil Wattal “When Prosocial Opportunities Collide: Tipping vs. Charity on Digital Platforms” Under Review at Information Systems Research.

  Conference presentations: WISE 2024, CIST 2025

• Meixian Wang, Jaehwuen Jung. “Personalization–Privacy Tradeoffs and AI Identity Disclosure: Evidence from a Large-Scale Field Experiment with Hyper-Realistic AI Agents.” Field experiment and data analysis in progress.

  Conference presentations: ACR 2025

• Meixian Wang, Keren Zhao Jason Bennett Thatcher. “AI versus Human? Investigating the heterogeneous effect of online shopping live streamers.” Data analysis and draft.

  Conference presentations: AMCIS 2022, INFORMS 2024, WITS 2024

Honors and Awards

• 2025 Research Enhancement for Competitive and Accelerated Publishing (RECAP) Award, $2000
• 2025 Exceptional presentation at the 2025 Graduate Symposium for Research and Creative Works, $100
• 2024 1st Research Advancement and Impact Seed funding (RAIS) Symposium Award, $2000
• 2021-2023 Young Scholars Interdisciplinary Funding, $10,900
• 2022 Best Paper Award Nomination of Workshop on Information Technologies and Systems (WITS)
• 2019 Technology Incubation Program Scholarship, $5000

Teaching and Interests

• Business analytics (including python for business analytics, data mining, data visualization, and machine learning)
• Databases management
• Information systems management and AI in business

Teaching Experience

• Instructor, MIS2502: Data and Analytics (Undergraduate level; MIS Core class)
• Evaluation: 4.4 / 5 (Fall 2024), Enrollment: 54
• Self-built Course Website: https://community.mis.temple.edu/mis2502sec003fall2024/