Research Interests

Substantive: Team collaboration, Learning Process, Network Evolution, Layout Design

Methodological: Deep learning, Graph/Hypergraph Neural Networks, Econometric Models

Job Market Paper

• Collaboration and Knowledge Sharing Within and Across Teams: A Hypergraph Neural Network Modeling Approach 

Collaboration and knowledge sharing are foundational to modern organizations, especially in environments where teamwork drives performance. However, understanding how individuals contribute to and learn from multiple teams remains empirically and methodologically challenging. This paper introduces a Two-Stage Hypergraph Neural Network (TSHyGNN) framework to model agent behavior in multi-agent, multi-team settings. Leveraging hypergraph structures, the model captures both within-team collaboration and cross-team knowledge transfer. To address endogeneity in team formation, we adopt a two-stage estimation approach: the first-stage hypergraph model predicts team formation and produces a correction term, which is incorporated into the second-stage performance model to account for selection bias. Using salesforce data from a large financial services firm, we show that TSHyGNN outperforms traditional econometric and network-based models in predicting team outcomes. Simulation results suggest that replacing a novice with an experienced salesperson can generate approximately $10K in direct team collaboration gains and $16K from knowledge spillovers across teams, with sales increasing by 1-3%. The deep learning architecture is designed to be interpretable by aligning its components with collaboration and knowledge-sharing processes. This approach offers a scalable and flexible tool for team design, with broad applicability to settings characterized by overlapping team structures and dynamic collaboration networks.

Presentations:

• AMA Sheth Doctoral Consortium, Manchester, UK, June 2024

• ISMS Marketing Science Conference, Sydney, Australia, June 2024

Working Papers

• Qianyin Xia, Yi Zhao, Cheng He: “Optimizing Shopping Mall Layout and Foot Traffic Using Graph Neural Networks.” 

Effective shopping mall layout design is critical for optimizing foot traffic flow and maximizing overall profitability. Retail tenants often encounter challenges in selecting optimal store locations when relying solely on historical traffic data, which can lead to suboptimal decisions. Likewise, shopping mall owners must balance revenue maximization with ensuring seamless consumer movement throughout the mall. Addressing these challenges requires a holistic understanding of the entire mall layout rather than evaluating individual store performance in isolation. This study proposes a novel approach that utilizes Graph Neural Networks (GNNs) to analyze and optimize shopping mall layouts by leveraging large-scale daily foot traffic data from one of the largest malls in China. The proposed GNN framework enables predictive analysis at both the node and edge levels, allowing for the forecasting of tenant store sales and foot traffic flows. The study evaluates the impact of different tenant placement strategies, comparing clustered (same-category) and mixed (cross-category) configurations. It also investigates the strategic positioning of essential non-sales service units such as information desks, play areas, and restrooms, which serve as key traffic attractors. The findings of this research provide actionable insights for shopping mall property owners and offer data-driven recommendations for designing layouts that enhance both profitability and the consumer experience. 

Presentations:

• ISMS Marketing Science Conference, Washington D.C., June 2025

• Qianyin Xia, Yi Zhao, and Sarang Sunder: “Modeling Agent Performance with Dynamic GNNs: The Role of Network Evolution, Office Culture, and Peer Influence” 

Presentations:

• ISMS Marketing Science Conference, Miami, FL, June 2023

• GSU-Emory Brown Bag Seminar, Atlanta, GA, Aug 2023

• AI Machine Learning and BA Conference, Philadelphia, Dec 2023

• AMA Winter Conference, St Peter, Feb 2024

• Amit Agarwal, Qianyin Xia, Yi Zhao: “Modeling Scholarly Collaboration Across Marketing Journals: A Graph Neural Network Analysis of Cross-Channel Structures on the AMA Platform” 

Presentations

·   Modeling Salesperson Networks Using Dynamic Graph Neural Networks

ISMS Marketing Science Conference, Miami, FL, June 2023

GSU-Emory Brown Bag Seminar, Atlanta, GA, Aug 2023

AI Machine Learning and BA Conference, Philadelphia, Dec 2023

AMA Winter Conference, St Peter, Feb 2024

·   Modeling Learning in Teams using Deep Hypergraph Neural Networks: An Application to Salesperson Networks

    AMA Sheth Doctoral Consortium, Manchester, UK, June 2024

  ISMS Marketing Science Conference, Sydney, Australia, June 2024