Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

3-2026

Abstract

This study is based on consumer movement trajectory data and examines the relationship between traffic spillover among stores within shopping centres and their rent ratios, responding to the practical limitation that traditional rental pricing mechanisms often overlook the dynamic structure of customer flows. Using micro-level consumer movement data and store rental data from three shopping centres in Shenzhen, Nanjing, and Chongqing, this paper constructs a store transition network reflecting customer movements between stores. Social network analysis is employed to measure stores’ network influence indicators (such as degree centrality, betweenness centrality, and structural holes) as well as traffic spillover indicators derived from one-step and multi-step transition probabilities. While controlling for traditional determinants of rent—including vertical location, horizontal location, brand tier, and store category—the study examines the relationship between network structure variables and rent ratios.

The results show that a store’s structural position in the consumer movement network and its traffic spillover capacity are not consistently or systematically capitalised into lower rent ratios across all samples, and certain differences exist among shopping centres of different scales. In some contexts, weighted closeness centrality and spillover intensity indicators that incorporate customer flow scale display a negative relationship with rent ratios. This suggests that stores undertaking customer attraction and path-extension functions may contribute to the overall value creation of the shopping centre through rental discount mechanisms, rather than being directly reflected in higher rent levels. Overall, the current rental structure remains primarily driven by static location attributes and brand factors, while the dynamic traffic value revealed by the consumer movement network has not yet been fully incorporated into the pricing system.

The theoretical contribution of this study lies in integrating Customer Journey Theory, tenant mix and rental strategy theory, and social network theory, while introducing the concepts of network influence and traffic spillover to address the empirical gap in quantifying store interaction value using real consumer movement data. Methodologically, the study improves the accuracy of traffic network identification by designing spillover measurement formulas and conducting threshold-pruning robustness tests. From a practical perspective, it provides an analytical framework and decision-making basis for shopping centres to move from experience-based pricing towards structure-based pricing, and to explore traffic-based rent mechanisms and data-driven rental optimisation strategies.

Keywords

Consumer Movement Data, Social Network Analysis, Traffic Spillover, Rent Ratio, Traffic-based Rent Mechanism

Degree Awarded

Doctor of Bus Admin (CKGSB)

Discipline

Marketing | Real Estate

Supervisor(s)

CHANG, Han-Wen Hannah

First Page

1

Last Page

242

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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