Toward Resilience: Assessing Retail Location’s Complex Impact Mechanism Using PLS-SEM Aided by Machine Learning

Because urban retail faces challenges in sustaining vitality and viability, risking decay in urban centers, retail space resilience (RSR) has become a pressing concern. Retail location presents an opportunity because it aligns with RSR in maximizing store vitality and adopting a long-term perspective. This study uses PLS-SEM to examine the complex impact mechanism of retail location attributes (LAs) on retail space resilience (RSR), based on 304 retail spaces in central Shanghai. LAs and RSR are assessed based on a metrics system, followed by Random Forest for variable selection. An impact pathway framework grounded in key theoretical models is then constructed. The results from the PLS-SEM analysis show that Amenity exerts the strongest direct influence on RSR (β = 0.383), followed by Agglomeration (β = 0.294) and Accessibility (β = 0.291), while the results of the mediation effect further reveal that RSR is primarily shaped by consumers’ trip-chaining behaviors, with agglomeration effects and the spatial interaction model playing secondary roles. Notably, the scale of the retail space negatively affects RSR (β = −0.016), suggesting that large retail centers may be less resilient due to weaker consumer attachment. Overall, our research suggests that consumers’ perceptions and behaviors play key roles in RSR. Based on this insight, this study proposes placemaking strategies aimed at fostering consumer attachment and developing neighborhood-oriented retail nodes aligned with consumers’ preferences.