Abstract In Malaysia, the rapid proliferation of shopping malls has heightened consumer expectations, driving malls to diversify their scale, formats, and characteristics. This study employs block clustering to simultaneously classify consumers (rows) alongside area brand asset factors in Subang Jaya and store image factors at Sunway Pyramid (columns). Compared to traditional methods like K-means, this machine learning technique more efficiently processes complex data interrelationships. The analysis revealed minimal overlap between area-specific and mall-specific factors within the expectation clusters, suggesting a relatively weak correlation between the two. Furthermore, the identified consumer clusters reflect distinct trends in age, transportation, number of shopping companions, and the prioritization of the area versus the mall. These findings offer strategic insights into leveraging area brand assets and store image factors to enhance regional appeal and shopping mall satisfaction.
Yamada et al. (Sun,) studied this question.