Distribution Personalization and Content Decentralization on Brand Recall

Abstract

Structural equation modeling examines the impact of distribution personalization and content decentralization on brand recall among Gen-Z consumers to analyze the causal relationship between these factors. The research used Covariance Based–Structural Equation Modeling (CB-SEM) and Partial Least Squares–Structural Equation Modeling (PLS-SEM) based on review of related literature. The 385 samples were Gen Z (age 12 to 27 as of year 2024 randomly from Region III, Philippines, comprising the provinces of Aurora, Bataan, Bulacan, Nueva Ecija, Pampanga, and Tarlac. Questionnaires with rating scales were used as research tools. Confirmatory factor analysis was used to analyze model fit, reliability, and validity. A structural model and path analysis were used to examine the links. Causal relationships were analyzed using PLS-SEM. The findings indicated that the model had a reasonable fit with empirical data and exhibited acceptable reliability and validity. The results demonstrate that distribution personalization has a direct positive effect on Gen Z’s ability to recall a brand. In addition, content decentralization acted as a partial mediator or indirect effect on the relationship between distribution personalization and Gen Z brand recall. Based on these results, sellers of clothing brands reliant on short-form video ads may consider employing advanced data analytics technologies to acquire insights into the preferences, behavior, and demographics of the audience and personalize video content to specific audience segments like Gen Z. Furthermore, it is advisable to optimize distribution channels, incorporate user-generated content into distribution strategies, and emphasize usercentric storytelling to create favorable associations with the brand. Future research should be qualitative or mixed methods to obtain a richer qualitative interpretation, and cohort or longitudinal research should be conducted to study these effects eventually.