A Comparative Analytical Approach for Predicting Continuance Intention in Mutual Fund Investment Apps
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Regression Models, Service Quality, Continuance Intention, Mutual Fund Investment Apps
Abstract
Global economic challenges have spurred a rise in household investments worldwide, including in Indonesia. By 2023, retail investors in Indonesia’s capital market have quadrupled, reaching over 12 million, with mutual fund investors driving this surge. Many new investors use mutual fund investment applications to purchase and manage their portfolios. Despite this growth, providers face challenges in maintaining user loyalty and increasing average investments due to Indonesia’s low financial literacy rate of 38.03%. This study investigates the factors influencing users’ adoption and continuous use of mutual fund investment apps. It examines the relationships between perceived service quality, perceived security and privacy, trust, familiarity, user satisfaction, and continuance intention. The research uses a comparative approach, combining Structural Equation Modeling (SEM) with machine learning regression models, including linear regression, support vector regression, and multilayer perceptron, to create a robust and comprehensive model. The findings reveal that trust significantly impacts both user satisfaction (β = 0.463, p < 0.001) and continuance intention (β = 0.194, p < 0.05). Perceived service quality strongly influences user satisfaction (β = 0.523, p < 0.001), while familiarity plays a key role in fostering trust (β = 0.224, p < 0.001) and encouraging continued use (β = 0.206, p < 0.001). Based on these results, strategies to enhance user loyalty, such as improving security, services, and providing transparent recommendations, are proposed. This study contributes to understanding digital investment behavior in emerging markets and offers insights for expanding financial inclusion.
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