Hartoyo
School of Business, IPB University, Bogor, Indonesia

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Understanding Preference and Usage Intention in Online Food Delivery among Generation Z in Greater Jakarta Kristin Elisabeth Siregar; Hartoyo; Popong Nurhayati
Journal of Consumer Sciences Vol. 11 No. 2 (2026): Journal of Consumer Sciences
Publisher : Department of Family and Consumer Sciences, Faculty of Social and Human Ecology, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jcs.11.2.347-369

Abstract

Background: Generation Z is the primary users of Online Food Delivery services (OFD). Therefore, it is important to understand their preferences and intentions regarding the use of OFD. Purpose: This study examines how the five dimensions of the Theory of Consumption Values, consisting of functional, social, emotional, epistemic, and conditional values, influence usage preferences and usage intentions among Generation Z in the Greater Jakarta area. Method: This study employed a cross-sectional survey design, collecting data from 120 Generation Z respondents aged 18 years and older who had used OFD services in the past three months through voluntary sampling. Structural Equation Modeling Partial Least Squares (SEM-PLS) was used for analysis. Findings: Emotional and conditional values significantly influenced preference, while functional, social, and epistemic values did not. Emotional value also had a significant direct effect on intention, while emotional and conditional value indirectly affected intention through preference. In addition, preference significantly influences intention. Conclusions: The findings indicate that Generation Z’s OFD behavior is driven more by enjoyable experiences and situational relevance than by utilitarian, social, or novelty-based considerations. Research implication: OFD businesses should strengthen the emotional appeal and practical convenience of their offerings in specific situations to enhance user preference and increase usage intention. Future research could expand the model by including additional variables.