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Annals of Data Science

13.09.2024

Determining the Correlation among the Users' Satisfaction and Familiarity with Malay Entrepreneurs Food Delivery Mobile Applications in Malaysia

verfasst von: Muhamad Redha Iqbal Bin Daud, Norhidayah Abdullah, Lovelyna Benedict Jipiu

Erschienen in: Annals of Data Science

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Abstract

The rise of mobile technology has significantly transformed numerous aspects of our everyday lives, especially within food delivery services. The investigation aims to explore the food delivery mobile apps (FDMA) satisfaction (SAT) and the influence of familiarity (FAM). Data was gathered from 381 individuals who have experience in using any FDMA services specifically in Shah Alam, Selangor with the aid of online questionnaires. The study findings indicate user satisfaction (US) with FDMA is strongly influenced by the level of familiarity users have with the platform. The research result shows the satisfaction of users with FDMA is strongly linked to how easy they find the platform to use. The research provides a unique contribution by exploring the influence of familiarity on the US with FDMA. Investigating how users' prior experiences and comfort levels impact their satisfaction provides valuable insights for enhancing app design and user experience in the rapidly evolving food delivery industry. The study contributes by elucidating the significant impact of FAM on FDMA satisfaction. This insight aids in refining app design and strategies to enhance user experience. The study suggests optimizing FDMA by prioritizing features that enhance user FAM, ultimately developing higher SAT levels and improving overall user experience. The research findings indicate a notable correlation between the US and the inclination to maintain the usage of FDMA systems.

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Titel: Determining the Correlation among the Users' Satisfaction and Familiarity with Malay Entrepreneurs Food Delivery Mobile Applications in Malaysia
verfasst von: Muhamad Redha Iqbal Bin Daud
Norhidayah Abdullah
Lovelyna Benedict Jipiu
Publikationsdatum: 13.09.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00568-7

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