Integrated Grocery Application for Enhanced User Convenience and Efficiency

Abstract

Therising interestinsmart,easy-to-use,andrespon- sibly created mobile apps has resulted in a flexible framework designed for advanced grocery shopping applications. This study puts forward an all-encompassing mobile app framework that aims to enhance the grocery shopping experience by incorporat- ing AI-powered meal planning, suggestions for items based on recipes, budget monitoring in real-time, and voice commands. Rather than developing a single application, the intention is to offerareusableandadjustablemodelthatcan actasthebase for future grocery-related mobileapplications. To achieve this aim, the suggested framework relies on An- droid for mobile app creation and Supabase to handle backend functions such as real-time databases, user authentication, and data storage. Careful consideration has been given to developinga user interface that focuses on accessibility and intentionally avoids dark patterns, which are deceptive design techniques that can mislead users and undermine their freedom of choice. These design aspects are crucial for building trust and ensuring inclu- sivity, particularly for users dependent on assistive technologies. Anapplicationprototypewascreatedandtestedbyagroup of 15 students from Amity University. The app’s usability and effectiveness were assessed using three primary tools: the Cog- nitive Response Questionnaire Index (CRQI), the Engagement Satisfaction Index (ESI), and the App Usability Feedback Form. The findings showed that users were highlysatisfied, interactions were intuitive, and task efficiency was enhanced, especially when managing grocery lists and budgets through voice commands. In summary, this framework offers a practical and user- focused approach for intelligent grocery shopping that can be adapted for different user demographics. Its modular structure, along with a strong backend, positions it as an excellent founda- tionfordevelopers aimingtocreateethical, efficient, andscalable grocery mobile applications.

Introduction

The grocery shopping experience has shifted significantly toward personalized and efficient online platforms, with the global online grocery market expected to grow from USD 354 billion to USD 800 billion by 2029. Despite this growth, many current grocery apps face challenges such as cognitive overload, lack of personalization, poor accessibility, and the use of unethical "dark pattern" designs.

This research proposes a modular, reusable framework for intelligent grocery shopping apps that incorporates AI-driven meal planning, recipe-based item suggestions, budget monitoring, and voice-controlled list management. These features aim to reduce user effort, support budget-conscious shopping, and improve accessibility, especially for college students, working adults, and small families who struggle with meal organization and financial planning.

The study emphasizes ethical design, avoiding manipulative UI practices common in many top shopping apps, and follows accessibility standards like WCAG 2.1. It utilizes Supabase as a backend for scalable, secure development, and an Android-native frontend to integrate voice commands effectively.

Testing with university students showed high user satisfaction, highlighting the effectiveness of AI meal planning, voice interactions, and real-time budget alerts in reducing mental load and improving engagement. Users recommended adding features like dark mode and theme customization for enhanced usability.

Conclusion

This study focused on creating, developing, and assessinga versatile smart grocery app framework that emphasizes AI functionalities, ethical considerations, and accessibility. In contrast to conventional grocery applications that tend to prioritize aggressive profit strategies and standard features, this framework incorporates voice-enabled organization, AI-driven recipe recommendations, and real-time budget monitoring, all while following user-centered designprinciples. Utilizing Android for the user interface and Supabase as a reliable open-source backend, the prototype illustrated that it is possible to build effective yet lightweight systems without sacrificing performance. The modular nature of the framework allows for flexibility, making it suitable for various settings whether for private users, groups of students, or larger com- munity food access initiatives. Perhaps the most important aspect of this project is its commitment to anethically drivendesignapproach.Inan era where deceptive practices and manipulative user interface strategies are frequent, this app framework presents a clear, respectful,andinclusiveoption.Thisethicalframework,paired with intelligent automation and user-friendliness, could setnew standards in the grocery and retail technology fields. In summary, the suggested framework serves not only as a prototype but also as a fundamental resource for developers andresearcherswhowish tocreateimpactful, user-centric gro- cery applications. Future developments could include features such as multilingual voice capabilities, AI-oriented nutrition tracking, and machine learning technologies for customized groceryoptimization.

References

[1] Statista.(2024).GlobalOnlineGroceryMarketValue2024[Online]. [2] Availableat:https://www.statista.com [3] NationalInstitutesofHealth.(2023).SurveyonCollegeStudents’EatingHabits [Online]. Available at: https://www.nih.gov [4] CentersforDiseaseControlandPrevention(CDC).(2021).FoodAller-gies [Online]. Available at: htt ps://www.cdc.gov [5] PewResearch Center.(2022).VoiceAssistantsandDisabilities[Online]. [6] Availableat:https://www.pewresearch.org [7] ACM. (2022). Dark Patternsin Mobile Shopping Apps[Online]. ACM.(2022). Dark Patterns in Mobile Shopping Apps [Online]. Available at:https://www.acm.orgAvailable at:https://www.acm.org

Copyright

Copyright © 2025 Prithvi Raj Sharma, Rachna Jain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.