• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.375-387
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• 2022

Purpose - The food delivery mobile application market is growing rapidly in the catering service industry. The purpose of this study was to investigate the effects of E-service quality on consumer attitudes and satisfaction in the food delivery service field in China. Design/methodology/approach - A total of 390 copies of this questionnaire were distributed between February 17 and March 25, 2021 on the Chinese survey site (https://www.wjx.cn). Three hundred forty-nine parts were used for the final analysis. Validity and reliability were analyzed using SPSS 25.0 statistical program, and correlation and regression analysis were performed. Findings - The study results showed among the e-service quality components of food delivery application software, trust, convenience, proximity and reactivity have an impact on consumer attitudes. Research implications or Originality - The service quality of food application software is an important factor determining consumer attitude. The limitations of this study and suggestions for future research were discussed.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.112-114
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• 2022

This paper proposes an E-Commerce service that supports large quantities of food waste sales generated by collective residences, including apartments, to consumers in urban areas, such as livestock farmers, through online transactions. Unlike general E-Commerce, the proposed service uses a smart food waste bin equipped with an IoT-based sensor and communication module to automatically collect the location information of each apartment and the amount of food waste to be displayed in a specialized E-Commerce platform. The key of this system is to provide information and sell it to consumers. The smart food waste bin periodically delivers its current capacity and location using a weight sensor, GPS sensor and LoRa communication module to a cloud-based database to be used in web or mobile applications. The proposed E-Commerce service is expected to help resolve the food waste disposal problem and revitalize the local economy by linking with a service that delivers food waste from each apartment to a nearby location where the buyer is located.