TY - GEN
T1 - Predicting the Impact of Restaurant Automation and Food Safety in China
T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
AU - Lo, Ying Tuan
AU - Ma, Wenbo
AU - Tan, Andrew Huey Ping
AU - Majeed, Anwar P.P.Abdul
AU - Lau, Teck Chai
AU - Razak, Siti Suraya Abd
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The food service industry in China is rapidly growing, but food safety remains a concern, with over 200,000 cases of food poisoning reported from 2003 to 2017. As the industry modernises, it is important to maintain food safety standards while adopting technical advancements. However, many restaurants in China have low levels of automation due to a lack of understanding of customer acceptance. The lack of customer acceptance is due to: (1) the predictors of dining experience are different between smart and traditional restaurants, (2) where the smart dining experience might also be affected by automation and food safety. This conceptual paper aims to address these issues by identifying the fundamental predictors of smart dining experience and exploring how these predictors can be leveraged to improve food safety in automated restaurants. By doing so, we will develop a smart dining experience model that can replace traditional models as more restaurants adopt automation. This study aligns with China’s Made in China 2025 national plan, which encourages the use of automation and digital technologies in the food industry. Ultimately, this conceptual paper aims to present a novel theoretical framework and seeks to contribute to a safer and more efficient food service industry in China.
AB - The food service industry in China is rapidly growing, but food safety remains a concern, with over 200,000 cases of food poisoning reported from 2003 to 2017. As the industry modernises, it is important to maintain food safety standards while adopting technical advancements. However, many restaurants in China have low levels of automation due to a lack of understanding of customer acceptance. The lack of customer acceptance is due to: (1) the predictors of dining experience are different between smart and traditional restaurants, (2) where the smart dining experience might also be affected by automation and food safety. This conceptual paper aims to address these issues by identifying the fundamental predictors of smart dining experience and exploring how these predictors can be leveraged to improve food safety in automated restaurants. By doing so, we will develop a smart dining experience model that can replace traditional models as more restaurants adopt automation. This study aligns with China’s Made in China 2025 national plan, which encourages the use of automation and digital technologies in the food industry. Ultimately, this conceptual paper aims to present a novel theoretical framework and seeks to contribute to a safer and more efficient food service industry in China.
KW - Automation
KW - Food safety
KW - Intelligent restaurant
KW - Smart dining experience
UR - http://www.scopus.com/inward/record.url?scp=85187786535&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8498-5_14
DO - 10.1007/978-981-99-8498-5_14
M3 - Conference Proceeding
AN - SCOPUS:85187786535
SN - 9789819984978
T3 - Lecture Notes in Networks and Systems
SP - 181
EP - 187
BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
A2 - Tan, Andrew
A2 - Zhu, Fan
A2 - Jiang, Haochuan
A2 - Mostafa, Kazi
A2 - Yap, Eng Hwa
A2 - Chen, Leo
A2 - Olule, Lillian J. A.
A2 - Myung, Hyun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2023 through 23 August 2023
ER -