서빙로봇 인식 분석을 통한 푸드테크 발전 방안 탐색

Objective: This study analyzes unstructured data extracted from social media and online reviews to explore perceptions of serving robots and identify strategies for advancing food technology. Methods: This study used online reviews, social media posts, and survey data to examine emotional responses and perception changes related to serving robots. This study also conducted a semantic network analysis to identify relationships among key concepts. Then, implications were drawn regarding the role of serving robots and customer acceptance in the food tech industry. Results: This study identified keywords related to serving robots through text mining and centrality analysis to analyze their frequency and importance. Additionally, CONCOR analysis revealed five clusters: ‘service operations,’ ‘serving robot,’ ‘technology and industry,’ ‘food and menu,’ ‘smart technology,’ and ‘customer experience.’ Implications: The analysis results showed that serving robots are not merely automated devices but play a crucial role in enhancing operational efficiency and customer service in the restaurant industry. These findings highlight the need for food tech research that goes beyond a technology-centric approach to incorporate a human-centered perspective, improving service experiences and customer satisfaction.