Robots have been increasingly common in hospitality and tourism, especially being favored under the threat of COVID-19. However, people generally do not think robots are appropriate for cooking food in hotels and restaurants, possibly because they hold low quality predictions for robot-cooked food. This study aimed to investigate the factors influencing people's quality prediction for robot-cooked food. In three experiments, participants viewed pictures of human and robotic chefs and dishes cooked by them, and then made food quality predictions and rated their perceptions of the chefs. The results showed that participants predicted the foods cooked by robotic chefs were above average quality; however, they consistently held lower food quality prediction for robotic chefs than human chefs, regardless of dishes' cooking difficulty level, novel cues in chefs and food, or the anthropomorphism level of robotic chefs. The results also showed that increasing the appearance of robotic chefs from low or medium to high anthropomorphism, or enabling robotic chefs to cook high cooking difficulty level food could promote food quality prediction. These results revealed the current acceptance of robot-cooked food, suggesting possible ways to improve food quality predictions.
Keywords: Anthropomorphism; Cooking difficulty level; Food quality prediction; Novel cues; Robotic chef; ‘Like-me’ hypothesis.
© The Author(s), under exclusive licence to Springer Nature B.V. 2022.