With the rapid development of artificial intelligence and automation technology, many companies in the hotel and tourism industries have adopted intelligent robots to enhance their services. Especially in the post pandemic era, the demand for automated and contactless services from customers has sharply increased. However, most hotels face the dilemma of whether to focus more on customer or employee acceptance of robots.
From the perspectives of resource-based view and information processing theory, our associate professor Dr. Fan Hua and collaborators explored the differences in the impact of the "imbalanced acceptance" strategy implemented by hotels (i.e., customers' acceptance of robots is higher/lower than employees' acceptance) on improving service quality under uncertain needs on the customer side (i.e., demand diversity) and the employee side (i.e., task duality).
Polynomial regression analysis of 1066 customer employee matching datasets shows that the "imbalanced acceptance" strategy is superior to the "balanced acceptance" strategy in improving service quality. In addition, when the diversity of customer needs is high, a customer-centered "imbalanced acceptance" strategy (i.e., customers' acceptance of robots is higher than that of employees) is the best choice to improve service quality. However, when the flexibility of employees' tasks is high, the positive impact of the "imbalanced acceptance" strategy on service quality will quickly weaken.
In terms of theoretical value, this study has contributed to the study of robot acceptance by integrating customer and employee perspectives; This study also contributes to information processing theory by proposing that demand diversity and task duality are two important sources of uncertainty for customer and employee needs; At the same time, this study also represents a groundbreaking attempt to apply different models to measure robot acceptance in field experiments. In terms of practical value, this study breaks through the research paradigm of laboratory experiments and provides thoughtful, insightful, high-level, and valuable practical guidance for the deployment of intelligent robots from the perspective of "customer robot employee" through practical human-machine interaction research on the acceptance of service robots.
Fan, Hua, Wei Gao, and Bing Han. "How does (im) balanced acceptance of robots between customers and frontline employees affect hotels’ service quality?." Computers in Human Behavior 133 (2022): 107287.
