Sungwoo Choi successfully defended his dissertation on “Exploring Service Failures and Recovery Efforts Made by Service Robots” at Penn State School of Hospitality Management last year, and he is finalist of the 2021 SERVSIG Best Dissertation Award.
We asked him to brief us about his research on service innovation and technology.
Consumers like interactions with service robots that look and talk like them, but they can sometimes spoil service experiences when their “social skills” do not operate well, according to our research.
The emergence of service robots is increasingly replacing human service providers in various service sectors. Despite our lay beliefs about highly reliable robot performance, it appears such high-tech machines are not so perfect. Robot failures have already made headlines in real life. In Japan, Henn-Na Hotel had pulled the plug on more than half of their service robots following complaints from unhappy guests. The hotel’s voice-controlled assistant robot in rooms interrupted people’s conversations, messed up room service orders and repeatedly woke up guests during the night after mistaking snoring for requests for help. How would customers react when service robots’ performance fails to meet expectations? We focus on robot appearance to explain how and why.
Practitioners take not only robots’ functionality but also their design elements into consideration so that consumers can easily accept the use of robots. Robot engineers try to design robots to look and behave like humans (e.g., humanoids) in order for people to interact with robots in a more intuitive and natural way. However, our findings suggest that their human resemblance may increase a person’s annoyance at service failures, especially those involving inattentive or slow service. This is mainly because humanlike features influence the extent to which consumers attribute social capabilities like friendliness and helpfulness to service robots. Consequently, service managers and robot designers should be attentive to the design of robot features to ensure an appropriate match with social interaction programming.
Another managerial insight we provide is how to recover from service failures made by robots. Given their greater sociality perceptions, humanoids appear to be able to effectively recover from service failures by delivering apologies and explanations. Thus, humanoids may be programmed to autonomously analyze and detect service failures and apologize or explain such failures to customers. With current advanced technologies, robots can use facial expression recognition, voice stress analysis, or natural language processing to detect whether customers are unhappy. Once a robot detects service failure or customer dissatisfaction with such ways, it should be able to provide a sincere apology by verbal — vocally, by displaying a message, or both — and through non-verbal means, such as with facial expressions, if possible.
Our findings also suggest humans and robots can be good teammates if the right robot design is selected and collaborations with humans are managed correctly. For example, a non-humanoid robot might require a little bit more monitoring and assistance when things go wrong than a humanoid robot will. When a non-humanoid causes a problem, humans may need to step in to give apology and explanation, ensuring customer satisfaction.
Lastly, we suggest the need for a broader strategy regarding the deployment of robots in service settings. Service firms should map out their service blueprints in order to figure out what type of a service robot to be deployed at each stage of the service delivery process (i.e., matching service task and robot type). By doing so, service managers can better predict what customers expect, to manage occasional service failures, and thus maximize service satisfaction. For instance, companies should be more cautious in deploying humanoids than non-humanoids in tasks with frequent interactions with customers that create more opportunities for process failures.
Research Assistant Professor
School of Hotel and Tourism Management
Chinese University of Hong Kong (CUHK) Business School
Credit Image: Mathew Schwartz