Application of humanization to survey chatbots: Change in chatbot perception, interaction experience, and survey data quality

The use of chatbots as an online survey tool is becoming increasingly popular owing to their convenience, particularly when face-to-face interactions are difficult. However, with longer surveys, interaction experience and data quality can decrease due to several factors, such as increased fatigue. In this study, we compared how applying humanization techniques to survey chatbots can affect survey-taking experience in three aspects: respondents' perceptions of chatbots, interaction experience, and data quality. To address our research goal, two different versions of survey chatbots were compared: a humanization applied survey chatbot (HASbot) and a baseline chatbot (baselinebot). The HASbot simultaneously incorporates four humanization techniques: use of self-introduction, addressing by name, using adaptive response speed, and echoing respondents' answers. Our experimental study with 59 middle school-aged adolescents showed that compared to the baselinebot, respondents’ perceptions of the HASbot were more positive, with higher levels of anthropomorphism and social presence. In terms of interaction experience, the respondents spent more time interacting with the HASbot and showed a higher level of satisfaction. For data quality, the HASbot outperformed the baselinebot in terms of self-disclosure; however, the HASbot also elicited a higher social desirability bias. No difference was observed in the response differentiation between the two chatbots.