Heterogeneity of Attitudes toward Robots in Healthcare among the Chinese Public: A Latent Profile Analysis

Int J Environ Res Public Health. 2022 Dec 28;20(1):508. doi: 10.3390/ijerph20010508.

Abstract

Attitudes are deemed critical psychological variables that can determine end users' acceptance and adoption of robots. This study explored the heterogeneity of the Chinese public's attitudes toward robots in healthcare and examined demographic characteristics associated with the derived profile membership. The data were collected from a sample of 428 Chinese who participated in an online survey. Latent profile analysis identified three distinct subgroups regarding attitudes toward robots-optimistic (36.9%), neutral (47.2%), and ambivalent (15.9%). Interestingly, although participants in the ambivalent attitude profile held more negative attitudes toward interaction with or social influence of healthcare robots, their attitudes tended to be positive when it came to emotional interactions with healthcare robots. All the respondents reported negative attitudes toward the social influence of healthcare robots. Multivariable regression analysis results showed that there were significant differences in age, education level, monthly income, experience with computers, experience with wearable devices, and whether to follow robot-related news or not. This study confirmed the heterogeneity of the Chinese public's attitudes toward robots in healthcare and highlighted the importance of emotional interaction with and social influence of healthcare robots, which might facilitate a better understanding of the needs and expectations of potential end users for robots in healthcare to make them more acceptable in different situations.

Keywords: China; attitude; cross-sectional study; healthcare; latent profile analysis; robot.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Attitude
  • China
  • Delivery of Health Care
  • Emotions
  • Humans
  • Robotics* / methods

Grants and funding

This research was funded by the National Natural Science Foundation of China (grant number 71974196).