Older and fearing new technologies? The relationship between older adults' technophobia and subjective age.

OBJECTIVE: This article aimed to identify different technophobia subgroups of older adults and examine the associations between these distinct subgroups and the subjective age. METHODS: A sample of 728 retired older adults over the age of 55 was recruited in China. Latent profile analysis was conducted to identify technophobia subgroups using three indicators: techno-anxiety, techno-paranoia and privacy concerns. Analysis of Variance was applied to determine whether a relationship exists between the identified technophobic subgroups and subjective ages (Feel-age, Look-age, Do-age and Interests-age). RESULT: Four technophobia types were identified: 'low-technophobia' (24.59%), 'high-privacy concerns' (26.48%), 'medium-technophobia' (28.38%), and 'high-technophobia' (20.55%). Privacy concerns play a major role in the profiles of older adults who belong to the profiles of 'high-privacy concerns' and 'high-technophobia' (47.03%). A series of ANOVAs showed that older adults in the 'low-technophobia' were more likely to be younger subjective ages of the feel-age and interest-age. CONCLUSION: The majority of Chinese older adults do not suffer from high levels of technophobia, but do concerns about privacy issues. It also pointed out the younger subjective age might have a protective effect on older adults with technophobia. Future technophobia interventions should better focus on breaking the age stereotype of technology on older adults.

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

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.