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BackgroundPoor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field.ObjectiveThis study aimed to investigate the general public’s acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people’s intention to use these devices.MethodsWe proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care–related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model’s path effects, significance, goodness of fit, and mediation and moderation effects were analyzed.ResultsStandardized factor loadings of items were between 0.583 and 0.876. Composite reliability of 9 constructs ranged from 0.673 to 0.841. The discriminant validity of all constructs met the Fornell and Larcker criteria. Model fit indicators such as standardized root mean square residual (0.057), comparative fit index (0.915), and root mean squared error of approximation (0.049) demonstrated good fit. Intention to use (R2=0.515) is significantly affected by subjective norms (beta=.408; P<.001), perceived usefulness (beta=.336; P=.03), and resistance bias (beta=–.237; P=.02). Subjective norms and perceived behavior control had an indirect impact on intention to use through perceived usefulness and perceived ease of use. Eye health consciousness had an indirect positive effect on intention to use through perceived usefulness. Trust had a significant moderation effect (beta=–.095; P=.049) on the effect path of perceived usefulness to intention to use.ConclusionsThe item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products.

Background An artificial intelligence (AI)–assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy. Yet, there is much hope and fear surrounding such technologies, and this fear can manifest as resistance from health care professionals, which can lead to the failure of AI projects. Objective The objective of this study was to develop and test a model for investigating the factors that drive radiation oncologists’ acceptance of AI contouring technology in a Chinese context. Methods A model of AI-assisted contouring technology acceptance was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding the variables of perceived risk and resistance that were proposed in this study. The model included 8 constructs with 29 questionnaire items. A total of 307 respondents completed the questionnaires. Structural equation modeling was conducted to evaluate the model’s path effects, significance, and fitness. Results The overall fitness indices for the model were evaluated and showed that the model was a good fit to the data. Behavioral intention was significantly affected by performance expectancy (β=.155; P=.01), social influence (β=.365; P<.001), and facilitating conditions (β=.459; P<.001). Effort expectancy (β=.055; P=.45), perceived risk (β=−.048; P=.35), and resistance bias (β=−.020; P=.63) did not significantly affect behavioral intention. Conclusions The physicians’ overall perceptions of an AI-assisted technology for radiation contouring were high. Technology resistance among Chinese radiation oncologists was low and not related to behavioral intention. Not all of the factors in the Venkatesh UTAUT model applied to AI technology adoption among physicians in a Chinese context.

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Purpose Although user engagement has been paid increasing attention, the work on user disengagement is scarce, and little is understood about how overloads elicited by excessive social commerce activities affect user disengagement. Based on the stimulus-organism-response (SOR) framework and psychological reactance theory (PRT), the authors aimed to investigate the effects of social commerce overloads (SCOs) on user disengagement, its influential mechanism, and the buffer effect of guanxi. Participants and Methods The authors conducted an online survey to collect the data and then examined our theoretical model and hypotheses. This study employed SPSS 20.0 software and Amos 24.0 software to examine the hypothesized relationships and the model. Results Social commerce overloads (ie, information overload (IO), social overload (SO), and communication overload (CO)) positively impact reactance via inferences of manipulative intent (IMI) and compulsive perception (CP); IMI and CP positively influence reactance; IMI, CP, and reactance positively affect user disengagement (ie, neglecting behavior and blocking behavior); guanxi has the buffer effect on the relationship between IMI (CP) and user disengagement, negatively moderates the impacts of IMI on user disengagement (ie, neglecting behavior and blocking behavior), and negatively moderates the effects of CP on blocking behavior but not neglecting behavior. Conclusion The findings of this study contribute to the literature on PRT and user disengagement by displaying the effects of excessive social commerce activities on user disengagement and uncovering the buffer effect of guanxi, which can help social e-commerce practitioners better reduce the negative effect of social commerce overloads.

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