Principal results
This study aimed to analyse the factors influencing the acceptance of the MyData platform ‘HiMD’ leading to actual use behaviour.
This study set the main variables of performance expectancy, effort expectancy, social influence, and facilitating conditions as factors affecting behavioural intention to use and actual use behaviour based on the UTAUT model. We surveyed 1153 participants who used the system for more than four weeks, and measured their actual use behaviour using the system usage log record.
Of the eight hypotheses, two were rejected (H2 and H5), four were accepted (H1, H3, H4, and H6), and one was partially accepted (H8a) as a result of the moderating effect tests for age and gender. Performance expectancy had significant positive effects on behavioural intention to use MyData. This result is consistent with those of previous studies on mHealth and eHealth services [
33,
55,
56]. If users believe that they can derive healthcare management benefits from HiMD, their willingness to use it will be stronger. Therefore, to increase the behavioural intention of users on the HiMD platform, it is necessary for various partners and companies to provide services using personal health records to collaborate to provide functions that benefit users' healthcare.
Previous studies have shown that effort expectancy on behavioural intention to use has a significant effect on and is an important factor in behavioural intention to use [
30,
31]. Contrariwise, the results of this study did not show any significant effect of effort expectancy on behavioural intention to use HiMD. This finding was unexpected; in other words, effort expectancy did not significantly affect a users’ intention to use the MyData platform, We might speculate on the reason for this. Effort expectancy might have no direct effect on usage behaviour; it may also have an indirect effect on user adoption through performance expectancy [
55,
57]. However, we did not consider whether effort expectancy had an indirect effect in this study. Effort expectancy is a valuable factor in technology acceptance; therefore, developers should consider designing usable and easy-to-use user interfaces (UI). In addition, users considered systems with a well-designed UI that aligned with their needs as easy to understand and use [
58]. Considering that user interface fit (UIF) positively affects effort expectancy, investigations into UIFs could also be conducted in a future study.
Social influence and facilitating conditions showed different results for each study. In this study, social influence and facilitating conditions had significant positive effects on behavioural intention to use HiMD, similar to the results of Park et al. [
30] and Zhang et al. [
55]. To reorganise this, facilitating conditions affected behavioural intention to use the HiMD, but it did not directly impact actual use behaviour. The influence of significant others on eHealth acceptance is an aspect of interactions among colleagues, other patients, and experts in clinical settings [
59]. In other words, considering that social influence exerted an effect on behavioural intention to use HiMD, the MyData platform should expand functions that enable interaction between colleagues or healthcare experts. We have provided a variety of support to users, before and after using the platform, especially when they felt discomfort or experienced trouble. This method reduces the dropout rate of the users. System developers and planners should provide continuous assistance services and consider guidelines to support users. This is because facilitating conditions affect behavioural intention to use HiMD, and intention to use affects actual use behaviour. Many studies of healthcare systems using the UTAUT model have been conducted [
30,
31], although they were limited to 'intention to use'. The contribution of this study is that we objectively measured 'actual use behaviour' using system log data. Scores were calculated according to the degree of use of the platform functions, such as consent, data check, data download, and data sharing. By combining subjective and objective data, we confirmed that intention to use significantly affects actual use behaviour.
The impact of the main factors in UTAUT was not moderated by age or gender, except for performance expectancy. Gender was partially supported as a moderator of performance expectancy, indicating that the effect of the link between performance expectancy and behavioural intention to use was significant for the male group but not for the female group. Our sample included more women than men. This is one of the limitations of this study, and this imbalance may have affected the results. However, as we only included participants who had used the HiMD, this gender gap might ne fairly representative of the HiMD user population, asin real life, women are more likely to engage in eHealth activities [
60]. This result related to gender needs to be interpreted with caution because finding the main barriers to adopting a data-sharing system is highly important. Future work needs to departmentalise the factors influencing the use of HiMD and build various hypotheses regarding the relationships between the factors to identify additional influential factors.
Another limitation is that this study adopted the main factors of UTAUT. Therefore further research is needed to investigate this more thoroughly by adding perceived security to the model [
50]. Additionally, the available personal health records utilised by the users were limited to health check-up data, drug prescription data, and depression scale testing data. Further studies with more general data are necessary.
Unlike one study in which MyData acceptance was rejected in most hypotheses [
5], we confirmed many hypotheses of the UTAUT model for the MyData platform based on personal health record data sharing. Our results suggest that performance expectations for healthcare, social influence, and facilitating conditions modulate the acceptance of eHealth based on a personal health record data sharing system. This may account for the differences before and after the system was used. In addition, this study confirmed the usage behaviour of the MyData platform by utilising the system’s actual usage log for each function. The effect of intention to use on actual use was also analysed.
Limitations
First, data items should be expanded so that more data from the HIS can be linked for implemention within the platform. In addition, it is necessary to expand the functions of the platform and healthcare services provided. The expansion of this data sharing and utilisation platform will contribute to the construction of a data ecosystem. The research model should then be expanded to increase its explanatory power in predicting behavioural intention regarding the MyData platform, as by including additional factors of the UTAUT2 model and perceived security.
Second, each factor was separated like the original UTAUT factors, but the item loading of SI construction was lower than the others and was observed to have an association with PE in the analysis of the correlations. The correlation coefficient between the factor items checked, but the correlation coefficient of each item between PE and SI was below 0.71. This seems to indicate the possibility that the SI is related to the PE; further research is thus needed on this correlation.Third, studies have shown that of the characteristics of participants, education might mediate some of the relationships between UTAUT items and technology acceptance [
61,
62]. We considered education as a moderating factor, but 95.4% of the platform users received education above college graduation. Most participants who visited the health examination centre at university hospitals had an education level above college graduate, and for this reason, we excluded education from the moderating factors. We need to consider and research various moderating factors in the future to enhance the acceptance of healthcare technology.
Conclusions
This study is the first to examine the factors influencing the use of the MyData platform based on the personal health record data sharing system in Korea. In addition, this study is significant in using the eHealth system and in conductingat survey during the COVID-19 pandemic. In conclusion, the results of our study provide pioneering empirical evidence for the UTAUT model in the MyData platform-based personal health record data sharing system. In other words, we provide managerial insights that may increase acceptance and usage of the MyData platform. Focusing on the significant constructs that we found, future research may create other variants of the MyData platform to more appropriately provide various healthcare services.
The paradigm of healthcare has changed, and COVID-19 has accelerated this trend. MyData, which reflects the right to self-determination, is an important component of this paradigm shift. Personal data portability can create value by actively utilising data in the platform economy, and its expansion requires policy management, technical support, and user participation [
10,
35]. This research will serve as a significant foundation for accepting data portability and data sharing concepts and expanding the data economy and data ecosystem. Finally, we plan to conduct future research using an expanded research model with the expansion of data sharing platforms.