Perceived Factors Influencing the Public Intention to Use E-Consultation: Analysis of Web-Based Survey Data

Abstract

Background Unbalanced distribution of medical resources is becoming a major challenge, particularly in the selection of doctors. e-Consultation could provide patients with more choices of doctors and break the constraints of time and space. However, the acceptance of e-consultation is still poor and the mechanism of adoption is unclear. Objective The aim of this study was to identify the factors influencing the public intention to use e-consultation and explore the effect path of the factors and behavior intention. Methods The hypotheses of our research model were developed based on the technology acceptance model and perceived risk theory. A web-based survey was conducted by an electronic questionnaire collection platform; this survey that consisted of a 29-item questionnaire with 5-point Likert scales was completed by 934 respondents. Structural equation modeling was used to analyze the data. Item evaluation and reliability, validity, path loading, goodness of fit, and multiple group analysis were used to check the moderation effects. Results The standardized factor loadings of the items were between 0.551 and 0.873. The composite reliability of 9 constructs ranged from 0.706 to 0.840. The average variance extracted ranged from 0.387 to 0.640. The fitness indices showed that the collected data fitted well with the research model. Perceived usefulness was the strongest positive factor effecting behavior intention (β=.399, P<.001). Perceived ease of use had a positive effect on behavior intention but it was not statistically significant (β=.117, P=.07) and it had a positive effect on perceived usefulness (β=.537, P<.001). Perceived risk could be well explained by financial risk (β=.972, P<.001), privacy risk (β=.774, P<.001), social risk (β=.871, P<.001), time risk (β=.894, P<0.001), and psychological risk (β=.774, P<.001). Perceived risk had negative effects on perceived usefulness (β=–.375, P<.001) and behavior intention (β=–.297, P<.001). Personal innovativeness had a positive influence on perceived ease of use (β=.241, P<.001) and a slight effect on behavior intention (β=.124, P=.001). Age (χ258=133.5, P<.001) and usage experience (χ258=82.5, P=.02) had a slight moderation effect on the paths. Conclusions Perceived usefulness and perceived risk have significant effects on public intention to use e-consultation. Therefore, platform and manufacturer must improve the function of e-consultation, which will promote the public intention to use e-consultation fundamentally. In order to control the perceived risk of public, government should play an important role in enforcing management of e-consultation markets and approving corresponding medical insurance policies. Besides, personal innovativeness had an effect on behavior intention. Moreover, the paths of factors had some heterogeneity among people with different characteristics. Therefore, it is necessary to adjust the strategies to fit more groups better.

The Impact of Signals Transmission on Patients’ Choice through E-Consultation Websites: An Econometric Analysis of Secondary Datasets

“…The COVID-19 pandemic has triggered public health concerns around the globe. In certain special cases (e.g., the COVID-19 crisis), e-consultation could accomplish the target of public isolation and fulfill the patients’ needs for high-quality care [ 79 ]. In comparison to physical consultation, e-consultation can only be used to diagnose mild and high-risk diseases in the absence of appropriate medical exams and treatment systems.…”

“…Therefore, it is essential to strengthening the publicity of e-consultation for the public. In the promotion of physicians on e-consultation platforms, e-consultation providers should offer advanced services for innovative and stable services for the common [ 79 ].…”

Translation and Validation of the Questionnaire on Acceptance to Telemedicine from the Technology Acceptance Model (TAM) for Use in Malaysia

“…The Technology Acceptance Model (TAM) was developed to measure how well a technology “fits” with user tasks [ 15 ]. It is currently the most widely applied and empirically tested model in vast areas and the study setting [ 17 , 18 ] and has been translated to many different languages such as Arabic [ 19 ], French [ 20 ], and Chinese [ 21 , 22 ]. This model has been shown to be capable to explain about 40% of information technology (IT) use [ 23 ].…”

“…Measurement of acceptance using TAM has focused on the acceptance of health informatics [ 18 ] with only limited number of studies on videoconferencing or telemedicine [ 22 , 24 ].…”

“…Most of the studies tried to understand perspective among healthcare providers in comparison to patients. In the studies that involved patients, perceived usefulness was found to have significant effects on telemedicine acceptance [ 22 ]. Among the studies on healthcare providers, perceived usefulness was also a strong determinant in physician-to-physician and physician-to-specialist remote consultation and referral in the Malaysian setting [ 24 ].…”