Prediction Of Doctors’ Returns In Online Health Communities Based On Signaling Theory

With the rapid development of information technology and the continuous improvement of people’s health needs,online health community has become an important platform for people to seek online consultation services and obtain health information,and also provides a new way for doctors to obtain economic and social returns.The competition in the health care market is increasingly fierce.It is very important for doctors to maintain a favorable position in the community.In addition,doctors are important participants in online health communities.The continuous participation and knowledge contribution of doctors is pivotal for the sustainable development of the community.Economic and social returns are important drivers for doctors to participate in online health communities.Although many scholars have paid attention to online health communities,most of the researches are conducted from the perspective of patients,and researches focused on doctor group especially the prediction of doctors’ returns are relatively scarce.Based on signaling theory,doctor-delivered information and system-generated information play an important role in the purchase decision-making of patients.These two kinds of information make contributions to reducing the information asymmetry between patients and doctors,so economic and social returns of doctors can increase.By analyzing the operation process and current situation of doctors’ returns in online health communities,this study derives professional capital,self-representation,knowledge contribution and knowledge cooperation factors from social exchange theory,function volume,function complexity and function heterogeneity factors from competitive repertoire theory,popularity and honor factors from brand related theory,and constructed the research framework to predict doctors’ returns.Based on nine influencing factors,this study proposes research hypothesizes.The empirical data comes from haodf.com.Using Python crawler,the statistical information and service information of 12353 doctors are obtained.Firstly,the distribution of doctors’ economic and social returns,the characteristics of doctor-delivered information and systemgenerated information are analyzed by using descriptive statistics.Secondly,based on multiple regression model,the proposed hypothesizes are verified,and prediction model of doctors’ economic and social returns are constructed.Finally,the validity of the models is verified.Results showed that compared with system-generated information,doctordelivered information affect economic returns of doctors much more.Popularity,function volume and the knowledge contribution have a great positive impact on economic returns of doctors,while knowledge cooperation and function heterogeneity have a negative impact.Compared with doctor-delivered information,systemgenerated information has a greater influence on doctors’ social returns.Popularity,honor and knowledge contribution have a great positive impact on doctors’ social returns,while professional capital,self-representation and function complexity have a negative one.The goodness of fit of economic returns prediction model is 0.79,and the goodness of fit of social returns prediction model is 0.77.This study extends the economic theories like competitive repertoire theory,brand premium theory to online health communities,which rich the research field of Information Science.The accuracy of prediction models is high.The prediction results can not only help doctors adjust personal disclosure information,set up service functions to improve their economic and social returns,but also assist communities develop more scientific evaluation system,improve the platform function design to maintain old users and attract new users,and promote sustainable development of communities.