Research On The Effect Of Doctor’ Online Word-of-mouth On Patient Selection Behavior Based On The Perspective Of Trust

The deep integration of healthcare and information technology not only innovate healthcare service models,but also change patient’s medical habit.In particular,online healthcare community(OHC),the poster child in Digital Health,follows the business logic of traditional E-commerce to a certain extent,but OHC confronts new challenges concerning operation and development model due to the characteristics of credence goods in healthcare service.Considering patient requirement,OHC gather massive quality healthcare service resources and generate huge amounts of healthcare information that further form multi-dimensional doctor’s online word-of-mouth(WOM).Patient acquires and uses doctor’s online WOM data to perceive and judge doctor’s professional competence,which alleviates the asymmetric information problem between doctor and patient and help them build strong trust relationship.Patient can not make the next medical decision until he or she trust the doctor.Therefore,the research related to Digital Health need to be extended beyond hospital.OHC offer a good research context to explore the relationship between doctor online WOM,patient trust and selection behavior.Based on current situation of healthcare industry in China,this paper focuses on full-fledged Chinese OHC Haodaifu Online,to research how patient employ diverse healthcare data to assist in making medical decision in the Digital Health era.Firstly,we collect and comb literatures and materials concerning OHC,trust and patient’s selection behavior,providing solid theoretical basis for sequel research to build research model and put forward hypotheses.Secondly,we make clear the demand of data according to research hypotheses.We program crawler to get data from certain website.4,532 doctor’s information and 234,605 comments data are saved after filtering and cleaning.Then extracting the topics and analysising the sentiment for unstructured data have been done.With sentiment dictionary,emotional factors in comments are converted to scores used to quantitative analysis.At length,considering the characteristics of data,we construct multivariable linear regression model to test hypotheses.Meanwhile,both of the mediation effect of patient initial trust and the moderation effect of disease risk are tested significantly,which enriches research on OHS.Results show that doctor online WOM have a significant and positive effect on patient initial trust and selection behavior.Doctor’s medal,online satisfaction,number of comments,gift are the key factors that patient concern.In different situations,patient initial trust elaborates full or partial mediation effect between doctor online WOM and patient selection behavior.In addition,disease risk plays moderation effect in models.This paper combines with natural language processing and econometrics to exploit the value of all kinds of data in OHC.It verifies the relation between doctor online WOM,trust,behavior and disease risk,which provides new thought and theoretical basis for follow-up study on OHC.The results also provide practical suggestion for doctor and patien to effectively participate in OHC,eventually achieving mutual benefit and win-win for doctors,patients,platforms and other multiple participants.