| Internet healthcare has effectively alleviated the shortage and uneven distribution of medical resources in China for a long time.Online health communities(OHCs),as an emerging product of Internt healthcare,is to extend healthcare services to the Internet environment.OHCs have made a new development of healthcare model,which is of significance in reducing healthcare costs,improving the allocation efficacy of medical resources and enhancing the fairness of medical resources.However,due to the particularity of healthcare services and online virtual environments,OHCs have the development status of low user conversion rate and low service utilization rate.This paper analyzes OHC patients’ behaviors and influencing factors in the four stages of online decision-making process,including information search,option evaluation,consultation decision and consultation feedback,and analyzes the OHC conversion rate and influencing factors.Therefore,this paper aims to provide OHC managers and service providers with effective strategies to improve user conversion rate and service utilization rate.First,drawing on the information adoption model,this paper analyzed how the characteristics of health information,providers and users in OHCs,as well as interactions between them,influenced patients’ health information adoption at information search stage.By collecting data of users’ information adoption behaviors in a Chinses OHC,text mining and sentiment analysis were used to measure variables.The logit regression results show that physicians’ attitude certainty has a positive effect on patients’ intention to adopt health information,online seniority has a negative moderating effect on this relationship,while offline seniority has a positive moderating effect.In addition,there is a negative three-way interaction of attitude certainty,online seniority and personal motivation factor—disease severity on patients’ health information adoption.Second,this paper divided patient service adoption in OHCs into first adoption and post-adoption.Drawing on elaboration likelihood model(ELM)and service quality theory,the influencing factors of two-stage service adoption were analyzed and compared in a research model.By using text mining and sentiment analysis methods to process user adiotion data and interactive content in a Chinese OHC,relevant constructs were measured.The negative binomial regression results show that the central cue interaction quality has a positive impact on first adoption of service,while the peripheral cue eWOM is the main influencing factor of first adoption and post-adoption of services.In addition,the path comparison results show that interaction quality plays a stronger role at first adoption stage,while eWOM plays a stronger role at post-adoption stage.Next,this paper focused on the behaviors and motivations of patients’online review-writing in OHCs.Drawing on expectation-disconfirmation theory and service quality theory,the effects of service quality perception(i.e.,outcome quality and process quality perceptions)and disconfirmation(i.e.,outcome quality and process quality disconfirmations)on emotional response and reviewing effort in patients’ textual reviews were analyzed.The ordinary least squares(OLS)regression results show that both service quality perception and disconfirmation have a positive effect on patients’ positive emotional intensity in textual reviews,and disease severity enhances these relationships of process quality.The zero-truncated negative binomial(ZINB)regression results show that there is an asymmetric U-shaped relationship among service quality perception,disconfirmation and reviewing effort.Patients who perceive low service quality have higher reviewing effort,while service quality disconfirmation has the opposite relationship.The results also determine the perceived and disconfirmation values of outcome quality and process quality at the lowest level of reviewing effort.Finally,combined with the characteristics of patients’ online decision-making process and the definition of service site conversion rate,this paper defined the OHC conversion rate,that is,the proportion of consumers of a physician’s personal website who have chosen the physician’s services in the OHC to visitors.This is also the conversion rate of physicians’personal websites.In this paper,online health information was divided into physician-generated,patient-generated and system-generated information according to the sources.The OLS regression results show that multi-source OHC information:physician-generated,patient-generated and system-generated information positively affect the conversion rate.And physician-generated and patient-generated information has a substitute relationship rather than a complementary relationship.In addition,the usage time of personal website could positively moderate patient-generated information,but negatively moderate physician-generated information.Taking OHCs as the research context,this paper explored the patients’decision-making process and influencing factors from macro and micro levels.At the macro level,the thesis is user-centered.We have continuously tracked patients’ behaviors at all stages of the online decision-making process,and have analyzed the factors that affect user conversion rate and service utilization rate.At the micro level,the thesis is the problem-centered.We have analyzed the information adoption behavior at information search stage,the service adoption behavior at option evaluation and consultation decision stages,and the review-writing behavior at consultation feedback stage,as well as the OHC conversion rate and influencing factors.This paper extends the current literature about e-health and the theories of dual-process,information adoption model,expectation-disconfirmation,and service quality.In addition,this paper provides a certain theoretical and practical implications on comprehensively understanding the influencing mechanism of patient participation,and provides a theoretical framework for future in-depth research on OHCs.The research results emphasize the importance of patients’behaviors in OHCs to improve user conversion rate and service utilization rate,and provide guidance and suggestions for community managers to better design OHCs and service providers to efficiently provide healthcare services. |
