{"title":"The incentive effects of experts: Evidence from an online mental health platform","authors":"Lini Kuang , Tingting Hou","doi":"10.1016/j.ipm.2025.104289","DOIUrl":null,"url":null,"abstract":"<div><div>A prevalent form of online healthcare comprises hybrid support services, allowing help seekers to pose health-related questions, assess answers from various healthcare experts and ordinary supporters, and vote on the usefulness of answers they find most satisfactory. However, the impact of healthcare experts' engagement on the subsequent supporters' performance within this hybrid service remains unclear. While concerns persist that experts' involvement may diminish subsequent supporters' performance due to a reduced likelihood of recognition, there is a plausible scenario in which it fosters learning and competition, thereby improving performance of subsequent supporters. Leveraging data from a Chinese online mental health platform, we utilize the mediation effects model to investigate the impact of healthcare experts' engagement on the performance of subsequent supporters. Within our model, we focus on the mediating effects of the effort and quality of answers from these subsequent supporters. Our research findings indicate that the engagement of counselors can directly enhance the social recognition obtained by subsequent supporters and can also indirectly boost this recognition by increasing the effort put into their answers. However, the quality of the subsequent supporters' answers does not play a significant mediating role in this relationship. These results fill a gap in the literature on online health services and expert effects, offering valuable insights for online health platforms aiming to enhance the performance of supporters by involving healthcare experts.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 6","pages":"Article 104289"},"PeriodicalIF":7.4000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457325002304","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
A prevalent form of online healthcare comprises hybrid support services, allowing help seekers to pose health-related questions, assess answers from various healthcare experts and ordinary supporters, and vote on the usefulness of answers they find most satisfactory. However, the impact of healthcare experts' engagement on the subsequent supporters' performance within this hybrid service remains unclear. While concerns persist that experts' involvement may diminish subsequent supporters' performance due to a reduced likelihood of recognition, there is a plausible scenario in which it fosters learning and competition, thereby improving performance of subsequent supporters. Leveraging data from a Chinese online mental health platform, we utilize the mediation effects model to investigate the impact of healthcare experts' engagement on the performance of subsequent supporters. Within our model, we focus on the mediating effects of the effort and quality of answers from these subsequent supporters. Our research findings indicate that the engagement of counselors can directly enhance the social recognition obtained by subsequent supporters and can also indirectly boost this recognition by increasing the effort put into their answers. However, the quality of the subsequent supporters' answers does not play a significant mediating role in this relationship. These results fill a gap in the literature on online health services and expert effects, offering valuable insights for online health platforms aiming to enhance the performance of supporters by involving healthcare experts.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.