Department of Management Science and Engineering, School of Management, Harbin Institute of Technology, Harbin, People's Republic of China.
J Biomed Inform. 2019 Oct;98:103272. doi: 10.1016/j.jbi.2019.103272. Epub 2019 Aug 31.
With the growth in Internet technology, online rating websites encourage patients to contribute actively in rating their physicians. These rating sites provide more information for patients, such as electronic word of mouth (eWOM) and physician trustworthiness. Although several studies in e-commerce have investigated the role of eWOM and seller trustworthiness in the consumer purchase decision-making process and the price premium for products or services, studies on the role of different information sources that reflect the service quality and delivery process in choosing a competent physician remain scarce. This research develops a two-equation model to examine the effect of different signals, i.e., patient-generated signals (PGSs) and system-generated signals (SGSs), on patient choice, which is an important predictor of physicians' economic returns.
A secondary data econometric analysis and structural modeling using 2896 physicians' real data from a publicly available online physician rating site, i.e., Healthgrades.com, were conducted using a mixed-methods approach. A hybrid text mining approach was adopted to calculate the sentiment of each review.
We find that both PGSs and SGSs have a significant impact on patient choice at different stages of health consultation. Furthermore, disease risk negatively moderates the association between PGSs and information search, while the impact of both signals on patient willingness to pay a price premium is positively moderated by the disease risk.
Our study contributes to the unified framework of signaling theory and Maslow's hierarchy of needs theory by making a clear distinction between PGSs or SGSs and their influence on patient decision-making across different disease risks. Moreover, PGSs and SGSs are two essential factors for physicians to increase their income.
随着互联网技术的发展,在线评分网站鼓励患者积极参与医生评分。这些评分网站为患者提供了更多信息,如电子口碑(eWOM)和医生可信度。尽管电子商务领域的几项研究已经调查了 eWOM 和卖家可信度在消费者购买决策过程中的作用,以及产品或服务的溢价,但关于反映服务质量和交付过程的不同信息源在选择合格医生方面的作用的研究仍然很少。本研究建立了一个两方程模型,以检验不同信号(即患者生成信号(PGS)和系统生成信号(SGS))对患者选择的影响,这是医生经济回报的重要预测指标。
使用混合方法,对 Healthgrades.com 等公开在线医生评分网站的 2896 名医生的真实数据进行二次数据分析和结构建模。采用混合文本挖掘方法计算每条评论的情绪。
我们发现,PGS 和 SGS 在健康咨询的不同阶段对患者选择都有重大影响。此外,疾病风险负向调节 PGSs 和信息搜索之间的关系,而两种信号对患者支付溢价意愿的影响则被疾病风险正向调节。
我们的研究通过明确区分 PGSs 或 SGSs 及其对不同疾病风险下患者决策的影响,为信号理论和马斯洛需求层次理论的统一框架做出了贡献。此外,PGS 和 SGS 是医生增加收入的两个重要因素。