Scholarlink

<p>Crowdsourced question-and-answer (Q&amp;A) services on online healthcare platforms provide a wide range of information for patients. Owing to information asymmetry, evaluating healthcare providers’ services often leads to skewed decision-making, but it remains unclear whether heuristic cues play a role. Considering potential heuristic information processing, we explore the existence of patient evaluation deviation in a crowdsourced context and how it is affected by the similarity among the physicians’ answer text, the question, and the first answer text. Data were collected from 1,074,885 answer records on a leading Chinese online crowdsourced Q&amp;A healthcare platform. Our findings show that the text similarity of questions and answers in physician‒patient interactions, as well as of first and subsequent answers, increases the evaluation deviation of online physicians’ consultations (specifically, increasing patient underestimation and reducing patient overestimation). Moreover, the information differentiation of all answers to the same question has a negative moderating effect on the above relationship. The results of this study provide empirical evidence regarding the factors that influence online evaluation deviation in crowdsourced Q&amp;A services and suggestions for strategies for different stakeholders.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The influence of similarity heuristics on evaluation deviation: Evidence from an online crowdsourced Q&A healthcare platform

  • 期刊论文

  • 发表日期：

    2025年1月11日

查看全文
  • Yifan He,
  • Min Zhang,
  • Tianshi Wu,
  • Xitong Guo,
  • Doug Vogel

摘要

Crowdsourced question-and-answer (Q&A) services on online healthcare platforms provide a wide range of information for patients. Owing to information asymmetry, evaluating healthcare providers’ services often leads to skewed decision-making, but it remains unclear whether heuristic cues play a role. Considering potential heuristic information processing, we explore the existence of patient evaluation deviation in a crowdsourced context and how it is affected by the similarity among the physicians’ answer text, the question, and the first answer text. Data were collected from 1,074,885 answer records on a leading Chinese online crowdsourced Q&A healthcare platform. Our findings show that the text similarity of questions and answers in physician‒patient interactions, as well as of first and subsequent answers, increases the evaluation deviation of online physicians’ consultations (specifically, increasing patient underestimation and reducing patient overestimation). Moreover, the information differentiation of all answers to the same question has a negative moderating effect on the above relationship. The results of this study provide empirical evidence regarding the factors that influence online evaluation deviation in crowdsourced Q&A services and suggestions for strategies for different stakeholders.