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Exploring the influence of doctor–patient social ties and knowledge ties on patient selection

  • School of Management, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: With the advent of the Digital 2.0 era, online doctor–patient (D–P) interaction has become increasingly popular. However, due to the fact that doctors use their fragmented time to serve patients, online D–P interaction inevitably has some problems, such as the lack of pertinence in the reply content and doctors' relative unfamiliarity with their individual patients. Therefore, the purpose of this study is to excavate whether potential D–P social ties and D–P knowledge ties accentuate or attenuate the influence of patient selection (online and offline selection). Design/methodology/approach: The authors used the methods of text mining and empirical analysis on the structured and unstructured data of an online consultation platform in China to examine the research hypotheses. Findings: The findings illustrate that the potential D–P social ties increase the influence on patient selection, as do the potential D–P knowledge ties. Specifically, the effect of social ties on patient selection is positively moderated by patient health literacy. Conversely, health literacy weakens the link between knowledge ties and patient selection. In addition, the doctor's title weakens the influence of social ties on patient selection, in contrast to knowledge ties (partially). Originality/value: This study provides guidance for doctors and patients on how to communicate effectively and alleviate tension within D–P relationships. The study’s findings have both theoretical and practical implications for both doctors' and online platforms' decision-making.

Original languageEnglish
Pages (from-to)219-240
Number of pages22
JournalInternet Research
Volume32
Issue number1
DOIs
StatePublished - 18 Jan 2022
Externally publishedYes

Keywords

  • Knowledge ties
  • Online doctor–patient interaction
  • Social ties
  • Text mining and empirical analysis

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