Physician popularity in online healthcare communities: a causal investigation by integrating DEMATEL-machine learning approach

Purpose Physicians' online popularity plays a vital role in the development and success of online healthcare communities (OHCs). However, key factors driving this popularity are yet to be fully explored. Thus, this study aims to identify and prioritize the key factors contributing to physicians' online popularity in OHCs using a comprehensive, multilayered analytical approach. Design/methodology/approach The multilayered 4-step methodology was adopted to identify and prioritize the factors influencing physicians' online popularity in OHCs. Initially, relevant factors were identified through a comprehensive literature review and validated by experts using the Fuzzy Delphi Method (FDM). Semi-structured interviews with diverse stakeholders were then analyzed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, based on stakeholder theory, to develop a causal framework. To validate the findings, machine learning (ML) models, including Artificial Neural Networks (ANN), Decision Tree, XGBoost and Random Forest were applied. Findings The analysis revealed that “active social media presence” and “quality of communication” are the most influential factors contributing to physicians' online popularity. The causal relationships derived from DEMATEL were further supported by machine learning validations, confirming the reliability and significance of the identified factors. Originality/value This study offers a novel integration of FDM, DEMATEL, and machine learning models to investigate physician popularity in OHCs. By combining expert insights with empirical data and robust validation techniques, the research provides both theoretical and practical contributions to understanding how physicians can enhance their popularity and impact within digital health environments.