Evidence map›Paper›PMID 39656521›Full record

ArticleJournal of medical Internet research2024

Spillover Effects of Paid Functions on Physicians' Unpaid Knowledge Activities: Quasi-Experimental Approach.

Xuan Liu, Xiaotong Chi, Ming Chen, Wen Sun, Jia Li

Abstract read
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Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xuan Liu *East China University of Science and Technology, Shanghai, China.ORCID 0000-0001-5370-937X
Xiaotong Chi *East China University of Science and Technology, Shanghai, China.ORCID 0000-0001-5805-2661
Ming ChenEast China University of Science and Technology, Shanghai, China.ORCID 0000-0001-5579-1821
Wen SunEast China University of Science and Technology, Shanghai, China.ORCID 0009-0005-3752-2173
Jia LiEast China University of Science and Technology, Shanghai, China.ORCID 0000-0002-4422-2647

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo promote sustained contributions by physicians to online health care communities, these platforms have introduced a content payment model that offers economic incentives for physicians' online knowledge activities. However, the impact of these paid features on unpaid knowledge activities remains unexplored.

objectiveThis study investigated how the introduction of economic incentives in online medical communities affects physicians' unpaid knowledge activities in the community.

methodsThe data for this study were obtained from the Haodf Online platform in China, which has implemented paid scenarios for its science popularization function, providing economic benefits to physicians. The dataset, which comprises panel data, includes 7453 physicians who participated in both paid and unpaid knowledge contributions on the website. This study examined the impact of paid knowledge activities on physicians' free knowledge contributions, focusing on dimensions including knowledge quantity, quality, and diversity. To address the timing discrepancies in physicians' participation in paid activities, we used a quasi-experimental design that combined the approach of propensity score matching and multi-period difference in differences.

resultsIn the balance test results of the propensity score matching, the absolute values of the SDs of all matching variables were mostly <5% after matching, ensuring the accuracy of the results obtained from the difference in differences method. This study found that participation in paid knowledge activities had a positive spillover effect on physicians' free knowledge contributions, which manifested in the increase in post quantity (473.1%; P<.001), article length (108%; P=.009), function word frequency (0.6%; P=.001), causal word frequency (0.2%; P<.001), and content information entropy (6.6%; P=.006). The paid function led to a decrease in the consistency between titles and content (-115.5%; P<.001).

conclusionsThe findings of this study contribute to the existing literature on the impact of economic incentives in the medical context. For the platform, providing economic incentives to physicians can have positive significance in promoting the development of the platform's knowledge ecosystem and can effectively encourage physicians to contribute to both paid and free knowledge activities. This study provides a valuable reference for the platform to introduce a paid knowledge model, which is beneficial to the sustainable development of the platform.

Indexed as

PhysiciansAdultChinaFemaleHumansKnowledgeMaleMotivationdiversityeconomic incentiveshealth knowledge contributionmulti-period difference in differencespropensity score matching

Identifiers

PMID39656521
PMCPMC11668984

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.