Impact of WeChat public platforms on blood donation behavior: a big data-based study

Abstract

Purpose

The purpose of this paper is to explore the impact of WeChat public platforms (abbreviated as WPP) on blood donation behavior using data from the platforms’ backend and information system.

Design/methodology/approach

First, this paper established a time-varying difference-in-difference (DID) model to evaluate the change before and after following the WPP under normal scenarios. The difference-in-difference-in-difference (DDD) method was further used to analyze the heterogeneous effects of gender, age, occupation and education. Second, a logit model was used to examine the impact of WPP on blood donation behavior under emergency scenarios (i.e. COVID-19).

Findings

The research shows that following WPP has a positive impact on donation volume. For each donor, the average blood donation volume after following WPP increased by 12.94% compared to before following. The WPP has a greater impact on groups with males, medical staff, middle-aged individuals and those with primary school education. Following WPP also enhanced blood donation behavior in emergency scenarios. During the COVID-19 pandemic, the probability of fans donating blood was 2.6% higher than non-fans, and the average blood donation volume of fans was 7.04% higher than non-fans, which was 5.9% lower than in normal scenarios.

Originality/value

For theory, this paper quantified the impact of WPP on blood donation behavior in normal and emergency scenarios and addressed the research gap surrounding the impact exerted by social media on blood donation behavior. For methodology, the time-varying DID model, DDD model and logit model were applied to the field of blood donation, which expanded the application scenarios. For practice, the findings are of great significance for recruiting blood donors and providing evidence for promotion on WPP.

Citation

Guo, X. and Chen, X. (2022), "Impact of WeChat public platforms on blood donation behavior: a big data-based study", Industrial Management & Data Systems, Vol. 122 No. 4, pp. 983-1001. https://doi.org/10.1108/IMDS-09-2021-0588