Back to Search Start Over

Joint-sensemaking, innovation, and communication management during crisis: Evidence from the DCT applications in China

Authors :
Jingjing Qu
Liwei Chen
Hui Zou
Hui Hui
Wen Zheng
Jar-Der Luo
Qingyuan Gong
Yuwei Zhang
Tianyu Wen
Yang Chen
Source :
Big Data & Society, Vol 11 (2024)
Publication Year :
2024
Publisher :
SAGE Publishing, 2024.

Abstract

As exemplified by the COVID-19 pandemic, the design and implementation of data-driven health surveillance, like digital contact tracing (DCT) apps, carry significant implications for society. However, its rushed development calls for careful consideration from all involved stakeholders to achieve a shared understanding and engage in joint-sensemaking in order to implement DCT collaboratively and effectively utilize it in the fight against the pandemic. Yet, the empirical ground truth and theoretical mechanism of joint-sensemaking are both unclear. Drawing on this gap, this article applies a multistep approach, including sentiment analysis, topic analysis coupled with regression and unique network analysis, to thoroughly explore, examine, and explain the dynamic process of joint-sensemaking in the context of a public crisis. Based on evidence from 113,264 Weibo posts, we illustrate two joint-sensemaking pathways and three key interventions using the case of China's Health Code in the context of the DCT. We reveal that the effectiveness of different interventions and contributions made by stakeholders vary significantly between different joint-sensemaking pathways. Specifically, we find that official media and opinion leaders act as crucial mediators in bridging intervention conductors and the public. However, their influence presents heterogeneity toward different network modularity, thus leading to distinct patterns. Additionally, inconsistent with previous literature, we find that within the context of the China Health Code, official media has a greater impact on opinion leaders in engaging the public.

Subjects

Subjects :
General Works

Details

Language :
English
ISSN :
20539517
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Big Data & Society
Publication Type :
Academic Journal
Accession number :
edsdoj.0435288fe7745d5ac3a4877a01981e3
Document Type :
article
Full Text :
https://doi.org/10.1177/20539517241270714