The 3N Model and collective support for extreme measures to combat COVID-19

Understanding resistance to COVID-19 measures is crucial, since it undermines public health efforts during crises. Building on prior research showing the crucial role of individual psychological factors in shaping responses to such efforts, we focus on psychological factors involved. Specifically, we examine such psychological factors that shape support for COVID-19 measures by conceptualizing them as extreme measures-restrictions that significantly alter daily life-thus enabling the application of the 3N model of extreme behavior. Drawing on data from the multinational dataset (N = 62,983) across 114 countries, we tested the role of two types of needs/losses: collective loss and personal loss. The results reveal a differential pattern: collective loss is associated with lower support for extreme measures, while personal loss is associated with higher support. Both effects are mediated by perceived social network norms, though the strength of mediation differs. These findings extend the 3N model by highlighting how distinct types of loss shape responses to extreme measures, and they offer implications for designing public policies that address both individual and collective concerns.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2025-11-04
DOI
https://doi.org/10.1371/journal.pone.0335241
Primary Topic
COVID-19 and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The 3N Model and collective support for extreme measures to combat COVID-19

Haykal Hafizul Arifin, Winnifred R. Louis, Mirra Noor Milla, Jocelyn J. Bélanger et al.
PLoS ONE
COVID-19 and Mental Health
article

The 3N Model and collective support for extreme measures to combat COVID-19

Haykal Hafizul Arifin, Winnifred R. Louis, Mirra Noor Milla, Jocelyn J. Bélanger, Aly Lamuri, Umar H. Sulaiman, Norberta Fauko Firdiani
article en

Abstract

Understanding resistance to COVID-19 measures is crucial, since it undermines public health efforts during crises. Building on prior research showing the crucial role of individual psychological factors in shaping responses to such efforts, we focus on psychological factors involved. Specifically, we examine such psychological factors that shape support for COVID-19 measures by conceptualizing them as extreme measures-restrictions that significantly alter daily life-thus enabling the application of the 3N model of extreme behavior. Drawing on data from the multinational dataset (N = 62,983) across 114 countries, we tested the role of two types of needs/losses: collective loss and personal loss. The results reveal a differential pattern: collective loss is associated with lower support for extreme measures, while personal loss is associated with higher support. Both effects are mediated by perceived social network norms, though the strength of mediation differs. These findings extend the 3N model by highlighting how distinct types of loss shape responses to extreme measures, and they offer implications for designing public policies that address both individual and collective concerns.

PLoS ONEVol. 20(11)
Diponegoro University (ID), The University of Queensland (AU), University of Indonesia (ID), Carnegie Mellon University Qatar (QA)
Openalex Percentile: Top 56%
COVID-19 and Mental Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The 3N Model and collective support for extreme measures to combat COVID-19 — Haykal Hafizul Arifin, Winnifred R. Louis, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS