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

TL;DR

The study explores how personal and collective losses influence public support for extreme measures during the pandemic, using a psychological model.

Contribution

The research extends the 3N model by showing how different types of loss affect support for extreme measures through social norms.

Findings

Collective loss is linked to lower support for extreme measures.

Personal loss is linked to higher support for extreme measures.

Both effects are mediated by perceived social network norms.

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.

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