The impact of social norms on diffusion dynamics: A simulation of e-cigarette use behavior
Health Education & Behavior ยท 2025
Published2025Cited by1
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Abstract
Diffusion of innovations theory can be used to understand how to prevent or slow the spread of harmful behaviors, such as e-cigarette use in adolescent social networks. This study explores how different network intervention strategies could impact diffusion dynamics through network simulations based on observed social norms and e-cigarette use data. Simulations were initialized with baseline network data collected from 10 schools in a prospective cohort study of adolescent social networks and health behaviors in Southern California. Diffusion conditions varied by changes in social norms for intervention nodes (pro-e-cigarette, anti-e-cigarette, or neutral norms) and intervention strategy, where greater pro- and anti-tobacco norms were assigned to 15% of the network based on four intervention seeding conditions: opinion leadership, betweenness centrality, segmentation, and random selection. For each network, simulations were run using the netdiffuseR package in R and multivariate generalized linear models were estimated to examine changes in diffusion dynamics. Diffusion prevalence and rate were greater in denser networks and networks with more initial e-cigarette users. Anti-e-cigarette norms significantly decreased average prevalence across all intervention conditions. Strategically selecting high betweenness centrality nodes and opinion leader nodes significantly decreased the average prevalence of e-cigarette use. The results of this study show that achieving a change in norms for 15% of a network can substantially impact e-cigarette use prevalence. Furthermore, this study enhances our knowledge of how personal and network factors affect diffusion dynamics and demonstrates that targeting social norms through network-based interventions is one avenue for slowing the spread of harmful behaviors.
Cite
@article{piombo2025,
title = {The impact of social norms on diffusion dynamics: A simulation of e-cigarette use behavior},
author = {{Piombo}, {Sarah} and {Vega Yon}, {George G.} and {Valente}, {Thomas W.}},
year = {2025},
month = {3},
journal = {Health Education & Behavior},
volume = {52},
number = {4},
pages = {428โ438},
doi = {10.1177/10901981251327189},
url = {https://doi.org/10.1177/10901981251327189},
}