Signed networks - The role of negative links for the diffusion of innovation

Matthias Mueller and Shyaam Ramkumar

Technological Forecasting and Social Change, 2023, vol. 192, issue C

Abstract: We study the effects of negative links for the innovation diffusion process. To do so we employ an agent-based simulation model of a threshold diffusion process. Our results reveal that negative relationships can have a negative but also positive impact on the innovation diffusion. If network actors simply weigh the positive/negative signals coming from peers that have adopted the innovation, already few negative links will have a significant negative impact on the diffusion performance. If, however, network actors additionally consider the signals coming from peers that have not adopted the innovation, networks with a medium to high number of negative links create a ‘double-negative’ effect which drives the diffusion. Finally, an empirical network shows that our results hold also for real world network data. Our results emphasize the need to better understand how negative relationships, foe effects, and non-adopter effects can hinder or support the diffusion of innovations, potentially explaining why key innovations fail to spread and others succeed. The implications of this research are relevant for the discussion on the diffusion of innovation but also for the diffusion of information, opinions etc. It offers insights for researchers, companies, and policymakers wishing to scale and diffuse innovations more quickly.

Keywords: Innovation diffusion; Diffusion in networks; Agent-based modeling; Signed networks; Friendship-foe networks; Distinction effects (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002603

DOI: 10.1016/j.techfore.2023.122575

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