Social Networks · 1996 · 1K citations · 52 references
EngineeringSocial InfluenceSocial NetworkKnowledge DiffusionComputational Social ScienceTechnology DiffusionSocial MediaThreshold ModelsDiffusion Of InnovationSocial Network AnalysisOpinion LeadershipSocial NetworksInnovationMarketingNetwork ScienceAdopter CategorizationNetwork ThresholdsSocial ComputingSociologyInformation DiffusionSocial InnovationArtsInfluence ModelSocial Network Thresholds
Threshold models have been postulated as one explanation for the success or failure of collective action and the diffusion of innovations. The study develops a social network threshold model of innovation diffusion using Ryan and Gross adopter categories, aiming to define these categories at both system and individual network levels. The model employs a dual typology of adopter categories—system‑wide and personal network—applied to three diffusion datasets to examine external influence and opinion leadership. Network thresholds enable variation of contagion definitions, prediction of diffusion patterns, and identification of opinion leaders and followers, thereby enhancing understanding of the two‑step flow hypothesis.
Threshold models have been postulated as one explanation for the success or failure of collective action and the diffusion of innovations. The present paper creates a social network threshold model of the diffusion of innovations based on the Ryan and Gross (1943) adopter categories: (1) early adopters; (2) early majority; (3) late majority; (4) laggards. This new model uses social networks as a basis for adopter categorization, instead of solely relying on the system-level analysis used previously. The present paper argues that these four adopter categories can be created either with respect to the entire social system, or with respect to an individual's personal network. This dual typology is used to analyze three diffusion datasets to show how external influence and opinion leadership channel the diffusion of innovations. Network thresholds can be used (1) to vary the definition of behavioral contagion, (2) to predict the pattern of diffusion of innovations, and (3) to identify opinion leaders and followers in order to understand the two-step flow hypothesis better.
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