Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns

Autores

Laciana, C., Rovere, S.L. and Podestá, G.P.

Publicado en

Physica A: Statistical Mechanics and its Applications

Año de publicación

2013

Afiliaciones

Grupo de Aplicaciones de Modelos de Agentes (GAMA), Facultad de Ingeniería, Universidad de Buenos Aires, Avenida Las Heras 2214, Ciudad Autónoma de Buenos Aires, C1127AAR, Argentina
Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149-1098, USA

Programa

CRN3

Proyecto

CRN3035

Keywords

Innovation diffusion, Bass model, Agent-based models, Technology adoption

DOI

https://doi.org/10.1016/j.physa.2012.12.023

Resumen

A micro-level agent-based model of innovation diffusion was developed that explicitly combines (a) an individual&rsquos perception of the advantages or relative utility derived from adoption, and (b) social influence from members of the individual&rsquos social network. The micro-model was used to simulate macro-level diffusion patterns emerging from different configurations of micro-model parameters. Micro-level simulation results matched very closely the adoption patterns predicted by the widely-used Bass macro-level model (Bass, 1969 [1]). For a portion of the domain, results from micro-simulations were consistent with aggregate-level adoption patterns reported in the literature. Induced Bass macro-level parameters and responded to changes in micro-parameters: (1) increased with the number of innovators and with the rate at which innovators are introduced (2) increased with the probability of rewiring in small-world networks, as the characteristic path length decreases and (3) an increase in the overall perceived utility of an innovation caused a corresponding increase in induced and values. Understanding micro to macro linkages can inform the design and assessment of marketing interventions on micro-variables&ndashor processes related to them&ndashto enhance adoption of future products or technologies.

Highlights

•We develop a microscopic model of processes influencing individual adoption behavior.
•Micro-level simulations match closely adoption curves predicted by the Bass model.
•Bass parameters for many actual adoptions can be induced by the microscopic model.
•We established linkages between micro-level parameters and innovation takeoff time.
•We illustrate use of the micro-model to assess outcomes of marketing interventions.