Untitled - Universidade de Lisboa

Innovation Diffusion in Organizations- An Evolutionary Approach
Luciano Sampaio 1 , João Varajão 2,3 , E.J. Solteiro Pires 2,4 , P.B. de Moura Oliveira 2,5
1
Instituto de Estudos Superiores de Fafe, Lda. - Escola Superior de Tecnologias de Fafe, Portugal
2 University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
3 Centro ALGORITMI, Guimarães, Portugal
4 CITAB Research Center, Vila Real, Portugal
5 CIDESD Research Center, Vila Real, Portugal
luciano.sampaio@optimus.clix.pt, jvarajao@utad.pt, epires@utad.pt, oliveira@utad.pt
Extended abstract
Over the last years several theoretical models have been proposed to explain the innovation adoption
process, each one using different approaches [1-2]. Therefore, various mathematical models, based on
simple expressions have been developed to describe the diffusion of an innovation among a given
potential adopters set. Today, these models are a fundamental tool to predict the innovation adoption
growth. Several examples can be found in literature, as the Gompertz and Bass macro-logistics models
[3], among many others.
The application of these models is made accordingly to the estimation of certain coefficients. These
coefficients are typically estimated from time series. However, in several problems there is not any
historical data, which constitutes a serious drawback in applying this type of methods. Thus, to
simulate the process sometimes is necessary to use features of other similar innovations [3, 4]. This
could introduce some entropy in the process. Moreover, the models based on the logistic equation have
a limited application. On the other hand, modeling at macro level is inaccurate because it assumes a
perfect social mix where everyone interacts with everyone and does not measure whether the
interpersonal are the same [4]. Other type of models has been proposed, based on the study of the
individual and / or organization within a social system [5-6]. Some approaches consider that the
diffusion is made through the influence of social network. In this case, the influence is transmitted by
direct contact between individuals [4,7]. For instance, Young [7] proposed and built a model based on
a graph.
Taking the former work in consideration, particularly the Young model [7], and evolutionary
computation [8], this work proposes a new model using evolutionary algorithms to describe the
innovation adoption process. Moreover, the proposed model under development is based in Roger,
Valente and Davis models concepts [5,4,9] and Young interaction network [7]. The main advantage of
a model with these features is that historical data is not required to simulate the innovation diffusion,
since its construction is focused on the individual characteristics, in their behavior towards innovation,
external influences (media) and their interpersonal relationships. Moreover, it only requires taking into
account the individual adoption trends and social systems (businesses, schools, municipalities, etc.)
converted into mathematical models, as well as the network of direct contacts between individuals. The
predisposition of an individual to adopt a particular innovation can also be evaluated probabilistically.
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