Extended innovation diffusion models and their empirical performance on real propagation data

Sergei Sidorov (), Alexey Faizliev, Vladimir Balash, Olga Balash, Maria Krylova and Aleksandr Fomenko
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Sergei Sidorov: Saratov State University, Russian Federation
Alexey Faizliev: Saratov State University, Russian Federation
Vladimir Balash: Saratov State University, Russian Federation
Olga Balash: Saratov State University, Russian Federation
Maria Krylova: Saratov State University, Russian Federation
Aleksandr Fomenko: Povolzhsky Institute of Management named after P.A. Stolypin
Authors registered in the RePEc Author Service: Владимир Алексеевич Балаш

Journal of Marketing Analytics, 2021, vol. 9, issue 2, No 3, 99-110

Abstract: Abstract This paper proposes a new class of innovation diffusion models which are extensions of the standard logistic model, the Bass model, and the Gompertz model for the case when the observed process is the result of the interaction of several unobserved processes, e.g., for the case when the process allows the possibility of repeated use of innovation by each subject of the process over time. In order to check the viability of the models and their ability to adequately describe and predict the process of diffusion of innovations, the time series data of mobile phone subscribers are used in this paper. These time series are employed to compare the performance of the proposed models with the classical innovation diffusion models. Empirical results show that the extended models surpass the classical models, and the examined models have a better performance on real data.

Keywords: Innovation diffusion models; Diffusion of innovations; Logistic model; Bass model; Gompertz model; Mobile communications (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1057/s41270-021-00106-x

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