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.

本文提出了一种新的创新扩散模型，它是标准逻辑模型，Bass模型和Gompertz模型的扩展，适用于当观察到的过程是几个未观察到的过程相互作用的结果的情况，例如当该过程允许该过程的每个主题随时间重复使用创新的可能性时。为了检查模型的可行性及其充分描述和预测创新扩散过程的能力，本文使用了手机用户的时间序列数据。使用这些时间序列将建议模型的性能与经典创新扩散模型进行比较。实证结果表明，扩展模型优于经典模型，