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A discrete innovation diffusion model incorporating change in the adoption rate

机译:包含采用率变化的离散创新扩散模型

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New products play a significant role in the success of firms concerned with the introduction of innovative products. Mathematical modeling that can describe the life cycle of these products can provide major contribution in their successful diffusion. The Bass model of innovation diffusion is a main representative of the diffusion models. Many modifications have been made to the model since its development to answer the changing needs and limitations. The model was developed in continuous time, which limits its application on many real life applications having discrete time data. Due to this reason a discrete version of this model was proposed by another author Hirota. The model was based on Riccati's equation in mathematics. Although the model can be solved to exact solution but it is difficult to modify this model further and solve to get the exact solution. Further, in practice the pace of diffusion varies not only because of the life cycle phase but due to many other variations such as changes in advertising strategies, little product modifications, competitive products etc. In marketing this concept can be termed as change point. In the present article, we propose an approach to model the diffusion process using a discrete logistic function whose exact solution can be obtained using probability generating function (PGF), incorporating the aforesaid change point concept. The model is validated on the real life data sets. Therefore, the proposed model provides accurate parameter estimates, making it possible to predict when a product can be launched.
机译:新产品在与引进创新产品有关的公司的成功中起着重要作用。可以描述这些产品生命周期的数学模型可以为产品的成功推广做出重大贡献。创新扩散的巴斯模型是扩散模型的主要代表。自从模型开发以来,已经对其进行了许多修改,以适应不断变化的需求和局限性。该模型是在连续时间内开发的,这限制了它在具有离散时间数据的许多现实生活中的应用。由于这个原因,另一位作者Hirota提出了该模型的离散版本。该模型基于Riccati的数学方程式。尽管可以将模型求解为精确解,但是很难进一步修改该模型并求解以获取精确解。此外,在实践中,扩散的速度不仅因生命周期阶段而异,还由于许多其他变化而异,例如广告策略的变化,产品的很少改动,竞争性产品等。在营销中,这一概念可以称为变化点。在本文中,我们提出了一种使用离散逻辑函数对扩散过程进行建模的方法,该函数可以使用概率生成函数(PGF)结合上述变化点概念来获得精确解。该模型在现实生活的数据集上得到了验证。因此,所提出的模型提供了准确的参数估计,从而可以预测何时可以发布产品。

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