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An integrated two-stage diffusion of innovation model with market segmented learning

机译：具有市场细分学习的创新模型的集成两阶段扩散

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摘要

With the aid of the Internet, both firms and customers have access to vast amounts of data. The aim of the proposed model is to provide a method that utilizes data to understand and predict how potential customers will value innovations, and communicate thoughts in a non-fully connected market. Innovation diffusion models have been studied extensively, and are often formulated using either macro-level approaches that aggregate much of the market behavior, or using micro-level approaches that employ microeconomic information pertaining to the potential market and the innovation. We propose a two-stage integrated model that benefits from both the macro- and micro-level approaches, and we add emphasis to modeling when, what, and how customers communicate and process information. The proposed model incorporates heterogeneous potential customers and adopters, segmented Bayesian learning, and the adopter's satisfaction levels to describe biasing and word-of-mouth behavior in a non-fully connected market.
机译：借助互联网，公司和客户都可以访问大量数据。提出的模型的目的是提供一种利用数据来理解和预测潜在客户将如何评价创新并在不完全连通的市场中交流思想的方法。对创新扩散模型进行了广泛的研究，通常使用汇总了许多市场行为的宏观方法或采用利用有关潜在市场和创新的微观经济信息的微观方法来制定创新扩散模型。我们提出了一个两阶段的集成模型，该模型可以从宏观和微观两种方法中受益，并且我们更加强调建模时间，内容以及客户如何交流和处理信息。提议的模型结合了异构的潜在客户和采用者，细分的贝叶斯学习以及采用者的满意度，以描述非完全连通市场中的偏见和口碑行为。

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