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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|42573||2015||15 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Sciences, Volume 307, 20 June 2015, Pages 95–109
With the development of social media, online social networks offer potential opportunities for firms to analyze user behaviors. Among many, one of the fundamental questions is how to predict product adoption, and the answer to this question lays the foundation for product adoption maximization and demand estimation in large social networks. However, due to the inherent challenges resulting from the dynamic diffusion mechanism in online social networks, such as modeling of activation thresholds and influence probability, differentiating between influence and adoption, and incorporating review content, traditional diffusion models are often not adequate enough to predict product adoption accurately. In order to tackle these challenges, we propose a linear threshold-hurdle model to predict product adoption incorporating social network effects. First, we present a fine-grained activation threshold model based on the five categories of adopters. In addition, we identify three operational factors underlying social network effects, including interaction strength, structural equivalence, and social entity similarity, to model influence probabilities. Furthermore, we distinguish influence spread from adoption spread by introducing a tattle state, in which users express opinions without adopting the product. Finally, we introduce the notion of hurdle to capture the monetary aspect in users’ decision making process of product adoption. Based on the proposed linear threshold-hurdle model, two data mining methods based on the rough set technique, namely, decision rules and decomposition trees, are employed to predict product adoption in a large social network. An empirical study of Kindle Fire HD 7 in. tablets is used to illustrate the potential and feasibility of the proposed model. The results demonstrate the predictive power of the proposed model with average F-scores of 89.8% for the week prediction model and of 86.7% for the bi-week prediction model.