Social Media and the Diffusion of an Information Technology Product
Social Media and the Diffusion of an Information Technology Product
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DOI：
10.1007/978-981-13-3149-7_13
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发表时间：
2016-08
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影响因子：
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通讯作者：
Yinxing Li;Nobuhiko Terui
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文献类型：
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作者：
Yinxing Li;Nobuhiko Terui
互联网的扩张导致消费者通过论坛、博客、产品评论等社交媒体平台在线发布海量信息。本研究提出了一个扩散模型，容纳发射前的社交媒体信息，并结合它与发射后的销售信息的低音模型，以提高销售预测的准确性。该模型的特征在于扩展的Bass模型，其参数随时间变化，其演化受消费者在社交媒体中的通信的影响。具体地，我们通过情感分析和主题分析从社交媒体中构造变量。这些变量作为扩散模型演化过程中的关键参数，以填补时不变关键参数模型与实测销售量之间的差距。对2006年至2007年第一代iPhone的实证研究表明，使用从BBS上的情感和主题分析中提取的附加变量的模型在几个标准的基础上表现最好，包括偏差信息准则（DIC）、边际似然和保持样本的预测误差。我们讨论了社会媒体信息在这项研究的扩散过程中的作用。
The expansion of the Internet has led to a huge amount of information posted by consumers online through social media platforms such as forums, blogs, and product reviews. This study proposes a diffusion model that accommodates pre-launch social media information and combines it with post-launch sales information in the Bass model to improve the accuracy of sales forecasts. The model is characterized as the extended Bass model, with time varying parameters whose evolutions are affected by the consumer’s communications in social media.Specifically, we construct variables from social media by using sentiment analysis and topic analysis. These variables are fed as key parameters in the diffusion model’s evolution process for the purpose of plugging the gap between the time-invariant key parameter model and that of observed sales.An empirical study of the first-generation iPhone during 2006 and 2007 shows that the model using additional variables extracted from sentiment and topic analysis on BBS performs best based on several criteria, including DIC (Deviance Information Criteria), marginal likelihood, and forecasting errors of holdout samples. We discuss the role of social media information in the diffusion process for this study.