摘要
本文通过互联网上的产品评论及其回帖的数据,研究了产品网络口碑传播的动态交互过程。我们采用分层贝叶斯选择模型建模,并用马尔可夫链蒙特卡洛(MCMC,Markov Chain Monte Carlo)方法对参数进行估计。结果发现,已有回帖的特征(如正面回帖的比例、负面回帖的比例等)对当前回帖的产品态度有显著影响,并且这种影响在不同的产品评论之间存在很大差异。这种异质性可以通过引入产品评论(即主帖)的特征得到很好的解释。总体而言,已有回帖对产品的态度,以及主帖的特征等均对之后回帖的产品态度有显著影响。此外,本文还发现,在网络口碑传播过程中,正面回帖的影响比负面回帖的影响更大。最后,本文讨论了该研究对营销理论和实践的贡献。
Online word-of-mouth （WOM） plays a critical role in shaping consumers＇ attitudes toward new products. Consumers usu- ally consult online reviews to obtain others＇ opinions of the new products, and then form their own ones. In a typical online review, one main message initiates the communication, followed by many responses from other consumers to express their attitudes. The in- teraction process reshapes consumers＇ attitudes, and is of special importance for firms. This study investigates whether and how prior responses in a review and the main messages＇ characteristics influence the current responder＇s attitude toward the new products. We collected 26 new product reviews from various websites and kept the first 40 to 50 responses for each review, which results in 1173 responses in total. We specify a Hierarchical Bayesian Ordinal Choice Model to address the research questions. Parameters are estimated by the Markov Chain Monte Carlo （MCMC） method. We find that the proportion of positive and negative responses in a review significantly influence the product attitudes of the following responses. An interesting finding is that positive responses exert stronger influence on product attitudes than negative ones. We also test two psychological effects with this empirical data, namely, re- cency and primacy effects on product attitudes. The results show that both the most recent and the earliest responses exert significant negative effects on the product attitudes of the following responses. This finding provides some evidence for the existence of recency and primacy effects in the setting of online WOM. Besides, the dispersion of prior responses has a positive impact on product atti- tudes, which suggests that the presence of some negative responses help increase the credibility of the review. We also find huge hetero- geneity across reviews, which can be well explained by the charac- teristics of the main messages at the second-level specification. At the end of the paper, the theoretical and manage
出处
《南开管理评论》
CSSCI
北大核心
2012年第5期141-151,共11页
Nankai Business Review
关键词
网络口碑
产品评论
主帖
回帖
分层贝叶斯选择模型
Online Word-of-Mouth
Product Reviews
Main Mes- sages
Responses
Hierarchical Bayesian Ordinal Choice Model
Primacy Effect
