外文翻译--在线口碑和产品销售动态——对电影行业的实证调查

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1、中文 3310 字 毕业 论文外文翻译 一、外文原文 标题： The dynamics of online word-of-mouth and product salesAn empirical investigation of the movie industry 原文： Introduction Word-of-mouth (WOM) has been recognized as one of the most influential resources of information transmission since the beginning of human society (Go

2、des and Mayzlin 2004； Maxham and Netemeyer 2002； Reynolds and Beatty 1999). However, conventional interpersonal WOM communication is only effective within limited social contact boundaries, and the influence diminishes quickly over time and distance (Bhatnagar and Ghose 2004； Ellison and Fudenberg 1

3、995). The advances of information technology and the emergence of online social network sites have profoundly changed the way information is transmitted and have transcended the traditional limitations of WOM (Laroche et al. 2005). The otherwise fleeting WOM targeted to one or a few friends has been

4、 transformed into enduring messages visible to the entire world. As a result, online WOM plays an increasingly significant role in consumer purchase decisions. Online WOM presents both challenges and opportunities to retailers. On the one hand ,WOM provides an alternative source of information to co

5、nsumers, thus reducing retailers ability to influence these consumers through traditional marketing and advertising channels. Prior studies show that a variety of aspects of WOM influence retail sales. Some found that WOM dispersion (Godes and Mayzlin 2004) and valence (Chevalier and Mayzlin 2006； F

6、orman, Ghose, andWiesenfeld 2008) have significant effects on product sales, while others found that WOM volume serves as the key driver of product sales (Chen, Wu, and Yoon 2004； Liu 2006). On the other hand, online WOM provides a new venue for retailers to reach consumers and to strategically infl

7、uence consumer opinions. Anecdotal evidence has surfaced in recent years suggesting that online WOM could be successfully leveraged as a new marketing tool (Dellarocas2003). A unique aspect of the WOM effect that distinguishes it from more traditional marketing effects is the positive feedback mecha

8、nism between WOM and product sales. That is, WOM leads to more product sales, which in turn generate more WOM and then more product sales. The positive feedback mechanism indicates that WOM is not only a driving force in consumer purchase but also an outcome of retail sales ( Godes and Mayzlin 2004；

9、 Srinivasan, Anderson, and Ponnavolu 2002). Prior studies on WOM have not fully recognized this unique nature of WOM effect and often treat WOM as exogenous, like traditional marketing effects (Chen et al. 2004； Liu 2006). Ignoring WOMs dual roles of precursor and outcome may misplace causality and

10、lead to erroneous results. The objectives of this study, therefore, are to explicitly model the positive feedback mechanism between WOM and retail sales and identify their dynamic interrelationship. We propose a simultaneous equation system to fully capture the dual nature of online WOM and its dyna

11、mic evolution in a panel data setting. We have chosen the movie industry as our research context because industry experts agree that WOM is a critical factor underlying a movies staying power, which leads to its ultimate financial success (Elberse and Eliashberg 2003). In addition, the movie industr

12、y has by far received the most attention in marketing literature on WOM, which allows in-depth comparison of our results with those of previous studies. We, however, note that movies are a unique type of experience goods and the results from the industry do not necessarily generalize to other retail

13、ing sectors. Rather, our goal is to use the movie industry as a context to highlight the importance of considering the dynamics of and the interrelationship between retail sales and online WOM and to demonstrate the validity of the simultaneous equation approach in this setting. We found that both a

14、 movies box office revenue and WOM valence significantly influence WOM volume. WOM volume in turn leads to higher box , office performance. Our results clarify conflicting results reported in earlier studies with regard the influence of user ratings on box office revenue. We show that user ratings d

15、o not directly influence box office revenue. However, they affect box office revenue indirectly through WOM volume. Online WOM in the movie industry takes many forms, including online reviews, discussion boards, chat rooms, blogs, wikis, and others. In this study, we focus on online user reviews bec

16、ause statistics suggest that user reviews are more prevalent than other forms of WOM communication in the movie industry. Beyond volume, another subtle but important difference between online user reviews and other types of WOM is that user reviews usually reflect user experience and consumer satisf

17、action, which are mainly viewed as a source of product information (Chen and Xie 2004； Li and Hitt 2008). Meanwhile, other types of WOM, such as discussions in online community sites, reflect more about consumer expectation, which could be heavily influenced by social structure (Gopal et al. 2006； L

18、iu 2006). The rest of the paper is organized as follows. The next section provides the literature review followed by the discussion of our conceptual framework and research hypo theses. We then describe our sources of data and the empirical model and estimation. Main findings are presented and discu

19、ssed next, and the paper ends with a discussion of implications, limitations, and future research. Empirical model specification The development of our empirical model is guided by the following considerations. First, as we are interested in the drivers of both box office revenue and WOM, we constru

20、ct a system of two interdependent equations: one equation with daily revenue as the dependent variable (the revenue equation) and the other with WOM volume as the dependent variable (the WOM equation). We assume that in each time period (i.e., day), the errors in the two equations may be correlated,

21、 which implies that factors not included in our model could simultaneously influence both movie revenue and WOM. Second, recognizing that interactions between consumers movie-going behavior and WOM can go beyond the concurrent term (Elberse and Eliashberg 2003), we develop a system of dynamic equations. That is, in the revenue equation, we include not only the contemporaneous term of daily WOM volume, but also multi-lag terms.