Source: Statist. Sci. Volume 21, Number 2
(2006), 277-285.
The emergence of online communities has enabled firms to monitor consumer-generated online word-of-mouth (WOM) in real-time by mining publicly available information from the Internet. A prerequisite for harnessing this new ability is the development of appropriate WOM metrics and the identification of relationships between such metrics and consumer behavior. Along these lines this paper introduces a metric of a purchasing population’s propensity to rate a product online. Using data from a popular movie website we find that our metric exhibits several relationships that have been previously found to exist between aspects of a product and consumers’ propensity to engage in offline WOM about it. Our study, thus, provides positive evidence for the validity of our metric as a proxy of a population’s propensity to engage in post-purchase online WOM. Our results also suggest that the antecedents of offline and online WOM exhibit important similarities.
References
Anderson, E. W. (1998). Customer satisfaction and word of mouth. J. Service Research 1 5--17.
Bayus, B. L. (1985). Word-of-mouth: The indirect effects of marketing efforts. J. Advertising Research 25 31--39.
Bowman, D. and Narayandas, D. (2001). Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. J. Marketing Research 38 281--297.
Breusch, T. and Pagan, A. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica 47 1287--1294.
Brown, J. J. and Reingen, P. (1987). Social ties and word-of-mouth referral behavior. J. Consumer Research 14 350--362.
Dellarocas, C., Awad, N. and Zhang, M. (2005). Using online ratings as a proxy of word-of-mouth in motion picture revenue forecasting. Working paper, Smith School of Business, Univ. Maryland.
Dichter, E. (1966). How word-of-mouth advertising works. Harvard Business Review 44(6) 147--160.
Eliashberg, J. and Shugan, S. M. (1997). Film critics: Influencers or predictors? J. Marketing 61(2) 68--78.
Engel, J. F., Blackwell, R. D. and Miniard, P. W. (1995). Consumer Behavior, 8th ed. Dryden, Fort Worth.
Godes, D. and Mayzlin D. (2004). Using online conversations to study word of mouth communication. Marketing Science 23 545--560.
Granovetter, M. (1973). The strength of weak ties. American J. Sociology 78 1360--1380.
Greene, W. (2003). Econometric Analysis, 5th ed. Prentice Hall, Upper Saddle River, NJ.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G. and Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? J. Interactive Marketing 18 38--52.
King, K. W. and Tinkham, S. F. (1990). The learning and retention of outdoor advertising. J. Advertising Research 29 47--51.
Liu, Y. (2004). Word-of-mouth for movies: Its dynamics and impact on box office receipts. Working paper.
Muñoz, L. (2003). High-tech word of mouth maims movies in a flash. Los Angeles Times August 17.
Neelamegham, R. and Chintagunta, P. (1999). A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science 18 115--136.
Reingen, P. and Kernan, J. (1986). Analysis of referral networks in marketing: Methods and illustration. J. Marketing Research 23 370--378.
Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. J. Marketing 47(1) 68--78.
Simonoff, J. S. and Sparrow, I. R. (2000). Predicting movie grosses: Winners and losers, blockbusters and sleepers. Chance 13(3) 15--24.
Sundaram, D. S., Mitra, K. and Webster, C. (1998). Word-of-mouth communications: A motivational analysis. Adv. in Consumer Research 25 527--531.
Wu, O. (2005). Dynamics of online movie ratings. Term paper, Research Interactive Team, VIGRE program, Univ. Maryland.
