Statistical Science

A Statistical Measure of a Population’s Propensity to Engage in Post-Purchase Online Word-of-Mouth

Chrysanthos Dellarocas and Ritu Narayan

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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.

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Statist. Sci. Volume 21, Number 2 (2006), 277-285.

First available in Project Euclid: 7 August 2006

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Word-of-mouth metrics online communities viral marketing motion picture reviews


Dellarocas, Chrysanthos; Narayan, Ritu. A Statistical Measure of a Population’s Propensity to Engage in Post-Purchase Online Word-of-Mouth. Statist. Sci. 21 (2006), no. 2, 277--285. doi:10.1214/088342306000000169.

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  • 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.