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The role of online buzz for leader versus challenger brands: the case of the MP3 player market

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Abstract

Online buzz reflects the perceived quality of products in a positive, negative, or neutral way. We have limited understanding of how customers’ quality perceptions, often referred to as e-sentiment, affect the movement of prices. In this paper, we examine the effect of e-sentiment on the daily price fluctuations of MP3 players by using daily buzz information collected from diverse online documents. Econometric panel data modeling reveals that e-sentiment is a leading indicator of price fluctuations. Furthermore, we find the effect is moderated by the brand’s market position: the leading (challenger) brand’s price responds more strongly to negative (positive) online buzz. In other words, negative buzz has a greater adverse effect on leading brands, whereas positive buzz has a greater beneficial effect on challenger brands. These findings establish the relevance of e-sentiment information to online price movements and suggest that managers should frequently monitor the online buzz surrounding their products, especially as it relates to their relative perceived quality.

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Notes

  1. Subjective quality assessment is often called ‘quality perceptions,’ ‘perceived value,’ or ‘perceived quality’ in the marketing literature. Quality perceptions consist of three components: (1) abstract dimensions of intrinsic attributes (e.g., safety, durability), (2) perceived monetary price, and 3) reputation formed through advertising and brand name [61].

  2. A few notable exceptions include [19, 34, 43, 44, 56]. But they collect online buzz data from a single source, such as a movie review site (e.g., Yahoo!Movie) [19, 43], an online forum [34], or a single firm [56].

  3. Accordingly, researchers often rely on a proxy, such as sales rank. Even though sales rank information is known to approximate actual sales to some extent (e.g., [32]), it is still not a perfect measure.

  4. http://archive.fortune.com/magazines/fortune/fortune_archive/2005/01/10/8230982/index.htm.

  5. To help ‘confused’ customers, the CNET MP3 Player Buying Guide suggests “10 Key MP3 Play Features” in addition to basic attributes such as sound quality and design (http://reviews.cnet.com/4520-7964_7-5134106.html).

  6. http://www.bloomberg.com/apps/news?pid=conewsstory&refer=conews&tkr=AAPL:US&sid=aggTRzHFt1Do.

  7. Note that T = 61 for all products except the iPod Mini, which was not available at Amazon.com for the first seven days (June 2–8). Accordingly, our database contains 54 observations for the iPod Mini.

  8. The ADF and PP panel unit root tests can be used to check whether a focal variable is stationary. If stationary, we can perform a conventional regression analysis. If non-stationary, we can make the variable stationary by differencing. For more details, see Enders [28].

  9. Note that the iPod Mini was replaced by the iPod Nano in 2006. During the data collection period in 2007, one to two e-vendors carried the iPod Mini at a price approximately 60 % cheaper than the original price.

  10. For a specification with K = 2 and above, we found that either the estimation does not converge or the coefficients of additional lag terms are statistically insignificant. Accordingly, we conclude that K = 1 is the best specification, even aside from the parsimony issue.

  11. Price elasticity reported in the table is calculated as the average of each individual product’s price elasticity, which is calculated by multiplying the estimated coefficient of the e-sentiment variable by the average value of the variable divided by the average value of price in the sample.

  12. See http://onlineprofitable.com/ebay/online-retailing-amazon-vs-ebay-selling-on-amazon-or-selling-on-ebay-which-is-the-best-option-for-online-vendors-part-1.

References

  1. Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., & Sawyer, A. (1997). Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. Journal of Marketing, 61(3), 38–53.

    Article  Google Scholar 

  2. Arndt, J. (1968). Word-of-mouth advertising and perceived risk. In H. Kassarjian & T. Robertson (Eds.), Perspectives in consumer behavior. IL: Scott Foresman, Glenview.

    Google Scholar 

  3. Bajari, P., & Benkard, C. L. (2005). Demand estimation with heterogeneous consumers and unobserved product characteristics: A hedonic approach. Journal of Political Economy, 113(6), 1239–1276.

    Article  Google Scholar 

  4. Bajari, P., Cooley-Fruehwirth, J., Kim, K. I., & Timmins, C. (2012). A rational expectation approach to hedonic regressions with time-varying unobserved product attributes: The price of pollution. American Economic Review, 102(5), 1898–1926.

    Article  Google Scholar 

  5. Bajari, P., & Hortacsu, A. (2004). Economic insights from internet auctions. Journal of Economic Literature, 42(2), 457–486.

    Article  Google Scholar 

  6. Bakos, Y. (2001). The emerging landscape for retail e-commerce. Journal of Economic Perspectives, 15(1), 69–80.

    Article  Google Scholar 

  7. Barwise, P., & Meehan, S. (2004). SIMPLY BETTER: Winning and keeping customers by delivering what matters most. Boston, MA: Harvard Business School Press.

    Google Scholar 

  8. Bass, F. (1969). A new product growth model for consumer durables. Management Science, 15, 215–227.

    Article  Google Scholar 

  9. Baye, M. B., Morgan, J., & Scholten, P. (2004). Price dispersion in the small and large: Evidence from an internet price comparison site. Journal of Industrial Economics, 52(4), 463–496.

    Article  Google Scholar 

  10. Baye, M. B., Morgan, J., & Scholten, P. (2004). Temporal price dispersion: Evidence from an online consumer electronics market. Journal of Interactive Marketing, 18(4), 101–115.

    Article  Google Scholar 

  11. Bridges, E., Yim, C. K., & Briesch, R. A. (1995). A high-tech product market with customer expectations. Marketing Science, 14(1), 61–81.

    Article  Google Scholar 

  12. Brynjolfsson, E., & Smith, M. (2000). Frictionless commerce? A comparison of internet and conventional retailers. Management Science, 46, 563–585.

    Article  Google Scholar 

  13. Brynjolfsson, E., Hu, Y., & Smith, M. D. (2006). From niches to riches: The anatomy of the long tail. Sloan Management Review, 47(4), 67–71.

    Google Scholar 

  14. Chen, Y., & Xie, J. (2005). Third-party product review and firm marketing strategy. Marketing Science, 24(2), 218–240.

    Article  Google Scholar 

  15. Chen, P., Dhanasobhon, S., & Smith. M. D. (2008). All reviews are not created equal: The disaggregate impact of reviews and reviewers at Amazon.com. Available at SSRN: http://ssrn.com/abstract=918083 or http://dx.doi.org/10.2139/ssrn.918083.

  16. Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491.

    Article  Google Scholar 

  17. Chevalier, J. A., & Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and BarnesandNobles.com. Quantitative Marketing & Economics, 1(2), 203–222.

    Article  Google Scholar 

  18. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word-of-mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Article  Google Scholar 

  19. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Article  Google Scholar 

  20. Clay, K., Krishnan, R., Wolff, E., & Fernandes, D. (2002). Retail strategies on the Web: Price and non-price competition in the online book industry. Journal of Industrial Economics, 50(3), 351–367.

    Article  Google Scholar 

  21. Davidson, R., & MacKinnon, J. G. (1993). Estimation and inference in econometrics. NY: Oxford University Press.

    Google Scholar 

  22. Dellarocas, C. N. (2003). The digitization of word-of-mouth: Promise and challenges of online reputation mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  23. Dellarocas, C. N. (2006). Strategic manipulation of internet online forums: Implications for consumers and firms. Management Science, 52(10), 1577–1593.

    Article  Google Scholar 

  24. Dellarocas, C. N., Zhang, X., & Awad, N. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45.

    Article  Google Scholar 

  25. Dellarocas, C. N., & Wood, C. A. (2008). The sound of silence in online feedback: Estimating trading risks in the presence of reporting bias. Management Science, 54(3), 460–476.

    Article  Google Scholar 

  26. Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.

    Article  Google Scholar 

  27. East, R., Hammond, K., & Lomax, W. (2008). Measuring the impact of positive and negative word of mouth on brand purchase probability. International Journal of Research in Marketing, 25, 215–224.

    Article  Google Scholar 

  28. Ellison, G., & Ellison, S. F. (2005). Lessons about markets from the internet. Journal of Economic Perspectives, 19(2), 139–158.

    Article  Google Scholar 

  29. Enders, W. (2004). Applied econometric time series (2nd ed.). New York: Wiley.

    Google Scholar 

  30. Fiske, S. T. (1980). Attention and weight in person perception: The impact of negative and extreme behavior. Journal of Personality and Social Psychology, 38(6), 889–906.

    Article  Google Scholar 

  31. Gantenbein, D. Good reasons to post customer reviews on your site, Microsoft.com. Downloaded on 19 November, 2010 from http://www.microsoft.com/midsizebusiness/business-goals/crm-solutions/obtaining-customer-reviews.mspx.

  32. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.

    Article  Google Scholar 

  33. Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfeiffer, B., et al. (2005). The firm’s management of social interactions. Marketing Letters, 16(3/4), 415–428.

    Google Scholar 

  34. Gopinath, S., Chintagunta, P. K., & Venkataraman, S. (2013). Blogs, advertising and local-market movie box-office performance. Management Science (Articles in Advance), 59, 1–20.

    Google Scholar 

  35. Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  36. Gu, B., Park, J., & Konana, P. (2012). The impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research, 23(1), 182–196.

    Article  Google Scholar 

  37. Herr, P., Kardes, F., & Kim, J. (1991). Effects of word-of-mouth and product attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17, 454–462.

    Article  Google Scholar 

  38. Hotho, A., Staab S., & Stumme, G. (2003). Ontologies improve text document clustering. In Third IEEE international conference on data mining (pp. 541–544).

  39. Jarvis, J. (2009). Googlenomics (1st ed.). New York: HarperCollins.

    Google Scholar 

  40. Landsman, S. (2013). Love it or leave it: Growing power of customer reviews. CNBC.com. downloaded on August 31, 2013 from http://www.cnbc.com/100792646.

  41. Lee, J., Park, D., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352.

    Article  Google Scholar 

  42. Li, X., & Hitt, L. M. (2010). Price effects in online product reviews: An analytical model and empirical analysis. MIS Quarterly, 34(4), 809–831.

    Google Scholar 

  43. Liu, Y. (2006). Word-of-mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  44. McAlister, L., Sonnier, G., & Shively, T. (2012). The relationship between online chatter and firm value. Marketing Letters, 23(1), 1–12.

    Article  Google Scholar 

  45. Meyer, R., & Johnson, E. J. (1995). Empirical generalizations in the modeling of consumer choice. Marketing Science, 14(3) Part 2 of 2 G180-189.

  46. Nasukawa, T., & Yi J. (2003). Sentiment analysis: Capturing favorability using natural language processing. In Second international conference on knowledge capture (pp 70–77) (October).

  47. Phillips, J., & Buchanan, B.G. (2001). Ontology-guided knowledge discovery in databases. In International conference on knowledge capture (pp. 123–130).

  48. Rao, A. R., & Monroe, K. B. (1996). Causes and consequences of price premiums. Journal of Business, 64, 511–536.

    Article  Google Scholar 

  49. Reingen, P., Foster, B., Brown, J. J., & Seidman, S. (1984). Brand congruence in interpersonal relations: A social network analysis. Journal of Consumer Research, 11, 1–26.

    Article  Google Scholar 

  50. Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47, 68–78.

    Article  Google Scholar 

  51. Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the Web. Journal of Interactive Marketing., 4, 76–94.

    Article  Google Scholar 

  52. Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159–169.

    Article  Google Scholar 

  53. Schindler, R. M., & Bickart, B. (2004). Published word of mouth: Referable, consumer-generated information on the internet. In C. Haugtvedt, K. A. Machleit, & R. F. Yalch (Eds.), Online consumer psychology: Understanding and influencing customer behavior in the virtual world, chap. 2 (pp. 35–60). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  54. Schneider, H., & Albers, S. (2008). Retailer competition in shopbots. SSRN Working Paper. Available at SSRN: http://ssrn.com/abstract=1078505.

  55. Shin, H. S. (2008). Strategic and financial implications of new product quality. UCLA Doctoral Dissertation.

  56. Sonnier, G. P., McAlister, L., & Rutz, O. J. (2011). A dynamic model of the effect of online communications on firm sales. Marketing Science, 30(4), 702–716.

    Article  Google Scholar 

  57. Sotiriadis, M. D., & Zyl, C. (2013). Electronic word-of-mouth and online reviews in tourism services: the use of twitter by tourists. Electronic Commerce Research, 13(1), 103–124.

    Article  Google Scholar 

  58. Tellis, G. J., & Johnson, J. (2007). The value of quality. Marketing Science, 26(6), 758–773.

    Article  Google Scholar 

  59. Wagner, M. (2008). The power of customer reviews, internetretailer.com (February 2008). Downloaded on March 4, 2008 from www.internetretailer.com/printArticle.asp?id=25215.

  60. Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2004) Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In The third IEEE international conference on data mining (pp. 427–434) (November).

  61. Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52, 2–22.

    Article  Google Scholar 

  62. Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.

    Article  Google Scholar 

  63. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1988). Communication and control processes in the delivery of service quality. Journal of Marketing, 52, 35–48.

    Article  Google Scholar 

  64. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The nature and determinants of customer expectations of service. Journal of the Academy of Marketing Science, 21(1), 1–12.

    Article  Google Scholar 

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Acknowledgments

We thank the editor, associate editor, and three reviewers for their invaluable feedback and constructive comments. We are also grateful to Mr. Bharath Gajula for making data available. The first author acknowledges generous support by the research fund of Hanyang University (HY-2015). This paper is based on the first author’s doctoral dissertation.

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Authors and Affiliations

  1. School of Business, Hanyang University, Seoul, South Korea

    Hyun S. Shin

  2. UCLA Anderson School of Management, Los Angeles, CA, USA

    Dominique M. Hanssens

  3. Department of Economics, Michigan State University, East Lansing, MI, USA

    Kyoo il Kim

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Shin, H.S., Hanssens, D.M. & Kim, K.i. The role of online buzz for leader versus challenger brands: the case of the MP3 player market. Electron Commer Res 16, 503–528 (2016). https://doi.org/10.1007/s10660-016-9218-7

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