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Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance

Author

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  • Shyam Gopinath

    (David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112)

  • Jacquelyn S. Thomas

    (Edwin L. Cox School of Business, Southern Methodist University, Dallas, Texas 75275)

  • Lakshman Krishnamurthi

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

Abstract

We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth (OWOM) from its valence , which has three dimensions---attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model (DHLM) for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model. Our estimation accounts for potential endogeneity in the key measures. Among the different OWOM measures, only the valence of recommendation OWOM is found to have a direct impact on sales; i.e., not all OWOM is the same. This impact increases over time. In contrast, the impact of attribute advertising and emotion advertising decreases over time. Also, consistent with prior research, we observe that rational messages (i.e., attribute-oriented advertising) wears out a bit faster than emotion-oriented advertising. Moreover, the volume of OWOM does not have a significant impact on sales. This suggests that, in our data, “what people say” is more important than “how much people say.” Next, we find that recommendation OWOM valence is driven primarily by the valence of attribute OWOM when the product is new and driven by the valence of emotion OWOM when the product is more mature. Our brand-level results help us classify brands as consumer driven or firm driven, depending on the relative importance of the OWOM and advertising measures, respectively.

Suggested Citation

  • Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    • Handle: RePEc:inm:ormksc:v:33:y:2014:i:2:p:241-258
    DOI: 10.1287/mksc.2013.0820
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    References listed on IDEAS

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