Abstract
Prior marketing research on eWOM has focused on the effect of overall sentiment (valence) of online conversation on product or brand performance, whereas little research has examined the impact of sentiment polarization, that is, the degree to which positive and negative sentiments are simultaneously strong. Through a combination of experimental study and quantitative modeling of archival social media data, the present study examined the impact of eWOM polarization on consumer new product adoption. Our experimental study shows that eWOM polarization increased consumer attitudinal ambivalence, which in turn decreased new product adoption intention. In our quantitative modeling study, we developed a measure for quantifying eWOM polarization on social media, and estimated its impact on sales of video game consoles. The result replicated the negative impact of eWOM polarization and further showed that the negative impact is more pronounced at the early (vs. later) stage of product life cycle.
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Data Availability
The datasets generated and analyzed during the experimental study (Study 1) are available from the corresponding author on reasonable request. The data that support the findings of the quantitative modeling study (Study 2) are available from NPD Inc., but restrictions may apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of NPD Inc.
References
Barron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Bessi, A., Zollo, F., Del Vicario, M., Puliga, M., Scala, A., Caldarelli, G., Uzzi, B., & Quattrociocchi, W. (2016). Users polarization on Facebook and Youtube. PLoS ONE, 11(8), e0159641.
Binken, J. L. G., & Stremersch, S. (2009). The effect of superstar software on hardware sales in system markets. Journal of Marketing, 73(2), 88–104.
Breckler, S. J. (1994). A comparison of numerical indexes for measuring attitude ambivalence. Educational and Psychological Measurement, 54(2), 350–365.
Brown, Jo., Broderick, A. J., & Lee, N. (2007). Word of mouth communication within online communities: Conceptualizing the online social network. Journal of Interactive Marketing, 21(3), 2–20.
Casey, S. (2017). "2016 Nielsen social media report," The Nielsen Company (US), LLC.
Darke, P. R., et al. (1998). Accuracy motivation, consensus information, and the law of large numbers: Effects on attitude judgment in the absence of argumentation. Personality and Social Psychology Bulletin, 24(11), 1205–1215.
Gopinath, S., Thomas, J. S., & Krishnamurthi, L. (2014). Investigating the relationship between the content of online word of mouth, advertising, and brand performance. Marketing Science, 33(2), 241–258.
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach: The Guilford Press.
Herzenstein, M., Posavac, S. S., & Joško Brakus, J. (2007). Adoption of new and really new products: The effects of self-regulation systems and risk salience. Journal of Marketing Research, 44(2), 251–260.
Hogenboom, A., Van Iterson, P., Heerschop, B., Frasincar, F., & Kaymak, U. (2011). "Determining negation scope and strength in sentiment analysis," in 2011 IEEE International Conference on Systems, Man, and Cybernetics: IEEE.
Iyengar, R., Van den Bulte, C., & Lee, J. Y. (2015). Social contagion in new product trial and repeat. Marketing Science, 34(3), 408–429.
Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological Bulletin, 77(5), 361.
Kim, E. E. K., & Lee, C. H. (2015). How do consumers process online hotel reviews? The effects of eWOM consensus and sequence. Journal of Hospitality and Tourism Technology, 6(2), 113–126.
Ma, Z., Gill, T., & Jiang, Y. (2015). Core versus peripheral innovations: The effect of innovation locus on consumer adoption of new products. Journal of Marketing Research, 52(3), 309-324.
Mackie, D. M. (1987). Systematic and nonsystematic processing of majority and minority persuasive communications. Journal of Personality and Social Psychology, 53(1), 41.
Malshe, A. (2019). Data analytics applications, https://ashgreat.github.io/analyticsAppBook/. Accessed 6 Apr 2022
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. https://repositories.lib.utexas.edu/bitstream/handle/2152/31333/LIWC2015_LanguageManual.pdf?Sequence=3. Accessed 1 Oct 2022
Petty, R. E., & Cacioppo, J. T. (2012). Communication and persuasion: Central and peripheral routes to attitude change: Springer Science & Business Media.
Schweidel, D. A., & Moe, W. W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research, 51(4), 387–402.
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23(4), 323–355.
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of marketing, 27(3), 39–50.
Thompson, M. M., Zanna, M. P., & Griffin, D. W. (1995). Let’s not be indifferent about (attitudinal) ambivalence. Attitude Strength: Antecedents and Consequences, 4, 361–386.
Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198–215.
Triandis, H. C. (1979). “Values, attitudes, and interpersonal behavior,” in Nebraska symposium on motivation. University of Nebraska Press.
van Harreveld, F., van der Pligt, J., & de Liver, Y. N. (2009). The agony of ambivalence and ways to resolve it: Introducing the MAID model. Personality and Social Psychology Review, 13(1), 45–61.
West, P. M., & Broniarczyk, S. M. (1998). Integrating multiple opinions: The role of aspiration level on consumer response to critic consensus. Journal of Consumer Research, 25(1), 38–51.
Funding
This study is supported by the Social Sciences and Humanities Research Council (SSHRC) of Canada awarded to Dr. Zhenfeng Ma (Grant# 415–2-17–0495).
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Study 1 (the experimental study which involves interviewing human subjects) has been reviewed and approved by the Research Ethics Board of the Wilfrid Laurier University (REB# 5493), as part of a larger project on the role of emotions in product adoption. Please see below for an electronic copy of the approval letter. For further inquiry on the research ethics approval, please contact the REB Chair Dr. Jayne Kalmar (jkalmar@wlu.ca; + 1 519.884.0710) × 2033).
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Zhao, P., Ma, Z., Gill, T. et al. Social media sentiment polarization and its impact on product adoption. Mark Lett 34, 497–512 (2023). https://doi.org/10.1007/s11002-023-09664-9
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DOI: https://doi.org/10.1007/s11002-023-09664-9