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Discovering heterogeneous consumer journeys in online platforms: implications for networking investment

Author

Listed:
  • Ho Kim

    (University of Missouri-St. Louis)

  • Juncai Jiang

    (Virginia Polytechnic Institute and State University)

  • Norris I. Bruce

    (University of Texas at Dallas)

Abstract

We model consumer journeys for user-created programs published in an online programming platform (OPP) and uncover factors that predict their occurrence. We build our model on a theoretical framework where consumer journeys involve three latent stages (Learn, Feel, Do), in which users gather information about, express fondness toward, and try the published items, respectively. Using a dataset from an OPP where users publish multimedia items and follow other users, we find that there is no one dominant consumer journey; instead, the sequences of stages in a journey (e.g., Learn → Feel → Do) vary across published items. Furthermore, we find that the social capital (i.e., social network) of a publisher influences the occurrence of spillover effects between latent stages (the phenomenon that one stage in a period triggers another stage in the next period) for the items posted by the publisher. We also find that a publisher’s social capital has only a transient impact on the consumer journeys for the publisher’s projects, underlining the importance of consistently making new network connections in order to promote the growth of user activities surrounding the publisher’s projects. We apply our findings to the publishers’ networking investment decisions to show that publishers’ networking investment would be severely suboptimal if journey heterogeneity is not considered.

Suggested Citation

  • Ho Kim & Juncai Jiang & Norris I. Bruce, 2021. "Discovering heterogeneous consumer journeys in online platforms: implications for networking investment," Journal of the Academy of Marketing Science, Springer, vol. 49(2), pages 374-396, March.
  • Handle: RePEc:spr:joamsc:v:49:y:2021:i:2:d:10.1007_s11747-020-00741-3
    DOI: 10.1007/s11747-020-00741-3
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    References listed on IDEAS

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    1. Miniard, Paul W, et al, 1991. "Picture-Based Persuasion Processes and the Moderating Role of Involvement," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(1), pages 92-107, June.
    2. Raghuram Iyengar & Christophe Van den Bulte & Jae Young Lee, 2015. "Social Contagion in New Product Trial and Repeat," Marketing Science, INFORMS, vol. 34(3), pages 408-429, May.
    3. Yaniv Dover & Jacob Goldenberg & Daniel Shapira, 2012. "Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data," Marketing Science, INFORMS, vol. 31(4), pages 689-712, July.
    4. Shuba Srinivasan & Oliver J. Rutz & Koen Pauwels, 2016. "Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity," Journal of the Academy of Marketing Science, Springer, vol. 44(4), pages 440-453, July.
    5. Kim, Ho & Bruce, Norris I., 2018. "Should sequels differ from original movies in pre-launch advertising schedule? Lessons from consumers' online search activity," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 116-143.
    6. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    7. Peters, Kay & Chen, Yubo & Kaplan, Andreas M. & Ognibeni, Björn & Pauwels, Koen, 2013. "Social Media Metrics — A Framework and Guidelines for Managing Social Media," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 281-298.
    8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    9. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    10. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    11. Onishi, Hiroshi & Manchanda, Puneet, 2012. "Marketing activity, blogging and sales," International Journal of Research in Marketing, Elsevier, vol. 29(3), pages 221-234.
    12. Guiyang Xiong & Sundar Bharadwaj, 2014. "Prerelease Buzz Evolution Patterns and New Product Performance," Marketing Science, INFORMS, vol. 33(3), pages 401-421, May.
    13. Shyam Gopinath & Pradeep K. Chintagunta & Sriram Venkataraman, 2013. "Blogs, Advertising, and Local-Market Movie Box Office Performance," Management Science, INFORMS, vol. 59(12), pages 2635-2654, December.
    14. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
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