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Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings

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  • Christen, Tatjana
  • Hess, Manuel
  • Grichnik, Dietmar
  • Wincent, Joakim

Abstract

Value-based pricing is known to be challenging, especially on online platforms, but is considered a superior pricing strategy. We investigate cross-platform pricing and other factors that influence perceived customer value in the context of the accommodation industry. This industry is characterized by powerful platforms (e.g., Booking.com) as well as small and medium-sized enterprises (SMEs) selling across platforms. We compare the importance of platform choice and seller history as underlying signals conveying value and thus defining pricing beyond core product attributes. Such actor-signaling-actions for value are neglected in previous research. We pay particular attention to how time-based price discrimination affects the importance of these non-core product signals. As cross-platform efforts increase the complexity of value-based pricing, we apply machine learning methods to model how SMEs can successfully predict pricing across platforms. We discuss our methodological and theoretical contributions to value-based pricing and signaling theory.

Suggested Citation

  • Christen, Tatjana & Hess, Manuel & Grichnik, Dietmar & Wincent, Joakim, 2022. "Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings," Journal of Business Research, Elsevier, vol. 152(C), pages 82-92.
    • Handle: RePEc:eee:jbrese:v:152:y:2022:i:c:p:82-92
    DOI: 10.1016/j.jbusres.2022.07.042
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