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Dynamische Preisgestaltung in der digitalisierten Welt
[Dynamic Pricing in a Digitized World]

Author

Listed:
  • Martin Spann

    (Ludwig-Maximilians-Universität München)

  • Bernd Skiera

    (Goethe-Universität Frankfurt am Main)

Abstract

Zusammenfassung Digitale Technologien begünstigen den Einsatz einer dynamischen Preisgestaltung, also von Preisen, die für ein prinzipiell gleiches Produkt unangekündigt variieren. Dabei werden in der öffentlichen Diskussion unterschiedliche Ausgestaltungsformen dynamischer Preise oftmals vermischt, was eine sinnvolle Analyse der Vor- und Nachteile der dynamischen Preisgestaltung erschwert. Das Ziel des Beitrags ist die Darstellung der ökonomischen Grundlagen und die Diskussion sowie Klassifikation der Ausgestaltungsmöglichkeiten der dynamischen Preisgestaltung. Darüber hinaus erfolgt eine Bewertung der Vor- und Nachteile der dynamischen Preisgestaltung aus Käufer- und Verkäufersicht. Abschließend werden Implikationen für die betriebswirtschaftliche Forschung diskutiert.

Suggested Citation

  • Martin Spann & Bernd Skiera, 2020. "Dynamische Preisgestaltung in der digitalisierten Welt [Dynamic Pricing in a Digitized World]," Schmalenbach Journal of Business Research, Springer, vol. 72(3), pages 321-342, September.
    • Handle: RePEc:spr:sjobre:v:72:y:2020:i:3:d:10.1007_s41471-020-00095-0
    DOI: 10.1007/s41471-020-00095-0
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    References listed on IDEAS

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    1. Katharina Dowling & Daniel Guhl & Daniel Klapper & Martin Spann & Lucas Stich & Narine Yegoryan, 2020. "Behavioral biases in marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 449-477, May.
    2. Torsten Olderog & Bernd Skiera, 2000. "The Benefits Of Bundling Strategies," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 52(2), pages 137-159, April.
    3. Erik Brynjolfsson & Michael D. Smith, 2000. "Frictionless Commerce? A Comparison of Internet and Conventional Retailers," Management Science, INFORMS, vol. 46(4), pages 563-585, April.
    4. Erik Brynjolfsson & Avinash Collis & Felix Eggers, 2019. "Using massive online choice experiments to measure changes in well-being," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(15), pages 7250-7255, April.
    5. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    6. Anna Priester & Thomas Robbert & Stefan Roth, 2020. "A special price just for you: effects of personalized dynamic pricing on consumer fairness perceptions," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(2), pages 99-112, April.
    7. Gurumurthy Kalyanaram & Russell S. Winer, 1995. "Empirical Generalizations from Reference Price Research," Marketing Science, INFORMS, vol. 14(3_supplem), pages 161-169.
    8. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
    9. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
    10. Kelly L. Haws & William O. Bearden, 2006. "Dynamic Pricing and Consumer Fairness Perceptions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 33(3), pages 304-311, October.
    11. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    12. Homburg, Christian & Lauer, Karin & Vomberg, Arnd, 2019. "The multichannel pricing dilemma: Do consumers accept higher offline than online prices?," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 597-612.
    13. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    14. Drechsler, Wenzel & Natter, Martin, 2011. "Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 95-109.
    15. Martin Spann & Marc Fischer & Gerard J. Tellis, 2015. "Skimming or Penetration? Strategic Dynamic Pricing for New Products," Marketing Science, INFORMS, vol. 34(2), pages 235-249, March.
    16. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    17. Peter P. Belobaba, 1989. "OR Practice—Application of a Probabilistic Decision Model to Airline Seat Inventory Control," Operations Research, INFORMS, vol. 37(2), pages 183-197, April.
    18. Dickson, Peter R. & Kalapurakal, Rosemary, 1994. "The use and perceived fairness of price-setting rules in the bulk electricity market," Journal of Economic Psychology, Elsevier, vol. 15(3), pages 427-448, September.
    19. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
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