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Impact of Pricing Schemes on a Market for Software-as-a-Service and Perpetual Software

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Author Info

  • Juthasit Rohitratana

    ()
    (TEMEP, College of Engineering, Seoul National University)

  • Jorn Altmann

    ()
    (TEMEP, College of Engineering, Seoul National University)

Abstract

In this paper, we present an agent-based simulation system that allows modeling the interactions between software buyers and vendors in a software market. The market offers Software-as-a-Service (SaaS) and perpetual software (PS) licenses under different pricing schemes. Four dynamic pricing schemes are analyzed: derivative-follower pricing, demand-driven pricing, skimming pricing, and penetration pricing. Customer (buyer) agents respond to these prices by selecting the most appropriate software license scheme based on four criteria using the Analytic Hierarchy Process (AHP) decision support mechanism. The four decision criteria relate to finance, software capability, organization, and vendor. The simulation results show that the demand-driven pricing scheme is the most effective method but hard to implement since it requires perfect knowledge about market conditions. As an alternative, penetration pricing and skimming pricing could be used. In addition to this, it can be stated that SaaS is most attractive for small enterprises while PS is attractive for large enterprises.

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File URL: ftp://147.46.237.98/DP-88.pdf
File Function: First version, 2012
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Bibliographic Info

Paper provided by Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) in its series TEMEP Discussion Papers with number 201288.

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Length: 32 pages
Date of creation: Mar 2012
Date of revision: Mar 2012
Publication status: Published in Journal of Future Generation Computer Systems Journal, Elsevier, 2012.
Handle: RePEc:snv:dp2009:201288

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Related research

Keywords: SaaS; Software-as-a-Service pricing; perpetual software pricing; agent-based simulation; Analytic Hierarchy Process (AHP); dynamic pricing; decision support; demand-driven pricing; derivative-follower pricing; penetration pricing; skimming pricing.;

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References

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  1. Yasushi Masuda & Seungjin Whang, 1999. "Dynamic Pricing for Network Service: Equilibrium and Stability," Management Science, INFORMS, vol. 45(6), pages 857-869, June.
  2. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics, Springer, vol. 5(3), pages 239-292, September.
  3. Marcel Risch & Jorn Altmann, 2009. "Enabling Open Cloud Markets Through WS-Agreement Extensions," TEMEP Discussion Papers 200920, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Oct 2009.
  4. Jorn Altmann & Juthasit Rohitratana, 2009. "Software Resource Management Considering the Interrelation between Explicit Cost, Energy Consumption, and Implicit Cost: A Decision Support Model for IT Managers," TEMEP Discussion Papers 200936, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
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Cited by:
  1. Price, Michael & Tamm, Gerrit & Stantchev, Vladimir, 2012. "SaaS marketplaces: Visions from theory and experience from practice," 19th ITS Biennial Conference, Bangkok 2012: Moving Forward with Future Technologies - Opening a Platform for All 72513, International Telecommunications Society (ITS).

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