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Agent-Based Simulations of the Software Market under Different Pricing Schemes for Software-as-a-Service and Perpetual Software

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Author Info

  • Juthasit Rohitratana

    ()
    (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University)

  • Jorn Altmann

    ()
    (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University)

Abstract

In this paper, we present agent-based simulations that model the interactions between software buyers and vendors in a software market that offers Software-as-a-Service (SaaS) and perpetual software (PS) licensing under different pricing schemes. In particular, scenarios are simulated, in which vendor agents dynamically set prices. Customer (or buyer) agents respond to these prices by selecting the software license scheme according to four fundamental criteria using Analytic Hierarchy Process (AHP) as decision support mechanism. These criteria relate to finance, software capability, organization, and vendor. Three pricing schemes are implemented for our simulations: derivative-follower (DF), demand-driven (DD), and competitor-oriented (CO). The results show that DD scheme is the most effective method but hard to implement since it requires perfect knowledge about market conditions. This result is supported through a price sensitivity analysis

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File URL: ftp://147.46.237.98/DP-64.pdf
File Function: First version, 2010
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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 201064.

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Length: 22 pages
Date of creation: Jul 2010
Date of revision: Jul 2010
Publication status: Published in GECON2010, Workshop on Grids, Clouds, Systems, and Services, Ischia, Italy, 2010
Handle: RePEc:snv:dp2009:201064

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Related research

Keywords: Software-as-a-Service pricing; perpetual software pricing; agent-based simulation; Analytic Hierarchy Process (AHP); dynamic pricing; decision support;

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References

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  1. Fu, Feng & Liu, Lianghuan & Wang, Long, 2008. "Empirical analysis of online social networks in the age of Web 2.0," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 387(2), pages 675-684.
  2. Galeotti, Andrea & Goyal, Sanjeev & Kamphorst, Jurjen, 2006. "Network formation with heterogeneous players," Games and Economic Behavior, Elsevier, Elsevier, vol. 54(2), pages 353-372, February.
  3. Junseok Hwang & Jorn Altmann & Kibae Kim, 2009. "The Structural Evolution of the Web2.0 Service Network," TEMEP Discussion Papers, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) 200914, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2009.
  4. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, Elsevier, vol. 34(10), pages 1608-1618, December.
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Cited by:
  1. Khin Swe Latt & Jorn Altmann, 2010. "A Cost-Benefit-Based Analytical Model for Finding the Optimal Offering of Software Services," TEMEP Discussion Papers, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) 201070, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Dec 2010.

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