Analyzing Software as a Service with Per-Transaction Charges

Software as a Service (SaaS) delivers a bundle of applications and services through the Web. Its on-demand feature allows users to enjoy full scalability and to handle possible demand fluctuations at no risk. In recent years, SaaS has become an appealing alternative to purchasing, installing, and ma...

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Main Authors: MA, Dan, SEIDMANN, Abraham
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/2470
https://doi.org/10.1287/isre.2015.0571
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spelling sg-smu-ink.sis_research-34692018-09-03T09:29:29Z Analyzing Software as a Service with Per-Transaction Charges MA, Dan SEIDMANN, Abraham Software as a Service (SaaS) delivers a bundle of applications and services through the Web. Its on-demand feature allows users to enjoy full scalability and to handle possible demand fluctuations at no risk. In recent years, SaaS has become an appealing alternative to purchasing, installing, and maintaining modifiable off-the-shelf (MOTS) software packages. We present a game-theoretical model to study the competitive dynamics between the SaaS provider, who charges a variable per-transaction fee, and the traditional MOTS provider. We characterize the equilibrium conditions under which the two coexist in a competitive market and those under which each provider will fail and exit the market. Decreasing the lack-of-fit (or the cross-application data integration) costs of SaaS results in four structural regimes in the market. These are MOTS Dominance → Segmented Market → Competitive Market → SaaS Dominance. Based on our findings, we recommend distinct competitive strategies for each provider. We suggest that the SaaS provider should invest in reducing both its lack-of-fit costs and its per-transaction price so that it can offer increasing economies of scale. The MOTS provider, by contrast, should not resort to a price-cutting strategy; rather, it should enhance software functionality and features to deliver superior value. We further examine this problem from the software life-cycle perspective, with multiple stages over which users can depreciate the fixed costs of installing and customizing their MOTS solutions on site. We then present an analysis that characterizes the competitive outcomes when future technological developments could change the relative levels of the lack-of-fit costs. Specifically, we explain why the SaaS provider will always use a forward-looking pricing strategy: When lack-of-fit costs are expected to decrease (increase) in the future, the SaaS provider should reduce (increase) its current price. This is in contrast with the MOTS provider, who will use the forward-looking pricing strategy only when lack-of-fit costs are expected to increase. Surprisingly, when such costs are expected to decrease, the MOTS provider should ignore this expectation and use the same pricing strategy as in the benchmark with invariant lack-of-fit costs. 2015-06-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/2470 info:doi/10.1287/isre.2015.0571 https://doi.org/10.1287/isre.2015.0571 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University software as a service game theory model pricing based on transactions competitive strategies lack-of-fit costs economies of scale Computer Sciences E-Commerce
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic software as a service
game theory model
pricing based on transactions
competitive strategies
lack-of-fit costs
economies of scale
Computer Sciences
E-Commerce
spellingShingle software as a service
game theory model
pricing based on transactions
competitive strategies
lack-of-fit costs
economies of scale
Computer Sciences
E-Commerce
MA, Dan
SEIDMANN, Abraham
Analyzing Software as a Service with Per-Transaction Charges
description Software as a Service (SaaS) delivers a bundle of applications and services through the Web. Its on-demand feature allows users to enjoy full scalability and to handle possible demand fluctuations at no risk. In recent years, SaaS has become an appealing alternative to purchasing, installing, and maintaining modifiable off-the-shelf (MOTS) software packages. We present a game-theoretical model to study the competitive dynamics between the SaaS provider, who charges a variable per-transaction fee, and the traditional MOTS provider. We characterize the equilibrium conditions under which the two coexist in a competitive market and those under which each provider will fail and exit the market. Decreasing the lack-of-fit (or the cross-application data integration) costs of SaaS results in four structural regimes in the market. These are MOTS Dominance → Segmented Market → Competitive Market → SaaS Dominance. Based on our findings, we recommend distinct competitive strategies for each provider. We suggest that the SaaS provider should invest in reducing both its lack-of-fit costs and its per-transaction price so that it can offer increasing economies of scale. The MOTS provider, by contrast, should not resort to a price-cutting strategy; rather, it should enhance software functionality and features to deliver superior value. We further examine this problem from the software life-cycle perspective, with multiple stages over which users can depreciate the fixed costs of installing and customizing their MOTS solutions on site. We then present an analysis that characterizes the competitive outcomes when future technological developments could change the relative levels of the lack-of-fit costs. Specifically, we explain why the SaaS provider will always use a forward-looking pricing strategy: When lack-of-fit costs are expected to decrease (increase) in the future, the SaaS provider should reduce (increase) its current price. This is in contrast with the MOTS provider, who will use the forward-looking pricing strategy only when lack-of-fit costs are expected to increase. Surprisingly, when such costs are expected to decrease, the MOTS provider should ignore this expectation and use the same pricing strategy as in the benchmark with invariant lack-of-fit costs.
format text
author MA, Dan
SEIDMANN, Abraham
author_facet MA, Dan
SEIDMANN, Abraham
author_sort MA, Dan
title Analyzing Software as a Service with Per-Transaction Charges
title_short Analyzing Software as a Service with Per-Transaction Charges
title_full Analyzing Software as a Service with Per-Transaction Charges
title_fullStr Analyzing Software as a Service with Per-Transaction Charges
title_full_unstemmed Analyzing Software as a Service with Per-Transaction Charges
title_sort analyzing software as a service with per-transaction charges
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2470
https://doi.org/10.1287/isre.2015.0571
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