TY - JOUR
T1 - On the optimal fixed-up-to pricing for information services
AU - Wu, Shinyi
AU - Pavlou, Paul A.
N1 - Funding Information:
Shinyi Wu is an associate professor of information systems at the W. P. Carey School of Business of Arizona State University. He received his PhD in operations and information management and MS in operations research both from the Wharton School of the University of Pennsylvania, and his MBA and BBA in information management from National Taiwan University. His research interests include strategic pricing of information goods and services, telecommunications and efficient allocation of wireless network resources, and business analytics. His work has been published in leading journals such as Management Science, Operations Research, Information Systems Research, Journal of the Association for Information Systems, and European Journal of Operational Research.
Funding Information:
Paul A. Pavlou is the Dean of the C. T. Bauer College of Business at the University of Houston and is also the Cullen Distinguished Chair of Information Sciences. Paul received his PhD from the University of Southern California. He was ranked #1 in the world in publications in the top two information systems journals (MISQ & ISR) in 2010-2016. His research has been cited over 42,000 times by Google Scholar, and he was recognized among the “World’s Most Influential Scientific Minds” by Thomson Reuters, based on an analysis of “highly cited” authors in Economics & Business for 2002-2012. Paul has won several Best Paper recognitions for his research, including the Sheth Foundation nomination for the “Long-Term Contribution to Marketing” in the Journal of Marketing in 2019, the Maynard Award nomination for the “Most Significant Contribution to Marketing” in the Journal of Marketing in 2015, and the ISR Best Paper award in 2007. He has also received about $2,000,000 in grants from funding agencies such as the National Science Foundation (NSF). Paul’s research spans several disciplines (information systems, marketing, strategy, operations, decision sciences), focusing on data science, analytics, artificial intelligence, digital business strategy, and research methods. His research has appeared in Management Information Systems Quarterly, Information Systems Research, Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, Decision Sciences, Journal of Management Information Systems, Journal of the Association of Information Systems, among other outlets.
Publisher Copyright:
© 2019 by the Association for Information Systems.
PY - 2019
Y1 - 2019
N2 - Fixed-up-to (FUT) pricing allows consumers to purchase a fixed usage amount of an information service for a certain fixed price chosen from a set of options. In this study, we derive an optimal analytical solution for FUT pricing without imposing the strong single-crossing assumption. Further, we illustrate the analytical solution by leveraging mixed integer nonlinear programming to derive an optimal FUT pricing scheme for information services and also investigate when and by how much FUT pricing improves upon commonly used “flat rate” pricing. Our numerical results show that FUT pricing improves the service provider’s profits while enhancing social welfare when consumers face different maximum consumption-level bounds. Notably, in terms of optimal pricing, our numerical results show that the consumers’ maximum consumption-level bounds are more important than their utility functions. Most importantly, our results show that FUT pricing performs better than flat rate pricing under conditions of incomplete information. Finally, we empirically show that it is not necessary to treat over-the-limit rates as a decision variable in terms of optimal FUT pricing since both FUT pricing and three-part tariffs are reasonable approximations of nonlinear pricing in terms of both firm profits and social welfare. We conclude with a discussion of theoretical and practical implications for the design of optimal FUT pricing in terms of enhancing firm profits, consumer surplus, and social welfare.
AB - Fixed-up-to (FUT) pricing allows consumers to purchase a fixed usage amount of an information service for a certain fixed price chosen from a set of options. In this study, we derive an optimal analytical solution for FUT pricing without imposing the strong single-crossing assumption. Further, we illustrate the analytical solution by leveraging mixed integer nonlinear programming to derive an optimal FUT pricing scheme for information services and also investigate when and by how much FUT pricing improves upon commonly used “flat rate” pricing. Our numerical results show that FUT pricing improves the service provider’s profits while enhancing social welfare when consumers face different maximum consumption-level bounds. Notably, in terms of optimal pricing, our numerical results show that the consumers’ maximum consumption-level bounds are more important than their utility functions. Most importantly, our results show that FUT pricing performs better than flat rate pricing under conditions of incomplete information. Finally, we empirically show that it is not necessary to treat over-the-limit rates as a decision variable in terms of optimal FUT pricing since both FUT pricing and three-part tariffs are reasonable approximations of nonlinear pricing in terms of both firm profits and social welfare. We conclude with a discussion of theoretical and practical implications for the design of optimal FUT pricing in terms of enhancing firm profits, consumer surplus, and social welfare.
KW - Fixed-Up-To (FUT) Pricing
KW - Information Services
KW - Nonlinear Mixed Integer Programming
KW - Pricing
UR - http://www.scopus.com/inward/record.url?scp=85074401878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074401878&partnerID=8YFLogxK
U2 - 10.17705/1jais.00574
DO - 10.17705/1jais.00574
M3 - Article
AN - SCOPUS:85074401878
SN - 1558-3457
VL - 20
SP - 1447
EP - 1474
JO - Journal of the Association for Information Systems
JF - Journal of the Association for Information Systems
IS - 10
ER -