Marketing Papers
Document Type
Technical Report
Date of this Version
2015
Publication Source
Marketing Science
Volume
34
Issue
1
Start Page
116
Last Page
133
DOI
10.1287/mksc.2014.0877
Abstract
Multipart tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit farsightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness.
Copyright/Permission Statement
Originally published in Marketing Science © 2015 INFORMS
This is a pre-publication version. The final version is available at http://dx.doi.org/10.1287/mksc.2014.0877
Keywords
multi-part tariffs, dynamic allocation, consumer learning, dynamic decision making, intertemporal discounting
Recommended Citation
Gopalakrishnan, A., Iyengar, R., & Meyer, R. J. (2015). Consumer Dynamic Usage Allocation and Learning under Multi-Part Tariffs. Marketing Science, 34 (1), 116-133. http://dx.doi.org/10.1287/mksc.2014.0877
Included in
Applied Behavior Analysis Commons, Behavioral Economics Commons, Cognitive Psychology Commons, Marketing Commons, Sales and Merchandising Commons, Taxation Commons
Date Posted: 15 June 2018
This document has been peer reviewed.