期刊
MARKETING SCIENCE
卷 34, 期 1, 页码 116-133出版社
INFORMS
DOI: 10.1287/mksc.2014.0877
关键词
multipart tariffs; dynamic allocation; consumer learning; dynamic decision making; intertemporal discounting
类别
资金
- Russell Ackoff Doctoral Student Fellowship
- Wharton Risk Management and Decision Processes Center
- Amazon Web Services in Education Grant
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.
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