A primary tool that consumers have for comparative shopping is the
shopbot, which is short for shopping robot. These shopbots automatically
search a large number of vendors for price and availability. Typically
a shopbot searches a predefined set of vendors and reports all results,
which can result in time–consuming searches that provide redundant
or dominated alternatives. Our research demonstrates analytically
how shopbot designs can be improved by developing a utility model
of consumer purchasing behavior. This utility model considers the
intrinsic value of the product and its attributes, the disutility
from waiting, and the cognitive costs associated with evaluating
the offers retrieved. We focus on the operational decisions made
by the shopbot: which stores to search, how long to wait, and which
offers to present to the user. To illustrate our model we calibrate
the model to price and response time data collected at online bookstores
over a six–month period. Using prior expectations about price and
response time, we show how shopbots can substantially increase consumer
utility by searching more intelligently and then selectively presenting
offers.
%0 Journal Article
%1 Montgomery:2004:ms
%A Montgomery, Alan L.
%A Hosanagar, Kartik
%A Krishnan, Ramayya
%A Clay, Karen B.
%D 2004
%J Mgmt. Science
%K imported thesis
%N 2
%P 189--206
%R 10.1287/mnsc.1030.0151
%T Designing a Better Shopbot
%V 50
%X A primary tool that consumers have for comparative shopping is the
shopbot, which is short for shopping robot. These shopbots automatically
search a large number of vendors for price and availability. Typically
a shopbot searches a predefined set of vendors and reports all results,
which can result in time–consuming searches that provide redundant
or dominated alternatives. Our research demonstrates analytically
how shopbot designs can be improved by developing a utility model
of consumer purchasing behavior. This utility model considers the
intrinsic value of the product and its attributes, the disutility
from waiting, and the cognitive costs associated with evaluating
the offers retrieved. We focus on the operational decisions made
by the shopbot: which stores to search, how long to wait, and which
offers to present to the user. To illustrate our model we calibrate
the model to price and response time data collected at online bookstores
over a six–month period. Using prior expectations about price and
response time, we show how shopbots can substantially increase consumer
utility by searching more intelligently and then selectively presenting
offers.
@article{Montgomery:2004:ms,
abstract = {A primary tool that consumers have for comparative shopping is the
shopbot, which is short for shopping robot. These shopbots automatically
search a large number of vendors for price and availability. Typically
a shopbot searches a predefined set of vendors and reports all results,
which can result in time–consuming searches that provide redundant
or dominated alternatives. Our research demonstrates analytically
how shopbot designs can be improved by developing a utility model
of consumer purchasing behavior. This utility model considers the
intrinsic value of the product and its attributes, the disutility
from waiting, and the cognitive costs associated with evaluating
the offers retrieved. We focus on the operational decisions made
by the shopbot: which stores to search, how long to wait, and which
offers to present to the user. To illustrate our model we calibrate
the model to price and response time data collected at online bookstores
over a six–month period. Using prior expectations about price and
response time, we show how shopbots can substantially increase consumer
utility by searching more intelligently and then selectively presenting
offers.},
added-at = {2017-03-16T11:50:55.000+0100},
author = {Montgomery, Alan L. and Hosanagar, Kartik and Krishnan, Ramayya and Clay, Karen B.},
biburl = {https://www.bibsonomy.org/bibtex/22104d8b2905a34cd7e9bba4cb30c1ba0/krevelen},
doi = {10.1287/mnsc.1030.0151},
interhash = {0530dd4d1c82d122aadcd8d7c4271115},
intrahash = {2104d8b2905a34cd7e9bba4cb30c1ba0},
journal = {Mgmt. Science},
keywords = {imported thesis},
month = feb,
number = 2,
owner = {Rick},
pages = {189--206},
timestamp = {2017-03-16T11:54:14.000+0100},
title = {Designing a Better Shopbot},
volume = 50,
year = 2004
}