Role of Spatial Demand on Outlet Location and Pricinig (Record no. 30462)

MARC details
000 -LEADER
fixed length control field 01962pab a2200205 454500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140923b0 xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency Welingkar Institute of Management Development & Research, Mumbai
Original cataloging agency Welingkar Institute of Management Development & Research, Mumbai
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title ENG
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number
Item number Dua
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Duan Jason A
245 ## - TITLE STATEMENT
Title Role of Spatial Demand on Outlet Location and Pricinig
250 ## - EDITION STATEMENT
Edition statement 2
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc.
Name of publisher, distributor, etc. April 2009
Date of publication, distribution, etc. 0
300 ## - PHYSICAL DESCRIPTION
Extent 260-278 Pp.
490 ## - SERIES STATEMENT
Volume/sequential designation XLVI
520 ## - SUMMARY, ETC.
Summary, etc. In this article, the authors consider the problem of outlet pricing and location in the context of unobserved spatial demand. The analysis constitutes a scenario in which capacity-constrained firms set prices conditional on their location, demand, and costs. This enables firms to develop maps of latent demand patterns across the market in which they compete. The analysis further suggests locations for additional outlets and the resultant equilibrium effect on profits and prices. Using Bayesian spatial statistics, the authors apply their model to seven years of data on apartment location and prices in Roanoke, Va. They find that spatial covariation in demand is material in outlet choice; the 95% spatial decay in demand extends 3.6 miles in a region that measures slightly more than 9.5 miles. They also find that capacity constraints can cost complexes upward of $100 per apartment. As they predict, price elasticities and costs are biased downward when spatial covariance in demand is ignored. Costs are biased upward when capacity constraints are ignored. Using the analysis to suggest locations for entry, the authors find that properly accounting for spatial effects and capacity constraints leads to an entry recommendation that improves profitability by 66% over a model that ignores these effects.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Outlet Location, Pricinig
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://192.168.6.13/libsuite/mm_files/Articles/AR10639.pdf">http://192.168.6.13/libsuite/mm_files/Articles/AR10639.pdf</a>
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 31924
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
        Main Library Main Library 18/05/2009 0.00   Dua AR10639 23/09/2014 0.00 23/09/2014 Articles

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