Model of Brand Choice with a No-Purchase Option Calibrated to Scanner-panel Data (Record no. 27215)

MARC details
000 -LEADER
fixed length control field 02224pab a2200217 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 Chi/See
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Chib Siddhartha
245 ## - TITLE STATEMENT
Title Model of Brand Choice with a No-Purchase Option Calibrated to Scanner-panel Data
250 ## - EDITION STATEMENT
Edition statement 2
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc.
Name of publisher, distributor, etc. May 2004
Date of publication, distribution, etc. 0
300 ## - PHYSICAL DESCRIPTION
Extent 184-196 Pp.
490 ## - SERIES STATEMENT
Volume/sequential designation XLI
520 ## - SUMMARY, ETC.
Summary, etc. In usual practice, researchers specify and estimate brand-choice models from purchase data, ignoring observations in which category incidence does not occur (i.e., no-purchase observations). This practice can be problematic if there are unobservable factors that affect the no-purchase and the brand-choice decisions. When such a correlation exists, it is important to model simultaneously the no-purchase and the brand-choice decisions. The authors propose a model suitable for scanner-panel data in which the no-purchase decision depends on the price, feature, and display of each brand in the category and on household stock of inventory. They link the no-purchase model to the brand-choice outcome through marketing-mix covariates and through unobservables that affect both outcomes. The authors assume that model parameters are heterogeneous across households and allow for a flexible correlation structure between the coefficients in the no-purchase model and those in the brand-choice model. The model formulation is more general than what is possible from either a nested logit model or a translog utility model and from models in which the no-purchase outcome is an additional outcome with the deterministic component of its utility set equal to zero. The authors estimate the proposed model using Bayesian Markov chain Monte Carlo estimation methods. They then apply the estimation methods to scanner panel data on the cola product category and compare the results with those from the widely used nested logit model.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Brand Choice,
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Seetharaman P B
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://192.168.6.13/libsuite/mm_files/Articles/AR7282.pdf">http://192.168.6.13/libsuite/mm_files/Articles/AR7282.pdf</a>
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 20823
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 23/09/2014 0.00   Chi/See AR7282 23/09/2014 0.00 23/09/2014 Articles

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