Realizing the value of extensive replication: A theoretically robust portrayal of double jeopardy
Cullen Habel and
Larry Lockshin
Journal of Business Research, 2013, vol. 66, issue 9, 1448-1456
Abstract:
Researchers have spent almost 50years developing, refining and applying the NBD-Dirichlet in repeat purchase markets, in particular with extensive replication of the Goodhardt, Ehrenberg, and Chatfield (1984) model. Recent research that employs a double jeopardy (DJ) line appears to have progressed with little regard for this body of theory. This paper reviews the varied applications of the NBD-Dirichlet and extends the theory to the portrayal of a double jeopardy line. This DJ line, an x–y plot of brands' penetration versus purchase frequency in a category, is tested against three alternative ways of drawing the line. A linear model for double jeopardy is adequate when used under limited conditions yet the model breaks down with a broad scope of analysis. This meta-analysis of 37 cross-sectional product categories and a 40-period (quarters) longitudinal dataset evaluates the best approximation of the DJ line to empirical data.
Keywords: Market modeling; Loyalty; NBD-Dirichlet; Double jeopardy; Marketing theory (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296312001464
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:66:y:2013:i:9:p:1448-1456
DOI: 10.1016/j.jbusres.2012.05.012
Access Statistics for this article
Journal of Business Research is currently edited by A. G. Woodside
More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().