A case for replication: fitting product variants data to the Dirichlet model
A case for replication: fitting product variants data to the Dirichlet model
To-date replication has been undervalued in marketing resulting in a focus on single studies. Non-replicated results have thus become disseminated as established knowledge. This paper discusses replication and why it is important. It looks at datasets from different countries and time periods as an example of how easy it can be to conduct differentiated replications. The model tested is the well-established NBD Dirichlet model of buyer behavior. Here it is extended to look at its application to purchasing of product variants (e.g. purchasing across different pack sizes). The results show the usefulness of replication studies and open up further areas of investigation.
374-378
Kennedy, Rachel
a7ff463a-7ab9-4f08-b943-f52c3743282c
Singh, Jaywant
db6316ed-e404-4c5a-873c-6e97c94fe531
2002
Kennedy, Rachel
a7ff463a-7ab9-4f08-b943-f52c3743282c
Singh, Jaywant
db6316ed-e404-4c5a-873c-6e97c94fe531
Kennedy, Rachel and Singh, Jaywant
(2002)
A case for replication: fitting product variants data to the Dirichlet model.
ACR Asia-Pacific Advances, 5, .
Abstract
To-date replication has been undervalued in marketing resulting in a focus on single studies. Non-replicated results have thus become disseminated as established knowledge. This paper discusses replication and why it is important. It looks at datasets from different countries and time periods as an example of how easy it can be to conduct differentiated replications. The model tested is the well-established NBD Dirichlet model of buyer behavior. Here it is extended to look at its application to purchasing of product variants (e.g. purchasing across different pack sizes). The results show the usefulness of replication studies and open up further areas of investigation.
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Published date: 2002
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Local EPrints ID: 435884
URI: http://eprints.soton.ac.uk/id/eprint/435884
PURE UUID: 45afe1df-d264-4fee-b991-d7e114b98746
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Date deposited: 22 Nov 2019 17:30
Last modified: 17 Mar 2024 03:59
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Author:
Rachel Kennedy
Author:
Jaywant Singh
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