The Ambiguous Identifier Clustering Technique

Michael Scholz (), Markus Franz and Oliver Hinz
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Michael Scholz: University of Passau
Markus Franz: University of Passau

Electronic Markets, 2016, vol. 26, issue 2, No 5, 143-156

Abstract: Abstract Investigations of online transaction data often face the problem that entries for identical products cannot be identified as such. There is, for example, typically no unique product identifier in online auctions; retailers make their offers at price comparison sites hardly comparable and online stores often use different identifiers for virtually equal products. Existing studies typically use data sets that are restricted to one or only a few products in order to avoid product heterogeneity if a unique product identifier is not available. We propose the Ambiguous Identifier Clustering Technique (AICT) that identifies online transaction data that refer to virtually the same product. Based on a data set of eBay auctions, we demonstrate that AICT clusters online transactions for identical products with high accuracy. We further show how researchers benefit from AICT and the reduced product heterogeneity when analyzing data with econometric models.

Keywords: Product heterogeneity; Clustering; Online transaction data; E-commerce (search for similar items in EconPapers)
JEL-codes: C18 D44 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s12525-016-0217-2

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