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Penalty Reward Contrast Analysis (PRCA) for Categorizing Service Components: A New Approach

Stöcker, Björn; Nasseri, Aydin


Ever since Noriaki Kano’s research, we have known that the relationship between performance and customer satisfaction is not just linear. Depending on the performance, different customer requirements exist, which are visualized in the Kano Model with three curves. In this article, we would like to present a new method that uses Kano’s model to characterize different service components using a cubic term. We then compare the results of the Penalty Reward Contrast Analysis (PRCA) and the cubic terms and recommend how the cubic terms can be interpreted, based on two surveys of an online retailer collected via CATI (study 1 in 2011 with n=480 and study 2 in 2013 with n=500). This paper makes three contributions: 1) We compare three different and popular applications of the PRCA on real customer data, then 2) contrast the results with our new approach of using cubic terms and 3) give hints towards causal relations of different service components to the overall customer satisfaction in the fashion online business.

Verlagsausgabe §
DOI: 10.5445/KSP/1000098012/03
Veröffentlicht am 26.05.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000133326
Erschienen in Archives of Data Science, Series A
Band 6
Heft 2
Seiten P03, 20 S. online
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