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Ordinal Evaluation: A New Perspective on Country Images

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 5633))

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Abstract

We present a novel use of ordinal evaluation (OrdEval) algorithm as a promising technique to study various marketing phenomena. OrdEval algorithm has originated in data mining and is a general tool to analyze data with ordinal attributes, including surveys. Its many favorable features, including context sensitivity, ability to exploit meaning of ordered features and ordered response, and robustness to noise and missing values in the data, offer marketing practitioners a perspective, not available with classical analytical toolbox.

We present a case study applying OrdEval algorithm on country-of-origin (COO) information. We demonstrate some interesting advantages it has to offer and show how to extract and interpret new insights allowing marketing practitioners to further optimize the management of products abroad.

Data for the empirical study was gathered by means of 1225 questionnaires. Results indicate that, contrary to the classical view on COO-effects, the processing of country-related cognitions, affects and conations is a non-linear and asymmetric phenomenon. The practical implications of this finding for marketers are discussed more in detail.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1001, Ljubljana, Slovenia

    Marko Robnik-Šikonja

  2. Department of Economics, University of Hasselt, Universitaire Campus, 3590, Diepenbeek, Belgium

    Kris Brijs & Koen Vanhoof

Authors
  1. Marko Robnik-Šikonja

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  2. Kris Brijs

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  3. Koen Vanhoof

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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland

    Petra Perner

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© 2009 Springer-Verlag Berlin Heidelberg

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Robnik-Šikonja, M., Brijs, K., Vanhoof, K. (2009). Ordinal Evaluation: A New Perspective on Country Images. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_21

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