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Analysis of three-way non-symmetrical association of food concepts in cross-cultural marketing

Author

Listed:
  • Rosaria Lombardo

    (University of Campania)

  • Eric J. Beh

    (University of Newcastle)

  • Luis Guerrero

    (IRTA-Monells)

Abstract

This paper analyses the non-symmetrical association among some key-words in a food context, given the European countries and gender of participants to a survey. The aim is to understand the meaning of the food concepts traditional and innovation associated to selected key-words in cross-cultural marketing. For studying the association among three categorical variables, usually, one can refer to Pearson’s three-way statistic, but in case of non-symmetrical association we prefer to consider Marcotorchino’s three-way predictability index and its related $$C_M$$ C M -statistic. Doing so, we present a generalisation of three-way non-symmetrical correspondence analysis to portray the predictability of food concepts given the knowledge of participants’country and gender.

Suggested Citation

  • Rosaria Lombardo & Eric J. Beh & Luis Guerrero, 2019. "Analysis of three-way non-symmetrical association of food concepts in cross-cultural marketing," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2323-2337, September.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:5:d:10.1007_s11135-018-0733-6
    DOI: 10.1007/s11135-018-0733-6
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    References listed on IDEAS

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    1. Antonello D'Ambra & Anna Crisci & Pasquale Sarnacchiaro, 2015. "A generalized analysis of the dependence structure by means of ANOVA," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(10), pages 2192-2202, October.
    2. Kiers, Henk A. L., 1998. "Three-way SIMPLIMAX for oblique rotation of the three-mode factor analysis core to simple structure," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 307-324, September.
    3. Beh, Eric J. & Simonetti, Biagio & D'Ambra, Luigi, 2007. "Partitioning a non-symmetric measure of association for three-way contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1391-1411, August.
    4. Henk Kiers & Jos Berge & Roberto Rocci, 1997. "Uniqueness of three-mode factor models with sparse cores: The 3 × 3 × 3 case," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 349-374, September.
    5. Henson, Spencer, 1995. "Demand-side constraints on the introduction of new food technologies: The case of food irradiation," Food Policy, Elsevier, vol. 20(2), pages 111-127, April.
    6. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    7. Henk Kiers, 1997. "Three-mode orthomax rotation," Psychometrika, Springer;The Psychometric Society, vol. 62(4), pages 579-598, December.
    8. André Carlier & Pieter Kroonenberg, 1996. "Decompositions and biplots in three-way correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 355-373, June.
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