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Using Neural Network Analysis to Uncover the Trace Effects of National Culture

Author

Listed:
  • John F Veiga

    (University of Connecticut)

  • Michael Lubatkin

    (University of Connecticut and Groupe ESC Lyon)

  • Roland Calori

    (Groupe ESC Lyon)

  • Phillipe Very

    (Groupe ESC Lyon)

  • Y Alex Tung

    (University of Connecticut)

Abstract

The primary objective of this paper is to demonstrate the usefulness of an artificial intelligence technique known as neural network analysis as an aid to uncovering the underlying patterns, or trace effects, of national culture. To make our case, we provide an application of the technique's pattern recognition capability utilizing survey data from top executives in French and British firms. We conclude by interpreting the trace effects found and encouraging the use of this tool by cross-cultural researchers in the future.© 2000 JIBS. Journal of International Business Studies (2000) 31, 223–238

Suggested Citation

  • John F Veiga & Michael Lubatkin & Roland Calori & Phillipe Very & Y Alex Tung, 2000. "Using Neural Network Analysis to Uncover the Trace Effects of National Culture," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 31(2), pages 223-238, June.
    • Handle: RePEc:pal:jintbs:v:31:y:2000:i:2:p:223-238
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    Citations

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    Cited by:

    1. Hu, Michael Y. & Zhang, G. Peter & Chen, Haiyang, 2004. "Modeling foreign equity control in Sino-foreign joint ventures with neural networks," European Journal of Operational Research, Elsevier, vol. 159(3), pages 729-740, December.
    2. Messner, Wolfgang, 2022. "Advancing our understanding of cultural heterogeneity with unsupervised machine learning," Journal of International Management, Elsevier, vol. 28(2).
    3. Stavrou, Eleni T. & Charalambous, Christakis & Spiliotis, Stelios, 2007. "Human resource management and performance: A neural network analysis," European Journal of Operational Research, Elsevier, vol. 181(1), pages 453-467, August.
    4. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).
    5. Messner, Wolfgang, 2024. "Distance is the spice, but not the whole enchilada: Country-pair psychic distance stimuli and country fixed effects in a deep learning implementation of the trade flow model," International Business Review, Elsevier, vol. 33(1).
    6. Zhang, Jianhong & van Witteloostuijn, Arjen & Zhou, Chaohong & Zhou, Shengyang, 2024. "Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis," Journal of World Business, Elsevier, vol. 59(2).
    7. Brouthers, Lance Eliot & Mukhopadhyay, Somnath & Wilkinson, Timothy J. & Brouthers, Keith D., 2009. "International market selection and subsidiary performance: A neural network approach," Journal of World Business, Elsevier, vol. 44(3), pages 262-273, July.
    8. Thomas Lindner & Jonas Puck & Alain Verbeke, 2022. "Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(7), pages 1307-1314, September.
    9. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
    10. Jesús Manuel De Sancha-Navarro & Juan Lara-Rubio & María Dolores Oliver-Alfonso & Luis Palma-Martos, 2021. "Cultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approach," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    11. Nair, Anand & Hanvanich, Sangphet & Tamer Cavusgil, S., 2007. "An exploration of the patterns underlying related and unrelated collaborative ventures using neural network: Empirical investigation of collaborative venture formation data spanning 1985-2001," International Business Review, Elsevier, vol. 16(6), pages 659-686, December.

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