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A Principle Component Analysis — Numerical Taxonomy Approach For Export Performance Assessment: The Case Of Iranian Chemical Units

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  • A. AZADEH

    (Department of Industrial Engineering, Center of Excellence for Intelligent Based Mechanical Experiments and Department of Engineering Optimization Research, College of Engineering University of Tehran, Iran, P. O. Box 11365–4563, Iran)

  • G. H. ATAEI

    (National Iranian Oil Company (NIOC), Department of Strategic Planning, Tehran, Iran)

Abstract

This paper presents an integrated approach for assessment of export performance based on principal component analysis (PCA) and Numerical Taxonomy (NT). The integrated assessment is influenced by shaping factors such as export value, production value, export growth, R&D expenditure and value added. Iranian chemical industries are selected as a case study according to the format of International Standard for Industrial Classification (ISIC) for a five-year period. The modeling approach of this paper could be used for analyzing other sectors and countries. This study shows how total export efficiency is obtained through the proposed approach whereas previous studies consider conventional productivity approach by a single indicator.

Suggested Citation

  • A. Azadeh & G. H. Ataei, 2009. "A Principle Component Analysis — Numerical Taxonomy Approach For Export Performance Assessment: The Case Of Iranian Chemical Units," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 54(04), pages 689-707.
    • Handle: RePEc:wsi:serxxx:v:54:y:2009:i:04:n:s0217590809003446
    DOI: 10.1142/S0217590809003446
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    References listed on IDEAS

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    1. Charles R. Hulten & Edwin R. Dean & Michael J. Harper, 2001. "New Developments in Productivity Analysis," NBER Books, National Bureau of Economic Research, Inc, number hult01-1, July.
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    Cited by:

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    2. Sarah Cheah & Yuen-Ping Ho & Shiyu Li, 2018. "Business Model Innovation for Sustainable Performance in Retail and Hospitality Industries," Sustainability, MDPI, vol. 10(11), pages 1-14, October.

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