An Empirical Study of the Relationship between Business Performance Measures and Environmental and Competitive Strategy Variables

Abstract:

This paper aims to investigate the relationship among business strategies and business performance in global and local manufacturing companies. The research is based an International Manufacturing Strategy Survey (IMSS2005) of about 700 companies from more than 20 countries. We first proposed a new method to construct a generic measure of business performance from the available performance measures in IMSS. Based on this new measure we developed a simple discriminant analysis to allocate each entry (company in the IMSS) into a particular category of business performance. Of course we performed some statistical tests to confirm the validity of this classification. We then analysed the contribution (importance) of various environmental and competitive variables to each category of the generic business performance in order to identify the most influential variables and notice of their difference among different business performances. This was done intuitively using graphical analysis, but we also carried out statistical tests to confirm that companies at different performance levels do operate at different environments and pay different attentions to competitive strategies. The models we developed are simple but novel and very practical. The results showed significant differences among business performances in terms of influential variables, and we conclude that this method can also be used to analyse other part of the data from the IMSS.

• Periodical: Applied Mechanics and Materials (Volumes 10-12)
• Pages: 73-83
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.10-12.73
• Online since: December 2007
• Authors: W.B. Wang, Ye Zhuang Tian, H.Y. Sun
• Keywords: Business Performance, Chi-Squared Tests, Competitive Strategy, Discriminant Analysis, International Manufacturing Strategy

References

[1] B.H. Rho, K. Park, and Y.M. Yu: International Journal of Production Economics, Vol. 70 (2001) No. 1, pp.89-97.

[2] T.L. Bjorge, A. Nuran, B. Harry and et al: International Journal of Operations and Production, vol. 25 (2005) No. 2, pp.131-150.

[3] G. Ulusoy: International Journal of Operations and Production Management, Vol. 22 (2001) No. 7-12, pp.1020-1041.

[4] F.L. DuBois, B. Toyne, and M.D. Oliff: Journal of International Business Studies, Vol. 24 (2nd Otc, 1993) No. 2, pp.307-333.

[5] A. Größler, and A. Grübner: International Journal of Operations and Production Management, Vol. 26 (2006) No. 5, pp.458-485.

[6] R. Mellor, and P. Gupta: International Journal of Operations and Production Management, Vol. 22 (2002) No. 12, pp.1411-1428.

[7] M. Kendal, and A. Stuart, The advanced theory of statistics, Vol. 2 Inference and relationship, Carles Griffin & Company Limited, London, (1979).

[8] J.T. McClave, P.G. Bension, and T. Sincich: A first course in business statistics, Prentice and Hall, London, (1998).