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How firms grow: clustering as a dynamic model of internationalization

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Abstract

This paper provides a methodology for identifying the dynamics of international firm expansion, demonstrating systematic evidence of both ‘toe in the water’ and concentrated bursts of internationalization by US multinational enterprises. We use the methodology of statistical process control to analyze initial investments in host countries and develop a numerical measure of temporal clustering or bunching. Using this measure, we identify two distinct patterns of growth; whereas some firms cluster their investments over time, others do not cluster their investments. For firms that cluster, three types of clustering strategy are identified: concentrating, hibernating, and hybrid. Arguments based on internal firm architecture, experience, and learning are advanced to explain these findings.

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Notes

  1. For more complete descriptions of SPC in a variety of contexts see Ishikawa (1976) and Evans and Lindsay (1996).

  2. These industries are: food (SIC 20); paper (SIC 26); chemicals (SIC 28); petroleum and coal (SIC 29); rubber and plastic (SIC 30); stone, clay, and glass (SIC 32); primary metal (SIC 33); fabricated metal (SIC 34); machinery (SIC 35); electrical equipment (SIC 36); transportation equipment (SIC 37); and instruments (SIC 38).

  3. We model ln(C i +0.1), rather than ln(C i ), to permit the retention of observations for which C i = 0. The addition of the small constant value does not materially affect the interpretation of the estimated coefficients; ln(C i ) and ln(C i +0.1) are perfectly correlated.

  4. This is the R 2 value for the regression of ln(IC i ) on the two experience variables.

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Acknowledgements

We are grateful to Lou Wells for providing access to the Harvard MNE database. We also thank the Editor, Arie Lewin, for his guidance and support, three anonymous referees for their constructive and developmental suggestions and insights, and the Australian Research Council for providing financial support.

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Authors and Affiliations

  1. School of Organisation and Management, University of New South Wales, Sydney, Australia

    Elizabeth Maitland

  2. Victoria University of Wellington, and University of Hawaii at Manoa, New Zealand, USA

    Elizabeth L Rose

  3. University of Sydney, Sydney, Australia

    Stephen Nicholas

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  1. Elizabeth Maitland
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  2. Elizabeth L Rose
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Correspondence to Elizabeth Maitland.

Additional information

Accepted by Arie Y Lewin, Editor in Chief, 3 November 2004. This paper has been with the authors for two revisions.

Appendix

Appendix

The chart below details the point allocation scheme for Company A; see Figure 1 for the zone chart. The single investment in year 1 is within one standard error of the mean, so the observation attracts no points. Year 2 had no new investment, which is between one and two standard errors below the mean, so it attracts two points. Year 3 was also without investment. As the observation falls on the same side of the mean as its predecessor, its own two-point contribution is added to that from year 2, resulting in a point total for year 3 of four points. The summation for this ‘run’ is stopped at year 4, when the mean line is crossed. Similar summations occur in years 6–9, 10–11, 12–14, and 15–21. The sum of the points in the last column is 96, so C A=96÷22=4.36.

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figure a

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Maitland, E., Rose, E. & Nicholas, S. How firms grow: clustering as a dynamic model of internationalization. J Int Bus Stud 36, 435–451 (2005). https://doi.org/10.1057/palgrave.jibs.8400140

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