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Improving generalizations from multi-country comparisons in international business research

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Abstract

In this paper we address the problem of questionable generalizations from comparing small numbers of countries in international business (IB) research. We illustrate the misleading results that can arise from sparse samples, whether the relationship between national-level variables is strong (economic development and corruption) or weak (population density and trust). We show that 35% of recent international comparisons in leading IB journals examined just two or three countries, and present an exploratory analysis of 123 variables that reveals typical correlations across countries to be rather moderate (average r=0.24). To help interpret extant study findings, we provide formulas and graphs based on Bayesian analysis, and introduce a method of combining results from multiple international comparisons. We also describe methods for designing studies to give stronger evidence of relationships between variables. Our results suggest that a minimum of 7–10 countries may support credible international generalizations, but only when overall trends are very strong. A key strategy for improving IB generalizations is to use larger samples of countries, because research based on common sample and effect sizes may lead to generalizations that the findings do not justify.

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Notes

  1. These alternatives are presented to illustrate the risks of interpreting results from small samples, not as purposeful hypotheses. Previous research shows a non-significant correlation of just 0.03 between population density and trust levels with a sample of 60 countries (Delhey & Newton, 2005).

  2. The correlation of 1.0 results from sampling error with n=3 countries rather than redundancy between variables.

  3. Effects of selecting countries for an extreme range of cultural values rather than sampling at random are discussed in a later section.

  4. Hyperbolic tangent and inverse hyperbolic tangent functions are implemented as TANH and ATANH, respectively, in Microsoft Excel. Corresponding functions in SAS are TANH and ARTANH. Many hand and online calculators (e.g., http://www.wolframalpha.com/) also perform these calculations. When r=±1, as when n=2, the formula is not usable, because the inverse hyperbolic tangent equals plus or minus infinity.

  5. This result is calculated as (0.682 × 0.5)/[(0.682 × 0.5)+(0.318 × 0.5)]=0.682. To confirm this analysis, we generated multiple samples of two observations, each with two random normal variables taken from a population in which the variables correlated r. Values of r varied randomly from −1 to +1. Across 10 million pairs of observations, the probability of a positive pairwise comparison given a positive r was 0.682, and the probability of a positive r given a positive pairwise comparison was likewise 0.682.

  6. As implied by the definition of a ij , the diagonal entries a ii =0 and a ij =a ji =0 for two objects C i and C j that have not been compared.

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Acknowledgements

The authors gratefully acknowledge the helpful suggestions of the editor and reviewers. Earlier versions of this paper were presented at the 12th Cross-Cultural Research Conference, at Iowa State University, and at the University of Oklahoma.

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  1. University of Alabama, Tuscaloosa, USA

    George R Franke & R Glenn Richey Jr

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  1. George R Franke
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  2. R Glenn Richey Jr
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Correspondence to George R Franke.

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Accepted by Daniel Bello, Area Editor, 4 March 2010. This paper has been with the authors for two revisions.

APPENDIX

APPENDIX

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Table a1 Statistical concepts and terms used in the paper
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Franke, G., Richey, R. Improving generalizations from multi-country comparisons in international business research. J Int Bus Stud 41, 1275–1293 (2010). https://doi.org/10.1057/jibs.2010.21

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