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Regional diversification and firm performance

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Abstract

This study examines how regional diversification affects firm performance. The results indicate that regional diversification has linear and curvilinear effects on firm performance. Regional diversification enhances firm performance linearly up to a certain threshold, and then its impact becomes negative. The results also show that firms of developed countries maximize their performance when they operate across a moderate number of developed regions and a strictly limited number of developing regions. This explains why internationalization by most international firms is regional rather than global.

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Notes

  1. For a firm to be included in the sample, the requisite data for the entire 5-year period were required for the firm. Because there were some missing data on many of the firms during this period, we were left with a sample of 189 firms.

  2. There are 13 industry groups based on their Standard Industrial Classification (SIC). Firms are placed in the group that represents the greatest volume of their sales. The industries are: Beverages; Chemicals; Food; Paper and Wood Products; Electrical; Industrial and Farm; Office Equipment (with computers); Metal Products; Measurement, Scientific and Photographic Equipment; Motor Vehicles; Non-Electrical Machinery; Pharmaceuticals; and Textiles.

  3. Firm performance (e.g., ROA and ROS) has, for example, self-accumulation characteristics that tend to be dynamic, as its past level is related to its current level (Bond, Klemm, Newton-Smith, Syed, & Vlieghe, 2002).

  4. Accordingly, two hypotheses are proposed. Hypothesis 1: Both the slope and the intercept are the same at different cross-sectional units and periods. Hypothesis 2: The slope is the same but the intercept is different at different cross-sectional units and periods. Apparently, if we accept Hypothesis 1, then it is unnecessary to conduct further tests. If we do not, then we have to test Hypothesis 2 to ensure that the slope is equal. If Hypothesis 2 is rejected, then we can be sure that our panel data fall into Situation 3, in which both the intercept and the slope are different. We used the F-statistic (both F 1 and F 2) to test Hypothesis 1 and Hypothesis 2 for model 4 of all sample firms. The observed F 1 value of 1.256 exceeds the critical F value that is obtained from the F table [F(375, 556)=1.127], while the F 2 value of 0.886 does not exceed the critical value F [F(181,556)=1.162] at the 10% level. Hypothesis 2 is therefore accepted, which implies that the panel data fit nicely into Situation 2, whereas Hypothesis 1 is rejected. Hence the model specified should be a dynamic variable intercept model.

  5. The results (both chi-square and p-value of chi-square) indicate that the three models are statistically significant, and the level of significance for the first-order lagged model is almost the same as for both the second-order and third-order lagged dependent models. illustration

    figure a

  6. The Hausman test is a multi-purpose test designed to compare two estimators that present the following contrast. Hypothesis 0: Both estimators are consistent, but the first estimator is efficient. Hypothesis 1: Only the second estimator is consistent. This test is widely used to compare between fixed effects and random effects in the analysis of panel data. If H0 is to hold, then there should be no systematic difference between the coefficients of the efficient estimator. If the two models display a systematic difference in the estimated coefficients, then we have reason to doubt the assumptions on which the efficient estimator is based. The formal representation of the test statistic is

    which is asymptotically distributed as a chi-square with degrees of freedom equal to the rows of each coefficient vector. If its p-value is smaller than the 0.05 significant level, we have to reject the null hypothesis, which means the acceptance of the fixed effect model and the invalidity of the random effect model. In our study, the test statistic [chi-square (12)] equals 32.60. The Hausman specification test indicates that the p-value is 0.001, which is clearly smaller than the 0.05 level, thus favoring the fixed effect model.

  7. This classification is based on the medium of the entropy measure of RD (cf. Qian, Wang, Li, & Yang, 2000).

  8. Moreover, we conducted an additional regression diagnosis using the variance-inflating factor (VIF) to determine whether there was any multicollinearity among the variables. The results (the highest value of VIF=1.04) further confirm that multicollinearity is not a major concern in this study.

  9. We test whether the orthogonal conditions hold using both the Sargan test of over-identifying restrictions and the p-values of the first-order serial correlation test. The Sargan test is a test of the validity of instrumental variables (or a test of the overidentifying restrictions). In the first test, the null hypothesis is that the instrumental variables are uncorrelated to a specified set of residuals. If the hypothesis is accepted, the instrument variables are valid. In the second test, the null hypothesis is that the error term is not first-order serially correlated. If the p-value is larger than the 0.05 level, the hypothesis is accepted, and thus the estimation is reliable.

  10. A strict procedure is followed to ensure that the strict analytical assumption of the homogeneity of variability is met: that is, that the error variance of the dependent variable is equal across the groups (Maxwell & Delaney, 1990). If the error variance is different within each group, then there is an increased probability of the occurrence of a Type I error, which would thus affect the power of the statistical test (Wilcox, 1987). Accordingly, we conducted the univariate homogeneity of variance test to see whether our data meet the assumption of the homogeneity of variability. The results are significant: ROA[Cochran C(4, 9)=0.412, p=0.014; Bartlett–Box F(8, 1166)=2.49, p=0.011] and ROS[Cochran C(4, 9)=0.426, p=0.013; Bartlett–Box F(8, 1166)=3.303, p=0.002]. All of the statistics indicate that the assumption is fully met.

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Hong Kong, China

    Gongming Qian

  2. York University, Canada

    Lee Li

  3. Hong Kong Baptist University, Hong Kong

    Ji Li

  4. Xiamen University, Xiamen, China

    Zhengming Qian

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  1. Gongming Qian
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  3. Ji Li
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Correspondence to Gongming Qian.

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Accepted by Nicolai Juul Foss, Departmental Editor, and Arie Y Lewin, Editor-in-Chief, 23 August 2007. This paper has been with the authors for two revisions.

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Qian, G., Li, L., Li, J. et al. Regional diversification and firm performance. J Int Bus Stud 39, 197–214 (2008). https://doi.org/10.1057/palgrave.jibs.8400346

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