Abstract
This study examines how industrial characteristics at home affect Chinese overseas acquisition activities from an industry-level resource-based view. Analyzing data on 512 Chinese overseas acquisition deals across 36 industries in 60 countries over 2003–2008, we find that: (1) firms in industries with higher levels of technology intensity are more likely to acquire foreign firms, while firms in labor-intensive industries are more likely to stay at home; and (2) the relationship between technology gap and international acquisition activity is hill-shaped. This implies that overseas acquisition is particularly attractive for developing Asian firms in industries with high technology intensity, and a medium-sized technology gap is essential for identifying potential targets.
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Notes
Suppose that two industries X and Y have a crucial technology x and y, respectively, and assume that technology intensity is larger in X than Y. Then, the price in the factor market is higher for x than for y. This implies that the degree of value, scarcity, inimitability and non-substitutability of x is larger than that of y.
Supposes that two industries W and Z have two kinds of crucial labor – w and z, respectively – and assume that labor intensity is larger in W than in Z. Then, the price in the factor market is lower for w than for z. This implies that the degree of value, scarcity, immobility and non-substitutability of w is lower than that of z.
The industry-level value of announced deals could be an alternative dependent variable. However, the Thomson Financial Merger & Acquisition database for this variable has more missing values than for the count measure. Therefore, to secure sufficient sample size, we worked with the count measure as our dependent variable. This variable is the aggregate number of the completed acquisition deals within an industry, which can be regarded as a proxy of industry-level acquisition activity. In some firm-level studies concerning acquisition (for example Dikova et al, 2010; Zhang et al, 2011; Muehlfeld et al, 2007, 2012), measures based on these count data are used to examine M&A deal success.
Such normalization gives a density term. For the sector-specific measurement, industry-level sales are often chosen to normalize the variables of interest (see, for example. Nicholson, 2007). Yet we may also use other variables such as industry-level aggregate assets and output for the same purpose. Our preference here for sales is based on two arguments: maximization of the number of observations and minimization of correlations with other regressors of interest.
In fact, this is a relative measure, as p ijt +p jt controls for the impact of the scale of the technological capacity of both countries as a whole. We illustrate this with a numerical example. Suppose that the US–China difference for the biotech patent number is 30–10=20, while the US–China difference for the food-processing patent number is 120–100=20. With the p ijt -p jt measure only, one may infer that the US–China technology gaps for the biotech and food-processing industries are the same. However, our current measure indicates that the US–China technology gap for the biotech industry is 0.5 and for the food-processing industry 0.17, indicating a significant difference.
Export intensity and import intensity are calculated by the following formulae: XI ch =((x ch )/(m h ))/((x c )/(m w −m c )) and MI ch =((m ch )/(x h ))/((m c )/(x w −x c )), respectively, where x ch denotes the export of China to the target country, m h the import of the target country, x c the export of China, m w world import, m c the import of China, x ch the import of China from the target country, m ch China’s import from the target country, x h the export of the target country, x w world export, and x c the export of China (Zhang and van Witteloostuijn, 2004).
Alternatively, the common measures of cultural distance are the Kogut and Singh (1988) Index and the Euclidean distance (for a detailed review for the measures, see Drogendijk and Slangen (2006)), which are calculated based on the scores on Hofstede’s (1980, 2001) dimensions of national culture. However, in Hofstede’s data, the number of the countries is limited, which does not fit our current research covering 60 countries.
Data cropped from the PRS Group’s ICRG database, accessed from http://www.prsgroup.com.
We tested H0: α=0 against H1: α>0, based on the equation Var(y|x)=E(y|x)+α2E(y|x). After estimating the Poisson model, constructing fitted values and running auxiliary OLS regressions (Cameron and Trivedi, 2005, 2009), the regression result indicates that α>0 with P=0.05, suggesting the presence of significant overdispersion.
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We gratefully acknowledge the University of Antwerp (Belgium) for financial support. We also thank the general editor Michael A. Witt and the two anonymous reviewers for their insightful comments, and Ad van den Oord for providing patent data.
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Zhou, C., van Witteloostuijn, A. & Zhang, J. The internationalization of Chinese industries: Overseas acquisition activity in Chinese mining and manufacturing industries. Asian Bus Manage 13, 89–116 (2014). https://doi.org/10.1057/abm.2014.1
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DOI: https://doi.org/10.1057/abm.2014.1