National culture and human resource management: assumptions and evidence

Abstract

Abstract It is generally understood that human resource practices and strategies differ according to contextual factors such as size, industry and so forth. However, the globalization of business means that country differences in the environment for organizations have received increasing attention. Although countries can differ in many important ways (e.g. institutional and regulatory environments, labour-force characteristics such as education), a dominant focus of the international management literature is on national differences in culture, especially cultural values (Hofstede, Citation1980). We examine the assumptions that underlie this emphasis on the importance of national culture differences in international management and re-analyse findings from previous research, such as Hofstede's, to test these assumptions. We find, contrary to the interpretations of the original authors, that these assumptions receive only weak support when country effects are evaluated using variance explained estimates, rather than relying solely on statistical significance tests. We conclude that, while national culture differences can be important and must be understood, their role needs to be put in the context of other important contextual factors, including organizational culture. Keywords: Human resource managementnational culturestrategic choice Notes 1 Calculation of ICC(1,1) requires knowledge of the mean square between and mean square within from the ANOVA. If only the F statistic (the ratio of the mean square between to the mean square within) is reported, one can fix the mean square within equal to 1.0 and then use the F statistic itself as the estimate of the mean square between component. 2 According to Hofstede, ‘the eight questions were selected because of their ability to discriminate between countries’ (1980: 71). This admission suggests that the F statistics (and the related variance explained percentages) are optimistic because of capitalization on chance. 3 In the case of single degree of freedom F statistic, the following formula can be used: r = (F/(F+error degrees of freedom))1/2. 4 Further, even though Hofstede refers to his 1990 study as examining data on ‘20 organizations’ and in the ANOVA table refers to ‘20 organizations’, a close reading of his article indicates that he actually had data on ‘20 units from 10 different organizations’. If, as seems likely, units from the same organization are more similar than would be completely different organizations, then his F statistic for organizations (and the corresponding ICC) would be biased downward. 5 The reason that country explains 2.1 per cent of the variance here versus 4 per cent of the variance earlier, despite the fact that both analyses are based on Hofstede (Citation1980)'s data, is explained by the fact that Hofstede et al. (Citation1990) reported the median F statistic for country, whereas we used the mean F statistic earlier.