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Commitment across cultures: A meta-analytical approach

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Abstract

The present study reports two sets of meta-analyses of employee commitment across cultures. First, using three-level hierarchical linear modeling, differences in mean levels of commitment were investigated. We examined the effects of individualism–collectivism and power distance values and practices (Hofstede, GLOBE) while controlling for study and industry effects. The findings showed that affective commitment (across 48 countries), continuance commitment (31 countries) and normative commitment (30 countries) were influenced by country-level individualism and power distance. Greater collectivism was associated with higher normative commitment, and greater power distance was associated with higher continuance and normative commitment. Economic variables were found to exert a strong influence on affective and normative commitment means. Second, relationships between affective commitment and turnover intentions (across 26 countries) were found to be somewhat stronger in individualistic settings, whereas the normative commitment–turnover intentions relationships (across 10 countries) were stronger in collectivistic settings. Overall, absolute cross-cultural differences in all analyses were relatively small compared with differences due to study and industry effects, but country-level predictors accounted for substantive proportions of the variance between countries. Implications for commitment and cross-cultural research are discussed, and particular attention is drawn to the need to explore the meaning of commitment across cultural and economic contexts.

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Notes

  1. House et al. (2004) distinguished between two different types of individualism–collectivism, and also measured these dimensions using two different formats (“as is” and “should be” indicators). We do not make differential predictions, since the interpretation and the meaning of these constructs are debated (Fischer, 2009; Hofstede, 2006; Peterson, 2004; Smith, 2006).

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Acknowledgements

We would like to thank the Editor, Lorraine Eden, the former Departmental Editor, Mark Peterson, and five anonymous reviewers for their helpful comments and suggestions, in particular one reviewer who checked our analyses. We would also like to thank Peter B. Smith and Birgit Schyns for comments on previous drafts. We would like to acknowledge Michael Riketta and Arzu Wasti for their support and willingness to share information, as well as all the scholars who provided additional information on published or unpublished studies. A previous version of this paper has been presented at the International Congress of the International Association for Cross-Cultural Psychology in Xian, PRC in July 2004.

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Authors and Affiliations

  1. Centre for Applied Cross-Cultural Research, School of Psychology, Victoria University of Wellington, Wellington, New Zealand

    Ronald Fischer

  2. Ministry of Health, Wellington, New Zealand

    Angela Mansell

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  1. Ronald Fischer
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Correspondence to Ronald Fischer.

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Accepted by Lorraine Eden, Editor-in-Chief, 15 November 2008. This paper has been with the authors for five revisions.

APPENDIX

APPENDIX

Three-Level Meta-analysis With HLM

Meta-analysis is a special form of a hierarchical model, since individuals are nested within studies. One main difference between meta-analytical data and other hierarchical data sets is that in meta-analysis the variance of each effect size is assumed to be known (the sampling variance of individuals within studies). Therefore meta-analytical problems belong to a category of so-called “variance-known” or “v-known” models. For meta-analysis, level 1 is typically the effect size, and level 2 includes variables at the study level. Using a random-effects approach to meta-analysis, this set-up better reflects the reality of real-world data structures, and prevents spuriously significant effects (Field, 2003). Cross-cultural meta-analysis adds another level to this hierarchy, since individual studies are nested within countries.

At level 1, the base-level of a random-effects meta-analysis is

where ES ijk is the estimated effect size i of study j in country k; γ000 is an estimate of the true unknown effect size (the population parameter, the intercept); r0 is the random study effect – that is differences between studies that are the result of sampling variation; and u00 is the random country effect.

In our analysis, we were interested in the effects of level 3 (country) variables on the average mean or correlation, while controlling for level 2 variation. Therefore our model is an intercepts-as-outcomes model, with variables at level 2 and 3 to explain the variability in effect sizes (the intercepts). The following equations follow the format of the equations as used in HLM.

At level 2, we can add study effect variables that can explain variability in mean effect sizes:

where X jk is a fixed effect of study-level variables at level 2. In our analysis this includes both study variables (type of scale being used, number of items, number of response options) and sample variables (professional group, sector).

At level 3, we can add country effect variables that also explain variability in mean effect sizes:

where Z k is the effect of country-level variables on mean effect sizes. In our analysis this includes macroeconomic variables and culture-level values and practices.

To give an example of an equation including predictors at level 2 and 3:

In our analysis, we have expanded this model by including a larger set of predictors at each level. Continuous variables were grand-mean-centered. Dummy-coded variables (professional group, sector) were entered uncentered.

These models can be run in the Hierarchical Linear and Nonlinear Modeling (HLM) program (Raudenbush & Bryk, 2002; Raudenbush et al., 2004). HLM is a standard program for analyzing hierarchical or nested data. Two options are available for running two-level meta-analyses in HLML: the batch mode and the windows interface. Multivariate v-known analysis is available only via the batch mode, but is restricted to two levels only (see Raudenbush et al., 2004: 184–189). Three-level analysis can be conducted with HLM 3 via the windows interface (see Raudenbush et al., 2004: 189). The data preparation and construction of the MDM file (multivariate data matrix) follows the same sequence as for normal multilevel analyses. Three data files (one for each level) are needed. The effect size and sampling variance of each effect size need to be included in the level 1 file. To run the analysis, the researcher needs to specify the sampling variance at level 1. In HLM 6, this can be done by going to “Other Settings”, selecting “Estimation Settings” and “Weighting”. There, the known level 1 variance can be selected using the pull-down menu under “Known variance”. This will automatically set sigma^2 to 1.0 (see Hox & de Leeuw, 2003). The default estimation method is Full Information Maximum Likelihood Estimation. The rest of the analysis is identical to standard multilevel analyses.

A more detailed discussion of traditional vs multilevel meta-analysis and fixed vs random-effects meta-analytical approaches can be found in Field (2003), Hedges and Olkin (1985), Hox and de Leeuw (2003), Konstantopoulos and Hedges (2004), and van den Noortgate and Onghena (2001).

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Fischer, R., Mansell, A. Commitment across cultures: A meta-analytical approach. J Int Bus Stud 40, 1339–1358 (2009). https://doi.org/10.1057/jibs.2009.14

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