Mapping world cultures: Cluster formation, sources and implications

Abstract

This paper extends and builds on Ronen and Shenkar’s synthesized cultural clustering of countries based on similarity and dissimilarity in work-related attitudes. The new map uses an updated dataset, and expands coverage to world areas that were non-accessible at the time. Cluster boundaries are drawn empirically rather than intuitively, and the plot obtained is triple nested, indicating three levels of similarity across given country pairs. Also delineated are cluster adjacency and cluster cohesiveness, which vary from the highly cohesive Arab and Anglo clusters to the least cohesive Confucian and Far Eastern clusters. Exploring predictors of cluster formation, we draw on the ecocultural perspective and other inputs, and examine the combined role of language, religion, and geography in generating cluster formation. We find that these forces play a prominent yet complex role: for instance, the religion and language brought by the Spanish fail to create a singular, cohesive Latin American cluster akin to the Anglo cluster. The role of economic variables is similarly considered. Finally, comparing the current map with that of 1985, we find strong support for the divergence (vs convergence) argument. Implications for international business are delineated.

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Notes

  1. 1.

    www.merriam-webster.com/dictionary/religion.

  2. 2.

    In deliberating whether to include studies based on student samples, we opted to reject them, considering that the psychological variables (values, beliefs, etc.) we are investigating are contextual, relating to working environments and personal work experience occurring at the time of the survey, or prior to that.

  3. 3.

    Several cross-cultural studies were not included in our analysis, as they failed to meet the criteria for inclusion, namely: (a) studies that did not utilize work-related values or attitudes (e.g., Biswas-Diener & Diener, 2006; Biswas-Diener, Vitterso, & Diener, 2005; Diener, Scollon, Oishi, Dzokoto, & Suh, 2000; Ng, Diener, Aurora, & Harter, 2009; Sriram & Gopalakrishna, 1991; Wirtz, Chiu, Diener, & Oishi, 2009); (b) studies that used student samples, and did not survey practicing or potential workforce respondents (e.g., Bond, 1988; Connection, 1987; Newman & Nollen, 1996; Oishi & Diener, 2001; Schimmack, Oishi, & Diener, 2002; van deVliert & Janssen, 2002); (c) studies that included less than the minimum of 15 countries (e.g., Rotondo Fernandez, Carlson, Stepina, & Nicholson, 1997); (d) studies that decided on a clustering solution a priori, and did not base their clustering solution on an empirical analysis of psychological variables (e.g., Georgas et al., 2004; Minkov & Blagoev, 2009; Schmitt et al., 2007); (e) studies that did not analyze raw data, or did not use an independent database (e.g., Hickson & Pugh, 1995; Pearce & Osmond, 1999); and (f) studies that did not provide a clustering solution or individual country scores from which a clustering solution could have been generated (e.g., Basabe, Paez, Gonzalez, Rimé & Diener, 2002; Furman, Porter, & Stern, 2002; Laurent, 1983; Mole, 1990; Tixier, 1994).

  4. 4.

    These sources have been updated since publication, but their data have not been published in any available academic outlet.

  5. 5.

    An updated version was published in 1998, but Smith et al. employed the 1994 edition.

  6. 6.

    Clustering of three studies was achieved through our secondary analysis: Foley’s two cluster sets originating in different MNEs were amalgamated; individual country scores of Smith et al. (2002) were hierarchically clustered. The two MDS plots of Smith et al. (1995, 1996), both derived from Trompenaars’ (1994) data, were combined.

  7. 7.

    Cohesiveness represents the dissimilarity of countries within a cluster. It is computed as the weighted average proportion apart of intra-cluster members, p w , defined as where n is the number of elements in the Consensus cluster, a ij is the number of studies in which the ith and jth countries in the Consensus cluster appear in different clusters, and m ij is the number of studies that consider both the ith and jth countries in the Consensus cluster. Thus p w measures the average weighted proportion apart for countries within a given cluster, which can be translated into cluster cohesiveness. Clusters whose members always occur together in a cluster have p w =0. The value increases as clusters become less cohesive.

  8. 8.

    Table available on demand.

  9. 9.

    Roy Gelbard, Bar-Ilan University (2007, personal communication).

  10. 10.

    www.worldbank.org.

  11. 11.

    www.cia.gov/library/publications/the-world-factbook.

  12. 12.

    www.heritage.org.

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Acknowledgements

The authors wish to acknowledge the generous support provided by the CIBER at the Fisher College of Business, The Ohio State University, as well as the generous support of the Israel Science Foundation, ISF Grant No. 1390/10. We gratefully acknowledge the comments and suggestions made by the anonymous reviewers, and the insightful guidance provided by the consulting editor, Paula Caligiuri, which resulted in a better manuscript. We would also like to thank Dr Michael Radmacher, who so capably helped in solving methodological issues and provided statistical assistance, and Dr Shlomit Friedman, who served as a key team member throughout this endeavor. Finally, we wish to thank Ms Halo (Hilla) Ben-Asher, who administrated the project with persisting determination, and whose intensive editing work assisted in generating valuable insights and the enticing visual representations.

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Correspondence to Simcha Ronen.

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Accepted by Paula Caligiuri, Area Editor, 25 June 2013. This paper has been with the authors for two revisions.

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Ronen, S., Shenkar, O. Mapping world cultures: Cluster formation, sources and implications. J Int Bus Stud 44, 867–897 (2013). https://doi.org/10.1057/jibs.2013.42

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Keywords

  • globalization
  • cultural values
  • crosscultural management
  • clustering
  • incorporating
  • country variables