Cluster analysis using multi-algorithm voting in cross-cultural studies

Roy Gelbard, Abraham Carmeli, Ran M. Bittmann, Simcha (Simi) Ronen

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The goal of this study was to overcome three main shortcomings in using a single algorithm to determine a particular clustering of a phenomenon. We addressed this issue by considering cross-cultural research as a case in point and applied Multi-Algorithm Voting (MAV) methodology to cluster analysis. Specifically, this study was designed to provide more systematic supportive decision tools for researchers and managers alike when attempting to cluster analyzing phenomena. To assess the merits of the methodology of MAV for cluster analysis, we analytically examined cross-cultural data from Merritt [Merritt, A. (2000). Culture in the cockpit Do Hofstede's dimensions replicate? Journal of Cross-Cultural Psychology, 31, 283-301] study as well as data scored and ranked by Hofstede [Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage; Hofstede, G. (1982). Values survey module (Tech. Paper). Maastricht, The Netherlands: Institute for Research on Intercultural Cooperation]. Our study contributes to the literature in several ways. From a methodological point of view, we show how researchers can avoid arbitrary decisions in determining the number of clusters. We provide the researcher with more compelling and robust methodologies not only for analyzing the results of cluster analysis, but also for more better-grounded decision-making through which theoretical insights and implications can be drawn.

Original languageEnglish
Pages (from-to)10438-10446
Number of pages9
JournalExpert Systems with Applications
Issue number7
StatePublished - Sep 2009


  • Cluster analysis
  • Cross-cultural research
  • Multi-Algorithm Voting (MAV)


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