TY - JOUR
T1 - Cluster analysis using multi-algorithm voting in cross-cultural studies
AU - Gelbard, Roy
AU - Carmeli, Abraham
AU - Bittmann, Ran M.
AU - Ronen, Simcha (Simi)
PY - 2009/9
Y1 - 2009/9
N2 - 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.
AB - 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.
KW - Cluster analysis
KW - Cross-cultural research
KW - Multi-Algorithm Voting (MAV)
UR - http://www.scopus.com/inward/record.url?scp=67349240409&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2009.01.071
DO - 10.1016/j.eswa.2009.01.071
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AN - SCOPUS:67349240409
SN - 0957-4174
VL - 36
SP - 10438
EP - 10446
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 7
ER -