Cluster Analysis in Marketing Research: Review and Suggestions for Application

By Girish N. Punj, David W. Stewart. 1983

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Citations in the corpus

Is data clustering in adversarial settings secure? (2013, background): “Applications of clustering span across a large number of different domains, such as market segmentation [14, 26], classification of web pages [10], and image segmentation [12].”
Attitudes and behaviour in everyday finance: evidence from Switzerland (2009, methodology): “K-means cluster analysis (with simple Euclidean distance) was used because it appeared to be more robust than any of the hierarchical methods (Punj and Stewart, 1983).”
Maybe they’re born with it, or maybe it’s experience: Toward a deeper understanding of the learning style myth. (2020, methodology): “K-means cluster analyses allow researchers to identify structure in their data by classifying participants into groups based on their responses to multiple survey questions without necessitating the researcher to make assumptions regarding the exact nature of those groups (Punj & Stewart, 1983).”
Enhancing Chinese SME performance through innovative HR practices (2009, methodology): “Based on the recommendations of Punj and Stewart (1983)”
The Effect of Retail Store Environment on Retailer Performance (2000, background): “Punj and Stewart (1983) recommend that hierarchical and nonhierar- The Effect of Retail Environment on Sales chical clustering be used sequentially.”
Open Innovation in Small and Medium‐Sized Enterprises (SMEs): External Knowledge Sourcing Strategies and Internal Organizational Facilitators (2015, unknown): “chose a random subsample, carried out clustering and compared results with those based on the overall sample (Hair et al. 1998; Punj and Stewart 1983).”