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This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison, 2002). Focus is given to the multivariate normal distribution, and 9 separate decompositions (i.e., class structures) of the covariance matrix are investigated. To provide a link to the current literature, comparisons are made with K-means clustering in 3 detailed Monte Carlo studies. The findings have implications for applied researchers in that mixture-model clustering techniques performed best when the covariance structure and number of clusters were known. However, as the information about the shape and number of clusters became unknown, degraded performance was observed for both K-means clustering and mixture-model clustering.

This research is the first to examine service sweethearting, an illicit behavior that costs firms billions of dollars annually in lost revenues. Sweethearting occurs when frontline workers give unauthorized free or discounted goods and services to customer conspirators. The authors gather dyadic data from 171 service employees and 610 of their customers. The results from the employee data reveal that a variety of job, social, and remuneration factors motivate sweethearting behavior and several measurable employee traits suppress its frequency. The results from the customer data indicate that although sweethearting inflates a firm's satisfaction, loyalty, and positive word-ofmouth scores by as much as 9%, satisfaction with the confederate employee fully mediates these effects. Thus, any benefits for customer satisfaction or loyalty initiatives are tied to a frontline worker that the firm would rather not employ. Marketing managers can use this study to recognize job applicants or company settings that are particularly prone to sweethearting and as the basis for mitigating a positive bias in key customer metrics.

cluster analysis, K-means partitioning, variable selection, heuristics,

cluster analysis, variable selection,