Retention of latent segments in regression-based marketing models

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Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles (2013, methodology): “Consistent with the Monte Carlo simulation study of Andrews and Currim (2003), we use a modiﬁed Akaike information criterion (MAIC) to determine the number of latent regimes.”
Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats (2013, methodology): “In mixture models, these heuristics include model-selection criteria that are well known from the model-selection literature (e.g., AIC, BIC, and CAIC) and can also be used to approximate the best fitting number of segments (Andrews and Currim 2003a; Sarstedt et al. 2011a).”
Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models (2008, unknown): “In a recent Monte Carlo study Andrews and Currim (2003) concluded that the AIC 3 (the AIC with a penalty factor of 3) was the best segment-retention criterion to use across a large variety of multinomial data configurations.”
Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture (2009, methodology): “simulation study by Andrews and Currim (2003) concluded that the AIC was the best criterion for LC model.”