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Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

The usage of the WTO Dispute Settlement System (DSS) is dominated by high-income countries. Since the ultimate enforcement threat of the system is based on retaliation, countries may take their economic size as well as their specific bilateral retaliatory capacity into account when deciding whether or not to respond to a detrimental infringement of a trade agreement by filing a costly complaint. Hence, various scholars conjecture that lawsuits surfacing in the record of the WTO constitute only the biased tip of an iceberg of trade disputes. In order to investigate such a potential bias, this chapter sets up a sequential game of the DSS. Subsequently, a binary choice model is employed to empirically explain a country's decision whether or not to litigate against a trading partner. The results suggest that a country is more likely to file a complaint if (i) it is large, (ii) its trading partner is small, (iii) the trade value of the commodity at stake is large, and (iv) its retaliatory capacity is large.