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The explanatory potential of four forms of expectancy theory with additive and multiplicative expectancy terms and linear and nonlinear valence functions were contrasted. A behavioral decision-making theory approach was used when 101 subjects were asked to make 128 hypothetical job-choice decisions. More than 25,800 decisions under a within-subjects framework were analyzed. Results indicate that the majority (83 percent) of subjects employed additive or multiplicative expectancy models with linear valence functions. However, the predictive efficacy of the expectancy theory model was improved for 17 percent of the subjects when nonlinear Tralence terms were introduced. The findings imply that different functional forms of expectancy theory may be needed to model individuals' decision-making processes.

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