This dissertation develops and tests a model of the client acceptance decision that involves two phases: a diagnostic reasoning/risk evaluation phase and an adversarial problem solving/strategic risk adaptation phase. The model proposes causal linkages between the two phases such that changes in the risk evaluation phase are linked to changes in strategic risk adaptation behavior, which then affects the client acceptance decision outcome. Structural equation modeling is used to statistically estimate the proposed client acceptance model.^ There are three main findings. First, audit partners\u27 perceptions of various client acceptance risks impact each other during the diagnostic process of risk evaluation. Second, audit partners use adversarial problem solving when making the client acceptance decision since they link their mental model of the client developed during the diagnostic phase to their suggestions for strategic risk adaptation behavior. Third, strategic risk adaptation behavior is not used to moderate the inverse relationship between client acceptance risks and the client acceptance decision as expected. Instead, audit partners distinguish between two client acceptance sub-tasks: deciding whether to accept the client and determining the terms of the client acceptance proposal that would make accepting the client reasonable.^ There are two primary contributions of this dissertation. First, the client acceptance model illustrates both the structure of the relationship between risk evaluation and strategic risk adaptation behavior (e.g., audit fee, audit evidence, personnel expertise allocation, amount of information collected during the client acceptance process) and also provides a cognitive characterization of that relationship. Second, this dissertation illustrates the usefulness of structural equation modeling as a research methodology well-suited to studying complex situations in accounting and auditing domains. The structural equation modeling approach allows the researcher to test a comprehensive model of how audit partners evaluate and adapt to various complex and interrelated risks in the context of the client acceptance decision. Therefore, this dissertation advances the understanding of expert auditor judgment in the context of the client acceptance decision and demonstrates that structural equation modeling can be a valuable research methodology for understanding expert auditor judgment.