Count data may exhibit excess zeros, within-group overdispersion, and heterogeneous relationships between the response and its covariates. Conventional zero-inflated regression models typically impose a single count-regression structure, whereas Poisson count components cannot accommodate residual within-component overdispersion. We propose a zero-inflated negative binomial cluster-weighted model that combines a structural-zero component with multiple negative binomial regression components and jointly models the response and mixed-type covariates.We establish sufficient conditions for identifiability of the proposed model and its Poisson counterpart and develop an expectation-maximization algorithm for estimation. Numerical studies demonstrate improved regression coefficient estimation and clustering performance relative to the Poisson counterpart under within-component overdispersion.

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