Kubernetes has become a cornerstone of cloud-native software systems, yet empirical evidence on its architectural and quality impact remains inconclusive. Existing studies often report differences between Kubernetes-Native, adopted, and non-adopting projects, but typically overlook a critical methodological issue: Kubernetes adoption is not random. Instead, adoption decisions are shaped by pre-existing architectural, process, and organizational characteristics that also influence software quality, leading to potentially biased conclusions. We present a work-in-progress, data-driven approach to confounder identification in studies of cloud-native technology adoption, using Kubernetes as a representative case. Leveraging the SQuaD dataset, which provides hundreds of architectural, process, and organizational metrics for hundreds of mature open-source projects, we systematically analyze pre-adoption characteristics across three Kubernetes adoption scenarios. We combine regularized regression and survival analysis to identify confounders that discriminate adoption scenarios, predict adoption timing.