As the number of published scientific articles has increased exponentially and the interdisciplinary nature of scientific research has strengthened over the past decades, the process of conducting efficient literature reviews has played an increasingly important role in helping scholars make sense of previous research results. Although current literature review methods provide insightful results, they are either cross-sectional or longitudinal studies and are unable to simultaneously model the structure and evolution of a research field. In addition, only a few methods apply the iterative refinement of the extracted categories, and none integrate the powerful multilayer network theory during the literature review. To fill this gap, the current paper develops a systematic iterative multilayer literature review (SIMILAR) method. The proposed method helps researchers to (1) refine the initial classification rules of the selected papers through iterations, (2) integrate the multilayer network theory into the literature review process, and finally (3) conduct longitudinal and cross-sectional analyses at the same time. We demonstrate the added value of the SIMILAR method by extending research results recently obtained in the field of information systems.