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.

在过去的几十年中，随着发表的科学文章数量呈指数级增长并且科学研究的跨学科性质不断增强，进行有效的文献综述的过程在帮助学者们理解以前的研究结果方面发挥着越来越重要的作用。尽管当前的文献综述方法提供了有见地的结果，但是它们是横断面研究或纵向研究，并且无法同时对研究领域的结构和演化进行建模。此外，只有少数方法对提取的类别进行迭代细化，并且在文献综述期间都没有方法集成强大的多层网络理论。为了填补这一空白，当前论文开发了系统的多层文献综述（SIMILAR）方法。所提出的方法可帮助研究人员（1）通过迭代细化所选论文的初始分类规则，（2）将多层网络理论整合到文献综述过程中，最后（3）在同一时间进行纵向和横断面分析时间。通过扩展最近在信息系统领域获得的研究结果，我们证明了SIMILAR方法的附加价值。