When One Size Does Not Fit All: A Systematic Literature Review and Taxonomy of Multidimensional Maturity Models

Abstract

This paper presents the findings of a systematic literature review (SLR) of 47 peer-reviewed studies on digital maturity models, focusing on their multidimensionality. First, it introduces a taxonomy that distinguishes Multi-Aspect Coverage (MAC) from Interdependent Multidimensionality (IMD), which employs formal weighting to reflect dimension-specific priorities and thereby improves diagnostic accuracy. Second, it reconfigures 977 indicators into 29 sub-dimensions and nine overarching dimensions through a deductive-inductive coding procedure. Third, drawing on contingency theory, the discussion offers context-sensitive implications for selecting an aggregation stance, thereby offering actionable guidance on balancing methodological rigor with pragmatic considerations. Hence, this review offers a systematic delineation of weighting processes in digital-maturity models, providing an operational boundary between MAC and IMD.