🤖 AI Summary
Existing Digital Maturity Models (DMMs) exhibit significant inconsistencies and semantic ambiguities in their dimensional definitions and structural formulations, which hinder effective assessment of digital transformation. This study addresses these limitations through a systematic literature mapping approach, combining automated retrieval with snowballing techniques to conduct a multi-source comparative and content analysis of 76 DMMs. For the first time, it integrates and harmonizes the definitions and constituent elements of ten frequently occurring dimensions—such as organization, strategy, and technology—thereby resolving the prevailing inconsistencies and structural ambiguities across existing models. The work establishes a coherent theoretical foundation and offers practical guidance for developing more consistent, actionable frameworks for evaluating digital maturity.
📝 Abstract
Digital Transformation (DT) initiatives frequently face high failure rates, and while Digital Maturity Models (DMMs) offer potential solutions, they have notable shortcomings. Specifically, there is significant disparity in the dimensions considered relevant, a lack of clarity in their definitions, and uncertainty regarding their components. This study aims to provide a clearer understanding of DMMs by proposing integrative definitions of the most frequently used dimensions. Using a Systematic Mapping approach, including automatic search and snowballing techniques, we analyzed 76 DMMs to answer two Research Questions: (RQ1) What are the most frequent dimensions in DMMs? and (RQ2) How are these dimensions described, including their components? We reconcile varying interpretations of the ten most frequent dimensions -- Organization, Strategy, Technology, Culture, Process, Operations, People, Management, Customer, and Data -- and propose integrative definitions for each. Compared to previous analyses, this study provides a broader and more recent perspective on Digital Maturity Models.