Design features of digital transformation maturity models: a systematic literature analysis and future research directions

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-06-21 DOI:10.1108/jm2-11-2022-0271

Mehmet Kirmizi, Batuhan Kocaoglu

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Abstract

Purpose This study aims to analyze and synthesize the design features of existing digital transformation maturity models with a developed classification scheme and propose a generic maturity model development wireframe based on design science research. Design/methodology/approach A systematic literature review is conducted on digital transformation maturity models in peer-reviewed journals, including the Emerald Insight, Science Direct, Scopus, Taylor & Francis and Web of Science databases, which resulted in 21 studies. A concept-centric tabular approach is used to analyze the studies, and intersectional demonstrations are used to synthesize the findings regarding the design features. Findings The classification scheme derived from the tabular concept-centric approach and iteratively evolved results in three main and 25 subcategories related to the design features. Analysis and synthesis of the studies reveal the granularity of the existing digital transformation maturity models concerning the design features. Furthermore, considering the design features in the classification scheme, a generic maturity model development wireframe is proposed to guide the researchers. Research limitations/implications The generic maturity model development wireframe and the classification scheme that represents the design features of existing maturity models guide the researchers for the maturity model development roadmap. Originality/value The existing literature review studies do not focus on the design feature of digital transformation maturity models within a systematic literature review perspective. A unique classification scheme derived from the tabular concept-centric approach aims to analyze the granularity level of the existing models. Furthermore, the generic maturity model development wireframe includes the guidelines and recommendations of design science studies and presents a roadmap for maturity model researchers.

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数字化转型成熟度模型的设计特点：系统的文献分析和未来的研究方向

目的本研究旨在分析和综合现有数字化转型成熟度模型的设计特征，并提出一种基于设计科学研究的通用成熟度模型开发线框。设计/方法论/方法在同行评审期刊上对数字化转型成熟度模型进行了系统的文献综述，包括Emerald Insight、Science Direct、Scopus、Taylor&Francis和Web of Science数据库，共进行了21项研究。使用以概念为中心的表格方法来分析研究，并使用交叉演示来综合有关设计特征的发现。发现该分类方案源自以表格概念为中心的方法，并经过迭代演变，产生了与设计特征相关的三个主要类别和25个子类别。对研究的分析和综合揭示了现有数字化转型成熟度模型的粒度设计特点。此外，考虑到分类方案中的设计特点，提出了一个通用的成熟度模型开发线框来指导研究人员。研究局限性/含义通用成熟度模型开发线框和代表现有成熟度模型设计特征的分类方案指导研究人员制定成熟度模型的开发路线图。原创性/价值现有的文献综述研究并没有从系统的文献综述角度关注数字化转型成熟度模型的设计特征。从以表格概念为中心的方法派生出一种独特的分类方案，旨在分析现有模型的粒度级别。此外，通用成熟度模型开发线框包括设计科学研究的指导方针和建议，并为成熟度模型研究人员提供了路线图。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.

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