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Digital Transformation Journey Guidance: A Holistic Digital Maturity Model Based on a Systematic Literature Review

Design, Technology and Society PhD Program, Özyeğin University, Istanbul 34794, Turkey
Department of Industrial Engineering, Galatasaray University, Istanbul 34357, Turkey
Author to whom correspondence should be addressed.
Systems 2023, 11(4), 213;
Submission received: 11 March 2023 / Revised: 15 April 2023 / Accepted: 18 April 2023 / Published: 20 April 2023


For a successful digital transformation, organizations must create an accurate roadmap and manage the process effectively. A digital maturity model is a critical success factor as it enables organizations to assess their current situation and create roadmaps aligned with their goals; however, a comprehensive systematic literature review covering the maturity models proposed by academia and consultancy firms is hard to find. Further, the existing models are sector-oriented, not organization-oriented, and do not consider the transformation journey holistically, but instead focus on model dimensions. This study first undertakes a comprehensive and up-to-date systematic literature review by applying the PRISMA approach using a bibliometric analysis tool capable of providing visual maps, then developing a unique holistic digital maturity model that covers several aspects of an organization’s digital transformation journey, from strategy to governance, and asking relevant questions. The hierarchical structure, comprising dimensions and sub-dimensions, presents content beyond the scope of other models. The results of the digital maturity assessment can be interpreted in parallel with the stages of the digital transformation. Consequently, the new holistic and sector-independent digital maturity model can be used by organizations in both the private and public sector.

1. Introduction

Digital transformation (DT) is defined as the application of novel digital technologies to facilitate significant business improvements, leading to either improved client experience and streamlined operations or the development of new business models [1]. It involves identifying organizational needs, designing new processes, or redesigning existing ones by utilizing digital technologies to provide value to customers, businesses, and other key stakeholders [2,3,4,5]. Given that DT has social, technological, and managerial impacts across all levels of the organization, it should be managed from a holistic perspective [6,7]. Additionally, the transformation component implies substantial changes forthcoming in the organization in terms of structure and strategies [4]; therefore, DT can be regarded as an adoption process that must be actively designed, initiated, and implemented [8]. It can also be viewed as a journey that enables organizations to create value by bringing together internal and external capabilities to achieve their goals with digital solutions. In this journey, each institution passes through certain stages according to its own vision and maturity.
Kurmann and Arpe identified top management support, cross-functional collaborations, flatter hierarchies, and intensified people management as crucial success factors in DT implementation [9]. In addition, companies should utilize digital technologies and customer-centered performance indicators as business practices for DT [10]. They should also assess their business models to avoid situations in which they are unable to compete or even survive [11]. Therefore, the success of DT is not only dependent on technology but also on the strategies implemented to change business processes. Managers in diverse sectors agree that DT should bring the organization from a state of being satisfied by marginal efficiency improvements to a state of implementing basic innovation principles and developing disruptive strategies [12].
Digitization, digitalization, and DT are the three stages of digital advancement in organizations [7,13]. Digitization is defined as the encoding of analog data into a digital arrangement, which enables computers to store, process, and disseminate such information, while digitalization refers to how information technology (IT) or digital technologies can be utilized to change existing business processes. The creation of new mobile communication channels enables clients to easily connect with the organization can be seen as digitalization [7]. IT is a primary enabler of digitalization because it offers novel business opportunities by transforming processes such as distribution, communication, and business relationship administration [7,13]. The last stage of digital advancement, DT, redesigns critical processes to augment a firm’s business approach to value creation [13,14].
Organizations face the challenge of matching appropriate digital approaches and actions during their DT journey due mainly to the basic complexity of IT administration and the lack of research on how firms can systematically adopt DT [15]. Many organizations face diverse problems, including cultural and talent gaps and weak collaboration between IT and other business processes [16]. According to a McKinsey report [17], DT requires organizations to reskill human resources, adjust their culture, promote closer IT–business process connections, and meticulously measure digital value; therefore, the success of DT is not only dependent on technology but also on the strategies implemented to change business processes. Employees and their working styles also help to bring these processes to life. In this context, it is unavoidable to examine an organization’s capabilities, culture, and human capital profiles. The impact of these dimensions on digitalization studies can also be seen in the elements examined and supported in the literature [18].
The need for balanced and holistic management of different activities in DT requires that digital maturity models are put into practice. Hence, this study focuses on three critical research questions to investigate the key roles digital maturity models play in DT.
  • Q1: What is the importance of maturity models within DT?
  • Q2: In what contexts is a digital maturity model considered?
  • Q3: How should a holistic and generic model be designed? What dimensions should it have?
The study makes two key contributions. First, noting that the most recent survey covers papers published until 2020, the present study includes an up-to-date comprehensive survey for the identification of relevant maturity models for DT that can be applied to organizations in different sectors. The comprehensiveness of the survey is mainly based on the fact that digital maturity models developed by both academia and consultancy firms are considered. Another distinctive feature of the survey is that, in addition to the frequently used Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach, a bibliometric analysis tool called Biblioshiny [19] was utilized to generate visual maps. Second, a more significant contribution is the development of a new holistic digital model that can be applied to all organizations, independent of their sector and size.
The remainder of this paper is organized as follows. Section 2 provides background information on the concepts that are utilized in the paper for the description of digital maturity and digital maturity models. Section 3 outlines the methodology used to conduct a systematic literature review of papers published until the end of 2022. Section 4 presents the results of this review, while Section 5 introduces the new digital maturity model and its novelties compared to existing models. Lastly, Section 6 concludes the paper.

2. Background

We devote this section to clarifying the concepts that are related to digital maturity, its levels, its assessment, and digital maturity models which are used for the assessment of the digital maturity level of organizations. We note that the effective management of DT depends heavily on digital maturity models.

2.1. Digital Maturity

Digital maturity is closely related to DT and is defined by Gökalp and Martinez [14] as the state in which an entity’s digital technology has transformed its activities, skill engagement, and business frameworks. Hägg and Sandhu [13] call it a situation where a transformation has occurred in an organization which has managed to address problems associated with digital business landscapes. Schumacher et al. [20] define maturity as a condition of being perfect or complete, which implies the advancement of a system’s development phase. Teichert [21] uses the term DT maturity to identify that the linkage between DT and digital maturity encompasses technological and managerial components. Gartner [22] defines digital maturity as the level at which an organization has implemented digital technologies and processes to drive business performance and enable DT. Based on these definitions, digital maturity can be summarized as a critical indicator that reveals the performance of DT adaptation.
Digital maturity assessment is a process of evaluating an organization’s level of digital maturity by assessing its capabilities, readiness, and progress in implementing digital technologies to transform its business operations and remain competitive in the digital age. This assessment involves analyzing an organization’s performance in key areas, such as strategy, culture, processes, technology, and data analytics, using various frameworks, models, and tools to measure the organization’s digital maturity level. According to literature and shared sources such as Ross and Beath [23]; Nambisan and Sawhney [24]; Westerman, Bonnet, and McAfee [25]; Berman and Marshall [26]; and Kagermann, Wahlster, and Helbig [27]; digital maturity assessment is crucial for organizations looking to thrive in the digital age. The process enables organizations to identify gaps in their digital capabilities and provides them with insights into areas they need to improve to stay competitive.

2.2. Digital Maturity Models

According to Berghaus and Back [28], a maturity model offers guidance on the approach companies adopt to plan and implement DT. Maturity frameworks primarily facilitate the evaluation of the status quo and implies a potential, expected, or usual development path to the target position [14]. Digital maturity models assist organizations in analyzing their capacity to respond to DT based on predefined milestones [29].
Berghaus and Back [28] argue that digital maturity models include dimensions and sub-dimensions that outline areas of improvement and measure maturity at distinct levels, pointing to the path of evolution toward full maturity. Specifically, a dimension refers to a measurable and isolated element that portrays a substantial, critical, and separate component of digital maturity [21]. In a later section, we review 60 maturity models developed by academia and consultancy firms and mention their dimensions as well as sub-dimensions.

3. Methodology

A systematic literature review was conducted to analyze existing studies on the concept of digital maturity. The stages of the review are shown on the right-hand side of Figure 1 and are based on the PRISMA approach, the steps of which are depicted on the left-hand side [30]. The PRISMA approach was introduced in 2009 by renaming and updating the standards set in the Quality of Reporting of Meta-analyses (QUOROM) conference for improving the quality of reporting systematic reviews [31].
The first step comprised scanning the literature using a wide range of concepts so that all the studies related to the digital maturity model in the context of DT are considered. More than 10,000 publications were found based on the keywords listed in Table 1. These keywords are indicative of the relevance of these publications to the concept of digital maturity.
In the second step, the source documents were determined by eliminating duplicate and irrelevant documents by reading abstracts and removing the duplicates across different databases. The Scopus and Web of Science (WoS) databases were scanned for academic studies, while Google search engines were used to find studies by consultancy firms. The Scopus and WoS databases were compared, and the former, being larger, was found to include the documents found in the latter; therefore, we decided to proceed with the Scopus database.
The third step involved analyzing the documents to find the answers to the research questions aimed at (i) marking the importance of digital maturity models in DT and (ii) presenting a holistic digital maturity model based on the analysis and the gaps in the literature.
This section is divided into the literature review analysis targeted to answer the first two questions and the digital maturity models analysis that checked if the documents selected in the literature review focused on digital maturity models and answers the third question. The last was further divided into academic articles, consultancy firm reports, and white papers.
The fourth step was bibliometric analysis, a research method that supports measurement-based analysis of scientific literature. Bibliographic mapping tools generate maps that summarize various attributes of documents and their relationships [32]. An inspiring example of bibliometric analysis is the paper by Uribe-Toril et al. [33] where the authors review the literature in the field of Energy, Economy, and Environment for a duration of 17 years. This paper has been a motivation for using bibliometric analysis in our study to boost the systematic literature review.
The Biblioshiny tool was selected owing to its superior features and visualization capabilities than those of other tools [19]. Table 2 presents a comparison of existing tools for bibliometric analysis and their visualization capabilities.
Figure 2 outlines the Biblioshiny tool process comprising three phases: preparation, data analysis, and data visualization and evaluation. The preparation phase involved downloading a dataset comprising all relevant documents as a .bib file from Scopus, and the data analysis phase involved inputting the .bib file into Biblioshiny, which performs a bibliometric analysis step by reducing the data and creating a network matrix. The last phase involves generating various mappings for visualization and evaluation.

3.1. Method Used for Literature Review Analysis

The literature review analysis conducted in this study utilizes the two types of analyses provided by the Biblioshiny software: one focused on the domain and another focused on the knowledge structure, as shown in Figure 3. Domain-focused analysis is based on sources (journals, conference proceedings, etc.), documents, and authors, whereas knowledge-structure-focused analysis makes use of conceptual, intellectual, and social issues. This study considers the sources and documents in terms of domain to obtain a general overview of the publications and carry out document analysis. The general overview provides a summary of the number of sources, publications, and citations that can be referred to as metadata. Document analysis was performed based on keywords. In terms of knowledge-structure-focused analysis, we used only the conceptual structure. This enabled us to analyze the relationships and trends of the concepts using the three Biblioshiny networks: co-occurrence networks, thematic maps, and thematic evolution.
A co-occurrence network, which helps to study the evolution of the subject area over time, was used to understand the topics covered by the subject area under investigation. In the network, each node is represented by a keyword used by the authors in the documents. The size (diameter) of a node increases when the associated keyword is used in a larger number of documents. An edge or link exists between a pair of nodes (keywords) if both the keywords exist in the same document. The strength of a link, which measures the degree of association between a pair of keywords, increases as the number of co-occurrences of the corresponding keywords increases.
The second type of network, called a thematic map, groups the documents into four clusters represented by a bubble based on two features of the keywords: centrality degree and density degree [36]. The centrality degree shows the importance of the keywords, whereas the density degree measures the development of keywords over time. In a thematic map, the size of the bubbles representing the clusters depends on the number of keywords assigned to the clusters. The position of the bubbles was set according to the Callon centrality and the density of the cluster [19]. Both types of maps have been utilized in many recent studies regarding various research disciplines [37,38].

3.2. Digital Maturity Model Analysis Method

This analysis groups the documents containing digital maturity models into two groups, as shown in Figure 4. Irrespective of their origin, the documents were analyzed based on the dimensions used in the proposed maturity model. A dimension was defined as a criterion in the model for the assessment of digital maturity.

4. Literature Review Results

The results from the literature review of all documents until the end of 2022 and the digital maturity model analysis are presented in the following subsections.

4.1. Results Based on Literature Review Analysis

4.1.1. Domain-Focused Analysis

Focusing on the domain of the documents, the development of publications over the years and publication performance based on citations and document types were analyzed. The first analysis provides a general overview of the number of sources, publications, and citations that can be referred to as metadata.
General Overview: As seen in Figure 5, the first document with the scope of digital maturity was published in 2004. Since then, the documents have grown at 3.7%. A total of 1481 authors contributed to the publications, and international cooperation in authorship was 14%. To date, over 20,000 references have been provided to 497 scanned documents, and the average citation performance of the articles was 6.2.
In parallel to the growth of DT studies, the annual number of publications on digital maturity has also increased steadily since 2018, as can be seen in Figure 6a. A significant share of the increase can be attributed to the conference papers as Figure 6b indicates.
As Figure 7 shows, along with the number of publications, the average number of citations received by each publication increased significantly after 2016.
Document Analysis: This analysis is performed based on keywords. As Figure 8a,b show, when all keywords or the 100 most frequently used are considered, the papers are concentrated across only four: Industry 4.0, digital maturity, maturity model, and digital technologies.
When the cumulative occurrences of the keywords are plotted in Figure 9 on an annual basis, it is observed that digital maturity ranks second after DT. Note that Figure 9 only displays the total number of times the most frequent keywords appeared by year 2022 while the numbers for other years can be read on the plot. For example, DT and digital maturity appeared 131 and 106 times, respectively, by 2022.
The most commonly used keywords are shown in Figure 10. When the trend topics of the documents are analyzed, digital maturity is still the winner; digitalization creates a new wave after digitization and progresses in parallel with digital maturity.
However, the importance of digital solutions and big data, which reveal the solutions and effects of DT, increased, as shown in Figure 11.

4.1.2. Knowledge-Structure-Focused Analysis

This analysis enables the investigation of the relationships of the concepts and their trends using the Biblioshiny network types: co-occurrence network, thematic map, and thematic evolution. As shown in Figure 12, the co-occurrence network indicates that DT occurs most frequently with digital maturity, digitalization, and Industry 4.0. According to the results of the thematic map, which shows clusters with respect to the density and centrality of the keywords, the keywords DT, digital maturity, and digital model form a separate and powerful cluster among all publications (Figure 13). The second significant cluster was obtained by the keywords digitalization and Industry 4.0. Digital readiness ranks third, followed by the COVID-19 cluster.
Biblioshiny is an R package that provides an interactive interface for bibliometric analysis. One of the features of Biblioshiny is the ability to generate a thematic evolution map of research themes that have developed over different time periods. Themes are basically clusters of author-defined keywords selected in each paper. The map is created as follows: First, for each time period in question, a network is created from the articles in the database where the nodes of the network represent the keywords found in the articles, and the edge between a pair of keywords implies that these two keywords co-occur in a number of articles. Then, as the number of articles having the two keywords increases, the strength of the edge (or equivalently, the similarity of the keywords) also increases.
A clustering algorithm is utilized to group the keywords based on the similarities among them. Each cluster corresponds to a research theme. Hence, the colored boxes found at each time period of Figure 14 denote different research themes. When one or more keywords exist both in a theme at time period t and another theme at time period t + 1, there is a connection between these themes across periods.
Owing to the sharp increase in the number of documents in 2018, the time horizon was divided into three time periods: 2004–2017, 2018–2020, and 2021–2022. In the thematic evolution map in Figure 14, the digital maturity theme in period 2004–2017 is seen to have connections with itself and DT theme in period 2018–2020; moreover, it has also ties with the theme of maturity model in the first two time periods.
The analyses made in this section reveal two main results as the answer to the first research question (Q1) with regard to the place of digital maturity in DT. First, the number of publications where DT and digital maturity are discussed together has increased over the years. Second, the connection between these two concepts is much stronger and more closely related in comparison with the connection between all keywords.

4.2. Results Based on Digital Maturity Model Analysis

The increasing importance of DT, its successful implementation, and its failures have triggered both academia and consultancy firms to develop models allowing the systematic monitoring of projects related to DT. We found five review papers on digital maturity models, two of which were published in 2019 and three in 2020, as shown in Table 3. Within the scope of digital maturity, the most recent review paper considers documents published until 2020; this study, therefore, updates the literature review by two years. Moreover, existing review papers focus mainly on maturity models applicable to small- and medium-sized enterprises, while this study focuses on all papers independent of the sector, making it possible to propose a holistic and comprehensive model.
After eliminating documents with models that do not qualify as digital maturity models, the remaining models are examined in detail by dividing them into two categories: models developed by academic papers and models proposed by consultancy firms. For each category, a subsection is devoted to the dimensions included in these models and targeted application areas.

4.2.1. Digital Maturity Models of Academic Papers

Table 4 presents the list of academic works on a digital maturity model, 49% of which are conference papers, 41% journal papers, and the remaining 10% book chapters; 80% of these book chapters were developed for specific sectors, and only 20% had an enterprise-wide perspective. All publications were analyzed with respect to four criteria. The first relates to whether a maturity model is proposed based on an analysis of the existing models; only 30% of the studies developed a new maturity model. The second criterion was that the developed model should be holistic; only 21% of the models were holistic. The third criterion asks whether the model is developed with a specific sector in mind; 65% of the models were sector-oriented. The last addresses the existence of a case study that supports the model; 40% of the models related to a case study. Only one study was found to have developed a new holistic model based on the analysis of existing models in the literature and presented an application of the model in the retail sector [43]. Thus, the results of the analysis show a holistic model based on a comprehensive literature review has not been developed yet.
A total of 236 dimensions were identified in the 38 publications analyzed and was reduced to 123 by eliminating the common ones. Finally, 12 dimensions were obtained by grouping similar and frequently used ones: strategy, technology, operations, products and services, governance, people, customers, processes, innovation, culture, value and value chains, and leadership. It was found that both alignment with the strategies to maximize the value targeted by DT and the technology, as well as the processes, products, services, and operations where the technology is applied to realize the strategies, play a crucial role. Further, governance and leadership must support the impact of transformation on employees and culture.

4.2.2. Digital Maturity Models of Consultancy Firms

Table 5 displays the publications that developed digital maturity models proposed by consultancy firms. It can be observed that 90% of these publications present a sector-independent model. An analysis was conducted to determine the main dimensions considered. This results in strategy, culture, technology, operations, process, organization, and customer experience.

5. Discussion and a New Holistic Digital Maturity Model

Given that digital maturity models play a paramount role in DT, act as a catalyst in the DT journey, and there does not exist a holistic model in the literature, the goal of this study is set as developing a comprehensive new digital maturity model that addresses all of the critical factors and capabilities required by the entire DT regardless of sector or size of an organization. After the model development, the next goal is determined as devising an approach for implementing the new model.
The analysis of the existing models developed by both academia and consultancy companies reveals the scope of the existing digital maturity models. Furthermore, this analysis sheds light on the common dimensions that are utilized in the models and also helps to identify some specific dimensions that may provide new perspectives. Thus, the output of this analysis provides an answer to the research question Q2. An important conclusion one can draw from the analysis is that a systematic methodology is not adopted in the process of model development, and there is a need for a holistic digital maturity model.

5.1. Digital Transformation Journey

The term “digital transformation journey” has been around for a while, but it is difficult to pinpoint the specific person or organization that first mentioned it; however, one of the earliest mentions of DT in a business context can be traced back to a report published by MIT Center for Digital Business in 2011 titled “Digital Transformation: A Roadmap for Billion-Dollar Organizations”. The report discusses how digital technologies are disrupting traditional business models and emphasizes the need for companies to embrace DT to stay competitive. Since then, the term “DT journey” has become more widely used since companies of all sizes and different sectors recognize the importance of DT. In this paper, the DT journey is defined as the endeavor to acquire digital capabilities and turning them into an asset. This journey requires identifying current areas of improvement, creating a roadmap according to the goals, and ensuring that plans and projects help to reach strategies and goals.
The literature review indicates that no holistic maturity model can be implemented within a DT journey. The existing models had been modified and extended with new dimensions and sub-dimensions to incrementally increase their coverage and usability. This study argues that, to maximize the value that a digital maturity model can provide to the DT journey, the latter must be defined as comprising four stages: awareness, readiness, planning, and execution (see Figure 15).
  • Awareness: This pertains to an organization’s decision to initiate DT within the framework of a strategy or strategic initiative to achieve its goals.
  • Readiness: The first of this two-step process is the development of a reference model that can reveal the extent and scope of DT, the gaps the organization needs to close, and the improvement areas that require new approaches to implement the intention set forth in the awareness stage. The second step is to perform an assessment based on this model.
  • Planning: This provides an input for the creation of a digital roadmap by scoring the assessment results in parallel with the goals and priorities set by the organization.
  • Execution: This deals with the systematic implementation and maintenance of the continuity and sustainability of the roadmap.
This concretization of the DT journey and the place and importance of the maturity model in this journey carries the proposition based on the literature study and analysis made for the first research question (Q1).

5.2. The New Holistic Digital Maturity Model

The results and limitations of the systematic literature review studies published to date (Table 4) show that there is a lack of a systematic approach in the creation of the dimensions and sub-dimensions of proposed models. These dimensions and sub-dimensions are rather developed based on existing model comparisons. There is also a need for a new holistic model. In line with these needs, first of all, the design approach for developing dimensions and sub-dimensions of the holistic model was revealed. Then, the main dimensions of the model were determined based on six questions given below that are inspired by the WH questions.
The proposed holistic digital maturity model, illustrated in Figure 16, is expected to play a catalyst role in the defined DT journey by answering the following six questions:
  • What is DT aimed at?
  • What value does it offer?
  • In which processes should the organization apply digital projects?
  • Which technologies support the DT?
  • Who implements the DT?
  • How is DT sustained?
The design based on these questions and the dimensions as well as sub-dimensions defined in this context also provide the concrete proposition to the third research question (Q3).
The model enables the organization, based on its assessment, to identify the gaps between the current situation and goals. It has 6 dimensions and 24 sub-dimensions, which not only includes all the dimensions and sub-dimensions of the existing models but also all the issues that need to be addressed in a DT journey.
As the first dimension of the model, digital strategy focuses on assessing the extent to which corporate-level vision, strategic direction, and goals for DT can create value. Five sub-dimensions were defined to serve this purpose. The “vision” sub-dimension examines the DT vision and what is understood by DT. The “strategies” sub-dimension deals with the DT strategy and how the competitive advantage that can be achieved by DT is defined. The “business model” sub-dimension is related to the new value proposition presented with digitalization and the extent to which a new extension is desired in the existing product and service portfolio. The “operation model” sub-dimension investigates to what extent digitalization affects the current operation model and the organization. The “leadership” sub-dimension examines the level at which digitalization studies are embraced and internalized by the leaders. In this model, the strategy sub-dimension stands out as a standalone component, and in a limited number of models, the business model and leadership are defined as separate dimensions.
Digital value, which is the second dimension, focuses on the assessment of both the impact of DT on the value and product portfolio offered to the customer and the scope and dimensions of differentiation in customer value processes. Three sub-dimensions were defined within this dimension. The “create value” sub-dimension questions the role of digitalization in offering new value to the customer and the difference it creates in the value proposition. The “deliver value” sub-dimension investigates the impact of digitalization on the capability of offering new and innovative products/services. In the “capture value” sub-dimension, the focus is on evaluating the new and innovative products offered to customers through digitalization, new customers, existing-customer market share, and competitive advantage. Although the holistic view of this dimension is not perceived under the umbrella of digital value dimensions in the reviewed models, new products are handled in different dimensions in the context of new product launch, innovation, and offering new value to customers.
Digital processes are the third dimension, focused on the assessment of the extent to which DT has been implemented for the processes. Four sub-dimensions were defined here. The “process architecture & business process management” sub-dimension examines how the processes and process architecture of the organization are affected by strategies, and how the digitalized processes are managed. The “value chain” sub-dimension deals with the effect of digitalization on the value chain and the interaction among processes. The “core processes” sub-dimension investigates basic processes, such as operations, supply chain, and sales, in which digitalization will be carried out and to what extent it will create impact and changes in these processes. The “management & support processes” sub-dimension concentrates on the effects and changes that digitalization will create in management and support processes. In the reviewed models, this completeness is not observed under the umbrella of digital processes; instead, the focus is mainly on operational processes. In the proposed model, the evaluation of the effect of digitalization in all processes of the organization is valuable in terms of both enabling the application of a sector-independent model and uncovering the additional benefits of digitalization.
The fourth dimension, “digital technology and data,” focuses on the evaluation of technologies and solutions that put DT into practice in a sustainable way. Three sub-dimensions exist here, the first one being “data & analytics,” which examines how digitalization affects data-based decision-making and transforms it into value. The “management information systems (MIS) and applications” sub-dimension is related to how technological solutions are selected and managed in the digitalization process. The “security and risk management” sub-dimension examines how security and risk factors that emerge in parallel with digitalization are taken into account and managed. In fact, the reviewed models mostly focus on information technology, rather, MIS, and enterprise resource planning (ERP) applications, whereas security and data are considered in separate sub-dimensions in only a limited number of models.
Digital work, as the fifth dimension, addresses the assessment of the effects of DT on an organization’s work pattern as well as the scope and dimensions of new skills that employees must acquire. The first of the four sub-dimensions is the “working style.” It studies the change in the working style that needs to emerge in parallel with digitalization and the management of this change. Working styles stand out with both the difference in the way of doing business and the diversity of participation. The focus is on employees who digitalize their jobs in parallel with the DT, benefit more from digital solutions, and conduct their work through digital platforms. The “skills and capabilities” sub-dimension examines the new knowledge and skills that employees should possess in parallel with digitalization, and the new capabilities the organization needs. The “training and development” sub-dimension focuses on how the development of the employees should be supported. The “collaboration” sub-dimension examines how digitalization affects the cooperation of the employees with technology. In the reviewed models, the digital work dimension does not cover all these sub-dimensions, and focuses mainly on the knowledge, skills, and development of employees under “people.” Considering the effects of the working environment and collaboration sub-dimensions on employees, in addition to employee focus, this may contribute to the literature within the scope of maturity models.
Digital governance, the sixth and last dimension, focuses on the assessment of how managerial and cultural issues are handled to ensure the successful implementation and sustainability of DT. Five sub-dimensions served this goal. The scope of the “organization and roles” sub-dimension is related to the identification of the changing organizational structure and roles as a result of digitalization and management. The “program management” sub-dimension addresses how digitalization is managed within the scope of a program and how the attainment of the results is ensured in alignment with the strategic objectives. The “culture” sub-dimension studies the effects of the changes to be experienced in the digitalization process on the existing culture and how the process is managed. The “ethics” sub-dimension examines how ethical issues that arise within the scope of digitalization are defined and managed. In the “ecosystem” sub-dimension, the focus is on how the external interaction provided by digitalization is managed in a different way and how to ensure that this interaction is transformed into value. Digital governance is included in existing maturity models with the exception of ethical issues; therefore, its inclusion in the proposed model is believed to be a contribution, because it is considered a critical component in the success of the DT process.

5.3. Comparison of the New Holistic Digital Maturity Model with Existing Models

To clearly indicate the similarities and dissimilarities of the proposed model, the dimensions of the models examined in the literature review are compared to those of the proposed model, as shown in Table 6. The comparison is based on the extent and frequency of each sub-dimension of the proposed model being met in the existing models. The focus of the existing academic models appears to be on the digitization of key processes. Other important focuses are customer value creation, digitalization strategy, digitalization program management, and culture. The proposed model’s sub-dimensions that have not been addressed in existing models are vision, operation model, processes other than operational processes, security, ethics, training and development issues that support employee adaptation, and managing changes in the working environment.
In the maturity models developed by consultancy firms, the focus is on creating value, implementing digitalization in basic processes, innovating by means of digitalization, and supporting it with cultural change. The sub-dimensions that are not considered in comparison with the proposed model are the vision and operation model, which are important parts of digital strategy management and enterprise process management, ensuring the management of digitalized processes and support of digital work, including digitalization and employee-technology collaboration. Another advantage of the new model is its structure, where the answers to the questions framed based on the sub-dimensions are related to targeted capabilities, as shown in Table A1, Table A2, Table A3, Table A4 and Table A5 in Appendix A. This allows the model to be used both independently of sectors and the creation of a development plan depending on the gap between the current and targeted situations.
To use this model for the assessment of an organization, five maturity levels were defined in each dimension. These levels are called intention, beginner, adopter, performer, and transformer. Several questions were prepared for each sub-dimension; the answers to these questions measure the digital maturity (or readiness) level in each dimension. The intention level is the most immature and implies ad hoc studies. This means that there is no systematic approach or planned roadmap. At the beginner level, commitment and improvement studies have been initiated or planned, but no output or value has been created. The adopter level indicates that fundamental structures and studies have been completed and are ready to create value. In addition, there were some valuable outputs. The performer level corresponds to the status where value is created, and digital projects are common and mature. The transformer level implies that revenue is obtained as a result of digital projects or new business, or new products are created, and the organization has adopted DT.
The maturity levels can also be associated with the widely accepted stages of Industry 4.0, which are digitization, digitalization, and DT, as shown in Figure 17. The digitization stage refers to the action or process of digitizing, that is, the conversion of analog data to digital data. Digitalization is a more fundamental change compared to digitizing existing processes or work products, whereas DT, the most advanced stage, implies a significant change in the business model caused by digital technologies [7,13].
The intention level, where digital examples are not observed in the processes at the beginning of the digitalization process, can be associated with the digitization stage. At the beginner level, pilot studies are conducted, in which digital solutions are introduced into business processes. The adaptation level is where efficiency and effectiveness are achieved in the organization’s business processes by means of digital solutions. At the performer level, efficiency and effectiveness are ensured through digital solutions implemented in all business processes to ensure operational excellence. At the transformer level, a new product with digital characteristics is offered to the market to attract existing and new customers, leading to a competitive advantage. While the maturity levels defined in the proposed model are parallel to those used in the literature, the main difference is that the reference competencies created under each level are associated with digital stages. This feature allows for a qualitative analysis based on digital stages. The maturity assessment also generates a score with a maximum of 1000. Score intervals are defined based on expert opinions, helping to identify the digital stage that best describes the organization.

6. Conclusions

Organizations strive to implement DT successfully to achieve sustainable success in their operations. This requires the assessment of the organization’s current digital maturity level based on several dimensions. The best approach for this assessment and building a roadmap to implement DT is to use a digital maturity model. This study focuses on establishing the place and importance of digital maturity models and revealing their catalyst role in DT. The contributions of this paper can be divided into three categories: theoretical, practical and socio-technical contributions.
Theoretical contributions are made in the contexts of DT and digital maturity. First, the paper provides a more comprehensive and effective approach in assessing digital maturity, which is a key aspect of the DT journey. This is achieved through a rigorous and widely recognized methodology known as the PRISMA approach, which was used in a comprehensive and up-to-date systematic literature review. This review, conducted for the first time in the literature, was complemented by a bibliometric analysis through the Biblioshiny tool, which allowed for a clear and easy observation of the trends of the topics considered in a vast amount of articles related to DT and digital maturity. Second, the study highlights the importance of maturity models in the DT process, which includes awareness, readiness, models and model-based evaluations, planning, and execution steps. The development of a digital maturity model and evaluations based on this model can act as a catalyst and establish the milestones of the DT journey. Overall, these theoretical contributions enhance our understanding of DT and digital maturity and provide valuable insights for researchers and practitioners in the field.
The practical contributions of the study provide useful implications for organizations seeking to successfully implement DT. To this end, a “Holistic Digital Maturity Model” is proposed to extend existing maturity models in the literature by incorporating novel sub-dimensions such as vision, operation model, ethics, and process architecture. These sub-dimensions refer to various capabilities and critical success factors in the DT process, and the proposed model’s unique structure enables a clear and specific assessment of an organization’s digital maturity level. Furthermore, the model’s capability of identifying an organization’s current status with respect to the digitalization stages can effectively guide organizations towards a desired status. By focusing on targeted capabilities, organizations can prioritize their efforts and resources for implementing DT and ensure a more effective and efficient transition towards digitalization. The model provides a roadmap for planning and executing DT activities for corporations in the business sector, public organizations, and NGOs. The model’s structure can also help organizations evaluate their progress and measure the effectiveness of their DT initiatives over time.
The study also makes a socio-technical contribution by recognizing that ethics, technology, and the human perspective are all relevant considerations in DT. The model includes sub-dimensions related to these aspects. Ethics is recognized as a sub-dimension, enabling organizations to identify potential ethical issues that may arise during the DT process. Technology is acknowledged as a critical component in the journey towards digital maturity, and the model includes sub-dimensions related to technology infrastructure and usage. The human perspective is also recognized as important, and the model includes sub-dimensions related to culture, capabilities, and training and development, among others. By considering these aspects, organizations can ensure a more balanced and sustainable DT that takes into account not only technological advancements but also ethical considerations and human aspects. By encouraging improved collaboration through the use of digital tools, enabling remote work and flexibility, requiring upskilling and reskilling to remain competitive, and creating individualized employee experiences, DT has had a significant impact on employees. Additionally, it has prompted the adoption of agile work procedures, enabling organizations to react to changes more quickly. Overall, as both employees and organizations adjust to the constantly changing business landscape, these changes are reshaping the workplace and defining the future of work [96]. The digital work dimension within the proposed holistic model allows for the examination and management of all these elements within the scope of the DT journey.
There are some limitations to this study. First, it was not possible to include maturity models that have been developed specifically for individual companies. Second, while the proposed maturity model is generic, it may be necessary to determine the importance of dimensions and sub-dimensions based on the size and sector of the company. Therefore, it may be appropriate to use multicriteria decision-making techniques to assign suitable weights to each dimension and sub-dimension rather than assigning equal weight to all sub-dimensions as is currently the case. Despite these limitations, the maturity model can be applied to companies of different sizes and sectors to assess their maturity level across each dimension and sub-dimension, providing an opportunity to develop targeted solutions to accelerate DT.

Author Contributions

Conceptualization, A.A.; writing—original draft preparation, A.A.; writing—review and editing, A.A. and G.B. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Digital Maturity Assessment Questionary for Strategy Dimension

Table A1. Sub-dimension-based questions and capabilities for the Digital Strategy—Vision (1a).
Table A1. Sub-dimension-based questions and capabilities for the Digital Strategy—Vision (1a).
Maturity Levels and Capabilities
Vision (1a)At what stage are you in the digitalization process?Understanding and embodying the benefits to be gained from digitalizationIdentifying visions and directions for digitalizationGetting started with realizing the vision for digitizationProviding gains that affect business results with digitalizationEntering new business areas with digitalization, producing digital products
What is your vision for DT, and how big is it?No defined vision, no concrete strategy or objectives definedLimited to automation
Creating competitive advantage with digitalization in some functions/processes (functional strategy)Creating a competitive advantage with digitalization that spans the entire organization or its intended scope (business strategy)Creating disruptive competitive advantage by directing new product and service strategies (DT strategy) to achieve a share of the digital economy
At what level and to what extent is digitization addressed?Triggered by employeesSome functions, business units have set targets related to
Some functions are being implemented on the basis of business unitsSupporting the targets with digitalization studiesStrategies are implemented through digitalization studies
Table A2. Sub-dimension-based questions and capabilities for the Digital Strategy—Leadership (1b).
Table A2. Sub-dimension-based questions and capabilities for the Digital Strategy—Leadership (1b).
Maturity Levels and Capabilities
Leadership (1b)How important and prioritized is digitalization for senior management, and what level of
is demonstrated?
Top management does not have any determination, it is not among their prioritiesInterested in trends and competitive conditions, brought up by some
Seems strategically important and supported in targeted
business units
All of the digitalization process and studies are supported, and senior management takes a role as a sponsor in all critical projectsConsidered as part of corporate change and transformation, owned and led by the entire senior management team
What is the size of the resource/budget allocated to the DT process?No budget and resource allocation providedCreating budget projections for necessary improvements and infrastructureEnsuring minimum budget allocation for necessary improvements and infrastructureBudget planning and realization that supports corporate-level plans and targetsLong-term budget planning and allocation, covering all resource and investment requirements for transformation
Table A3. Sub-dimension-based questions and capabilities for the Digital Strategy—Strategies (1c).
Table A3. Sub-dimension-based questions and capabilities for the Digital Strategy—Strategies (1c).
Maturity Levels and Capabilities
Strategies (1c)What dimensions of strategic competitive advantage are targeted in the DT
Not in a defined stateReducing costsReducing costs, reducing risks, optimization,
process excellence
Reducing costs, reducing risks, optimization, operational excellenceEarn additional income from new products, business
Have strategic
performance indicators and targets for digitalization been
NoKPIs for the process have been
KPIs to evaluate the impact on operational performance have been determinedKPIs to evaluate the impact on strategic performance have been determinedKPIs to evaluate performance against the competition have been determined
Table A4. Sub-dimension-based questions and capabilities for the Digital Strategy—Business Model (1d).
Table A4. Sub-dimension-based questions and capabilities for the Digital Strategy—Business Model (1d).
Business Model (1d)What is the new value proposition offered?Not in a defined stateNo additional value propositionActivation of existing products and service delivery processesSupport the development/improvement of existing products and servicesAllowing us to introduce brand new products and services
Do digital strategies require defining a new business model?NoImprovement in the existing business modelDefining a new business modelOperating the new business modelCreating value with the new business model
Table A5. Sub-dimension-based questions and capabilities for the Digital Strategy—Operating Model (1e).
Table A5. Sub-dimension-based questions and capabilities for the Digital Strategy—Operating Model (1e).
Operating Model (1e)To what extent is the current operating model capable of supporting digital
Not at allOn a functional basis, independent of other functions and
business units
Process-based functions require interregional standardization and integrationsRequires standardization and integrations on the basis of
business units
To what extent is the current operating model capable of supporting digital
How does the
current operating model require us to address digital targets?
NothingLimitedFunctional basedProcess basedEnterprise wide


  1. Warner, K.S.R.; Wäger, M. Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Plann. 2018, 52, 326–349. [Google Scholar] [CrossRef]
  2. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2010, 36, 101385. [Google Scholar] [CrossRef]
  3. Moreira, F.; Ferreira, M.J.; Seruca, I. Enterprise 4.0—The emerging digital transformed enterprise? Procedia Comput. Sci. 2018, 138, 525–532. [Google Scholar] [CrossRef]
  4. Reis, J.; Amorim, M.; Melão, N.; Matos, P. Digital transformation: A literature review and guidelines for future research. In Trends and Advances in Information Systems and Technologies; Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 411–421. [Google Scholar]
  5. Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Sys. 2021, 28, 118–144. [Google Scholar] [CrossRef]
  6. Henriette, E.; Feki, M.; Boughzala, I. The Shape of Digital Transformation: A Systematic Literature Review. In Proceedings of the 9th Mediterranean Conference on Information Systems (MCIS 2015), Samos, Greece, 3–5 October 2015; pp. 1–13. [Google Scholar]
  7. Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Qi Dong, J.; Fabian, N.; Haenlein, M. Digital transformation: A multidisciplinary reflection and research agenda. J. Bus. Res. 2021, 122, 889–901. [Google Scholar] [CrossRef]
  8. Berghaus, S. The Fuzzy Front-end of Digital Transformation: Three Perspectives on the Formulation of Organizational Change Strategies. In Proceedings of the 29th Bled eConference: Digital Economy, Bled, Slovenia, 19–22 June 2016; pp. 129–144. [Google Scholar]
  9. Kurmann, P.; Arpe, B. Managing Digital Transformation: How Organizations turn Digital Transformation into Business Practices. Master’s Thesis, Lund University, Lund, Sweden, 2019. [Google Scholar]
  10. Hrynko, P. Improvement of the digital transformation strategy of business on the basis of digital technologies. Eureka Soc. Humanit. 2019, 6, 10–18. [Google Scholar] [CrossRef]
  11. Kotarba, M. Digital transformation of business models. Found. Manag. 2018, 10, 123–142. [Google Scholar] [CrossRef]
  12. World Economic Forum. Surviving Digital Disruption. WEF White Paper digital transformation of industries: In collaboration with Accenture Digital Enterprise. Available online: (accessed on 9 February 2023).
  13. Hägg, J.; Sandhu, S. Do or Die: How Large Organizations Can Reach a Higher Level of Digital Maturity. Master’s Thesis, Luleå University of Technology, Luleå, Sweden, 2017. [Google Scholar]
  14. Gökalp, E.; Martinez, V. Digital transformation capability maturity model enabling the assessment of industrial manufacturers. Comput. Ind. 2021, 132, 103522. [Google Scholar] [CrossRef]
  15. Ikegami, H.; Iijima, J. Unwrapping efforts and difficulties of enterprises for digital transformation. In Digital Business Transformation; Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 237–250. [Google Scholar]
  16. Dhasarathy, A.; Frazier, R.; Khan, N.; Rahul, A. Managing the Fallout from Technology Transformations. Available online: (accessed on 9 February 2023).
  17. De la Boutetière, H.; Montagner, A.; Reich, A. Unlocking Success in Digital Transformations. Available online: (accessed on 9 February 2023).
  18. Griva, A.; Kotsopoulos, D.; Karagiannaki, A.; Zamani, E.D. What do growing early-stage digital start-ups look like? A mixed-methods approach. Int. J. Inf. Manag. 2023, 69, 102427. [Google Scholar] [CrossRef]
  19. Aria, M.; Cuccurullo, C. Bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  20. Schumacher, A.; Erol, S.; Sihn, W. A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 2016, 52, 161–166. [Google Scholar] [CrossRef]
  21. Teichert, R. Digital transformation maturity: A systematic review of literature. Acta Univ. Agric. Silvic. Mendel. Brun. 2019, 67, 1673–1687. [Google Scholar] [CrossRef]
  22. Gartner. Gartner IT Glossary: Digital Maturity. 2020. Available online: (accessed on 9 February 2023).
  23. Ross, J.W.; Beath, C.M. Designed for Digital: How to Architect your Business for Sustained Success; MIT Press: Cambridge, MA, USA, 2018. [Google Scholar]
  24. Nambisan, S.; Sawhney, M. Digital transformation: Business models and global implications. Commun. ACM 2019, 62, 58–67. [Google Scholar]
  25. Westerman, G.; Bonnet, D.; McAfee, A. Leading Digital: Turning Technology into Business Transformation; Harvard Business Press: Boston, MA, USA, 2014. [Google Scholar]
  26. Berman, S.J.; Marshall, A. Digital transformation in the innovation process: Theory and practice. RD Manag. 2019, 49, 1–5. [Google Scholar]
  27. Kagermann, H.; Wahlster, W.; Helbig, J. (Eds.) Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Final Report of the Industrie 4.0 Working Group; Acatech—National Academy of Science and Engineering: Frankfurt, Germany, 2013. [Google Scholar]
  28. Berghaus, S.; Back, A. Stages in Digital Business Transformation: Results of an Empirical Maturity Study. In Proceedings of the the 2016 Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, 4–6 September 2016; pp. 1–17. [Google Scholar]
  29. Gill, M.; Van Boskirk, S. The Digital Maturity Model 4.0—Benchmarks: Digital Business Transformation Playbook. Available online: (accessed on 9 February 2023).
  30. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
  31. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Med. 2009, 6, e1000100. [Google Scholar] [CrossRef]
  32. Linnenluecke, M.K.; Marrone, M.; Singh, A.K. Conducting systematic literature reviews and bibliometric analyses. Aust. J. Manag. 2020, 45, 175–194. [Google Scholar] [CrossRef]
  33. Uribe-Toril, J.; Ruiz-Real, J.L.; Milán-García, J.; de Pablo Valenciano, J. Energy, economy, and environment: A worldwide research update. Energies 2019, 12, 1120. [Google Scholar] [CrossRef]
  34. Moral-Muñoz, J.A.; Herrera-Viedma, E.; Santisteban-Espejo, A.; Cobo, M.J. Software tools for conducting bibliometric analysis in science: An up-to-date review. Prof. Inf. 2020, 29, 1–20. [Google Scholar] [CrossRef]
  35. Bibliometrix. From Data Collection to Data Visualization. Available online: (accessed on 9 February 2023).
  36. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. J. Infometr. 2011, 5, 146–166. [Google Scholar] [CrossRef]
  37. Rashid, S.; URRehman, S.; Ashiq, M.; Khattak, A. A scientometric analysis of forty-three years of research in social support in education (1977–2020). Educ. Sci. 2021, 11, 149. [Google Scholar] [CrossRef]
  38. Armenta-Medina, D.; Díaz de León, C.; Armenta-Medina, A.; Perez-Rueda, E. A bibliometric analysis of Mexican bioinformatics: A portrait of actors, structure, and dynamics. Biology 2022, 11, 131. [Google Scholar] [CrossRef]
  39. Hajoary, P.K. Industry 4.0 maturity and readiness models: A systematic literature review and future framework. Int. J. Innov. Technol. Manag. 2020, 17, 2030005. [Google Scholar] [CrossRef]
  40. Hizam-Hanafiah, M.; Soomro, M.A.; Abdullah, N.L. Industry 4.0 readiness models: A systematic literature review of model dimensions. Information 2020, 11, 364. [Google Scholar] [CrossRef]
  41. Soomro, M.A.; Hizam-Hanafiah, M.; Abdullah, N.L. Digital readiness models: A systematic literature review. Compusoft 2020, 9, 3596–3605. [Google Scholar]
  42. Williams, C.; Schallmo, D.; Lang, K.; Boardman, L. Digital Maturity Models for Small and Medium-sized Enterprises: A Systematic Literature Review. In Proceedings of the International Society for Professional Innovation Management (ISPIM) Innovation Conference, Florence, Italy, 16–19 June 2019. [Google Scholar]
  43. Akdil, K.Y.; Ustundag, A.; Cevikcan, E. Maturity and readiness model for Industry 4.0 strategy. In Industry 4.0: Managing the Digital Transformation; Ustundag, A., Cevikcan, E., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 61–94. [Google Scholar]
  44. Al-Ali, M.; Marks, A. A digital maturity model for the education enterprise. Perspect. Policy Pract. High. Educ. 2022, 26, 47–58. [Google Scholar] [CrossRef]
  45. Barry, A.S.; Assoul, S.; Souissi, N. Benchmarking of Digital Maturity Models according to the Dimension Component. In Proceedings of the 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, 3–4 March 2022; pp. 1–8. [Google Scholar]
  46. Yang, H.; Xu, X. Research on Computer Evaluation Index System of Digital Maturity of Automotive Supply Chain. In Proceedings of the 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China, 25–27 February 2022; pp. 442–446. [Google Scholar]
  47. Duncan, R.; Eden, R.; Woods, L.; Wong, I.; Sullivan, C. Synthesizing dimensions of digital maturity in hospitals: Systematic review. J. Med. Internet Res. 2022, 24, e32994. [Google Scholar] [CrossRef]
  48. Goumeh, F.; Barforoush, A.A. A Digital Maturity Model for Digital Banking Revolution for Iranian Banks. In Proceedings of the 26th International Computer Conference, Computer Society of Iran (CSICC 2021), Tehran, Iran, 3–4 March 2021; pp. 1–6. [Google Scholar]
  49. Alsufyani, N.; Gill, A.Q. A review of digital maturity models from adaptive enterprise architecture perspective: Digital by design. In Proceedings of the IEEE 23rd Conference on Business Informatics (CBI), Bolzano, Italy, 1–3 September 2021; Almeida, J.P.A., Bork, D., Guizzardi, G., Montali, M., Eds.; IEEE: New York, NY, USA, 2021; pp. 121–130. [Google Scholar]
  50. Cordes, A.-K.; Musies, N. Accelerating the Transformation? The Impact of COVID-19 on the Digital Maturity of Retail Businesses. In Proceedings of the 23rd Conference on Business Informatics (CBI), Bolzano, Italy, 1–3 September 2021; pp. 102–110. [Google Scholar]
  51. Yezhebay, A.; Sengirova, V.; Igali, D.; Abdallah, Y.O.; Shehab, E. Digital Maturity and Readiness Model for Kazakhstan SMEs. In Proceedings of the 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan, 28–30 April 2021; pp. 74–79. [Google Scholar]
  52. Almasbekkyzy, A.; Abdikerim, D.; Nabi, D.; Abdallah, Y.O.; Shehab, E. Digital Maturity and Readiness Model for Multiple-case of Kazakhstan Large Companies. In Proceedings of the 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), Nur-Sultan, Kazakhstan, 28–30 April 2021; pp. 617–623. [Google Scholar]
  53. Salume, P.K.; Barbosa, M.W.; Pinto, M.R.; Sousa, P.R. Key dimensions of digital maturity: A study with retail sector companies in Brazil. Rev. Adm. Mackenzie 2021, 22, 1–29. [Google Scholar] [CrossRef]
  54. Kljajić Borštnar, M.; Pucihar, A. Multi-attribute assessment of digital maturity of SMEs. Electronics 2021, 10, 885. [Google Scholar] [CrossRef]
  55. Aslanova, I.V.; Kulichkina, A.I. Digital maturity: Definition and model. In Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: From Regional Development to Global Economic Growth” (MTDE 2020), Yekaterinburg, Russia, 16–17 April 2020; pp. 443–449. [Google Scholar]
  56. Weritz, P.; Braojos, J.; Matute, J. Exploring the Antecedents of Digital Transformation: Dynamic Capabilities and Digital Culture Aspects to Achieve Digital Maturity. In Proceedings of the 26th Americas Conference on Information Systems (AMCIS), Virtual, 15–17 August 2020. [Google Scholar]
  57. Colli, M.; Berger, U.; Bockholt, M.; Madsen, O.; Møller, C.; Wæhrens, B.V. A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era. Annu. Rev. Control 2019, 48, 165–177. [Google Scholar] [CrossRef]
  58. Bandara, O.; Vidanagamachchi, K.; Wickramarachchi, R. A Model for Assessing Maturity of Industry 4.0 in the Banking Sector. In Proceedings of the 2019 International Conference on Industrial Engineering and Operations Management, Bangkok, Thailand, 5–7 March 2019; pp. 1141–1150. [Google Scholar]
  59. Schumacher, A.; Nemeth, T.; Sihn, W. Roadmapping towards Industrial Digitalization based on an Industry 4.0 Maturity Model for Manufacturing Enterprises. In Proceedings of the 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Gulf of Naples, Italy, 18–20 July 2018; pp. 409–414. [Google Scholar]
  60. Canetta, L.; Barni, A.; Montini, E. Development of a digitalization maturity model for the manufacturing sector. In Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, Germany; pp. 1–7.
  61. Rossmann, A. Digital Maturity: Conceptualization and Measurement Model. In Proceedings of the 39th International Conference on Information Systems (ICIS 2018), San Francisco, CA, USA, 13–16 December 2018; pp. 1–9. [Google Scholar]
  62. Gimpel, H.; Hosseini, S.; Huber, R.; Probst, L.; Röglinger, M.; Faisst, U. Structuring digital transformation: A framework of action fields and its application at ZEISS. J. Inf. Technol. Theory Appl. 2018, 19, 3. [Google Scholar]
  63. Horvat, D.; Stahlecker, T.; Zenker, A.; Lerch, C.; Mladineo, M. A conceptual approach to analysing manufacturing companies’ profiles concerning Industry 4.0 in emerging economies. Procedia Manuf. 2018, 17, 419–426. [Google Scholar] [CrossRef]
  64. Bibby, L.; Dehe, B. Defining and assessing industry 4.0 maturity levels–case of the defence sector. Prod. Plan. Control 2018, 29, 1030–1043. [Google Scholar] [CrossRef]
  65. Botha, A.P. Rapidly arriving futures: Future readiness for Industry 4.0. South Afr. J. Ind. Eng. 2018, 29, 148–160. [Google Scholar] [CrossRef]
  66. Hamidi, S.R.; Aziz, A.A.; Shuhidan, S.M.; Aziz, A.A.; Mokhsin, M. SMEs maturity model assessment of IR4.0 digital transformation. Adv. Intell. Syst. Comput. 2018, 739, 721–732. [Google Scholar]
  67. Sjödin, D.R.; Parida, V.; Leksell, M.; Petrovic, A. Smart factory implementation and process innovation. Res. Technol. Manag. 2018, 61, 22–31. [Google Scholar] [CrossRef]
  68. Mittal, S.; Romero, D.; Wuest, T. Towards a smart manufacturing maturity model for SMEs (SM3E). IFIP Adv. Inf. Commun. Technol. 2018, 536, 155–163. [Google Scholar]
  69. De Carolis, A.; MacChi, M.; Negri, E.; Terzi, S. Guiding Manufacturing Companies towards Digitalization a Methodology for Supporting Manufacturing Companies in Defining their Digitalization Roadmap. In Proceedings of the 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), Madeira, Portugal, 27–29 June 2018; pp. 487–495. [Google Scholar]
  70. Gökalp, E.; Şener, U.; Eren, P.E. Development of an assessment model for industry 4.0: Industry 4.0-MM. In Software Process Improvement and Capability Determination; Mas, A., Mesquida, A., O’Connor, R.V., Rout, T., Dorling, A., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 128–142. [Google Scholar]
  71. De Carolis, A.; Macchi, M.; Negri, E.; Terzi, S. A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies. In Proceedings of the IFIP WG 5.7 International Conference on Advances in Production Management Systems: The Path to Intelligent, Collaborative and Sustainable Manufacturing, Hamburg, Germany, 3–7 September 2017; Lödding, H., Riedel, R., Thoben, K.D., von Cieminski, G., Kiritsis, D., Eds.; Springer: Cham, Switzerland, 2017; Volume 513, Part I. pp. 1313–2020. [Google Scholar]
  72. Von Leipzig, T.; Gamp, M.; Manz, D.; Schöttle, K.; Ohlhausen, P.; Oosthuizen, G.; Palm, D.; Von Leipzig, K. Initialising customer-orientated digital transformation in enterprises. Procedia Manuf. 2018, 8, 517–524. [Google Scholar] [CrossRef]
  73. Klötzer, C.; Pflaum, A. Toward the Development of a Maturity Model for Digitalization within the Manufacturing Industry’s Supply Chain. In Proceedings of the Hawaii International Conference on System Sciences, Waikoloa Beach, Hawaii, 4–7 January 2017; pp. 4210–4219. [Google Scholar]
  74. Leino, S.-P.; Anttila, J.-P. Digimaturity in manufacturing industry. In Proceedings of the 2nd Annual SMACC Research Seminar, Tampere, Finland, 7 November 2017; Aaltonen, J., Virkkunen, R., Koskinen, K.T., Kuivanen, R., Eds.; Tampere University of Technology: Tampere, Finland, 2017; pp. 25–28. [Google Scholar]
  75. Valdez-de-Leon, O. A digital maturity model for telecommunications service providers. Technol. Innov. Manag. Rev. 2016, 6, 19–32. [Google Scholar] [CrossRef]
  76. Rogers, D.L. The Digital Transformation Playbook: Rethink your Business for the Digital Age; Columbia University Press: New York, NY, USA, 2016. [Google Scholar]
  77. Ganzarain, J.; Errasti, N. Three stage maturity model in SME’s toward industry 4.0. J. Ind. Eng. Manag. 2016, 9, 1119–1128. [Google Scholar] [CrossRef]
  78. Leyh, C.; Bley, K.; Schäffer, T.; Forstenhäusler, S. SIMMI 4.0-A Maturity Model for Classifying the Enterprise-wide IT and Software Landscape Focusing on Industry 4.0. In Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland, 11–14 September 2016; pp. 1297–1302. [Google Scholar]
  79. Bain & Company. Digital Readiness Survey. Available online: (accessed on 9 February 2023).
  80. Deloitte. Digital Maturity Index Survey: Digital Value Creation in an Unsettled Environment. Available online: (accessed on 9 February 2023).
  81. Earley Information Science. Building a Successful Digital Transformation Roadmap. Available online: (accessed on 9 February 2023).
  82. EY. Maturity Assessment: Global Business Services. Available online: (accessed on 9 February 2023).
  83. Felch, V.; Asdecker, B.; Sucky, E. Maturity Models in the Age of Industry 4.0—Do the Available Models Correspond to the Needs of Business Practice? In Proceedings of the 2019 Hawaii International Conference on System Sciences, Maui, Hawaii, 8–11 January 2019; pp. 5165–5174. [Google Scholar]
  84. Gartner. Gartner Digital Execution Scorecard. Available online: (accessed on 9 February 2023).
  85. Geissbauer, R.; Vedso, J.; Schrauf, S. Industry 4.0: Building the Digital Enterprise. PwC 2016 Global Industry 4.0 Survey. Available online: (accessed on 9 February 2023).
  86. The Digital Maturity Model 4.0. Available online: (accessed on 9 February 2023).
  87. IMPULS Industry 4.0 Readiness Online Self-Check for Businesses. Available online: (accessed on 9 February 2023).
  88. KPMG. Digital Maturity Model. Available online: (accessed on 9 February 2023).
  89. Li, C.; Akhtar, O.; Etlinger, S.; Terpening, E.; Moser, T.; Littleton, A. The 2020 State of Digital Transformation: Benchmarking Digital Maturity in the COVID-19 Era. Altimeter Research Report. Available online: (accessed on 9 February 2023).
  90. Runfrictionless. Reviewed: Top 5 Digital Transformation Frameworks. Available online: (accessed on 9 February 2023).
  91. Schuh, G.; Anderl, R.; Gausemeier, J.; ten Hompel, M.; Wahlster, W. Industrie 4.0 Maturity Index—Managing the Digital Transformation of Companies. Acatech STUDY; Herbert Utz Verlag: Munich, Germany, 2017. [Google Scholar]
  92. Tmforum. Digital Transformation & Maturity: Practical Tools for Navigating the Maze of Digital Transformation. Available online: (accessed on 9 February 2023).
  93. Tmforum. Open Digital Framework (ODF). Available online: (accessed on 9 February 2023).
  94. Tmforum. Digital Maturity Model Toolkit. Available online: (accessed on 9 February 2023).
  95. World Eonomic Forum. Maximizing the Return on Digital Investments. Available online: (accessed on 9 February 2023).
  96. Williams, S.P.; Schubert, P. Designs for the digital workplace. Procedia Comput. Sci. 2018, 138, 478–485. [Google Scholar] [CrossRef]
Figure 1. Methodology for systematic literature review.
Figure 1. Methodology for systematic literature review.
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Figure 2. Bibliometric analysis using BiblioShiny [19,35].
Figure 2. Bibliometric analysis using BiblioShiny [19,35].
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Figure 3. Literature analysis scope using BiblioShiny.
Figure 3. Literature analysis scope using BiblioShiny.
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Figure 4. Digital maturity model dimensions focused literature analyis scope.
Figure 4. Digital maturity model dimensions focused literature analyis scope.
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Figure 5. The main information provided by BiblioShiny based on literature review metadata.
Figure 5. The main information provided by BiblioShiny based on literature review metadata.
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Figure 6. (a) The annual number of publications. (b) The annual number of document type.
Figure 6. (a) The annual number of publications. (b) The annual number of document type.
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Figure 7. Average number of citations per publication by year.
Figure 7. Average number of citations per publication by year.
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Figure 8. (a) Word cloud for all keywords. (b) Word cloud for 100 keywords.
Figure 8. (a) Word cloud for all keywords. (b) Word cloud for 100 keywords.
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Figure 9. The dynamics of frequently used keywords.
Figure 9. The dynamics of frequently used keywords.
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Figure 10. Most frequent keywords.
Figure 10. Most frequent keywords.
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Figure 11. The evolution of trend topics.
Figure 11. The evolution of trend topics.
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Figure 12. Co-occurrence network.
Figure 12. Co-occurrence network.
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Figure 13. Thematic map.
Figure 13. Thematic map.
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Figure 14. Thematic evolution map.
Figure 14. Thematic evolution map.
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Figure 15. Digital transformation journey.
Figure 15. Digital transformation journey.
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Figure 16. The new holistic digital maturity model.
Figure 16. The new holistic digital maturity model.
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Figure 17. The relationship of digital maturity levels to digital stages and scores.
Figure 17. The relationship of digital maturity levels to digital stages and scores.
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Table 1. Digital-maturity-related keywords.
Table 1. Digital-maturity-related keywords.
“Digital Maturity”“Digital Readiness”
“Digital Transformation” AND “Digital Maturity”“Digital Readiness Model”
“Digital Transformation Maturity”“Digital Readiness Assessment”
“Digital Maturity Assessment”“Stages of Digital Transformation”
“Digital Transformation Assessment”“Digital Maturity Levels”
“Digital Transformation Capability Maturity Model”“Phases of Digital Transformation”
Table 2. Comparision of bibliometric analysis and visualization tools [34].
Table 2. Comparision of bibliometric analysis and visualization tools [34].
Thematic NetworkAuthor NetworkReference NetworkOther NetworksEvolutionPerformanceBurst DetectionSpectrogramGeospatialVisualization
Science Mapping Analysis Tools
Bibexcel External software
BiblioshinyNetwork, three-fields plot, word cloud, tree map, historiograph, strategic diagram,
evolution map, and world map
BiblioMaps Network
CiteSpace Tree ring, geospatial map
CitNetExplorer Network
SciMAT Strategic diagram, cluster network,
overlapping map, evolution map
Sci2 Tool Temporal, geospatial map, topical, network
VOSviewer Network, overlay, density
BibliometrixNetwork, three-fields plot, word cloud, tree map, historiograph, strategic diagram,
evolution map, and world map
BiblioTools Network
Citan Bars, bow plots, and pie chart
Metaknowledge Timeline graph, spectrogram, and network
SxientoPy Timeline graph, bar graph, evolution graph, and word cloud
Table 3. Review papers on digital maturity models.
Table 3. Review papers on digital maturity models.
ReferenceFocus Years of Literature ReviewNumber of Examined ModelsScope of ModelsAnalysis MethodContribution/
2011–202053 Industry 4.0
maturity and
readiness models
Academia and consultancy-firm basedPreferred
Reporting Items for
Reviews and Meta-Analysis (PRISMA)
Proposes a holistic model in 10 main dimensionsBased on the Industry 4.0 literature and considers only manufacturing organizations
Hizam-Hanafiah et al. [40].2000–201930 Industry 4.0
readiness models
Academia and consultancy-firm basedPreferred
Reporting Items for
Reviews and Meta-Analysis (PRISMA)
Performs dimension analysis and groups them under 6 main dimensionsFocuses only on SMEs and analyzes a limited number of maturity models
Soomro et al. [41].2007–201922 digital readiness modelsAcademia basedDefines 4 critical success factors to achieve digital readinessAcademic and technology focused and analyzes a limited number of maturity models
2018–201824 digital maturity modelsAcademia and consultancy-firm basedDetermines that most of the digital maturity models are manufacturing-oriented and that a wide variety of digital maturity model stages existLiterature review of only academic publications from 2018
Williams et al. [42].2011–20166 maturity modelsAcademia basedProposes a digital maturity model for SMEs based on 6 dimensionsFocuses only on SMEs and analyzes a limited number of maturity models
Table 4. Maturity models existing in academic papers.
Table 4. Maturity models existing in academic papers.
ReferenceDocument TypeDimensionsDAHMSMCS
AL-Ali and Marks
Article1. Digital Transformation Vision, Strategy, Leadership, and Communication, 2. Digital Transformation Talent, Skills, and Knowledge, 3. Digital Transformation Processes, Controls, and Digital Technologies, 4. Digital Transformation
Technology Infrastructure, 5. Approach to Understand and
Communicate Customers
Barry et al. [45].Conference Paper1. Structural, 2. Informational, 3. Environmental, 4. Security, 5. Quality, 6. Financial, 7. Cultural, 8. Innovation,
9. Participate
Yang and Xu [46].Conference Paper1. Strategy and Organization, 2. Infrastructure construction, 3. Business Innovation and Transformation, 4. Supply
Chain Ecological Construction, 5. Digital Performance
Duncan et al. [47].Article1. Strategy, 2. IT Capability, 3. Interoperability, 4. Governance and Management, 5. Patient-Centered Care, 6. People, Skills, and Behavior, 7. Data Analytics
Goumeh and Barforoush [48].Conference Paper1. Customer, 2. Ecosystem, 3. Law, 4. Strategy, 5. Operation, 6. Technology
Alsufyani and Gill
Conference Paper1. Interaction Layer, 2. Technology Layer, 3. Human Layer,
4. Security Layer, 5. Environment Layer
Cordes and Musies
Conference Paper1. Customer Experience, 2. Innovation, 3. Process Digitalization, 4. Information Technology, 5. Digital Skills, 6. Strategy, 7. Culture, 8. Governance, 9. Organization, 10. Collaboration
Yezhebay et al.
Conference Paper1. People, 2. Leadership, 3. Strategy, 4. Technology,
5. Operation, 6. Product
Almasbekkyzy et al.
Conference Paper1. Strategy, 2. Technology, 3. Operations, 4. Organization and Culture
Salume et al. [53].Article1. Strategy, 2. Leadership, 3. Market, 4. Operations, 5. Culture, 6. People, 7. Governance, 8. Technology Capability
Borštnar and Pucihar
Article1. Digital Technology, 2. Management, 3. HR, 4. Strategy,
5. Digital Business Model, 6. Role of Informatics
Aslanova and Kulichkina [55].Conference Paper1. Strategy, 2. Organization, 3. People, 4. Technologies,
5. Data
Weritz et al.
Conference Paper1. Capabilities Absorptive Capacity, 2. Agility and Flexibility, 3. Cross-functional Collaboration, 4. Innovation Capability, 5. Market Orientation, 6. Relational Capability
Colli et al.
Article1. Governance, 2. Technology, 3. Connectivity, 4. Value Creation, 5. Competences
Bandara et al.
Conference Paper1. Products and Services, 2. Technology and Resources,
3. Strategy and Organization, 4. Operations, 5. Customers,
6. Governance, 7. Employees
Schumacher et al.
Article1. Technology, 2. Products, 3. Customers and Partners,
4. Value Creation Processes, 5. Data and Information,
6. Corporate Standards
Canetta et al. [60].Conference Paper1. Strategy, 2. Processes, 3. Technologies, 4. Product and
Services, 5. People
Conference Paper1. Strategy Capability, 2. Leadership Capability, 3. Market Capability, 4. Operational Capability, 5. People and Expertise Capability, 6. Cultural Capability, 7. Governance Capability, 8. Technology Capability
Akdil et al.
Book Chapter1. Strategy and Organization, 2. Smart Products and Services, 3. Smart Business Processes
Gimbel et al.
Article1. Organization, 2. Product, 3. Value Chain, 4. Ecosystem,
5. Operations, 6. Customer, 7. Transformation Management,
8. Cloud and Data
Horvat et al.
Article1. Organization of Product and Logistics, 2. Employees and Communication, 3. Management and Strategy, 4. Technology, 5. Interim Cooperation
Bibby and Dehe [64].Article1. Factory of the Future, 2. People and Culture, 3. Strategy
Article1. Technology, 2. Behavior, 3. Events
Hamidi et al. [66].Conference Paper1. Strategy and Organization, 2. Smart Factory, 3. Smart
Operations, 4. Smart Product, 5. Data-driven Services,
6. Employees
Sjödin et al.
Article1. People, 2. Process, 3. Technology
Mittal et al.
Conference Paper1. Finance, 2. People, 3. Strategy, 4. Process, 5. Product
De Carolis et al.
Conference Paper1. Organization, 2. Processes, 3. Technologies, 4. Monitoring and Control
Gökalp et al.
Book Chapter1. Asset Management, 2. Data Governance, 3. Application
Management, 4. Process Transformation, 5. Organizational Alignment
De Carolis et al. [71].Book Chapter1. Organization, 2. Processes, 3. Technologies, 4. Monitoring and Control
Von Leipzig et al.
Article1. Strategy, 2. Technologies, 3. People, 4. Governance,
5. Culture, 6. Product, 7. Operations, 8. Leadership
Klötzer and Pflaum
Conference Paper1. Competence(s), 2. Innovation Culture, 3. Cooperation,
4. Strategy Development, 5. Process Organization, 6. Complementary IT System, 7. Smart Product and Factory,
8. Offering to Customer, 9. Structural Organization
Leino and Anttila
Conference Paper1. Strategy, 2. Information Technology, 3. Business Model,
4. Customer Interface, 5. Organization and Processes,
6. People and Culture
Valdez-de-Leon [75].Article1. Strategy, 2. Organization, 3. Technologies, 4. Ecosystem,
5. Operations, 6. Customers, 7. Innovation
Schumacher et al.
Article1. Product, 2. Customers, 3. Operations, 4. Technologies,
5. Strategy, 6. Leadership, 7. Governance, 8. Culture,
9. People
Berghaus and Back
Conference Paper1. Customer Experience, 2. Product Innovation, 3. Strategy, 4. Organization, 5. Process Digitization, 6. Collaboration,
7. Information Technology, 8. Culture Expertise,
9. Transformation Management
Book Chapter1. Customer, 2. Cloud and Data, 3. Innovation, 4. Competition, 5. Value
Ganzarain and Errasti
Article1. Processes, 2. Product, 3. Value Network, 4. Market
Leyh et al.
Conference Paper1. Basic Digitization Level, 2. Cross-Departmental Digitization, 3. Horizontal and Vertical Digitization, 4. Full Digitization, 5. Optimized Full Digitization
DA: Dimension Analysis, HM: Holistic Model, SM: Sector-Based Model, CS: Case Study and Assessment Results.
Table 5. Consultancy-firm-based maturity models and dimensions.
Table 5. Consultancy-firm-based maturity models and dimensions.
ReferencesConsultancy Firms’ Model NameDimensions
Bain & Company
Digital Readiness Survey1. Business Model, 2. Digital Strategy, 3. Enablers,
4. Orchestration
Deloitte Digital Maturity Survey1. Strategy, 2. Innovation, 3. Experience, 4. Digital Channels and Sales, 5. Digital Marketing, 6. Data and Insights, 7. Cyber Security
Earley Information
Digital Transformation Roadmap1. Technology, 2. Process, 3. People, 4. Content
Ernst & Young (EY)
Global Business Service Maturity (GBS)1. Strategy, 2. Operations, 3. Control and
Felch et al.
Digital Capability Assessment (DCA)1. Strategy and Leadership, 2. People and Culture,
3. Product and Service, 4. Customer Experience, 5. Enterprise Enablement
(internal source)
Digital Business Maturity Model1. Digital Strategy and Execution, 2. Customer Experience Management, 3. Digital Product, Service and Digital Revenue, 4. Infonomics, 5. Digital Channels and Ecosystem, 6. Business Agility, 7. Innovation Culture, 8. Digital Leadership, 9. Digital Workplace
Gartner Digital
Execution Scorecard
Digital Execution Scorecard1. Generate Digital Revenue, 2. Excel in Customer Experience, 3. Organizational Excellence, 4. Optimize Asset Utilization, 5. Minimize Risk
Geissbauer et al.
Digital Transformation Framework1. Digitalization Value Chain, 2. Digital Business Model and Customer Access, 3. Digitalization of Product and Service
Gill and Van Boskirk [86].Digital Maturity Model 4.01. Technology, 2. Insight, 3. Organization, 4. Culture
Industry 4.0 Readiness1. Strategy and Organization, 2. Smart Factory,
3. Smart Operations, 4. Smart Products, 5. Data-Driven Services, 6. Employees
Digital Business Aptitude (DBA)1. Strategy, 2. Governance, 3. Talent, 4. Process,
5. Infrastructure
Li et al.
Altimeter’s Digital Maturity Assessment1. Customer Experience, 2. Leadership and Culture,
3. Marketing and Sales, 4. Technology and Innovation, 5. Data and Analytics
(internal source)
Digital Quotient (DQ)1. Strategy, 2. Culture, 3. Organization, 4. Capabilities
BSC’s The Digital Acceleration Index (DAI)1. Business Strategy Driven by Digital, 2. Customer offer and Go-To-Market, 3. Operations, 4. Support Functions, 5. New Digital Growth, 6. Changing Ways of Working, 7. Leveraging the Power of Data and Technology, 8. Integrating Ecosystems
Digital Transformation Framework1. Digitize the Customer Experience, 2. Digitize the Products and Services, 3. Digitize Operations,
4. Digitize the Organization
Industry 4.0 Digital Operations Self-Assessment1. Business Models, Product and Service Portfolio,
2. Value Chain and Processes, 3. Market and Customer Access, 4. IT Architecture, 5. Organization and Culture, 6. Compliance, Legal, Risk, Security and Tax
Digital Transformation Framework1. Customer Experience, 2. Operational Processes, 3. Business Model, 4. Digital Capabilities
Schuh et al.
Industry 4.0 Maturity Index1. Information Systems, 2. Culture, 3. Process,
4. Organizational Structure
Digital Maturity Model1. Strategy, 2. Customer, 3. Operations, 4. Technology, 5. Organization and Culture
Open Digital Framework (ODF)
DMM Readiness Check Assessment
1. Information Systems, 2. Deployment and Runtime, 3. Implementation, 4. Governance
Digital Maturity Model1. Strategy, 2. Customer, 3. Operations,
4. Technology, 5. Data, 6. Culture
World Economic
Digital Competency Framework1. Company Transformation, 2. Market
Transformation, 3. Digital Workforce
Table 6. Comparison with existing academic and consultancy-firm models.
Table 6. Comparison with existing academic and consultancy-firm models.
DimensionsSub-DimensionsNumber of Times
Existing in Other
Models (Academic)
Number of Times
Existing in Other Models
1. Digital Strategy1.1. Vision00
1.2. Leadership33
1.3. Strategies611
1.4. Business Model23
1.5. Operating Model00
2. Digital Value2.1. Create Value (Innovation)710
2.2. Deliver Value39
2.3. Capture Value311
3. Digital Processes3.1. Process Architecture and Business Process
3.2. Value Chains (E2E Processes)02
3.3. Core Processes1613
3.4. Management and Support Processes04
4. Digital
Technology and Data
4.1. Data Analytics26
4.2. MIS and Applications412
4.3. Security and Risk Management03
5. Digital Work5.1. Working Style02
5.2. Skills and Capabilities34
5.3. Training and Development01
5.4. Collaboration3
6. Digital
6.1. Organization and Roles42
6.2. Ecosystem22
6.3. Program Management64
6.4. Culture610
6.5. Ethics00
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Aras, A.; Büyüközkan, G. Digital Transformation Journey Guidance: A Holistic Digital Maturity Model Based on a Systematic Literature Review. Systems 2023, 11, 213.

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Aras A, Büyüközkan G. Digital Transformation Journey Guidance: A Holistic Digital Maturity Model Based on a Systematic Literature Review. Systems. 2023; 11(4):213.

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Aras, Arzu, and Gülçin Büyüközkan. 2023. "Digital Transformation Journey Guidance: A Holistic Digital Maturity Model Based on a Systematic Literature Review" Systems 11, no. 4: 213.

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