Hoang Pham Minh
Hanoi University of Science and Technology – HUST, Hanoi, Vietnam.
Hong Pham Thi Thanh
Hanoi University of Science and Technology – HUST, Hanoi, Vietnam.
In recent years, digital transformation has become one of the most popular trends for enterprises worldwide. The global trend of digital technologies and the COVID-19 pandemic have made the growth speed of digital transformation steadier than ever. In this condition, practitioners and academic researchers believe that the Digital Maturity Model is one of the most effective weapons in helping managers and the workforce manage to transform their businesses digitally. However, the Digital Maturity Model (DMM) is a type of maturity model (MM) that is relatively new in model development and digital maturity assessment methodologies, especially when integrated into an extensive digital transformation process. With this paper, the authors aim to conduct a comprehensive review to clarify the current state of the DMM field, including its essential characteristics, popular elements belonging to its structures, the number of methods, and techniques used in developing and applying them. In addition, these papers identify significant areas of research underway. Moreover, the authors raise some challenges for the field in the capture of results by reviewing them: i) the need to standardize its component names; ii) a contextualized but low-cost DMM for SMEs to use in their business; iii) the need for positioning DMM applied processes in a master digital transformation process and in a dynamics context that help applications of DMM more efficient. The authors proposed a solution for the third challenge through a conceptual model integrating DMM into a continuous digital transformation process.
Keywords: Digital Transformation; Digital Maturity Model; Continuous Transformation Process; Change Management.
Most modern-day enterprises are being confronted with dealing with digital transformation challenges. Digital transformation (DT/DX) is defined as “the use of technology to radically improve the performance or reach of enterprises” (Westerman et al., 2014b). DX is seen as a radical and complex type of Enterprise Transformation, commonly referring to a disruptive process that profoundly changes companies’ ways of competing, interacting, and creating value. Moreover, Bordeleau & Felden (2019) state that high levels of digitalization are presented as good for a country’s economic performance because they increase an organization’s efficiency and productivity.
According to IDC (2020a), despite the challenges presented by the COVID-19 pandemic, global spending on DX investment will continually grow from 10.4% in 2020 to $1.3 trillion. Even though this is significantly smaller than the 17.9% growth in 2019, the growth remains one of the few bright spots if overall technology spending reduces dramatically. The global consulting giant also reveals that direct DX investment is growing at 15.5% annually, driving over 6.8 trillion from 2020 to 2023 as companies struggle to become digital-at-scale future enterprises. By 2022, the digitalized economy will account for about 65% of global GDP (IDC, 2020b).
Figure 1. Worldwide spending for DX in 2020 Source: IDC (IDC, 2020a)
The maturity model (MM) concept first appeared in the 1970s and is dedicated to software engineering (Chanias & Hess, 2016; Rafael et al., 2020). Since then, the MM concept has evolved into an important tool for improving business practices (Schäffer et al., 2018) by assessing their status-quos, establishing a desirable path for advancing them, and making internal or external benchmarking to realize gaps in competencies manner (Röglinger et al., 2012).
Due to the broad range of potential applications, MMs have gained popularity in management and science (Becker et al., 2009; Rafael et al., 2020). There are lots of MMs published focusing on different fields of organizations’ capabilities, such as Process Management (ISO, 2015), Six Sigma (ISO, 2011), “IT service capability, innovation management, program management, enterprise architecture, strategic alignment, or knowledge management maturity” (De Bruin et al., 2005). The most well-known MM is the Capability Maturity Model (CMM), derived from Phillip Crosby’s Quality Management Maturity Grid (QMMG) model, which aims to help evaluate the quality of the information systems and processes (Williams et al., 2019).
Meanwhile, DX is a modern revolution where companies use new digital technologies such as SMACIT (Warner and Wäger, 2019) to enable significant business improvements such as enhancing customer experience, advancing operations excellence, and innovating in business models (Fitzgerald et al., 2014). It is a strategic change that must follow several aspects (Singh & Hess, 2017), such as operational, functional, financial, and corporate strategy (Matt et al., 2015). However, all previously mentioned MMs just applied to improve specific organizations’ capabilities, meaning the need to develop a type of maturity model that covers the number of capabilities required for DX (Kane, 2017). The Digital Maturity Model (DMM) is a type of MM focused on supporting firms to assess and develop their digital capabilities (Becker et al., 2009). With the booming of the DX trend, DMM has become one of the most important fields for both academia and practitioners to research and pursue.
By understanding DMM’s importance in assisting companies’ transformation to become future digital-at-scale enterprises, this paper aims to investigate research papers to gain insights into DMMs in general and DMM applications in particular. To this end, we raise and research answers to the following research questions:
• What are the different types of models, approaches, methods, techniques, dimensions, and maturity levels that are used to develop and apply DMMs?
• What are the potential research areas in the field of DMM development?
The authors collected papers that were peer-reviewed and published between 2000 and May 2021 through structured keyword search and cross-referencing to ensure the quality and reliability of this review. The keywords applied to the search for articles in the database of Google Scholar were: "Digital transformation" OR "digital maturity" OR "maturity model" OR "readiness index". The authors limit the sources of papers to several well-known databases, including Elsevier, EBSCOhost, Emerald, Taylor & Francis, AIS eLibrary, IEEE, and ResearchGate. We only considered articles in English, not those for literature reviews and enterprises.
Within our research, characteristics, structured elements, methods, techniques, focus, and challenges of DMM research are defined and classified. To this end, our review analysis research papers have new contributions to this research field, such as:
• Specifying DMMs’ functions and roles in the DX process.
• Developing and/or implementing a new DMM for a firm.
• Empirically investigating how firms from specific sectors apply their DMMs.
After carrying out screening titles, abstracts, and conclusions to choose the appropriate papers to review, we selected and reviewed 96 papers altogether.
The authors used the content analysis method defined by Berelson (1952) and developed by Mayring (2015) to investigate the collected papers. This method is quite efficient at combining rich meaning qualitative approaches with robust quantitative analyses by (i) enabling manifest content of text and documents and (ii) uncovering latent content and more profound meaning embodied in the text and document (Duriau et al., 2007; Wilding et al., 2012).
Figure 2. Categories to analyze reviewed papers
Firstly, we coded selected papers according to a number of categories that were also revised during the coding process. Figure 2 presents our analytic categories that include two groups, namely descriptive analysis and content analysis. Secondly, in the analysis phase, we synthesized and linked two groups to gain insights into critical points and trends in DMM applications in the DX space.
Our review investigated the theoretical-based (77 papers) and empirical-based (19 papers) research papers. Figure 3 shows the distribution of reviewed papers by published year. In line with DMM prevalence in particular and DX in general, the number of papers has increased over time. Figure 4 shows the distribution of reviewed papers by publishers.
Concerning the research questions, the content of the reviewed papers is analyzed as follows: i) to clarify the characteristics, structure, methods, and techniques used in the DMM field; and ii) to find potential research areas. Firstly, to gain insights into the DX phenomenon, it is necessary to understand the characteristics and structure of DMMs (Berghaus & Back, 2016; Chanias & Hess, 2016; Rafael et al., 2020; Zapata et al., 2020). The characteristics of DMMs are analyzed, and synthetics are in Table 1. The most important DMM attributes presented in Table 1 are their purposes, scope, and approach type. The purpose attribute includes descriptive, prescriptive, and benchmarking functions. It is suggested that the descriptive function leads to a contextualized context so that the prescriptive function can give context-specific recommendations for firms that have similar digital maturity levels. DMMs’ scopes can cover a specific industry or cross-industries so that firms decide to select an appropriate DMM for them. DMMs’ approach can cover a specific capability that the firms’ are concerned with or all the capabilities (multi-dimensions) they need to advance as digital enterprises. Table 2 shows the popular components used to construct DMMs: dimension, scale items, weighting factors, maturity level, assessment tools, and evolution path. A comprehensive comparison of well-known DMMs is shown in Table 3, showing that the most important dimensions are Organization, Process, Strategy, Customer, People, Culture, and IT Technology. The table also reveals that only a few rather complex DMMs use weighting factors for firms to prioritize their initiatives on reducing digital gaps as addressed by assessments. The assessment tools are built based on assessment methods and techniques that are detailed in Table 4, which shows various methods ranging from qualitative to quantitative and mixed methods, cover different techniques, and use different types of data and supported tools. These methods and techniques are used in the assessment process and model development projects. As for its evolution path, most DMMs develop their evolution paths based on their maturity levels, which implies a linear path to the next maturity level. This implication is criticized for its oversimplification of the current context of firms, which cannot give them context-specific and particular paths to their next levels (Remane et al., 2017).
Next, from the reviewed papers, the authors can find potential research areas that are ongoingly researched and could be embedded into DMMs in the future. They are: Change Management, Dynamics capabilities, Firm size, Non-linear evolution path, Evaluation methods, and DMM Dynamics. From Table 3, the Transformation Management dimension is the least popular one, but due to DX, it is a type of complex change, which should not only focus on what capabilities need to be changed but also on how these changes are managed (Bordeleau & Felden, 2019). For this reason, Change Management, Dynamics capabilities should be seen as capabilities that need to be assessed by DMMs. Firm size is another factor that should be considered because big companies tend to create their own DMM for their specific and frequent use (Schallmo et al. 2020). The non-linear evolution path is also a potential research area due to giving context-specific recommendations for firms to escalate their digital maturity (Remane et al., 2017). The firms’ evaluation methods to select a suitable DMM for their digital visions need to be researched because they do not have any current guidance for this activity (Felch et al., 2019). The last one is the DMMs’ dynamics, which means that DMMs are currently seen in a one-time static manner rather than gradually enhanced and accessed to reflect the fast pace of change of external environments (Gollhardt et al., 2020).
Table 1: Characteristics of Digital Maturity Models
Table 2: Principles elements of Digital Maturity Models
Table 3. Comparison of well-known Digital Maturity Models
Legend: A: Academy, P: Practitioner; C: Cross-Industry, S: Specific Industry; o – DMM does not have sub-dimensions; x - DMM has sub-dimensions; *: weighting
Table 4. Methods & techniques used in Digital Maturity Model applications
Table 5: Focuses on the field of Digital Maturity Model
Although DMM brings huge benefits to DX activities, the development of these models in academia and industry faces many challenges. Firstly, it lacks standardization in naming, especially in the naming of structured components of models. Different authors used these terms in different contexts with different meanings, including the dimensions (Gill & Vanboskirk, 2016; Lichtblau et al., 2017; Open Roads, 2017; Pirola et al., 2019; SIRI, 2019; Santos & Martinho, 2019; Schuh et al., 2018; Szaniawski et al., 2020; Trotta & Garengo, 2019; Valdez-de-Leon, 2016), action fields (Bumann & Peter, 2019; Gimpel et al., 2018), focus areas (Corver & Elkhuizen, 2014; De Carolis et al., 2017a), capabilities (Rossmann, 2018; Westerman et al., 2011), congruence (Kane et al., 2016), domain (Rogers, 2016), and track (EARLEY, 2017). Due to the majority use of “dimensions” in recent years, and with the popularity of this term in other management frameworks such as ITIL 4 (2019), the authors suggest that “dimensions” should be used as a standard name for the first level components of DMM. Similarly, the authors suggest that “capabilities” should be used as a standard name for the second-level components.
Secondly, the majority of the models (72%) have a descriptive purpose (Canetta et al., 2018), thus limiting their scope to providing companies with some insights on their adoption level of Industry 4.0 technologies (von Leipzig et al., 2017; Canetta et al., 2018). In addition, multi-dimensional models are usually too high-level (Matt et al., 2015), i.e., they provide too little detail or are too general, which means that they do not consider industry-related characteristics (Berghaus and Back, 2016a) to deliver necessary insights for organizations. Meanwhile, specific models only focus on particular isolated dimensions or functional areas, resulting in potential risks (Schumacher et al., 2019). These limitations raise rather high requirements for both sides of DMM application contexts. From the development side, they require establishing development teams who can conduct multi-discipline approaches to build multi-dimensional models for their clients. Table 3 shows that the team must be composed of experts in diverse domain areas, such as Organizations Development and Design, Operation & Quality Management, Strategic Management, Business Management, IT Technology, Digital Technologies, Human Resource Management, Service Management, and Change Management. On the flip side, firms that use DMMs should make significant investments in DMM assessment missions to obtain significant results that are context-specific to their companies. The context-specific DMM assessment can lead to the application of multi-model assessments and multi-method assessments, including 360-degree (expert survey and interview) assessments (Colli et al., 2019), IoT integration (Nygaard et al., 2020), DES simulation (Gajsek et al., 2019), and Fuzzy analysis (Caiado et al., 2021; Wagire et al., 2020). These serious DMM assessment deployments will lead to only some big companies paying for these types of assessments to achieve particular recommendations. The challenge of providing cheaper ways for SME firms to assess their digital maturity should be an outlook for future research.
Figure 5. A proposal to integrate DMM into a continuous digital transformation process
Thirdly, as the DXs are integrated into the firms' strategies that are gradually revised to respond to the dynamic context of the environment, the DX implementation incrementally and continuously is suggested (Kane et al., 2018; Rogers, 2016). Hence, the DMM that reflects the impacts of digital technologies on firms should be applied to the DX process in a closed-loop manner. However, few models mentioned their assessment process (Colli et al., 2019), and in that case, they only introduced a one-time assessment context such as Deloitte’s DMM (Anderson & William, 2018). These limitations raise a critical requirement for guidance that shows DMM actions in their whole lifecycle regarding the continuous DX process. The next section presents a suggestion for this challenge.
As the analysis in previous sections, it should be critical to the need for guidance on how to apply DMMs in an integrated manner with the DX processes to reflect the frequent changes in customer expectations (Chanias, 2017) and the dynamics of external conditions, including digital technology disruptions (Römer et al., 2017; Vial, 2019).
In this section, the paper’s authors propose a conceptual model for integrating DMMs into the DX process that respects the above requirements. The proposed model is based on the DX process suggested by Vial (2019) and focuses on showing DMM applications in its Strategic Response block, as presented in Figure 5. The process in Figure 5 shows that, after realizing the disruptions from markets, firms should redefine their business strategy, which should be based on the advancement of digital technology (El Sawy et al., 2015; Hess et al., 2016), and then identify the capabilities necessary for implementing the newly adjusted strategies (Ng et al., 2018). Then the firms develop a suitable DMM that reflects the firms’ strategic visions and future needs. After that, the DMM assesses the firms contextually to consult the weaknesses they need to heal in the short-term and their gap from the current business model to the visions business model (Colli et al., 2019; Pierenkemper & Gausemeier, 2020). The DMM assessment also helps firms understand their gaps in terms of digital capabilities (Brunner & Jodlbauer, 2020). The assessment outputs will be used as guidelines for firms to plan and implement their transformations, consisting of transforming business models in parallel with the development of digital capabilities (Pavlou & El Sawy, 2010; Ng et al., 2018). The change management that should be considered (Bordeleau & Felden, 2019; Gimpel, 2018) due to transformation is a type of radical strategic and cultural change (Westerman et al., 2014a) and is the strongest and riskiest change for any organization (By, 2006). After each incremental loop within transformation action plans, the firms make a revision to the current DMM with respect to its performance (Felch et al., 2019) and the newest disruptions from outside, and make decisions to reuse them or build new ones (Gollhardt et al., 2020).
This paper used keyword search and cross-references to collect units of analysis and the method of content analysis to review gathered research papers from 2000 to 2021. This paper provided an overview of (i) characteristics and components of DMMs and (ii) methods and techniques used in DMM development and assessment. Moreover, besides the major focus subjects (iii) that are currently under development, the paper raises a need for further consideration of challenges (iv). One of these challenges that show the need to address the DMM position in the DX master process, the authors propose an integration of DMM development and assessment steps into the DX process in a continuous context. The integration is supplementary to the DMMs’ reviewed studies and, together with them, provides both the development and application sides (enterprise) of DMMs’ clearer functions and their position in the DX process. The continuity of the integration model suggests that not only the DMMs’ assessment but also their development should be continuously conducted. Other challenges, especially the need to study appropriate development methods for multi-dimensional DMMs that SME firms can freely customize and effectively apply to their businesses without serious investment expenses, are also an outlook for future research.
This research is funded by VNPT Group1, and HUST University2.
1Vietnam Post and Telecommunication Group (https://vnpt.vn)
2Hanoi University of Science and Technology (https://hust.edu.vn)
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Hoang Pham Minh is a Ph.D. student at the School of Economics and Management (SEM) of Hanoi University of Science and Technology (HUST) and the Director of the Product Quality Department at Vietnam Telecommunication Group (VNPT) in Hanoi, Vietnam. He earned a B.S. in Electronics and Telecommunication from the Hanoi University of Science and Technology (HUST), Vietnam; a Master’s of Science in Electronics and Telecommunication from Hanoi University of Science and Technology (HUST), Vietnam; and a master’s of Business Administration from La Trobe University, Australia. Presently, he is joining a Ph.D. degree program in Economics and Management (SEM) at the University of Science and Technology (HUST). He has published journal and conference papers in Vietnamese science journals such as Economics Study and posted them on his Research Gate page. Mr. Hoang has completed research projects with the Posts and Telecommunications Institute of Technology (PTIT), Vietnam Telecom Services Company (VinaPhone), VNPT-Media Corporation, VNPT-Information Technology Company, Hanoi University of Science and Technology, Vietnam Institute for Development Strategies (VIDS), and MIT. His research interests include quality and operations management, business intelligence, lean, six-sigma, Agile Enterprise, DevOps, digital services, and platform economy. He is a member of the TM Forum and GSMA.
Hong Pham Thi Thanh is the Deputy Dean of the School of Economics and Management (SEM) at Hanoi University of Science and Technology (HUST). She earned a Ph.D. in Operations and Management from the Asian Institute of Technology (AIT), Thailand. She has published journal and conference papers for a number of magazines, such as the International Conference on Electronic Business (ICEB), International Conference on e-Technology, e-Commerce, and e-Service (EEE). Dr. Hong has completed research projects with the Vietnam government and many leading organizations in Vietnam and Thailand, such as VNPT, Viettel, and AIT. Her research interests include the digital economy, smart manufacturing, and digital transformation. Dr. Hong is the Director of the International Conference on Emerging Challenges (ICECH).
Received: April 5, 2022
Approved: April 5, 2022
How to cite: Minh, H.P., Thanh, H.P.T. (2022). Comprehensive review of a Digital Maturity Model and proposal for a continuous digital transformation process with Digital Maturity Model integration. Revista S&G 17, 1. https://revistasg.emnuvens.com.br/sg/article/view/1789