Posted: March 24th, 2023
The work entails a literature review and identification of gaps and a framework for future research on Maturity Models for Digital Transformation. The study provides a background for the analysis before, including other sources related to the topic. The appraisal of sources on Maturity Models for Digital Transformation is conducted through a classification methodology, which shows how the literature on the subject is reviewed and analyzed to generate comparisons and conclusions. The following section explains the classification of the review before delving deep into the critical assessment.
A literature search was conducted from online databases to identify sources relevant to the research question. The process allowed the collection, organization, and synthesizing of current knowledge on Maturity Models for Digital Transformation. The keywords used to search for the sources were digital models, maturity models, digitization, and digital transformation. A general search produced over 1,000 possible sources from various databases, such as EBSCOhost, Google Scholar, and Jstor. However, after filtering the source, only 11 references were found relevant and included in the review. Some of the criteria for filtering were the date of publication (2013-2019), the language of publication (English), and relevance to the subject matter.
The review uses 11 sources relating to maturity models for digital transformation. The table below presents a critical analysis of the ten studies included in the literature review:
Figure 2.2: Literature Analysis
|Year||Author(s)||Objectives||Applied techniques||Field of application|
|To explore the use of a capability maturity model for digital investigations.||Capability maturity models||Software engineering|
|2016||Valdez-de-Leon||To establish the efficacy of a digital maturity model for telecommunications service providers.||Social media model||The telecommunications industry|
|2016||Gill & VanBoskirk
|Introduces the use of digital maturity model 4.0||Forrester’s eBusiness model||Digital marketing|
|2016||Schumacher, Erol, and Sihn||To test the readiness and maturity of manufacturing enterprises for the use of Industry 4.0
|Industry 4.0 maturity model||Manufacturing enterprises|
|2017||Remane, Hanelt, Wiesboeck, and Kolbe
|To explore the use of Digital Maturity in Traditional Industries.||Practice-oriented practice||Traditional supply chain environments.|
|2017||Klötze and Pflaum
|To explore the development of a maturity model for digitalization.||“Broad” and dispersed “mega-trend” of digital applications||Manufacturing industry’s supply chain.|
|2017||Coreynen, Matthyssens and Van Bockhaven||To explore how digitization boosts servitization.||Digital assets and pertinent skills||Manufacturing processes|
|2017||Ustundag and Cevikcan
|To explore the use of Industry 4.0 to manage digital transformation.||Industry 4.0||Manufacturing industries.|
|2017||Weber, Königsberger, Kassner, and Mitschang||To explore the use of M2DDM, a maturity model for data-driven manufacturing.||M2DDM||Data-driven manufacturing.|
|To propose the use of Laserfiche Digital Transformation Model||Laserfiche Digital Transformation Model||All industries.|
|2019||Pflaum, Prockl, Bodendorf, and Chen
|The use of technologies, applications, and business models in the digital supply chain.||Technologies, applications, and business models||Digital supply chain.|
Internationally, businesses aim at increasing their participation in the innovative digital world. They are implementing digital transformation programs to take advantage of emerging opportunities in the market. However, the scope of the revolutions differs from one business to another and along the models applied to the digitization process. Some of the commonly used models are digital product development, digital customer engagement, and digital operations. Remane, Hanelt, Wiesboeck, and Kolbe (2017) explore the implications of the diffusion of new digital technologies in organizations and industries. The changes have rendered digital transformation relevant to all businesses in all sectors. As a result, the maturity of firms depends on the capacity of the organization to master innovative change recommended in practice-oriented research.
However, Remane, Hanelt, Wiesboeck, and Kolbe (2017) revealed some limitations in the practice-oriented exercise relevant to digital transformation. Firstly, digital maturity is usually discussed along a linear perspective and the assumption that all companies should proceed along a common path. Secondly, the authors propose the need to change the narrative to ensure that all firms benefit from digital transformation through the use of effective maturity models. Remane, Hanelt, Wiesboeck, and Kolbe (2017) revealed the need for a monolithic chunk of digital maturity that allows differentiated organization-specific assessments and implementation of digitization.
Research focuses on organizations that use digital transformation to revolutionize their activities. The telecommunications industry is one of the sectors that use the models extensively. Valdez-de-Leon (2016) traces the emergence of the model in the industry to the so-called over-the-top (OTT) services. They have used services, such as WhatsApp and Skype, to provide services to clients. However, the research identifies some limitations in the models applied, such as a lack of a framework and tools to support the radical changes in the telecommunication industry.
Besides the service industry, other researchers have focused on the use of digital models in the manufacturing sector. Schumacher, Erol, and Sihn (2016) focus on the use of digital processes to support maturity in manufacturing enterprises amid challenges in the use of disruptive technologies. Some of the methods and concepts applicable in the process include the Internet of Things, Cloud-based Manufacturing, and Cyber-Physical Systems. The use of digital technologies has improved servitization according to a study by Coreynen, Matthyssens, and Van Bockhaven (2017). Their research revealed that digitization results in a three-servitization pathway: “industrial, commercial and value servitization” (p.42). The study affirms the need for managers to leverage digital assets and relevant skills to integrate digitization into their manufacturing processes.
Pflaum, Prockl, Bodendorf, and Chen (2019) further supported the use of maturity models in the supply chain. One of the areas most affected by digital transformation in organizations is the supply chain. Companies have implemented digital supply chains to streamline their processes, ensuring enough supplies and inventories. Klötzer and Pflaum (2017) assume a similar approach to Schumacher, Erol, and Sihn (2016) to investigate the use of maturity models for digitization in the manufacturing sector, specifically in the supply chain. According to Klötzer and Pflaum (2017), the “broad” and dispersed “mega-trend” of digital applications will continue to play a significant role in supply chains within the manufacturing sector. Models applicable to the sector aim at addressing essential components, complementary innovative ideas, and pertinent technologies, such as Cyber-Physical Systems (CPS), smart products, and Big Data Analytics.
One of the models driving digitization today is Laserfiche’s model, which provides an innovative framework for digital efficiency in the workplace. The model includes five strategic phases: digitize, organize, automate, streamline, and transform (Laserfiche, 2017). The model is useful for managers who are unsure about the necessary steps to implement digitization. Firstly, the company converts paper into electronic formats. Secondly, they organize documents and manage information. Thirdly, they automate by digitizing processes through electronic formats. Fourthly, they streamline and create visibility of operations to increase efficiency. Finally, the firms transform by leveraging analytics to align processes with the organizational objectives.
Researchers have developed numerous digital transformation models for use in diverse companies to implement digitization. Ustundag and Cevikcan (2017) explored the application of Industry 4.0 as a part of the strategy to capitalize on digitalization opportunities in production and service activities. The authors term it as the fourth industrial revolution achieved through an integration of digital and physical technologies, such as cloud computing, artificial intelligence, augmented reality, adaptive robotics, and the Internet of Things (IoT). Companies use the digital transformation model to achieve resource efficiency and productivity. Weber, Königsberger, Kassner, and Mitschang (2017) further investigated the use of Industrie 4.0 an IT infrastructure for use in the manufacturing industry. The authors define it as a data-driven manufacturing model that integrates digitization vertically and horizontally through the product life cycle.
Gill and VanBoskirk (2016) explored the use of Forrester’s eBusiness and digital marketing assessments to support digitization in organizations. (figure 2.4 shows how the model works). The authors affirm that the model is aimed at maturing businesses towards excellence. The digital maturity model supports interactive marketing and the application of eBusiness in the company’s activities. Schumacher, Erol, and Sihn (2016) applied a similar model to research the use of a maturity model in the manufacturing sector. The model of focus in the study is Industry 4.0, which is specifically targeted at success in manufacturing. The Industry 4.0 maturity model is proven effective for the implementation of digitization in production. Schumacher, Erol, and Sihn (2016) further revealed that the model is applicable to nine dimensions in manufacturing, including “Products,” “Customers,” “Operations,” and “Technology” as well as “Strategy,” “Leadership,” “Governance,” “Culture” and “People” (p. 161). The model allows businesses to integrate the dimensions and achieve success in their production functions.
Kerrigan (2013) introduces the concept of capability maturity models into the study of Maturity Models for Digital Transformation. The particular model used is the Software Engineering Institute at Carnegie Mellon University, which is applied to the assessment of the capability to implement software projects. The author suggests that the model is also useful outside software development projects as a framework for process improvement. Kerrigan (2013) further elucidated that the Capability Maturity Model Integration or CMMI (see figure 2.5 for the model) can support the analysis of organizational digitization readiness to benefit the business through productivity outcomes.
Scientific rigor is possible in the research using grounded theory and other theoretical models and conceptual frameworks in collecting, analyzing, and evaluating data. However, the area of Maturity Models for Digital Transformation is relatively new in research. Therefore, research is still limited, especially in the identification of a comprehensive list of the relevant maturity models. Most of the studies included in the literature review focus on single models. Therefore, it is essential to explore the appropriate maturity models that can be applied to digital transformation across organizations and industries.
Coreynen, W., Matthyssens, P., & Van Bockhaven, W. (2017). Boosting servitization through digitization: Pathways and dynamic resource configurations for manufacturers. Industrial Marketing Management, 60, 42-53.
Gill, M., & VanBoskirk, S. (2016). The digital maturity model 4.0. Benchmarks: Digital Transformation Playbook.
Kerrigan, M. (2013). A capability maturity model for digital investigations. Digital Investigation, 10(1), 19-33.
Klötzer, C., & Pflaum, A. (2017). Toward the development of a maturity model for digitalization within the manufacturing industry’s supply chain. In Proceedings of the 50th Hawaii International Conference on System Sciences
Laserfiche (2017). Driving innovation with the Laserfiche Digital Transformation Model. Retrieved from http://hemingwaysolutions.net/wp-content/uploads/2017/08/digital-transformation-model.pdf
Pflaum, A., Prockl, G., Bodendorf, F., & Chen, H. (2019, January). The digital supply chain of the future: Technologies, applications, and business Models. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Remane, G., Hanelt, A., Wiesboeck, F., & Kolbe, L. (2017). Digital Maturity in Traditional Industries–An Exploratory Analysis. Association for Information Systems.
Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia Cirp, 52, 161-166.
Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: managing the digital transformation. New York: Springer.
Valdez-de-Leon, O. (2016). A digital maturity model for telecommunications service providers. Technology Innovation Management Review, 6(8), 1-6
Weber, C., Königsberger, J., Kassner, L., & Mitschang, B. (2017). M2DDM–a maturity model for data-driven manufacturing. Procedia CIRP, 63, 173-178.
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