An Approach for a Digital Maturity Model for SMEs Based on Their Requirements

Abstract

This study aimed to develop an approach for a digital maturity model (DMM) based on Small and Medium Enterprises’ (SMEs’) requirements. Our research is based on a systematic literature review in which we analyzed and compared existing approaches of DMMs with regard to their stakeholders. In addition, we conducted several interviews with SMEs for the InnoSÜD research project “Digitaler Reifegrad@Mittelstand.” Our findings show that DMMs are currently a significant topic for SMEs across various industry sectors, but there is no consensus on the required dimensions to examine when applying a DMM. The chapter offers an approach to the development of a DMM consisting of the following dimensions: digital strategy, partner interface, the company’s processes, employees and technologies, products and services, and customer interface. Moreover, the chapter offers recommendations for further research.

Full text

Das dieser Veröffentlichung zugrundeliegende Vorhaben wurde mit Mitteln des Bundesministeriums für Bildung und Forschung und den
Ländern im Rahmen des Programms „Innovative Hochschule“ (IHS) unter dem Förderkennzeichen 03IHS024C gefördert. Die
Verantwortung für den Inhalt dieser Veröffentlichung liegt beim Autor (bzw. den Autoren).
1
An Approach for a Digital Maturity Model for SMEs
based on Their Requirements
Daniel Schallmo
University of Applied Sciences Neu-Ulm
Wileystrasse 1, 89231 Neu-Ulm, Germany
E-Mail: daniel.schallmo@hnu.de
Klaus Lang
University of Applied Sciences Neu-Ulm
Wileystrasse 1, 89231 Neu-Ulm, Germany
E-Mail: klaus.lang@hnu.de
Daniel Hasler
University of Applied Sciences Neu-Ulm
Wileystrasse 1, 89231 Neu-Ulm, Germany
E-Mail: daniel.hasler@hnu.de
Katharina Ehmig-Klassen*
University of Applied Sciences Neu-Ulm
Wileystrasse 1, 89231 Neu-Ulm, Germany
E-Mail: katharina.ehmig-klassen@hnu.de
Christopher A. Williams
Johannes Kepler University
Altenbergerstraße 69, 4040 Linz
E-Mail: chrs.a.williams@gmail.com
* Corresponding author

This paper was presented at The ISPIM Innovation Conference – Innovating Our Common Future,
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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-467-8
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Abstract: This study aimed to develop an approach for a digital maturity model
(DMM) based on Small & Medium Enterprises’ (SMEs’) requirements. Our
research is based on a systematic literature review in which we analyzed and
compared existing approaches of DMMs with regard to their stakeholders. In
addition, we conducted several interviews with SMEs for the InnoSÜD
research project “Digitaler Reifegrad@Mittelstand.” Our findings show that
DMMs are currently a significant topic for SMEs across various industry
sectors, but there is no consensus on the required dimensions to examine when
applying a DMM. The paper offers an approach to the development of a DMM
consisting of the following dimensions: digital strategy, partner interface, the
company’s processes, employees and technologies, products and services, and
customer interface. Moreover, the paper offers recommendations for further
research.
Keywords: digital maturity models; maturity models; stakeholder
requirements; requirements for SMEs; digital transformation tools; SME; SLR;
deductive method
1 Introduction
“Digitalization” and “digital transformation” are currently some of the most used
buzzwords in consulting, economics, and management sciences. The media constantly
seems to report that Germany is at risk of losing touch with the latest trends, but,
according to the digital economy and society (DESI) index of the European Commission,
Germany is placed slightly above average with countries such as Finland, Sweden, the
Netherlands, and Denmark leading the field (European Commission 2019). Some might
speak of “Digital Darwinism” (Kreutzer and Land 2015), suggesting that technology and
society are changing faster than businesses can adjust.
The bigger the company, the higher their perception of digital maturity (Lichtblau et al.
2018; Brandt 2018). Taking a deeper look, it is particularly the German SMEs that will
have to adapt their current business models to new consumption patterns and disruptive
technologies or risk losing their competitive advantages in a globalized marketplace.
Only one in four companies uses digital marketing or sales concepts, reorganized
workflows to prepare for the digital age, or digitalized their products and services
(Zimmermann 2019).
A possible and efficient solution to correctly determine the status quo of a company’s
state of digitalization can be the use of a digital maturity model (DMM). Maturity models
are rather practical tools that have been present in different areas of actions, e.g., project
management (Cook-Davies 2002: 16–20), for quite some time but have become
extremely popular in recent years in the context of digital transformation (Hess 2019). In
our understanding, a DMM serves to clarify the current state of digitalization of a
company based on different questions and variables, sometimes compared to other
companies in the same sector or cross-sectoral, and recommends further actions to
improve the company’s state of digitalization.

Although the Internet is currently being flooded with practical tools provided by different
stakeholders, there is little theoretical consensus on what a DMM is. The problem here
lies within the lack of clarity of tools and literature as well as objectivity when it comes
to application, execution, and analysis of a DMM in practice. Therefore, we seek to
provide insight into what requirements SMEs have and how they can be integrated into
future DMMs.
2 Theoretical Background
A common opinion or standard procedure is not apparent regarding either maturity
models or the degree of DT.
Digital Transformation
Various definitions of DT have been presented (e.g., BMWi 2015; Bowersox et al. 2005;
Bouée and Schaible 2015; PwC 2013). In our understanding, DT can be seen as follows:
…the networking of actors such as businesses and customers across all value-added
chain segments, and the application of new technologies. As such, DT requires skills
that involve the extraction and exchange of data as well as the analysis and conversion
of that data into actionable information. This information should be used to calculate
and evaluate options, in order to enable decisions and/or initiate activities. In order to
increase the performance and reach of a company, DT involves companies, business
models, processes, relationships, products, etc. (Schallmo et al. 2017)
Maturity Models
Becker et al. (2009b: 2–3) state that many maturity models often deal with similar topics,
deriving from the field of business informatics or considering the use of information
technologies in companies or other organizations. For example, there have been around
30 different maturity models in the domain of “project management” (Cook-Davies 2002:
16–20) and even 150 maturity models for “IT service capability, strategic alignment,
innovation management, program management, enterprise architecture, or knowledge
management maturity” (Bruin et al. 2005: 3).
The authors criticize that only in rare cases is it even disclosed how the development of a
new maturity model was motivated, in which steps it was developed, who was involved
in these steps, and whether and how it was evaluated that the new model fulfilled its
function (Becker et al. 2009b: 2–3).
Degree of Maturity
Basically, the degree of maturity of a research object deals with the fulfilment of certain
objectives, characteristics, or indicators (Becker et al. 2009a: 213). The characteristic
values or dimensions necessary to achieve a degree of maturity are generally predefined

This paper was presented at The ISPIM Innovation Conference – Innovating Our Common Future,
Berlin, Germany on 7-10 June 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-467-8
4
(CMMI Product Development Team 2011: 464); the point in time can be arbitrary
(Pfeifer-Silberbach 2005) but is usually the actual state of a company and its products,
services, business model, and processes considering the point in time of the
measurement.
Digital Maturity Models
Considerable research has been done on maturity models focusing on digital capabilities
in the areas of IT management (Becker et al. 2009b) and business processes (Tarhan et al.
2016; Williams et al. 2019). Maturity models for digitization in companies must
summarize certain characteristics in particular dimensions at a specific time (Becker et al.
2009a; Pfeifer-Silberbach 2005; CMMI Product Development Team 2011: 464). They
serve to determine the current state and the degree of digital maturity in the context of DT
(e.g., regarding competence, performance, and level of experience) and allow
recommendations for future actions deriving from the current degree of maturity.
Small & Medium Enterprises (SMEs) and Their Requirements
According to the Institut für Mittelstandsforschung (2020), SMEs are companies that
employ fewer than 500 persons and have an annual turnover not exceeding 50 million
euros.
SMEs are also typically seen as long-term, stable, and independent (Bundesverband
der Deutschen Industrie e. V. 2015). Therefore, they have their own needs and
requirements, especially when it comes to new and radically changing issues like DT.
They do not rely much on theoretical approaches and prefer quick and easy, pragmatic
solutions. Their requirements must consist of practical facts and recommendations for
action.
Furthermore, Arendt (2008: 93–108) found that knowledge and skills were the biggest
barriers for the SMEs with regard to digital initiatives. Zimmermann (2019: 11) adds data
security and governance as well as Internet infrastructure.
3 Research Questions and Research Design
Research Questions
Based on the previous sections, we propose the following research questions:
• What are their main requirements for the creation of a DMM to support SMEs?
• What DMMs exist?
• What does a suitable maturity model for SMEs look like?

Research Design
Our research design consists of three parts. First, we collected practical qualitative data
by interviewing SMEs for their requirements regarding DMMs. Second, we conducted a
systematic literature review (SLR) to gain insight into existing approaches for DMM. In
the last step, we compared theoretical and practical results to see how DMMs for SMEs
can be improved in the future.
For the qualitative data, we used action research as this method helps to “address
complex real-life problems and the immediate concern of practitioners” (Avison et al.
1999: 95), and we can test and refine a DMM approach for SMEs with the help of SMEs’
feedback.
In the context of the InnoSÜD research project “Digitaler Reifegrad@Mittelstand” at
University of Applied Sciences Neu-Ulm, in various workshops and interviews with
regional SMEs, we are currently in the process of obtaining data and requirements for
developing and testing an SME-oriented DMM. The goal of the InnoSÜD university
network is to use innovative transfer formats to facilitate a sustainable and effective
exchange between science, business, and society. The focus is on topics that are
important for the region, such as transformation management. In this case, the transfer
refers to SMEs. With the support of the Institute for Digital Transformation of the
University of Applied Sciences Neu-Ulm, they should determine their digital maturity to
be able to derive a digitization strategy and implement it in their own company.
We interviewed five regional SMEs on the topics of digital maturity and DT in their
companies to determine necessary requirements for an SME-oriented DMM.
The central questions asked were:
• What is the status quo of your company regarding the DT?
• Where do you see the biggest problem field in your company regarding the DT?
• Where do you see the greatest need for action regarding digitalization in your
company?
• What are your expectations for determining digital maturity?
Furthermore, we conducted an SLR to gain insight into existing approaches for DMMs as
“[s]ystematic reviews are undertaken to clarify the state of existing research and the
implications that should be drawn from this” (Feak and Swales 2009: 3). This formal and
methodical approach aims to reduce bias in choosing literature selectively and to increase
the reliability of the chosen literature (Tranfield, Denyer and Smart 2003).
For the SLR, we used the keywords “Digitalisierung,” “digitalization,” “Digitaler
Reifegrad,” “digital maturity,” “Reifegradmodell,” “maturity model,” “digital
assessment,” “digital readiness,” and “digital fit” to retrieve sources from the Internet as
well as Web of Science, SpringerLink, Ebsco, Emerald, ScienceDirect, and Wiley
databases.

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To refine the review, we applied the following exclusion criteria. First, we only kept
sources for analysis that were available in German or originated in Germany, Austria, or
Switzerland. We conducted our workshops and interviews in Southern Germany (Bavaria
and Baden-Wurttemberg), and our objective was to rely on available additional data with
a minimum of cultural-related bias as DT and maturity might be perceived differently in
other areas of the world.
Second, we focused on maturity models with the core topic of DT. As mentioned above,
maturity models are present in various areas of action, but our focus is on digitalization
and DT.
Third, sources had to be generally or at least cross-sectionally applicable. To achieve
transparency and possible comparison among the different SMEs interviewed, it was not
possible to rely only on industry specific DMMs.
Sources were further examined using the following criteria:
• Group: Who designed the model?
• Sector: What are the main target sectors of the maturity model?
• Methodology: How was the survey conducted, and how were data collected?
• Model structure: How is the model structured? How many questions, dimensions,
rating levels (degrees of maturity) does it consist of?
The results are summarized in Table 3 in the appendix of this paper. As a last step, we
present the following four DMMs and compare them to SME requirements from the
interviews:
• Digitaler Reifegrad of Schweizer KMU (Wyss 2017)
• Quick Check Industrie 4.0 Reifegrad (Digital in NRW n.d.)
• Industry 4.0 / Digital Operations Self-Assessment (Geissbauer et al. 2016)
• Potentialanalyse Arbeit 4.0 (Offensive Mittelstand – Gut für Deutschland 2018)
These were chosen because (1) the questions were simple, understandable, and minimally
complex so that they could be used in a workshop context; (2) each of them comes from a
different group; and (3) they all include recommendations for further actions and
therefore seem to have a good overall fit for an application to SMEs.

4 Findings
We analyzed four DMMs and examined how they meet requirements of SMEs deriving
from interviews and workshops of the InnoSÜD research project “Digitaler
Reifegrad@Mittelstand.” None of the existing models met all of the requirements.
Consequently, suggestions for improving future model constructs can be derived.
Requirements for SMEs based on “Digitaler Reifegrad@Mittelstand”
The results of the workshops are summarized in Table 1. We clustered the SMEs’
responses into various dimensions, such as (digital) strategy; the interaction with partners
and suppliers via a partner interface; the company’s processes, employees, and used
technologies; the interaction with customers via a customer interface; and the company’s
products and services.
The most important areas for improvement are internal processes, products, and services
and the overall digital strategy. Processes are often “highly analogue” and “still use a lot
of paper,” which “impedes the processing of important data” internally and towards
customers, partners, and suppliers. In this context, IT systems are very old or the IT
infrastructure is not harmonized.
Regarding products and services, the potential of new technologies, such as artificial
intelligence or mobile apps, to upgrade existing products and expand the service portfolio
have already been detected, but these initiatives progress slowly due to a lack of capacity
and knowledge of the company’s employees.
Table 1 Required dimensions for digital maturity models provided by SMEs
Requirements / Dimensions
SME 1
SME 2
SME 3
SME 4
SME 5
Strategy
●
●
●
Partner interface
○
●
●
Processes
●
●
●
●
Employees
○
○
○
●
Technologies
○
○
●
●
Customer interface
○
○
●
●
Products & services
○
●
●
●
● strong need for further actions (top priority)
○ need for further actions
(blank) no immediate need for further actions
This leads to the third core topic: digital strategy. The companies know that “something
has to be done” but often “do not know where to start.” Determining the digital maturity

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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-467-8
8
is seen as a good way to discover “potentials and recommendations for further actions” as
well as to create a “digitalization roadmap including priorities.”
Existing Approaches for Digital Maturity Models
Table 3 in the appendix of this paper summarizes the results of the SLR. In general, there
is a large number of maturity models, which are based on different dimensions and are
therefore neither generally comparable nor applicable. Studies differ in terms of the
industries and sectors, company sizes, and the number of participating companies.
A wide variety of methodologies have been applied from (online) questionnaires and
online self-checks (e.g., Hochschule Neu-Ulm (HNU), minnosphere GmbH 2017;
techconsult n.d.; Mittelstand 4.0 Kompetenzzentrum Kaiserslautern n.d.) over conceptual
modelling (Westerman et al. 2012) and literature reviews (Back et al. 2016, 2017)
towards more qualitative methods, such as interviews (Geissbauer et al. 2016), focus
groups, workshops (e.g., Acatech n.d.; H&D 2016) and assessments (fme AG n.d.).
We see the following main groups as creators of DMMs.
• Consulting firms use DMMs as a practical supporting tool for providing information
and consultancy services to companies needing to improve their digital strategy.
Their objective is profit-orientated, like the companies they are consulting, operating
in one or various industry sectors.
• Associations are representations of a sum of companies with the intention to inform
and strengthen the industry sector in which the respective companies are operating.
Digital maturity should help create benchmarks and comparison for the members.
• Universities and research institutes, in this context, have the goal to inform, educate,
and support the public, e.g., companies, citizens, etc., on actual topics like
digitalization, DT, and digital maturity.
• Big companies, e.g., Deutsche Telekom (Techconsult n.d.), sometimes create their
own DMM to improve their status quo with regard to DT and to collect market data.
We also encountered various combinations of the groups, e.g., an association contracting
a research institute for conducting a survey on digital maturity (e.g., IMPULS-Stiftung
2015), a university partnering with a company for transforming research results into a
product or service (e.g., Universität St. Gallen & Crosswalk AG 2016, 2017), or a
company using their knowledge for their own consulting branch (e.g., Rockwell
Automation 2014).
Moreover, the model structures differ largely in the number of dimensions, questions, and
rating levels. While some DMMs only deal with three (Rockwell Automation 2014),
others consist of up to nine different dimensions (e.g., Frauenhofer Austria Research
GmbH 2017) while the majority presents five central fields of action. The number of
questions range from 15 (Digital in NRW n.d.) to 166 (Offensive Mittelstand – Gut für

Deutschland 2018). The number of different degrees of maturity is usually in between
three and six rating levels. Only one DMM (Industrie- und Handelskammer (IHK)
München & Oberbayern n.d.) offers 11 different maturity degrees.
Furthermore, not all information on dimensions, questions, and rating levels have been
publicly available, which complicates detailed comparison of existing approaches.
Comparing SME Requirements to Existing Approaches
As Table 2 shows, none of the four analysed DMMs fully considers every dimension of
digitalization mentioned by the interviewed SMEs during the InnoSÜD research project
“Digitaler Reifegrad@Mittelstand.” The four existing models, however, all consider to
some extent the aspects of the company (processes, employees, and technologies) as well
as the overall digital strategy. The latter as well as the internal processes have been
detected as the most important areas of improvement by the interviewees as well. The
partner interface and sometimes the customer interface are neglected in some of the
analysed existing approaches.
Nevertheless, an approach for a DMM for SMEs should consist of all of the requirements
mentioned in Tables 1 and 2. For the upcoming data collection process, the questionnaire
has to include questions to determine the digital maturity of all aspects of digitalization.
Table 2 Existing digital maturity models vs. SME requirements
Requirements /
Dimensions
Industrie 4.0-
Readiness-
Modell
Digitaler
Reifegrad von
Schweizer KMU
Industry 4.0 /
Dig. Operations
Self-Assessment
Potential-
analyse 4.0
Strategy
○
●
●
○
Partner interface
○
○
Processes
○
●
●
●
Employees
○
●
●
●
Technologies
○
●
●
●
Customer
interface
●
●
○
Products &
services
○
●
●
● included in the model
○ partly included in the model
(blank) not included in the model

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5 Contributions
This study aims to determine the requirements that are currently lacking in DMMs for
companies through analysis and a deductive method. The results give readers a deeper
look into requirements of SMEs in relation to DMMs. These results and the indication
that requirements are lacking in current DMMs can be used in the development of future
DMMs.
6 Practical Implications
First of all, practitioners will get an overview of and deeper insights into existing DMMs.
In addition, they will find the analysis of the requirements of SMEs for DMMs and first
approaches to build a model that meets the requirements of SMEs.
7 Limitations
This paper aimed to report our current research-in-progress regarding the necessary
requirements for standardized DMMs to meet stakeholder interests. We see the following
limitations to this paper. Due our focus on the German-speaking area, the results may not
be generalizable on a global level.
Furthermore, it is debatable whether companies are willing to publish their data on digital
maturity for a common goal. Although it would be helpful to create more transparency in
the context of benchmarking, they could interpret this as an exposure of their own
shortcomings, endangering their market position.
8 Recommendations for Further Research
Practitioners should be even more included into further research as the model could
intensively be tested and more company data would be available for comparison. It would
be interesting to create an overall accessible and anonymized database to be able to
strengthen which dimensions are truly necessary for a DMM. This database would allow
researchers to get insights from different industries, regions, or countries; practitioners
would get a reliable benchmarking tool providing recommendations for further actions
inside their companies.

References and Notes
1. Acatech (n.d.). Industrie 4.0 maturity index [online]. [Viewed January
2020]. Available from: https://www.acatech.de/projekt/industrie-4-0-
maturity-index/
2. Arendt, L., (2008). Barriers to ICT adoption in SMEs: how to bridge
the digital divide? Journal of Systems and Information Technology.
10(2), 93–108.
3. Avison, D., Lau, F., Myers, M. and Nielsen, A., (1999). Action
Research: to make academic research relevant, researchers should try
out their theories with practitioners in real situations and real
organizations. Communications of the ACM. 42(1), 94–99.
4. Back, A., Berghaus, S., & Kaltenrieder, B. (2016). Digital maturity &
transformation report 2016. Universität St. Gallen (Institut für
Wirtschaftsinformatik) and Crosswalk AG.
5. Back, A., Berghaus, S. and Kaltenrieder, B., (2017). Digital maturity &
transformation report 2017. Universität St. Gallen (Institut für
Wirtschaftsinformatik) and Crosswalk AG.
6. Becker, J., Knackstedt, R. and Pöppelbuß, J., (2009a). Developing
maturity models for IT management. Business & Information Systems
Engineering. 1(3), 213–222.
7. Becker, J., Knackstedt, R. and Pöppelbuß, J., (2009b).
Dokumentationsqualität von Reifegradmodellentwicklungen, working
paper 2009 (123) [online]. [Viewed January 2020]. Available from:
https://www.econstor.eu/bitstream/10419/59549/1/718173538.pdf
8. BMWi, (2015). Industrie 4.0 und digitale wirtschaft—impulse für
wachstum, beschäftigung und innovation. Berlin: Bundesministerium
für Wirtschaft und Energie.
9. Boueé, C. and Schaible, S., (2015). Die digitale transformation der
industrie. Studie: Roland Berger und BDI.
10. Bowersox, D. J., Closs, D. J. and Drayer, R. W., (2005). The digital
transformation: technology and beyond. Supply Chain Management
Review. 9(1), 22–29.
11. Brandt, M., (2018). Je größer desto digitaler [online]. Statista GmbH.
[Viewed January 2020]. Available from: https://de-statista-
com.ezproxy.hs-neu-ulm.de/infografik/14216/einschaetzung-von-

This paper was presented at The ISPIM Innovation Conference – Innovating Our Common Future,
Berlin, Germany on 7-10 June 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-467-8
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unternehmen-in-deutschland-zum-eigenen-stand-bei-der-
digitalisierung/
12. Bruin, T., Freeze, R., Kulkarni, U. and Rosemann, M., (2005).
Understanding the main phases of developing a maturity assessment
model. 16th Australasian Conference on Information Systems. Sidney
[online]. [Viewed December 2019]. Available from:
http://aisel.aisnet.org/acis2005/109/
13. Bundesverband der Deutschen Industrie (BDI) e. V., (2015).
Faktencheck [online]. Mittelstand und Familienunternehmen. [Viewed
January 2020]. Available from:
https://bdi.eu/media/presse/publikationen/mittelstand-und-
familienunternehmen/Faktencheck_Mittelstand_Familienunternehmen_
230915.pdf
14. CMMI Product Development Team, (2011). CMMI® für Entwicklung.
Version 1.3. Software Engineering Institute. Carnegie Mellon
University, pp. 464.
15. Cook-Davies, T., (2002). Project management maturity models. Does it
make sense to adopt one? Project Manager Today, 16–20.
16. Digital in NRW. (n.d.). Online-Fragebogen zur Bewertung des
Industrie 4.0-Reifegrades [online]. [Viewed January 2020]. Available
from: https://www.digital-in-nrw.de/de/termine-
themen/workshop/online-fragebogen-zur-bewertung-des-industrie-4-0-
reifegrades
17. European Commission, (2019). The digital economy and society index
(DESI). EC website. [Viewed January 2020]. Available from:
https://ec.europa.eu/digital-single-market/en/desi
18. Feak, C. B. and Swales, J. M., (2009). Telling a research story: writing
a literature review. University of Michigan Press.
19. fme AG. (n.d.). Digitaler reifegrad [online]. [Viewed January 2020].
Available from: https://www.fme.de/lan/digitaler-reifegrad/
20. Frauenhofer Austria Research GmbH, (2017). Jahresbericht 2017:
industrie 4.0 reifegradmodell [online]. [Viewed January 2020].
Available from:
https://www.fraunhofer.at/de/publikationen/jahresbericht2017_e-
paper.html; 20–21.
21. Geissbauer, R., Vedso, J. and Schrauf, S., (2016). Industry 4.0: building
the digital enterprise [online]. [Viewed January 2020]. Available from:
https://www.pwc.com/gx/en/industries/industries-4.0/landing-
page/industry-4.0-building-your-digital-enterprise-april-2016.pdf

22. H&D, (2016). Industrie 4.0. forschungsprojekt smartTCS gestartet:
service-plattform vernetzt produktionshalle mit technischem
kundendienst [online]. [Viewed January 2020]. Available from:
https://www.hud.de/2016/02/15/industrie-4-0/
23. Hess, T., (2019). Digitale transformation strategisch steuern. Vom
Zufallstreffer zum systematischen Vorgehen. Berlin Heidelberg:
Springer-Verlag.
24. Hochschule Neu-Ulm (HNU), minnosphere GmbH, (2017). Digitales
reifegrad analysetool [online]. [Viewed January 2020]. Available from:
http://reifegradanalyse.hs-neu-ulm.de/
25. Industrie und Handelskammer (IHK) München & Oberbayern, (n.d.).
Digitalisierung im Mittelstand [online]. Leitfaden Industrie 4.0.
[Viewed January 2020]. Available from: https://ihk-industrie40.de/
26. IMPULS-Stiftung, (2015). Industrie 4.0-readiness-modell [online].
[Viewed January 2020]. Available from: https://www.industrie40-
readiness.de/
27. Institut für Mittelstandsforschung, (2020). Institut für
mittelstandsforschung - IfM Bonn. Zugriff am 31. März 2020 [online].
[Viewed January 2020]. Available from: https://www.ifm-
bonn.org/definitionen/
28. Kreutzer, R. T. and Land, K. -H., (2015). Digital Darwinism –
branding and business models in jeopardy. Berlin Heidelberg:
Springer-Verlag.
29. Lichtblau, K., Schleiermacher, T., Goecke, H. and Schützdeller, P.,
(2018). Digitalisierung der KMU in Deutschland [online]. IW Consult.
[Viewed January 2020]. Available from:
https://www.iwconsult.de/fileadmin/user_upload/projekte/2018/Digital
_Atlas/Digitalisierung_von_KMU.pdf
30. McKinsey & Company, Inc. (n.d.). Digital Digital Maturity and Value
Assessment für den öffentlichen Sektor. Retrieved January 2020, from
https://docplayer.org/8484104-Mckinsey-digital-digital-maturity-and-
value-assessment-fuer-den-oeffentlichen-sektor.html
31. Mittelstand 4.0 Kompetenzzentrum Kaiserslautern, (n.d.). Readiness-
check [online]. [Viewed January 2020]. Available from:
https://kompetenzzentrum-kaiserslautern.digital/readiness-check/
32. Offensive Mittelstand – Gut für Deutschland, (2018). Potentialanalyse
Arbeit 4.0 [online]. [Viewed December 2019]. Available from:
https://www.offensive-mittelstand.de/om-
praxisvereinbarungen/potenzialanalyse-arbeit-40

This paper was presented at The ISPIM Innovation Conference – Innovating Our Common Future,
Berlin, Germany on 7-10 June 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-467-8
14
33. Pfeifer-Silberbach, U., (2005). Ein beitrag zum monitoring des
reifegrades in der entwicklung eines produktes. Aachen: Shaker.
34. PwC, (2013). Digitale transformation – der größte Wandel seit der
Industriellen Revolution. Frankfurt: PricewaterhouseCoopers.
35. Rockwell Automation, (2014). The connected enterprise maturity
model [online]. [Viewed January 2020]. Available from:
https://literature.rockwellautomation.com/idc/groups/literature/docume
nts/wp/cie-wp002_-en-p.pdf
36. Schallmo, D., Williams, C. A. and Boardman, L., (2017). Digital
transformation of business models—best practice, enablers, and
roadmap. International Journal of Innovation Management. 21(08).
37. Tarhan, A., Turetken, O. and Reijers, H. A., (2016). Business process
maturity models: a systematic literature review. Information and
Software Technology. 75, 122–134.
38. Techconsult, (n.d.). Digitalisierungsindex[online]. [Viewed December
2019]. Available from: https://www.digitalisierungsindex.de/
39. Tranfield, D., Denyer, D. and Smart, P., (2003). Towards a
methodology for developing evidence‐informed management
knowledge by means of systematic review. British Journal of
Management. 14(3), 207–222.
40. tmforum, (2020). Digital maturity model overview [online]. [Viewed
December 2019]. Available from: https://www.tmforum.org/digital-
maturity-model-metrics/model-overview/
41. Westerman, G., Tannou, M., Bonnet, D., Ferraris, P. and McAfee, A.,
(2012). The digital advantage: how digital leaders outperform their
peers in every industry. MA: MITSloan Management and Capgemini
Consulting; pp. 2–23.
42. Williams, C., Schallmo, D., Lang, K. and Boardman, L., (2019).
Digital maturity models for small and medium-sized enterprises: a
systematic literature review. Florence: The ISPIM Innovation
Conference.
43. Wyss, M., (2017). Digitaler Reifegrad von Schweizer KMU.
Hochschule Luzern - Informatik.
44. Zimmermann, V., (2019). KfW-Digitalisierungsbericht Mittelstand
2018 [online]. Digitalisierung erfasst breite Teile des Mittelstands –
Digitalisierungsausgaben bleiben niedrig. [Viewed January 2020].
Available from: https://www.kfw.de/PDF/Download-
Center/Konzernthemen/Research/PDF-Dokumente-

Digitalisierungsbericht-Mittelstand/KfW-Digitalisierungsbericht-
2018.pdf

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Annex
Table 3 Digital maturity models
Maturity model
Group
Sector
Methodology
Structure
Industry 4.0
Maturity Model
Frauenhofer Austria
Research GmbH (2017)
University /
Research
institute
Industry,
production,
manufacturing
questionnaire, software
supported calculation,
visualization and report
in a roadmap
9 dimensions
62 questions
5 rating levels
The Connected
Enterprise
Maturity Model
Rockwell Automation
(2014)
Consulting,
Company
Industry,
production,
manufacturing
five steps: assessment,
secure and updated
network and controls,
defined and organized
working data capital,
analytics, collaborations
3 dimensions
? questions
5 rating levels
Industry 4.0 /
Digital Operations
Self-Assessment of PwC*
Geissbauer et al. (2016)
Consulting
Industry,
production,
manufacturing
interviews and surveys
7 dimensions
? questions
4 rating levels
The Digital Advantage of
MIT Center for digital
Business and Capgemini
Consulting
Westerman et al. (2012)
Consulting
Industry,
production,
manufacturing
conceptual model but
refers to data (no
references) with MNCs
? dimensions
? questions
4 rating levels
Digital Maturity &
Transformation Study of
the university St Gallen
and Crosswalk AG
Back et al. (2016, 2017)
University /
Research
institute,
Company
cross-sectoral
literature review, expert
interviews, focus groups
9 dimensions
64 questions
5 rating levels
IDT-Quickcheck –
Digitales Reifegrad-
Analysetool
Hochschule Neu-Ulm
(HNU), minnosphere
GmbH (2017)
University /
Research
institute,
Company
cross-sectoral
online self-assessment,
based on answering 10
core questions on the
current status and the
planned status in 3 years
5 dimensions
50 questions
5 rating levels
Digitalisierungsindex –
Deutsche Telekom
Techconsult (n.d.)
Company
cross-sectoral
(selection at the
beginning)
online self-check to
determine your own
degree of digitalization
5 dimensions
71 questions
5 rating levels
Industrie 4.0-
Readiness-Modell IdW
Köln for VDMA*
IMPULS-Stiftung (2015)
University /
Research
institute,
Association
cross-sectoral
with focus on
technological
aspects
online self-check to
determine the individual
industry 4.0 maturity
level
6 dimensions
27 questions
6 rating levels

Readiness Check
Mittelstand 4.0
Kompetenzzentrum
Kaiserslautern (n.d.)
Consulting
cross-sectoral
online self-check
5 dimensions
25 questions
5 rating levels
Leitfaden Industrie 4.0
Industrie- und Handels-
kammer (IHK) München
& Oberbayern (n.d.)
Association
cross-sectoral
with focus on
technological
aspects
online self-check for
digital maturity level
with a total of 19 main
questions
4 dimensions
19 questions
11 rating levels
Quick Check Industrie
4.0 Reifegrad
Digital in NRW (n.d.)
University /
Research
institute
cross-sectoral
with focus on
technological
aspects
online questionnaire with
five possible answers to
each question for self-
evaluation
9 dimensions
15 questions
5 rating levels
Industrie 4.0-Readiness-
Index
H&D (2016)
Consulting
cross-sectoral
with focus on
technological
aspects
cooperative maturity
analysis in cooperation
with the respective
company
5 dimensions
? questions
? rating levels
Industrie 4.0-
Maturity-Index
Acatech (n.d.)
University /
Research
institute
cross-sectoral
identification of status
quo of industry 4.0 in
companies via
workshops
4 dimensions
? questions
6 rating levels
Digitaler Reifegrad von
Schweizer KMU*
Wyss (2017)
University /
Research
institute
cross-sectoral
with focus on
SMEs
study / survey
7 dimensions
54 questions
5 rating levels
Digital Maturity and
Value Assessment
McKinsey & Company,
Inc. (n.d.)
Consulting
public sector
survey - representative
sample of authorities /
departments
4 dimensions
76 questions
3 rating levels
Digital Maturity Model
tmforum (2020)
Association
cross-sectoral
online presentation with
different implementation
ideas
5 dimensions
110 questions
? rating levels
fme Reifegradmodell für
die dig. Transformation
fme AG (n.d.)
Consulting
cross-sectoral
assessment
5 dimensions
25 questions
5 rating levels
Potentialanalyse 4.0*
Offensive Mittelstand –
Gut für Deutschland
(2018)
Association
cross-sectoral
with focus on
SMEs
self-check with
implementation support
6 dimensions
166 questions
3 rating levels
*Models marked with the asterisk are analyzed more closely in chapter 4.