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This paper was presented at ISPIM Connects Global 2020: Celebrating the World of Innovation -
Virtual, 6-8 December 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-566-8
1
How SMEs digitally mature: Conceptual research
framework and initial findings
Christopher A. Williams*
Johannes Kepler University, Altenbergerstrasse 69, 4040, Linz,
Austria.
E-mail: chrs.a.williams@gmail.com
Daniel Schallmo
Neu-Ulm University of Applied Science, Wileystrasse 1, 89231, Neu-
Ulm, Germany.
E-mail: Daniel.schallmo@hnu.de
* Corresponding author
Abstract: The objective of this paper is to present essential developmental
principles for the development of digital maturity model for SMEs. Our initial
findings provide insights into concept and methodological principles of
developing a digital maturity model for SMEs. Our primary findings show that
there are significant concept and methodological deficiencies evident in the
existing maturity model for SME literature. These deficiencies have limited
SMEs’ ability to use a validated digital maturity as an important way to promote
strategic digital transformation and ultimately provide them with a proven digital
direction. The paper offers an important concept and methodology challenges on
what needs to solve. Furthermore, the paper outlines an appropriate research
methodology to advance this research field.
Keywords: This is; an example; of the style; for keywords. Please use about 10
keywords and separate them with semi-colons.
1 Initial Situation
In recent years, there has been a substantial interest in digital transformation initiatives for
SMEs (Maensam, 2019), in particular, in the research field of digital maturity model
(DMM) (Colli et al., 2019). However, research to date has consistently shown the
significant research gaps in the DMM field. From our previous systematic literature review
(SLR) on DMMs (Williams et al., 2019), it can be seen that the DMM research needs to
consider both contextual and methodological principles when developing any DMM
particularly for SMEs. For example, senior managements’ perceptions of current and future
digital states (i.e. a maturity model) is a valid method to later map out SMEs’ future digital
initiatives but if SMEs do not already have experience with conceptualizing and
implementing digital initiatives, it is difficult to do this successfully without some external
support (Gökalp, Şener, & Eren, 2017). The objective of this paper is to extend on our
This paper was presented at The ISPIM Innovation Conference – Innovating in Times of Crisis,
7-10 June 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
2
initial findings and propose a research methodology for developing a holistic DMM for
SMEs.
2 Theoretical Background
Digital Maturity Model
Based on existing definitions and our initial results, the following definition of a maturity
model (MM) is presented. MM involves the evaluation of maturity levels for organizational
silos (Becker et al., 2009) and a thorough gap analysis based on existing knowledge from
internal and external resources, digital data and real-life success factors.
Digital Maturity Models & Digital Transformation
Researchers argue that MM dimensions need to be based on real world SME success
factors to properly develop a DMM (Rosemann, Bruin & Power, 2006) but it is difficult to
identify these SME-oriented digital success factors when they are not visible. To address
this issue, future MMs should be internally developed with SMEs using primary and
secondary data sources which could allow for these hidden success factors to emerge
(Schumacher, Erol, & Sihn, 2016) and external experts. This is another argument for
developing holistic MMs in different countries and industries so that these models are not
embedded in one industrial and cultural context and allow more opportunities to discover
universal, common digital success factors. Additionally, the SME-oriented MMs
developed, which include success factors, will provide more relevant evidence of an
appropriate path to successfully digital transform that is currently lacking in current
research. Although cultural influences should be closely considered with DMMs (Williams
et al., 2019), due to the scope of this paper, cultural considerations will not be addressed.
Digital Maturity Models Developmental Principles
Based on our literature (Schumacher, Nemeth, & Sihn, 2019) and other initial findings, we
identified the following DMM development categories; 1) content principles and 2)
methodological principles. In the following section, we will outline the content and
methodological principles use to evaluate MMs.
Content Developmental Principles
We have identified essential content principles to consider when developing a DMM for
SMEs. As shown in Table 1, are the concept development principles.
Table 1 Content Principles
Content principles
Reasonings
Dimensions
Most MMs include dimensions (Schumacher,
Nemeth, & Sihn, 2019)
Sub-dimensions including criteria
20
Common terminology
Most popular MMs (CMMI, SPICE, etc.) use common
terminology with maturity levels
Digital success stories
Success stories are concrete example of improved
digital maturity with an organization (Becker,
Knackstedt, & Pöppelbuß, 2009)
Methodological Developmental Principles
We have identified essential methodological principles to consider when developing a
DMM for SMEs. In Table 2, the methodological developmental principles are shown.
Table 3 Methodological Principles
Methodological principles
Reasonings
Scope
Metalevel, development, application, and validation
(Williams et al., 2019)
Development methods
Mixed methods are ideal for MM development
(Schumacher, Erol, & Sihn, 2016)
3 Research Objectives and Questions
The objective of this paper is to explore essential developmental principles for the
development of DMM for SMEs. Based on the objective, we propose the following
research questions:
• What are the contextual and methodology principles of developing a digital
maturity model for SMEs?
• What is the most appropriate research methodology to investigate digital maturity
model for SMEs?
4 Research Design
This paper builds on our previous DMM work and extends on our analysis of twenty-five
models from different industries and different countries evaluated with methodological and
contextual research principles (Williams et al., 2019). Glaser and Strauss’ (1967) grounded
theory and Wieringa’s (2014) SLR procedure is considered a methodical and pragmatic
approach to conduct a literature review in innovation management research. The approach
This paper was presented at The ISPIM Innovation Conference – Innovating in Times of Crisis,
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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
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of our literature review derives directly from the design science methodology which is
called a SLR.
5 Initial Findings
To better visualize these content and methodological results and to answer RQ1, first Table
1 illustrates the content findings with existing MM research. Figure 1 shows an evaluation
tool and partial findings of the methodological principles.
Table 4 Content Principles Evaluation Findings
Authors
Content Findings
Anggrahini et al.
(2018)
• Lack of information and insights about smart manufacturing
systems
• Criteria were only formulated using experts (not real success
stories)
• Mentioned a gap analysis but did not outline how it was done
Singapore Smart
(2018)
• Insightful development path (e.g. common terminology)
• Unclear how data was collected
• MM too complex
Paavel et al. (2017)
• Focused only on product lifecycle management
• Only web-based questionnaire
VDMA (2016)
• Focused only on products
• Briefly mentioned gap analysis
• Only online questionnaire
Raber et al. (2013)
• Focus only on BI
• Only a questionnaire
• Quantitatively conducted a gap analysis
Depaoli & Za
(2013)
• Focus only on CMC
• Design science as overarching approach
• Proposed QCA
Ganzarain & Errasti
(2016)
• Weak focus on digital transformation
• Three stage process model
Mittal et al. (2018)
• Focused only on manufacturing
• Dimensions formed based on literature review (no primary
sources)
Buchalcevova
(2015)
• Focus only on Green ICT
• Only questionnaire
• Used Becker’s (2009) model but Becker also recommends
multiple sources
Isoherranen et al.
(2016)
• Focus only on process alignment
• Reported multiple case studies based on Yin (2017) but
aggregated interviews
Leyh et al. (2017)
• Design science as overarching methodology (4th phase)
• Only questionnaire
Igartua et al., (2018)
• Focused on general digital transformation
• Unclear how the model was tested and/or validated
Coleman et al.
(2016)
• Focused on big data
• Unclear what primary sources were collected to form
dimensions
Prashar (2017)
• “Hypothetical-deductive” (i.e. SLR) methodology (no primary
sources)
• Case studies used to validate the instrument but not the model as
a whole
• Gap analysis mentioned
Rad et al. (2017)
• Focus only on software processes
• Used qualitative and quantitative methods
• Validated the model as a whole (e.g. focus group and Delphi
method)
Hamidi et al. (2018)
• Good focus on digital transformation but aimed at
manufacturing
• Only questionnaires
Butzer (2016)
• Focused well on digital transformation (European Foundation
for Quality Management) but only manufacturing
• Validation was mentioned but not clearly reported
Ormazabal et al.
(2016)
• Focus only on environment management
• Case studies were conducted but only surveys and interviews
Häberer et al. (2017)
• Good focus on digital transformation but aimed at
manufacturing
• Only questionnaires
Kim et al. (2018)
• Focus only on software test processes
• Heavily influenced by CMMI and TMM
Parra et al. (2019)
• Mixture of manufacturing and service industries
• Only used surveys and two interviews
Warnecke et al.
(2018)
• Focus on environment IS
• Only used surveys and interviews
Becker (2008)
• Focus only on project management
• The dimension requirements (i.e. items) were not reported
Reckin &
Brandenburg (2016)
• Focus only on usability
• Only used questionnaire (pre- and post-test)
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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
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Reza et al. (2018)
• Focused only on information modeling
• Only questionnaire
Content Principle Issues
The problematic areas of the content of the analyzed twenty-five DMM for SMEs are listed
below.
• Several MMs focus on specific areas/industries, but very few provide a
holistic/general DMM for SMEs
• A majority of the DMMs focus on manufacturing a product; lack of services
• Absence of common terminology to describe digital maturity levels
• Far too often MMs were only conceptualized
• Few MMs published their digital requirements
• Dimensions were formed without using real digital transformation success factors
• Lack of success factors could be due to the lower research rigor
As shown in Figure 1, a sample of the evaluation tool used to evaluate the
methodological principle is shown. All of the twenty-five DMMs were evaluated based on
these criteria. To help identify the research gaps, we categorized as either a) black dot
(well-covered), b) white dot (partly covered) c) or a blank (not covered).
Figure 1 Methodological Evaluation Tool
Method Principles Issues
The problematic areas of the methodological principles used in the analyzed twenty-five
DMMs for SMEs are listed below.
• Often DMMs only used quantitative research methods
• Case studies were mentioned, but only completed (expert) interviews
• Some instruments were validated, but the model as a whole was not validated
Based on these MM deficiencies discovered, these two research gaps were identified in
Table 5:
Table 5 Maturity Model Research Gaps
Research gaps
Evidence of research gaps
Current MMs for SMEs are
inadequate
• Very few holistic/general DMMs for SMEs
• DMMs focusing on services not well represented
• Absence of common terminology to describe
digital maturity levels
• Frequently digital maturity models only
conceptualized
• MMs digital requirements rarely published
• Digital transformation success factors for SME
not present in models
• Lack of success factors could be due to the lower
research rigor
Current studies in DMMs for SMEs
suffer from low research rigor
• The MM as a whole is seldomly validated
• There is strong evidence of a poor mixed method
approach
• Inconclusive case studies results
• Often DMMs only used quantitative research
methods
To answer RQ2, we first identified well-defined research criteria. It is quite clear that future
MM studies demand well-defined (specific) research quality criteria (Drost, 2011). In
Table 6, an overview of the quality criterion for future MM studies.
This paper was presented at The ISPIM Innovation Conference – Innovating in Times of Crisis,
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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
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Table 6 Maturity Model Methodological Criteria
Quality Criteria
Methodological Criteria
Construct validity
Top-down/bottom-up approach
Digital success stories
Internal validity
Iterative approach
External validity
Validated Digital Maturity Model
Academic & Practitioner driven
Reliability
Overarching research strategy
Increased research rigor
Based on the methodological quality criteria for MMs, design science is recommended as
the overarching research methodology and case studies as the research method. In the next
section, the design science methodology and case study method approach will be discussed.
Design Science Research Methodology
The research procedure is based on Peffers, Tuunanen, Rothenberger, and Chatterjee’s
(2007) design science research methodology (DSRM). There is clear evidence from design
science literature that conducting highly rigorous research is essential for providing
valuable results to academics and practitioners (Venable, Pries-Heje, & Baskerville, 2012).
The design science methodology is considered a highly structured research approach that
leads to better transparency and higher validity (van Aken, 2016). Design sciences
encourages multiple evaluation methods (i.e. data collection methods) to be used which
allows for data triangulation and increased internal validity and reliability. Additionally,
design science aims to develop and evaluate generic artifacts which can be used in different
contexts. For example, design science was responsible for developing the Business Model
Canvas which is a popular innovation management tool used by companies of all sizes
(Joyce & Paquin, 2016) to conceptualize business models. Design science has been
recommended as an ideal methodology to develop and investigate MMs (Becker et al.,
2009) as well as empirically assess MMs (Donnellan & Helfert, 2010). Design science is
considered a multi-disciplinary approach but also highlights the challenge of researchers
who do not possess the appropriate research skills.
Recent trends in the field of management research have led to an interest in case studies
(Patton & Appelbaum, 2003). To better develop well-developed theoretical propositions
and workable assumptions at the end of the dissertation, a multiple case study approach is
recommended at the beginning and at the end of the design science research procedure.
The concept of using case studies in two different instances during the development of an
artifact has been proposed but to our knowledge, has not been empirically tested.
Figure 2 Proposed DSRM framework with case studies
The attractiveness of using case studies within the DSRM is our DMM should not only be
evaluated but more importantly validated in an authentic business setting. Yin (2017)
recommends several initiatives to increase the validity of the research design which will be
described later in the quality criteria section (Yin, 2017). With case studies, the possibility
of using multiple data collections methods allows for data triangulation. Yin emphasizes
the importance of data triangulation which he describes as an analytic strategy which
increases the likelihood of the accuracy of the research findings.
Another reason why a multiple case approach was chosen is the ability to make
generalizations. Yin points out, in quantitative research, statistical generalizations are used
to make inferences about a population. There have been valuable insights of using
statistical analysis within a case study approach (Raber, Wortmann & Winter, 2013), but
we argue that qualitative data can serve to validate these results by providing rich data
which can better evaluate the phenomena. Critics of single case studies have pointed to the
inability to make generalizations with small sample sizes (Mayring, 2010), but to address
this issue, a multiple case study was chosen to better detect relationships in different
contexts (Eisenhardt & Graebner, 2007). Multiple cases allow the comparison of the
findings across different cases (e.g. SMEs) and in doing so, develop generalizable findings
which Yin calls analytic generalizations. The goal of using multiple case studies as an
evaluation and validation method is to develop usable theories for SMEs in different
industries. Yin (2017) claimed that “the case study, like the experiment, does not represent
at “sample”, and in doing case study research, the goal will be to expand and generalize
theories” (pg. 273). The generalizable findings are used to develop a conceptual framework
(i.e. DMM for SMEs) and the lessons learned during the multiple cases study would
provide as working hypotheses for future studies (Cronbach, 1975).
The existing literature review of the development of MMs clearly shows the interest of
using case studies as a research method (Isoherranen et al., 2016; Ormazabal et al., 2016);
Prashar, 2017). Although case studies can be useful to develop and validate MMs, many
of these studies suffer from some limitations. For example, several studies only reported
using one or two data collection methods. Another limitation of these studies is the lack of
This paper was presented at The ISPIM Innovation Conference – Innovating in Times of Crisis,
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Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
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transparency which is needed to increase the overall validity of the study. Although
validation has not been considered a priority in previous research, properly executed case
studies can provide high validation for MMs (Garcia & Giachetti, 2010). Based on our
literature review and lack of validation in MM research, another focus of our research
design will be the emphasis of high validation. In summary, this paper chose a multiple
case research approach to offer a fresh perspective on using case studies in developing MM
by putting a significant emphasis on high validation and making generalizations based on
the cross-case synthesis.
Sample
A multiple case study approach provides the ability of replication logic where after
collecting the data, there is the flexibility of comparing a direct replication or contrasting
cases (Yin, 2003). In contrast to quantitative research where large sample sizes are
desirable, this paper requires smaller sample sizes so that we gain a deeper understanding
of the phenomena (Eisenhardt, 1998). In the future research studies, the research findings
should report whether multiple highly digitally mature SMEs or contrasting digitally
mature SMEs best address the research questions.
Unit of analysis and participants
Appropriate existing MM theories are necessary to propose the unit of analyses (Yin,
2003). The proposed unit of analysis is the digital transformation in SMEs. The four
proposed embedded unit of analyses are:
• The perceived understanding of what digital transformation is, how does it affect
employees’ business and personal lives
• Senior and mid-level managers
• The overall corporate culture
• Direct reports to the senior and mid-level managers
To achieve the data triangulation needed to make generalizations, a pragmatic data
collection approach is proposed (Jick, 1979; Eisenhardt, 1989; Eisenhardt & Graebner,
2007; Yin, 2017) Figure 3 outlines the proposed data collection methods for the stage 2.
Figure 3 Detailed overview of stage 2
This paper proposes that the primary researcher visits all the SMEs. During these visits,
the primary researcher interviews various senior and mid-level managers. Additionally,
telephone interviews are conducted with Chief Executive Officers (CEOs) of SMEs.
Finally, SMEs should grant access to internal documentation and records to the primary
researcher.
The focus of the data collection is on each of the initial DMM dimensions. The participants
are asked about their current understanding of the dimensions, their previous experiences
related to the dimensions and the future importance of the dimensions.
The interviews, surveys, and internal documentation come from various organizational
levels: senior level (e.g. CEO), mid-level (e.g. sales manager), low level (e.g. assembly
line worker) and if possible, one external stakeholder (e.g. customer). Once the data is
collected, the primary researcher generates separate databases for each of the SMEs. Each
database contains quantitative and qualitative data on the five topics.
Data Analysis
At the start of any research project, a literature review is performed. Design science is a
methodology that ultimately aims to develop and assess a developed artifact in a real-life
setting (Hevner & Chatterjee, 2010; March & Smith, 1995). The benefits of using the SLR
are the methodology’s well-defined, structured processes to evaluate the literature and also
its high transparency. The high transparency is particularly important because higher
transparency allows for the literature review to be reproduced and improve validity which
is important for this paper.
The outcome of the SLR allowed for an initial DMM to be developed before the collection
of any primary data. It is important to briefly outline the data analysis method of our case
studies so that theory can be developed. The development of theory can either be inductive,
deductive or abductive. The deductive approach is considered an approach to test existing
theories (Eisenhardt, 1998). In this sense, a solely deductive approach would be more
appropriate quantitative research design where statistical analyses bear more fruitful
findings. Purely inductive reasoning is described as theory development which begins from
the start of the original data but does not consider previous literature (Suddaby, 2006). One
of the most cited inductive theories is the grounded theory (Glaser & Strauss, 1967);
Eisenhardt, 1989). The studies, which are purely inductive, claim that existing literature
and theories would not assist in answering the paper’s research questions (Eisenhardt &
Graebner, 2007). In MM research using case studies, researchers should use other well-
documented case studies as a guide (Barratt, Choi & Li, 2011); therefore, a purely inductive
approach is not suitable. This paper recognizes the importance of existing MM research
but also the need for original data to help revise and form new MM dimensions. To
accomplish this task, an abductive approach is recommended.
The outcome of the SLR allowed for an initial DMM to be developed before the collection
of any primary data. It is important to briefly outline the data analysis method of our case
studies so that theory can be developed. The development of theory can either be inductive,
deductive or abductive. The deductive approach is considered an approach to test existing
This paper was presented at The ISPIM Innovation Conference – Innovating in Times of Crisis,
7-10 June 2020.
Event Proceedings: LUT Scientific and Expertise Publications: ISBN 978-952-335-466-1
12
theories (Eisenhardt, 1989). In this sense, a solely deductive approach would be more
appropriate quantitative research design where statistical analyses bear more fruitful
findings. Purely inductive reasoning is described as theory development which begins from
the start of the original data but does not consider previous literature (Suddaby, 2006). One
of the most cited inductive theories is the grounded theory (Glaser & Strauss, 1967;
Eisenhardt, 1989). The studies, which are purely inductive, claim that existing literature
6 Contributions
The paper contributes to the MM research by providing content and methodological
principles which need to be closely considered for future DMM for SMEs. Additionally,
this paper offers research methodology approach to further the advancement of the MM
field. Based on these findings, we discovered considerable content and methodological
deficiencies with existing DMMs for SMEs. Moreover, the findings and future research
recommendations proposes an excellent opportunity to close some of the MM research
gaps.
7 Practical Implications
Senior managers and business developers, who are involved in strategic management and
developing strategic digital directions for their organizations, would benefit from the
findings particularly the content principles. Scholars, who are active in strategic and
innovation management field, profit particularly from the methodological principles and
methodological gaps to provide future recommendations for their own MM research. The
conceptual research design outlines an opportunity of using DSRM and case studies to
develop a state-of-the-art artifact which would allow scholars and practitioners to further
their digital initiatives.
8 Limitations
The aim of this paper was to report our content and methodological principle findings and
based on these findings, propose an appropriate research methodology. Readers need to be
aware that due to theoretical constraints and the lack of primary data sources that the
presented results may not be generalizable. Furthermore, the outlined principles and
proposed research methodology need to be further investigated.
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