ArticlePDF Available

Abstract and Figures

A Maturity Model is a widely used technique that is proved to be valuable to assess business processes or certain aspects of organizations, as it represents a path towards an increasingly organized and systematic way of doing business. A maturity assessment can be used to measure the current maturity level of a certain aspect of an organization in a meaningful way, enabling stakeholders to clearly identify strengths and improvement points, and accordingly prioritize what to do in order to reach higher maturity levels. This paper collects and analyzes the current practice on maturity models, by analyzing a collection of maturity models from literature.
Content may be subject to copyright.
Procedia Computer Science 100 ( 2016 ) 1042 1049
1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of CENTERIS 2016
doi: 10.1016/j.procs.2016.09.279
ScienceDirect
Available online at www.sciencedirect.com
Conference on ENTERprise Information Systems / International Conference on Project
MANagement / Conference on Health and Social Care Information Systems and Technologies,
CENTERIS / ProjMAN / HCist 2016, October 5-7, 2016
Maturity Models for Information Systems - A State of the Art
Diogo Proençaa,b
*
, José Borbinhaa,b
aINESC-ID, Rua Alves Redol 9, 1000-029 Lisboa, Portugal
bInstituto Superior T
l
cnico, Universidade de Lisboa, Avenida Rovisco Pais 1, 1049-001 Lisboa, Portugal
Abstract
A Maturity Model is a widely used technique that is proved to be valuable to assess business processes or certain aspects of
organizations, as it represents a path towards an increasingly organized and systematic way of doing business. A maturity
assessment can be used to measure the current maturity level of a certain aspect of an organization in a meaningful way, enabling
stakeholders to clearly identify strengths and improvement points, and accordingly prioritize what to do in order to reach higher
maturity levels. This paper collects and analyzes the current practice on maturity models, by analyzing a collection of maturity
models from literature.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of SciKA - Association for Promotion and Dissemination of Scientific Knowledge.
Keywords: Maturity Model, Maturity Assessment, State of the Art.
1. Introduction
A Maturity Model (MM) is a technique that has been proved to be valuable in measuring different aspects of a
process or an organization. It represents a path towards increasingly organized and systematic way of doing business
in organizations. A MM consists of a number of “maturity levels”, often five, from the lowest to the highest, Initial,
Managed, Defined, Quantitatively Managed and Optimizing (however, the number of levels can vary, depending on
the domain and the concerns motivating the model). This technique provides organizations: (1) A measuring for
auditing and benchmarking; (2) A measuring of progress assessment against objectives; (3) An understanding of
* Corresponding author. Tel.: +351213100300; fax: +351213145843.
E-mail address: diogo.proenca@tecnico.ulisboa.pt
© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of CENTERIS 2016
1043
Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
strengths, weaknesses and opportunities (which can support decision making concerning strategy and project portfolio
management).
In 2 maturity is defined as a specific process to explicitly define, manage, measure and control the evolutionary
growth of an entity. In turn, in 3 maturity is defined as a state in which an organization is perfectly able to achieve the
goals it sets itself. In 5 and 6 it is suggested that maturity is associated with an evaluation criterion or the state of being
complete, perfect and ready and in 7 as being a concept which progresses from an initial state to a final state (which is
more advanced), that is, higher levels of maturity. Similarly, in 4 maturity is related with the evolutionary progress in
demonstrating a particular capacity or the pursuit of a certain goal, from an initial state to a final desirable state. Still,
in 11 it is emphasized the fact that this state of perfection can be achieved in various ways. The distinction between
organizations with more or less mature systems relates not only to the results of the indicators used, but also with the
fact that mature organizations measure different indicators when comparing to organizations which are less mature 12.
While the concept of maturity relates to one or more items identified as relevant14, the concept of capability is
concerned only with each of these items.
In 1 maturity models are defined as a series of sequential levels, which together form an anticipated or desired
logical path from an initial state to a final state of maturity. In 9 maturity models are defined as tools used to evaluate
the maturity capabilities of certain elements and select the appropriate actions to bring the elements to a higher level
of maturity. Conceptually, these represent stages of growth of a capability at qualitative or quantitative level of the
element in growth, in order to evaluate their progress relative to the defined maturity levels.
Some definitions found involve organizational concepts commonly used, such as the definition of 8 in which the
authors consider a maturity model as a "... a framework of evaluation that allows an organization to compare their
projects and against the best practices or the practices of their competitors, while defining a structured path for
improvement." This definition is deeply embedded in the concept of benchmarking. In other definitions, such as in
the presented by 10 there appears the concern of associating a maturity model to the concept of continuous
improvement. In 14, the maturity models are particularly important for identifying strengths and weaknesses of the
organizational context to which they are applied, and the collection of information through methodologies associated
with benchmarking.
2. Maturity Models Analysis
This section provides a synthesis of the analysis of a set of maturity models. The references used for this section
were found after searches on several services, such as, Google; Google Scholar; IEEE Xplore; ACM Digital Library;
Springer Link; CiteSeerX. The search terms included the keywords maturity, ͒maturity model, capability maturity,
capability maturity model, stages theory, process maturity, among others. The search resulted in an initial set of
papers, of which the references were analysed and selected, if relevant. ͒In this section, we are analysing maturity
models from the most diverse domains, from software engineering to asset management and information governance.
The goal here was to select different maturity models that reflect the most diverse approaches, some influenced by
other maturity models, others with no name for the maturity levels, others that have a level 0, without focusing on just
one domain.
In 14 it is summarized the major features of various maturity models. According to these authors, the maturity
models can be classified according to several items the most important being the number of maturity levels that make
up the model, its discrete or continuous nature, if the results obtained are quantitative or not, and if they adopt a
philosophy of continuous improvement. In addition to the inherent characteristics of the model design the authors
refer to characteristics that influence model applicability and dispersion. In particular, the associated costs, ease of
use, simplicity of interpretation, consistency in terms of continuity between model versions arising from its iterative
nature and the relative difficulty of the necessary training 15.
Table 1, Table 2 and Table 3 synthesize the information collected through the selected literature sources in a way
that eases the analysis and according to the variables detailed in 14 and 4. The simplicity associated with the
development and use of maturity models has its "price", and several authors point out some limitations to maturity
models 16, 12, for example: (1) Overly simplistic in relation to reality; (2) Lack of fundamentals; (3) They focus on a
single path to reach maturity, neglecting potentially advantageous alternative paths; (4) Its applicability may be
constrained by internal factors (available technology, intellectual property, supplier relations) or external factors
1044 Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
(market conditions); (5) There are multiple identical maturity models; (6) Lack of information regarding the maturity
model development method. Minimizing the limitations pointed to maturity models can be achieved by ensuring a
continuous and iterative evaluation, as well as a comparison with other models used for the same purpose16, 17.
2.1. Model Structure
The model structure analysis focuses on the structural aspects of the maturity model. We analyze the number of
levels, the name and number of attributes, whether the model provides a maturity definition and the practicality of the
model. Table 1 synthetizes the analyzed maturity models regarding the model structure. It uses a set of variables
selected from 14 and 4. The following variables were selected:
1. Name of the Maturity Model: The name of the maturity model and the main references;
2. Number of Levels: The quantity of maturity levels of the model;
3. Name of the attributes: The name of attributes the maturity model uses, there are several attributes being used.
The attributes aim at three things, (1) Decompose the Maturity Model into easily understandable sections; (2)
Aggregate several business processes into process areas that aggregate processes meeting the same business goal
and (3) Provide different viewpoints of the maturity level subject;
4. Number of Attributes: The number of attributes used by the maturity model;
5. Maturity Definition: Shows if the maturity model contains a definition of maturity;
6. Practicality: Details if the practicality of the recommendations is problem-specific or general in nature.
2.2. Model Assessment
The model assessment analysis focuses on the application of the maturity model. In order to measure the maturity
level of a certain reality there must be available a way to calculate the maturity levels. This can be done by following
a self-assessment questionnaire or by following a full-fledged maturity assessment method. Table 2 synthetizes the
analyzed maturity models regarding the model assessment. It uses a set of variables selected from 14 and 4. The
following variables were selected:
1. Name of the Maturity Model: The name of the maturity model and the main references;
2. Assessment Method Described: Details if the maturity model has an associated assessment method or not;
3. Assessment Cost: Shows the degree of expenditure of an assessment project;
4. Strong/Weak Points Identification: Details if the maturity model identifies weaknesses and strong points of
the organization;
5. Continuous Assessment: Shows if the maturity model strives for a continuous assessment;
6. Improvement Opportunities Prioritization: Details if the maturity model determines a priority of
improvement in the organization.
2.3. Model Support
The model support analysis focus on the support to the model provided by the maturity model authors or stewards.
The analysis focus on whether training is available, what is the availability of the author regarding model support,
whether there is continuity from different versions of the model, what is the origin of the model, as well as, the
accessibility of the model. Table 3 synthetizes the analyzed maturity models regarding the model support. It uses a set
of variables selected from 14 and 4. The following variables were selected:
1. Name of the Maturity Model: The name of the maturity model and the main references;
2. Training Available: Details if there is training available for the maturity model, in order to become an expert
on the model or an assessor;
3. Author Support Availability: Shows the degree of the support the author provides for the maturity model;
4. Continuity from different versions: Details, when there is more than one version of the maturity model, if there
is continuity between different versions of the model. This is important to show if the model is adaptable or not;
5. The origin of the model: Whether it originated in the academia or from practitioners;
6. Accessibility: Whether there is documentation readily available for free or not.
1045
Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
Table 1. Synthesis of the Analysed Maturity Models regarding Model Structure
Maturity Model
Number of levels
Name of the
attributes
Number of
Attributes
Maturity
Definition
ISO/ IEC 1550435
6
Process Groups
9
Yes
Software Engineering Institute
Capability Model Integration
(CMMI)34
5
Process Areas
22
Yes
Model-driven Development
(MDD) Maturity Model26
5
MDD Practices
3
No
Metrics Based Verification and
Validation Maturity Model
(MB-V2M2)24
5
Fundamental
Factors
4
No
Documentation Process
Maturity Model23
4
-
-
No
Business Process Maturity
Model (BPMM)25
5
Elements
4
No
OMG Business Process
Maturity Model18
5
Process Areas
30
No
Gartner BPM Maturity Model19
6
Critical Success
Factors
6
No
Group IT Controlling (GITC)
Maturity Model37
6
Dimension / Sub-
dimension
3 / 6
No
IT Capability Model
Framework (IT-CMF)39
5
Process Areas
4
Yes
Business-IT Alignment
Maturity Model20
5
Key Process
Areas
5
No
The IT Service CMM21
5
Categories /
Dimensions
3 / 13
No
Records Management Maturity
Model30
5
Categories /
Dimensions
4 / 15
No
Gartner Enterprise Information
Management Maturity Model22
5
Dimensions /
Category
4
No
Research Data Management
(RDM) Maturity Model38
5
IT-business
alignment criteria
6
No
Enterprise Content
Management (ECM) Maturity
Model32
5
Process Areas
21
No
Digital Asset Management
(DAM) Maturity Model31
4
Section
9
No
Asset Management Maturity
Model29
6
-
-
No
Risk Maturity Model27
4
Attributes
4
No
COBIT Maturity Model40
6
Attributes
6
Yes
Information Governance
Maturity Model33
5
Principles
8
No
Stanford Data Governance
Maturity Model28
5
Dimensions
3
No
1046 Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
Table 2. Synthesis of the Analysed Maturity Models regarding Model Assessment
Maturity Model
Assessment
Method
Described
Assessment
Cost
Strong/Weak
Points
Identification
Continuous
Assessment
Improvement
Opportunities
Prioritization
ISO/ IEC 1550435
Yes
High
Yes
Yes
Yes
Software Engineering Institute
Capability Model Integration
(CMMI)34
Yes
High
Yes
Yes
Yes
Model-driven Development
(MDD) Maturity Model26
No
?
Yes
?
?
Metrics Based Verification and
Validation Maturity Model
(MB-V2M2)24
Yes
?
Yes
?
?
Documentation Process
Maturity Model23
Yes
?
No
No
?
Business Process Maturity
Model (BPMM)25
No
?
Yes
?
?
OMG Business Process
Maturity Model18
No
Medium
Yes
Yes
?
Gartner BPM Maturity Model19
No
Low
Yes
?
?
Group IT Controlling (GITC)
Maturity Model37
No
?
No
?
?
IT Capability Model
Framework (IT-CMF)39
Yes
High
Yes
Yes
Yes
Business-IT Alignment
Maturity Model20
No
?
No
?
?
The IT Service CMM21
No
?
No
?
?
Records Management Maturity
Model30
Yes
?
No
?
?
Gartner Enterprise Information
Management Maturity Model22
Yes
?
No
?
?
Research Data Management
(RDM) Maturity Model38
Yes
?
Yes
?
?
Enterprise Content
Management (ECM) Maturity
Model32
No
?
No
No
?
Digital Asset Management
(DAM) Maturity Model31
Yes
?
No
No
No
Asset Management Maturity
Model29
No
Low
Yes
Yes
?
Risk Maturity Model27
Yes
?
No
No
No
COBIT Maturity Model40
Yes
High
Yes
Yes
Yes
Information Governance
Maturity Model33
No
Medium
Yes
Yes
?
Stanford Data Governance
Maturity Model28
Yes
?
Yes
?
?
1047
Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
Table 3. Synthesis of the Analysed Maturity Models regarding Model Support
Maturity Model
Author
Support
Availability
Training
Available
Origin
Accessibility
Continuity
from different
versions
ISO/ IEC 1550435
High
Yes
Academic
Charged
Yes
Software Engineering Institute
Capability Model Integration
(CMMI)34
High
Yes
Academic
Free
Yes
Model-driven Development
(MDD) Maturity Model26
Low
No
Academic
Free
No
Metrics Based Verification and
Validation Maturity Model
(MB-V2M2)24
Low
No
Academic
Free
No
Documentation Process
Maturity Model23
Low
No
Academic
Free
No
Business Process Maturity
Model (BPMM)25
Low
No
Academic
Free
No
OMG Business Process
Maturity Model18
Medium
No
Practitioner-
based
Free
No
Gartner BPM Maturity Model19
Medium
No
Practitioner-
based
Free
No
Group IT Controlling (GITC)
Maturity Model37
Medium
No
Academic
Free
No
IT Capability Model
Framework (IT-CMF)39
High
Yes
Academic
Charged
Yes
Business-IT Alignment
Maturity Model20
Medium
No
Academic
Free
No
The IT Service CMM21
Medium
No
Academic
Free
No
Records Management Maturity
Model30
Medium
No
Practitioner-
based
Free
No
Gartner Enterprise Information
Management Maturity Model22
Low
No
Practitioner-
based
Free
No
Research Data Management
(RDM) Maturity Model38
Low
No
Academic
Free
No
Enterprise Content
Management (ECM) Maturity
Model32
Low
No
Practitioner-
based
Free
No
Digital Asset Management
(DAM) Maturity Model31
Low
No
Practitioner-
based
Free
No
Asset Management Maturity
Model29
Medium
No
Practitioner-
based
Free
No
Risk Maturity Model27
Low
No
Academic
Free
No
COBIT Maturity Model40
High
Yes
Practitioner-
based
Charged
Yes
Information Governance
Maturity Model33
High
Yes
Practitioner-
based
Charged
No
Stanford Data Governance
Maturity Model28
Medium
No
Academic
Free
No
1048 Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
3. Conclusions and Future Work
This work presented a state of the art on the subject of maturity models. Future research will help Maturity Models
become more relevant for both academia and industry. In this paper we also described the concepts which form the
foundation of maturity models. A description of the different aspects of current maturity models was presented,
combining knowledge from the different domains analyzed.
As future work resulting from this paper, we concluded that current maturity assessment methods focus on highly
complex and specialized tasks being performed by competent assessors in an organizational context36. These tasks
mainly focus on manually collecting evidence to substantiate the maturity level calculation34. Because of the
complexity of these methods, maturity assessment becomes an expensive and burdensome activity for organizations.
As such, one major area to invest is to develop methods and techniques to automate maturity assessment. Due to
the wide spread of modeling practices of business domains, assisted by modeling tools, makes it possible to have
access, for processing, to the data created and managed by these tools. Also, the recent state of the art41 demonstrating
how business processes and Enterprise Architecture models in general can be represented as ontologies has raised the
potential relevance of the semantic techniques for the automated processing of these models. As such, the objective is
to analyze the potential, and the main limitations, of the existing semantic techniques to automate methods for the
assessment of MM through the analysis of an existing model representation of a reality.
There are several examples of models used to represent an organization architecture, such as, Archimate44, BPMN43
or UML45. These models are descriptive and can be detailed enough to allow to perform, to some extent, maturity
assessment. However, in order for these models to become relevant for maturity assessment there should be a formal
representation for both MMs and model representations. One hypothesis is that building on the knowledge of
ontologies from the computer science and information science domains, these can be used to represent MMs and
model representations. This can be achieved by developing a generic ontology that expresses all these core concepts
(or at least a relevant group of them) and relationships among them, as also the rules for a generic maturity assessment
accordingly Then, by representing MMs and models representations of concrete organizational scenarios using
ontologies we can verify if an organization models representations matches the requirements to reach a certain
maturity level using ontology query and reasoning techniques, such as SPARQL and Description Logics46 inference.
The final objective is thus to identify how these methods and techniques can be used in existing maturity assessment
methods36,42, so that they can be proven as relevant to enable the automation of certain aspects of maturity assessment,
such as, the maturity level determination. In order to do this, there should be an exploration of what types of analysis
can be performed using the information on model representations that is relevant in a maturity assessment effort.
Acknowledgements
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference
UID/CEC/50021/2013, and by the European Commission under the Competitiveness and Innovation Programme
2007-2013, E-ARK Grant Agreement no. 620998 under the Policy Support Programme. The authors are solely
responsible for the content of this paper.
References
1. M. Röglinger, J. Pöppelbuß, “What makes a useful maturity model? A framework for general design principles for maturity models and its
demonstration in business process management,” In proceedings of the 19th European Conference on Information Systems, Helsinki, Finland,
June. 2011.
2. M. Paulk, B. Curtis, M. Chrissis, C. Weber, “Capability Maturity Model for software,” Version 1.1 CMU/SEI-93-TR-24, Pittsburgh,
Pennsylvania, USA, Carnegie Melon University. 1993.
3. E. Anderson, S. Jessen, “Project Maturity in Organizations,” International Journal of Project Management Accounting, Vol. 21, pp. 457-461.
2003.
4. T. Mettler, “A design science research perspective on maturity models in information systems,” St. Gallen: Institute of Information Management,
Universtiy of St. Gallen. 2009.
5. Franz, “Proposta de um modelo para avaliação e ações de melhoria na gestão da segurança e saúde no trabalho,” Tese de doutoramento, Escola
de Engenharia, Universidade Federal de Rio Grande do Sul. 2009.
6. R. Fitterer, P. Rohner, “Towards assessing the networkability of health care providers: a maturity model approach,” Information Systems E-
business Management, Vol. 8, pp. 309-333. 2010.
1049
Diogo Proença and José Borbinha / Procedia Computer Science 100 ( 2016 ) 1042 – 1049
7. A. Sen, K. Ramammurthy, A. Sinha, “A model of data warehousing process maturity,” In IEEE Transactions of Software Engineering. 2011.
8. A. Korbel, R. Benedict, “Application of the project management maturity model to drive organisational improvement in a state owned
corporation,” In proceedings of 2007 AIPM Conference, Tasmania, Australia, 7-10, October. 2007.
9. M. Kohlegger, R. Maier, S. Thalmann, “Understanding maturity models: Results of a structured content analysis,” In proceedings of the I-KNOW
’09 and I-SEMANTICS ’09, 2-4 September 2009, Graz, Austria. 2009.
10. G. Jia, Y. Chen, X. Xue, J. Chen, J. Cao, K. Tang, “Program management organization maturity integrated model for mega construction
programs in China,” International Journal of Project Management, Vol. 29, pp. 834-845. 2011.
11. A. Amaral, M. Araújo, “The organizational maturity as a conductive field for germinating business sustainability,” In proceedings of Business
Sustainability I Conference, Póvoa do Varzim, Portugal. 2008.
12. T. Cooke-Davies, A. Arzymanowc, “The maturity of project management in different industries: An investigation into variations between
project management models,” International Journal of Project Management, Vol. 21, No 6, pp. 471-478. 2003.
13. D. Hillson, “Maturity - good or bad?,” Project Manager Today, March, pp. 14. 2008.
14. M. Koshgoftar, O. Osman, “Comparison between maturity models,” In proceedings of the 2nd IEEE International Conference on Computer
Science and Information Technology, Vol. 5, pp. 297-301. 2009.
15. L. Bourne, A. Tuffley, “Comparing maturity models: CMMI, OPM3 and P3M3,” In proceedings of the PMOZ Conference, 28-31 August.
2007.
16. J. Becker, R. Knackstedt, J. Pöppelbuβ, “Developing maturity models for IT management: A procedure model and its application,” Business
and Information Systems Engineering, Vol. 3, pp 213-222. 2009.
17. Y. Helgesson, M. Höst, K. Weyns, “A review of methods for evaluation of maturity models for process improvement,” Journal of Software
Maintenance and Evaluation Research and Practice, Vol. 24, No 4, pp. 436 -454. 2012.
18. Open Management Group, “Business Process Maturity Model (BPMM) - Version 1.0,” 2008.
19. M. J. Melenovsky, J. Sinur, “BPM Maturity Model Identifies Six Phases for Successful BPM Adoption,” Gartner, 2006.
20. J. Luftman, “Assessing Business-IT Alignment Maturity,” In Strategies for Information Technology Governance, Idea Group Publishing, 2004.
21. F. Niessink, V. Clerc, H. Vliet, “The IT Service Capability Maturity Model,” IT Service CMM Release L2+3-0.3, 2002.
22. D. Newman, D. Logan, “Gartner Introduces the EIM Maturity Model,” Gartner, 2008.
23. M. Visconti, C. R. Cook, “Evolution of a maturity model - critical evaluation and lessons learned,” Software Quality Journal, vol. 7, pp. 223-
237, 1998.
24. J. Jacobs, J. Trienekens, “Towards a Metrics Based Verification and Validation Maturity Model,” In Proceedings of the 10th International
Workshop on Software Technology and Engineering Practice, pp. 123-128, 2002.
25. J. Lee, D. Lee, S. Kang, “An overview of the Business Process Maturity Model (BPMM),” In Proceedings of the APWeb/WAIM 2007
International Workshops, pp. 384-395, 2007.
26. E. Rios, T. Bozheva, A. Bediaga, N. Guilloreau, “MDD Maturity Model: A Roadmap for Introducing Model-Driven Development,” In
Proceedings of the Second European Conference ECMDA-FA 2006, pp. 78-89, 2006.
27. D. A. Hillson, “Towards a Risk Maturity Model,” The International Journal of Project & Business Risk Management, vol. 1, no. 1, pp. 35-45,
1997.
28. Stanford University, “Data Governance Maturity Model.” [Online]. Available: http://web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/
29. T. Lei, A. Ligtvoet, L. Volker, P. Herder, “Evaluating Asset Management Maturity in the Netherlands: A Compact Benchmark of Eight Different
Asset Management Organizations,” In Proceedings of the 6th World Congress of Engineering Asset Management, 2011.
30. JISC InfoNet, “Records Management Maturity Model.” [Online]. Available: http://www.jiscinfonet.ac.uk/tools/maturity-model/
31. Real Story Group, DAM Foundation, “The DAM Maturity Model.” [Online]. Available: http://dammaturitymodel.org/
32. A. Pelz-Sharpe, A. Durga, D. Smigiel, E. Hartmen, T. Byrne, J. Gingras, “Ecm Maturity Model - Version 2.0,” Wipro - Real Story Group -
Hartman, 2010.
33. ARMA International, “Generally Accepted Recordkeeping Principles - Information Governance Maturity Model.” [Online]. Available:
http://www.arma.org/principles
34. CMMI Product Team, “CMMI for development, version 1.3,” Software Engineering Institute - Carnegie Mellon University, Tech. Rep.
CMU/SEI-2010-TR-033, 2010.
35. ISO/IEC 15504:2004, “Information technology - Process assessment,” International Organization for Standardization and International
Electrotechnical Commission Std. 2004.
36. ISO/IEC 15504-3:2004, “Information technology - Process assessment - Part 3: Guidance on performing an assessment,” International
Organization for Standardization and International Electrotechnical Commission Std. 2004.
37. F. Hamel, T. P. Herz, F. Uebernickel, W. Brenner, “IT Evaluation in Business Groups: A Maturity Model,” In Proceedings of the 28th
Symposium on Applied Computing, 2013.
38. Syracuse University, “A Capability Maturity Model for Research Data Management.” [Online]. Available:
http://rdm.ischool.syr.edu/xwiki/bin/view/Main/
39. Innovation Value Institute, “The IT-CMF Framework.” [Online]. Available: http://ivi.nuim.ie/it-cmf.
40. IT Governance Institute, “COBIT 4.1 Framework, Control Objectives, Management Guidelines, Maturity Models,” 2007.
41. G. Antunes, “Analysis of Enterprise Architecture Models: An Application of Ontologies to the Enterprise Architecture Domain,” PhD Thesis,
University of Lisbon, 2015.
42. SCAMPI Upgrade Team, “Standard CMMI Appraisal Method for Process Improvement (SCAMPI) A, Version 1.3: Method Definition
Document,” Software Engineering Institute - Carnegie Mellon University, Tech. Rep. CMU/SEI-2011-HB-001, 2011.
43. Object Management Group, Business Process Model and Notation (BPMN), Version 2.0, OMG Standard, formal/2011-01-03, 2011.
44. The Open Group. ArchiMate 2.0 Specification, Van Haren Publishing, 2012.
45. Object Management Group, “OMG Unified Modelling Language (OMG UML), Version 2.5.” 2015.
46. R. Vaculin, Process Mediation Framework for Semantic Web Services. PhD thesis, Department of Theoretical Computer Science and
Mathematical Logic, Faculty of Mathematics and Physics, Charles University, 2009.
... Most of the models described in the literature are holistic [24]. However, some authors present DMMs in which the area of maturity assessment is strictly defined, for example, assessing maturity knowledge-intensive business processes [28], big data usage [29], digital information systems [26], but also logistics 4.0 [30][31][32]. ...
... Characteristics of the maturity assessment model (based on[24][25][26]). ...
Article
Full-text available
(1) Background: Industry 4.0 and the COVID-19 pandemic have resulted in an acceleration of digital transformation, primarily in production systems and logistics. This raises the need to assess where a company is in its digital transformation today and what measures must be taken to improve logistics processes. This article aims to present the results of a study assessing the digital maturity of logistics processes in a group of selected enterprises located in Poland. The research was conducted among companies that are business partners of the Poznan School of Logistics. (2) Methods: The DMM-OP digital process maturity assessment model was used in the study. Digital maturity was assessed on a five-point scale in four areas of company activity: process management, performance measurement, employee support, and technology. The research procedure included four stages. (3) Results: The results indicate that companies in the process management and performance measurement dimensions achieved the highest level of digital maturity. In commercial enterprises, the level of digital transformation is at the lowest level. Large enterprises achieved the best results, but there were also very good results in the group of small enterprises. (4) Conclusions: The results presented in the article can be used by industry and academia. The research was not statistical but can form the basis for benchmarking analyses.
... Maturity assessments are used to measure the current maturity level of a certain organizational aspect, project, or product in a meaningful way, thereby "enabling stakeholders to clearly identify strengths and improvement points, and accordingly prioritize what to do in order to reach higher maturity levels" [11]. This concept was first popularized by Philip B. Crosby's Quality Management Maturity Grid (QMMG) [12], which laid out a five-level maturity map for the software industry. ...
Article
Full-text available
The concept of circular economy (CE) has gained momentum in the construction industry to mitigate the effects of climate change and decouple economic growth from environmental impact. There is a growing body of research related to the circularity of specific construction materials, as well as to the entire building. However, there remains a lack of understanding at the construction product level, and this lack of transparency prevents informed decisions when choosing which products to use in projects and how those products support the CE. A maturity assessment is one methodology that can provide insights for both product decisionmakers and product suppliers. Maturity assessments are a way to evaluate the level of development or progress towards a certain goal, whether at the organization, project, or product level. This paper proposes a conceptual framework to assess construction product system circularity maturity. Through a systematic literature review, the authors analyze existing CE maturity assessments and CE indicators for construction products to develop the framework. The functional unit is defined as a construction product, which is defined as an integrated system with multiple materials (i.e. a prefabricated wall system). This research finds that while there are many CE assessment frameworks for the construction sector, these must be translated into a construction product context, which requires a tailored subset of circularity indicators and maturity levels. The paper proposes construction product maturity levels ranging from “initial” to “optimizing” for key circularity indicators at the construction product level, including, material procurement, manufacturing, product use phase, and end-of-life. This conceptual framework serves as a practical tool for decisionmakers and as an educational tool for suppliers on how to support the CE in construction.
... The lower levels represent a more immature approach to business operations, whereas the upper levels signify a more developed, systematic, and organized approach. The implementation of maturity models offers organizations several key benefits (Proença & Borbinha, 2016): ...
Article
Full-text available
Strategy and Performance Management has evolved in the last decades to become a critical capability adopted and nurtured by more and more organizations worldwide. While several types of performance management architectures are set in place to facilitate strategy execution and performance excellence within organizations, less emphasis is attributed to understanding how well performance management tools, processes, and resources are set to enable proper integration and allow the maturity of such architectures. To address this gap, this research focuses on developing and applying an Integrated Performance Management Maturity Model that can test the dynamics of performance management architectures. Using a structured research protocol, 5 public organizations from different sectors in Saudi Arabia were chosen to test the impact of the maturity model designed and diagnose their maturity level. Results from the study suggest moderate to advanced performance management practices among governmental. The application of the maturity model assessment allowed a diagnosis of performance management architectures’ status quo while enabling the development of improvement roadmaps for the entities included in the study.
... Maturity stages represent typical stages of maturity that exhibit a unique set of characteristics (Fraser et al., 2002). Irrespective of the fact that maturity models are widely used in practice (e.g., Proença & Borbinha, 2016), research points out different weaknesses, such as the oversimplification of the real world through the multi-stage approach (de Bruin et al., 2005) and apparent path dependencies prescribed by the outlined maturity path, which is presented as the single true path to reach the final stage (Teo & King, 1997). Further, maturity models often imply that the final stage is the end stage, neglecting the continuous change and permanent transformation of organizations and their environment (King & Kraemer, 1984). ...
Article
Full-text available
Digital transformation and sustainability transformation are at the top of organizations’ agendas to remain competitive. While guidance on both transformations exists separately, even more research on integrating digital and sustainability transformation, namely twin transformation, is required. Specifically, deeper knowledge about relevant twin transformation capabilities and progress is needed for effective implementation. To enhance the understanding and provide corresponding guidance, we developed a twin transformation capability maturity model focusing on dynamic capabilities required to realize twin transformation based on a structured literature review and interviews with 13 experts. Further, we demonstrated its use with a technology service provider. Our contribution is twofold: First, accounting for organizations’ twin transformation starting points in terms of their digitalization and sustainability experience and expertise, we reveal three pathways to becoming a twin transformer. Second, our work provides an overview of 45 relevant twin transformation capabilities structured along six capability dimensions and four maturity stages. Our work also provides relevant practical implications supporting organizations in assessing their twin transformation maturity building the foundation for targeted capability development.
... There are methods available for measuring the current maturity level of a particular area of an organization, enabling stakeholders to identify strengths and areas for improvement [24]. Adopting a maturity model allows the organization to evaluate its methods and processes according to best management practices, following a set of defined parameters [12]. ...
... However, despite its implementation, the maturity level of DHIS2 has not yet been assessed. A maturity assessment is used to measure the current maturity status of a certain HIS to identify the strengths and improvement points and accordingly prioritize the next steps to reach higher maturity levels [11]. ...
Article
Full-text available
Background Although Ethiopia has made remarkable progress in the uptake of the District Health Information System version 2 (DHIS2) for national aggregate data reporting, there has been no comprehensive assessment of the maturity level of the system. Objective This study aims to assess the maturity level of DHIS2 implementation in Ethiopia and propose a road map that could guide the progress toward a higher level of maturity. We also aim to assess the current maturity status, implementation gaps, and future directions of DHIS2 implementation in Ethiopia. The assessment focused on digital health system governance, skilled human resources, information and communication technology (ICT) infrastructure, interoperability, and data quality and use. Methods A collaborative assessment was conducted with the engagement of key stakeholders through consultative workshops using the Stages of Continuous Improvement tool to measure maturity levels in 5 core domains, 13 components, and 39 subcomponents. A 5-point scale (1=emerging, 2=repeatable, 3=defined, 4=managed, and 5=optimized) was used to measure the DHIS2 implementation maturity level. Results The national DHIS2 implementation’s maturity level is currently at the defined stage (score=2.81) and planned to move to the manageable stage (score=4.09) by 2025. The domain-wise maturity score indicated that except for ICT infrastructure, which is at the repeatable stage (score=2.14), the remaining 4 domains are at the defined stage (score=3). The development of a standardized and basic DHIS2 process at the national level, the development of a 10-year strategic plan to guide the implementation of digital health systems including DHIS2, and the presence of the required competencies at the facility level to accomplish specific DHIS2-related tasks are the major strength of the Ministry of Health of Ethiopia so far. The lack of workforce competency guidelines to support the implementation of DHIS2; the unavailability of core competencies (knowledge, skills, and abilities) required to accomplish DHIS2 tasks at all levels of the health system; and ICT infrastructures such as communication network and internet connectivity at the district, zonal, and regional levels are the major hindrances to effective DHIS2 implementation in the country. Conclusions On the basis of the Stages of Continuous Improvement maturity model toolkit, the implementation status of DHIS2 in Ethiopia is at the defined stage, with the ICT infrastructure domain being at the lowest stage as compared to the other 4 domains. By 2025, the maturity status is planned to move from the defined stage to the managed stage by improving the identified gaps. Various action points are suggested to address the identified gaps and reach the stated maturity level. The responsible body, necessary resources, and methods of verification required to reach the specified maturity level are also listed.
... Maturity models are defined as a series of sequential levels/stages, which together form an anticipated or desired logical path from an initial state to a final state of maturity. In this case, maturity models represent stages of evolutionary growth of websites, in order to evaluate their progress relative to the defined maturity levels (Anderson & Jessen, 2003;Proença & Borbinha, 2016, Paulk, 2009. In this paper, the Capability Maturity Model (CMM) and the Web Content Management Maturity Model (WCMMM) were discussed and related to the results of this study. ...
Article
Full-text available
Since the introduction of the Internet, organisations have employed the use of websites in their business dealings. Supermarkets have also taken up this challenge in order to take advantage of the benefits of websites in an effort to gain a competitive advantage in today's e-business environment. The objectives of this paper were to explore the adoption and use of supermarket websites in Zimbabwean supermarkets and to assess the quality of these websites. The results were also related to existing models and the eventual development of the Website Maturity Model. A mixed method approach was employed where questionnaires and interviews were used to collect data from supermarket managers. The results indicated that although Zimbabwean supermarkets had set up websites for their organisations, the websites were mainly used for work purposes and their rate was low, particularly in terms of quality and depth of content and frequency of update. When related to existing models, the supermarket websites were found to be at low maturity stages. With regard to the developed Website Maturity Model, the websites were also found to be at low maturity stages and at the lower end of the Website Maturity Continuum. The study recommends supermarkets to introduce online selling of groceries in order to upgrade their websites to become transactional websites so that they reach the higher maturity stages/levels that can thrive in tomorrow’s technological era where e-business will be the norm. Keywords: Websites, maturity, website maturity models, informational and transactional websites, website maturity continuum
... A Maturity Model is a technique that has been proved to be valuable in measuring different aspects of a process or an organization. It represents a path towards increasingly organized and systematic way of doing business in organizations (Proença & Borbinha, 2016). The maturity models are usually divided into progressive maturity levels, allowing the organization to plan how to reach higher maturity levels and to evaluate their outcomes on achieving that. ...
Chapter
Full-text available
Organizations today need to provide better products and services. The issues they face require integrated approaches and effective management of their resources. Maturity models help integrate traditionally separate organizational functions, define process improvement goals and priorities, provide guidance for quality processes, and provide a benchmark for evaluating the current processes. The benefits management approach emerges as a complement to traditional management practices and proposes a continuous mapping of benefits, implementing and monitoring intermediate results. Based on a case study, it is shown how a set of business objectives can be obtained from the identification, structuring, and monitoring of business benefits, supported by information technology enablers and organizational transformations and by a certain level of maturity. The authors state that the focus of a successful investment is not only on the implementation of technology, but mainly on changes in organizational performance and business efficiency through process improvements and changes in the way of working.
Article
Purpose The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education. Design/methodology/approach As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis. Findings After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made. Originality/value Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.
Article
Full-text available
Maturity models are popular instruments used, e.g., to rate capabilities of maturing elements and select appropriate actions to take the elements to a higher level of maturity. Their application areas are wide spread and range from cognitive science to business applications and engineering. Although there are many maturity models reported in scientific and non-scientific literature, the act of how to develop a maturity model is for the most part unexplored. Many maturity models simply and vaguely build on their, often well-known, predecessors without critical discourse about how appropriate the assumptions are that form the basis of these models. This research sheds some light on the construction of maturity models by analysing 16 representative maturity models with the help of a structured content analysis. The results are transformed into a set of questions which can be used for the (re)creation of maturity models and are answered with the help of the case example of a knowledge maturity model. Furthermore, a definition of the term maturity model is developed from the study's results.
Chapter
Strategic alignment focuses on the activities that management performs to achieve cohesive goals across the IT (Information Technology) and other functional organizations (e.g., finance, marketing, H/R, R&D, manufacturing). Therefore, alignment addresses both how IT is in harmony with the business, and how the business should, or could, be in harmony with IT. Alignment evolves into a relationship where the function of IT and other business functions adapt their strategies together. Achieving alignment is evolutionary and dynamic. It requires strong support from senior management, good working relationships, strong leadership, appropriate prioritization, trust, and effective communication, as well as a thorough understanding of the business and technical environments. The strategic alignment maturity assessment provides organizations with a vehicle to evaluate these activities. Knowing the maturity of its strategic choices and alignment practices make it possible for a firm to see where it stands and how it can improve. This chapter discusses an approach for assessing the maturity of the business-IT alignment. Once maturity is understood, an organization can identify opportunities for enhancing the harmonious relationship of business and IT. Purchase this chapter to continue reading all 30 pages >
Article
Maturity models are valuable instruments for IT managers because they allow the assessment of the current situation of a company as well as the identification of reasonable improvement measures. Over the last few years, more than a hundred maturity models have been developed to support IT management. They address a broad range of different application areas, comprising holistic assessments of IT management as well as appraisals of specific subareas (e. g. Business Process Management, Business Intelligence). The evergrowing number of maturity models indicates a certain degree of arbitrariness concerning their development processes. Especially, this is highlighted by incomplete documentation of methodologies applied for maturity model development. In this paper, we will try to work against this trend by proposing requirements concerning the development of maturity models. A selection of the few well-documented maturity models is compared to these requirements. The results lead us to a generic and consolidated procedure model for the design of maturity models. It provides a manual for the theoretically founded development and evaluation of maturity models. Finally, we will apply this procedure model to the development of the IT Performance Measurement Maturity Model (ITPM3).
Conference Paper
Ensuring the effectiveness and efficiency of IT is a substantial aim of IT evaluation which is part of the strategic IT management. Weighing costs and benefits of IT is per se a complex and difficult process but gets even more challenging in the context of business groups. Business groups are a collective of legally independent entities that are owned and managed by a holding or parent company respectively. The purpose of this paper is to develop a maturity model for IT evaluation on the group level as a governance instrument to analyze and evaluate the current setup as well as to identify possible areas for improvement. In this way, maturity models facilitate the evolutionary reengineering of IT functions as they allow benchmarking assessments and roadmap planning to be carried out. The development of the maturity model is based on design science research and evaluated through various expert interviews, a focus group workshop and a real-world implementation.
Conference Paper
Verification and validation (V&V) is only marginally addressed in software process improvement models like CMM and CMMI. A roadmap for the establishment of a sound verification and validation process in software development organizations is badly needed. This paper presents a basis for a roadmap; it describes a framework for improvement of the V&V process, based on the Testing Maturity Model (TMM), but with considerable enhancements. The model, tentatively named MB-V2M2 (Metrics Based Verification and Validation Maturity Model), has been initiated by a consortium of industrial companies, consultancy & service agencies and an academic institute, operating and residing in the Netherlands. MB-V2M2 is designed to be universally applicable, to unite the strengths of known (verification and validation) improvement models and to reflect proven work practices. It recommends a metrics base to select process improvements and to track and control implementation of improvement actions. This paper outlines the model and addresses the current status.
Article
Maturity models can improve the effectiveness and capability of the organizations. However, the existing maturity models are only for one single organization and cannot deal with the specific problems of mega construction programs (MCPs) in China. The program management subjects of MCPs in China consist of the owner, the general design contractor, and the general construction contractor, which are not mature in the organizational management and process management. This paper presents a program management organization maturity integrated model for MCPs (PMOMIM-MCPs) in China, which integrates the program management subjects of MCPs and can improve the capability of them. Two submodels of PMOMIM-MCPs—Organizational Management Submodel (OMS) and Process Management Submodel (PMS) are given. In the end, a case study is given to justify and optimize the model.