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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.
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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
?
?
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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.
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... Development of society and ICT solutions are considered according to 16 levels of the SLMHM model (see [5] and Fig 1 for details). The discussion includes conception of SC [6][7][8][9][10][11][12] and several maturity models [8,10,[12][13][14][15][16][17][18][19][20]. Understanding of demand in information technologies and information systems (IT/IS) according to the five patterns and SLMHM levels is a success factor of ICT projects. ...
... Different Maturity Models for Information Systems were studied. The number of levels in the considered models varies from 4 to 6 [20]. ...
... 21 (Proença & Borbinha, 2016) To analyze the potential, and major limitations, of existing semantic techniques to automate methods for assessment through analysis of existing model representations of reality. An area of research in availability optimization, the Redundancy Allocation Problem (RAP) addresses the issue of achieving an optimal trade-off between resource availability and consumption. ...
... Design Factor Achievement, Reconsideration, talent, technology, improvement, transition, service, implementation and deliver, creating the portfolio, structure, processes, behavior, collaboration (Yamamoto, 2017), (Hosono & Shimomura, 2017), (Al-Matari, Helal, Mazen, & Elhennawy, 2021), (Ariffin & Ahmad, 2021), (Alimam, Bertin, & Crespi, 2017), (Shrestha, Cater-Steel, Toleman, Behari, & Rajaeian, 2020), (Proença & Borbinha, 2016), (Schmitz, Schmid, Harborth, & Pape, 2021), (Yandri, Suharjito, Utama, & Zahra, 2019), (Orta & Ruiz, 2019) ...
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... Moreover, research that focuses on the innovative capability of trucking companies is also rare, especially those that examine the maturity level of the innovative capability in trucking companies. This assessment is useful for mapping the maturity level of the trucking company's innovative capability based on the three factors above, to then be used in determining the next strategic step (Proença & Borbinha, 2016). The data obtained plays an important role in knowing industry developments in identifying gaps to ensure success and assist companies in maintaining their status as market leaders (Antunes et al., 2014). ...
... Of course, when the company's processes are more mature, they will be better prepared to face challenges and carry out business innovations (Rudnicka, 2017). The model is a tool or roadmap to compare, describe, measure, and determine steps (Proença & Borbinha, 2016). The maturity model is used to assess the company's state according to the state described by the model as a starting point for improving the company's processes. ...
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Trucking companies are important part of supply chains, as they connect most of product movements. While a lot of studies have been done to assess supply chain maturity level, very limited studies have been done to specifically address trucking companies. In this study we develop a model and measurement instruments to assess maturity of trucking companies in terms of their innovative capabilities. Innovative capabilities are critical in today's fast technological development such as industry 4.0 and trucking companies have opportunities to use those technologies to play a better role in the supply chain they involve in. We then use the instruments to assess the maturity level of 52 companies that involved in transporting goods using trucks. The results show that the maturity level has a widespread, ranging from the lowest to the highest level, but most of them are in the category of partial maturity to mature. This indicates that most companies have an opportunity to improve their innovative capabilities.
... Apart from the AI based contribution discussed above, validation is mostly performed through literature review or expert interviews, while empirical evidence is still pending [27]. While the best empirical evidence is available for maturity models in software development [14] [25], the situation appears unsatisfactory in other technology-related areas such as Industry 4.0 [27]. ...
... The next activity consists of the collection of data that will be afterwards used to determine the result of the assessment. Depending on the needs of the assessment, data could come from different sources such as interviews [26,27,28,23], questionnaires [29,30], surveys [28,31], workshops [32], documentation of the organization [26,27], process models [33], data from a Business Process Management System (BPMS) [34], among others. The data validation in this phase consists of confirming that the data is representative enough of the assessed entity, along with the checking of its consistency, objectivity, consistency and sufficiency [22]. ...
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Enterprises are constantly transforming to adapt to an ever-changing and competitive environment. In this context, assessments allow to understand the state of different organisational aspects before performing transformation activities. One of these aspects is the capability of business processes. Evaluating the quality of business processes is relevant to guide improvement initiatives, considering that the way that processes are designed and executed in organisations has direct impact on the quality of products and services. However, assessments are expensive in terms of resources if they are performed by humans. In this sense, recent trends in Artificial Intelligence provide means to improve process capability assessment through the automation of some of its tasks. Following this line, this work presents a method to perform process capability assessment using raw text as input data with the aid of a smart system, able to reduce the need of human intervention to provide reliable assessment results. For this purpose, we introduce a hybrid approach to perform assessments in enterprises using text data as assessment evidence. The method combines the Long Short-Term Memory Network (LSTM) approach and the use of an Ontology named Process Capability Assessment Ontology (PCAO), which also contains a set of rules to calculate process attribute ratings, capability levels, among other aspects. The approach is grounded on the Smart Assessment Framework, a conceptual model devised to guide the development of intelligent assessments in enterprises. We introduce a demonstration of the assessment of a process based on the management of chemical samples from a research institute.
... The Hybrid Matuirty model is Combining the best features of the development model and capability maturity. This model supports the achievements in the progression model and adds to the ability to measure capabilities with the capabilities of the capability maturity model (Proença and Borbinha 2016). Model assesment analysis focuses on the application of the maturity model. ...
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... Different Maturity Models for Information Systems were studied. The number of levels in the considered models varies from 4 to 6 (Proençaa & Borbinha, 2016). ...
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There are different Maturity, Motivation, and Development models. The models can be applied to the development of organizations, businesses, information technology infrastructure, human resources, and so on. This paper discusses society patterns that can be used in modeling society and team development. The model discussed has many advantages over existing ones. It assumes the Age of Creativity and the Creative Society Pattern as the upmost level of development. The patterns are juxtaposed with the 16 levels Simple Learning Motivation Hierarchy Model that allow modeling of dynamic processes with Expansion and Totality as the upmost levels. This approach eliminates the limitations of existing models and allows detailed modeling and planning. Explanation of the future development of humanity (up to the Age of Creativity) is one of the advantages of the model. The paper contains the description of the main peculiarities of society patterns and creates a basis for practical implementation of the model for society and team development. Organizations and teams can benefit from this model through its implementation in consulting and coaching processes. The model can be used in regional/organizational development and investment planning.
Chapter
This chapter has the main purpose of structuring and analyzing the available literature in the field of research on Maturity Models of Digital Transformation, both in the academic literature and in the publications of Consulting companies and Market studies. The research method was content analysis and it was possible to find 11 categories in which comparative analyzes were performed between DTMM The result of the comparison and analysis of the models helps professionals from different areas of activity, specialists, and academics to understand the similarities and differences between the models, and to assess the organization’s readiness and capacity to make significant changes, such as, strategy, business model, products and services, and technology.
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Chapter
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