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IT Infrastructure Automation Maturity
Model (ITIAMM): Exploring the
dimensions and attributes of a maturity
model for IT Infrastructure Automation
A thesis submitted in fulfilment of the requirements
for the degree of Master of Enterprise IT Architecture (MSc)
Author: ing. Henk-Jan Hopman BSc
Promotor: Prof. dr. Yuri Bobbert
Program: Master of Enterprise IT Architecture 2021 -2023
Date: May 15th, 2023
DOI https://doi.org/10.5281/zenodo.7928725
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
2
Declaration of Authorship
I, Henk-Jan Hopman, declare that this thesis titled, 'IT Infrastructure Automation Maturity
Model (ITIAMM): Exploring the dimensions and attributes of a maturity model for IT
Infrastructure automation' and the work presented in it are my own. I confirm that:
• This work was done wholly or mainly during candidature for a research degree at this
University.
• Where any part of this thesis has previously been submitted for a degree or any
other qualification at this University or any other institution, this has been clearly
stated.
• Where I have consulted the published work of others, this is always clearly
attributed.
• Where I have quoted from the work of others, the source is always given. Apart from
such quotations, this thesis is entirely my own work.
• I have acknowledged all main sources of help.
• Where the thesis is based on work done by myself jointly with others, I have made
clear exactly what was done by others and what I have contributed myself.
Signed:
------------------------------------------------------------
Date:
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Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
3
"The first rule of any technology used in a business is that automation
applied to an efficient operation will magnify the efficiency. The second is
that automation applied to an inefficient operation will magnify the
inefficiency."
Bill Gates
"Automation is good, so long as you know exactly where to put the
machine."
Eliyahu Goldratt
"Technology, through automation and artificial intelligence, is definitely
one of the most disruptive sources."
Alain Dehaze
"Complexity shall increase with new technologies, and we have to simplify
management of Complex IT Infrastructure in the Future."
Amar Prusty
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
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Abstract
Faculty Enterprise Engineering
Executive Master Enterprise IT Architecture
Master of Enterprise IT Architecture (MSc)
IT Infrastructure Automation Maturity Model (ITIAMM): Exploring the dimensions and
attributes of a maturity model for IT Infrastructure automation.
by ing. Henk-Jan Hopman BSc
Digital-centric businesses use IT infrastructure to create business value. Today, an IT
infrastructure is made up of different parts. These parts are on-premises, cloud, and legacy
IT infrastructure. As a result, the IT complexity is tremendously increased. By automating IT
infrastructure management, organizations can reduce overall complexity. An automation
maturity model helps define a clear roadmap and steps to implement and bring an IT
infrastructure to a certain desired maturity level. This research aims to explore whether it is
feasible to develop a maturity model for IT infrastructure automation. Design science
research is used together with the Delphi method for the iterative development of the
maturity model. A new definition and model of the IT infrastructure and IT infrastructure
automation are created to understand the environment in which the maturity model
operates. The developed maturity model has five levels, allowing automation of the IT
infrastructure management to evolve from a stand-alone technology to a fully integrated
cross-technological domain environment. Intending to enhance quality and productivity,
strengthen a business's competitive advantage, and reduces IT complexity. The maturity
model defines three dimensions: People, Processes, and Technology. The people dimension
addresses the knowledge and skills needed to understand and implement different levels of
automation per maturity level. The technology dimension covers the technical aspects
required to operate and maintain an automated IT infrastructure platform. The process
dimension concerns the structured and documented workflows guiding IT infrastructure
management. Conclusively we can state that this research provides a maturity model for
organizations to assess their current IT infrastructure automation level and identify areas for
improvement, ultimately strengthening their digital transformation efforts and being better
prepared to adopt industry trends. Future research aims to develop and validate the IT
infrastructure model and should focus on exploring the "how" aspect of automation and
conducting practical validation. Another research topic would be to explore whether
reaching maturity level five is possible. With the IT infrastructure automation maturity
model, this thesis contributed to both the body of knowledge and the practitioners that can
immediately apply the developed maturity model.
Keywords: IT infrastructure, Automation, IT infrastructure automation, Maturity Model, IT
infrastructure management, IT infrastructure automation maturity model
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
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Executive Summary
Digital-centric businesses use IT infrastructure to create business value. IT infrastructure
refers to the physical and virtual components that enable a business to build and run its
applications. Today, an IT infrastructure is made up of different parts. These parts are on-
premises, cloud, and legacy IT infrastructure. As a result, IT complexity is increased
enormously. This led to the following problem statement:
By automating the IT infrastructure, organizations can reduce overall complexity. An
automation maturity model helps to define a clear roadmap and necessary steps to
implement and bring it to a certain maturity level, aligning with the business's goals and
reducing IT complexity. It also allows people to understand the required capabilities per
maturity level since automation is not only about technology.
This research aims to explore whether it is feasible to develop an IT infrastructure
automation maturity model. The following main research question is formulated to guide
this research:
MRQ:
What dimensions and attributes constitute an IT infrastructure
automation maturity model?
Design science research is used for the iterative development of the maturity model. This
method is a systematic and transparent approach to problem-solving, while rigor ensures
that the solutions created are scientifically sound. The Delphi method is used together with
design science research to incorporate practical knowledge from subject matter experts in a
structured and iterative way.
The scientific body lacks recent definitions of IT infrastructure and IT infrastructure
automation. These definitions are necessary for the environment in which the maturity
model operates. This thesis presents the following new definition of IT infrastructure in the
cloud age:
An IT infrastructure platform is a shared foundation consisting of self-
service APIs, tools, services, and knowledge. This foundation enables
organizations to develop and implement present and future business
applications and data. The platform comprises both virtual resources and
supporting physical technology, which work together in an automated way
to provide the necessary services and a managed environment.
Definition 6-2 IT Infrastructure (created by the researcher)
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
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IT Infrastructure automation is defined as follows:
Automation of the IT infrastructure platform consists of the use of
software tools and technologies to automatically provision, configure,
deploy, and manage physical and virtual resources such as servers,
storage, and networking. It involves using automation tools, scripts, and
workflows to perform provisioning, patching, and scaling tasks.
Definition 6-2 Automation for the IT infrastructure (created by the researcher)
The thesis suggests a maturity model for the automation of IT infrastructure management.
The proposed maturity model answers the main research question by defining three
dimensions: People, Processes, and Technology. The people dimension addresses the
knowledge and skills needed to understand and implement different levels of automation.
The technology dimension covers the technical aspects required to operate and maintain an
automated IT infrastructure platform. The process dimension concerns the structured and
documented workflows guiding IT infrastructure management.
The developed maturity model consists of five levels for providing a roadmap for
automation to develop, see Figure 1. The first level concerns manually managing the IT
infrastructure without any automation. For level two, some of the management tasks are
automated. At level three, automation is the cornerstone of infrastructure management. At
level four, automation is widespread, and policies and access management are handled per
technology domain. At level five, the highest maturity level, IT infrastructure management
becomes entirely policy driven. Increasing automation maturity enhances quality and
productivity, strengthens a company's competitive advantage, and reduces IT complexity.
Figure 1 High-level overview of the maturity model for automation of the IT infrastructure (created by the researcher)
By establishing an IT infrastructure automation maturity model, this research provides a
framework for organizations to assess their current IT infrastructure automation level and
identify areas for improvement, ultimately strengthening their digital transformation efforts
and being better prepared to adopt industry trends.
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
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Acknowledgments
In 2021, I started this journey to complete the executive master's degree at Antwerp
Management School (AMS). An intensive period during which I gained a lot of insight into
the world of 'rigorous academic research.' The combination of the rigor theory and how this
can be executed in practice, and how it was taught at AMS has enriched my way of thinking
in my private and professional life. I would like to thank AMS and its staff for this learning
experience.
Special thank goes to my fellow students; it was a unique and interesting time to start a
study in the aftermath of the COVID pandemic and meet each other for the lectures on
campus. I enjoyed the talks during class, lunches, dinners, and afterward during drinks.
Also, I would like to thank the following people.
The Expert Panel
Thank goes to the expert panel members for their patience, expertise, and inspiration
during the one-on-one interviews, their time to fill in the questionnaire, and some for the
fruitful discussion during the expert meeting. I would like to thank; Tristan Suerink, Geert
Haerens, Rick van 't Spijker, Jory van Dam, Earl Zmijewski, Leif Bergman, Sébastien Godier,
Johan Verrips, Aad over de Vest, Barry Dukker, Jan Schoonderbeek, Stefan van Haaster, Bert
Dingemans, Bas Visscher, Niels, Fredric Meulenijzer, Victor Grenu, Olivier van der Wiele, Erik
van de Zee, Andre van der Waal, Jos Mohle, Jeffery Barends, Sjaak Laan, Mehmet, Arie
Groenveld, Ewout Hofman, Thomas Reichel, Jan-Willem Lammers, Mark de Graaf, Robert
Nieuwehuis, Piet Kempenaar, and Hugo Wessel.
The thesis support group
A word of special thanks goes to Ide Hingstman and Tim van der Fuhr for their patience,
constructive criticism, inspiration, feedback, and support during our weekly peer review
sessions in the process of writing and researching for this thesis. Also, I would like to thank
Earl Zmijewski, Barry Dukker, Cindy Ferrier, Dennis Verslegers, and Jésus Caetano for their
time proofreading this thesis.
A special word of thanks goes to prof. dr. Yuri Bobbert, for his guidance and support during
the research journey in creating this thesis.
Finally, I would like to thank my wife, Andrea; this would not have been possible without
your support over the past two years.
Henk-Jan, May 2023
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
ing. H.J. Hopman BSc – Antwerp Management School
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Table of Contents
1 Introduction ..................................................................................................................... 16
1.1 Introducing the Research Problem .......................................................................... 16
1.2 The Problem Statement ........................................................................................... 17
1.3 Research Question ................................................................................................... 18
1.4 Research Methodology at a Glance ......................................................................... 19
1.5 Impact and Relevance .............................................................................................. 20
1.5.1 Practical Impact................................................................................................ 21
1.5.2 Theoretical Impact ........................................................................................... 22
1.6 Thesis Structure ....................................................................................................... 23
2 Theoretical Foundation .................................................................................................... 24
2.1 Introduction ............................................................................................................. 24
1.1 IT Infrastructure ....................................................................................................... 24
2.2 Automation .............................................................................................................. 31
2.2.1 IT automation ................................................................................................... 33
2.2.2 IT Infrastructure Automation ........................................................................... 34
2.3 Maturity Models ...................................................................................................... 36
2.4 Overview of the Maturity Model Related to IT Infrastructure Automation ............ 40
2.4.1 IT Infrastructure Maturity Model (ITI-MM) ..................................................... 42
2.4.2 Maturity Model for IT Management ................................................................ 43
2.4.3 Gartner IT Infrastructure and Operations Maturity Model ............................. 44
2.4.4 The Gartner Infrastructure Maturity Model. ................................................... 45
2.4.5 Maturity Model for Implementing ITIL v3 ....................................................... 47
2.4.6 Cisco Digital Network Readiness Model .......................................................... 47
2.4.7 Cisco Network Automation Maturity ............................................................... 48
2.4.8 Cloud Maturity Model ...................................................................................... 49
2.4.9 DevOps Maturity Model .................................................................................. 50
2.4.10 Open Group Service Integration Maturity Model ........................................... 51
2.4.11 Netbrain Network Automation Maturity Model ............................................. 53
2.4.12 Infrastructure Automation Maturity Model .................................................... 53
2.5 Summary .................................................................................................................. 55
3 Research Design and Approach ....................................................................................... 56
3.1 Research Design and Approach ............................................................................... 56
3.1.1 Design Science Research .................................................................................. 56
3.1.2 Delphi Method ................................................................................................. 58
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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3.2 Research Approach .................................................................................................. 59
3.2.1 Mapping Research Questions to Research Methods ...................................... 60
3.2.2 Conceptual Model ............................................................................................ 60
3.2.3 Research Scope ................................................................................................ 62
3.2.4 Applied Research Approach ............................................................................. 63
3.3 Literature Review Strategy ...................................................................................... 64
3.4 Data-gathering Techniques ...................................................................................... 68
3.4.1 Semi-structured Interview ............................................................................... 68
3.4.2 Digital Survey ................................................................................................... 71
3.4.3 Explorative study with a single expert ............................................................. 72
3.5 Summary .................................................................................................................. 73
4 Exploring IT Infrastructure, Automation, and Maturity................................................... 74
4.1 Qualitative semi-structured interview ..................................................................... 74
4.2 Automation in General ............................................................................................ 75
4.2.1 Implications for Automation ............................................................................ 76
4.3 Exploring IT Infrastructure ....................................................................................... 76
4.3.1 Implications for the IT Infrastructure .............................................................. 77
4.4 Exploring Automation .............................................................................................. 79
4.4.1 Automation Themes ........................................................................................ 79
4.4.2 Implications for Automation ............................................................................ 81
4.5 Exploring the Maturity Model.................................................................................. 83
4.5.1 Development of the Preliminary Maturity Model ........................................... 86
4.6 Summary .................................................................................................................. 89
5 The Preliminary Maturity Model Design.......................................................................... 91
5.1 The Digital Survey .................................................................................................... 91
5.2 Exploring the Definition of IT Infrastructure............................................................ 91
5.2.1 IT Infrastructure Model Evaluation .................................................................. 91
5.2.2 IT infrastructure definition ............................................................................... 94
5.2.3 Explorative study with expert .......................................................................... 96
5.2.4 Refined IT Infrastructure Model ...................................................................... 97
5.3 Exploring the Definition of Automation for IT Infrastructure ................................ 100
5.3.1 Refined Conceptual Framework for Automation in the IT Infrastructure ..... 102
5.4 Exploring maturity model design ........................................................................... 103
5.4.1 Exploring the Type of a Maturity Model ........................................................ 103
5.4.2 Exploring the Maturity Model Domains ........................................................ 104
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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5.4.3 Exploring the Maturity Model Levels ............................................................. 107
5.5 Summary ................................................................................................................ 108
6 The IT Infrastructure Automation Maturity Model ....................................................... 109
6.1 Definition of IT infrastructure ................................................................................ 109
6.2 Definition of IT Infrastructure Automation............................................................ 111
6.3 Design of IT Infrastructure Automation Maturity Model ...................................... 114
6.3.1 General Maturity Model Design Criteria ....................................................... 114
6.3.2 Design Criteria for Automation in the IT Infrastructure ................................ 115
6.3.3 Design Criteria for Levels ............................................................................... 119
6.3.4 Design Criteria for Dimensions ...................................................................... 124
6.4 IT Infrastructure Automation Maturity Model ...................................................... 126
6.5 Summary ................................................................................................................ 129
7 Discussion....................................................................................................................... 130
7.1 Reflection on the Research Approach ................................................................... 130
7.2 Reflection on Qualitative Semi-Structured Interviews .......................................... 131
7.3 Reflection and Discussion on the Digital Survey .................................................... 131
7.4 Discussion on the IT Infrastructure ........................................................................ 132
7.5 Discussion on IT Infrastructure Automation .......................................................... 132
7.6 Discussion on the maturity model ......................................................................... 133
7.7 Summary ................................................................................................................ 134
8 Conclusions .................................................................................................................... 135
8.1 Research Findings .................................................................................................. 136
8.2 Answering the Main Research Question ............................................................... 141
8.3 Research Limitations .............................................................................................. 144
8.4 Recommendations ................................................................................................. 145
8.5 Future Work ........................................................................................................... 145
9 Bibliography ................................................................................................................... 147
10 Appendix – Transcripts of Interviews ........................................................................ 153
10.1 Transcript 01 .......................................................................................................... 153
10.2 Transcript 02 .......................................................................................................... 154
10.3 Transcript 03 .......................................................................................................... 157
10.4 Transcript 04 .......................................................................................................... 159
10.5 Transcript 05 .......................................................................................................... 161
10.6 Transcript 06 .......................................................................................................... 163
10.7 Transcripts 07 and 08 ............................................................................................. 165
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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10.8 Transcript 09 .......................................................................................................... 167
10.9 Transcript 10 .......................................................................................................... 170
10.10 Transcript 11 ...................................................................................................... 171
10.11 Transcript 12 ...................................................................................................... 174
10.12 Transcript 13 ...................................................................................................... 176
10.13 Transcript 14 ...................................................................................................... 178
10.14 Transcript 15 ...................................................................................................... 180
10.15 Transcript 16 ...................................................................................................... 182
10.16 Transcript 17 ...................................................................................................... 184
11 Appendix – Themetic Analysis of Interviews ............................................................. 186
11.1 Thematic Analysis for Interview Question 1 .......................................................... 186
11.2 Thematic Analysis for Interview Question 2 .......................................................... 190
11.3 Thematic Analysis for Interview Question 3 .......................................................... 195
11.4 Thematic Analysis for Interview Question 4 .......................................................... 198
11.5 Thematic Analysis for Interview Question 5 .......................................................... 200
11.6 Thematic Analysis for Interview Question 6 .......................................................... 205
12 Appendix – Digital Survey .......................................................................................... 208
12.1 Invitation through Email ........................................................................................ 208
12.2 Invitation through Signal........................................................................................ 208
12.3 Survey ..................................................................................................................... 209
13 Appendix - Analysis of Digital Survey ......................................................................... 222
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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List of Figures
Figure 1 High-level overview of the maturity model for automation of the IT infrastructure
(created by the researcher) ....................................................................................................... 6
Figure 2 Characteristics of Automation Maturity (source: Gartner's leadership vision for
2022 (H. Chris, 2021)) .............................................................................................................. 17
Figure 3 The research approach of this thesis (created by the researcher) ............................ 20
Figure 4 Relations between business objectives and how maturity models contribute
(adapted from (Nedzelskay et al., 2020)) ................................................................................ 21
Figure 5 Outline of the thesis (created by researcher) ............................................................ 23
Figure 6 Process of creating a theoretical framework (adapted from (Chumney, 2016)). ..... 24
Figure 7 Service of GRAAL framework (adapted from (van Eck et al., 2004)) ......................... 25
Figure 8 The elements of IT infrastructure (adapted from Broadband et al. (Broadbent et al.,
1999)) ....................................................................................................................................... 25
Figure 9 Views on IT infrastructure (adapted from (Laan, 2017)) ........................................... 27
Figure 10 The IT infrastructure model (adapted from (Laan, 2017)) ...................................... 27
Figure 11 Building blocks of a cloud IT infrastructure (adopted from (Musse & Alamro,
2016)) ....................................................................................................................................... 28
Figure 12 Cloud service models and delivery models (adapted from (Dijk, 2017)) ................ 29
Figure 13 IT infrastructure flexibility (adapted from (Anwar & Masrek, 2014)) ..................... 31
Figure 14 Levels of automation (adapted from (Sheridan & Verplank, 1978)) ....................... 32
Figure 15 Level of automation (adapted from (Vagia et al., 2016)) ........................................ 32
Figure 16 Deployment pipeline (adapted from (Humble & Farley, 2011)) ............................. 33
Figure 17 Characteristics of CMMI maturity models (adapted from (Chrissis et al., 2011)) ... 38
Figure 18 General representation of a maturity model structure (adapted from (Lasrado et
al., 2015)) ................................................................................................................................. 40
Figure 19 IT Infrastructure Maturity Model (ITI-MM) (adapted from (Haris, 2010)) .............. 43
Figure 20 The IT management maturity model (adapted from (Becker et al., 2009)) ............ 44
Figure 21 The levels of Gartner's I&O Maturity Model (adapted from (Scott et al., 2007) ) .. 45
Figure 22 The Gartner Infrastructure maturity model (adapted from (Hidas, 2006)) ............ 46
Figure 23 Gartner Infrastructure Mature through People, Process, and Technology (adapted
from (Hidas, 2006)) .................................................................................................................. 46
Figure 24 Maturity model for implementing ITL v3 (adapted from (de Sousa Pereira & da
Silva, 2010) ) ............................................................................................................................. 47
Figure 25 Transformation from a traditional network to a digital-ready network (adapted
from (Montanez, 2020))........................................................................................................... 48
Figure 26 Cisco Digital Network Readiness Model (adapted from (Greene et al., 2017)) ...... 48
Figure 27 Cloud maturity model (adapted from (Dijk, 2017)) ................................................. 49
Figure 28 High-level DevOps maturity model (adapted from (Radstaak, 2019)) .................... 51
Figure 29 Open Group Service Integration Maturity Model (adapted from (Group, 2009)) .. 52
Figure 30 Network Automation Maturity Model (adapted from (Netbrain, 2022)) ............... 53
Figure 31 IT infrastructure automation lifecycle (adapted from (Quali, 2021) ) ..................... 53
Figure 32 IT Infrastructure Automation Maturity Model (adapted from (Quali, 2021)) ......... 54
Figure 33 Design Research Framework (adapted from (Hevner & Chatterjee, 2010)) ........... 57
Figure 34 DSR methodology process model (adapted from (Vom Brocke et al., 2020)) ........ 59
Figure 35 Overall Conceptual Framework (created by the researcher) .................................. 61
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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Figure 36 Conceptual Framework (created by the researcher) .............................................. 61
Figure 37 Scope of the research in the context of the IT infrastructure (created by the
researcher) ............................................................................................................................... 62
Figure 38 The conceptual research framework of this thesis (created by the researcher) .... 63
Figure 39 Divergent and Convergent Process (created by the researcher) ............................ 64
Figure 40 Literature review stages (created by the researcher) ............................................. 65
Figure 41 Number of publications selected per database (created by the researcher) ......... 67
Figure 42 Number of publications per topic (created by the researcher)............................... 67
Figure 43 Distribution of professions in the panel of subject matter experts(Created by the
researcher) ............................................................................................................................... 71
Figure 44 Distribution of experts in sectors of the economy (created by the researcher) ..... 71
Figure 45 Distribution of experts from the sectors of the economy for the digital survey
(created by the researcher) ..................................................................................................... 72
Figure 46 Word cloud of the participant’s professions (created by the researcher) .............. 72
Figure 47 New proposed IT infrastructure model version 1 (created by the researcher) ...... 78
Figure 48 Conceptual automation framework in the new IT infrastructure model version 1
(created by the researcher) ..................................................................................................... 82
Figure 49 Proposed combined CI/CD pipelines (created by the researcher) .......................... 82
Figure 50 Results of the survey on ‘way‘ to describe an IT infrastructure (created by the
researcher) ............................................................................................................................... 92
Figure 51 Results of the survey on the proposed IT infrastructure model (created by the
researcher) ............................................................................................................................... 93
Figure 52 Survey result of IT infrastructure definitions (created by the researcher) ............. 95
Figure 53 The Infrastructure Model Version 2 (created by the researcher) ........................... 99
Figure 54 Results of the survey on automation for the IT infrastructure (created by the
researcher) ............................................................................................................................. 101
Figure 55 Survey results on CI / CD statements (created by the researcher) ....................... 102
Figure 56 Conceptual framework for automation in the IT infrastructure version 2 (created
by the researcher) .................................................................................................................. 102
Figure 57 Examples of a staged and continuous maturity model (created by the researcher)
................................................................................................................................................ 103
Figure 58 Results of the survey on the types of maturity models (created by the researcher)
................................................................................................................................................ 104
Figure 59 Results of the survey on the dimensions of a maturity model (created by the
researcher) ............................................................................................................................. 106
Figure 60 Results of the survey on statements to describe the different maturity levels
(created by the researcher) ................................................................................................... 107
Figure 61 Results of the survey on the preliminary maturity model (created by the
researcher). ............................................................................................................................ 108
Figure 62 The IT Infrastructure model version 3 (created by the researcher) ...................... 110
Figure 63 Automation workflows in IT (created by the researcher) ..................................... 112
Figure 64 Deming Cycle (adapted from (Nedzelskay et al., 2020)) ....................................... 112
Figure 65 Conceptual Automation Framework in IT Infrastructure Version 3 (created by the
researcher) ............................................................................................................................. 113
Figure 66 General Structure of a Maturity Model based on (Lasrado et al., 2015) .............. 115
Figure 67 Maturity Model for Automation of the IT Infrastructure (created by the
researcher) ............................................................................................................................. 126
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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Figure 68 A generalized growth pattern of automation and reduced manual contributions to
all forms of human activity (adapted from (Hancock, 2014). ................................................ 134
Figure 69 IT infrastructure model version 3 (created by the researcher). ............................ 137
Figure 70 Conceptual model for automation workflows in the IT infrastructure (created by
the researcher)....................................................................................................................... 139
Figure 71 High-level overview of the maturity model for the automation of the IT
infrastructure (created by the researcher). ........................................................................... 142
List of Tables
Table 1 IT infrastructure changes through time (adapted from (Morris, 2020)) .................... 28
Table 2 Difference in the way of working for infrastructure types (adapted from(Morris,
2020)) ....................................................................................................................................... 35
Table 3 Maturity Model Concepts (adapted from (Serrano & Pereira, 2020)) ....................... 37
Table 4 Types of improvement paths (adapted from (Serrano & Pereira, 2020)) .................. 37
Table 5 Specific purposes of the maturity model (adapted from (Serrano & Pereira, 2020)) 39
Table 6 Overview-related maturity models (created by the researcher)................................ 40
Table 7 Related maturity model and the requirements they fulfill (created by the researcher)
.................................................................................................................................................. 41
Table 8 Dimensions of ITI-MM (adapted from (Haris, 2010) ................................................... 42
Table 9 Overview of dimensions and sub-dimensions (adapted from (Scott et al., 2007)) .... 45
Table 10 Consolidation of capabilities into dimensions (adapted from (Dijk, 2017)) ............. 50
Table 11 Mapping the research questions to the research methods (created by the
researcher) ............................................................................................................................... 60
Table 12 Steps to design and conduct semi-structured interviews (adapted from
(Dejonckheere & Vaughn, 2019)) ............................................................................................ 68
Table 13 Framework for thematic analysis (adapted from (Braun & Clarke, 2006) ). ............ 69
Table 14 Thematic analysis of interview question 1 (created by the researcher) .................. 76
Table 15 Thematic analysis of interview question 2 (created by the researcher) .................. 77
Table 16 Thematic analysis of interview question 3 (created by the researcher) .................. 79
Table 17 Thematic analysis of interview question 4 (created by the researcher) .................. 80
Table 18 Thematic analysis of interview question 5 (created by the researcher) .................. 83
Table 19 Thematic analysis of interview question 6 (created by the researcher) .................. 85
Table 20 Distribution of dimensions across IT management domains (created by the
researcher) ............................................................................................................................... 87
Table 21 Maturity level of the preliminary maturity model (created by the researcher) ...... 89
Table 22 A preliminary maturity model for IT infrastructure automation (created by the
researcher) ............................................................................................................................... 89
Table 23 Overview change between IT infrastructure models (created by the researcher) .. 98
Table 24 Overview of possible dimensions for a maturity model (created by the researcher)
................................................................................................................................................ 105
Table 25 Top 10 dimensions based on statistical analysis (created by the researcher) ....... 107
Table 26 Proposed naming schemes for maturity model (created by the researcher) ........ 107
Table 27 Principles for implementing automation in the IT infrastructure (adapted from
(Morris, 2020)) ....................................................................................................................... 116
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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Table 28 List of maturity models that influences the maturity model for automation of the IT
infrastructure (created by the researcher) ............................................................................ 118
Table 29 Mapping the levels of different maturity models (created by the researcher)...... 120
Table 30 Description of the maturity levels (created by the researcher). ............................ 123
Table 31 Overview of dimensions and sub-dimensions (created by the researcher). .......... 125
Table 32 Overview of design criteria for infrastructure automation maturity model (created
by the researcher) .................................................................................................................. 127
Table 33 Detailed Maturity Model for Automation in the IT Infrastructure (created by the
researcher) ............................................................................................................................. 128
Table 34 Overview of the used maturity model (created by the researcher). ...................... 140
Table 35 Overview of dimensions and subdimension for the maturity model (created by the
researcher). ............................................................................................................................ 140
Master Thesis - IT Infrastructure Automation Maturity Model (ITIAMM)
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1 Introduction
This chapter provides an overview of the research topic by introducing the research
problem, the problem statement, and the associated research questions. In the remaining
part of the chapter, the applied research methodology is briefly described, followed by a
discussion of the practical and scientific impact and relevance of this research. The chapter
closes with the structure of this thesis.
1.1 Introducing the Research Problem
In today's IT landscape, technologies are changing rapidly, and the landscape is shifting
towards a more hybrid environment where it is hard to differentiate between the IT
infrastructure and applications (Sinclair & Keane, 2019). The IT infrastructure enables an
organization to build and run the applications that underlie its business and helps to achieve
cost optimizations, flexibility, and efficiency (Rimol, 2022). Over the past few decades, the IT
infrastructure has undergone a significant shift from the "iron age" to the "cloud age"
(Morris, 2020). Several technological advances, such as virtualization, software-defined
networking, and the widespread availability of the high-speed Internet, have driven this
shift. The shift towards cloud-based infrastructure has transformed the way organizations
operate. An organization must change its operating model and move towards a digital-
centric business to remain competitive (Bhandari, 2021). Digital-centric businesses use
technology to create new business value. Generally speaking, the role of IT within
organizations is changing due to this shift toward the "cloud age," which affects the function
of IT infrastructure.
Unfortunately, with this shift comes an increase in complexity (Help_Net_Security, 2022;
Nallappan, 2022; Saunderson, 2021). This complexity is enhanced because IT infrastructures
often rely on legacy IT systems. These legacy systems introduce what is called technical
debt. Technical debt is a term that describes the accumulation of inefficiencies and
limitations in an organization's IT landscape (Ashok Vasa, 2023). Outdated software,
hardware, skills, processes, or practices can cause technical debt. Many organizations have a
tremendous amount of technical debt due to an extensive patchwork of different
technologies and processes that are often not optimized, lean, connected, consistent, or
explicit. On top of that, technical debt makes it difficult to modernize your business and
adapt to changing markets because it diverts resources away from meaningful investments.
As a result of the shift towards the "cloud age" and the presence of technical debt, the
complexity of IT infrastructure is tremendously increased (Atchison, 2022).
To overcome the complexity of IT in an organization, it is essential to have a clear
understanding of the business objectives and IT requirements. Adopting an agile approach,
simplifying processes, and investing in modern technologies, such as automation, can also
help reduce complexity and improve organizational efficiency (Atchison, 2022).
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1.2 The Problem Statement
The traditional IT infrastructure and its applications are operated and maintained manually.
As a result, organizations waste money, time, and talent, sacrifice quality, and are exposed
to significant security risks (Rimol, 2022). For organizations to overcome their technical debt
challenge and quickly adopt new technologies in their environment, automation of the IT
infrastructure is needed (Stephen, 2020). Automation refers to the use of technology and
software to perform tasks or processes that would otherwise require human intervention
(Ninja, 2021). Automation aims to improve processes' efficiency, accuracy, and speed while
reducing potential errors or variations. In addition, organizations are increasingly using
automation to automate IT infrastructure deployment, configuration, monitoring, and
management.
When the IT infrastructure is not properly managed, it affects business performance and
reduces revenue (Serrano & Pereira, 2020). By automating the IT infrastructure,
organizations can reduce overall complexity and increase security with fewer resources and
operational costs while simultaneously providing faster and more flexible services and
keeping up with the competition (Bigelow, 2019; Maxima_Consulting, 2021). Furthermore,
by implementing automation, organizations can reduce human error and increase efficiency
by eliminating manual human actions (McHugh, 2022).
Furthermore, the automation of IT infrastructure is not a one-time thing. It needs
continuous attention. Implementing automation and bringing it to a certain maturity level
takes time, requires planning and coordination, and is probably done in different steps
(Rimol, 2022). However, implementing automation is not achieved overnight and can be
complex.
A framework offers a structured way to support an organization that implements processes
and products, such as automation (Chrissis, Konrad, & Shrum, 2011). A maturity model
provides this framework by defining a clear roadmap for the organization, describing the
necessary steps to achieve the goals, and providing a means to measure progress (Becker,
Knackstedt, & Pöppelbuß, 2009). The roadmap to implement automation and bring it to a
preferred maturity level requires the right resources, skills, tools, and a focused plan that
aligns with the goals of the enterprise organization. In Figure 2 Characteristics of
Automation Maturity (source: Gartner's leadership vision for 2022 (H. Chris, 2021))) an
example of characteristics of a maturity roadmap is presented.
Figure 2 Characteristics of Automation Maturity (source: Gartner's leadership vision for 2022 (H. Chris, 2021))
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Maturity models allow an organization to assess its current state and guide it toward its
preferred end state (Becker et al., 2009). Therefore, an automation maturity model for the
IT infrastructure can help organizations overcome their IT complexity. It allows an
organization to assess its current automation maturity level and identify gaps in its
automation capabilities by performing a maturity assessment.
1.3 Research Question
This research aims to explore whether it is feasible to develop an IT infrastructure
automation maturity model. This maturity model must be a practical framework with a
scientific foundation for implementing automation. The following main research question is
formulated to provide guidance to this research:
MRQ:
Which dimensions and attributes constitute an IT infrastructure
automation maturity model?
IT infrastructure refers to the physical and virtual components required to support an
organization's information technology services to deliver storage, processing, and security
(Haris, 2010). As mentioned above, there is a shift toward a cloud-based IT infrastructure,
which supports computing that relies on a network of remote servers hosted on the
Internet to store, manage, and process data instead of relying on local servers or personal
computers (Dijk, 2017). This research needs a clear definition of this cloud-based IT
infrastructure. Hence, the first sub-question is to define a clear description of an IT
infrastructure.
RQ01:
What is an IT infrastructure?
The second sub-question is about automation, to define and scope the role and place in the
context of an IT infrastructure. Automation is a broad concept within information
technology. Automation in IT infrastructure management involves the use of software tools
to manage and optimize IT infrastructure, such as automating server provisioning and
configuration management. In contrast, application automation typically requires software
tools to streamline the development process, such as automated testing and deployment
tasks (Humble & Farley, 2011).
RQ02:
What is IT infrastructure automation?
The following research question investigates the availability and current state of maturity
models for automation of the IT infrastructure and identifies if already existing models could
be re-used as a starting point.
RQ03:
What models of IT infrastructure automation maturity are available in the
current scientific literature?
A maturity model framework (Poeppelbuss, Niehaves, Simons, & Becker, 2011) usually
includes maturity levels, dimensions, and attributes. The model defines a set of maturity
levels through which an organization can progress. Each level represents a higher degree of
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maturity. The model also identifies the key dimension that an organization must develop to
move to a higher maturity level. Attributes are defined to measure and assess the progress
of dimensions (Lasrado, Vatrapu, & Andersen, 2015). The following sub-questions aim to
identify these dimensions, attributes, and how they can be placed within a maturity model.
RQ04:
What are the useful dimensions along which the maturity of IT
infrastructure can be assessed?
RQ05:
What are the useful attributes of each of these dimensions?
1.4 Research Methodology at a Glance
Design science research is a problem-solving approach that involves creating and evaluating
artifacts in real-world settings (Hevner & Chatterjee, 2010; Recker, 2021). Design science
research aims to develop innovative solutions to practical problems by leveraging
knowledge from various fields, such as academic research and practical information. This
information refers to the knowledge gained from stakeholders, users, and other relevant
parties.
One of the key characteristics of design science research is the use of rigorous methods to
evaluate the effectiveness and efficiency of their artifacts. The research methodology used
for this research is design science research due to the combination of rigor and practical
information, which leads to the creation of innovative solutions that meet academic and
practical needs. This research only uses the different phases of design science research to
develop the artifact. The phases for evaluation are not part of this research.
Design science research provides a systematic and transparent approach to problem-
solving, while rigor ensures that the solutions created are scientifically sound. Most
importantly, practical information ensures that the artifact created is practical and usable in
the problem context. The iterative framework used within design science research (Vom
Brocke, Hevner, & Maedche, 2020) and adapted to the research of this thesis is presented in
Figure 3 The research approach of this thesis (created by the researcher)
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Figure 3 The research approach of this thesis (created by the researcher)
The Delphi method (Alarabiat & Ramos, 2019; Turoff & Linstone, 2002) is used as a
structured, organized, and iterative process to gather data. The strength of the Delphi
method is to create consensus with opinions from a panel or individual experts. The Delphi
method conceives data in a setting with incomplete knowledge of the 'research problem.'
The Delphi method is combined with design science research to incorporate practical
knowledge fully.
The research approach is visualized in the middle part of the above figure. This research
aims to explore and define these different steps during the following phases.
1. With scientific rigor and practical knowledge from subject matter experts, find and
define ingredients for the automation of an IT infrastructure.
2. These ingredients are the basis for a prototype artifact that a panel of experts can
validate.
3. Based on the panel's outcome, a prototype artifact (Maturity model (Poeppelbuss et
al., 2011)) is designed to evaluate and implement automation initiatives in steps.
1.5 Impact and Relevance
Automation has become a crucial component in the IT infrastructure of organizations (Josh
Chessman, 2020; Saunderson, 2021), and a maturity model framework can be used to
assess an organization's level of automation in its IT infrastructure. This model can provide
practical benefits and relevance to organizations to overcome their IT complexity. As a
result, efficiency increases, costs reduce, and quality improves.
Maturity models have a clear relation with the IT processes of an organization. This relation
is described by Nedzalskay et al. (Nedzelskay, Moiseyev, Bikineeva, & Bulakina, 2020); see
Figure 4 Relations between business objectives and how maturity models contribute
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(adapted from (Nedzelskay et al., 2020)). For the evaluation of IT processes, there are three
approaches. The first is efficiency, and the second is results; both can be measured with
performance indicators. The third is maturity, which is measured with maturity models. In
addition, they point out that technical competence is needed for infrastructure
management.
This research is about a maturity model in a technical environment. The following sections
discuss the potential practical impact and scientific relevance of an automated IT
infrastructure maturity model.
Figure 4 Relations between business objectives and how maturity models contribute
(adapted from (Nedzelskay et al., 2020))
1.5.1 Practical Impact
The practical impact of an IT infrastructure maturity model for automation is significant for
organizations seeking to overcome their IT complexity. By providing a framework for
assessing the level of automation in an organization's IT infrastructure, the model can help
organizations identify areas for improvement and guide automation efforts. This is also
stated by K. Morris (Morris, 2020). He describes that for organizations to be successful in
their digital transformation and operation, automation is essential for the high-quality
delivery of IT infrastructure services.
A survey conducted by the consulting firm Gartner in 2021 revealed that 70% of information
and operations leaders view IT Infrastructure automation as a strategic topic for the next
four years, and 50% of these leaders plan to develop skills in IT Infrastructure automation
(Rimol, 2022). The IT automation trends for 2022 indicate that automation is rapidly
increasing due to a rise in data volume and data diversity, driving enterprise organizations
toward new IT infrastructure technologies (McHugh, 2022). Furthermore, the network
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vendor Cisco presents in their trend paper “Network Automation Trends and Strategy” that
it is also relevant for an organization to have an automation strategy (Pinto & Lacunza,
2021). A white paper from the VMware Infrastructure Virtualization Vendor (VMWare,
2022) presents the same conclusion.
1.5.2 Theoretical Impact
The question arises: Is a maturity model already available in the scientific body of
knowledge? This research explores the development of a new maturity model. A study of
the body of knowledge conducted during this research shows that different researchers
report that there are different maturity models are available. The research of Wendlers
(Wendler, 2012) maps a total of two hundred and thirty-seven scientific papers about
maturity models to twenty different domains, mainly the domains in computer science and
information science. Researchers Proença and Borbinha (Proença & Borbinha, 2016)
researched the current practice of the maturity model in the available scientific literature.
They found twenty-two maturity models with the same number of domains. A study by
Poeppelbuss et al. (Poeppelbuss et al., 2011) found 76 relevant articles about maturity
models in different science fields. The findings of Poeppelbuss et al. and Wendlers are also
mentioned in the related work part of the research of Monteiro et al. (Monteiro & Maciel,
2020). This recent research from around 2020 found more than 600 academic papers about
maturity models. As can be said, the scientific body of knowledge contains many maturity
models that cover many domains. Only one maturity model is found that covers the IT
infrastructure; This is a model from Ferry Haris called 'IT Infrastructure Maturity Model'
(Haris, 2010). Another observation is that limited information about the IT landscape's
technological part (IT infrastructure and automation) is available in the body of knowledge.
In addition to the scientific body of knowledge, practical information, such as white papers
and technical journals, is available in the information and technology industry. Several
maturity models can be found within the industry body of knowledge, but all address a
specific technology or product range. As an example, a network automation maturity model
developed by NetBrain (Netbrain, 2022), a network automation maturity model developed
by Cisco (Howell, 2022), and a more general IT automation model from EMA (Drogseth &
Twing, May 2020). The consultancy firm Gartner has developed a more generic maturity
model for the IT infrastructure (Scott, Pultz, T, Bittman, & McGuckin, 2007).
Answering the question at the beginning of this section, this research contributes to the
scientific body of knowledge by adding a new maturity model. Additionally, it adds a new
maturity model for the automation of the entire IT infrastructure instead of one of the
different technology-based constituent parts.
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1.6 Thesis Structure
The thesis is organized into four parts (see Figure 5 Outline of the thesis (created by
researcher)). Part 1 of this thesis presents background information on the main concepts
and research approach, covering both theoretical and practical aspects. Chapter 2 delves
into the theoretical concepts of IT infrastructure, automation, and maturity models. Moving
on to Chapter 3, the research approach is described, including the research methods used
and the literature review conducted.
Part 2 will cover the exploration phase of the thesis. In this phase, the artifact's creation,
namely a maturity model for IT infrastructure automation, is described in detail. Chapter 4
begins by examining the three core concepts of IT infrastructure, automation, and maturity
models. Followed by the development of the preliminary maturity model in Chapter 5.
Figure 5 Outline of the thesis (created by researcher)
The development of the artifact, the IT infrastructure automation maturity model, and the
definitions of IT infrastructure and automation are presented and described in Chapter 6,
which is Part 3.
Part 4 is about critically looking back and forward. Chapter 7 reflects on and discusses the
research process and results. Chapter 8 answers the research questions, and conclusions,
limitations, and recommendations for future work are presented.
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2 Theoretical Foundation
This chapter describes the theoretical foundation that is relevant to this research. The
theoretical foundation is the context in which this research takes place and provides the
pertinent theory to be analyzed to answer the research questions. For a good foundation,
the different theoretical concepts are described from a rather generic perspective to a more
specific one. The first concept described is the IT infrastructure, followed by the concept of
automation. The next concept is IT infrastructure automation, a compound of the first two
concepts. Another key topic that is described is maturity models. The central concept
behind a maturity model is introduced and explained. At the end of the chapter, an
overview of the available maturity model related to IT infrastructure automation is
provided.
2.1 Introduction
The theoretical framework for this thesis is constructed using the following iterative steps:
finding many studies and understanding relationships and theory. These steps result in the
theoretical framework (Chumney, 2016) (see Figure 6).
Figure 6 Process of creating a theoretical framework (adapted from (Chumney, 2016)).
The iterative way of working on creating this theoretical framework is described in more
detail in Section 3.3, Literature Review Strategy. The theoretical framework consists of three
primary sources: scientific literature, industry publications, and books.
1.1 IT Infrastructure
Information technology (IT) infrastructure is a strategic capability for organizations to
perform their business (Broadbent, Weill, & Neo, 1999). The purpose of an IT infrastructure
is to support different business processes. These processes are supported not only by the IT
infrastructure but also by applications. Applications are the technology that directly
supports the business environment, and the IT infrastructure supports applications. This
relationship and alignment are also captured in the Guidelines Regarding Architecture
Alignment (GRAAL) (van Eck, Blanken, & Wieringa, 2004), which defines how different
service layers can support business processes; see Figure 7.
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Figure 7 Service of GRAAL framework (adapted from (van Eck et al., 2004))
A more formal definition of the business IT alignment is provided by Santana Tapia (Santana
Tapia, 2009), which is:
The process of making the services offered by IT support the requirements
of the business – whether such services are offered individually by one
participant in the collaborative networked organization or collaboratively
by the entire network – so that value is created for the participating
organizations of the collaborative networked organization.
Definition 2-1 Business-IT alignment according to (Santana Tapia, 2009)
The definition of Santana Tapia makes clear that IT services can support the business
processes of your organization and those of others. The essence of this definition was
already captured in a model by Broadbent et al. (Broadbent et al., 1999) see Figure 8. This
model perfectly shows the IT infrastructure's supporting function to the business processes.
Figure 8 The elements of IT infrastructure (adapted from Broadband et al. (Broadbent et al., 1999))
Broadbent et al. state that the IT infrastructure capability comprises three aspects: service,
human, and technical. These aspects stand for:
• Shared Information Services. Refers to the infrastructure as services users can
understand, draw upon, and share to support conducting businesses.
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• Human Information Technology Infrastructure. Refers to knowledge, skills,
standards, and experience required to operate and manage IT components.
• Information Technology Components. Refers to the IT components, the technology
commodities readily available in the market, such as computers, routers, printers,
database software packages, operating systems, etc.
Bryd and Truner's (Byrd & Turner, 2015) research supports this view. They also see that the
IT infrastructure is not only a technical IT infrastructure but also contains the human aspects
of the IT infrastructure. According to them, the definition of IT infrastructure is as follows:
The IT infrastructure is the shared IT resources consisting of a technical, physical
base of hardware, software, communications technologies, data, and core
applications and a human component of skills, expertise, competencies,
commitments, values, norms, and knowledge that combine to create IT services
that are typically unique to an organization. These IT services provide a
foundation for communication interchange across the organization and for
developing and implementing current and future business applications.
Definition 2-2 IT infrastructure according to (Byrd & Turner, 2015)
On the other hand, Santana Tapia points out that the IT infrastructure is based on the
following two IT entities, which are based on the GRAAL framework (van Eck et al., 2004):
o The software infrastructure consists of the collection of standard general-purpose
software needed to run all information system services. It ranges from operating
systems, middleware, and network software to database management software.
o Physical infrastructure consists of computers, cables, wireless access points, printers,
and user interface devices to support software running in an organization.
In the research of Geert Haerens (Haerens, 2016), he defines the IT infrastructure as follows:
‘Infrastructure = Physical Infrastructure (according to GRAAL) + Software
Infrastructure (according to GRAAL)’
Definition 2-3 IT infrastructure according to (Haerens, 2016)
An almost similar definition of the IT infrastructure is presented by Gartner (Gartner, 2022):
The IT infrastructure is the system of hardware and software installations
and service components that support the delivery of business systems and
IT-enabled processes.
Definition 2-4 IT infrastructure according to (Gartner, 2022)
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Based on the different definitions mentioned above, the IT infrastructure can be described
as a physical infrastructure with software components that provide services to the business.
Generally speaking, based on all these different definitions, a general statement can be
made that the IT infrastructure supports and facilitates the business.
Figure 9 Views on IT infrastructure (adapted from (Laan, 2017))
Although all these different definitions suggest that it is clear how an IT infrastructure can
be defined, it still isn’t. It is still a bit ‘vague’ how an IT infrastructure can be seen. This
vagueness is also highlighted by Sjaak Laan ((Laan, 2017). He describes that it depends on
your perspective on how a person or organization sees or defines an IT infrastructure and
visualizes the different perspectives, see Figure 9.
Sjaak Laan presents the most comprehensive model for describing an IT infrastructure in his
book IT Infrastructure Architecture (Laan, 2017); see Figure 10. Although this model is a
simplified representation, it contains all the essential building blocks to build an
infrastructure.
Figure 10 The IT infrastructure model (adapted from (Laan, 2017))
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In addition, this model combines more or less the mentioned definitions and incorporates
the relationship with the processes, which connects to the business. Business IT alignment is
not fully covered in this model because it lacks the business environment defined by the
GRAAL framework. However, because it contains the essential building block for
infrastructure, this model is the basis for the perspective of how the IT infrastructure is seen
in this thesis.
In the introduction to this thesis's problem statement, the IT infrastructure change in the
last decade is already mentioned. This change is called the shift from the ‘iron age’ to the
‘cloud age’ (Morris, 2020). The IT infrastructure model (Figure 10) represents the ‘iron age’
IT infrastructure. Due to the introduction of the Cloud, the IT infrastructure has changed.
Table 1 provides a summary of these changes. The most significant change is the
introduction of virtualization technology (virtualized resources in the table).
Iron Age
Cloud Age
Physical Hardware
Virtualized resource
Provisioning takes weeks
Provisioning takes minutes
Manual Processes
Automated Processes
Table 1 IT infrastructure changes through time (adapted from (Morris, 2020))
Like the IT infrastructure model, the cloud IT infrastructure can be represented by building
blocks (Musse & Alamro, 2016); see Figure 11. The adaption of the Cloud shifts the
traditional IT infrastructure landscape, based on personal computers and enterprise
computing in its own data centers (on-premises), towards an IT infrastructure based on
virtual and distributed computing. With distributed computing, the necessary infrastructure
components are in other data centers than in the on-premises data center of an
organization.
Figure 11 Building blocks of a cloud IT infrastructure (adopted from (Musse & Alamro, 2016))
Within Cloud, services and how they are delivered are separated, where the service model
supports the application and business needs. The Cloud does not change how applications
support the business and conforms to the models and definitions of GRAAL (van Eck et al.,
2004), Broadbent et al. (Broadbent et al., 1999) and Santana Tapia (Santana Tapia, 2009).
Cloud service models and delivery models are illustrated in Figure 12 Cloud service models
and delivery models (adapted from (Dijk, 2017)). These models are essential for grasping the
essence of the research, as they provide the theoretical background.
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Figure 12 Cloud service models and delivery models (adapted from (Dijk, 2017))
The service models for a cloud IT infrastructure are software-as-a-service (Saas), platform-
as-a-service (PaaS), and infrastructure-as-a-service (IaaS).
1. Software-as-a-Service (SaaS): With SaaS, a provider offers its (proprietary) software
products in a cloud model. Consumers can access the software through a thin client
interface (e.g., a web interface) or a program interface. In this model, the customer
does not manage or control the underlying cloud infrastructure but only has limited
available customization options built into the software (Dijk, 2017; Liu et al., 2011;
Musse & Alamro, 2016).
2. Platform-as-a-Service (PaaS): PaaS is one step further down in its level of
abstraction, allowing the customer to deploy their applications on the cloud
architecture (be it consumer-created or acquired). The cloud provider manages the
underlying cloud architecture, where the consumer controls the deployed
applications and possibly configuration settings for the hosting environment (Dijk,
2017; Liu et al., 2011; Musse & Alamro, 2016).
3. Infrastructure-as-a-Service (IaaS): With IaaS, the service provider offers an
environment where the consumer can deploy and run software, ranging from
operating systems to applications. The cloud provider manages the cloud
infrastructure, but the consumer controls all functionalities, such as operating
systems, storage, and deployed applications (Dijk, 2017; Liu et al., 2011; Musse &
Alamro, 2016).
A delivery model for cloud computing refers to how cloud service models are provided to
customers. It outlines the methods by which cloud service models are deployed, managed,
and accessed. The main delivery models for cloud computing include public, private,
community, hybrid, and virtual private Cloud.
• Public Cloud: Public Cloud is a type of cloud computing delivery model where a third-
party service provider offers computing resources and services, such as virtual
machines, storage, and applications, over the internet to a broad range of users. This
model allows organizations to rent computing resources on demand and pay only for
what they use without investing in and maintaining their own infrastructure (Dijk,
2017; Liu et al., 2011; Musse & Alamro, 2016).
• Private Cloud: Private Cloud is a type of cloud computing where the computing
resources, including servers, storage, and networking, are dedicated to a single
organization or user. It is a cloud delivery model that offers the same benefits as the
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public cloud, such as scalability and on-demand resources, but with added security
and control. Private Cloud can be deployed on-premises or hosted by a third-party
provider (Dijk, 2017; Liu et al., 2011; Musse & Alamro, 2016).
• Community Cloud: A community cloud is a cloud computing deployment model in
which a group of organizations with common interests shares a cloud infrastructure
to meet their specific needs. The community cloud model allows organizations to
achieve cost savings and economies of scale by sharing resources while maintaining
a level of control and privacy that is impossible in public cloud deployments. The
community cloud model can facilitate collaboration and information sharing among
member organizations (Dijk, 2017; Liu et al., 2011; Musse & Alamro, 2016).
• Hybrid Cloud: A hybrid cloud is a delivery model that combines two or more different
cloud deployment models, typically a combination of public and private cloud
environments. It allows organizations to take advantage of both models while
addressing specific needs and requirements, such as data security and compliance
regulations. The hybrid cloud model can also provide greater flexibility, scalability,
and cost-effectiveness than a single deployment model (Dijk, 2017; Liu et al., 2011;
Musse & Alamro, 2016).
• Virtual Private Cloud: Refers to a cloud computing model that provides a private
cloud environment using public cloud infrastructure. It enables users to create
isolated virtual networks within a public cloud provider's network, allowing
organizations greater control over their resources and security. With a virtual private
cloud, users can configure and manage virtual servers, storage, and networking
resources as if using an on-premises infrastructure (Dijk, 2017; Liu et al., 2011;
Musse & Alamro, 2016).
At the beginning of this section, the IT infrastructure capability is described as containing
three aspects (service, human, and technical). In addition, this capability is essential to
support the development of information technology within the business. As pointed out by
Anwar et al. (Anwar & Masrek, 2014), Seppo Sirkemaa (Sirkemaa), and Byrd and Turner
(Byrd & Turner, 2015) to have an organization that profits from information technology
development, the business needs to be flexible. Additionally, the IT infrastructure must be
flexible to support this.
Anwar et al. (Anwar & Masrek, 2014) developed a framework for the IT infrastructure that
positively influences the flexibility needed in a business. This framework comprises three
aspects: process, human, and technology. The framework is shown in Figure 13 IT
infrastructure flexibility (adapted from (Anwar & Masrek, 2014)). This flexibility is needed
due to changes in markets or applications (Anwar & Masrek, 2014; Byrd & Turner, 2015).
This flexibility requires an IT infrastructure that is built to meet the standards. These
standards make it possible to be compatible and easily accommodate the change in the
existing IT infrastructure.
In addition to the influence of technology and the human aspect on the IT infrastructure, it
is crucial to note that the process aspect is the most important. Serrano et al. also describe
this essential point (Serrano & Pereira, 2020). They have found that processes are essential
to be implemented to manage the whole IT infrastructure. In addition, IT infrastructure is a
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complex universe consisting of different building blocks with various technologies. And due
to the Cloud, there are also different service and delivery models.
Figure 13 IT infrastructure flexibility (adapted from (Anwar & Masrek, 2014))
The different resources of the cloud service and delivery models must be managed; this can
be time-consuming. Automation is the solution for an organization’s IT department to
manage its IT infrastructure seamlessly on-premises and in the cloud (the combination is
known as hybrid) (Nedzelskay et al., 2020). In the next section, the concept of automation
will be explained in more detail.
2.2 Automation
To understand why automation helps to manage IT infrastructure, it is essential to
understand the concept of automation. Automation is mentioned in the previous section as
a methodology to manage IT infrastructures. In essence, automation can be defined as
(Ghazizadeh, Lee, & Boyle, 2012; Parasuraman, Sheridan, & Wickens, 2000; Vagia, Transeth,
& Fjerdingen, 2016; Wickens, Li, Santamaria, Sebok, & Sarter, 2010):
Automation is the use of technology to execute a function that was
previously performed by humans.
Definition 2-5 Automation (adapted from(Ghazizadeh et al., 2012))
This definition shows that automation can be used in many different ways. It also implies
that automation is not all or none. The use of automation can vary across a continuum of
levels, from the lowest to the highest level of full automation (Parasuraman et al., 2000;
Vagia et al., 2016). Research by Vagia et al. (Vagia et al., 2016) shows that different models
describe levels of automation. These levels define that a human only executes the function
at the lowest level; at the highest level, a computer performs all functions autonomously.
Sheridan and Verplank already described in their research the first levels of automation in
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1978 (Sheridan & Verplank, 1978), where they represent the shift of the role of an operator
in the industry from manual to supervisory control; see Figure 14.
Figure 14 Levels of automation (adapted from (Sheridan & Verplank, 1978))
In a research conducted by Vagia et al. (Vagia et al., 2016), they investigated different
models of levels of automation. After comparing these models, they proposed an eight-level
model that fits better within modern information technology; see Figure 15.
Figure 15 Level of automation (adapted from (Vagia et al., 2016))
Organizations face a challenge when levels of automation increase. This challenge is called
the automation problem (Endsley, 2018):
The more automation is added to a system, and the more reliable and
robust that automation is, the less likely human operators overseeing the
automation will be aware of critical information and able to take over
manual control when needed. More automation refers to automation
used for more functions, longer durations, higher automation levels, and
automation that encompasses longer task sequences.
Definition 2-6 Automation problem (adapted from (Endsley, 2018))
Hancock (Hancock, 2014) discusses in his paper ‘Automation: How much is too much?’ the
loss of level of control for humans. Endsley and Hancock conclude that organizations need
to adopt technology and the use of automation to remain accurate in their business.
However, organizations must be very aware of when and if they want to lose control. The
most important message that both researchers give is that human operators of automated
systems must always be informed and able to interact effectively and safely with the
system.
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2.2.1 IT automation
In the previous section, the purpose of automation was explained. The definition of
automation can be explicitly defined for information technology. Within information
technology, automation can be defined as follows (Bigelow, 2019; Ninja, 2021; Stephen,
2020):
IT Automation can be defined as the use of software and scripts to execute
repeatable tasks and reduce human interaction with IT systems.
Definition 2-7 IT automation (created by the researcher)
IT automation ranges from a scope of single on-off tasks to more routine repeatable tasks
and ultimately to tasks that are executed autonomously. Software tools and application
frameworks with minimum human intervention perform these tasks. Before these tools or
applications can be released in a production environment, they must be tested. The
different steps involved are named a deployment pipeline or workflow. A deployment
pipeline is, in essence, an automated implementation of the application build, deploy, test,
and release process (Humble & Farley, 2011); see Figure 16.
Figure 16 Deployment pipeline (adapted from (Humble & Farley, 2011))
This deployment pipeline is the main driver for delivering and deploying software in an
automated and continuous way, as described by Humble et al. (Humble & Farley, 2011;
Humble, Kim, & Forsgren, 2018). The pipeline releases software (applications) quickly and
with a repeatable process. The application platform includes these pipelines for these
applications' continuous integration (CI) and continuous deployment (CD). The application
runs after deployment on the resources of the IT infrastructure. Both building blocks are
depicted in Figure 10 The IT infrastructure model (adapted from (Laan, 2017)). The
combined interaction between these layers is responsible for the delivery of software as an
automated process. Therefore, automation is a key successor to application delivery.
Automatization between the application platform and the infrastructure is about the
provisioning of virtual (and sometimes physical) resources. The main difference between
automation for applications and automation for infrastructure resource provisions is the
following. Automation for application source code is used to build and deploy an
application, and automation for the infrastructure is used to provision components with
configuration parameters (Humble & Farley, 2011; Morris, 2020). The similarity is that the
same automation application workflow (pipeline) mechanism can be used and that the
configuration parameters are handled just like the source code. The automated way to
provision the infrastructure is called Infrastructure as Code (IaC) (Morris, 2020). With
infrastructure as code, infrastructure resources can repeatedly recreate the same
application environment.
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Infrastructure as code is what is called a cloud-age approach. This approach embraces the
continuous change of the application and provides higher reliability and quality. It also
reduces errors and makes governance, security, and compliance control visible (Morris,
2020).
2.2.2 IT Infrastructure Automation
IT infrastructure automation is the automation to operate and manage the infrastructure
(Maxima_Consulting, 2021). As seen in the IT infrastructure model (Figure 10), the
infrastructure is a compound of different building blocks. Based on this context, IT
infrastructure automation can be defined as follows:
Infrastructure automation uses technology to manage and operate
physical and virtual resources, such as computer hardware and operating
systems, networking components, and data storage systems, to deliver IT
services and solutions.
Definition 2-8 IT infrastructure automation (created by the researcher)
Within the IT infrastructure, two types of infrastructure can be defined that need to be
managed and operated (Maxima_Consulting, 2021); these are:
• Traditional infrastructure: refers to all physical components, such as computer
hardware and operating systems, networking components, data storage systems,
and other equipment owned and managed by the business within its facilities and
data centers. This type of infrastructure is usually quite expensive to manage and
operate. (Haris, 2010; Maxima_Consulting, 2021; Morris, 2020)
• Cloud infrastructure: refers to all components and resources related to cloud
computing, and the service model for cloud infrastructure is only Infrastructure-as-a-
Service (IaaS) and supports all the delivery models (Dijk, 2017; Liu et al., 2011;
Maxima_Consulting, 2021; Musse & Alamro, 2016; Redhat, 2019).
However, there is a significant difference between the way of working with these two types.
These differences are shown in Table 2.
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Traditional infrastructure
Cloud Infrastructure
The cost of change is high.
The cost of change is low.
Change has a high impact on stability.
Changes have a low impact on stability.
Reduce the opportunities to fail.
Maximize the speed of improvement.
Deliver in large batches, test at the end
Deliver small changes, and test
continuously.
Long release cycles.
Short release cycles.
Monolithic architecture (fewer, larger
moving parts).
Microservices architecture (more,
smaller parts).
GUI-driven or physical configuration.
Configuration as Code.
Table 2 Difference in the way of working for infrastructure types (adapted from(Morris, 2020))
Another aspect within the realm of IT infrastructure automation is life cycle management.
Life cycle management involves several stages: planning, design, implementation,
operation, and maintenance. These stages can be described as day-n operations (Codiline,
2023; Rami, 2015), which are:
1. Day-0 involves designing and implementing the infrastructure based on the organization's
requirements.
2. Day-1 focuses on configuring and operating the infrastructure to ensure it functions
efficiently and effectively.
3. Day-2 or Day-N involves periodic upgrades and modifications of the infrastructure to keep
up with the changing needs.
Based on these insights, IT infrastructure automation can be defined as:
IT infrastructure automation supports processes of the different lifecycle
phases to have a predictable and repeatable interaction with the different
IT infrastructure building blocks of a traditional and cloud infrastructure.
Definition 2-9 IT Infrastructure Automation (created by the researcher)
This definition directly highlights the challenge of IT infrastructure automation. On the one
hand, it must be able to deal with traditional IT infrastructure building blocks and, on the
other hand, with cloud-based IT infrastructure building blocks. Managing and operating
both types of IT infrastructure at once increases the complexity of IT (Atchison, 2022;
Sinclair & Keane, 2019). This complexity is exacerbated because IT infrastructures often rely
on legacy IT systems. These legacy systems introduce what is called technical debt.
Technical debt is a term that describes the accumulation of inefficiencies and limitations in
an organization's IT landscape (Ashok Vasa, 2023; Atchison, 2022). Although technical debt
sounds like a traditional ‘thing,’ only technical debt covers the building blocks that are often
outdated and do not support the automated way of working. In contrast, traditional is about
on-premises IT infrastructure with updated building blocks.
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IT infrastructure automation must deal with three different aspects to be successful,
namely:
• Traditional IT Infrastructure Building Blocks
• Cloud IT Infrastructure Building Blocks
• Legacy IT Infrastructure Building Blocks
2.3 Maturity Models
For the management of software and systems within information technology, organizations
constantly seek ways to improve quality, increase efficiency, and reduce costs (Becker et al.,
2009; Nedzelskay et al., 2020; Serrano & Pereira, 2020). Information technology
management's primary goal is to improve IT performance continuously (Becker et al., 2009).
A maturity model provides a roadmap for the organization, describing the necessary steps
to achieve this goal and providing a means to measure progress (Becker et al., 2009). The
concept of a maturity model is defined by Becker J et al. (Becker et al., 2009) as follows:
A maturity model consists of a sequence of maturity levels for a class of
objects. It represents an anticipated, desired, or typical evolution path of
these objects shaped as discrete stages.
Definition 2-10 Maturity Model (adapted from (Becker et al., 2009))
Based on this definition, we can state that a maturity model can be used as a "tool" to
define the current maturity level and the corresponding gap to reach the next maturity level
(Proença & Borbinha, 2016). As stated by Santana Tapia (Santana Tapia, 2009), the maturity
model also gives a path to evolve toward a higher level over time.
In their research on IT infrastructure management, Serrano and Pereira (Serrano & Pereira,
2020) describe the concepts of a maturity model. This concept comprises three elements:
maturity, capability, and maturity levels. These descriptions are shown in Table 3 Maturity
Model Concepts (adapted from (Serrano & Pereira, 2020)).
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Concepts
Description
Maturity
The maturity concept has been described as a 'state in which an
organization is perfectly able to achieve the goals it sets itself.'
This concept is recognized as a measure to assess the
organization's capabilities. The “component” of maturity may be
an object, a system, or a person.
Capability
Capability is characterized as the ability of an organization to
produce value. Organizations strategically use their capabilities
to improve their 'abilities' to another efficient and effective level.
Maturity Levels
Maturity levels or stages are a sequential path, not just to give an
improvement path to the organization but also to “situate”
organization capabilities at a hierarchal level. There are often five
maturity levels, each with procedures to implement to achieve
that level.
Table 3 Maturity Model Concepts (adapted from (Serrano & Pereira, 2020))
The most well-known maturity model in the information technology field is the Capability
Maturity Model Integration (CMMI) (Chrissis et al., 2011). CMMI is a framework that
contains the knowledge and experience of many experts. In addition, this model is the basis
for most maturity models used today due to basic principles and concepts (Santana Tapia,
2009; Serrano & Pereira, 2020). A CMMI model can be categorized into two types, namely,
staged and continuous (Chrissis et al., 2011; Santana Tapia, 2009). Both types use the same
concepts but are organized in different ways. Where levels describe maturity objectives,
these types describe the improvement path only in different ways. The description of both is
given in Table 4 Types of improvement paths (adapted from (Serrano & Pereira, 2020)).
Paths
Description
Staged
The staged model helps an organization improve its capabilities as a
whole. Organizational capabilities must comply with those
characteristics of that level to achieve a certain maturity level. This
model helps organizations characterize the overall state of
organizational capabilities.
Continuous
In a continuous path is the description of the procedures to
improve/evaluate in-divided each capability of a domain to improve.
Each capability can be at a different maturity level. This helps the
organization develop and characterize the state of its individual
capabilities and abilities.
Table 4 Types of improvement paths (adapted from (Serrano & Pereira, 2020))
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The definition of a staged maturity model and continuous maturity model are described in
more detail by Pereira and Silva (de Sousa Pereira & da Silva, 2010). A CMMI model typically
has five maturity levels (Chrissis et al., 2011; Santana Tapia, 2009). These five levels have the
following characteristics:
• Level 1 is described as ‘initial’ processes that are still unpredictable, poorly
controlled, and reactive processes.
• Level 2 is described as ‘managed’; processes are focused on projects and are often
reactive.
• Level 3 is described as 'defined.' The organization now characterizes processes and is
not reactive but proactive.
• Level 4 is described as ‘quantitatively managed’ in which processes are measured
and controlled.
• Level 5 is described as 'optimizing,' focusing on process improvement.
The relationship of these levels is visualized in Figure 17 Characteristics of CMMI maturity
models (adapted from (Chrissis et al., 2011)).
Figure 17 Characteristics of CMMI maturity models (adapted from (Chrissis et al., 2011))
An example of a continuous maturity model with five maturity levels for IT infrastructure
management looks like this (de Sousa Pereira & da Silva, 2010):
• Level 1: Ad-hoc, success depends on individual effort and heroics.
• Level 2: Resources and training are provided. Responsibilities and roles are assigned. The
process is executed according to a plan and a policy. The process is controlled and
monitored.
• Level 3: The process is documented, and all documents become assets of the
organization in order to institutionalize the process so that it becomes a standard
process of the organization.
• Level 4: Measurements, audits, reviews, and reports are provided, managed, and
controlled. Quantitative objectives for quality and process performance are established
and used as criteria for process management.
• Level 5: Continuous process improvement is enabled by quantitative feedback from
processes and the pilot of innovative ideas and technology.
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And an example of a staged maturity model for IT infrastructure management with five
levels looks like this (de Sousa Pereira & da Silva, 2010):
• Level 1: Ad-hoc, success depends on individual effort and heroics.
• Level 2: Projects establish the foundations for an organization to become an effective
service provider. This enables organizations to understand what they provide, with/for
whom, and how they provide it. The focus is on customer satisfaction and organizational
training.
• Level 3: IT service processes are documented, standardized, and integrated into
standard service processes. Critical organizational processes that allow for high-
performance levels in IT management are included.
• Level 4: Manage and control the information. Both service processes and delivery
services are quantitatively understood and controlled.
• Level 5: Continuous process improvement is enabled by quantitative feedback from
processes and the pilot of innovative ideas and technology.
The purpose of a maturity model is to provide information about an organization on how it
can improve. The scientific literature defines three specific purposes for a maturity model
(Serrano & Pereira, 2020). The descriptions of these three purposes are presented in Table 5
Specific purposes of the maturity model (adapted from (Serrano & Pereira, 2020)).
Purpose
Description
Descriptive
A maturity model can be used for an as-is situation of an
organization, easing a basic assessment of the organization’s
capabilities. For descriptive purposes, the maturity model is used
as a 'diagnostic tool.'
Prescriptive
A maturity model has a prescriptive purpose when it gives an
improvement path to a higher maturity level, providing
guidelines and measures to an organization.
Comparative
Comparative purpose allows an organization to benchmark its
capabilities externally and internally, using a large number of
historical data from the assessments of another organization.
Table 5 Specific purposes of the maturity model (adapted from (Serrano & Pereira, 2020))
In his dissertation, Santana Tapia (Santana Tapia, 2009) argues that maturity models are
descriptive because they describe, in a sense, the characteristics or processes that
distinguish an organization at a specific maturity level. He also stated that the maturity
model is not prescriptive because it does not tell an organization how to improve. The
purpose of a maturity model is to define the 'What' and not the 'How.'
According to Lasrado et al. (Lasrado et al., 2015), most maturity models in information
technology can be described by a generic structure. This generic structure is visualized in
Figure 18 General representation of a maturity model structure (adapted from (Lasrado et
al., 2015)). This generic model shows that a maturity model consists not only of levels but
also of dimensions. A dimension or sub-dimensions in the model represents a specific aspect
of maturity. These dimensions provide a structured framework for organizations to assess
their current capabilities and identify areas for improvement. Organizations can better
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understand their strengths and weaknesses by evaluating their capabilities in these
dimensions and developing a roadmap to improve their maturity level (Proença & Borbinha,
2016). This thesis uses the term dimensions because a dimension represents the same
purpose as a capability: to improve a specific aspect of maturity.
Figure 18 General representation of a maturity model structure (adapted from (Lasrado et al., 2015))
2.4 Overview of the Maturity Model Related to IT Infrastructure Automation
This section provides an overview of the maturity models related to IT infrastructure
automation. These models are selected during the literature review. The methodology for
conducting the literature review is described in detail in Section 3.3. In Table 6 an overview
of the related maturity models is presented.
Maturity Model
Reference
Number
of
Maturity
Levels
IT Infrastructure Maturity Model (ITI-MM)
(Haris, 2010)
1 - 5
Maturity Model for IT Management
(Becker et al., 2009)
0 - 5
Gartner IT Infrastructure and Operations Maturity
Model
(Scott et al., 2007)
1 – 6
The Gartner Infrastructure Maturity Model.
(Hidas, 2006)
1 - 6
Maturity Model for Implementing ITIL v3
(de Sousa Pereira & da Silva,
2010)
1 – 5
Cisco Digital Network Readiness Model
(Greene, Parker, & Perry,
2017)
1 - 5
Cisco Network Automation Maturity
(Pinto & Lacunza, 2021)
1 - 5
Cloud Maturity Model
(Dijk, 2017)
1 – 5
DevOps Maturity Model
(Radstaak, 2019)
1 - 5
Open Group Service Integration Maturity Model
(Group, 2009)
1 – 7
Netbrain Network Automation Maturity Model
(Netbrain, 2022)
0 - 4
Infrastructure Automation Maturity Model
(Quali, 2021)
1 – 5
Table 6 Overview-related maturity models (created by the researcher)
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For a maturity model to be related, the characteristics of the model must meet at least two
or more of the following requirements:
• IT Infrastructure, the maturity model describes an improvement path for IT
infrastructure building blocks.
• Cloud Infrastructure, the maturity model, describes an improvement path for cloud
services and delivery models.
• Software development approach, the maturity model describes an improvement
path for automated software/application delivery and deployment workflows.
• IT management, the maturity model describes an improvement path for IT
management. The improvement path includes at least a dimension of processes,
humans, or technology.
• IT (infrastructure) Automation, the maturity model describes an improvement path
for automation. This improvement path includes at least a dimension on manual or
automated tasks.
In Table 7 an overview of the related maturity models and the requirements they fulfill is
presented.
Maturity Model
Infrastructure
Cloud
Software
Development
IT
management
Automation
IT Infrastructure Maturity Model
(ITI-MM)
Maturity Model for IT Management
Gartner IT Infrastructure and Operations
Maturity Model
The Gartner Infrastructure Maturity
Model.
Maturity Model for Implementing ITIL v3
Cisco Digital Network Readiness Model
Cisco Network Automation Maturity
Cloud Maturity Model
DevOps Maturity Model
Open Group Service Integration Maturity
Model
Netbrain Network Automation Maturity
Model
Infrastructure Automation Maturity
Model
Table 7 Related maturity model and the requirements they fulfill (created by the researcher)
In the following subsections, these maturity models are briefly described.
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2.4.1 IT Infrastructure Maturity Model (ITI-MM)
The IT infrastructure maturity model (ITI-MM) (Haris, 2010) describes a step-by-step
roadmap to mature a flexible IT infrastructure. It is a vendor-agnostic roadmap. The focus
of this roadmap is on technology and human aspects. The model covers five levels, from
basic to innovative, and twelve dimensions. A descriptive overview of all levels is given in
Figure 19 IT Infrastructure Maturity Model (ITI-MM) (adapted from (Haris, 2010)). The ITI-
MM model is used in a real-life context and is easily understood by several experts. For time
reference, the model is developed for a more traditional IT infrastructure. Although the
introduction of the cloud infrastructure started, it did not incorporate the cloud service and
delivery models.
Dimensions are described in more detail Table 8 Dimensions of ITI-MM (adapted from
(Haris, 2010). However, the ITI-MM model lacks quantitative key performance indicators for
every dimension. This lack prevents the use of this model to assess an IT infrastructure
better.
Table 8 Dimensions of ITI-MM (adapted from (Haris, 2010)
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Figure 19 IT Infrastructure Maturity Model (ITI-MM) (adapted from (Haris, 2010))
2.4.2 Maturity Model for IT Management
The Maturity Model for IT management (Becker et al., 2009) is based on other maturity
models. The maturity model presented in Figure 20 is developed with the research
procedures of Becker et al. This model provides an improvement path for measuring IT
performance in the field of IT management. The model contains six levels, from nonexistent
performance measurement to fully automated and integrated performance management.
The models cover three dimensions, namely contents, organization, and technology. Each
dimension can be assessed by five criteria, in which the dimension contents describe the
relevant measures to be applied for IT management. The dimension organization looks at
the IT management integration in the organization, and the dimension technology examines
the supporting components.
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Figure 20 The IT management maturity model (adapted from (Becker et al., 2009))
2.4.3 Gartner IT Infrastructure and Operations Maturity Model
The IT infrastructure and Operations Maturity Model from consulting firm Gartner (Scott et
al., 2007) aim to improve the Business IT alignment over time. The model describes five
levels, from little to no focus on IT infrastructure and operations to a trusted partner for
business to increase value. For many organizations, the level required to reach is level four.
Level four has a maturity level that supports a business IT alignment that supports business
priorities and competitive IT services. With the increase of every maturity level, there is a
substantially higher business value. A descriptive overview of this model is given in Figure
21. The model incorporates four dimensions to assess maturity: people, process,
technology, and business management. Every dimension can be measured with the
performance of several sub-dimensions; see Table 9 Overview of dimensions and sub-
dimensions (adapted from (Scott et al., 2007)). The model is developed at a moment in time
when the cloud infrastructure does not exist. Therefore, the model lacks the support to
assess cloud services and delivery models.
Dimensions
Sub-dimensions
People
• Organization
• Roles
• Culture
• Training
• Metrics
Process
• Focus
• Standards
• Integration
• Metrics
Technology
• Standard
• Efficiency
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Dimensions
Sub-dimensions
• Agility
• Service Quality
• Tools
Business Management
• Planning
• Financial Management
• Metrics
• Governance
• Sourcing
• Project Management
Table 9 Overview of dimensions and sub-dimensions (adapted from (Scott et al., 2007))
Figure 21 The levels of Gartner's I&O Maturity Model (adapted from (Scott et al., 2007) )
2.4.4 The Gartner Infrastructure Maturity Model.
The Infrastructure Maturity Model of the consulting firm Gartner (Hidas, 2006) aims to
improve the maturity of the IT infrastructure building blocks toward a real-time IT
infrastructure. A real-time IT infrastructure is, according to Gartner, an infrastructure that
provides shared services to the business. Business policies drive these services and
automatically configure the IT infrastructure. This description of an IT infrastructure looks
like the description of a cloud infrastructure. This model is also developed at a moment in
time when the cloud infrastructure does not exist, but it already incorporates the ideas
behind the cloud. The model consists of six levels and seven dimensions; see Figure 22.
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Figure 22 The Gartner Infrastructure maturity model (adapted from (Hidas, 2006))
The model describes a roadmap from a basic IT infrastructure, which is very ad hoc and
reactive, via a virtualized infrastructure where the IT management is proactive toward a
real-time infrastructure. The aspects in the model are derived from the Gartner IT
infrastructure and Operations Maturity Mode (see Section 2.6.3). To achieve the different
maturity levels, the model uses the dimensions of people, processes, and technologies. How
these dimensions support the roadmap for improvement is per dimension described in
Table 9.
Figure 23 Gartner Infrastructure Mature through People, Process, and Technology (adapted from (Hidas, 2006))
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2.4.5 Maturity Model for Implementing ITIL v3
The Maturity Model for ITIL
1
v3 (de Sousa Pereira & da Silva, 2010) describes an
improvement path for ITIL processes. Examples of ITIL processes are incident management,
configuration management, problem management, change management, security
management, service level management, and more. The model contains four levels and
fourteen dimensions; see Figure 24.
Figure 24 Maturity model for implementing ITL v3 (adapted from (de Sousa Pereira & da Silva, 2010) )
The dimensions are all the management processes as defined by ITIL v3. Details of the
different ITIL processes are needed for a good assessment of an organization. Therefore, a
good understanding of ITIL is required to use this model. The model is built around the two
types of improvement paths for a maturity model. Organizations that do not know how to
implement the different ITIL processes can use the staged improvement path. If an
organization already has some processes in place, then the continuous improvement path
can be used.
2.4.6 Cisco Digital Network Readiness Model
The Cisco Digital Network Readiness Model (Greene et al., 2017) provides an improvement
path from traditional to digital-ready networks. A network is one of the building blocks of an
IT infrastructure that provides communication and data transport. In Figure 25 the
characteristics of the traditional network and the digital ready network are shown.
1
Informaon Technology Infrastructure Library is a framework for managing IT services.
(hps://www.itlibrary.org/)
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Figure 25 Transformation from a traditional network to a digital-ready network (adapted from (Montanez, 2020))
The model contains five levels from best effort towards self-driving. Self-driving stands for
continuously and automatically adapting the changes needed for the business. The model is
shown in Figure 26.
Figure 26 Cisco Digital Network Readiness Model (adapted from (Greene et al., 2017))
The five dimensions of this model contribute to readiness at every level. The dimension
architecture defines the approaches to enhancing the network architecture, life cycle,
governance, and compliance. Automation is the ability to reduce manual tasks. When the
security dimension improves, the risk is reduced, and the compliance requirements are met.
Therefore, a security policy must be defined. Service assurance is the ability to continuously
align with the application's growth, supporting the business's demands. The fifth-dimension
analytics provides the necessary information for valuable insight into the network's
operation and management. The lack of this model is due to the lack of descriptions for
levels two, three, and four. Therefore, assessing a network with the help of this model is
challenging.
2.4.7 Cisco Network Automation Maturity
The Cisco Network Automation Maturity model (Pinto & Lacunza, 2021) describes a
roadmap for automated deployment, configuration, and testing/validation of network
components. The automated processes of this network automation maturity model involve
both types of IT infrastructure. Network automation also includes the day-n life cycle
management of components. This maturity model contains five levels, from manual
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configuration to automatic provisioning, and has only one dimension, the automation
dimension. The levels are:
1. Manual device configuration. The network still relies on manual provisioning and
configuration.
2. Basic configuration automation. There are some basic configuration automation and
advanced service provisioning.
3. Automated provisioning based on the controller per domain. There is controller-
based automation in one or more network domains that allow secure, scalable, and
consistent day 0 and day 1 provisioning.
4. Controller-based, network-wide automated provisioning. There is already controller-
based automation across the network for policy-based day 0 and day 1 provisioning
and configuration, delivered consistently by the cloud delivery models.
5. Automated provisioning of devices in a self-organized, self-diagnosing, and
dynamically updated network. The network has advanced self-optimization
capabilities.
2.4.8 Cloud Maturity Model
The Cloud Maturity Model (Dijk, 2017) provides an improvement roadmap for cloud service
models. The roadmap of this model describes the evolution of cloud infrastructure from an
on-premises deployment to a fully optimized cloud (native) infrastructure for the three
cloud service models, IaaS, PaaS, and SaaS. These three are the dimensions of the model.
The model contains five levels and is visualized in Figure 27. Behind these three dimensions
are fourteen so-called focus areas; these focus areas are called sub-dimension in this thesis.
The focus areas and their origination are presented in Table 10 Consolidation of capabilities
into dimensions (adapted from (Dijk, 2017)). The maturity model offers an improvement
path across all levels for every focus area (dimensions).
Figure 27 Cloud maturity model (adapted from (Dijk, 2017))
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Table 10 Consolidation of capabilities into dimensions (adapted from (Dijk, 2017))
2.4.9 DevOps Maturity Model
The DevOps Maturity Model (Radstaak, 2019) provides an improvement path for software
development. With the infrastructure model (see Figure 10), the software development is
placed in the development block of the application platform. The term DevOps stands for
development and operations. This maturity model contains five levels, which are based on
CMMI; the levels are:
• Initial, software development teams are isolated and organized around one specific
skill set. There is no automation for software delivery. Documentation and
configuration are saved per project and are not consistent.
• Managed, software development teams are still independent and structured to
short-term deliverables. Software delivery processes are scheduled, and the support
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environment is partially automated. The deployment is partially automated.
Documentation and configuration are up-to-date.
• Define, the software development team. The teams are structured around projects
and their processes to facilitate collaboration between teams. The delivery process is
standardized and automated. There is a basic deployment through a pipeline.
Documentation and configuration are regularly validated.
• Measured, software teams are structured around whole products. Software delivery
and deployment are automated. The documentation is updated with the gathered
experience and quality requirements.
• Optimized, the software teams are cross-functional and interdisciplinary. The
software delivery and deployment are optimized for a maximum throughput of
releases. Documentation and configuration are automated and generated.
There are five dimensions in the DevOps maturity model: communication and collaboration,
measurement, monitoring, automation, and culture. These dimensions represent how
DevOps is achieved. Each dimension has several sub-dimensions. Figure 28 presents a high-
level overview of this model.
Figure 28 High-level DevOps maturity model (adapted from (Radstaak, 2019))
2.4.10 Open Group Service Integration Maturity Model
The Open Group Service Integration Maturity Model (OSIMM) (Group, 2009) provides an
improvement path for service integration levels for an organization, IT systems, and
business applications. This model consists of seven levels, which are as follows:
1. Silo, Individual parts of the organization are developing their own software
independently, without integrating data, processes, standards, or technologies.
2. Integrated, technologies have been put in place to communicate between the silos
and to integrate the data and interconnections. Constructing an IT system that
integrates across different parts of the organization is possible.
3. Componentized, the IT systems in the silos have been analyzed and broken down
into component parts, with a framework in which they can be developed into new
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configurations and systems. Although components interact through defined
interfaces, they are not loosely coupled, which limits agility and interoperability
between different segments of the organization.
4. Service, composite applications are built from loosely-coupled services. Services run
on an IT infrastructure supported by appropriate protocols, security mechanisms,
data transformation, and service management capabilities. The way in which
services may be invoked is based on open standards and is independent of the
underlying application technology.
5. Composite Services, at this level of service maturity, it is now possible to construct a
business process for a set of interacting services, not just by bespoke development
but by using a composition or business process modeling language of information
and control through the individual services. Composite services include static,
process, and activity-based services.
6. Virtualized services, business, and IT services are now provided through a façade – a
level of indirection. The service consumer does not invoke the service directly but
rather by invoking a 'virtual service.' The virtual service becomes loosely coupled
with the infrastructure on which it is running, allowing more opportunities for the
composition of services.
7. Dynamically Re-Configurable Services, prior to this level, the business process
assembly, although agile, is performed at design time by developers (under the
guidance of business analysis and product managers) using suitable tooling. Now this
assembly may be performed at runtime, either assisted by the business analysts via
suitable tooling or by the system itself.
These maturity levels can be assessed by the following dimensions, business, organization
and governance, method, application, architecture, information, and infrastructure and
services. The dimensions of infrastructure and services are relevant to this thesis. Because it
addresses the IT infrastructure, service management, IT operations, IT management, and
service level agreements. An overview of the model is shown in Figure 29.
Figure 29 Open Group Service Integration Maturity Model (adapted from (Group, 2009))
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2.4.11 Netbrain Network Automation Maturity Model
The Netbrain Network Automation Maturity Model (Netbrain, 2022) provides an
improvement path process of automated deployment, configuration, and testing/validation.
The maturity model shows that the operational overhead decreases as automation
increases. A measure of automation maturity, according to Netbrain, is the Mean-Time-To-
Repair (MTTR). In Figure 30 Network Automation Maturity Model (adapted from (Netbrain,
2022)), the positive advantage of automation is the difference between the two curves.
Figure 30 Network Automation Maturity Model (adapted from (Netbrain, 2022))
The maturity model addresses only dimension automation, with five maturity levels.
2.4.12 Infrastructure Automation Maturity Model
The Infrastructure Automation Maturity Model (Quali, 2021) is an improvement path for the
IT infrastructure created by a vendor. The main driver behind this model is the adaptation of
the DevOps approach in the IT infrastructure. The maturity model is designed around the
infrastructure management lifecycle, see Figure 31. The lifetime of an IT infrastructure is
influenced by several factors for the business to provide several business deliverables.
Figure 31 IT infrastructure automation lifecycle (adapted from (Quali, 2021) )
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The maturity model has four dimensions: culture, people, process, and technology. Each
dimension has an improvement path of five levels, from ad-hoc to self-defining. The levels
can be briefly described as follows:
1. Ad-hoc, the IT infrastructure is a mix of old and new technology. IT teams are highly
siloed with skills focused on domains. Provisioning is accomplished by using multiple
methods. Changes take time.
2. Automated, the IT infrastructure remains a mix of old and new technology.
However, the cloud infrastructure has been adopted. DevOps is emerging with the
IT infrastructure.
3. Frictionless, optimization is the key initiative. There is a need to standardize, reduce
complexity, and create insight to understand DevOps activities. There are still
challenges to the management of the IT infrastructure. The IT infrastructure is still
too complex and is managed with too many tools.
4. Lifecycle, the IT infrastructure is managed holistically and end-to-end and entirely
according to the DevOps approach. In addition, governance, security, and change
control practices are in place.
5. Self-defining, IT infrastructure management is a high-scale, end-to-end environment
actively supported and enhanced by software delivery.
The description of the different levels and dimensions is shown in Figure 32.
Figure 32 IT Infrastructure Automation Maturity Model (adapted from (Quali, 2021))
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2.5 Summary
The theoretical framework of this thesis consists of three concepts. The first concept
introduced is IT infrastructure, where the two types of infrastructure are explained. The first
type is traditional infrastructure, and the second is cloud infrastructure. The positive
influence of humans, processes, and technology is introduced for operations and
management.
The next concept is automation. Automation is a broad concept. This thesis defines
automation as using software and scripts to execute repeatable tasks and reduce human
interaction.
The latest concept introduced is about maturity models, which provides a roadmap for
organizations to describe the necessary steps to achieve a more mature improvement and
means to measure progress. An overview of the twelve maturity models related to IT
infrastructure automation is provided.
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3 Research Design and Approach
This chapter discusses the overall design of the research and provides an overview of the
reasoning behind the selection of the research methods. It also provides information on the
research methods used and explains the conceptual framework, scope of the research, the
research method, and the research approach. Furthermore, the structure of the literature
research is described.
3.1 Research Design and Approach
The research design consists of two research paradigms: Design Science Research and the
Delphi Method. This section will introduce both methods and explain why these paradigms
were chosen.
3.1.1 Design Science Research
Design Science Research (DSR) is a research paradigm that aims to create and validate
innovative and effective solutions to practical problems (Alarabiat & Ramos, 2019;
Massaroli, Martini, Lino, Spenassato, & Massaroli, 2018; Okoli & Pawlowski, 2004). DSR
involves a systematic approach to designing, developing, and evaluating artifacts, such as
software applications, information systems, and processes. DSR is also a research paradigm
that combines rigor and practical information to create innovative and effective solutions to
practical problems. As mentioned in the books, 'Scientific Research in Information Systems'
(Recker, 2021) and 'Design Research in Information Systems' (Hevner & Chatterjee, 2010),
the main paradigm behind DSR is understanding a design problem in their environment and
finding the solution while creating the artifact. This research approach emphasizes the
importance of combining rigor and practical knowledge to ensure the relevance and
usefulness of the artifacts created (Vom Brocke et al., 2020).
DSR follows a cyclical process that includes three main cycles: the relevance cycle, the
design cycle, and the rigor cycle (Vom Brocke et al., 2020). The relevance cycle aims to
identify and understand a practical problem and determine its relevance to research. The
design cycle involves creating and testing a prototype artifact that is iteratively refined
based on subject matter expert feedback. Finally, the rigor cycle evaluates the effectiveness
of the artifact and refines the design principles used.
The three cycles of DSR are crucial to creating and validating artifacts. The conceptual DSR
framework, presented in Figure 33 Design Research Framework (adapted from (Hevner &
Chatterjee, 2010)), shows the three cycles and their relations. This conceptual framework
combines the real world, called the environment, and the practice and scientific theory,
called the knowledge base. The environment consists of the problem space in which the
research problem is defined. The knowledge base provides the foundational theories,
frameworks, methods, and constructs to build the theoretical base for the research.
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The motivation to use DSR as a research method is based on the following factors:
• The main goal of this research is to attempt to create a new practical artifact for a
real-world problem using a scientific approach. The DSR research paradigm supports
this goal.
• For the creation of this artifact, knowledge is needed from best practices, scientific
theory, practitioner experience, and knowledgeable experts. The key feature of DSR
is the combination of rigor and practical information, which supports this functional
requirement.
• The process of creating this artifact is not linear but iterative. It requires solid design
techniques and interaction with professionals and experts, and it is a creative
process. DSR is a cyclical process and supports the iterative approach needed for the
development of the artifact.
The environment and the knowledge base are bridged together with two cycles, these are:
1. The Relevance Cycle ensures the 'research problem' is clearly and precisely defined.
This cycle bridged the environment context with the iterative design science
research steps.
2. The Rigor Cycle connects the knowledge base with the design science research steps.
In this way, there is an iterative coupling during the research with the scientific
literature, best practices, experiences, and expertise used during the research. This
research study conducts a literature study to ensure the theoretical basis for creating
the artifact described in Section 3.3, Literature Review.
Figure 33 Design Research Framework (adapted from (Hevner & Chatterjee, 2010))
The framework contains a third cycle, the Design Cycle. The design cycle is a crucial part of
DSR, enabling researchers to create a prototype artifact that can be iteratively refined based
on feedback from subject matter experts. The Design Cycle is executed in collaboration with
the Delphi method (Turoff & Linstone, 2002) to ensure the creation of an artifact based on
rigor and practical information. The Delphi method is a structured communication
technique that involves multiple rounds of expert feedback and consensus building. This
method is particularly suited to DSR, as it supports the creation of artifacts that are
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grounded in best practices and validated by experts in the field. The Delphi method is
explained in more detail in the next section.
3.1.2 Delphi Method
The Delphi method is a structured communication technique that is widely used in various
fields, including social sciences, business, and engineering (Turoff & Linstone, 2002). This
method involves multiple rounds of expert feedback and consensus building to identify best
practices in a particular field. The Delphi method is particularly useful when dealing with
complex and uncertain problems, where experts' opinions can provide valuable insights and
perspectives (Hsu & Sandford, 2007).
The Delphi method typically involves several rounds to get a consensus about a topic, with
each round providing participant feedback and insights. The results of each round are
analyzed and summarized. Participants, who generally are subject matter experts in the
field, remain anonymous throughout the process, which helps to eliminate bias and
promote honest feedback. The selection of the subject matter experts is an essential step in
the method. Subject matter experts can be involved in the next round to have the
opportunity to review their earlier responses and provide feedback. The process continues
until a consensus is reached or a predetermined endpoint is reached (Alarabiat & Ramos,
2019; Hsu & Sandford, 2007).
The Delphi method offers several strengths that make it a valuable research technique. One
of its primary strengths is its ability to gather subject matter expert opinions and insights
while minimizing bias and groupthink. By allowing experts to remain anonymous and
providing multiple rounds of feedback, the Delphi method enables experts to provide
honest and independent assessments, which can be critical for decision-making and
problem-solving (Avella, 2016; Hsu & Sandford, 2007; Turoff & Linstone, 2002).
Another strength of the Delphi method is its flexibility and adaptability to different research
contexts. The method can be used in various fields, from healthcare and education to
technology and business. Its iterative and collaborative approach allows researchers to
adjust their research questions or objectives based on feedback received in each round,
making it an effective method for exploring complex and uncertain problems (Avella, 2016;
Hsu & Sandford, 2007; Turoff & Linstone, 2002).
The Delphi method also offers a cost-effective and efficient approach to data collection, as it
can be carried out remotely and does not require face-to-face meetings or extensive travel.
This makes it a practical method for collecting data from experts who are located in
different regions or countries (Avella, 2016; Belton, MacDonald, Wright, & Hamlin, 2019;
Turoff & Linstone, 2002).
Finally, the Delphi method provides a systematic approach to gathering and analyzing data,
enhancing research findings' rigor and validity. The structured and iterative nature of the
method ensures that data are collected and analyzed consistently and reliably, reducing the
potential for errors or biases (Okoli & Pawlowski, 2004; Turoff & Linstone, 2002).
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The mentioned strengths are the motivation to choose the Delphi method in the Design
Cycle of DSR. Additionally, using the Delphi method in the Design Cycle enables the creation
of artifacts based on rigor and practices validated by field experts.
3.2 Research Approach
To carry out design science research, vom Brocke et al. (Vom Brocke et al., 2020) created a
research process model. This process model consists of six steps: problem identification and
motivation, the definition of the objectives for a solution, design and development,
demonstration, evaluation, and communication; and four possible entry points: problem-
centered initiation, objective-centered solution, design, and development-centered
initiation, and client/context initiation; see Figure 34 DSR methodology process model
(adapted from (Vom Brocke et al., 2020)).
As a starting point for this research, the Design & Development process is chosen. By
starting at this process, all relevant rigor and practice necessary is collected during the
iterations in the artifact's creation. The main activities of this study are around the creation
of an artifact. But to create the artifact, according to Hevner et al. (Hevner & Chatterjee,
2010), all of the ingredients are needed to be in place. Therefore, the processes 'Identify
Problem & Motivation' and 'Define Objective of a Solution' are part of this research. The
other processes are not part of this research because they do not provide the necessary
ingredients. The three processes are outlined in the different chapters:
• The "Identify Problem & Motivation" process defines the research problem, and the
justification of the solution's value is determined; see Chapter 1.
• The "Define Objective of a Solution" process is where an objective of a solution can
be inferred from the available knowledge and explored of what is possible and
feasible; see Chapter 4 and Chapter 5.
• And the "Design & Development" process is where the artifact is created; see
Chapter 6.
Figure 34 DSR methodology process model (adapted from (Vom Brocke et al., 2020))
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3.2.1 Mapping Research Questions to Research Methods
As mentioned above, DSR is an iterative method with different iterative cycles. As a result,
the research method can differ during DSR activities. The different research questions are
mapped to the appropriate research method to answer each question. Table 11 Mapping
the research questions to the research shows the methods used to try to answer them.
Research Question
Research Methods
Data Collection Method
RQ01
What is an IT
infrastructure?
• Review of the
literature
• Delphi together with
qualitative analysis
methods
• Literature Review
• Expert Panel –
Interview
• Digital Survey
RQ02
What is IT infrastructure
automation?
• Review of the
literature
• Delphi together with
qualitative analysis
methods
• Literature Review
• Expert Panel –
Interview
• Digital Survey
RQ03
What models of IT
infrastructure automation
maturity are available in
the current scientific
literature?
• Review of the
literature
• Literature Review
RQ04
How can these attributes
be ordered over different
maturity stages as part of
a maturity model for IT
Infrastructure
automation?
• Design Science
Research Combined
with
• Delphi Method and
Qualitative Analyses
Methods
• Literature Review
• Expert Panel –
Interview
• Digital Survey
RQ05
What are the useful
attributes for each of
these dimensions?
• Design Science
Research Combined
with
• Delphi Method and
Qualitative Analyses
Methods
• Literature Review
• Expert Panel –
Interview
• Digital Survey
Table 11 Mapping the research questions to the research methods (created by the researcher)
3.2.2 Conceptual Model
Conceptual models are a vital component of research design, providing a theoretical
framework for understanding and analyzing complex phenomena. According to Recker
(Recker, 2021), a conceptual model is a simplified representation of a complex reality that
captures the essential elements and relationships among them. One of the key concepts in
conceptual modeling is abstraction, which involves simplifying complex phenomena by
focusing on the essential elements and the relationships among them. This abstraction
enables researchers to develop a clear and concise representation of the phenomenon
under study.
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The conceptual model for this research is shown in Figure 35 Overall Conceptual
Framework. The conceptual model is based on a conceptual framework of the flexibility of
the IT infrastructure (Anwar & Masrek, 2014). That model defines which concepts influence
an IT infrastructure. The conceptual model for this research explains that three relationships
influence the IT infrastructure. IT infrastructure is defined as an independent variable. The
dependent variable is the maturity level; this variable needs to be observed to determine
the effect of the independent variable. Automation is the moderator variable influencing
the relationship between independent and dependent variables. In this case, it influences
the maturity of the IT infrastructure.
Figure 35 Overall Conceptual Framework (created by the researcher)
One of the key concepts in conceptual modeling is abstraction, which involves simplifying
complex phenomena by focusing on the essential elements and the relationships among
them. This abstraction enables researchers to develop a clear and concise representation of
the phenomenon under study. Based on this overall conceptual framework, a more abstract
model can be defined; see Figure 36 Conceptual Framework. In this more abstract model,
the maturity of the IT infrastructure is seen as one concept and is the dependent variable.
Automation, on the other hand, is the independent variable; the way automation is present
or implemented says something about maturity. This conceptual model shows that this
research investigates only the relation or influence of automation on the maturity of an IT
infrastructure. In general, the research study concerns the maturity of automation in IT
infrastructure.
Figure 36 Conceptual Framework (created by the researcher)
Additionally, experts are often invited to participate in the research to refine further and
test the conceptual model. Their insights and perspectives on the phenomenon are used to
identify gaps in existing knowledge and highlight areas where further research is needed.
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3.2.3 Research Scope
In the theoretical foundation for this research, see Chapter 2, the IT infrastructure is
presented as a model that consists of different building blocks (Laan, 2017). Within the IT
infrastructure model, there is a separate building block for infrastructure management. It is
within this building block that automation of the IT infrastructure takes place. The maturity
of the IT infrastructure is influenced by the maturity of the IT infrastructure management
(Serrano & Pereira, 2020).
To limit and scope our research, the artifact created aims only at a maturity level of
automation of the IT infrastructure management building block. This is visualized in Figure
37 Scope of the research in the context of the IT infrastructure.
Figure 37 Scope of the research in the context of the IT infrastructure (created by the researcher)
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3.2.4 Applied Research Approach
Based on the information in the previous section, the research approach can be visualized in
the conceptual research framework, shown in Figure 38 The conceptual research framework
of this thesis.
Figure 38 The conceptual research framework of this thesis (created by the researcher)
In this model, the environment contains the topics that are part of this research. To create
the right artifact, the environment must be clear and precise. The theoretical framework,
see Chapter 2, shows that the IT infrastructure, automation, and IT infrastructure
management topics are not ambiguous. Section 1.1, Introducing the Research Problem, also
describes that there are challenges in understanding these topics. Therefore, part of this
research focuses on defining the environment, not the artifact. The sub-research questions
RQ01 and RQ02 address this research and aim at the topics of IT infrastructure and
automation. Therefore, during the DSR iterations, data was collected to answer the main
research questions to create the artifact, and data was gathered to define the environment
to answer the sub-research questions.
The knowledge base contains the theoretical framework for this research. This framework is
based on the literature review, as described in more detail in Section 3.3. As described, this
framework's rigor cycle connects the different DSR iterations.
In the DSR design cycle, the artifact is developed and created, as described in Chapter 6 of
this document. The design cycle is supported by sub-research questions RQ03, RQ04, and
RQ05. The development and creation of the artifact is carried out through a total of two
iterations. In the first iteration, a subject matter expert panel is interviewed, while a larger
group of experts is invited to participate in a digital questionnaire during the second
iteration. The data collected from both iterations define the exact environment and guide
the artifact's creation.
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To gather the necessary data during the iteration of the design cycle, Section 3.4 provides a
detailed explanation of the data-gathering methods used.
3.3 Literature Review Strategy
A literature review is conducted to create the body of knowledge that is relevant to this
research. The literature review uses an iterative divergent and convergent process (see
Figure 39 Divergent and Convergent ). This process is described in more detail by Edzo
Botjes (Botjes, 2020). The process of analyzing and synthesizing the literature is repeated
several times until a comprehensive understanding of the different topics is achieved. The
topics are IT infrastructure, automation, and maturity models. The divergent process is used
to find and discover various sources of information such as whitepapers, reports, and case
studies from vendors and industry experts. The convergent process involves the selection of
sources by identifying the different topics and keywords.
Figure 39 Divergent and Convergent Process (created by the researcher)
The motivation to choose this research approach is because the theoretical framework must
contain information about the topics of this research (IT infrastructure, automation, and IT
infrastructure management). As pointed out in Section 1.5.2, limited literature is available in
the body of knowledge about IT infrastructure and automation. Therefore, the search for
relevant information is extended outside the scientific body of knowledge. The diverge and
converge approach used during the research period allowed the researcher to search many
sources and filter only the relevant information. These sources can be categorized into
books, scientific databases, and the world wide web. The different stages of the literature
review are shown in Figure 40 Literature review stages.
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Figure 40 Literature review stages (created by the researcher)
The search strings used to find potential information are as follows:
• "IT Infrastructure"
• "Maturity Model"
• "IT Infrastructure" and "Maturity Model"
• "IT Infrastructure" and "Maturity”
• "Automation"
• "IT Infrastructure" and "Automation"
• "IT Infrastructure" and "Automation" and "Maturity"
• "IT Infrastructure" and "Automation" and "Maturity Model"
• "Automation" and "Levels"
• "Level of Automation"
• "Infrastructure as code" or "Infrastructure-as-Code"
The inclusion criteria for building the theoretical framework are as follows:
• Publications
2
must be written in English.
• Publications must be available for download.
• The title, Keywords, Abstract, and Introduction of the publication make it explicit
that the publication is related to "IT Infrastructure" or "Maturity Model" or
"Automation" or a combination of these three.
2
The term publicaons is used as an aggregate for papers, whitepapers, websites and blogs.