OPEN GOVERNMENT DATA: A STAKEHOLDER ANALYSIS. THE OPENCUP CASE - POLITesi

 
POLITECNICO DI MILANO
           Master of Science in Management Engineering

             OPEN GOVERNMENT DATA:
            A STAKEHOLDER ANALYSIS.
                  THE OPENCUP CASE

Supervisors: Prof. Giuliano Noci
            Dott. Luca Tangi
            Dott.ssa Irene Vanini

                                    Master Graduation Thesis by:
                                       Gianmarco Raho, 880415

                  Academic Year 2017/2018
II
ABSTRACT

       The Open Government Data (OGD) research field is driven by a growing desire for
transparency, responsibility and participation. In addition to this, the enormous availability
of new datasets has stimulated the OGD movement from the really beginning. However, the
lack of knowledge on the stakeholders surrounding OGD initiatives is negatively affecting
the achievement of the benefits sought.
       This thesis increases the knowledge on the OGD stakeholders providing an
identification framework and a tool that matches stakeholders with the skills they should
have in order to increase the effectiveness of the OGD system. The identification framework
is the result of an iterative analysis on Stakeholder Theory applied to different case studies,
that ended up with a list of stakeholders able to return a picture of a general OGD project.
The stakeholders/skills tool is a combination of the list of stakeholders, obtained by the
identification framework, and the Open Data Institute skills framework, that describes the
knowledge and the skills of anyone interacting with open data.
       Taking OpenCUP, an awarded Italian OGD platform, as case study, the identification
framework and the stakeholders/skills tool are tested. Various stakeholders are selected and
interviewed. Consequently, a qualitative content analysis is conducted on the interviews.
Finally, the case study highlighted the need of few integrations in order to achieve better
results and it opened up to future opportunities.

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IV
INDEX

                 EXECUTIVE SUMMARY .................................................... 2

1.1      INTRODUCTION ................................................................................................. 2
1.2      LITERATURE REVIEW ......................................................................................... 3
  1.2.1 Open Government Data ................................................................................ 4
  1.2.2 Stakeholder Theory ....................................................................................... 5
  1.2.3 Skills related to OGD projects ....................................................................... 6
  1.2.4 Discussion ...................................................................................................... 7
1.3      CASE STUDY .................................................................................................... 12
  1.3.1 OpenCUP ...................................................................................................... 12
  1.3.2 Discussion .................................................................................................... 13
1.4      CONCLUSION AND FUTURE OPPORTUNITIES ...................................................... 16

                  INTRODUCTION ............................................................. 18

                  LITERATURE REVIEW ................................................... 20

3.1      METHODOLOGY............................................................................................... 20
3.2      OPEN GOVERNMENT DATA .............................................................................. 21
  3.2.1 OGD preliminary findings .......................................................................... 29
3.3      STAKEHOLDER THEORY ................................................................................... 30
  3.3.1 Identification, classification and characterization models ....................... 33
  3.3.2 Stakeholder theory preliminary findings .................................................. 37
3.4      SKILLS RELATED TO OGD PROJECTS ................................................................. 44
  3.4.1 Skills definition ............................................................................................ 45
3.5      FINDINGS........................................................................................................ 48

                  CASE STUDY .................................................................... 51

4.1      OPENCUP ...................................................................................................... 52

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4.2    THE INTERVIEWS............................................................................................. 53
    4.2.1 Content summary: administrative stakeholders....................................... 53
    4.2.2 Content summary: user stakeholders ........................................................ 57
  4.3    DISCUSSION .................................................................................................... 62

                  CONCLUSION AND FUTURE OPPORTUNITIES............... 66

BIBLIOGRAPHY ................................................................................. 69

EXHIBIT 1 INTERVIEW WITH A. CHIACCHIARARELLI .................... 74

EXHIBIT 2 INTERVIEW WITH E. CAPPELLO AND M. PACIFICI ........ 76

EXHIBIT 3 INTERVIEW WITH G. ALBANESE .................................... 81

EXHIBIT 4 INTERVIEW WITH S. BERSELLI ...................................... 86

                                                                                                                       VI
Figure 1 ODI skills framework (Open Data Institute, 2016) ....................................... 7
          Figure 2 Stakeholders identified .................................................................................. 9
          Figure 3 Stakeholders/Skills matrix ........................................................................... 11
          Figure 4 Qualitative content analysis results ............................................................. 14
          Figure 5 Literature review phases .............................................................................. 20
          Figure 6 Pillars of Open Government Data ............................................................... 23
          Figure 7 Salience model (Mitchell, Agle, & Wood, 1997) .......................................... 35
          Figure 8 Importance/Influence matrix (Chigona, Roode, Nazeer, & Pinnock, 2010)
............................................................................................................................................... 36
          Figure 9 Stakeholders identified ................................................................................ 43
          Figure 10 ODI skills framework (Open Data Institute, 2016) ................................... 45
          Figure 11 Stakeholders/Skills matrix ......................................................................... 50
          Figure 12 Matrix filled by Andrea Chiacchiararelli, Emanuela Cappello and Michela
Pacifici ................................................................................................................................... 56
          Figure 13 Matrix filled by Giuseppe Albanese ........................................................... 58
          Figure 14 Matrix filled by Sara Berselli ...................................................................... 61
          Figure 15 Qualitative content analysis results ........................................................... 63

                                                                                                                                            VII
Table 1 Benefits of Open Government Data (Janssen, Charalabidis, & Zuiderwijk,
2012) ...................................................................................................................................... 26
          Table 2 Adoption barriers (Janssen, Charalabidis, & Zuiderwijk, 2012) .................. 29
          Table 3 List of stakeholders - first phase ................................................................... 39
          Table 4 List of stakeholders - second phase ..............................................................40

                                                                                                                                        VIII
EXECUTIVE SUMMARY

1.1 Introduction
       In recent years, a series of Open Government Data (hereinafter, OGD) initiatives
sprung up all over the world. These initiatives are driven by a growing desire for
transparency, responsibility and participation, in particular dealing with government data.
All these advantages, in addition to the enormous availability of new data sets, have
stimulated the OGD movement since the beginning. However, we will see in the literature
review that current researches show that the effectiveness of this OGD projects is really low.
A widespread opinion on this topic is that the knowledge on the stakeholders involved in
these projects is not enough.
       Facing this problem, this thesis aims to find out how those that are responsible for
managing OGD projects can be assisted in developing these initiatives.
       Therefore, the first step is to identify all the stakeholders involved into the OGD
projects. Afterwards, analysing the interactions that each stakeholder has with the OGD
system, it is possible to better define the stakeholder’s profile.
       So far, existing stakeholder models are exclusively related to the specific case they
analyse. Furthermore, none of them defines the skills and competences that each
stakeholder should have to make the most of the OGD system. Until now, the research is
lacking on who the stakeholders are and on what skills they should have.
       In this thesis, findings of a literature review on already existing stakeholder models
are presented. Furthermore, these findings are used here to define a list of stakeholders that
could be considered representative of a general OGD project.
       Subsequently, using the list of stakeholders, the paper presents a model that aims to
match the identified stakeholders with an already existing open data skills framework (Open
Data Institute skills framework).

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The list and the model are applied to the OpenCUP project, an awarded OGD
initiative in Italy. This project is led by the public administration department in charge of
the planning and coordinating national economic policy (DIPE – Dipartimento per la
programmazione e il coordinamento della politica economica della Presidenza del Consiglio
dei Ministri). This case study has the objective to verify if the list of stakeholders and the
stakeholders/skills model are applicable in any general situation.
       For this purpose, the research is guided by the following questions:
       •      Is it possible to identify a general-purpose list of stakeholders of an OGD
              project?
       •      Taking into considerations the stakeholders identification framework, what
              are the skills needed by the stakeholders identified, in order to increase the
              success of OGD projects?
       The structure of the executive summary is the following. Section 1.2 presents a
literature-based review of the three pillars that built the thesis: the OGD movement, in
particular the benefits it pursues and the barriers from which it is blocked; the stakeholder
theory, namely the models applied to OGD initiatives; the skills related to the open data,
thus the ODI framework; and also the models elaborated. Section 1.3 shows the results of
the OpenCUP’s case study and the findings related to the research questions. Finally, section
1.4 summarizes all the work and draws the conclusions.

1.2 Literature review
       The first step in the analysis is to get in contact with the Open Government Data
scenario, to understand the main features and the different perspectives through which it is
debated. A bunch of papers, generally known by researchers as remarkable for the topic,
represents the starting point of the review. With a “snowball approach”, many other papers
were collected and screened in order to obtain a general overview on the OGD, capturing all
the nuances, but at a superficial level.
       The second step aims to investigate a specific barrier, to shed light on the criticalities
involved. The lack of knowledge on the stakeholders involved into the projects is that barrier.
It is common in the research field to use a more structured and consolidated theory in order
to broaden the knowledge on a less mature one. With this approach, Stakeholder Theory is
used in order to have a theoretical description of the problem.

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The last part of the review has the aim to identify the skills that different stakeholders
should have to perform their role and their tasks.

     1.2.1 Open Government Data
         The concept of open data and specifically open government data has been around
for some years. Open data began to become visible in the mainstream in 2009, as various
governments (such as the USA, UK, Canada and New Zealand) announced new initiatives to
open up their public information (Open Knowledge International, 2012), launching an
actual OGD movement, motivated by values such as citizen collaboration and participation,
better government transparency, and stimulated innovation.
         The innovation flow that hit the OGD movement brought substantial benefits, but
also a series of problems that slow down the adoption of the OGD approach, and that must
be considered as barriers. This is why several researchers already discussed about the
positive and negative influences related to OGD (Janssen, Charalabidis, & Zuiderwijk,
2012).
         On the benefits side, there are many areas where open data is creating value. The first
visible positive impact is on transparency and accountability that the government gains
towards citizens, as decisions and operations are shared with the public (Hardy &
Maurushat, 2017). This is a mean to decrease corruption, build trust and improve citizen
satisfaction. Increased social control by citizens allows them to interact more actively with
the government and other public entities, overcoming the traditional governmental
structures (Attard, Orlandi, Scerri, & Auer, 2015).
         From an economic point of view, the main impacts of opening data are stimulating
innovation and promoting economic growth. In this specific field, the peculiarity of the open
data is that its value changes when it is used, meaning that the data itself has no value, but
it becomes valuable once it is introduced in other analysis. This is due to the difficulties
related to the prediction of the potential of the application derived by the OGD (Janssen,
Charalabidis, & Zuiderwijk, 2012). Unlocking data enable the public, entrepreneurs and the
government itself to better leverage this information using it as input into applications and
services (Pereira, Macadar, Luciano, & Testa, 2017).
         Even if datasets are usually published in their raw form, hence the value of the data
itself can be considered little, public entities can leverage on other stakeholders, such as
those coming from the private sector, community groups, and citizens, to bring innovation
upon the data published in order to exploit the utmost potential of open government data
initiatives (Edelmann, Höchtl, & Sachs, 2012). This is how the increased participation

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enhances data quality, together with the great contribution of users’ feedbacks (O'Hara,
2014).
         Yet, active participation is not given for sure. While open government data initiatives
set the basis for citizen participation and collaboration, the actual realization of them is not
guaranteed (Alexopoulos, Zuiderwijk, Charalabidis, Loukis, & Janssen, 2014).
         All these benefits, added to the huge availability of new datasets, spurred the OGD
movement from the beginning (Styrin, Luna-Reyes, & Harrison, 2017). However, data
coming from current researches show that data usage is scarce, the participation and
collaboration of citizens is almost non-existent, and other businesses involvement is limited
(Safarov, Meijer, & Grimmelikhuijsen, 2017).
         So, even if OGD can potentially provide a number of benefits, its adoption is affected
by numerous barriers (Zuiderwijk, Janssen, Choenni, Meijer, & Alibaks, 2012).
         Barriers can be classified as cultural, legal, technical and related to the data usage.
Literature is currently more focused on technical and cultural barriers. Most of the
initiatives and investments undertaken by many governments in order to transform the
internal processes addresses only functional quality attributes.
         It is a common acknowledgement that the OGD’s effects occur slower than expected,
and the influence generated by the various stakeholder groups plays a significant role in the
success of this innovation (Flak & Nordheim, 2006). Attention to the supply side and to
technology prevents us from analysing the different perspectives that each stakeholder
group entails. Although each OGD project is characterized by many stakeholders with
multiple value dimensions (financial, social and political), few studies try to understand the
different role played by each stakeholder group. Governments need to know more about who
their stakeholders are, in order to increase the success of their initiatives.
         In order to go further with the stakeholder analysis, Stakeholder Theory is here used
as approach.

     1.2.2 Stakeholder Theory
         Stakeholder theory is considered to assist and to increase the knowledge about OGD
topics (Flak & Rose, 2005). Since the expected positive effects do not correspond to reality,
a greater knowledge on stakeholders in OGD, and the awareness of the OGD’s potential
effects, would support policymakers in identifying strengths and weaknesses of the services
provided. The incorrect identification and analysis of the stakeholders involved is
considered one of the main reasons of failure in the development of OGD projects (Sánchez
& Macías, 2017). Therefore, the identification, the characterisation and the analysis of the

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different stakeholders’ perspectives and needs become a critical task in the development
process.
         In order to find useful insights, several models belonging to stakeholder theory used
to identify and classify stakeholders within OGD projects have been analysed. They differ on
the number and the type of categories, on the way each stakeholder is classified in a certain
group, on the data gathering approach and on the preliminary objectives that the authors
set at the beginning of their analysis.
         So, even if a number of researchers used stakeholder theory to deepen the knowledge
on the stakeholders into OGD projects, the issue cannot be considered as surpassed, for a
reason: OGD projects have peculiar characteristics, and often differ one from another, not
only for their objectives, but also for their organizational structure. These insights confirm
the problem raised with the research question. A general framework to identify
stakeholders, applicable on every project, is still missing.

     1.2.3 Skills related to OGD projects
         There are only isolated efforts to understand the characteristics of OGD stakeholders,
such as their skills and expertise, and type of tasks they have to perform (Martin & Begany,
2017).
         While there is no general agreement on who will interact with OGD systems, there is
also a lack of knowledge on the skills that the stakeholders should have to carry out that
tasks (Gascó-Hernández, Martin, Reggi, Pyo, & Luna-Reyes, 2018).
         Taking the users perspective, most of them lack the skills required to perform basic
activities, such as accessing data and assessing their quality, or even they are not aware of
what they can do with data (Safarov, Meijer, & Grimmelikhuijsen, 2017). The skills issue
concerns also the decision-makers and the operators involved in the project
implementation, because they have roles and perform tasks closely related to the final
success of these initiatives.

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In terms of skills needed in the OGD world, the ODI (Open Data Institute) has
developed a framework (Figure 10) based on six sets of basic skills (Open Data Institute,
2016). It describes the knowledge and the skills of anyone interacting with open data, in
order to enable users to identify where they are in their learning journey (Open Data
Institute, 2016).

                             Figure 1 ODI skills framework (Open Data Institute, 2016)

     1.2.4 Discussion
       The literature review brought two conclusions:
       •       A framework able to spot stakeholders of an OGD project, in a more general
               way is still missing;
       •       No one has applied stakeholder theory models to OGD with the aim to
               understand what the skills that each stakeholder should have to make the OGD
               systems more effective are.
       The solution developed to address the first issue is mapping all the stakeholders
identified in case studies already analysed by other scholars, using stakeholder theory, and
creating a categorization of these stakeholders, based on the role they perform within the
OGD project.
       The first phase of the mapping process consists of generating a list of stakeholders
previously identified by other researchers. The occurrences of this list are characterized by

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a high role specificity within the project they belong to. This means that the list is made of
stakeholders that are very similar to each other, nevertheless they are listed twice because
they appeared in their case study with different labels.
       The second phase aims to resolve the redundancies generated in the first one.
Therefore, an in-depth analysis of each document is necessary, in order to clearly state the
tasks each stakeholder carried out in its case study. Afterwards, redundancies are
eliminated, and identical stakeholders are aggregated under the same label.
       Starting from the list obtained by the second phase, stakeholders were grouped
according to a “common interest approach”. Basically, if a group of stakeholders is
considered heterogeneous in relation to the interests of its members, the group is divided
into two or more groups, until the final groups are internally homogeneous for their
interests. Instead, if different groups have similar interests, they are gathered together.
       The concept of interest is here meant as the stakeholders’ generic objective dealing
with an open data system. Benefits sought, job assignments and influences on OGD project
fulfilment are all considered interests (Rowley, 2011).
       For the purposes of the thesis, the final list is made up only by stakeholders directly
related to the data or related to the conceptual development of the projects. For instance, all
the stakeholders involved in the funding of these initiatives are not considered.
       This final list makes room for some considerations. To clearly understand the
different characteristics and perspectives of each group of stakeholders, it is useful to define
four macro-categories that can group the categories previously identified: users, business
users, administration and policy-makers. The first and the second groups represent
those that are involved in the project only on the demand-side, so they only perform
activities related to the research and download of data from platforms, and afterwards
analysis on that data. The difference between them is that the second group is made up of
users acting according to the willingness of the company they work for, while the users
belonging to the first group interact with the system on their own account. Instead, the
administration group represents those in charge of the OGD project itself. This means that
they are responsible of the platform, from the initial phase of the conceptual design, to the
feeding of data. The fourth group, policy-makers, has a big impact at strategic level, having
influences on the strategic plan, policies and laws.
       Figure 9 briefly summarizes all the stakeholders identified in the literature review.

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Figure 2 Stakeholders identified

       Once all the stakeholders of a project are clearly listed, the further step is towards
improving their interactions with the project. Interactions change on the basis of the
different stakeholder, so it is important to understand who is going to do it and how they
can do it in order to increase the adoption effectiveness. Therefore, the focus shifts on the
skills required to stakeholders in order to fulfil their tasks at the best.
       In order to introduce the second solution, the review identified only one study
inquiring the correspondence between stakeholders’ types and skills needed in an OGD
ecosystem. Gascó-Hernández, Martin, Reggi, Pyo, and Luna-Reyes (2018) reflect on the
extent to which training programs should take into account specific contexts, as well as tailor

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the content to specific interests, characteristics, and expectations of different types of
stakeholders.
       The review supported the answer to the second research question providing two
important elements: a list of stakeholders and a list of skills. Combining these two elements
it is possible to create a tool that can be used to match stakeholders and skills. This tool
is a matrix that has all the stakeholder groups on the rows and all the skills on the columns.
The intersection between a column and a row represents the possibility to associate a certain
skill to a certain stakeholder.
       Hence, this matrix is able to state a correlation between OGD’s stakeholders and open
data related skills, filling a gap appeared in the literature review, and moreover, it supports
project managers and policy-makers providing them with a picture of the as-is situation of
the skills owned by stakeholders present in the project, and eventually establishing a best
practice through the validation of some case studies.

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SKILLS
                                                                                                   PUBLISHING                                                                                 MANAGEMENT                                                                     BUSINESS                                                                                                        ANALYSIS                                                                                        LEADERSHIP

                                                                                                                                                                                                                                                                                                           generating revenue

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    developing strategy
                                                                                                                                                                                                                                                                                                                                using data analytics
                                                                                                                                                                                                                                                measuring success
                                                                                                                                                                                                                             managing changes

                                                                                                                                                                                                                                                                                      designing services
                                                                                                                                                       boasting usability
                                                                                                                                   improving quality

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              prioritizing action
                                                                                                                                                                                                                                                                                                                                                                                              applying statistics

                                                                                                                                                                                                                                                                                                                                                                                                                                                                         developing policy
                                                                                choosing formats

                                                                                                                                                                            sustaining open
                                                              using platforms

                                                                                                                                                                                                                                                                    innovating with

                                                                                                                                                                                                                                                                                                                                                                                                                                     interacting with
                                                                                                                                                                                                                                                                                                                                                       visualizing data

                                                                                                                                                                                                                                                                                                                                                                          finding insights

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          leading change
                                                                                                                                                                                                                                                                                                                                                                                                                                                        deploying data
                                                                                                                                                                                                                                                                                                                                                                                                                    trend analysis
                                                                                                                                                                                              licensing data
                                                                                                    cleaning data

                                                                                                                                                                                                               communities
                                                                                                                    linking data

                                                                                                                                                                                                                 building

                                                                                                                                                                                                                                                                                                                                                                                                                                                           science
                                                                                                                                                                                  data

                                                                                                                                                                                                                                                                         data

                                                                                                                                                                                                                                                                                                                                                                                                                                           data
                                         enthusiasts
                                         researchers
                 USERS

                                           citizens
                                  public agencies employees
                                   public agencies decision
                                           makers
                                       data journalists
               BUSINESS

                                          journalists
                USERS

                                     business employees
STAKEHOLDERS

                                  business decision makers
                                  data harvester employees
                                    data insert employees
                                       project manager
                                     data quality monitor
                 ADMINISTRATION

                                       success monitor
                                    privacy issue monitor
                                    business logic designer
                                     software developer
                                   infrastructure provider
                                  systems maintenence team
                                     hardware provider
                                          legislators
               MAKERS
               POLICY

                                   chief government board
                                     government board

                                                                                                                                                                               Figure 3 Stakeholders/Skills matrix

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1.3 Case study
      OpenCUP, an awarded OGD platform recognized for its success (Invitalia, 2019), is
chosen as case study.
      The case study was carried out with a double objective. The main one is to verify the
two results of this thesis, hence the list of stakeholders and the matrix that matches the
stakeholders previously identified with a set of skills. The second objective is to understand
if these two results are considered useful by those responsible for the management and
coordination of an OGD platform, and to determine the future opportunities they entail. A
group of representative stakeholders was selected, with the aim to collect responses from
different groups.
      In order to collect accurate recommendation, each interviewee has been asked to fill
the matrix personally, with a specific focus on the stakeholder groups they represent and on
the stakeholder groups they usually are in touch with. In order to extract meaningful
information from the data gathered from the interviews, the qualitative content analysis has
been considered suitable.

     1.3.1 OpenCUP
      DIPE (Dipartimento per la programmazione e il coordinamento della politica
economica della Presidenza del Consiglio dei Ministri) promoted the realization of the
OpenCUP portal, financed with 2014-2020 UE funds by the national operative program
governance and institutional capacity (PON-GOV14-20). This portal has the aim to release,
in open format, the whole database of all the Italian public investment projects. All these
projects are tracked by the CUP code (Codice Unico di Progetto), that is an identification
code used as an interoperability tool to interact with other portal such as OpenCoesione,
Italiasicura and OpenCantieri. The OpenCUP portal is addressed to civil society, central and
local public administration, data journalists and the academic world (OpenCUP, 2019).
      OpenCUP allows research on planned public investments and, thanks to the
publication in open format, it enhances citizens participation and data.
      The platform enables downloading data related to public investment decisions, easily
searching and visualizing projects, on maps and infographics, filtering projects on sector,
cost or location, or searching for the specific entity responsible for the realization of an
investment. These features are designed to make OpenCUP a useful tool able to improve the
quality of the decisional processes and to make public resources more accountable
(OpenCUP, 2019).
In the last 15 years, the CUP database has been compiled by people responsible for
recording public investment projects on a platform managed by DIPE. The number of
projects amounts roughly to 5 million. The current objective is to ensure a high data quality,
defining guidelines and models to improve the data release process (OpenCUP, 2019).

     1.3.2 Discussion
       In order to transform the raw data coming from the interviews into a standardized
form, a qualitative content analysis has been performed over the interviews.
       The first step was to identify the codes of interest through which the qualitative
data need to be classified. After an iterative analysis of the theory appeared in the literature
review and taking into account the objectives of the case study, the units of analysis
(clusters) were discovered.
       The following are the clusters used to analyse the interviews:
       •       Observations on the stakeholders list
       •       Observations on the stakeholders/skills matrix
       •       Tasks that the stakeholder has to perform
       •       Stakeholders’ needs
       •       Future opportunities
       This classification is used with the aim to test the tools designed at the end of the
literature review on the basis of the research questions. To deepen the analysis, it is
important to define what kind of information each cluster can provide.
       The observations on the stakeholders list and the observations on the
stakeholders/skills matrix collect the advices of the interviewees on the two tools under
analysis. These advices represent the requirements that, according to the interviewees, will
boast the usability of the two tools to different OGD project.
       Tasks that the stakeholder has to perform and stakeholders’ needs help to better
define the skills and the competencies that each stakeholder might need. While the former
seeks to understand the skills of the stakeholders from their point of view, the latter aims to
state the skills that a stakeho0lder should have according to the perspective of another
stakeholder.
       The future opportunities cluster is linked to the development of the two tools. This
category, providing the interviewees’ perspective, allows to understand how it is possible to
take advantage of the matrix stakeholders/skills and from the list of stakeholders.
       In Figure 15, the results of the qualitative content analysis are reported.

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Figure 4 Qualitative content analysis results

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The results of the qualitative content analysis generate some interesting
considerations. Starting from the list of stakeholders, the first outcome of this thesis, the
interviewees did not change the occurrences of the list, so they found it representative of
their situation.
       The main weakness of the list, come out during the interviews, is the need to adopt
an agreed language. This means that it is important for the interviewees to have clear in
their mind the meaning of each occurrence of the list. For instance, Cappello and Pacifici
suggest integrating the list of stakeholders with a glossary, in order to make clear to
everyone what each stakeholder group consists of. The list of skills identified by the ODI is
partially confirmed by the tasks that stakeholders perform and by the stakeholders’ needs.
None of the interviewees carried out tasks that are in contrast with the skills inserted in the
matrix. As for the stakeholders list, the agreed language is requested also for the skills.
The stakeholders/skills matrix is also affected by the need of an agreed language. The
interviewees belonging to OpenCUP stressed the importance of having some notes to
support the reading and the interpretation of the matrix.
       So, in order to obtain the best result in every situation, it is mandatory to take further
steps towards a model that fits the specific case. Since this model is going to assume the role
of a basis on which each specific case can develop its solution, it is very important to use an
easy and non-technical language, in order to facilitate the interpretation and to boast the
model usability to different projects.
       The considerations arisen by the future opportunities confirm that the tools will have
a positive impact if they are used as basis for further developments. Since the two tools are
built for general purposes, they could be integrated with other variables in order to achieve
more specific results. As emerged in the interviews, the tools create opportunities for
monitoring of the resource’s exploitation, for the definition of the degree of responsibility
within a project based on the skills everyone has, they could change the selection process for
the stakeholders related to the administrative side, and also the education programs for the
stakeholders on the user side.
       The way the interviewees filled the matrix is another useful indication. It is clear that
some stakeholders are perceived form the other in a different way than how they perceive
them self. This bias can lead to misconceptions during the development phase of the project.
This insight confirms once again that this model should be used as a starting point in a
project, and moreover, it could give the opportunity to the managers of the project to align
their vision with the one of the other stakeholders.

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1.4 Conclusion and future opportunities
         The initial goal of this work was to better understand the cloud of stakeholders that
surrounds every OGD initiatives, and then to figure out how the interactions between these
stakeholders and the OGD system could be made more effective.
         The literature review ended up with two answers to the research questions: the list
of stakeholders and the stakeholders/skills matrix.
         The list of stakeholders has the purpose to make aware those that are in charge of
develop OGD initiatives of the stakeholders that are likely to interact with the system. This
kind of tool wants to fill the gap found in the literature review, spotting stakeholders of an
OGD project in a way as general as possible. Moreover, it defines a framework that
practitioners can use in order to clearly understand the type of stakeholder they are dealing
with.
         The stakeholders/skills matrix is a tool that completely missed so far. As the literature
review showed, researchers’ focus has always been on the identification of the stakeholders
in each specific project. Instead, the matrix goes further the identification and it provides a
way to understand how to increase the usage effectiveness per each stakeholder. This tool
can change the way OGD initiatives are built up, since it can have an influence on selection
process for those stakeholders that actively participate to the project development, and it
can also provide useful indication in order to set guidelines to help users in perform their
tasks.
         The case study partially validated the purpose of the two tools. It appeared that the
tools need to be integrated with guidelines and definitions in order to facilitate their
application. This is due to the need of the interviewees to link their specific situation to the
general-purpose tools.
         In terms of future opportunities, the case study has already started the discussion on
the possible developments that the tools may have. They could be integrated with other
variables in order to obtain specific results, such as understanding the level of influence of
each stakeholder on the project according to their skills, or defining the degree of
responsibility that a stakeholder, belonging to the administration side, should have on the
basis of their level of competence, or even having a clear picture of the resources exploitation
on the basis of the skills owned.
         All these insights make clear that the results of this work should be tested again with
other case studies, possibly with a different scope, in order to collect more data and to
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understand the other opportunities that the tools entail. Furthermore, it is crucial to collect
other information on the way these instruments should be delivered, in order to facilitate
their usage.

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INTRODUCTION

       In recent years, a number of Open Government Data (hereinafter, OGD) initiatives
sprung up around the world. These initiatives are led by an increasing will for transparency,
accountability and participation, in particular dealing with governmental data. All these
benefits, in addition to the huge availability of new datasets, spurred the OGD movement
from the beginning. Nevertheless, current researches show that the effectiveness of this
OGD projects is really low. A widespread opinion on this topic is that the knowledge on the
stakeholders involved in these projects is not enough.
       Addressing this issue, this thesis has the aim to find out how those that are in charge
of the management of the OGD projects can be assisted in the development of these
initiatives.
       Therefore, all the stakeholders involved into the OGD projects need to be identify.
Afterwards, analysing the interactions that each stakeholder has with the OGD system, it is
possible to better define the stakeholder’s profile.
       So far, existing stakeholder models are exclusively related to the specific case they
analyse. Moreover, none of them defines the skills and the competences that each
stakeholder should have in order to exploit the OGD system at the best. Thus, there is not a
general framework that could be followed during the development of a project. Until now,
the research is lacking on who the stakeholders are and on what skills they should have.
       In this thesis, findings of a literature review on already existing models are presented.
Furthermore, these findings are here used in order to define a list of stakeholders that could
be considered representative of a general OGD project.
       Afterwards, using the list of stakeholders, the paper presents a model that aims to
match the stakeholders identified with an already existing open data skills framework (Open
Data Institute skills framework).

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The list and the model are applied to the OpenCUP project, an awarded OGD
initiative in Italy. This project is conducted by the public administration department in
charge of the planning and the coordination of the national economic policy (DIPE –
Dipartimento per la programmazione e il coordinamento della politica economica della
Presidenza del Consiglio dei Ministri). This case study has the objective to verify if the list of
stakeholders and the stakeholders/skills model are applicable in any general situation.
       For this purpose, the research is guided by the following questions:
       •      Is it possible to identify a list of stakeholders valid for any OGD project?
       •      Taking into considerations the stakeholders identification framework, what
              are the skills needed by the stakeholders identified, in order to increase the
              success of OGD projects?
       The structure of the paper is as follows. CHAPTER 3 presents a literature-based
review of the three pillars that built the thesis: the OGD movement, in particular the benefits
it pursues and the barriers from which it is blocked; the stakeholder theory, namely the
models applied to OGD initiatives; the skills related to the open data, thus the ODI
framework. CHAPTER 4 shows the results of the OpenCUP’s case study and the findings
related to the research questions. Finally, CHAPTER 5 summarizes all the work and draws
the conclusions.

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LITERATURE REVIEW

3.1 Methodology
       This literature review started from a widespread topic, argued by a number of authors
and in different ways, and eventually ended to light up a research gap that, even if narrow
and quite specific, involves an issue entailing huge consequences on the final success of the
OGD field. The analysis process is characterized by three distinct phases (Figure 5).

                                       Figure 5 Literature review phases

       The first stage of the analysis aims at getting in contact with the Open Government
Data scenario, to understand the main features and the different perspectives through which
it is debated. A bunch of papers, generally known by researchers as remarkable for the topic,
represents the starting point of the review. With a “snowball approach”, several other papers
were collected and screened in order to get a general overview on the OGD, catching all its
shades, but at a superficial level. The results of this first analysis led to a selection of themes,
considered by several authors as reasons for interest. In particular, adoption barriers, data
life-cycle, data quality and usability, stakeholders, benefits, and business model for OGD are
the main topics raising concerns in the academic world.
       The second step aims at deepening a specific barrier, to shed light on the criticalities
it involves. The considerations that lie behind this selection regard two main aspects: the

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extent to which the topic is discussed in literature and the possibility to go deeper and
generate new knowledge. After these considerations, the research focused on the
stakeholders of the OGD projects and their role within those initiatives.
       It is frequent in the research field to use a more structured and established theory in
order to broaden knowledge on a less mature one. With this approach, Stakeholder Theory
is used in order to have a theoretical description of the problem. Hence, a thorough analysis
on this new theme took place. Articles and conferences have been researched on databases,
using keywords as “Open Government Data” and “Stakeholder theory”, and applying again
the “snowball approach” on them. This section of the review stated that a number of
researchers already applied to e-Government, and in particular OGD, models belonging to
stakeholder theory, in different cases, reaching different conclusions. Anyway, each research
contributed to enrich the literature providing case by case categorizations of stakeholders,
different in the form and in the level of granularity.
       The last part of the review has the aim to identify the skills that different stakeholders
should have in order to fulfil their role and their tasks.
       So, the results coming from these three phases are going to set the basis for the
development of a model addressing the two research questions. In particular, the second
phase has a crucial role to identify the stakeholders involved in an OGD project, the third
phase is focused on the identification of the skills, while the first one is crucial for both.

3.2 Open Government Data
       The concept of open data and specifically open government data has been around
for some years. In 2009 open data started to become visible in the mainstream, since various
governments (such as the USA, UK, Canada and New Zealand) announced new initiatives
towards opening up their public information (Open Knowledge International, 2012),
launching an actual OGD movement, motivated by values such as citizen collaboration and
participation, better government transparency, and stimulated innovation.
       In order to understand the origin of the OGD topic, it is important to introduce four
concepts that contributed to its foundation and development, namely Open Data,
government data, Open Government and e-Government.
       The term Open Data is here used along this definition: “data that can be freely used,
re-used and redistributed by anyone - subject only, at most, to the requirement to attribute
and sharealike” (Open Knowledge International, 2012). This kind of data shall be open both
legally and technically. On the legal side, openness of the data can be achieved applying an
appropriate open license. This means that the terms of use of Open Data must not
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discriminate against fields of endeavour or against persons or groups and it must allow reuse
and redistribution of data. Technical openness, instead, is accomplished if the data is
available as a complete set in an open, machine readable format and at no more than a
reasonable reproduction cost (Open Knowledge International, 2012). Complete datasets
means that every element of the dataset must be available.
       Open data format is a platform independent format that makes available an
information without any restrictions that would impede to re-use it. Data are machine
readable if they are enough structured to allow data manipulation using software application
(Kucera, Chlapek, & Necaský, 2013). Finally, Open Data should be made available for free,
or at no more than a reasonable reproduction cost, since any kind of fee is seen as a barrier
in access to the data (Open Knowledge International, 2012).
       The notion of “government data” denotes any data created by a public sector body.
       Another contribution to the Open Government Data notion development is given by
the Open Government approach. It originates by the belief that government decision-
making should be more transparent and participative (Janssen, Charalabidis, & Zuiderwijk,
2012). Transparency, as thought by this approach, relates to the realization of a more
democratic society, where citizens and other stakeholders are able to monitor government
initiatives and their legitimacy. Participation is the other goal that the publishing of
government data pursues, since the opportunity to participate to governance process, such
as decision-taking and policy-making, is given to citizens only sporadically voting in an
election every number of years (Attard, Orlandi, Scerri, & Auer, 2015).
       Even if different definitions of e-Government exist in literature, here it is defined as
the use of technology by the Government in order to enhance the services it offers to other
entities, including citizens, business partners, employees, and other agencies (Attard,
Orlandi, Scerri, & Auer, 2015). The potential of this kind of initiative is related to build better
relationships between citizens and their government, delivering information more
efficiently. The concept of e-Government has evolved, indeed, with the introduction of the
Open Government concept, Open Government Data initiatives are considered a sub set of e-
Government (Attard, Orlandi, Scerri, & Auer, 2015).

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These are the pillars from which the Open Government data concept has been
developed (Figure 6).

                                  Figure 6 Pillars of Open Government Data

        The innovation flow that hit the OGD movement brought consistent benefits, but also
a number of issues that slow down the adoption of the OGD approach, and that must be
considered as barriers. This is why several researchers already discussed about the positive
and negative influences related to OGD.
        On the benefits side, there are many areas where open data is creating value,
especially open government data, since government is particularly involved in the
production and collection of a huge quantity of data. The success of this new approach of
managing data relays in the basic assumption that open data itself creates and generates
more value than the selling of data sets (Janssen, Charalabidis, & Zuiderwijk, 2012). It is
possible to point a large number of benefits related with the release of Open Government
Data.
        The first visible positive impact is on transparency and accountability that the
government gains towards citizens, as decisions and operations are shared with the public
(Hardy & Maurushat, 2017). This is a mean to decrease corruption, build trust and improve
citizen satisfaction. Increased social control by citizens allows them to interact more actively

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with the government and other public entities, overcoming the traditional governmental
structures. For instance, opening data related to public budget gives the possibility to
stakeholders such as citizens, NGOs and even private entities to provide feedback, to have
an impact on budget priorities and to monitor government activities (Attard, Orlandi, Scerri,
& Auer, 2015).
       From an economic point of view, the main impacts of opening data are stimulating
innovation and promoting economic growth. In this specific field, the peculiarity of the open
data is that its value changes when it is used, meaning that the data itself has no value, but
it becomes valuable once it is introduced in other analysis. This is due to the difficulties
related to the prediction of the potential of the application derived by the OGD (Janssen,
Charalabidis, & Zuiderwijk, 2012). Unlocking data enable the public, entrepreneurs and the
government itself to better leverage this information using it as input into applications and
services (Pereira, Macadar, Luciano, & Testa, 2017).
       Government increases its capacity to supervise those companies that provide public
services, guaranteeing quality commitment and increasing public satisfaction. In terms of
efficiency, sharing and control objectives and results of agencies might help them to better
manage their resources (Pereira, Macadar, Luciano, & Testa, 2017). It might result in an
increase in the overall satisfaction with the agencies and a consequent rise in citizens’
satisfaction. Likewise, opening data related to policy-making, users can validate and verify
from the data whether the work of policy-makers has been correct and justified (Janssen,
Charalabidis, & Zuiderwijk, 2012).
       Even if datasets are usually published in their raw form, hence the value of the data
itself can be considered little, public entities can leverage on other stakeholders, such as
those coming from the private sector, community groups, and citizens, to bring innovation
upon the data published in order to exploit the utmost potential of open government data
initiatives (Edelmann, Höchtl, & Sachs, 2012). This is how the increased participation
enhances data quality, together with the great contribution of users’ feedbacks (O'Hara,
2014). Yet, active participation is not given for sure. While open government data initiatives
set the basis for citizen participation and collaboration, the actual realization of them is not
guaranteed (Alexopoulos, Zuiderwijk, Charalabidis, Loukis, & Janssen, 2014). Error!
Reference source not found. taken from Janssen, Charalabidis & Zuiderwijk (2012)
summarizes the principle benefits associated to the release of OGD.

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CATEGORY      BENEFITS
POLITICAL     More transparency
AND
SOCIAL        Democratic accountability
              More participation and self-empowerment of citizens (users)
              Public engagement
              Creation of trust in government
              Scrutinization of data
              Equal access to data
              New governmental services for citizens
              Improvement of citizen services
              Improvement of citizen satisfaction
              Improvement of policy-making processes
              More visibility for the data provider
              Stimulation of knowledge developments
              Creation of new insights in the public sector
              New (innovative) social services
ECONOMIC      Economic growth and stimulation of competitiveness
              Stimulation of innovation
              Contribution toward the improvement of processes, products,
              and/or services
              Development of new products and services
              Use of the wisdom of the crowds: tapping into the intelligence of the
              collective
              Creation of a new sector adding value to the economy
              Availability of information for investors and companies
OPERATIONAL   The ability to reuse data/not having to collect the same data again
AND           and counteracting
TECHNICAL     unnecessary duplication and associated costs (also by other public
              institutions)
              Optimization of administrative processes
              Improvement of public policies
              Access to external problem-solving capacity
              Fair decision-making by enabling comparison

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Easier access to data and discovery of data
                        Creation of new data based on combining data
                        External quality checks of data (validation)
                        Sustainability of data (no data loss)
                        The ability to merge, integrate, and mesh public and private data

                Table 1 Benefits of Open Government Data (Janssen, Charalabidis, & Zuiderwijk, 2012)

       All these benefits, added to the huge availability of new datasets, spurred the OGD
movement from the beginning (Styrin, Luna-Reyes, & Harrison, 2017). However, data
coming from current researches show that data usage is scarce, the participation and
collaboration of citizens is almost non-existent, and other businesses involvement is limited
(Safarov, Meijer, & Grimmelikhuijsen, 2017).
       So, even if OGD can potentially provide a number of benefits, its adoption is affected
by numerous barriers. Barriers are those factors which hinder or block the use of open data
(Zuiderwijk, Janssen, Choenni, Meijer, & Alibaks, 2012). These barriers are cultural, legal
and technical (Hardy & Maurushat, 2017).
        Opening up government data seems to be impeded by public service culture, that
appears to favour secrecy of information by predisposition (Hardy & Maurushat, 2017).
Sometimes, civil servants perceive Open Data as a threat, because an adequate or inadequate
interpretation of data, often de-contextualized, may lead to protests against the public. This
fear makes civil servants reluctant to actively participate to the data opening process
(Martin, Foulonneau, Turki, & Ihadjadene, 2013). Other cultural barriers are a generational
limited understanding of the benefits that can be gained from open data and concerns about
the quality and the accuracy of the information released (Hardy & Maurushat, 2017). This
cultural inadequacy is perceived even from the political side. A lack of consistency in political
behaviour can produce a weak Open Data policy. Poor coordination at an administrative
level raises a risk of fragmentation of the initiative, affecting negatively the potential reuse
released data (Martin, Foulonneau, Turki, & Ihadjadene, 2013).
       Legal barriers raise the risk related to licences and conditions for reuse. Since many
different OGD services rely on multiple datasets, managing heterogeneous conditions of
reuse generates several challenges. Indeed, coherent licences and conditions to reuse are
supposed to facilitate the reuse of data (Martin et al., 2013). Privacy issue related to personal
information are significant: identifying information should clearly be removed from any
government data before it is released into the public domain (Hardy & Maurushat, 2017).

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On the technical side, different problems are considered. Data should be compliant
according to their reliability, their quality, and their format. Some data can be sensitive to
pressures coming from politics, in particular those data dealing with State funding may raise
concerns regarding manipulations by the State, affecting the accuracy of the data. Data
quality is also very sensitive to financial aspects. Finally, data raise risks also with format.
Indeed, the type of format determines the right software to use in order to read data and
work with them. So, using the appropriate format to publish documents that can be
considered open, facilitates the access and the reuse of data (Martin, Foulonneau, Turki, &
Ihadjadene, 2013). Datasets composition is described by metadata. Usually, metadata are
formatted according to Dublin Core (Dublin Core Metadata Initiative, 2012) and DCAT
vocabularies (W3, 2014). Despite this, there is not a single standard to describe Open
Datasets, so users have to deal with multiple vocabularies. Moreover, cases of lack of
metadata and lack of mechanisms guaranteeing the quality of metadata also represent risks
for the efficient reuse of Open Datasets (Martin, Foulonneau, Turki, & Ihadjadene, 2013).
       Reuse is not only affected by the bad practices of data producers, it also depends on
the skills of the potential re-users. This is why language barrier cannot be neglected.
Numerous countries deal with multilingualism, even the creation of services in the European
Union entails a data publication that should take into account different languages, in order
to avoid misinterpretation. Data literacy raised concerns about the actual ability of users to
get benefits from Open Data. While a certain minority has not even the necessary skills to
make use of the new mean of information, the others are not able to exploit it in order to
generate profit from Open Datasets (Martin, Foulonneau, Turki, & Ihadjadene, 2013). The
availability of all kinds of capabilities and knowledge levels of users seems to be an
underestimated subject. This type of barrier expresses the need for having good structures
and support for handling open data (Janssen, Charalabidis, & Zuiderwijk, 2012). Moreover,
there are only few studies trying to understand the characteristics of OGD users, such as
their usage objectives, skills, and kinds of tasks they desire to execute with the data (Gascó-
Hernández, Martin, Reggi, Pyo, & Luna-Reyes, 2018).
       Table 2 by Janssen, Charalabidis & Zuiderwijk (2012) lists the main barriers that
block the usage of OGD.

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