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TRANSCRIPT
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[Written by EY] [November – 2018]
Justice and
Consumers
Study on the Economic
Detriment to Small and Medium-Sized Enterprises
Arising from Unfair and
Unbalanced Cloud Computing Contracts
Final Report
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Study on the economic detriment to small and medium-sized enterprises arising from
unfair and unbalanced cloud computing contracts
EUROPEAN COMMISSION
Directorate-General for Justice and Consumers Directorate A: Civil and commercial justice Unit A2 – Contract Law Contact: Unit A2 E-mail: [email protected] European Commission B-1049 Brussels
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EUROPEAN COMMISSION
Directorate-General for Justice and Consumers Directorate A: Civil and commercial justice
Study on the economic detriment to small and
medium-sized enterprises arising from unfair and
unbalanced cloud computing
contracts
Final Report
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Study on the economic detriment to small and medium-sized enterprises arising from unfair
and unbalanced cloud computing contracts
This document has been prepared for the European Commission however it reflects the views
only of the authors, and the Commission cannot be held responsible for any use which may
be made of the information contained therein.
More information on the European Union is available on the Internet (http://europa.eu).
Print ISBN 978-92-79-45908-5 doi:10.2838/397707 DS-AU-15-001-EN-C
PDF ISBN 978-92-79-96750-4 doi: 10.2838/962608 DS-03-18-421-EN-N
© European Union, 2019
Printed by Imprimerie Central
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Luxembourg: Publications Office of the European Union, 2019
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Study on the economic detriment to small and medium-sized enterprises arising from unfair
and unbalanced cloud computing contracts
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CONTENTS
List of abbreviations ................................................................................................... 2
Glossary.................................................................................................................... 4
1 Introduction ........................................................................................................ 7
1.1 The importance of contract-related problems ..................................................... 7
1.2 Objectives and scope of the study .................................................................. 10
1.3 Structure of this report ................................................................................. 12
2 Research methodology ....................................................................................... 14
2.1 Overview of the approach ............................................................................. 14
2.2 Main limitations encountered ......................................................................... 35
3 Usage of cloud computing across EU SMEs: the state of play................................... 37
3.1 Cloud computing usage rate .......................................................................... 37
3.2 Types of cloud computing services contracted ................................................. 38
3.3 Types of contracts and relative costs .............................................................. 41
4 Contract-related problems encountered by SMEs ................................................... 46
4.1 Types and frequency of encountered problems ................................................ 46
4.2 The most serious problems encountered ......................................................... 49
4.3 Causes of the most serious problems encountered ........................................... 51
4.4 Actions taken by SMEs .................................................................................. 53
5 Consequences of the encountered problems .......................................................... 57
5.1 Qualitative assessment ................................................................................. 57
5.2 Quantitative assessment ............................................................................... 59
6 Impacts of the economic detriment ...................................................................... 72
6.1 Direct impacts ............................................................................................. 73
6.2 Indirect impacts ........................................................................................... 74
6.3 Other qualitative impacts of contract-related problems ..................................... 75
7 Conclusions ...................................................................................................... 77
Annex 1. Methodology for the Survey of SMEs ........................................................ 82
Annex 2. Stakeholders list .................................................................................... 84
Annex 3. Bibliography .......................................................................................... 86
Annex 4. Survey questionnaire.............................................................................. 90
Annex 5. SME Survey results .............................................................................. 101
Annex 6. Economic detriment – further results ...................................................... 114
Annex 7. Impacts on jobs and growth, detailed results .......................................... 136
Annex 8. Case study questionnaires by stakeholder type ........................................ 146
Annex 9. Case study analysis .............................................................................. 154
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List of abbreviations
Acronym Description
CATI Computer Assisted Telephone Interviews
CT&C Contract terms and conditions
DG JUST Directorate-General Justice and Consumers
EC European Commission
EEN Enterprise Europe Network
EU European Union
EU13 Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia
Eurostat Statistical Office of the European Communities
GDP Gross Domestic Product
GDPR General Data Protection Regulation
GE250 Large enterprises of more than 250 employees
GFD Gross Financial Detriment
GVA Gross Value Added
IaaS Infrastructure as a Service
IOS International Organization for Standardization
MS Member State
NFD Net Financial Detriment
PaaS Platform as a Service
PC Personal computer
PPP Purchasing power parity
R Value of any redress
SaaS Software as a Service
SLA Service level agreement
SLALOM Service Level Agreement ‐ Legal and Open Model
SMEs Micro, small and medium-sized enterprise
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EU Member States
AT Austria
BE Belgium
BG Bulgaria
CY Cyprus
CZ The Czech Republic
DE Germany
DK Denmark
EE Estonia
EL Greece
ES Spain
FI Finland
FR France
HR Croatia
HU Hungary
IE Ireland
IT Italy
LV Latvia
LT Lithuania
LU Luxembourg
MT Malta
NL The Netherlands
PL Poland
PT Portugal
RO Romania
SE Sweden
SI Slovenia
SK Slovakia
UK The United Kingdom
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Glossary
Term Definition Source
Active enterprise
An enterprise that had either turnover or employment at any time during the reference
period.
Eurostat
Cloud computing
A model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (such as networks, servers, storage, applications, and
services) that can be rapidly provisioned and released with minimal management effort and
service provider intervention.
National Institute of Standards and Technology, The NIST Definition of Cloud Computing, 2011
Cloud Service
One or more capabilities offered via cloud computing, invoked by using a defined interface.
European Commission, Cloud service level agreement standardisation guidelines,
2014
Cloud service provider
A party which makes cloud services available. European Commission, Cloud service level agreement standardisation guidelines, 2014
Cloud service agreement
The Cloud Service Agreement or CSA is the main document which sets out the terms and conditions of the contractual relationship between
the provider and the user in relation to the provision of cloud services.
Slalom Project, Model contract for Cloud Computing, 2016
Data portability
Ability to easily transfer data from one system to another without being required to re-enter data.
It is the ease of moving the data that is the essence here. This might be achieved by the source system supplying the data in exactly the format that is accepted by the target system. But even if the formats do not match, the
transformation between them may be simple and straightforward to achieve with commonly available tools. On the other hand, a process of printing out the data and rekeying it for the
target system could not be described as ‘easy’.
International Organisation for Standardization, Information technology — Cloud computing — Overview and vocabulary, 2014
EU13 Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia.
Eurostat
EU15 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the
United Kingdom.
Eurostat
EU28 Belgium, Bulgaria, Czech Republic, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands,
Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom.
Eurostat
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Term Definition Source
Frequency
(F)
The frequency shows how often the mentioned problem appeared and it is measured on a scale from 1 to 4: 1 meaning ‘rarely’ (at least once in the last 12 months);2 meaning ‘occasionally’ (at least once every 3 months); 3 meaning ‘frequently’ (at least once a month); and 4 meaning ‘very frequently’ (at least once a week).
EY Survey of SMEs, 2018
Incidence
(I)
Number of SMEs that encountered the problem during 2016-2017/Number of SMEs that contracted at least one cloud computing service during 2016-2017.
EY Survey of SMEs, 2018
Infrastructure as a Service (IaaS)
The capabilities provided to the cloud-service customer include processing, storage, networks, and other fundamental computing resources where the cloud-service customer is able to deploy and run arbitrary software, which may
include operating systems and applications. The cloud-service customer does not manage or control the underlying cloud infrastructure, but does have control over operating systems, storage, and deployed applications, and possibly limited control of select networking components
(such as host firewalls).
European Commission, Cloud service level agreement standardisation guidelines, 2014
NACE Statistical Classification of Economic Activities in
the European Community
Eurostat
Purchasing power parity
(PPP)
The rate of currency conversion that equalises the purchasing power of different currencies by
eliminating the differences in price levels between countries. In its simplest form, PPP shows the ratio of prices in the national currency of the same good or service in different countries.
Eurostat
Platform as a
Service (PaaS)
The capability provided to the cloud-service
customer is to deploy onto the cloud infrastructure customer-created or acquired applications created using programming languages, libraries, services, and tools
supported by the cloud-service provider. The cloud-service customer does not manage or control the underlying cloud infrastructure
including network, servers, operating systems, or storage, but does have control over the deployed applications and possibly configuration settings for the application-hosting environment.
European Commission, Cloud
service level agreement standardisation guidelines, 2014
Service level agreement
A Service Level Agreement (SLA) is the service contract component between a service provider
and customer. An SLA provides specific and measurable aspects related to service offerings.
European Commission, Cloud service level agreement
standardisation guidelines, 2014
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Term Definition Source
Software as a Service (SaaS)
The capability provided to the cloud-service customer to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through either a thin client interface, such as a web browser (for example, web-based e-mail), or a programme interface. The user does
not manage or control the underlying cloud infrastructure, including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application
configuration settings.
European Commission, Cloud service level agreement standardisation guidelines, 2014
Service availability
The property of being accessible and usable on demand by an authorised entity.
European Commission, Cloud service level agreement standardisation guidelines,
2014
Service performance
The problem faced by SMEs characterised by the difficulty in understanding the level of performance or the quality of services that has been promised in the contract.
European Commission, Cloud service level agreement standardisation guidelines, 2014
SMEs The category of small and medium-sized enterprises (SMEs) includes enterprises which employ fewer than 250 persons and which have
an annual turnover not exceeding €50 million, and/or an annual balance sheet total not exceeding €43 million.
Within the SME category, a small enterprise is defined as an enterprise which employs fewer than 50 persons and whose annual turnover and/or annual balance sheet total does not exceed €10 million.
Within the SME category, a microenterprise is defined as an enterprise which employs fewer
than 10 persons and whose annual turnover and/or annual balance sheet total does not exceed €2 million.
Commission Recommendation of 6 May 2003 concerning the definition of micro, small and
medium-sized enterprises (notified under document number C(2003) 1422)
Virtual Data Centre
A pool or collection of cloud infrastructure resources specifically designed for enterprise business needs. The basic resources are the
processor (CPU), memory (RAM), storage (disk space) and networking (bandwidth). It is a virtual representation of a physical data centre, complete with servers, storage clusters and many networking components, all of which reside in a virtual space being hosted by one or more actual
data centres.
Altman & Alt, A Digital Library for the Dissemination and Replication of Quantitative
Social Science Research: The Virtual Data Centre, 2001
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1 Introduction
This report represents the final deliverable responding to the request for services
JUST/2016/RCON/FW/CIVI/0176 (2017/03) concerning a Study on the economic
detriment to small and medium-sized enterprises arising from unfair and
unbalanced cloud computing contracts. The Request for service was issued by the
European Commission, Directorate-General Justice and Consumers (DG JUST), Unit A2 -
Contract Law.
1.1 The importance of contract-related problems
Cloud computing has been credited with increasing competitiveness through cost reduction,
greater flexibility and optimal resource utilisation.1 By using cloud computing services,
enterprises are only paying for the amount of storage they are actually consuming and are
not responsible for the daily maintenance of the storage infrastructure.
Demonstrable tangible economic benefits have been estimated in 2011. A report published
by the European Commission ‘Quantitative Estimates of the Demand for Cloud Computing in
Europe and the Likely Barriers to Up-take’2 identified as main benefits: the lower IT costs,
more effective mobile working, higher productivity, standardisation of processes, better ability
to enter new business areas, and the ability to open up in new locations.
All in all, increasing the usage of cloud computing can have a relevant impact in terms of
increasing EU Gross Domestic Product (GDP) by up to €250 billion and creating 3.8 million
jobs by 2020 compared to 2012.3
While the list of the above benefits is not exhaustive, it certainly provides a perspective on
the potential economic advantages of the usage of cloud computing compared to more
traditional alternatives such as on-premises information technology infrastructure.4
1 Rittinghouse, J., Ransome, J. (2009), Cloud Computing, Boca Raton, CRC Press, USA. 2 Based on a 2011 survey conducted on 479 enterprises already using cloud computing for their businesses, 81% reported lower IT costs with a 10% to 20% reduction being typical, but 12% reported
savings of 30% or more. Business benefits included more effective mobile working (46%), higher
productivity (41%), more use of standard processes (35%), better ability to enter new business areas (33%) and the ability to open up in new locations (32%). European Commission (2012), Quantitative Estimates of the Demand for Cloud Computing in Europe and the Likely Barriers to Up-take, SMART 2011/0045, Brussels. 3 European Commission (2012), Quantitative Estimates of the Demand for Cloud Computing in Europe
and the Likely Barriers to Up-take, SMART 2011/0045, Brussels. 4 Traditional on-premises infrastructures consist of various pieces of hardware, such as desktop computers, which are connected to a network via a remote server. This server is typically installed on the premises of an enterprise, and provides all employees using the hardware with access to the company’s stored data and applications. Enterprises with this type of information technology model must purchase additional hardware and upgrades in order to scale up their data storage and services to support more users. Mandatory software upgrades are also required with traditional information
technology infrastructure to ensure that failsafe systems are in place to in case a hardware failure occurs. For many enterprises with information technology data centres, an in-house information
technology department is needed to install and maintain the hardware.
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However, Eurostat data indicate that in 2016 only 21% of EU small and medium-sized
enterprises (SMEs, i.e. 10 – 250 employees) bought cloud computing services used over the
internet,5 mostly for hosting their e-mail systems and electronically storing files.
If micro enterprises (i.e. 1 – 9 employees) are considered, Eurostat data are available only
for few countries (the United Kingdom, Slovakia, Sweden, Portugal and Spain). For these
countries the percentage of micro enterprises that were buying cloud computing services used
over the internet in 2016 was around 11%.
With regard to the main factors affecting the adoption of cloud computing, in most
sectors, enterprises reported6 that insufficient knowledge of cloud computing prevented them
from using it (Figure 1). Expertise and sufficient knowledge of contractual and legal aspects
and the details of technical implementation are necessary prerequisites for an enterprise in
deciding to purchase cloud computing services. In addition, the risk of a security breach was
a key consideration for enterprises: the concern over the risk of a security breach scored
highest for both large enterprises and small and medium-sized enterprises (SMEs) (57%
and 38% respectively).7
Figure 1: Factors that limit cloud use
Source: Eurostat (2016)
Clearly, enterprises attach importance to the protection of their information technology
systems, but the issue can be seen in the wider context of resilience to possible security
breaches when using the cloud. Therefore, from the enterprises’ point of view (regardless of
their size), the risk of a security breach may be a matter of service providers’ liability and
accountability, as well as a technical issue.
A study commissioned by the Commission8 looked at the main contractual issues that are
of particular interest in a cloud context: description of the service and Service Level
Agreement (SLA), acceptable-use policy, data protection and disclosure of personal data,
intellectual property and other proprietary rights and duties over content, warranties, direct
and indirect liability, indemnification for third party claims, the effect on data preservation of
contract termination, modification of the contract terms and conditions (CT&C) and security,
protection of data and loss of data.
5 Eurostat data consider all enterprises with the exception of those in the financial sector, consulted in April 2018. 6 Eurostat, Cloud computing - statistics on the use by enterprises, Brussels. 7 Ibidem. 8 European Commission (2015), Comparative Study on Cloud Computing Contracts, Publications Office
of the European Union, Luxembourg.
http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.dohttps://ec.europa.eu/eurostat/statistics-explained/index.php/Cloud_computing_-_statistics_on_the_use_by_enterprises
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The Commission acknowledged the role of cloud contracts, notably in relation to data
portability,9 as ‘contracts often exclude, or severely limit, the contractual liability of the cloud
provider if the data is no longer available or is unusable, or they make it difficult to terminate
the contract’.10
Contract-related problems have been addressed by the Commission through various
initiatives.
Firstly, in 2013 the Commission set up an Expert Working Group on Cloud Computing
Contracts11 to work on identifying safe and fair contract terms for cloud computing services
with a view to addressing concerns of consumers and enterprises.
Secondly, specific actions have been taken forward on business-to-business cloud computing
contracts beyond the legislative field, including:
the work of the European Cloud Partnership and the ISO (International Organization
for Standardization) towards the standardisation of cloud SLAs;
the SLA standardisation guidelines prepared in June 2014 by the Cloud Select Industry
Group on Service Level Agreements Subgroup (C-SIG SLA);12
the ‘Service Level Agreement Legal and Open Model’ (SLALOM) project13 funded under
the Horizon 2020 Framework Programme, which delivered a set of common legal
clauses to cover cloud SLAs and contracts and a standard technical SLA specification.
Finally, in April 2016 the Commission gave a new impetus to its work on cloud computing by
adopting the Communication on the European Cloud Initiative - Building a competitive
data and knowledge economy in Europe14 where further complementary actions under the
Digital Single Market strategy covering cloud contracts for business users and switching of
cloud services providers have been announced.
Thus, over recent years, the Commission has adopted a true European Cloud Computing
Strategy aimed at removing barriers to cloud computing. Additionally, work is under way
towards a Code of conduct on switching and porting cloud data and services.
A further development of this topic is represented by the ‘Proposal for a Regulation of the
European Parliament and of the Council on a framework for the free flow of non-personal data
in the European Union’, launched in 2017.15
The objective of this proposal is to unlock the potential of the EU data market. In order to do
so, the proposal aims to address the following issues:
9 Please see the Glossary for further information. 10 European Commission (2012), Unleashing the Potential of Cloud Computing in Europe, COM(2012)529 final, Brussels. 11 The Group has been set up within the framework of the Commission Communication (2015) ‘A Digital
Single Market Strategy for Europe’, which aims at enhancing trust in cloud computing services and unlocking its potential for boosting economic productivity in Europe. 12 European Commission (2013), Cloud Select Industry Group on Service Level Agreements, accessed 01 May 2018. 13 The SLALOM project, accessed 1 May 2018. 14 European Commission (2016), European Cloud Initiative - Building a competitive data and knowledge economy in Europe, COM(2016) 178 final, Brussels. 15 European Commission (2017), Proposal for a Regulation of the European Parliament and of the Council on a framework for the free flow of non-personal data in the European Union, COM(2017) 495 final,
Brussels.
https://ec.europa.eu/digital-single-market/en/cloud-select-industry-group-service-level-agreementshttp://slalom-project.eu/
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improving the mobility of non-personal data across borders in the Single Market,
which is limited today in many Member States by localisation restrictions or legal
uncertainty in the market;
ensuring that the powers of competent authorities to request and receive access to
data for regulatory control purposes, such as for inspection and audit, remain
unaffected; and
making it easier for professional users of data storage or other processing services to
switch service providers and to port data, while not creating an excessive burden on
service providers or distorting the market.
The impact assessment study16 that accompanies the legislative proposal addresses barriers
such as the legislative and administrative localisation restrictions; data localisation driven by
legal uncertainty and the lack of trust in the market which is considered to inhibit data
mobility. The impact assessment identifies as a preferred option a policy that would enhance
legal certainty, maintain the current levels of security of data storage and processing and that
would rely on self-regulation by industry through the development of codes of conduct for
facilitating switching between providers and, finally, that would positive economic effects,
resulting in lower compliance costs for cloud service providers. The study took into
consideration that actions at the Member State level would not be able to achieve the legal
certainty necessary for conducting this type of business across the EU, or to remedy the lack
of trust that is slowing the development of a thriving data storage and/or processing sector.
It is presumed that an EU intervention would contribute to further development of secure data
storage capacity for the entire EU.
1.2 Objectives and scope of the study
Objectives
The study’s main objective is to deliver the necessary evidence to support the Commission
in its assessment of the need for, and extent of, any further EU efforts to increase SMEs’ trust
in cloud services and allow them to bring in the full potential benefits of these types of
services.
The main specific objectives of the study are:
to identify the prevalence, nature, scope and scale of the contract-related problems
that SMEs encounter in relation to cloud computing services;
to analyse whether and to what extent enterprises are able to protect themselves
against problems, for example by means of enforcement of rules on business-to-
business unfair contract terms (where they exist);
to quantify the financial detriment as well as the detriment arising from time loss and
consequential damages (such as business interruption) suffered by SMEs in relation
to the contract-related problems encountered;
to complement the quantitative measurement with a qualitative assessment of the
detriment suffered by SMEs;
to model (in quantitative terms) the impact on growth and jobs of the quantified SME
detriment identified and provide a qualitative evaluation of the overall impact of the
problems encountered by SMEs in terms of market functioning/failures and
competitiveness.
16 European Parliament (2017), Commission Staff Working Document, Impact Assessment
Accompanying the document, Proposal for a Regulation of the European Parliament and of the Council on a framework for the free flow of non-personal data in the European Union, SWD(2017) 304 final, Brussels.
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Scope
Cloud computing services covered
The study covers the cloud computing services most commonly used by SMEs, taking into
account: i) the general delivery models used by the provider, i.e. Infrastructure as a Service
(IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS);17 ii) the core
categories of cloud services available in the market.
The study covers cloud computing services provided both in exchange for money and provided
free of charge (i.e. not provided in exchange for a monetary counter-performance).
Contract-related problems encountered by SMEs
The study investigates the prevalence, nature, scope and scale of:
problems in relation to the conformity of the service with the contract;
problems with unfair CT&C;
problems in exercising the user's rights to remedies for non-conformity of the
service with the contract.
Territorial scope
The study assesses the contract-related problems faced by SMEs in all EU28 Member States.
Timeline
The study covers the usage of cloud computing services by EU SMEs over the period 2016 –
2017.
Stakeholders
The following categories of stakeholders are covered:
EU SMEs that contracted cloud computing services over the period 2016 – 2017;
cloud computing providers;
cloud computing brokers;
National authorities competent in fields related to cloud computing (such as data
privacy protection, personal data processing and data security);
the National SMEs Envoys Network;18
Chambers of Commerce;
the Enterprise Europe Network (EEN);19
SMEs associations.
Please refer to Annex 2 for the full list of stakeholders.
For the purpose of this study and in order to make the present report more intelligible
whenever the term SMEs is used in the text, it refers to:
medium-sized enterprises defined as enterprises which employ fewer than 250
persons (50-249) and whose annual turnover and/or annual balance sheet total does
not exceed €50 million;
17 See the Glossary for the definition of these terms. 18 The network of national SME Envoys was set up in 2011 as part of the review of the Small Business Act (SEC(2008) 2101). Each EU country has nominated a national SME Envoy to complement the role of the EU national SME Envoy who chairs the network. The group of national SME Envoys makes up an SBA advisory group that promotes SME friendly regulation and policy making in all EU countries. More
information is available at http://ec.europa.eu/growth/smes/business-friendly-environment/small-
business-act/sme-envoys_en. 19 The Enterprise Europe Network helps businesses innovate and grow on an international scale. It is the world’s largest support network for SMEs with international ambitions.
https://een.ec.europa.eu/
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small enterprises defined as enterprises which employ fewer than 50 persons (10-
49) and whose annual turnover and/or annual balance sheet total does not exceed
€10 million;
micro enterprises defined as enterprises which employ fewer than 10 persons and
whose annual turnover and/or annual balance sheet total does not exceed €2 million.
1.3 Structure of this report
The report is structured as follow:
Chapter 1 recaps the objectives and the scope of the study and provides information
about the background of the study that justified the request for service;
Chapter 2 presents the methodological framework and the main limitations
encountered. Chapter 3 covers the findings related to:
- the usage of cloud computing among EU SMEs over the period 2016-2017 and
the reasons for the limitations of the use of cloud computing;
- types of cloud computing services that were contracted by SMEs
(complexity of the subscribed cloud computing packages, the frequency of the
number of contracted services and the services contracted by SMEs size);
- types of cloud computing contracts and relative costs, including the
negotiability of contract terms and conditions (CT&T).
Chapter 4 is dedicated to the analysis of contract-related problems encountered
by SMEs, specifically types and frequency of encountered problems and actions taken
by SMEs to address these problems;
Chapter 5 presents a qualitative assessment of the consequences faced by SMEs due
to contract-related problems. These refer to:
- negative effects on the business activity (i.e. loss of turnover or profit, loss of
clients and/or reputation damages);
- extra work done with internal or external resources while trying to resolve the
encountered problems;
- legal actions taken to exercise the user's rights to remedies for non-conformity
of service.
Where possible these consequences have been quantified and monetised in order to
assess the economic detriment suffered by SMEs.
Chapter 6 is dedicated to the impacts of the economic detriment. The analysis
concerns:
- direct impacts, meaning the Gross Value Added (GVA) and employment
contents of the turnover lost by SMEs that are affected directly by the contract-
related problems;
- indirect impacts in the supplier sectors.
This chapter also discusses (in qualitative manner) impacts on market functioning and
competitiveness;
Chapter 7 presents the conclusions of the overall analysis:
Annexes, including the following:
- methodology for the survey of SMEs;
- the list of stakeholders that have been involved in the case studies;
- bibliography of consulted and relevant documents;
- survey questionnaire used for conducting the survey of SMEs;
- results of the survey of SMEs;
- further results of the economic analysis;
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- detailed results of the impacts on jobs and growth;
- case study questionnaires by stakeholder type;
- case study interviews and webinars analysis.
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2 Research methodology
2.1 Overview of the approach
The study envisaged three main Tasks:
Task 0 – Preparatory task. Defining the methodological framework and the topics
to be investigated during the research, as well as defining the standards and tools to
be used.
Task 1 - Survey of SMEs. Conducting an online survey on a representative sample
of SMEs and start-ups, which are using cloud computing for the purposes of
conducting their business.
Task 2 - Economic detriment. Quantification of the overall economic detriment
sustained by SMEs in relation to the problems identified under Task 1. Based on these
results, the impact on growth and jobs and on the economy as a whole has been
measured.
Figure 2 provides an overview of the approach followed, synthesising the main activities,
objectives, and methods/tools.
Figure 2: Overview of the project Tasks
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Task 0 – Preparatory Task
This Task envisaged undertaking desk research that allowed the team to clearly define the
methodological framework of the study in terms of:
cloud computing services commonly used by micro, small and medium-sized
enterprises;
potential contract-related problems that may be encountered while using cloud
computing;
problem definition, which provides an overview of the main causes and effects of the
identified problems. It was used to:
- better understand the context in which the problems occurred;
- identify the intermediate effects and the main drivers;
- structure the data collection tools (i.e. questionnaires for Computer Assisted
Telephone Interviews [CATI] and the online survey);
selecting a sample of countries and sectors to be analysed in depth in order to achieve
the project objectives. The sampling strategy included:
- the identification of a sample of Member States (MS) and economic sectors to
be analysed in depth;
- the definition of a target for a representative sample of micro, small and
medium-sized enterprises to be addressed through the survey under Task 1;
- ensuring representativeness of the sample size.
Table 1 provides the list of cloud computing services identified under Task 0.
Table 1: Cloud computing services commonly used
Cloud computing
model
Cloud computing services
Details
SaaS Business applications
Applications used for: accounting, invoicing, planning, payments, payroll, human resources management
Collaboration and communication services
Video conference system, business visualisation technology,
instant communications applications, e-mail account, virtual desktop, antivirus for server or PC, applications for customer relationship management, social media monitoring
Service and support tools
Project and portfolio management solutions, services operations management
Big data Business intelligence and data analytics applications
Networking services Creation of an internal private cloud and virtual local networks, utilising a pooled server for storing enterprise data and running applications needed each day
Security services Secure content management, end point protection, malware
protection
PaaS Sales and marketing services
Web configuration forms for marketing and sales purposes
Cloud enablement and information
technology operations
Application integration and monitoring, business process management, information technology operation/risk
management
Data management Data/content management
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Cloud
computing
model
Cloud computing services
Details
Mobile Usage of managed mobility to serve a multitude of mobile devices anywhere, anytime
Platform to create
software applications
Conception and creation of applications, through testing and
deployment
Internet of things Asset tracking, deployable components, intelligent spaces
IaaS Storage and hosting services
Server platform for online storage, sharing, hosting web-sites, files, images and similar content
Virtual data centre Usage of a pool of virtualised infrastructure as processor,
memory, storage space and networking components
High performance computing
Usage of high performance computing clusters, workloads and applications
Source: EY desk research20
The following potential contract-related problems have been identified:
lack of timely updates of the cloud service;
forced updates to the service that eliminated or changed a necessary function;
unsatisfactory amount of data that could be processed;
failure of the cloud provider to identify, analyse, and correct hazards and to prevent
future re-occurrences (incidence management);
low speed of the service;
unsatisfactory availability or discontinuity of the service (due to a crash, unannounced
maintenance or other interruptions of service);
unsatisfactory number of users that could access the service;
lack of compatibility of the service with user hardware or software;
limited data portability;
limited data retrievability;
extra costs imposed for data portability;
data deletion upon contract termination;
destruction or loss of data;
loss or alteration of data transmitted, stored or processed via the cloud service;
unauthorised disclosure of, or access to data;
lack of protection of the data stored in the cloud against theft or viruses;
lack of clarity and completeness of the instructions and information regarding access
and/or download of the cloud service;
unilateral modification of the service/contract by the cloud provider;
limited liability of the cloud provider;
termination of the contract and interruption of the service by the service provider with
no or little prior notice to the user;
lack of clarity and transparency of CT&C.
Based on the results of desk research the team finalised the methodological approach that
included: the definition of the problem tree to clearly understand the links between problems
encountered by enterprises and possible consequences; the preparation of the data collection
tools, including the questionnaires for the survey of SMEs and the case study template.
20 The bibliography serving as basis for the desk research can be found in Annex 3.
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Additionally, desk research revealed that cloud computing users may encounter the following
problems in exercising their rights to remedy the non-conformity of service with the
contract:
absence of a contact point to make a complaint or attempt to resolve issues;
length and high cost of the procedure;
applicability of foreign jurisdiction;
language difficulties;
impossibility to enforce the decision after the resolution.
The problem definition was structured around the above-mentioned key contract-related
problems and their short- and long-term effects. Essentially, the problem definition allowed
the team to:
identify the main/-core problems and the underlying causes;
identify who and what is affected and to what extent;
foresee the possible evolution of the problems identified.
Following the problem definition, a problem tree (Box 1) was structured in order to summarise
the identified problems and related causes, as well as the links between them.
Box 1: Definition of the problem tree
A problem tree or “hierarchy of problems” is useful in providing an overview of the main causes and effects of the identified problems. It is used to better understand the context in which the problems occur, what the intermediate effects and the main drivers are.
The analysis performed during the creation of a problem tree aims at identifying the real bottlenecks
to which stakeholders attach a high priority and which they wish to overcome. A clear problem analysis thus provides a sound foundation on which to develop a set of relevant and focused project objectives.
A problem tree analysis begins with the establishment of the core problem. Using desk research, the problem can be formulated in a more specific way. Once the core problem has been identified, we consider the direct causes and effects of the problem. Each cause statement is written in negative terms. These items should respect the MECE principle, in that they should be Mutually Exclusive and Collectively Exhaustive. In many respects the problem analysis is the most critical stage of project
planning, as it then guides all subsequent analysis and decision-making on priorities.
Once complete, the problem tree represents a summary picture of the existing negative situation that can be further used by the decision makers in order to analyse the objectives, by reversing the negative statements into possible solutions, and then choose the most appropriate solution to be implemented.
Source: European Commission - Aid Delivery Methods (Vol 1: Project Cycle Management Guideline)
Among the most important sources of information that helped in refining the problem tree,
especially with regard to the contract-related problems, were:21
European Commission (2012), ‘Unleashing the Potential of Cloud Computing in
Europe’, COM(2012)529 final, Brussels;
Cloud Computing Experts Groups meetings 2013-2014;
Cloud Service Level Agreement Standardisation Guidelines from 24 June 2014;
European Commission (2015), ‘Comparative Study on cloud computing contracts’;
European Commission (2016), ‘Study on measuring the economic impact of cloud
computing in Europe’;
European Commission consultations about Cloud Computing (2016);
IDC (2016), ‘Switching of Cloud Services Providers’;
Eurostat (2016), ‘Cloud computing - statistics on the use by enterprises and Business
Structure Statistics’;
21 The results of the survey of SMEs, as well as the case study results have been used in order to update the problem tree.
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European Commission (2017) ‘Final results of the European Market study measuring
the size and trends of the EU data economy’;
European Commission (2017), ‘Proposal for a Regulation of the European Parliament
and of the Council on a framework for the free flow of non-personal data in the
European Union’;
European Parliament (2017), Commission Staff Working Document, Impact
Assessment Accompanying the document, ‘Proposal for a Regulation of the European
Parliament and of the Council on a framework for the free flow of non-personal data
in the European Union’.
Figure 3 illustrates the problem tree that was created based on the study’s desk research.
The drivers of cloud computing problems are acknowledged in the different sources of
information consulted such as the Digital Single Market strategy. This strategy identifies
concerns related to ‘security, compliance with fundamental rights and data
protection’22 as a result of technical and legislative barriers.
Moreover, the fragmentation of regulation across the EU could be a factor that limits the
usage of cloud computing due to the need for large providers to adapt their services to each
country’s laws that regulate cloud computing.23
A lack of fairness and balance in CT&C is considered a driver as ‘contracts often exclude,
or severely limit, the contractual liability of the cloud provider’.24 By the same token,
the unilateral modifications of the contract and the difficulties in exercising the user’s
rights are problems arising from the unfairness of CT&C. Moreover, when targeting SMEs,
‘the contract is likely to be a take it or leave it contract’.25
The limited technical knowledge among SMEs is also a factor affecting the emergence of
the different problems shown in Figure 3. The limited technical knowledge of the users has
been considered when creating a Cloud Select Industry Group in Service Level Agreements
Subgroup.26 This subgroup worked towards the development of standardisation guidelines
between cloud providers and cloud customers and these efforts coincide with complementary
action under the Digital Single Market strategy.
The mentioned contract-related problems lead to a loss of business opportunities. Within one
of the Expert Group Meetings it was concluded that an uninformed choice of terms and
conditions may lead to damages.27 Other experts28 have stated that ‘certain losses are
potentially huge and cannot be compensated’.29 The present study tested the hypothesis that
contract-related problems may lead to losses of profit, turnover, clients and reputation
damage for SMEs over the short term. Over the long term, such problems can have a
negative impact on growth and jobs.
Over time, the lack of awareness of the benefits of cloud computing limits its usage and can
deter businesses from tapping into the full potential of the cloud. Over the long term this
could also have a negative effect on GDP and jobs creation. Although this scenario was
22 European Commission (2015), A digital Single Market Strategy for Europe, COM(2015) 192 final,
Brussels. 23 Ibidem. 24 Ibidem. 25 European Commission (2013), Cloud Select Industry Group on Service Level Agreements, accessed 01 May 2017. 26 Expert Group Meeting on Cloud Computing Contracts, Synthesis of the meeting of 29/30 January 2014. 27 Ibidem. 28 Expert Group Meeting on Cloud Computing Contracts, Synthesis of the meeting of 9/10 April 2014. 29 For example due to the unavailability of the service.
https://ec.europa.eu/digital-single-market/en/cloud-select-industry-group-service-level-agreements
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included in the problem tree, it is not the purpose of this study to examine these larger
macroeconomic impacts.30
The problem tree allowed the team to gain an understanding of the main issues to be
investigated within the study and for which evidence from stakeholders was needed.
This understanding was reflected in the data collection tools. These included:
The Survey questionnaire: The structure of the survey questionnaire31 reflected the
different layers of the problem tree. It first focused on the usage of cloud computing
and the types of contracts SMEs tend to sign, corresponding to the drivers section.
Furthermore, it addresses the problems that SMEs encounter, the actions taken in
order to resolve them and the associated economic detriment32 in order to provide
information for the problems and short-term effects sections of the tree.
Interview guidelines for case studies and webinars: Findings from the interviews and
the webinars were used to complement the quantitative analysis with detailed
information on the contract-related problems and the costs sustained by SMEs while
trying to resolve the encountered problems
All collected information supported the estimations on the loss of GDP and the loss of jobs at
the EU level.
30 The European Commission estimated that cloud computing can increase EU GDP up to €250 billion and create 3.8 million jobs. More information available in European Commission (2011), Quantitative Estimates of the Demand for Cloud Computing in Europe and the Likely Barriers to Up-Take, SMART 2011/0045, Brussels. 31 Annex 4 presents the structure of the CATI and online questionnaire. 32 The sections B, C, D and E cover: use of cloud computing, problems encountered with cloud computing services, actions taken by the enterprise in order to address the most serious problem and the economic detriment related to the most serious problem encountered.
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Figure 3: Problem tree33
Source: EY desk research34
33 L/T stands for ‘long-term effects’ and S/T for ‘short-term effects’. 34 Annex 3 contains the bibliography serving as basis for the desk research.
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Under Task 0, the team also defined the sampling strategy to select 12 MS, five economic
sectors to be investigated in depth as well as the sample size of SMEs to be involved in the
survey.
For the selection of the MS, three criteria were considered (Table 2). In particular, the
percentage of enterprises buying cloud computing services, as per 2016 Eurostat
statistics, was considered to be the best proxy variable35 available for the usage of cloud
computing and it has been used as one of the criteria for defining the sample of countries to
be analysed in greater depth:
Table 2: Criteria used for MS selection
Criteria for MS selection Details (Table 3) Source
Percentage of enterprises
buying cloud computing
services
Number of SMEs that
declared they bought at
least one cloud computing service in 2016 out of the total number of SMEs active in the MS
Data on cloud computing service
usage among SMEs Eurostat, 2017
[isoc_cicce_use]
Relevance of the country to the
study objectives
Number of SMEs that
declared they bought at least one cloud computing service in 2016 out of the total number of SMEs active in the EU28
Data on cloud computing service
usage among SMEs Eurostat, 2017 [isoc_cicce_use]
Structural Business Statistics, Eurostat, 2017 [sbs_sc_sca_r2]
Geographical balance Balanced number of EU15
and EU13 MS in the sample
N/A
Based on the above-mentioned criteria, the following 12 MS were selected: the Czech
Republic, Estonia, France, Germany, Ireland, Italy, Poland, Portugal, Romania, Sweden,
Spain, and the Netherlands (Table 3).
A high level of representativeness of the EU is ensured because the selected sample:
involves eight MS from the EU15 and four MS from the EU13;
refers to countries where more than 70% of the EU SMEs that bought at least one of
the cloud computing services in 2016 (Figure 4) were established.
35 A proxy variable is a variable that is used instead of the variable of interest when that variable of interest cannot be measured directly.
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_cicce_use&lang=enhttp://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_cicce_use&lang=enhttp://ec.europa.eu/eurostat/web/structural-business-statistics/data/database
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Table 3: SMEs buying cloud computing services, 2016
EU MS
A B C D E=B+D F=E/(A+C) E/Total EU28
EU15 /EU13
# of active enterprises with 10-250 employees
# of enterprises (10-250 empl.) buying CC services
# of enterprises with 1-9 employees
# of enter. (1-9 empl.) buying CC services 36
Total active SMEs
% (Country level)
% (EU28 level)
AT 40,393 6,462.88 280,850 31,959 38,422 11.96 1.37 EU15
BE 31,302 8,139 569,950 64,858 72,996 12.14 2.61 EU15
BG 26,991 1,619 298,559 33,975 35,594 10.93 1.27 EU13
CY n/a n/a n/a n/a n/a n/a n/a EU13
CZ 38,203 6,877 961,287 109,390 116,267 11.63 4.16 EU13
DE 411,527 61,729 1,985,471 225,938 287,667 12.00 10.29 EU15
DK 22,681 9,072 187,367 21,322 30,394 14.47 1.09 EU15
EE 6,439 1,417 61,513 7,000 8,416 12.39 0.30 EU13
EL 15,368 1,383 764,471 86,993 88,376 11.33 3.16 EU15
ES 125,000 21,250 2,337,621 116,881 138,131 5.61 4.94 EU15
FI 19,187 10,361 209,328 23,821 34,182 14.96 1.22 EU15
FR 139,335 22,294 2,765,282 314,677 336,970 11.60 12.05 EU15
HR 12,249 2,695 134,007 15,249 17,944 12.27 0.64 EU13
HU 30,852 3,394 504,904 57,456 60,850 11.36 2.18 EU13
IE 5,514 1,930 224,742 25,575 27,505 11.95 0.98 EU15
IT 182,182 38,258 3,497,783 398,032 436,290 11.86 15.60 EU15
LT 13,604 2,177 172,527 19,633 21,809 11.72 0.78 EU13
LU 3,939 670 27,841 3,168 3,838 12.08 0.14 EU15
LV 9,226 738 100,216 11,404 12,142 11.09 0.43 EU13
MT 1,885 509 24,123 2,745 3,254 12.51 0.12 EU13
NL 48,115 15,397 1,042,588 118,642 134,039 12.29 4.79 EU15
PL 69,282 4,850 1,534,086 174,572 179,422 11.19 6.42 EU13
PT 37,353 6,350 769,043 53,833 60,183 7.46 2.15 EU15
RO 50,987 3,569 405,493 46,143 49,712 10.89 1.78 EU13
SE 36,047 16,221 649,412 155,859 172,080 25.10 6.15 EU15
SI 6,912 1,382 127,603 14,521 15,903 11.82 0.57 EU13
SK 14,363 2,442 414,630 33,170 35,612 8.30 1.27 EU13
UK 199,528 65,844 1,734,989 312,298 378,142 19.55 13.52 EU15
Tot EU28
1,598,464 317,028 21,785,686 2,479,112 2,796,141 12 100
Selected Countries
Source: Eurostat (Cloud Computing Service [isoc_cicce_use], Structural Business Statistics [sbs_sc_sca_r2])
36 The percentage of micro enterprises (i.e. enterprises with fewer than 10 employees) that bought at least one CC service is only available for the United Kingdom, Slovakia, Sweden, Portugal and Spain. For the other EU countries, the average from those countries (11.38%) has been used.
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Figure 4: The percentage of enterprises which bought cloud computing services in the
selected EU countries as compared to the total EU28 (2016)
Source: Eurostat (Cloud Computing Service [isoc_cicce_use], Structural Business Statistics
[sbs_sc_sca_r2])
For the selection of the economic sectors to be analysed in depth, Eurostat statistics on the
percentage of enterprises buying cloud computing services were used in order to
identify those sectors with the highest usage of cloud computing.
Eurostat statistics are available only for some sectors (Table 4) and only for the category 10
persons employed or more which also includes enterprises with more than 250 employees (i.e.
large enterprises, GE250).
However, Eurostat statistics have been considered to be relevant in selecting the sectors to be
analysed in depth as the number of enterprises with more than 250 employees represents a
negligible percentage of the overall number of active enterprises at the EU28 level (i.e. around
45,000 out of 23 million enterprises, corresponding to 0.2% of the total).
The sampling strategy envisaged the use of the same criteria used for the selection of MS (i.e.
percentage of enterprises buying cloud computing services; relevance of the sector to the
study objectives).
As a result, the five sectors selected included (grey rows in Table 4):
manufacturing (NACE37 code: C);
wholesale and retail trade; repair of motor vehicles and motorcycles (NACE code: G);
professional, scientific and technical activities (NACE code: M69-M74, hereinafter
referred to as M);
administrative and support service activities (NACE code: N);
information and communication (NACE code: J).
37 Eurostat, Nomenclature des Activités Économiques dans la Communauté Européenne, Brussels.
https://ec.europa.eu/eurostat/web/nace-rev2
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Table 4: Economic sectors and usage of cloud computing services (2016)
NACE Code
Sectors
A B C = B/100 x
A C/D
# of active enter. (with
more than 10 empl.) at
EU28 level
% of enter. (with more
than 10 empl.)
buying CC services
# of enter. (with more
than 10 empl.)
buying CC services
% out of the total
C Manufacturing 364,432 17 61,953 23%
D+E Electricity, gas, steam, air conditioning and water supply
18,732 19 3,559 1%
F Construction 200,369 15 30,055 11%
G Wholesale and retail trade; repair of motor vehicles and
motorcycles
351,278 18 63,230 24%
H Transportation and storage 90,718 16 14,514 5%
I55 Accommodation 15,969 23 3,672 1%
J Information and communication
55,905 52 29,070 11%
L Real estate activities 10,661 24 2,558 1%
M Professional, scientific and technical activities
97,073 34 33,004 12%
N Administrative and support
service activities 121,657 22 26,764 10%
Total 1,326,794 21 268,384 (D) 100%
Selected Sectors
Source: Cloud computing services for 2016 (Eurostat [isoc_cicce_use]), Business Structure Statistics for 2015 (Eurostat [sbs_sc_sca_r2])
Although the financial and insurance activities sector (K under its respective NACE code) is
considered as one of the economic sectors with the highest usage of cloud computing,38 it has
been excluded because of its limited relative weight at the EU28 level.39
With the purpose of achieving statistically significant results, a target of 500 SMEs that
use cloud computing has been defined. This sample size has been chosen in order to ensure a
95% confidence interval and a maximum margin of error of 5% for all the estimates. The
sample target size, together with the random selection of the respondents, ensure that - with
a probability of 95% - the true values of a phenomenon (e.g. average incidence of cloud
computing problems) in the entire population (i.e. all EU SMEs) is equal to the average values
observed in the studied sample plus/minus the margin error.
The box below presents the methodology behind the definition of the sample size.
38 76% based on European Commission (2016), Measuring the economic impact of cloud computing in
Europe. 39 Based on the ‘Annual detailed enterprise statistics for services’ [sbs_na_1a_se_r2], it was observed that in 2015, for the respective financial and insurance activities, a total of approximately 14,000 enterprises were active in the EU28, that is 0.1% of the total active enterprises.
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_cicce_use&lang=enhttp://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_sc_sca_r2&lang=enhttp://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en
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Box 2: Definition of the size of the sample
The formula for computing the representative sample size is:
Sample size = [z2 *p(1-p)/e2]/[1 +( z2 *p(1-p)/e2N]
Where:
N= population size; e = margin of error (as a decimal); z = Z-score: constant value; e.g. 1.645 for a 90% confidence interval, 1.96 for a 95% confidence interval, 2.58 for a 99% confidence interval;
and p = average value of a specific indicator as observed in the population (as a decimal).
Our case:
From Table 3 we have
N= 2,796,141 is the estimated number of SMEs that are using CC services
e = 5%
Z = 1.96
P = when this value is not known conventionally is used the value 0.5
We can conclude that a target of 500 enterprise using CC services is appropriate to ensure a 95% confidence interval with a margin of error of 4.38%. When we put the confidence level and the confidence interval together, we can say that we are 95% sure that the true percentage of a phenomenon in the population is between +4.38% and -4.38% of the value observed in the studied sample. For example, if we ask a sample of 500 SMEs using CC if they encountered a certain problem,
and 70% say ‘Yes’, we can be 95% certain that between 65.62% and 74.38% of all EU SMEs using CC encountered the same problem.
Task 1 – Survey of SMEs
Task one was aimed at identifying:
the types of cloud services most frequently contracted;
the types of CT&C used to regulate the business relationships between users and
providers (for example, determining whether enterprises are able to negotiate CT&C
and/or to customise them to their needs);
the types, the incidence and the frequency of contract-related problems;
the types of actions taken by the enterprises in order to resolve the encountered
problems and the results of these actions.
The collection of data was based on a questionnaire (see Annex 8) submitted through two
complementary channels:
an online questionnaire (online Survey);
CATI.
The online questionnaire was translated into the official languages of the MS covered by the
study. Likewise, the CATI was conducted by native speakers in order to avoid language
barriers.
The online survey and the CATI have been developed, tested and conducted on a
representative sample of EU SMEs, which use the cloud for the purpose of conducting their
business.
In order to achieve the above presented target (i.e. 500 SMEs using CC services):
the contacts of 13,000 SMEs were identified during the design of the sampling strategy;
3,173 SMEs were reached within the 12 MS and the five sectors selected. Table 5
presents the distribution of the reached SMEs;
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1,009 SMEs were willing to participate in the survey
Table 5: Distribution of SMEs reached per country and sector (# of respondents: 3,173)
Sector
Country
Admin. and support service
activities
Inform. and comm.
Manufact.
Profess. scientific
and technical activities
Wholesale
and retail trade
Total
CZ 13 17 62 30 52 174
EE 5 22 8 12 15 62
FR 184 129 262 126 308 1,009
DE 99 123 185 226 249 882
IE 4 18 6 6 13 47
IT 8 9 100 7 15 139
NL 36 49
24 37 146
PL 24 69 16 78 92 279
PT 7 2 9 2 24 44
RO 8 20 2 27 15 72
ES 7 11
26 21 65
SE 41 33 59 30 91 254
Total 436 502 709 594 932 3,173
Source: Survey of SMEs (2018)
Table 6 presents the participation rate per MS of the 1,009 SMEs that agreed to provide
information about their use of cloud computing.
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Table 6: Participation rate by MS (# of respondents: 3,173)
Country
Number of
enterprises that expressed refusal
Number of enterprises
that were willing to participate in the survey
Total Participation
rate (%)
A B C = A + B B/C
CZ 131 43 174 25%
DE 33 158 191 83%
EE 715 29 744 4%
ES 724 54 778 7%
FR 16 294 310 95%
IE 24 31 55 56%
IT 97 115 212 54%
NL 152 49 201 24%
PL 11 127 138 92%
PT 48 33 81 41%
RO 11 24 35 69%
SE 202 52 254 20%
Total 2,164 1,009 3,173 32%
Source: Survey of SMEs (2018)
The study targeted all different size categories, the largest share of respondent being
represented by medium sized-enterprises (60%, Figure 5). Contrary to the SMEs distribution
at the EU level, the micro enterprises represented the smallest share of participants in the
survey. The smaller the size of the enterprise, the lower was its willingness to participate in
the survey.
Figure 5: Distribution of participants in the survey by SME size (# of respondents: 1,009)
Source: Survey of SMEs (2018)
The same applies to SME age: enterprises active for more than 10 years are better represented
in our sample as compared to younger enterprises (Figure 6). This reflects their willingness to
participate as well as the greater difficulties encountered in reaching younger enterprises.
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Figure 6: Distribution of enterprises involved in the survey of SMEs by age (# of respondents:
1,009)
Source: Survey of SMEs (2018)
Task 2 – Economic detriment and impacts on jobs and growth
Consequences of encountered problems at the micro level
The economic detriment was quantified along the lines recommended in the Guidelines on
impact assessment of the Better Regulation toolbox40 based on data collected from Task 1 and
on data from Eurostat Business Structure Statistics.
The measurement of economic detriment41 considered a monetary assessment of the effects
of the problems faced by each SME in the survey and includes the following main dimensions:
the gross financial detriment (GFD) associated with the problems encountered;
the net financial detriment (NFD) associated with the problems encountered which
corresponds to the GFD associated with the problem encountered minus any remedies
offered by the cloud provider.
The GFD incorporated the following components:
𝐺𝐹𝐷 = 𝐿 + 𝐻𝑅 + 𝑂𝐶
Where:
L is alternatively the loss of turnover or profits as perceived by each SME;
HR is the costs of the human resources invested by each SME while taking action to
resolve or remedy the most serious encountered problem;
OC other costs declared by each SME. These include costs such as the costs related to
ensuring an alternative service, the costs related to fixing the cloud service problem
using external support, any legal costs, costs related to reputation damage and costs
related to loss of client(s), and other residual costs.
To allow for the measurement of the NFD, the value of any redress (R) offered by the service
provider for the damages caused to each SME as reported in the survey questions was
computed.
40 The Better Regulation Toolbox is a set of general guidelines that set out the principles that the European Commission follows when preparing new initiatives and proposals and when managing and evaluating existing legislation. In this study, those principles were considered generally as they were central in
defining the survey, the sample and also the methodologies to measure economic detriment and estimate
the impact on growth and jobs. 41 Measured using the principles put forward in European Commission (2017), Operational guidance document on measuring personal consumer detriment, Brussels.
https://ec.europa.eu/info/better-regulation-toolbox_enhttps://ec.europa.eu/info/publications/study-measuring-consumer-detriment-european-union_enhttps://ec.europa.eu/info/publications/study-measuring-consumer-detriment-european-union_en
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The detailed procedure used to estimate the economic detriment at the EU28 level included
three main steps.
The first step was the computation of the following parameters from the survey with the 95%
confidence interval level and 5% margin of error:
the loss of turnover or profits perceived by each SME;
the costs of the human resources invested by each SME while taking action to resolve
or remedy the most serious problem;
the other costs covered by each SME while taking action to resolve or remedy the most
serious problem;
the value of any redress offered by the service provider for the damages caused to
each SME.
The second step required the definition of the number of enterprises affected by contract-
related problems (𝑁𝑝):
𝑁𝑝 = 𝑒𝑢 × 𝑒𝑝 × 𝐸
Where:
𝑒𝑢 is the proportion (i.e. the percentage) of enterprises that use cloud computing services;
𝑒𝑝 is the incidence (i.e. the percentage) of contract-related problems; E is the total number of active enterprises.
The incidence rates of contract-related problems (𝑒𝑝) by country and sector or country and size class, and the proportion of enterprises that use cloud-computing services (𝑒𝑢) by country and sector or country and size cannot be estimated from the sample with the 95% confidence
interval level and 5% margin of error, given that the sample is only representative at the EU
level overall.
In order to be able to estimate the total number of enterprises experiencing contract-
related problems by country and sector and by country and size class, an RAS-based42
procedure has been applied.
Box 3: RAS method for estimating the total number of enterprises experiencing contract-
related problems by country and sector
The RAS method is a well-known method for data reconciliation whose aim is to achieve consistency between the entries of some non-negative matrix and pre-specified row and column totals. Mathematically, the method is an iterative scaling method that can be applied in this case as we just
have non-negative matrices. We redistribute proportionally the differences arising from applying the EU level sectorial or size class incidence rates or from country incidence levels, considering in this
case as the binding constraint the country level incidence, which has been estimated with confidence from the sample (we ensure that the incidence per country is respected).
The procedure started with a preliminary estimation of the number of enterprises (𝑁𝑖𝑗𝑝)
experiencing contract-related problems in country i and sector or size class j using the following
formula:
𝑁𝑖𝑗𝑝 = (
𝑒𝑖𝑢 + 𝑒𝑗
𝑢
2) × (
𝑒𝑖𝑝 + 𝑒𝑗
𝑝
2) × 𝐸𝑖𝑗
42 See more information at https://ec.europa.eu/eurostat/cros/content/ras-method_en.
https://ec.europa.eu/eurostat/cros/content/ras-method_en
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Where:
for countries included in our sample
- 𝑁𝑖𝑗𝑝 is the preliminary estimation of the number of enterprises experiencing cloud
computing problems in country i and sector or size class j;
- 𝑒𝑖𝑢 is the proportion of enterprises using cloud computing services in country i43
computed from the survey;
- 𝑒𝑗𝑢 is the proportion of enterprises using cloud computing services in sector or
size class j44 for all the countries in the sample;
- 𝑒𝑖𝑝 is the incidence of cloud computing problems in country i computed from the
survey;
- 𝑒𝑗𝑝 is the incidence of cloud computing problems in sector or size class j
computed from the survey for the whole sample;
- 𝐸𝑖𝑗 is the total number of enterprises in country i and sector or size class j,
extracted from the Eurostat Business Structure Statistics for 2015 (Eurostat
[sbs_sc_sca_r2]).
While for countries not included in the sample
- 𝑁𝑖𝑗𝑝 is the preliminary estimation of the number of enterprises experiencing cloud
computing problems in country i and sector or size class j;
- 𝑒𝑖𝑢 is the average proportion of enterprises using cloud computing services in all
countries of the sample computed from the survey;
- 𝑒𝑗𝑢 is the proportion of enterprises using cloud computing services in sector or
size class j45 for all of the countries in the sample;
- 𝑒𝑖𝑝 is the average incidence of cloud computing problems in all countries of the
sample as computed from the survey;
- 𝑒𝑗𝑝 is the incidence of cloud computing problems in sector/size class j computed
from the survey;
- 𝐸𝑖𝑗 is the total number of enterprises in country i and sector or size class j,
extracted from the Eurostat, Business Structure Statistics for 2015 (Eurostat
[sbs_sc_sca_r2]).
In practice, this formula takes the joint incidence by country and sector or by country and size
class to be the average of the two marginal incidences, which would be the case if the two
distributions were independent. In this way, it allows a preliminary estimation of the number
of enterprises experiencing cloud computing problems in country i and sector or size class j.
43 We use two alternative measures of incidence, allowing us to develop two scenarios. From the survey data, we compute the incidence by country and sector and country and size class considering the answers given by respondents in two alternative ways. In one case, we compute the incidence as the ratio of
those enterprises that declared they are using cloud computing to all enterprises contacted that declared they are either using or not using cloud computing or that refused to respond to the survey based on criteria that can be seen as meaning that the survey does not apply to them (around 16%). Alternatively, also from the survey data we can compute the incidence of cloud computing usage as the ratio of those that declared they are using cloud services to the sum of those that explicitly mentioned that they are either using or not using cloud services (around 50%). This second scenario can be considered as a theoretical but most unlikely future scenario where a consistent number of EU SMEs use
cloud computing. 44 See the previous footnote. 45 See the previous footnote.
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After obtaining that preliminary estimation 𝑁𝑖𝑗𝑝, the RAS method of reconciliation is applied by
considering as the binding constraint the total number of enterprises experiencing problems,
as that value was estimated directly from the sample with the required confidence and error
levels.
This reconciliation of totals performed by the RAS method dismisses the assumption of
independence as it distributes the error across the margins.
Concerning the sectors not covered in the sample,46 the total number of enterprises
experiencing contract-related problems by country and sector can also be computed by
following the same procedure as above. In that case, sector incidence would be replaced by
the European average incidence on the other sectors. Given the sampling procedure that was
followed, in which the sectors with the highest incidence of cloud computing usage were
selected to be covered by the survey, applying those incidences of use of cloud computing as
representative to the other sectors would overestimate the impacts. Given this situation, only
the results for the selected sectors are presented and this constitutes a lower threshold for the
true detriment.47
Once the values of the number of enterprises experiencing cloud computing problems for each
country and sector and country and size class are computed, they can be aggregated to the
EU level just by summing up the number of enterprises experiencing cloud computing service
problems per country.
Finally, the third step was the computation of the total loss/cost/value of redress by country,
sector and size class, by multiplying the number of enterprises that experienced contract-
related problems (as computed using the RAS model) by the 95% trimmed mean values
per enterprise for each variable (from the survey).48
The GFD is then computed considering alternatively lost turnover and lost profits and adding
the human resource costs and the other costs. NFD is computed taking the two measures of
GFD and subtracting the value of any redress offered by the service provider for the damages
caused to each SME.
Afterwards, two measures are obtained, covering both GFD and NFD computed at country and
sector, country and size class, and at EU level.
Consequences of encountered problems at the macro level
Following the quantification of the economic detriment, the consequent impact on growth
and jobs was estimated using one of the methodologies suggested in the impact assessment
toolbox presented by the Joint Research Centre49 of the European Commission.
The goal was to estimate the overall GVA and employment content of the detriment. In order
to do that, the following methodology was used.
The starting point was the declared turnover loss, one of the components of total detriment,
aggregated by country and sector and by country and SME size. We first estimated the financial
detriment suffered by each enterprise by considering the different components of detriment,
then we aggregated these effects to the EU28 level by summing up the effects suffered by
46 As previously stated, only the following sectors were covered by the study: Manufacturing (C); Wholesale and retail trade; repair of motor vehicles and motorcycles (G); Professional, scientific and technical activities (M); Administrative and support service activities (N); and Information and communication (J). 47 Recall that the selected sectors comprise a total of 79.7% of SMEs and GE250 using cloud computing
services. 48 Given each variable, the 95% trimmed version was obtained by discarding the 5% lowest and highest observations: it is a statistic in the middle of the data range. 49 EU Science Hub (2016), Input-output economics, Brussels.
https://ec.europa.eu/jrc/en/research-topic/input-output-economics
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each enterprise across all enterprises. When estimating the macroeconomic effects of the
contract-related cloud computing problems, one must consider the overall turnover loss that
was suffered by all enterprises that experienced those problems as turnover is the variable
that best summarises the overall enterprise-level effects that are transmitted into the
economic system through the supply chains and that can be related to aggregate economic
variables, thus avoiding double counting.
This allowed the team to estimate the so-called direct impacts (the direct consequences in
the enterprises that face contract-related cloud c