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Master Thesis

MSc Business Information Management Rotterdam School of Management

Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Is the move to the cloud green-lighted by environmental factors? Dirk P. Zeilstra 294474 University Coach: Prof. Dr. Ir. Eric van Heck University Co-reader: Dr. Rob Zuidwijk Business Coach: Sabine Hess, Microsoft Date: 19 September 2012

Preface The author declares that the text and work presented in this Master thesis is original and that no sources other than those mentioned in the text and its references have been used in creating the Master thesis. The copyright of the Master thesis rests with the author. The author is responsible for its contents. RSM Erasmus University is only responsible for the educational coaching and beyond that cannot be held responsible for the content.

Dirk Zeilstra Department of Decision and Information Sciences Rotterdam School of Management Erasmus University September 2012

1 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

Acknowledgements In writing this thesis, I had the priceless guidance of my coach Prof. Dr. Ir. Eric van Heck, whose time, effort, help, guidance and continuous support was of great importance and influence for this research project. Guiding me through different theoretical models and giving endless feedback on my work was inspirational. He also contributed with putting me in contact with other researchers to spar my research ideas. I would like to express my deepest gratitude for everything he has done. I would like to express my sincere gratitude to my co-reader, Dr. Rob Zuidwijk for his constructive criticism and advice throughout the course of this study. He has been of importance when guiding me through the first phases of the project and by setting up my experiment. Next, I would give my special thanks to Sabine Hess, Environmental Sustainability Lead at Microsoft, who has been my company coach. She has supported me enormously by providing feedback on the practical relevance of my study as well as knowledge of the environment at Microsoft. I feel very fortunate to have had the opportunity to do research in this topic and in this company where they have continuously provided with new insights, content and of course contacts to complete my survey. The assistance of many people helped me lay the foundation of this work. I would like to gratefully thank Dr. Marcel van Oosterhout for giving me advice on pursuing the internship position at Microsoft. Also I would like to thank the Erasmus@Work group for all their feedback and input during the sessions and the work of Nick van der Meulen in guiding me through the experiment software. Furthermore, I would like to express my gratitude for all the people that have read my work in an early stage or have discussed the project with me. Also all the feedback on my proposal and during the Master Thesis trajectory at the university have guided me to successfully complete this work. I would also like to thank all people involved in testing my experiment and working on the questions. Last but certainly not least, I would like to offer my gratitude to my girlfriend, who gave me all the support and encouragement during the complete course of this project. I would not have been able to complete this study within the time without her help and time. I feel extremely fortunate to have had this kind of infinite support during the months which went into this research study.


Executive Summary The current research study provides a fuller understanding of the adoption of cloud computing and the impact of environmental variables on this adoption decision. The outcomes of this study provide insight in which environmental factors are enabling the adoption decision made by IT professionals. Cloud computing has received major attention over the last years and is one of the new emerging technologies. This study provides an indepth overview of cloud computing and the combination of environmental variables. Other empirical studies have studied the cloud computing phenomenon but not in combination with the environmental sustainability aspect. By means of an experiment, Dutch IT professionals were presented a cloud computing adoption decision where different characteristics of cloud computing were evaluated and rated. Through use of Real Options Theory, respondents were asked to rank the corresponding options when presented two different scenarios. After providing an overview of the different theories and previous academic literature concerning the topic, a conceptual framework is set up where different characteristics; being key characteristics of cloud computing and its major risk perceived lack of security were presented along with institutional influences. As an addition, environmental factors were included by means of power savings, carbon emissions and a more sustainable method of power. The preference of the different options is hypothesised in light of adoption concerning these different measurement variables. All variables proposed in the research seemed to have an impact on the decision to move to the cloud. Environmental factors were perceived influential in the cloud adoption decision, where power savings were recognised most. This study contributes in three different ways to existing academic literature. It is trying to fill the gap to answer which environmental factors are important, where real options has never been used before. Furthermore, this was never done by means of an experiment and in combination with cloud computing. Concerning practical relevance, cloud providers could use the outcomes of this study to show their ecological footprint in providing cloud computing to customers and advertise this aspect along with other factors.

Keywords Cloud Computing, Environmental Sustainability, Information Technology, IT Investments, Technology Adoption, Institutional Influences, Environmental factors, Real Options Analysis, Power Savings, Greener Method of Power Generated, Carbon emissions.


Table of Contents Chapter 1: Introduction ....................................................................................................7 1.1 Background ..................................................................................................................................................... 7 1.1.1 Cloud Computing ................................................................................................................................. 7 1.1.2 Environmental sustainability ............................................................................................................. 8 1.1.3 Real options theory .............................................................................................................................. 8 1.2 Structure of the Thesis ................................................................................................................................ 9

Chapter 2: Problem Statement and Research Questions ................................ 10 2.1 Research Objective ..................................................................................................................................... 10 2.2 Research Questions.................................................................................................................................... 10

Chapter 3: Literature Review ....................................................................................... 12 3.1 IT Investments .............................................................................................................................................. 12 3.2 Real Options Theory .................................................................................................................................. 13 3.3 Institutional Influences.............................................................................................................................. 15 3.4 Cloud Computing ....................................................................................................................................... 16 3.4.1 Economic and Operational Characteristics ............................................................................... 18 3.4.2 Environmental Influence Factors ................................................................................................... 19

Chapter 4: Conceptual Model ..................................................................................... 21 4.1 Conceptual Framework ............................................................................................................................. 21 4.2 Hypotheses Development ....................................................................................................................... 22 4.2.1 Institutional Influences Risk ............................................................................................................ 22 4.2.2 Key characteristics of Cloud Computing Risk .......................................................................... 23 4.2.3 Perceived Lack of Security Risk...................................................................................................... 23 4.2.4 Perceived Improved CO2 emissions Risk .................................................................................... 24 4.2.5 Power Savings Risk ............................................................................................................................. 25 4 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

4.2.6 More Sustainable Method of Power Generated Risk ............................................................ 25

Chapter 5: Methodology............................................................................................... 26 5.1 Research design .......................................................................................................................................... 26 5.2 Data Description.......................................................................................................................................... 27 5.3 Limitations of Field Experiments ........................................................................................................... 27 5.4 Measurement of Concepts ...................................................................................................................... 27

Chapter 6: Analysis & Discussion .............................................................................. 29 6.1 Demographics and General Characteristics ...................................................................................... 29 6.2 Scales Reliability .......................................................................................................................................... 31 6.3 Means, Variances & Medians ................................................................................................................. 32 6.3.1 Means and Variances for Institutional Influences................................................................... 32 6.3.2 Means and Variances for Key attributes of Cloud Computing .......................................... 33 6.4 Scenario Validation .................................................................................................................................... 34 6.5 Hypotheses Validation .............................................................................................................................. 36 6.5.1 Institutional influences ...................................................................................................................... 36 6.5.2 Key characteristics of cloud computing ..................................................................................... 37 6.5.3 Perceived Lack of Security ............................................................................................................... 38 6.5.4 Improved CO2 Emissions .................................................................................................................. 39 6.5.5 Power Savings ...................................................................................................................................... 40 6.5.6 Method of Power Generated.......................................................................................................... 41 6.6 Conceptual Model Validation ................................................................................................................ 42 6.6.1 Scenarios without Environmental Information ........................................................................ 43 6.6.2 Scenarios with Environmental Information ............................................................................... 45 6.6.3 CRM Application ................................................................................................................................. 47 6.6.4 E-mail, Calendar and Contacts Application .............................................................................. 47 6.7 Main Findings ............................................................................................................................................... 48 6.8 Discussion ...................................................................................................................................................... 51


Chapter 7: Conclusions .................................................................................................. 53 7.1 The organisational move to cloud computing ................................................................................ 53 7.2 Overall conclusion ...................................................................................................................................... 54 7.3 Limitations ..................................................................................................................................................... 55 7.4 Academic Relevance .................................................................................................................................. 55 7.5 Practical Relevance ..................................................................................................................................... 56 7.6 Future Research ........................................................................................................................................... 56

Glossary................................................................................................................................ 58 List of Figures & Tables ................................................................................................. 59 References........................................................................................................................... 60 Appendix A: Survey ......................................................................................................... 66


Chapter 1: Introduction The Greek myths tell of creatures plucked from the surface of the Earth and enshrined as constellations in the night sky. Something similar is happening today in the world of computing. (Hayes, 2008)

1.1 Background Without a doubt, cloud computing has gained major attention over the last couple of years. The shift from locally installed applications at home or at the office to having them running on a central server, or “in the cloud”, is just getting under way in earnest (Hayes, 2008). This change will affect everyone, from end-user to software developer to hardware manufacturer. More and more organisations are aware of this new technology and are seriously considering the move to the cloud on a larger scale. The adoption of cloud computing can be considered as an IT investment, where firms are trying to strategically leverage the outcome of their investments and gain competitive advantage. Environmental sustainability is on the top of every organisation’s agenda, but not yet a factor which has been studied with regard to cloud computing and other IT investment decisions. Because of dawning regulations concerning CO2 emission and power savings, environmental sustainability could be an enabling factor in the adoption of cloud computing. In this thesis, institutional influences, key characteristics of cloud computing and environmental factors will be analysed in light of Real Options Analysis. These factors will be elaborated in later sections.

1.1.1 Cloud Computing Computing power is shifting from people’s personal computers to big data centres. Almost fifty years ago, a similar movement happened with the time-sharing of computing power (Cusumano, 2010). Now, however the argument is not the lack of computing power, but centralising applications and their indispensable updates. A key factor of the cloud is the fact that people are able to work from any time and any place. Collaboration is being made easier than ever before, where working in the same document is one of the possibilities and changes are saved instantaneously. Essential characteristics of cloud computing that address almost the same needs as fifty years ago, are: on-demand access, elasticity, pay-per-use, connectivity, resource pooling, abstracted infrastructure and little or no commitment (Durkee, 2010). Computing demand is 7 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

being fulfilled rapidly in the amount required at that particular moment, and abandoned when unneeded. Much like the electric bill, cloud computing is on a quantity-based cost basis. The high-speed connection of the servers allows for a data flow over the Internet of computing and storage. Furthermore, computing power is shared between end-users, which provides economies of scale. Because of virtualisation of computing power and delivery over the Internet, it is very abstract for end-users, as he or she is unaware of the exact location or set-up of computers where their current applications are running. Not only mainstream software are moved to the cloud, like word processing, presentation making or doing calculations. More and more enterprises are putting major business applications, such like customer support, sales and marketing as an on-demand online service. Cloud computing can be divided into three basic service models. Each model has an answer for a certain business need. If looked at cloud computing from top to bottom, these three layers can be identified: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). SaaS is the highest layer in the cloud, where the end-user is purchasing a working application. PaaS is the next layer down, where end-users purchase an application environment on top of the bare-bone infrastructure. Lastly, at the base of cloud service models, end-users purchase raw computing, storage and network transfer. The subject of cloud computing will be elaborated in section 3.4, where academic literature covering the subject will be presented.

1.1.2 Environmental sustainability Environmental sustainability has been a subject of increased attention over the last few decades. Natural resources are slowly being depleted, a reason for the issue of sustainable development arising. Organisations are more aware of their impact and their ecological footprint on the planet. If growth continues in line with demand, the world will be using 122 million servers in 2020, up from 18 million today (Smart 2020 Report, 2008). Virtualisation architectures of Information and Communication Technologies (ICT) can help achieve changes in efficiency and eventually on carbon emissions. In data centres, the current utilisation rate of servers in data centres worldwide is very low (6 % average utilisation). To be able to overcome this underutilisation of computing power, organisations have the possibility to engage in outsourcing operations to the cloud. Main cloud providers see virtualisation along with the renewability and reuse of energy as their core business in making the data centre as environmental sustainable as possible.

1.1.3 Real options theory Real-options theory has been developed in the finance field and gained basis over the years (Boehm, 1991; Kumar, 2002; Kim and Sanders, 2002; Benaroch et al., 2006). Since options theory was developed to deal with financial options, this concept has been applied in many 8 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

other fields. The theory is especially valuable for projects that involve both a high level of uncertainty and opportunities to dispel that uncertainty as new information becomes available (Copeland and Tufano, 2004). The theory has been used in IT investment questions (like EDI and RFID) as well and can be particularly relevant for examining investments in other new technologies, such as cloud computing (Saya et al., 2010; Tallon et al., 2002; Benaroch et al., 2007).

1.2 Structure of the Thesis In this first section, the reader is introduced into the main topics: cloud computing, environmental sustainability and real options. This section is aimed at arising readers’ interest in these topics and the reasons to examine these phenomena. In Chapter 2, the objectives of the current study are clarified, by stating the problem and the questions this thesis tries to overcome. Following, in Chapter 3, academic literature will be reviewed and linked to the current problem situation. Chapter 4 provides a conceptual framework which links the attributes and where the hypotheses for this thesis are published. The practical and academic relevance of these problems and possible answers are explained. In the fifth chapter, the methodology of research is being treated. Following, in Chapter 6 the data analysis will be presented along with statistical outcomes. In the final chapter, conclusions will be made up and limitations, discussion and further research are described.


Chapter 2: Problem Statement and Research Questions If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions. (Albert Einstein)

Based on the previously stated introduction about the thesis topic: cloud computing and environmental sustainability, the focus will be formulated in this chapter. This study has the aim to gain an insight on which criteria IT professionals base their decisions concerning the adoption of cloud computing and in which way their decision is being impacted by environmental factors.

2.1 Research Objective The decision to move to cloud computing can be seen as an IT architectural decision. Architectural IT investments are the foundation of the organisations’ IT portfolio. As with every new technology, organisations are facing problems concerning the decision process involving cloud computing adoption. Environmental factors have not yet received much attention in prior research concerning these problems. The main problem this thesis is trying to overcome is how environmental factors impact the decision making process to move to cloud computing. Another fact that has not been covered by much attention is which environmental factors are specifically influencing this decision. The overall question this thesis is trying to overcome is how an organisational move to cloud computing is influenced by environmental factors.

2.2 Research Questions After having set up the problem definition, it can be formulated in research questions, which this thesis is trying to answer. The before mentioned problem can be translated into the following research questions:


1. How do environmental factors influence the decision to move to cloud computing? This question focuses on the impact of environmental factors in addition to key characteristics of cloud computing and if these variables accelerate adoption of cloud computing.

2. What kind of environmental factors encourage the investment in cloud computing? This question treats which environmental factors are encouraging adopting cloud computing and influence the decision process.


Chapter 3: Literature Review Reading is equivalent to thinking with someone else's head instead of with one's own. (Arthur Schopenhauer) The adoption of new innovations consist of a sequence of stages, where initial knowledge is being gained, an attitude towards this novelty is formed and eventually let us make a decision to whether to adopt or reject it (Rogers, 2003). Real Option Analysis (ROA) is a popular approach that offers different benefits relative to the valuation of capital investments or in this case, an IT investment. Behavioural decisions are also influenced by external sources, for example industry standards or regulatory measures. Institutional influences will impact the perceptions of individuals with regard to technological characteristics of cloud computing and their reactions. In this chapter, the literature concerning the problem statement and relevant subjects are presented. In section 3.1, IT investments and their link to the subject will be clarified according to existing literature. In section 3.2 Real Options Theory will be introduced, after which in section 3.3 Institutional Influences are described and finally in section 3.4 the cloud computing phenomenon will be elaborated.

3.1 IT Investments Firms are constantly exploring ways to strategically invest in new technologies. Research has shown that there is a positive relationship between IT investments, economic productivity and business value across distinct measures (Brynjolfsson and Hitt, 1996; Dewan and Min, 1997; Bharadwaj et al, 1999). Weill (1992) and Broadbent et al. (1999) have developed a framework which categorises IT investments into a portfolio of four different IT assets with a specific purpose, being infrastructural, transactional, informational and strategic. The decision to move to the cloud can be seen as an infrastructural decision. IT infrastructure provides the foundation of shared IT services used my multiple IT applications (Keen, 1991; Broadbent et al., 1999). This kind of investments is typically made to provide a flexible base for future business initiatives and needs (Aral and Weill, 2007). It is a long-term decision where the disruptive nature of these implementations creates high up-front costs and long benefit time horizons (Duncan, 1995; Broadbent et al., 1999). However, infrastructure investments enable new applications and functionalities which lay the groundwork for future operational performance and higher returns in the long run.


Architectural IT investments can be compared to other architectural expenditures. Like other architectural investments, investments in IT architecture consider long-term decisions where technology is changing rapidly. Environmental sustainability is a factor which has similar typologies and is constantly under evaluation. The conflicting goals faced by managers and engineers in developing and managing infrastructure systems, not only in Information Technology, are the core problem for balancing sustainability with the main goal (Sahely et al., 2005). In constructing new architecture, three different conflicting factors can be identified: (1) financial versus technical, (2) short-term versus long-term and (3) network versus project factors (Vanier, 2001). Similarities with IT infrastructural investments exist as these have the same conflicting factors. Weill and Ross (2009) recommend the use of options theory for IT infrastructural investments, which will be introduced in the next section.

3.2 Real Options Theory Real Options Analysis (ROA) is a method proposed to analyse capital investment value. The real-options theory was developed in the finance field and gained basis over the years (Boehm, 1991; Kumar, 2002; Kim and Sanders, 2002; Benaroch et al., 2006). Although options theory was initially set up to deal with financial options, this concept has been applied in many other fields. An option is a security which gives its owner the right to trade in a fixed number of shares of a certain stock at a fixed price at any time on or before a given date (Cox et al., 1979). A real option refers to the right, but not the obligation to make a managerial decision to take ownership of a real asset or to engage in a future project (Tallon et al., 2002; Wu et al., 2010). The theory is especially valuable for projects that involve both a high level of uncertainty and opportunities to dispel that uncertainty as new information becomes available (Copeland and Tufano, 2004). The theory has been used in IT investment questions as well and can be particularly relevant for examining investments in new technologies, such as Web 2.0 and cloud computing (Saya et al., 2010; Tallon et al., 2002; Benaroch et al., 2007). Real options are not necessarily pre-existent in IT projects, but they have to be actively embedded and managed (Benaroch, 2002). ROA is not always seen as being beneficial, since it reduces organisational commitment to a planned outcome or event (Busby and Pitts, 1997). Very few decision-makers seemed to be aware of the research in the field of real options but their intuitions agree with the qualitative prescriptions of earlier research. Some researchers have questioned assumptions which underlie ROA, such as the tradability and liquidity of a certain option (Tallon et al., 2002) and risk neutrality on part of the investor (Benaroch et al., 1999; 2000). If these assumptions are incorrect, this kind of analysis could lead to incorrect decisions concerning IT investments. Also accurate estimates of future cash flows are very difficult, just like Net Present Value analysis (Benaroch et al., 1999; 2000; Taudes et al., 2000). Another limitation might be that real options are too complex to communicate to business executives. However, Tiwana et al. (2006) found that managers recognise the value of real options and dedicate a higher value to projects with one or more embedded options than to 13 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

the same project without these embedded options. They were mainly motivated by the prospect of producing a positive economic return. Managerial flexibility is an important aspect considering ROA. It refers to the ability of IT project managers to change the strategy or course of a project in reaction of certain risks. Flexibility is an important success factor in IT projects as it enables countermeasures to be able to respond to a risk (Avison et al., 1995; Kim and Chung, 2003). Another factor is that option theory defines risk as a trait of an IT project that can negatively or positively affect the degree of variation in the expected outcome (Benaroch et al., 2006). In this thesis, the approach defined by Benaroch (2002) will be used, where at first the investments and its risks are defined. For IT investments, such as the adoption of cloud computing, there are several strategic purposes that may override a negative expectation of economic value. Sometimes, even though the expected economic value is below zero, organisations are still willing to push through a certain technology in order to capture future opportunities where growth opportunities may be the reason for these decisions (Benaroch et al., 1999). Real options can be classified into six types: defer, stage, switch use, scale down, abandon and growth (Trigeorgis, 1993; Fichman et al., 2005; Hilhorst et al., 2008). These different options are described in the following table along with the existing literature.

Option to… Includes learning and delaying of the investments (Benaroch, 2002;

Defer

Benaroch et al., 1999; Hubbard, 1994). The organisation avoids investing in what is destined to be a losing proposition, while the chances for making the right decision are increased.

Stage Switch use Scale down

Structuring it as a series of incremental stepping stones that allows the project owner to decide to stop when it becomes unfavourable. The project outcome is evaluated at every stage. Project is used for a different purpose than was originally intended (Trigeorgis, 1993). An organisation decides to allocate resources differently in order to change the scope or scale of the application (Kumar, 2002; Pindyck, 1988). Scaling down during unfavourable conditions is possible.

Abandon

The project is terminated prior to completion and funds are redistributed (Hubbard, 1993; Tiwana et al., 2007). Includes scaling up to engage follow-up investments a step further

Growth

than initially anticipated (Tiwana et al., 2007). Over time, the value of follow-up investments becomes visible and only positive projects are continued. Table 1 | Real Options


These options will be considered in the proposed model and research. This typology is not exactly the same as the option pricing model, which focuses more on deferring decisionmaking in order to obtain a larger expected value than a now-or-never type investment (Benaroch et al., 1999). From a managerial perspective it may be interesting to find whether IT professionals follow real options logic in real risk making decisions. Are they really recognising the value of the different options when facing risk? The decision maker has to assess the risks upfront when determining the potential real options in a project. Although real options can represent the value of a certain project, if there is no purpose to exercise the option, there is no value in having or creating it. Determining the value of the different real options and their execution minimum is out of the scope for this study.

3.3 Institutional Influences Organisations are faced with pressures to adjust their behaviour according to shared notions, for which violations may affect their political power, legitimacy and ability to secure customers and resources (Scott, 2008). Influences that shape social and organisational structures, schemes, rules, norms and routines which all have an outcome in the behaviour of social actors, are part of institutional theory (Scott, 2004). Institutional theory has been identified as an appropriate theoretical perspective to investigate IT related organisational changes (Robey and Boudreau, 1999). These factors have also been found to affect the intentions of organisations to adopt certain technologies, such as Electronic Data Interchange (Teo et al., 2003) and Radio Frequency Identification (Goswami et al., 2008). The conceptual model of Scott (1995, 2004) is integrated with the perspective by DiMaggio and Powell (1983) where it is stated that all organisations are operating within an institutional framework in a structure called “organisational field”. This organisational field consists of organisations that “in the aggregate, constitute a recognised area of institutional life: key suppliers, resource and product consumers, regulatory agencies and other organisations that produce similar services or products” (DiMaggio and Powell, 1983). Institutional influences can manifest itself in three different manners: coercive, mimetic and normative (Scott, 1995; 2004). Coercive influence treats the formal or informal pressure which is forced by other organisations upon which it is dependent (DiMaggio and Powell, 1983). These forces can be experienced through coalitions or regulatory bodies that control scarce and important resources, for instance originating in government departments or other regulative and legislative matters (Scott, 1995; 2004; Teo et al., 2003). Mimetic influence refers to the pressure for an organisation to copy behaviour of other organisations that are perceived more successful, which can be competitors, shareholders, non-governmental organisations or even society-at-large, (DiMaggio and Powell, 1983). 15 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

Normative influence arises from professionalism, in which organisations are seeking to define working conditions and methods (DiMaggio and Powell, 1983). These norms can be developed and reinforced through educational institutions or professional social networks that transcend organisational boundaries.

3.4 Cloud Computing The roots of cloud computing are founded in the advancement of different technologies. There are different motives that accelerated the upcoming of this new technology. Hardware virtualisation and the upcoming of multi-core chips has been an important factor. Because of Internet technologies available and by the rise of many Web services, Service Oriented Architectures and Web 2.0 with mash-ups, cloud computing was able to evolve. Cluster and grid computing have accelerated distributing computing power over several machines. In terms of software and systems management, the main technologies are autonomic computing and the automation of data centres. These technologies have been branded as hype in earlier stages, but are widely adopted by many organisations these days. Computing is delivered as a utility which can be defined as on-demand infrastructure, applications and business processes, running in a secure, shared and scalable environment in the cloud. People moved from large mainframes to personal computers (PCs) with the advent of fast and inexpensive microprocessors. Data centres moved to collections of commodity servers, but led to servers only running one dedicated process. Also, the unavailability of fast and reliable networks led to IT infrastructure in the proximity of business processes. Due to virtualisation, organisations were able to consolidate different dedicated servers to run different processes. The computing problems resemble the electricity generation stations, which used to power individual factories and were under-utilised. Now however, electricity is available hundreds of kilometres off of the generation facilities. The same is seen in the computing world, where optical fibres make it possible to share computing power at great speeds over great distances. Web services have contributed to advances in the domain of software integration. Many Web services have been glued together where applications run seamlessly, without the user noticing the different platforms on which it operates. These Web services provided a common mechanism for delivering services, which made the system ideal for a ServiceOriented Architecture (SOA). The purpose of SOA is to address requirements of loosely coupled, standards-based, and protocol-independent distributed computing (Voorsluys et al., 2011). The concept of gluing services together focused initially on enterprise webs, but with the upcoming of Web 2.0 this concept became available for consumers as well.


Figure 1 | Cloud Levels

Cloud computing as a form of SOA can be divided into the following three levels, namely Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). These levels can be viewed as layered architecture, where services can be composed of both services of a higher and lower layer. Cloud infrastructure enables an on-demand provision of server time, running a choice of operating systems and software. Infrastructure as a Service is the bottom layer of the architecture, as the upcoming layers are built on top of it. The cloud platform (Platform as a Service) offers an environment for developers to create and deploy applications. The most important aspect is that developers are initially not aware of the amount of processing power or memory that will be used. The platform is very scalable to build multiple programming models and specialised services. The applications and software reside on the top layer of the cloud stack (Software as a Service). These services can be accessed via Web portals, which allow consumers to switch from offline computer programs to their online equivalents. Traditional desktop applications as word processing, presentation making and spread sheets calculations can be accessed as a service over the Internet. Other implementations have been seen by CRM applications and email applications running on the Web.


The earlier discussed models mainly concerned public utilities. Cloud computing however is offered in many different deployment forms. These can be classified as public, private or hybrid, which is a combination of the aforementioned. The public cloud can be characterised as a cloud which is available in a pay-as-you-go manner to the general public, where private clouds are internal data centres of an organisation, which have no connection to the general public (Armbrust et al., 2009). The other forms of cloud computing can exist in the form of a community cloud, which is shared by several organisations and supports a specific community (Mell and Grance, 2009) and a hybrid cloud when a private cloud is supported by a public one (Sotomayor et al., 2009).

Figure 2 | Different cloud forms

Concerning cloud computing, a few characteristics and features can be defined. In this study, two fields of interest are being separated. Firstly, economic and operational features will be discussed with regard to the new way of computing. The other field includes environmental influence factors of cloud computing will be introduced and elaborated.

3.4.1 Economic and Operational Characteristics The key characteristics that can be defined from prior research and existing academic literature are accessibility, scalability and cost effectiveness. Accessibility is referring to the extent to which cloud computing applications and resources can be accessed from anywhere through any platform (Baker et al., 2002). All cloud features can be used using a web browser or at a programme level using Web services standards (Birman et al., 2009). A very important issue to this feature is that the use of the Internet enabled the delivery of computing resources available from anywhere and independent of the IT infrastructure of an organisation (Saya et al., 2010; Rochwerger et al., 2009; Erdogmus, 2009). The level of scalability deals with the available computing resources that can be dynamically adjusted to variable loads whenever there is a change in the number of users, required storage capacity and processing power (Stanoevska-Slabeva et al., 2010). Scalability is achieved through virtualisation where physical resources appear available to users, however 18 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

computing, storage and networking hardware and software are abstracted (Foster et al., 2008). Because of this virtualisation, many programmes are able to run simultaneously as they are only used on-demand. Cloud computing providers are setting up data centres at different geographical locations all over the world to serve their users in the best and fastest way possible. Existing systems however do not support the coordination of computing load to split between these locations. Also, these providers are unable to predict the geographical distribution of users loading their services (Buyya et al., 2009). Unfortunately, there is a lack of testing protocols to do research on the real scalability of existing cloud computing services (Birman et al., 2009). Cost effectiveness can be seen as an attribute of cloud computing. It refers to the benefits which can be derived from a computing resource and if that investment is worth its costs (Wells et al., 2003). By the shift of IT infrastructure from on-premise to outside the organisation, cloud computing can help to reduce the costs (Vaquero et al., 2009). Cloud computing providers have constructed and are operating extremely large-scale, commoditycomputer-based data centres at low-cost locations and mainly leverage economies of scale to decrease the costs of computing of five to seven percent (Armbrust et al., 2010). Providers offer these resources at low cost in a pay-per-use manner, where users only pay for resources they are actually using (De Assuncao et al., 2009). While cloud computing shows many advantageous features by providing accessible, scalable and cost-effective computing resources, users are focusing on what the cloud lacks. One of the main concerns regarding this new technology is security (Dillon et al., 2010; Foster et al., 2008). This concern refers to the ability to prevent unauthorised access or modification to information in storage, processing or transit (Joshi et al., 2001). Cloud computing can increase these risks as sensitive business data and information must be moved off of local server storage to the cloud provider (Abadi, 2009). As this is one of the most identified risks in academic literature, it is important to include this risk in this study.

3.4.2 Environmental Influence Factors While the operational benefits of cloud computing have been widely discussed in earlier studies, environmental factors have received less attention. Through the use of large shared servers and storage units, cloud computing can offer energy savings in the provision of computing and storage services. There are however also some concerns considering the environment. Because of the exponential growth of data centres required for cloud services, this raises sustainability concerns. It will lead to increases in network traffic and associated network energy consumption. The industry tries to overcome these problems by legislation, the operational limit of power grids and potential financial benefits. Virtualisation is their primary solution. 19 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

Data centres exist in different shapes and sizes and will always have a mixture of equipment and heat loads (Jing et al., 2011). A key concern which plays a significant role in data centre’s energy efficiency is heat recirculation (Tang et al., 2008; Moore et al., 2005). There has been a study on data centre cooling by directly reusing generated thermal energy for which the ultimate aim was a zero-emission data centre (Brunschwiler et al., 2009). Taking advantage of the environment can also significantly reduce the data centre energy consumption. Therefore, data centres are mainly located at geographical locations where the climate is ideal for operations. Also, the management of power consumption has led to a number of substantial improvements in energy efficiency (Hermenier et al., 2006; Chase et al., 2001). Techniques, such as virtualisation of computing resources and sleep scheduling improve energy efficiency (Liu et al., 2009). Cloud computing compared to conventional computing has been evaluated in previous studies and shown that it is significantly more sustainable (Baliga et al., 2011). Furthermore, cloud computing providers are aiming at using renewable energy for the production of its energy, therefore not overloading the grids and trying to be carbon neutral. Most data centres are equipped with a hydroelectric station to ensure the use of green energy.


Chapter 4: Conceptual Model A concept is stronger than a fact. (Charlotte Perkins Gilman)

4.1 Conceptual Framework Based on the academic literature review on the subjects related to the current study, the hypotheses have been formulated. To visualise the different attributes of this thesis, a conceptual model is constructed below.

Figure 3 | Conceptual Framework of the Thesis

The six different risks that have been identified, have an influence on the ranking of real options in the conceptual model. All options, except from the option to abandon, will have a positive effect on the further course of adoption, may it be in different ways.


4.2 Hypotheses Development In this section, the hypotheses for the current conceptual framework will be developed and elaborated. Real options theory concerns managerial flexibility which is dealing with risks. Flexibility represents the ability to react to a state of resolved risk, where the risk is the key presence of flexibility (Bräutigam et al., 2003). Real option analysis relies on these risks in a decision model where real options theory is used for a new IT implementation (Hilhorst, 2009). These risks are mentioned in the conceptual framework and will be discussed in the following sections along with their hypotheses.

4.2.1 Institutional Influences Risk Organisations are constantly responding to other organisations in their environment which are in turn responding to their environment. These inter-organisational reactions can be defined as institutional influences. In previous research, institutional influences have been found positively related to the intention to adopt certain new technologies, such as Electronic Data Interchange (Teo et al., 2003) and Radio Frequency Identification (Goswami et al., 2008). There are three isomorphic pressures identified: coercive, mimetic and normative which will be elaborated in this section (DiMaggio and Powell, 1983). Regardless of the technical value of a new innovation or product, an organisation tries to model itself after other organisations to confer status or social fitness (DiMaggio et al., 1983). Especially with uncertain solutions or technologies, decision makers may resolve to mimetic pressures from their surroundings to minimise searching costs, experimentation costs or to avoid first-mover risks (Cyert and March, 1963; Levitt and March, 1988; Lieberman and Montgomery, 1988). The regulative institutional pillar consists of rules, sanctions and directives from various institutional organisations, such as governments, industry and trade associations in the adoption process of cloud computing. The adoption of cloud computing can be impeded by regulation at local, national and international level. It can range from data privacy and access to audit requirements and data location requirements. When corporate data are stored in the cloud, regulations such as Sarbanes-Oxley come into play. These requirements can engage cloud computing customers into staging their adoption. Coercive pressures may come mainly from dominant suppliers, customers and the parent corporation. Prior knowledge or information about cloud computing is likely to generate an initial awareness of the technology and influence the perceptions about its properties before adopting (Frambach and Schillewaert, 2002). Managers may also be triggered by the different influences, for example through connections in different networks, which can be characterised as normative influences. Others may be forming perceptions by observing previous adopters or through the persuasion by stakeholders, as mimetic and coercive influences. Prior research has led to the insights that mimetic and coercive influences have 22 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

more impact on organisational behaviour than normative influences (Jennings and Zandbergen, 1995). Regulations give managers the obligation to move to the cloud, while managers may not be ready to move all applications to the cloud provider yet. Also because of mimetic influences, IT professionals may like to move to cloud computing, but not yet with all their applications and data. Therefore the option to grow gives possibilities to managers to first deploy their standard applications and later deploy other services to the cloud. Also the possibility to stage is being enabled by giving the managers options to gradually start the transition to the cloud. H1:

Institutional influences will lead to a higher valuation of the option to grow and the option to stage, in comparison with the other options.

4.2.2 Key characteristics of Cloud Computing Risk The adoption of cloud computing may generate growth options by enabling new business applications (Saya et al., 2010). Cloud computing generally relies on large data sets which are hosted in huge data centres with a high availability. Cloud computing resources are scalable and therefore adjustable to different loads. The cost effectiveness of cloud computing is one of the factors that steers growth options (Saya et al., 2010). However, the scalability factor of cloud computing leads generally to an abandoning option (Saya et al., 2010). The option for growth is highly valued by the decision maker, as this option enables the organisation to fully exploit its cloud architecture in the future. Scalability makes it possible to enlarge the computing resources used easily, while accessibility makes sure that users can access the applications from anywhere on any device, which makes growth possible. Not only desk workers are able to work with the applications, but people with handhelds will also benefit from online accessibility. Cost effectiveness ensures that computing resources are paid on a pay-per-use basis, which can be enhanced later when this growth has taken place. The option to abandon is another highly valued factor, as it gives managers the freedom to stop at any given time. Scalability is the main driver for this option, as altering scale of resources is easily possible and the subscription can be stopped at any time. H2:

Key characteristics of cloud computing will lead to a higher valuation of the option to grow and the option to abandon, in comparison with the other options.

4.2.3 Perceived Lack of Security Risk One of the main issues related to cloud computing is the perceived lack of security. Because all data is stored at the cloud provider, there is a possibility of data loss, due for instance to 23 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

the incompetence of the IT department of this cloud provider, or because misuse or theft of data has taken place. Due to failures at the cloud provider, violations of the confidentiality of the concerned data are a threat. Legal issues are also identified as important in most industries. The option to stage enables the decision maker to gradually outsource the data and checkup at every step. Because of the perceived lack of security, the IT department is able to first move less critical data to the cloud and after that move other applications, services and data to the cloud. The option to scale down will also reduce the risk of having your data off-premise. The perceived lack of security enables this option, as it gives decision makers the option to slowly make use of this new technology. H3:

The perceived lack of security will lead to a higher valuation of the option to stage and the option to scale down, in comparison with the other options.

4.2.4 Perceived Improved CO2 emissions Risk The awareness in environmental factors such as CO2 emissions and clean renewable energy has grown through governmental norms and regulations. Organisations are striving for lowering these emissions of their own IT equipment or looking for a reason to align this with their social responsibility strategy to align with these regulations and lower impact on the environment. By switching from on-premise mainframes or servers to the massive data centres of cloud providers, CO2 emissions improvements can be made, which is likely for managers to perceive as an advantage. The most attractive option is to stage the project, which implies to move certain applications step by step to the cloud. IT professionals cannot throw away their own IT equipment right away and in order to make these improvements on carbon emissions, they are making a move to the cloud application per application which leads to removal of one server at a time. The second most attractive option is to use growth to use more applications in the cloud at any time possible. IT professionals might see the opportunity to host additional users or services not on their own equipment, but start these in the cloud. This creates growth options to later even move more to the cloud, when for instance IT equipment has become obsolete. H4:

The perceived improved CO2 emissions will lead to a higher valuation of the option to stage and the option to grow, in comparison with the other options.


4.2.5 Power Savings Risk By moving to the cloud, managers can save on their power bill by turning off their onpremise mainframes or servers because they are no longer in use. Also with expanding business, it is possible to add cloud solutions to offer a hybrid solution. Because of lowering power consumption when adopting cloud computing, it is likely for decision makers to perceive this as an advantage. The first option is to stage the project to move certain applications to the cloud. The decision makers are thinking about what to do with their current equipment and are staging their entrance into the cloud provision market. The second option is to use growth to be able to make use of more applications in the cloud. The IT department can choose to host additional services to the current services in the cloud or add new users in a cloud solution, because of legacy equipment. H5:

Power savings will lead to a higher valuation of the option to stage and option to grow, in comparison with the other options.

4.2.6 More Sustainable Method of Power Generated Risk Environmental sustainable energy is promoted by many energy suppliers and governments. Managers perceive this method of power production in line with their corporate social responsibility strategy and are likely to engage in this movement. The move to a more sustainable method of power generated is possible through the closure of renewable energy contracts with energy providers or by own production of electricity. The IT department of an organisation is able for the same reasons as mentioned above to gradually move to the cloud and make use of the staging option. Cloud providers are highly dependent of their own renewable energy and energy reuse is an important aspect of their strategy to ensure the least power consumption. Therefore, the option to grow is valued highly by managers. H6:

A more sustainable method of power generated will lead to a higher valuation of the option to stage and option to grow, in comparison with the other options.


Chapter 5: Methodology At all times it is better to have a method. (Mark Caine)

5.1 Research design In order to answer the research questions and the underlying hypotheses, a field experiment is conducted with the use of Real Options Analysis. This kind of experiment is most suitable for modelling real life situations. In case of ROA, hypothetical situations are presented to the respondents, where they do not have to reveal their own situation. Each respondent is presented with some scenario descriptions including the different variables and the different options. The questionnaire was sent to IT professionals, which were at one hand IT managers from different Dutch Microsoft partner companies and customers of Microsoft products. Other Dutch IT professionals from different companies and industries were taken to ensure validity of the sample group. The total number of IT professionals in the Netherlands can be measured at 250.000 people, where around 80.000 can be counted as IT professionals on managerial levels (ICT Office, 2012). During this experiment four control variables were used, similar to Hilhorst et al. (2008) for rival explanations for the influence of the perceived risk. Firstly, the number of cloud projects that the respondent has assessed earlier, secondly the respondent’s prior cloud experience (measured in years), thirdly the respondent’s experience in this sector and lastly, the risk propensity. This last variable is included to measure the respondent’s tendency to risks, which is measured using a five-item scale (Keil et al., 2000). In developing the survey instrument, multiple item constructs were used. The complete questionnaire is included in Appendix A. For every question, a seven point Likert scale (from strongly disagree to strongly agree) was used to measure the perceived value. The survey was produced in an online survey tool, which was then distributed through an email notification to the entire population. This approach was used to ensure a random sample of IT professionals in different organisations. To achieve acceptable levels of measurement reliability and validity, a pilot study was performed along with a pre-test, where the guidelines suggested by Dillman (1991) were followed. The pre-testing was completed using faculty, graduate student and practitioner input. Various experts in the field of cloud computing were contacted for the testing of the different items in the instrument. Items were further clarified where needed and the completion time of the questionnaire was timed in order to ensure the instrument fit the potential time constraints of respondents in the final sample. 26 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

5.2 Data Description The data which is gathered from the experiment is analysed by using SPSS. To perform an analysis of the different valuations of options and which option is more preferable, KruskalWallis H-Test and paired-samples t-tests in the form of the Wilcoxon signed rank sum test are performed. This form is especially suitable for smaller samples, like in this study. To test the overall conceptual model, a regression analysis is performed. New variables are computed by summing the average of the options that influence cloud adoption against the option to abandon.

5.3 Limitations of Field Experiments The use of field experiments has several limitations. The first limitation is that the outcomes are not easily expandable to real life situations. This is because of the proposed real options are not initially embedded in every cloud adoption project. The second limitation deals with the assumption that the respondents are decision makers, while in real life they may not be. Therefore, generalisation of the outcomes of this experiment is risky. Concerning ROA, four limitations can be identified (Kumar, 2002). Firstly, the economic valuation of these options and their uncertainty may be poorly estimated. This has been seen in previous experiments and surveys (Busby and Pitts, 1997; Benaroch et al., 2007). Secondly, interaction effects within the different real options are possible (Trigeorgis, 1996), but assessing this is out of scope of this study. The third risk which has been identified is that it may not always be clear that the most preferred option is actually exercised by the decision maker (Taudes et al., 2000). The last problem is the uncertainty of when options are valid to exercise.

5.4 Measurement of Concepts In the development of the questionnaire, academic literature was searched for tests or scales that were already developed, used and tested. Each measuring construct was generated from existing measures (Moore and Benbasat, 1991). Construct definition played an important role in the operationalisation of the research. A distinction was made between reflective and formative constructs (Saya et al., 2010). Reflective constructs have indicators that are affected by an underlying latent, unobservable construct (Jarvis et al, 2003). Changes in the underlying constructs cause changes in the indicators and dropping an indicator should not harm the conceptual domain of this study. Formative constructs are composites of multiple indicators, where changes in the underlying construct are caused by changes in the indicators and may alter the conceptual domain (Jarvis et al., 2003). 27 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

The first part of the study focuses on the respondents’ view on cloud computing, in regard of institutional influences and some characteristics. These dimensions are measured my multiple items for larger reliability and on a 7 point Likert scale. Concerning institutional influences, a column was added in case a property was unknown to the respondent. The second part consists of some scenarios where the respondent is asked to rank the different options. The scenarios that are presented to the respondents are the following, at one hand a substitute for the current e-mail, agenda en contacts application and at the other hand a substitute for the current CRM application for customer accounts. These applications were chosen because of their contrasts. The CRM application is designed for a specific group within the organisation, while the e-mail, calendar and contacts application is used by everyone and used familiar concepts. Respondents were presented either the CRM or e-mail scenario with the information that the provider used clean energy, CO2 neutral hardware and saves energy. The third part consisted of demographics of the respondents. Their gender, age group, years of experience with cloud computing and number of cloud projects were asked. Furthermore, their experience in the IT industry was asked along with a risk propensity scale.


Chapter 6: Analysis & Discussion Although we often hear that data speak for themselves, their voices can be soft and sly. (Frederick Mostelle) In this chapter, the analysis of the gathered data will be discussed. At first, some general remarks will be made about the data and its characteristics. Then, each option type for all scenarios is being evaluated along with their control variables. The data is analysed to test the hypotheses and the total conceptual model is measured.

6.1 Demographics and General Characteristics In this section, general demographics concerning the respondents are given. Of all respondents to the survey (N = 155), there were 134 males (~ 86 %) and 21 females (~ 14 %), which seems representative for the IT industry in the Netherlands. The most represented group of IT professionals are between 36 and 45 years old (~ 48 %), followed by the age group of more than 46 years old (~ 28 %). The least common age group are the IT professionals under 25 (~ 4 %).

Most IT professionals had one or more years of experience with cloud projects, where most of them had one to three years of experience with the cloud (~ 50 %) and done one to three different cloud projects (~ 55%). The sample group was quite experienced, where more than 50 percent (~ 57 %) had ten or more years of working experience in the IT industry. The different characteristics are captured in the graphs depicted below. These characteristics hold for the whole sample of 155 respondents.


Gender

Age 7

21

< 25

15

43

26-30

15

Female

31-35

Male

36-45

134

> 46

75

Years of Experience with Cloud Computing 11 10

# Cloud computing implementations

41

0-1

23

3-5

77

Never 1-3

1-3

57

6

85

3-5 >5

>5

Years working in IT industry 7 5 21

10

Figure 4 | Demographic data on sample (N = 155)


The experiment is conducted with two different scenarios in order to make a distinction in risks between respondents. The overall risk propensity of the respondents has a mean of 3,44 on a 5-item scale with a standard deviation of 0,806. Interesting is to know if experience in IT functions influences risk propensity of the different IT professionals. As shown in the figure below, risk propensity shows a high willingness for risks when experience is gained, while inexperienced IT professionals only show average willingness. This could be explained by the fact that these people have not yet been in a position with real responsibilities to make this kind of decisions yet. This has been evaluated in multiple studies, where experience of decision makers and the evidence of overcoming their prior obstacles leads to undertake risks that less experienced decision makers would not take (March & Shapira, 1987; Sitkin & Pablo, 1992).

Willingness in Risk Taking High willingness Fairly high willingness Average willingness Fairly low willingness Low willingness

0-1

1-3

3-5

5-10

>10

Years of IT Experience Figure 5 | Willingness ranked on experience

6.2 Scales Reliability Some of the scales measuring institutional influences, accessibility, scalability, cost effectiveness, lack of security and the different environmental factors have been tested and validated in other studies. The reliability of multiple item scales refers to establishing whether the items within a scale measure a single concept. A Cronbach’s Alpha test can be carried out on all scales in the current study to determine their internal reliability. In general, it is accepted for Cronbach’s Alphas to have a value higher than 0,7 in order for the scale to be considered reliable in measuring the concept. However, occasionally it may be accepted for values that are higher than 0,5, especially for exploratory 31 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

research (Lewis et al., 2005). Deleting an item from any of the concepts could only provide a very minor increase in Cronbach’s Alpha; therefore no items have been deleted. They provide valuable data for an analysis of the concepts for this study and no Cronbach’s Alpha is below 0,7. Construct

# Items

Cronbach’s Alpha

Institutional influences

6

0,817

Accessibility

3

0,758

Scalability

3

0,924

Cost effectiveness

3

0,793

Lack of security

3

0,895

Environmental factors

3

0,891

Table 2 | Cronbach's Alpha for Constructs

6.3 Means, Variances & Medians The following sections discuss the means and variances for all variables which were measured in the experiment, such as the institutional influences, key attributes and finally each option of the different scenarios. The means and variances show the most preferred real option for each scenario in ranked order. Furthermore the medians are calculated to show if the data is distributed along normal distribution.

6.3.1 Means and Variances for Institutional Influences The first section of the questionnaire consists of six items used to measure the respondents’ vision on adoption of cloud computing in the institutional atmosphere. Respondents specified the extent to which the suggested institutions were using cloud computing. Responses were measured on a 7-point Likert scale, where 1 denotes “Is not used at all” and 7 “Is used solely” and a eighth factor where respondents could fill in that they did not know. The “do not know” answers were filtered out of the responses.


2 Suppliers

3

4

Customers

Strategic Partners

5 Competition

6 Industry

Government

Figure 6 | Institutional influences ranking (1 = Not used at all; 7 = Used solely); N = 138

Concerning the adoption by suppliers, the mean was 4,49 (standard deviation 1,326) where 33 % scored 3 and 36 % scored 6. Sixteen respondents were unaware of cloud adoption by their suppliers. For the cloud adoption by customers, respondents indicated a mean of 4,07 (standard deviation 1,264) where 35 % scored a 4. Strategic partners were considered more progressive towards cloud adoption (mean 4,89; standard deviation 1,149), as 42 % scored a 6, while competition was seen neutrally (43 % scored a 4; mean 4,04; standard deviation 0,924). Also industry was highly valued concerning the adoption of cloud (40 % scored a 5; mean 4,50; standard deviation 1,01), while institutions like governmental organisations seemed to lack in cloud adoption (mean 3,23; standard deviation 0,902).

6.3.2 Means and Variances for Key attributes of Cloud Computing The characteristics of cloud computing were measured through different questions which were used in earlier studies. Environmental questions were added to include this in the current study. Cloud computing was assessed quite positive by respondents with all means higher than the score of 4.


Mean

Std. Deviation

I have access to my data everywhere (independent of location) I have access to my data with any system (independent of system) Cloud computing is not sensitive for errors. Cloud computing is able to adapt to needs due to scalable resources. Cloud computing is able to work on different loads. Cloud computing is able to adapt to easily scale resources. Cloud computing applications are well priced. Cloud computing offers value for money. Cloud computing is a good product for the current price. Cloud computing is able to save my critical data. Cloud computing is able to perform monetary transactions. Cloud computing is able to download my company critical data and software. Cloud computing uses renewable energy. Cloud computing ensures lowering of carbon emissions. Cloud computing ensures less usage of power.

5,34

1,70

4,35

2,01

4,79

1,40

5,40

1,53

4,99

1,89

5,37

1,56

4,37

1,12

4,75

1,59

4,39

1,28

5,03

1,51

5,10

1,39

5,16

1,46

4,30

1,52

4,81

1,70

4,95

1,68

Table 3 | Means & Variances of Survey Results

6.4 Scenario Validation Respondents were presented a random selection of two different scenarios. Scenario 1 dealt with the Mail, Calendar and Contacts Application with Environmental Information stating that the cloud provider makes use of sustainable energy, uses hardware that is carbon neutral and saves energy and the CRM Application without this information. Scenario 2 was vice versa and the order of presentation was randomly selected. 34 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

The first scenario was distributed to 66 respondents where the option to stage and the option to grow are most preferred. The means and variances show the most preferred real option for each scenario and how much the other options deviate from the mean. Also the mean differences are presented, using Wilcoxon ranking method also stating the significance of this ranking. Mail, Contacts

CRM Application

Mean

and Calendar

Without

Difference

Application With

Environmental

Environmental

Information

Z

Sig.

Information

Stage

5,26 / 0,900

5,48 / 0,881

-0,220

-1,481

0,139

Growth

4,62 / 1,423

4,36 / 1,495

0,260

-1,036

0,300

3,53 / 1,026

0,380

-2,780

0,005

-1,413

0,158

Scale down Switch

3,91 / 0,818

3,86 / 1,122

3,61 / 0,909

0,250

Defer

2,20 / 0,789

2,44 / 1,360

-0,240

-0,952

0,341

Abandon

1,15 / 0,588

1,58 / 1,164

-0,430

-2,937

0,003

use

Table 4 | Means and Variances for Scenario 1 (N = 66)

The second scenario was presented to 89 respondents, where the option to stage and the option to grow were the most valued options. The means and variances show the most preferred real option for each scenario and how much the other options deviate from the mean. Also the mean differences are presented, using Wilcoxon ranking method also stating the significance of this ranking.


CRM Application

Mail, Contacts

Mean

With

and Calendar

Difference

Environmental

Application

Information

Without

Z

Sig.

Environmental Information

Stage

5,19 / 0,890

5,55 / 0,784

-0,360

-3,561

0,000

Growth

4,12 / 1,601

4,39 / 1,411

-0,270

-1,889

0,059

3,90 / 1,108

3,55 / 0,942

0,350

-2,771

0,006

3,83 / 1,100

4,03 / 0,935

-0,200

-2,169

0,030

Defer

2,21 / 1,071

2,08 / 0,787

0,130

-1,337

0,181

Abandon

1,74 / 1,549

1,39 / 0,912

0,350

-1,991

0,047

Scale down Switch Use

Table 5 | Means and Variances for Scenario 2 (N = 89)

6.5 Hypotheses Validation This section focuses on the validation of the different hypotheses with the use of multiple comparisons. Furthermore the variables of the hypotheses are tested on their significance. The outcomes and the acceptance or rejection of the different hypotheses are discussed. Using Kruskal-Wallis H test, the different valuations for the options have been found (Krusal and Wallis, 1952). It is the non-parametric equivalent of the parametric One Way Analysis of Variance (One-way ANOVA) where the Kruskal-Wallis H test is a variant where ranks are used. All ranks have been summed where a mean rank value is calculated. The higher this mean rank value, the higher the option has been ranked with regard to the score on that variable. The scale is linear. The significance of these findings are computed and mentioned in the tables.

6.5.1 Institutional influences The first hypothesis tests the real options valuation under conditions of institutional influences. In the hypothesis the option to grow and the option to stage are proposed to be more favourable than the others. H1:


Table 7 shows the comparison between the different mean ranks for the real options in this study. The table has been ordered along the score which respondents gave to the different 36 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

institutional influences combined. The most highly valuated options are the option to abandon and the option to switch use. The differences between the different options are however not very large, especially between the top three rated options.

Score

2

3

4

5

6

N

1

2

49

68

35

Real Options

Sig.

Normalised values

Mean Rank

Abandon

154,5

144,00

79,53

89,06

77,21

0,036

12651,08

Switch use

92,50

78,75

54,33

89,06

89,20

0,000

12063,72

Stage

1,00

64,50

73,98

69,20

103,70

0,001

12038,82

Scale Down

69,50

52,75

97,41

67,85

72,24

0,006

11557,45

Defer

150,50

145,00

83,83

83,75

52,77

0,000

11431,68

6,00

3,00

70,97

74,24

79,03

0,020

11155,60

Growth

Table 6 | Mean ranks between real options and Institutional Influences

The hypothesis that institutional influences lead to a higher valuation of the option to grow is not supported (Hypothesis 1a). It has been rated the lowest of all options. However the option to stage has been highly valuated and this option has been found significant. Therefore, the hypothesis that institutional influences lead to a higher valuation of the option to stage is supported (Hypothesis 1b).

6.5.2 Key characteristics of cloud computing The second hypothesis treats the valuation of real options concerning the identified key characteristics of cloud computing. The option to grow and the option to abandon are proposed to be more highly valued than the other options. H2:


Table 8 shows the comparison between the different mean ranks for the real options in this analysis, influenced by the key characteristics of cloud computing: accessibility, scalability and cost effectiveness. The option to stage and the option to grow are the most highly valued options. Valuations between the different options is very close and all results were considered significant.


Score

2

3

4

5

6

N

14

7

47

77

10

Real Options

Sig.

Normalised values

Mean Rank

Stage

69,71

43,79

56,54

98,02

60,25

0,000

10970,74

Growth

78,25

33,71

78,61

74,11

135,75

0,000

10870,99

Switch use

42,29

43,29

101,89

76,02

55,25

0,000

10766,25

Scale Down

108,07

21,50

64,38

87,51

66,25

0,000

10649,45

Abandon

79,00

128,21

91,98

68,62

48,00

0,000

10299,07

Defer

98,89

136,00

83,04

66,18

75,50

0,000

10249,15

Table 7 | Mean ranks between real options and the key characteristics of cloud computing

Therefore, the hypothesis that the key characteristics of cloud computing lead to a higher valuation of the option to stage (Hypothesis 2a), is supported. However, the option to abandon is the penultimate valuated option, and therefore Hypothesis 2b is not supported.

6.5.3 Perceived Lack of Security The third hypothesis tests the valuation of the different real options in the light of the perceived lack of security. The options to stage and the options to scale down are proposed to be more highly valued than the others. H3:


Table 9 shows the ranking of the different means along the axis of the perceived lack of security. The options to scale down and the option to switch use are the most highly valuated options.


Score

1

2

3

4

5

6

N

78

21

26

1

10

2

Real Options

Sig.

Normalised values

Mean Rank

Scale down

59,12

82,57

73,85

60,00

120,3

31,50

0,000

4018,92

Switch use

61,51

61,81

105,6

37,00

62,35

44,75

0,000

3779,22

Abandon

61,91

77,55

68,17

127,00

95,90

137,50

0,001

3741,27

Defer

73,22

45,90

77,96

132,50

48,30

136,50

0,000

3332,97

Stage

74,89

103,98

47,67

3,50

31,85

2,25

0,000

3106,67

Growth

86,99

51,60

51,10

19,00

36,60

4,50

0,000

2968,84

Table 8 | Mean ranks between real options and the perceived lack of security

The option to stage is not highly valuated and therefore Hypothesis 3a is not supported. The perceived lack of security has an influence on the valuation of the option to scale down and led to a higher valuation of that option. Therefore, Hypothesis 3b is supported.

6.5.4 Improved CO2 Emissions The fourth hypothesis deals with the perceived improved CO2 emissions and real options. The option to stage and the option to grow are proposed to be more highly valued than the other options. H4:


In Table 10, the ranking is shown of the different real options along the axis of improved CO2 emissions. The option to grow and the option to scale down are the most highly valuated options.


Score

1

2

3

4

5

6

7

N

2

10

22

51

9

19

42

69,9

90,44

55,42

Real

Sig.

Normalised values

Mean Rank

Options Growth

4,50

39,90

70,55

111,86 0,000

Scale

8999,63

0,002

down

36,25

73,50

89,66 74,78 50,50 113,03

68,90

Stage

2,50

62,60

82,95 91,44 78,22

67,74

70,94

0,020

8212,01

Abandon

154,5

80,75

82,82 74,51 78,61

77,79

75,38

0,194

8165,34

Defer

152,25 111,80 76,02 78,65 82,83

56,87

75,19

0,003

7988,40

Switch use

8304,24

0,000 55,25

140,00 70,75 83,31 88,33

73,66

61,42

7857,83

Table 9 | Mean ranks between real options and Improved CO2 Emissions

Hypothesis 4a is not supported as the option to stage is not highly valued as a real option concerning CO2 emissions. The option to grow however is the most highly valued option and therefore Hypothesis 4b is supported.

6.5.5 Power Savings The fifth hypothesis that was set up, tests the link between the perceived power savings when moving to the cloud and the different real options. The option to stage and the option to grow are proposed to be more highly valued than the others. H5:


Table 11 shows the ranking of the different options. The most highly valuated options are the option to grow and the option to stage.


Score

1

2

3

4

5

6

7

N

7

10

21

63

15

24

15

values

121,92 76,67 0,000

7918,11

75,65

7678,29

Real

Sig.

Normalised

Mean Rank

Options Growth

37,71

39,90

73,00 78,25

59,20

Stage

50,50

62,60

86,71 68,16 121,67

90,33 0,000

Scale

0,003

down

22,43

73,50

90,62 80,46 118,03

65,44

59,00

7440,37

Abandon

128,21

80,75

80,14 73,21

72,56

85,37 0,022

7251,49

71,17

Switch

0,000

use

53,93

140,00 71,86 84,87

53,50

71,44

62,67

7138,88

Defer

135,5

111,8

54,83

53,38

96,57 0,000

7068,81

72,76 78,47

Table 10 | Mean ranks between real options and Power Savings

Hypothesis 5a is supported, because the option to stage is one of the highest valued options. Also Hypothesis 5b is supported, as the option to grow is the most highly valued option.

6.5.6 Method of Power Generated The sixth hypothesis scores the different options and their relationship with a more sustainable method of power generated. The option to stage and the option to grow are proposed to be more highly valued than the other options. H6:


In Table 12, the ranking is shown of the different real options along the axis of a more sustainable method of power generated. The option to stage and the option to grow are the most highly valuated options.


Score

1

2

3

4

5

6

7

N

2

10

14

55

3

28

43

83,77

43,83

46,09

Real

Sig.

Normalised values

Mean Rank

Options Growth

4,50

39,9

65,04

110,28 0,000

Scale

9080,42

0,000

down

36,25

73,50

48,50

69,67

67,33

133,50

65,85

Abandon

154,5

80,75

85,43

67,45 111,33

80,63

80,84

0,029

8557,10

Stage

2,50

62,60

108,93 81,98

86,14

65,33

0,000

8431,93

68,00

Switch

8880,81

0,000

use

55,25

140,00 114,07 72,28

Defer

152,25

111,8

62,61

80,00

58,82

72,56

91,99 122,17

51,79

67,79

8075,05 0,000

8049,43

Table 11 | Mean ranks between real options and Sustainable Method of Power Generated

Hypothesis 6a is not supported, as the option to stage is not one of the most highly valued options. Hypothesis 6b is however supported as the option to grow is the most highly valued option concerning a greener method of power generated.

6.6 Conceptual Model Validation In order to test the relationships within the conceptual model and thus the main research question, a regression analysis is conducted. This analysis measures the influence of the independent variables on the dependent variable, in this case real options on the investment in cloud computing influenced by institutional influences, key characteristics, perceived lack of security and the different environmental factors. The dependent variable was not directly asked in the survey, but had to be constructed from the different options. The adoption decision is defined as the adoption of cloud computing by the respondents if they prefer the option to grow, the option to stage, the option to scale down, the option to switch use or the option to defer. The option to abandon is left out, because this is the option to not invest in cloud computing. The regression analysis shows the Betas for each variable which indicates how much the decision is influenced by this particular variable. These calculated Betas indicate a causal relation between the different risks in the conceptual model as the independent variables and the investment in cloud projects as the dependent variable.


6.6.1 Scenarios without Environmental Information In the regression analysis on the investments where no environmental information was given, the following results were found. There seemed to be a difference between the two different applications. In the CRM application, the most influential variables were gender, the years of experience in an IT function and the power generated variable, followed by accessibility. Accessibility was also considered a highly significant value (p = 0,014), as well as Years in IT Function and Gender. The most significant environmental value was the Method of Power Generated variable, which was significant at the p < 0,10 level. The e-mail, calendar and contacts application seemed more positively influenced by years of cloud experience and power savings and negatively on CO2 improvements. The Power Savings variable was also highly significant (p = 0,000) and had a high Beta outcome (1,146). The overall models had an adjusted R Square, or descriptive value of 65,7 % (CRM) and 53,8 % (Mail). In Figure 7 all Betas are shown, where the different variables are in order of highest average value. In Table 12 all variables are ranked in the same order and their T-value and significance is presented.


0,412

0,570

-1,030

-0,588

-0,146

0,093 -0,446

-0,984

-0,452 -0,261 -0,357

-0,425 -0,168 -0,297

-0,349 -0,314 -0,332

-0,386 -0,151 -0,269

-0,095 -0,602

-0,348

0,111

0,427 0,225

0,023

0,462 0,037 0,250

0,396 0,199 0,298

0,539 -0,068

0,315 0,296 0,306

1,220 0,649 0,078

BETAS IN REGRESSION ANALYSIS

1,146

INSTITUTIONAL INFLUENCES

Average

CLOUD PROJECTS EXPERIENCE

AGE

SCALABILITY

CO2 IMPROVEMENTS

PERCEIVED LACK OF SECURITY

Mail Without EI

METHOD OF POWER GENERATED

GENDER

COST EFFECTIVENESS

YEARS IN IT FUNCTION

ACCESSIBILITY

RISK PROPENSITY

POWER SAVINGS

YEARS OF CLOUD EXPERIENCE

CRM Without EI

Figure 7 | Regression results of Scenarios without Environmental Information (N = 155)

CRM Without EI

Mail Without EI

Beta

T-value

Sig.

Beta

T-value

Sig.

Years of Cloud Experience

0,078

0,383

0,704

1,220

6,444

0,000

Power Savings

-0,068

-0,328

0,744

1,146

5,263

0,000

Risk Propensity

0,315

2,105

0,040

0,296

1,699

0,094

Accessibility

0,396

2,538

0,014

0,199

1,706

0,092

Years in IT Function

0,462

2,365

0,022

0,037

0,223

0,824

Cost Effectiveness

0,023

0,143

0,886

0,427

3,426

0,001

Gender

0,570

2,835

0,007

-0,348

-3,377

0,001

Method of Power Generated

0,412

1,921

0,060

-0,602

-2,964

0,004

Perceived Lack of Security

-0,386

-2,675

0,010

-0,151

-1,066

0,290

Scalability

-0,425

-2,254

0,028

-0,168

-1,382

0,171

CO2 Improvements

-0,349

-1,497

0,140

-0,314

-1,382

0,171

Age

-0,452

-1,811

0,076

-0,261

-1,27

0,208

Institutional Influences

-0,984

-5,549

0,000

0,093

0,638

0,526

Cloud Projects Experience

-0,146

-0,710

0,481

-1,030

-5,454

0,000

Table 12 | Regression Results in Scenarios without Environmental Information (N = 155)


6.6.2 Scenarios with Environmental Information Concerning scenarios where environmental information about the certain applications were given, the following results were found. In the CRM substitute, the most important variables influencing the cloud investment decision seemed to be the perceived power savings, risk propensity, accessibility and the years in an IT function. These values were also significant (p < 0,10). Scalability and the Power Generated variable seemed to negatively influence the adoption of cloud computing significantly (p = 0,001). With regard to the e-mail, calendar and contacts application, the most important variables seemed to be risk propensity, accessibility, cost effectiveness and the perceived lack of security. Overall these variables were less significant as their CRM equivalent, but risk propensity is also highly significant and seemed to have a great influence in the adoption decision. The overall adjusted R Squares of both models were 40,3 % (CRM) and 41,8 % (Mail). The Betas are shown in Figure 8, where the different variables are in order of highest average value. In Table 13 all variables are ranked in the same order and their T-value and significance is presented.


-0,777

-0,474 -0,626

-0,173 -0,316

-0,458

-0,451

-0,177

-0,008 -0,041 -0,025 -0,346

-0,09 -0,281 -0,186

0,097

0,388

0,021

0,063 0,025 0,044

-0,139

0,067

0,273

0,16 0,052 0,106

0,274 0,176

0,078

0,368 0,179

0,200 -0,181

-0,011

0,465 0,446 0,456

0,914 0,627 0,339

BETAS IN REGRESSION ANALYSIS

0,581

Average


SCALABILITY

Mail With EI

CLOUD PROJECTS EXPERIENCE

AGE

CO2 IMPROVEMENTS


INSTITUTIONAL INFLUENCES


GENDER

COST EFFECTIVENESS


POWER SAVINGS

ACCESSIBILITY

RISK PROPENSITY

CRM With EI

Figure 8 | Regression results of Scenarios with Environmental Information (N = 155)

CRM With EI

Mail With EI

Beta

T-value

Sig.

Beta

T-value

Sig.

Risk Propensity

0,339

1,710

0,091

0,914

5,940

0,000

Accessibility

0,465

3,513

0,001

0,446

2,781

0,008

Power Savings

0,581

2,352

0,021

-0,181

-0,841

0,404

Years of Cloud Experience

0,368

1,710

0,091

-0,011

-0,052

0,958

Cost Effectiveness

0,078

0,550

0,584

0,274

1,669

0,101

Gender

0,160

1,371

0,175

0,052

0,252

0,802

Years in IT Function

0,273

1,461

0,148

-0,139

-0,691

0,493

Institutional Influences

0,063

0,380

0,705

0,025

0,110

0,913

Perceived Lack of Security

-0,346

-2,149

0,035

0,388

2,612

0,012

CO2 Improvements

-0,008

-0,033

0,974

-0,041

-0,170

0,866

Age

-0,451

-1,931

0,057

0,097

0,378

0,707

Cloud Projects Experience

-0,090

-0,420

0,676

-0,281

-1,332

0,189

Scalability

-0,458

-3,311

0,001

-0,173

-0,890

0,378

Method of Power Generated

-0,777

-3,369

0,001

-0,474

-2,149

0,036

Table 13 | Regression Results in Scenarios with Environmental Information (N = 155)


6.6.3 CRM Application In order to compare the different results between the scenarios with and without environmental information, these have been plotted in Figure 9. Few variables seem to be consistent with each other. Power savings has a positive influence on the adoption of the CRM application and also highly significant (p = 0,021). Accessibility as one of the key characteristics of cloud computing is also viewed as a positive factor in the adoption of the CRM application, where these results are in both cases highly significant (p < 0,05). Risk Propensity is also a constant factor in both scenarios. In the CRM application there can be found a positive relation when environmental information is given with regard to the recognition of that variable and its influence. Power savings and CO2 improvements lead to a higher valuation, while the method of power generated has a very high negative impact on the use of this CRM application in the cloud. 0,8

Beta of Regression Analysis

0,6 0,4 0,2 0 -0,2 -0,4 -0,6 -0,8 -1 Without Environmental Information

With Environmental Information

Average

Figure 9 | Regression analysis on CRM Application With and Without Environmental Information (N = 155)

6.6.4 E-mail, Calendar and Contacts Application Also in the adoption of the E-mail, Calendar and Contacts application, risk propensity has a significant positive role in the adoption, as well as accessibility. The environmental factor power savings plays a highly significant and very positive role in the adoption of the e-mail application where no environmental information is given. Cost effectiveness in the adoption of this application is also seen as an important variable. For a visual presentation of the differences between the two scenarios, the Betas have been plotted in Figure 10. In contrast to the CRM application, where environmental information is given, power savings have a 47 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

lower impact on the use of this application than in the situation with this information. CO 2 improvements, however have a positive effect. 1,5

Beta of Regression Analysis

1

0,5

0

-0,5

-1

-1,5 Without Environmental Information

With Environmental Information

Average

Figure 10 | Regression analysis on CRM Application With and Without Environmental Information (N = 155)

6.7 Main Findings This section discusses the main findings from the experiment conducted to give an overall description of the study and its outcomes. Table 14 shows that under different conditions, one without environmental information and one with environmental information, the chosen options are similar. IT professionals choose to stage the project or use growth options to later make full use of the cloud. A Wilcoxon test of mean rankings was performed to rank the different means. The results of the option to stage and the option to scale down were found significant.


Without

With

Mean

Environmental

Environmental

Difference

Information

Information

Stage

5,52 / 0,824

5,22 / 0,892

Growth

4,38 / 1,443

4,34 / 1,543

Switch

3,85 / 0,945

Use Scale

3,85 / 1,106

Z

Sig.

0,30

-3,628

0,000

0,04

-0,824

0,410

-0,304

0,761

-3,886

0,000

0,00

3,54 / 0,975

3,90 / 0,992

-0,36

Defer

2,23 / 1,080

2,21 / 0,958

-0,02

-0,306

0,759

Abandon

1,47 / 1,028

1,49 / 1,266

-0,02

-0,188

0,851

Down

Table 14 | Ranking of the real options with and without environmental information

In the light of the IT investment that IT professionals were asked to make, the overall influence of the different variables is analysed using regression analysis. The outcomes of this analysis can be found in Figure 11. The overall analysis can be identified as significant (p = 0,000), where the adjusted R Square is 38,3 %. 0,6 0,4

BETAS

0,2 0

-0,2 -0,4 -0,6 AGE


SCALABILITY


CLOUD PROJECTS EXPERIENCE *

INSTITUTIONAL INFLUENCES *

CO2 IMPROVEMENTS *

COST EFFECTIVENESS


GENDER

POWER SAVINGS


RISK PROPENSITY

ACCESSIBILITY

Figure 11 | Influences on Cloud Investment (N = 155); Variables marked with * are not significant at p =0,1


As can be derived from Figure 11, accessibility has the highest influence on the investment in cloud computing, which is also highly significant (See Table 14). These results are in line with previous studies (Saya et al., 2010; Erdogmus, 2008). Risk Propensity was another very significant variable found to be influential in the decision to adopt cloud computing, which is supported in another research done on the adoption of IaaS (Heinle & Strebel, 2010). The experience an IT decision maker has in his or her IT function also positive influenced the adoption of cloud computing. The influence of experience has been studied previously and results in this study are according to prior research (March & Shapira, 1987; Sitkin & Pablo, 1992). Power savings is the first environmental variable that has a significant positive influence on the adoption of cloud computing, which is also found in previous studies (Berl et al., 2009). Gender influenced the adoption of cloud computing in a way that males were more progressive toward the concept than females. The improvements in carbon emissions seem to have a low impact and also did not significantly influence the model, as well as institutional influences and number of cloud projects an IT professional had assessed. The perceived lack of security has a negative effect on the adoption of cloud computing, which is according to prior research (Saya et al., 2010; Foster et al., 2008). Scalability had a quite negative influence on the decision to move to the cloud which was highly significant and in line with prior study results (Saya et al., 2010). The method of Power Generated also impeded the adoption of cloud computing where managers did not perceive these as beneficial. Lastly, age had the most negative influence of all variables, where the older generations perceived the move to cloud computing less attractive than younger IT professionals.

Accessibility Risk Propensity Years in IT Function Power Savings Gender Years of Cloud Experience Cost Effectiveness CO2 Improvements Institutional Influences Cloud Projects Experience Perceived Lack of Security Scalability Method of Power Generated Age

Beta 0,525 0,392 0,312 0,277 0,277 0,225 0,188 0,026 -0,031 -0,133 -0,279 -0,447 -0,469 -0,489

T-value 5,055 2,984 2,596 2,073 2,467 1,716 1,767 0,176 -0,279 -0,948 -2,894 -4,512 -3,302 -3,146

Sig. 0,000 0,003 0,010 0,040 0,015 0,088 0,079 0,861 0,781 0,345 0,004 0,000 0,001 0,002

Table 15 | Results from Regression Analysis on Adoption of Cloud Computing (N = 155)


6.8 Discussion In this section the acceptance or rejection of the different hypotheses will be discussed based from the available data. H1:


Hypothesis 1a is supported because the option to grow was ranked third. Respondents valuated the option to switch use more highly, the normalised values however do not show a giant gap between the two options and the option to stage received a higher mean rank where respondents scored the option to stage most highly and was only ranked with the value 2 once. The option to stage has been valuated higher than other options and therefore Hypothesis 1b is supported. These options have been characterised in prior research (Cyert and March, 1963; Levitt and March; 1988; Lieberman and Montgomery, 1988). Therefore the results in this study are according to other studies and can therefore be generalised. H2:


Hypothesis 2a was supported, because the option to grow was the second highest valuated option, with little difference with the most highly valuated option. Accessibility and cost effectiveness led to the higher valuation of this option in comparison with the others, which is line with prior research (Saya et al., 2010). The second hypothesis (2b) however was not supported. The option to abandon was the least valuated option. The key characteristics cost effectiveness and accessibility were more influential in the decision the IT professionals were assessing than the scalability of cloud computing. It is assumed that IT professionals are more aware of these variables as these have come to mind in previous adoptions. Therefore the option to abandon received less attention and was valued lower. H3:


Because of the low valuation of the option to stage, Hypothesis 3a is not supported. IT professionals did not want to grasp the opportunities of growth options concerning the perceived lack of security. The option to scale down and switch use were valued higher. This might be explained by the fact that IT professionals perceive the lack of security as a higher risk than was expected and try other strategies to pursue the adoption of cloud computing. Hypothesis 3b however is supported, as it receives the highest valuation of all options. Scaling down was perceived an attractive option where IT professionals were able to alter the 51 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

scale of their cloud computing applications easily, which is in line with prior studies (Erdogmus, 2008). H4:


Hypothesis 4a is not supported, because the option to stage is not valued very highly and the difference between the different ranks is significant and quite large. IT professionals did not perceive the improved carbon emissions to be beneficial to stage the project. This could be explained by the fact that IT professionals are more aware of growth options to grasp future opportunities. Also staging might not have the effect of a beneficial factor to IT professionals. Hypothesis 4b is supported, because the option to grow is the most highly valuated option. Carbon emissions led to a high valuation of this option where the decision maker was able to use growth options to enable cloud computing in the future. H5:


Both hypotheses concerning power savings were supported (5a and 5b). The option to grow and the option to stage are highly valuated concerning this variable. The results were also significant, which leads to the conclusion that these are correct. IT professionals consider that power savings will positively influence the adoption of cloud computing and use growth and staging options to do so. This could be explained by the fact that IT professionals immediately see the results of their actions when their own IT equipment is moved to the cloud. Not only in terms of power savings and protecting the environment, but also in costs.

H6:


Hypothesis 6a is not supported, as the option to stage is not highly valued. The option to scale down and the option to abandon were considered more highly, although the option to abandon was less significant. IT professionals do not see a greener way of power generated to be beneficially for using staging options, which could be explained that IT professionals would like to take profit of green energy from the start and enlarge their influence on the cloud provider. Growth options however are the highest valued options, which leads to the acceptance of Hypothesis 6b. Respondents rated the option to grow highly to capitalise on the future possibilities of the cloud and a greener method of energy. An explanation might be that IT professionals are aware of the greener method of energy and through growth options can grasp on using future applications and not using traditional energy supply. 52 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

Chapter 7: Conclusions In the dime stores and bus stations, people talk of situations, read books, repeat quotations, draw conclusions on the wall. (Bob Dylan) This final thesis chapter answers the research questions, which were stated in section 2.2. The answers are based on the data analysis from the field experiment by means of an online survey. In this chapter, we will first look at the effects of environmental sustainable factors and the implications on the research questions. This chapter concludes with the limitations of the research, academic relevance, practical relevance and ideas for future research on this topic.

7.1 The organisational move to cloud computing The study performed in this thesis was triggered by the problem statement which was introduced in an earlier section. The problem statement about how an organisational move to cloud computing was influenced by environmental factors, resulted in two different research questions. How do environmental factors influence the decision to move to the cloud and which environmental factors encourage this investment in cloud computing. Institutional influences were considered important in the move to adopting cloud computing. These concern the influences IT professionals perceive from their environment, being suppliers, customers, industry or governmental institutions and in forms of mimetic, coercive or normative influences (Scott, 1995; 2004). In prior research these were found to affect organisations in adopting Electronic Data Interchange and Radio Frequency Identification (Teo et al., 2003; Goswami et al., 2008). The recognition of institutional influences led to IT decision makers preferring the option to stage and the option to grow, where these IT professionals could gradually start with using cloud computing and later fully exploiting the power of the cloud. In this study however these influences did not seem significant nor of great importance in the overall adoption of cloud computing. The key characteristics of cloud computing, which were captured in scalability, accessibility and cost effectiveness were introduced as being influential on a cloud computing investment. The characteristics together were proposed to lead to higher valuation of the option to grow and the option to abandon. The option to grow was highly valued by respondents, but the option to abandon was not recognised. Overall accessibility and cost effectiveness were more influential than scalability. Scalability is perceived in the overall model to be negatively 53 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

influencing the adoption of cloud computing, while accessibility is the strongest factor in this adoption. Cost effectiveness is also viewed as a positive factor in adopting the cloud. The perceived lack of security was a risk which was identified in prior research (Foster et al., 2008). The option to scale down is highly valued, where IT professionals are easily able to alter the scale of their cloud projects. Overall, the perceived lack of security has a negative influence on the adoption of cloud computing, which is in line with prior research. In this study a few environmental variables were proposed, which were improved carbon emissions, power savings and a more sustainable method of power generated. Concerning improved CO2 emissions, it was hypothesised that this variable will lead to a higher valuation of the option to grow. This variable was unfortunately not significant in the results of the regression analysis. Power savings were considered more influential in the overall adoption of cloud computing where the option to stage and the option to grow were most highly valued by IT professionals to adopt cloud computing. A more sustainable method of power generated led to a negative outcome concerning the adoption of cloud computing, although growth options were identified.

7.2 Overall conclusion The most highly valued options overall were the option to stage and the option to grow the cloud project. These two options were the most popular actions for respondents to take together with the option to switch use. The other real options to mitigate the different risks; the option to abandon, defer or scale down were perceived less interesting by the respondents. To answer the research questions, all variables seem to have a different impact on the decision to move to the cloud. The variables which were perceived most influential were the key characteristic accessibility, the risk the decision maker took and his or her experience in IT. The first environmental factor of substantial positive impact on the cloud project was power savings. Another environmental factor with a slight positive impact on the decision to move to the cloud was the improved carbon emissions. The more sustainable method of power generated had a very high negative impact on the decision. Overall, environmental information seems to have a positive impact on the adoption of cloud computing. Especially power savings are the enabling factor in the environmental side of cloud computing. All variables affect the investment in the cloud project through managerial flexibility and real options. Although the risk propensity of the respondents was rated slightly above average, they still are not willing to take the risk to immediately start the investment.


The option to stage is a move of decision makers to mitigate the risks and gradually make a move to the cloud. The same can be said of the option to grow, although in this case growth options give the opportunity for the decision maker to increase the scale of the cloud project.

7.3 Limitations The research questions and their answers only discuss how the risks affect the move to the cloud and what variables are influencing this move. However why the risks affect the investment is not taken into account. Respondents were only presented a few questions about cloud computing and could not reflect their own opinion. Also the environmental variables could be elaborated more and by trying different angles. That the research only took two different applications into account could also be a limitation of the research. Furthermore, there could be more general risks included into the research which could have an effect on the investment in cloud projects. The survey could have some external influences which affected the outcome, where respondents might be influenced. Interaction effects between the different options have not been taken into account in this study. Other problems may exist in the distribution of respondents, as they might not be from every industry or in every level of the organisation. Generalisation to real life situations may also be a problem. Hilhorst (2009) identified that managers have to deal with complex situations in IT projects where several types of risks exist and the different real options are not easily identifiable or embedded. Other literature also proves the difficulty of assessing real options and risks in practice (Busby and Pitts, 1997; Howell and Jägle, 1997). Also Benaroch et al. (2006) identify that assessment of real options cannot rely on perceptions as this may lead to sub-optimal decisions. The last limitation might be that managers were excluded from one important option: the option to do nothing and simply start the cloud project without assessing any risks. Although this is not included in Real Option Analysis, this option might still be realistic to some managers.

7.4 Academic Relevance Real Option Analysis has been performed before with regard to IT project valuations (Benaroch, 2001; 2002; 2006; Tiwana et al., 2006; Hilhorst, 2009). But little or no research has been done on the valuation of cloud projects and how environmental factors influence the move to the cloud. Also real options have not been used extensively in the light of these variables. This Master thesis makes three contributions to academic literature, where it is trying to fill the gap to answer the extent to which environmental variables influence the 55 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

move to the cloud by organisations and which environmental variables are responsible for this decision. The research is much in line with the dissertation of Hilhorst (2009). She studied the valuation of managerial flexibility in IT projects with the use of ROA. The difference between her study and this thesis is that that this thesis covers the risks in cloud computing projects. Furthermore, an experiment by means of an online survey has been performed to measure respondents’ behaviour and intentions to move to the cloud. In addition to general IT risks, cloud projects have to deal with certain risks that are related to cloud computing. Other research on the institutional influences on real options done by Saya et al. (2010) seems to find similar evidence of the different options that have been valued. However institutional influences have not been found significant in this study. As one of the first studies on environmental factors and their relation with cloud computing, this thesis makes the contribution to engage in future research to further investigate the role of environmental variables in the adoption of cloud computing and other technologies.

7.5 Practical Relevance In light of practical relevance to IT decision makers or cloud vendors, this section is trying to cover the outcomes of this study and their managerial implications. The findings of this Master thesis show that certain environmental factors influence the choice to move to the cloud. Cloud providers could use this hook to elaborate on their green character and advertise with the fact that “green” factors exist with cloud providers. Another aspect they could use is explain how the data centres are helping in CO2 improvements, power savings and show that renewable energy is being used. Cloud providers could also engage in evangelising the environmental sustainable purposes of the use of cloud to engage customers in the move to the cloud.

7.6 Future Research This study mainly focuses on what effect the different risks have on cloud projects, rather than why they have this effect. Also this research mainly elaborates on the environmental side of cloud computing. Future research could focus more on the why of these risks and investigate these reasons. Furthermore, interaction effects between the different options were out of the scope of this study. Moreover, future research could use other applications to get more data and 56 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

investigate deeper into the risks involved. Also general risks identified in IT projects could be studied in the light of cloud project investments. In regard of the survey done, in future research the scenarios could be elaborated more and a larger sample group could be gathered. With this larger group, also the pricing of the different real options could be taken into account. The price factor might be an important factor to influence the behaviour of IT decision makers when deciding to move to the cloud.


Glossary Cloud computing is the delivery of computing and storage

Cloud Computing

capacity as a service. Cloud computing is delivered over the Internet.

CRM

Customer Relationship Management software to capture customer data. On-premise software is installed and run on computers in the

On-premise

building of an organisation using the software, rather than at a remote facility, such as somewhere on the Internet. Real options analysis applies option valuation techniques to

ROA

investment decisions. A real option itself, is the right (but not the obligation) to undertake some business decision.

Environmental factors

Factors that influence the environment, such as carbon emissions, energy usage and the method of power generated. Service-oriented architecture is a group of services that communicate with each other. The process of communication

SOA

involves either simple data-passing or two or more services coordinating some activity. Intercommunication implies the need for some means of connecting two or more services to each other. Refers to the Cloud infrastructure which enables an on-demand

IaaS

provision of server time, running a choice of operating systems and software. It is the bottom layer of the architecture. The cloud platform offers an environment for developers to

PaaS

create and deploy applications. The platform is very scalable to build multiple programming models and specialised services. These services can be accessed via Web portals, which allow consumers to switch from offline computer programs to their

SaaS

online equivalents. Traditional desktop applications as word processing, presentation making and spread sheets calculations can be accessed as a service over the Internet.


List of Figures & Tables Table 1 | Real Options ........................................................................................................................................... 14 Figure 1 | Cloud Levels.......................................................................................................................................... 17 Figure 2 | Different cloud forms ........................................................................................................................ 18 Figure 3 | Conceptual Framework of the Thesis.......................................................................................... 21 Figure 4 | Demographic data on sample (N = 155) ................................................................................... 30 Figure 5 | Willingness ranked on experience ............................................................................................... 31 Table 2 | Cronbach's Alpha for Constructs .................................................................................................... 32 Figure 6 | Institutional influences ranking (1 = Not used at all; 7 = Used solely); N = 138 ....... 33 Table 3 | Means & Variances of Survey Results .......................................................................................... 34 Table 4 | Means and Variances for Scenario 1 (N = 66) ........................................................................... 35 Table 5 | Means and Variances for Scenario 2 (N = 89) ........................................................................... 36 Table 6 | Mean ranks between real options and Institutional Influences .......................................... 37 Table 7 | Mean ranks between real options and the key characteristics of cloud computing .. 38 Table 8 | Mean ranks between real options and the perceived lack of security ............................. 39 Table 9 | Mean ranks between real options and Improved CO2 Emissions ...................................... 40 Table 10 | Mean ranks between real options and Power Savings ........................................................ 41 Table 11 | Mean ranks between real options and Sustainable Method of Power Generated ... 42 Figure 7 | Regression results of Scenarios without Environmental Information (N = 155) ........ 44 Table 12 | Regression Results in Scenarios without Environmental Information (N = 155) ....... 44 Figure 8 | Regression results of Scenarios with Environmental Information (N = 155) ............... 46 Table 13 | Regression Results in Scenarios with Environmental Information (N = 155).............. 46 Figure 9 | Regression analysis on CRM Application With and Without Environmental Information (N = 155) ........................................................................................................................................... 47 Figure 10 | Regression analysis on CRM Application With and Without Environmental Information (N = 155) ........................................................................................................................................... 48 Table 14 | Ranking of the real options with and without environmental information ................. 49 Figure 11 | Influences on Cloud Investment (N = 155); Variables marked with * are not significant at p =0,1 ............................................................................................................................................... 49 Table 15 | Results from Regression Analysis on Adoption of Cloud Computing (N = 155) ...... 50


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Appendix A: Survey Welkom! Het invullen van deze vragenlijst neemt ongeveer 10 minuten in beslag. De vragenlijst heeft betrekking op cloud computing en duurzaamheid en maakt deel uit van een onderzoek opgezet door Microsoft Nederland in samenwerking met de RSM Erasmus Universiteit. Het doel van het onderzoek is het in kaart brengen van duurzame motieven bij het maken van een cloud adoptie beslissing. Ter introductie zullen de begrippen "cloud computing" en "duurzaamheid" worden besproken. Bij cloud computing draaien de computerprogramma's niet op de computer van de gebruiker, maar op (één of meerdere) machines in de cloud van een hosting partij. De gebruiker hoeft op deze manier geen eigenaar meer te zijn van de gebruikte hard- en software

en

is

niet

verantwoordelijk

voor

het

onderhoud.

De

details

van

de

informatietechnologische infrastructuur worden aan het oog onttrokken en de gebruiker beschikt over een eigen, in omvang en mogelijkheden schaalbare, virtuele infrastructuur. Cloud computing bestaat in meerdere vormen en kan worden gebruikt voor meerdere verschillende toepassingen. Duurzaamheid betekent op een maatschappelijk verantwoordelijke manier ondernemen, waarbij CO2 emissies en groene stroom worden gebruikt. Daarnaast wordt het energieverbruik geminimaliseerd. In de vragenlijst worden allereerst een aantal vragen gesteld over uw percepties met betrekking tot cloud computing. Daarna zullen een aantal scenario’s worden besproken, waar u wordt gevraagd een aantal projecten te beoordelen voordat deze gestart zijn. U kunt in de vragenlijst navigeren door de knop Next te gebruiken. Mocht u naar aanleiding van deze vragenlijst vragen hebben, kunt u mij bereiken via 020-500 1821 of [email protected]. U kunt nu aan de vragenlijst beginnen door op Next te klikken.


In dit gedeelte van het onderzoek vragen wij u om uw houding ten opzichte van de eigenschappen van cloud computing die zijn gedefinieerd in de begrippen toegankelijkheid, schaalbaarheid en kosten-effectiviteit. In hoeverre sluiten de volgende beweringen aan bij uw ervaringen.

Helemaal

(2) (3) (4) (5) (6)

Uitsluitend

Weet

niet

gebruikt

niet

gebruikt

(7)

(1) In hoeverre wordt cloud computing gebruikt door uw leveranciers? In hoeverre wordt cloud computing gebruikt door uw klanten? In hoeverre wordt cloud computing gebruikt door uw strategische partners? In hoeverre wordt cloud computing gebruikt door uw concurrenten? In hoeverre wordt cloud computing gebruikt in uw industrie? In hoeverre wordt cloud computing gebruikt door lokale en nationale overheidsinstanties? Helemaal niet

(2)

(3)

(4)

(5)

(6)

Helemaal mee

mee eens

eens

(1)

(7)

Cloud computing toepassingen zijn goed geprijsd. Cloud computing is een goed product voor de huidige prijs.


Cloud computing biedt waar voor zijn geld.

Helemaal niet

(2)

(3)

(4)

(5)

(6)

Helemaal mee

mee eens

eens

(1)

(7)

Cloud computing is gevoelig voor storingen. Ik heb met elk systeem toegang tot mijn data (onafhankelijk van systeem). Ik heb overal toegang tot mijn eigen data (onafhankelijk van locatie).

Helemaal niet

(2)

(3)

(4)

(5)

(6)

Helemaal mee

mee eens

eens

(1)

(7)

Cloud computing is in staat zich aan te passen aan behoeften door schaalbare inzet van middelen. Bij cloud computing is het mogelijk om de toewijzing van middelen te vergroten of verkleinen. Cloud computing is in staat om een wisselende belasting te verwerken.

In dit gedeelte vragen wij u de veiligheid en ecologische duurzaamheid van cloud computing te beoordelen. In hoeverre sluiten de volgende beweringen aan bij uw ervaringen. Helemaal niet mee

(2)

(3)

(4)

(5)

(6)

Helemaal mee eens (7) 68

Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

eens (1) Cloud computing is geschikt om mijn kritische data te bewaren. Cloud computing is geschikt om mijn geldtransacties uit te voeren. Cloud computing is geschikt om mijn bedrijf kritische data/software te downloaden. Helemaal niet

(2)

(3)

(4)

(5)

(6)

Helemaal mee

mee eens

eens

(1)

(7)

Cloud computing zorgt voor het verlagen van CO2 emissies. Cloud computing zorgt voor het verbruiken van minder stroom. Bij cloud computing wordt gebruik gemaakt van duurzame energie.

Hieronder worden een aantal scenario’s worden gepresenteerd, waarbij u wordt gevraagd deze te beoordelen naar aanleiding van de verschillende strategieën. Alle scenario’s bevatten dezelfde functionaliteiten als de huidige applicatie, maar worden geleverd via de cloud. Gemakshalve kunt u er vanuit gaan dat de kosten van ieder scenario gedekt zijn; de kosten blijven dan ook buiten beschouwing. U wordt gevraagd alle investeringsopties die u kunt nemen in volgorde van meest geschikt naar minst geschikt te ordenen. Scenario 1: Dit scenario betreft een project welke een vervanging moeten bieden voor de huidige e-mail, agenda en contactpersonen applicatie. Het beschrijft een applicatie die alle functionaliteiten bevat als de bestaande applicatie voor uw e-mail, agenda en contactpersonen, maar wordt geleverd over het Internet. Uw huidige gegevens (e-mail, agendapunten en contacten) worden opgeslagen bij de cloud leverancier 69 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

en dus niet in uw eigen database. Om gebruik te maken van de applicatie kunt u via uw web browser inloggen met uw persoonlijke gebruikersnaam en wachtwoord. De leverancier van de cloud diensten gebruikt groene stroom, gebruikt hardware die CO2 neutraal is en energie bespaart. Ik stel het project uit. Ik vergroot de schaal van het project, hierdoor wordt mijn belang bij de cloud leverancier groter. Ik verander het doel van het project, hierbij wordt het niet gebruikt ter vervanging, maar ter uitbreiding van de huidige applicatie. Ik verklein de schaal van het project, hierdoor zal ik niet al mijn klantgegevens bij de cloud leverancier opslaan, maar ook een gedeelte intern. Ik stop het project. Het risico hiervan is mij te hoog. Ik start het project in stappen, waarbij ik gefaseerd mijn klantgegevens bij de cloud leverancier zal opslaan.

Scenario 2: Dit scenario betreft een project welke een vervanging moeten bieden voor de huidige CRM applicatie waarin u alle klantgegevens beheert. Het beschrijft een applicatie die alle functionaliteiten bevat als de bestaande CRM applicatie voor uw klantgegevens beheer, maar wordt geleverd over het Internet. Uw klantgegevens 70 Master Thesis: Measuring the Environmental Sustainability Impact on Cloud Computing Adoption using Real Options Theory Rotterdam School of Management – MSc Business Information Management – Dirk P. Zeilstra - 294474

worden opgeslagen bij de cloud leverancier en dus niet in uw eigen database. Om gebruik te maken van de applicatie kunt u via uw web browser inloggen met uw persoonlijke gebruikersnaam en wachtwoord. Ik stel het project uit. Ik vergroot de schaal van het project, hierdoor wordt mijn belang bij de cloud leverancier groter. Ik verander het doel van het project, hierbij wordt het niet gebruikt ter vervanging, maar ter uitbreiding van de huidige applicatie. Ik verklein de schaal van het project, hierdoor zal ik niet al mijn klantgegevens bij de cloud leverancier opslaan, maar ook een gedeelte intern. Ik stop het project. Het risico hiervan is mij te hoog. Ik start het project in stappen, waarbij ik gefaseerd mijn klantgegevens bij de cloud leverancier zal opslaan.

Wat is uw geslacht: 

Man



Vrouw

Wat is uw leeftijd: 

5

Hoe vaak bent u betrokken geweest bij cloud computing implementaties? 

Nooit



1-3 keer



3-5 keer



Meer dan 5 keer

Hoe lang bent u reeds werkzaam in de IT industrie? 

1-3



3-5



5-10



>10

Hoe zou u uw bereidheid tot het nemen van risico’s bij investeringsbeslissingen classificeren op een 5-punts schaal? Heel laag (1)

(2)

Neutraal (3)

(4)

Heel hoog (5)

Hartelijk dank voor het invullen van de vragenlijst!
