Cloudrise: Exploring Cloud Computing Adoption and Governance With the TOE Framework

Cloudrise: Exploring Cloud Computing Adoption and Governance

With the TOE Framework

Hans P. Borgman

Bouchaib Bahli

Hauke Heier

IT Strategy & Transformation [email protected]

Fiona Schewski

IT Strategy & Transformation [email protected]

Abstract

Executives around the globe are investigating the benefits that cloud computing can deliver above and beyond cost savings. What is its contribution to competiveness - through improved agility, expanded business networks, and enhanced decision-making? At the same time, the organizational factors inhibiting or supporting cloud computing adoption are poorly understood. This paper builds on Tornatzky et al.’s Technology-Organization-Environment (TOE) frame-work to investigate the factors influencing cloud computing adoption. Another objective pursued is to conceptualize and understand how IT governance processes and structures moderate those factors. Our augmented TOE framework is developed in a set of hypotheses that are tested in a quantitative study of 24 global enterprises across various industries. Our results indeed indicate that the technology and organization context affect implementation decisions. A discussion and identification of next research steps round off the paper.

1. Introduction and Research Question

The purpose of this research is to investigate theoretically and empirically which organizational context factors inhibit or foster a firm’s adoption of cloud computing. We also aim to explore how IT governance processes and structures impact this adoption decision and its implementation processes.

The term “cloud” emerged from telecommun-ications in the early 1990s, when virtual private network (VPN) services were established for data communications. Those networks allowed for dynamic routing and balanced utilization - resulting in increased bandwidth efficiency in the “telecom cloud.” Today's cloud computing technology is very similar, essentially providing an environment that is dynamically allocated to meet organization/user needs.

Various definitions of cloud computing exist; most build on what the U.S. National Institute of Standards and Technology (NIST) lists as the essential characteristics of cloud computing. These are: 1)

on-demand self-service (automatic provisioning), 2) broad network access (accessible by any networked device), 3) resource pooling (multi-tenancy model with location independence), 4) rapid elasticity (automated load scaling), and 5) measured service (monitored, controlled, reported, and billed for) [1]. Resources are shared at various levels, each comprising a separate cloud offering [2, 3]: cloud infrastructure services, cloud-based development environments, and software-as-a-service (SaaS). All three cloud offerings are explored in this study.

We consequently employ Armbrust et al.’s [4] cloud computing definition which includes “both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services.” To avoid confusion with organizations’ (internal) data center virtualization initiatives we have only selected public clouds from the four NIST deployment modes [5]: “infrastructure […] made available to the general public or a large industry group and […] owned by an organization selling cloud services.”

An increasing body of publications indicates that those public clouds are high on corporate and IT agendas [6]. Farrell estimates the 2010 cloud computing enterprise utilization rate at 9%, while an additional 8% of organizations have plans for adoption within the next twelve months; 36% of firms have those solutions under evaluation. Main expected benefits are [7-9]:

- Fostering cost reduction, i.e. reducing IT spend through significant scale economics that public clouds can generate over private data centers and even over private clouds;

- Accommodating fluctuating computing needs, i.e. providing short-term and “infinitely” scalable computing resources to address demand peaks and troughs;

- Enabling the agile addressing of new markets and offerings, i.e. minimizing up-front commitments of cloud users to build transactional capabilities and creating new delivery mechanisms for information-based products;

- Integrating business partners and end customers, i.e. using cloud computing to streamline business

2013 46th Hawaii International Conference on System Sciences

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2013 46th Hawaii International Conference on System Sciences

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processes and information-sharing across value chains and geographies;

- Improving decision-making, i.e. employing analytical tools from the public cloud to derive insights from vast quantities of data and images within and beyond organizational boundaries - incl. social media.

While the above benefits are increasingly recognized and embraced by organizations around the globe, a recent study by Tata Consultancy Services (TCS) found surprising regional differences in cloud adoption rates [7]. An average large enterprise in Latin America has some 40% of its total applications in the cloud as SaaS. Asia Pacific follows closely behind with roughly a quarter (28%). In comparison, large firms in the United States have only shifted 19% of their applications to the cloud, in Europe the figure is closer to 12%. Can this lag be attributed to a different perception of implementation risks, a lack of top management support, or to bottlenecks in the legislatory environment?

Our study aims to improve the understanding of organizational context factors inhibiting or fostering the adoption of cloud computing. It sets out to structure the debate, link it to findings from existing research, and collect initial empirical evidence - summarized in the research question: What is the impact of technological, organizational, and environmental context factors on the adoption of cloud computing - and will those relations be influenced by the IT governance arrangements (structures and processes) an organization has adopted?

The remainder of this paper is organized into five sections. In the following section “conceptual foundations and hypotheses”, earlier research relevant to the research question is assessed and hypotheses are formulated. The third section “research methodology” describes the detailed research approach, the collection of data via structured interviews and a quantitative survey, as well as the analysis approach taken. The fourth section “results discussion” presents the descriptive statistics and non-parametric tests we have employed for analysis, as well as discusses findings and interpretations. The fifth and last section "conclusions and suggestions for further research" relates these findings to our overall research question and suggests next research steps.

2. Conceptual Foundations and Hypotheses

For the purpose of this study we have adopted the TOE framework as an organization-level theory "that explains how the firm context influences the adoption and implementation of innovations" [10]. The model

distinguishes between three building blocks determining the adoption and implementation of innovations: technology context, organizational context, and environmental context. Each context contains both constraints and opportunities for the adoption of innovative technology [11] - as depicted in Figure 1 - and provides the grounding for a set of hypotheses. IT governance has been conceptualized as a moderator since we assume that governance structures and processes have impact on decision-making structures and processes.

2.1. Tornatzky et al.’s TOE Framework

The TOE framework’s technology context refers to internal and external technologies which are relevant for the firm. Decisions to adopt technology innovations are determined by what is existing and innovations will fit with the existing technology landscape [11]. Frequently used constructs are relative advantage, complexity, and compatibility [12-14]. Relative advantage defines “the degree to which an innovation is perceived as being better than the idea it supersedes" [15] and has been positively associated with the adoption of innovative technology in previous research [12, 16].

Cloud computing makes it easier for firms or individual business functions to establish new services without managing or owning computer resources. It is a convenient means to avoid capital expenditures through a shift to operating expenditures. Initiatives - which would previously not fit the capital expenditure ceiling - are becoming economically feasible. Moreover, computing resources are scalable and can be easily be adjusted to fluctuating business needs; consumption is charged on a per-use-base [17]. We suggest that firms that perceive a higher relative advantage from cloud computing are more likely to adopt it (HT.1).

Complexity describes “the degree to which an innovation is perceived as […] difficult to understand and use" [15]. Higher (perceived) complexity will create higher uncertainty related to a successful implementation [14, 16]. Though the most complex aspects of cloud computing are hidden from the end user [18, 19] data security and confidentiality commonly surface as major adopter concerns [4]. In addition, the composition and integration of cloud computing with a firm’s existing IT landscape triggers new enterprise architecture (EA) challenges. We postulate that organizations which perceive cloud computing as less complex are more prone to adopt it

(HT.2).

Compatibility reflects “the degree to which an innovation is perceived as being consistent with the

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existing value, past experiences, and needs of receivers" [15]. Previous research most frequently singles out this factor’s influence on the adoption of innovative technology; it correlates positively with the diffusion of innovations [16]. Though the compatibility of different software platforms has improved over the last decades, data and process compatibility are still seen as major obstacles seem to be hindering the adoption of cloud computing [4].

Up to this point in time, the migration and integration of applications and data between different vendors’ clouds has not been standardized. A vendor change is likely to trigger migration and integration cost [17]. We hypothesize that perceived data, process, and vendor compatibility are positively related to the adoption of Cloud Computing (HT.3).

The TOE framework’s organizational context

comprises "... the characteristics and resources of a firm including linking structures between employees, intra-firm communication processes, firm size, and the amount of slack resources" [10]. For this study we have selected the constructs firm size, top management support, and cloud/IT skills of non-IT employees.

The size of an organization has been recognized as important facilitator for the adoption of technology innovations [11, 13]. Large firms usually have more slack resources available which can be used for pilots or larger scale investments [15]. On the other hand, we assume that small and mid-sized firms will embrace cloud computing more rapidly than large firms, which have invested in on-premise enterprise systems. Smaller firms are also more plentiful [...] though their IT spending is significantly less than it is at larger

firms” [20]. We postulate that firm size is negatively related to cloud computing adoption since larger organizations aim to explore internal economies of scale through on-premise solutions (HO.1).

Top management support can contribute to the adoption of innovations by creating a fertile environment and by providing resources [12, 14, 21]. It can take many forms: from true participation - i.e. activities or personal interventions in the management of IT - to executive involvement as a psychological state “reflecting the degree of importance placed on […] technology” [22]. Cloud computing can change budgets, processes, and responsibilities. Top management can support the transformation through "air cover" [23] - communicating a compelling vision for the IT governance initiative - and through allocating adequate resources [24-26]. We suggest that

top management support is positively associated with cloud computing adoption (HO.2).

The cloud/IT skills of non-IT employees are also expected to impact the diffusion of innovations. Previous researchers identified their technology knowledge as a crucial factor influencing adoption decisions [14, 27-29]. Considering that increasingly non-IT employees - or at least their management - are involved in strategic IT decisions, their perception and understanding of the targeted technologies is important. Van Grembergen et al. [30] also state that IT knowledge within business divisions contributes to a creative and innovative environment - and ultimately to the implementation of cloud computing (HO.3).

The TOE framework’s environmental context

relates to the area "in which a firm conducts its

Figure 1. Research Framework and Hypotheses

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business - its industry, competitors, access to resources supplied by others, and dealing with the government" [11]. For this study we have selected the constructs competition intensity - measured by the speed and intensity of product and service lifecycle changes - and the legislatory environment. [12, 13, 31]. Competition intensity defines “the degree that the company is affected by competitors in the market" [31] and reacts by adjusting its offerings accordingly. More competition forces a firm to allocate more resources to innovations [14].

They aim for three types of benefits: first, changes of the industry structure - ultimately affecting the rules of competition; second, increases in the relative competitive position; third, generation of new business opportunities [13, 32]. Cloud computing has the potential to introduce a step change to IT - increasing the standardization and modularization of IT services - and helping to quickly establish new services without major upfront investments. We postulate that competition intensity relates positively to cloud computing adoption since it increases innovation pressure (HE.1).

The legislatory environment can “either [have] a beneficial or a detrimental effect on innovation" [10]. Governments can support technology innovation by providing tax advantages by introducing regulation that force firms to adopt certain technology standards [31]. Adversely, governments can also pass constraining regulation - e.g. data security constraints in the health care and financial services industries which could make cloud computing less attractive [4]. Consequently we suggest that organizations that are subject to stricter regulatory requirements are less likely to become adopters (HE.2).

2.2. IT Governance Structures & Processes as Moderators

IT governance structures - our first moderator - is primarily reflected in the choice between centralized, decentralized, or federal IT governance arrangements [33, 34]. Centralized IT governance structures allocate decision-making responsibilities to a central IT function. Benefits of increased coordination and control are offset by more bureaucracy and less responsiveness to business demands. Decentralized IT governance structures delegate most decision authorities to business unit managers; however, the flexibility suited for turbulent environments must be balanced with standardization tradeoffs. A federal IT governance structure maintains central control of some IT domains while business units can deploy technology innovations at their discretion [35].

Since IT architecture and platform-related decisions are most commonly centralized in federal and centralized IT governance arrangements, we hypothesize that this moderator strengthens the relation between the technology context and cloud computing adoption: federal or centralized IT governance structures will positively impact the relations between the technology constructs from the TOE model (relative advantage, technology complexity and integration) on the one hand and adoption on the other

(HM.1). When IT management believes in the technological capabilities, as well as in compatibility with the existing systems landscape and business processes a centralized setup might reinforce its acceptance [36, 37]. Top management support will have more impact in a federal or centralized IT governance setup (HM.2).

IT governance processes - our second moderator - cover the following six major decision areas: request, prioritize, fund, monitor, enforce, and realign. Organizations can resort to a range of measures such as publishing guidelines, training employees, rewarding or punishing behavior, or implementing IT governance software to digitize and automate IT governance processes. We posit that mature IT governance processes will support adoption decisions when the technology context constructs are favorably perceived

(HM.3). A low relative advantage, high complexity, and a difficult integration with existing systems and processes increase adoption and implementation risk- those innovations are more likely to be taken out during prioritization and funding [38].

In addition, IT governance processes are aimed at accommodating audit trails and legislative compliance. Firms operating in a well-regulated environment have to balance legal requirements with the adoption of technology innovations. We hypothesize that mature IT governance processes will decrease the probability of cloud computing adoption in highly regulated industries(HM.4). Organizations will perceive the first mover advantage to be less important than ensuring that security standards and legal requirements are met. Well-governed firms will be slower adopters of innovation.

3. Research Methodology

The empirical research and testing of the hypotheses is based on data obtained via structured interviews. We targeted selected respondents to an earlier study by the Accenture Institute for High Performance’s High Performance IT (HPIT) survey [39]. This study -performed in fall 2009 - surveyed 669 global IT executives and other senior executive

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decision makers regarding their involvement with cloud computing and IT management-related topics. The survey data were used to select two company clusters: firstly those who strongly employed cloud computing - i.e. cloud infrastructure services, cloud-based development environments, or Software-as-a-Service (SaaS) - and secondly those who were non-adopters in all three areas.

Organizations who indicated that they were in the middle, or as the IT management survey phrased it “in a proof of concept (could still abandon)” were excluded. This approach allowed for comparing two extremes of the adoption spectrum and for better assessing the contrast. This screening phase left us with 40 “cloud adopter” companies and 60 “non-adopters”. Based on 2009 responses; we re-assessed their 2012 adopter/non-adopter status in the survey we did for the current study. We subsequently approached these 100 companies in May-August 2012 for our follow-up study. For 25 companies where we had personal contacts, we directly approached them to request an interview; the other 75 received an individual email invitation.

We are currently in the process of following up and scheduling interviews, and expect to be able to integrate the data in the presentation of this article in January 2013. So far we have completed 24 interviews, re-assessing each organization's “'adopter/non-adopter” status and discussing the factors that led to their decision to adopt or not adopt cloud computing. We also discussed the IT Governance arrangements at the time of the adoption decision to explore its possible impact.

Reflecting the regional focus of the Accenture study mentioned above, as well as that of our own network, the final group of respondents is predominantly European. Respondents to date include nine from Germany, five from Benelux, two from Italy, Austria and the United States, as well as five each from another country. The executives come from seven different industries (communications & high tech, financial services, health care, logistics, manufacturing, and professional services) as well as government and nonprofit sectors, primarily from organizations with revenues of $1.5 billion or higher.

Respondents were only included in the final tally if they were involved in their organization’s use or exploration of cloud computing, and had at least a basic knowledge of cloud computing. The targeted interview partners had a good knowledge of the company’s involvement with cloud computing, as well as with the context surrounding the adoption decision. Since we decided to reuse some secondary data from the predecessor study, we decided not to alter the starting and end points.

These preconditions meant that in most cases the (targeted) respondents were high-level managers in IT (typically the CIO) or in the business with a close link to IT. A professionally designed and personally addressed interview guideline (as shown in the appendix) and telephone interviews were used to obtain a high response rate - combined with a small reward (hifi earphones).

In total, 20 companies took part in the interviewing round, placing the response rate/quota (so far) at approximately 20%, and providing us with data to come to the results presented in the following section. In addition to the reasonably high response rate, the fact that no company type/group was systematically left out of the sample, improves the representativeness of the gathered data. Furthermore - and in order to ensure that there is no data distortion caused by the given response rate - the distributions of the early responses were compared against the late responses using the Mann-Whitney U test for 2 samples. The test found no significant differences between the early and late responses. Appendix 1 shows part of the interview guidelines. Individual items were taken from studies by Heier et al. [40] and Borgman et al. [41].

4. Results and Discussion

Our initial data set, described in the previous section, consists of 24 firms, split between the two extreme ends of the cloud-adoption spectrum: nine are “cloud adopters” and fifteen “non-adopters” in 2012. Descriptive statistics for all variables mentioned in the hypotheses are presented in Table 1. Given that the small sample size does not allow for very robust parametric statistical tests, we primarily looked at the descriptive statistics and non-parametric tests to explore and analyze the data. Where possible we did run statistical tests to investigate whether our hypotheses hold. In the interpretation we also refer to more qualitative data we gathered as a by-product of the structured interviews. Table 2 summarizes the outcomes.

HT.1 states firms that perceive a higher relative advantage from cloud computing are more likely to adopt it. Table 1 shows that T1 advantage is indeed higher for cloud adopters (4.00) than the mean for firms which were qualified as non-adopters (3.53). The independent samples Mann-Whitney U-test confirms this with a significance level of 0.02. At a more detailed level, we see that this is true for essentially each dimension: T1 advantage investment, T1 advantage scaling, and T1 advantage cost are higher for adopters. Findings for HT.2 are inconclusive.

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Though we assumed that firms which perceive cloud computing as less complex are more prone to adopt it, this only seems true for T2 complexity technology while T2 complexity security is essentially the same for adopters and non-adopters. Our interview partners pointed out that dealing with security concerns has always been a focus of their work, cloud computing did not present unusual or additional challenges. In some instances, the restricted configuration or customization possibilities of cloud offerings perceivably presented fewer security risks.

There are no significant differences between perceived data, process, and vendor compatibility in both groups. Based on our interviews and our experiences interacting with other firms outside this sample we think that large firms - which we have targeted for this study - have already assembled experience and built skills implementing heterogeneous/best-of-breed systems landscapes and working with a set of IT outsourcing partners. Potential

(data and process) compatibility issues introduced by cloud computing might not be seen as significantly different from day-to-day IT management challenges. The averages of the T3 compatibility factors are almost similar at 2.93 and 3.00 for non-adopters and adopters, respectively.

Note that this discussion about HT.1-3 is based here only on the means from Table 1. Employing Mann-Whitney U tests did only support our HT.1 hypothesis (at .02 significance level) for the T1 technology advantage mean. In summary, only the perceived relative advantage of cloud computing seems to matter for the technology context of the TOE framework.

The hypotheses HO.1-3 relates to organizational context of the TOE framework, again comparing adopters and non-adopters. Looking strictly at the means in Table 1, we can see that T2 top management supportand T2 top management risk acceptance scores clearly fall behind for the non-adopters (3.33 and 2.87

Non-Adopters (N=15) Adopters (N=9)

mean std. dev. min. max. mean std. dev. min. max. D1 adoption infrastructure 2.27 .70 1 4 4.78 .44 4 5 D2 adoption development 2.60 1.18 1 5 4.22 1.09 2 5 D3 adoption SaaS 3.60 .91 2 5 4.89 .33 4 5 T1 advantage investment 3.73 .80 2 5 4.00 .71 3 5 T1 advantage scaling 3.26 1.10 2 5 4.00 .50 3 5 T1 advantage cost 3.53 .74 2 5 4.00 .71 3 5 T1 advantage mean 3.53 .48 2.67 4.67 4.00 .41 3.67 4.67 T2 complexity security 2.27 1.22 1 5 2.33 1.12 1 4 T2 complexity technology 2.73 1.03 1 4 2.11 1.12 1 4 T2 complexity mean 2.50 0.93 1 4 2.22 .75 1 3.50 T3 compatibility data 3.20 1.10 2 5 3.22 1.56 1 5 T3 compatibility process 3.13 1.13 1 5 3.33 1.12 2 5 T3 compatibility vendor 2.47 1.06 1 4 2.44 1.24 1 5 T3 compatibility mean 2.93 .80 1.67 4 3.00 1.03 1.67 5 O1 firm size 3.15 1.52 1 5 2.86 1.57 1 5

O2 top management support 3.33 1.18 2 5 4.67 .71 3 5 O2 top management resources 3.33 .98 2 5 3.89 1.05 2 5 O2 top management risks 2.87 .99 1 4 3.89 .78 3 5 O2 top management mean 3.18 .83 2.00 4.67 4.14 .65 3 5 O3 skills non-IT employees 2.54 .75 1 4 2.85 1.44 1 5

E1 lifecycle changes 2.40 1.50 1 5 4.22 .83 3 5

E2 legislatory environment 3.73 1.03 1 5 3.44 .81 2.50 5 M1 IT governance structures 3.60 1.06 2 5 3.78 1.20 3 5 M2 IT governance processes 3.12 1.05 1.83 5.00 3.72 1.03 1.33 5

Table 1. Descriptive Statistics (N=24)

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vs. 4.67 and 3.89) suggesting that management can indeed provide a fertile environment of innovation diffusion through verbal endorsements and the willingness to tolerate risks being introduced by less mature technology. Interestingly, the provision of labor and financial resources is being perceived as less important. This might also be related to the funding of cloud initiatives - often from shadow or non-IT budgets - particularly in the case of small-scale pilots.

Given that our interview partners were almost all high-ranking IT executives who were discussing their own organization, one can realistically expect that there is a bias towards reporting a more positive picture, particularly when comparing later with earlier data, so the real situation may be somewhat overstated. A hindsight bias may also be at play, causing respondents to report reasons for a choice in the past influenced by today's insights. In our interviews we were particularly aware of both risks and tried to create an atmosphere of trust and academic objectivity to reduce these risks and their potential impact.

Testing HO.2 top management support using the independent-samples Mann-Whitney U test with a significance level of .01 we find support at an aggregate level (O.2 top management mean, for the average of all O.2 factors) as well as at the detailed level for O2 top management support and O2 top management risks. To further explore this we refined our adopter variable by not looking simply at whether firms are adopters or non-adopters but calculating an adopter score based on the average of the scores for D1, D2 and D3 (see Table 1).

We then used Spearman's rank correlation (2-tailed) to test the correlation between the different O2 factors and the average adopter score. Both O2 top management support (aggregate and detailed) as well as O2 top management risks show significant

correlations (at 0.01 significance level) of respectively 0.769 (aggregate), 0.691, and 0.605 (O2 top management risks). The results for O1 firm size and O3 skills of non-IT employees remain inconclusive and do not differ significantly between adopter and non-adopter groups. Some interview partners remarked that non-IT employees were influencing adoption but did not deem their technical skills relevant. Potentially, O3 should be refined to reflect the diversity of knowledge rather than skills - following Mishra et al. [42].

Moving to the third factor-set in the TOE model, E for environment context, we formulated and tested two hypotheses. HE.1 postulates that cloud computing adoption will be positively influenced by innovation pressure - in turn resulting from rapid lifecycle changes for the products and services a firm offers. The cloud adopters’ score for E1 lifecycle changes shows the biggest difference between cloud adopters and non-adopters of all individual factors. As expected, cloud computing has the potential to foster rapid technological changes - ultimately helping to quickly establish new services without major upfront investments. The Mann-Whitney U test supports this HE.1 hypothesis (at 0.01 significance level).

Findings for HE.2 are inconclusive. We hypothesized that firms that are afraid of complex legal issues involved in establishing cloud services - or afraid of unclear legal requirements - are less likely to adopt it, but the difference in means is inconclusive. The statistical tests we have employed also did not show a significant correlation. We hypothesize that this has been mandatory for organizations in well-regulated industries (financial services, health care) to familiarize themselves with data security and compliance requirements. The impact of cloud computing may not be perceived as a fundamental change.

Hypothesis Confirmation Rejection

HT.1 “perceived advantage” +

HT.2 “perceived complexity” O

HT.3 “perceived compatibility” O

HO.1 “firm size” O

HO.2 “top management support” +

HO.3 “skills of non-IT employees” O

HE.1 “lifecycle changes” +

HE.2 “legislatory environment” O

HM.1 “increased technology advantages through more centralization” inconclusive HM.2 “strengthened top management support through more centralization” inconclusive HM.3 “increased technology advantages through mature processes” inconclusive HM.4 “emphasis on regulatory requirements through mature processes” inconclusive

Table 2. Overview of Hypotheses (Mann-Whitney U test, sig =0.05, N=24)

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Testing the moderator variables, specifically the IT governance structure (more or less centralized) and the IT governance process maturity remains inconclusive at this point as the data points were not only few but also very unequally divided. For instance, 85% of the cases concerned federated or centralized IT governance structures, and only 15% (four cases) were decentralized. As more data points come in we expect to be able to assess the possible influence of the moderator variables as an extension to the TOE framework.

5. Conclusions and Suggestions for Further

Research

Our research has conceptualized the link between the TOE framework and the decision of organizations to adopt cloud computing, as well as the moderating effect of IT governance structures and processes on these relationships. We developed hypotheses and were able to test these for the main independent variables, showing that, specifically, the technology and organization context factors affect the decision whether organizations adopt cloud computing. A high perceived

relative advantage of cloud computing, a high level of

top management support and a high competition intensity (measured as a short lifecycle of products/services in the industry) are all three factors that are positively linked to the decision to adopt cloud computing.

We also conceptualized the potential moderating role of IT governance structures and processes within this framework. Our qualitative data on IT governance structures is in line with statistical results; the majority of our interview partners did not consider them relevant in the context of cloud computing adoption. On the other hand, IT governance structures were perceived as streamlining and expediting adoption decisions - regardless of the actual adoption decision that was made. IT executives remarked that innovations were selected and implemented faster with mature demand and portfolio management arrangements. Similarly, more mature arrangements could also block adoption decisions in unregulated legal environments.

While the results from this study indicate the general direction for the influence of TOE factors on adoption decisions regarding cloud computing, clearly more data are needed to solidify the results and to be able to understand the moderating role of IT governance structures and processes. The latter aspect is particularly interesting in exploring how these decisions pan out over time as the implementation process progresses. Do organizations reap benefits

from cloud computing, are there side effects such as back-door demand, and what is the impact on IT-business alignment [41]?

We are early in the process of cloud adoption, and while it is very important to have early results, future studies may well show differences over time, per industry or for different geographic regions. More qualitative studies, both regarding the factors influencing the decision itself as well as the decision process over time, may also help to interpret survey results and offer more detailed explanations for these results.

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Appendix 1. Interview Guideline Excerpt

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