2012 45th Hawaii International Conference on System Sciences
Technology Acceptance of Cloud Computing:
Empirical Evidence from German IT Departments
Nicky Opitz
University of Göttingen
[email protected]
Tobias F. Langkau
Nils H. Schmidt
Lutz M. Kolbe
University of Göttingen
University of Göttingen
University of Göttingen
[email protected] [email protected] [email protected]
Abstract
providers to sell infrastructure, platform and software
as a service. Building up on Koehler et al. [4] explanation about the value of different attributes (for example
costs) of cloud service configurations from a consumer
perspective, we resolve the research question:
In this article, we examine the technology acceptance of cloud computing by analyzing empirical data
from 100 CIOs and IT managers from stock indexed
companies using factor analysis. For a valid methodological background we use a modified version of the
common Technology Acceptance Model. The outcomes
indicate that user acceptance of cloud computing can
be explained and predicted by various non monetary
variables concerning social influence and cognitive
instrumental process. In particular, factors such as
image, job relevance and perceived usefulness play an
important role in cloud computing acceptance. These
findings advance theory and contribute to the foundation for future research aimed at improving our understanding of technology adoption behavior. They also
provide a structured and more systematic approach for
cloud service providers on how to build their services
and promoting them to the market.
Which factors explain the adoption of cloud computing?
Thereby we derive the factors of adoption from a
modified version of the common Technology Acceptance Model (TAM) by Davis [5] and review the ability of transferring the factors of TAM to the cloud computing technology by empirical research.
2. Related Research
Technology acceptance is the driver for cloud computing. This means that we first have to give an overview about the state of the art of cloud computing by
defining this term out of an unstructured literature
review. Next we reproduce chances and risks of cloud
computing from research literature. Afterwards we
explain the basis of the Technology Acceptance Model. It includes a description of the different factors
given by the model and the interdependencies between
these factors to explain the acceptance of a new technology. At the end of this paragraph the chosen conceptual research framework is described.
1. Introduction
Cloud computing has gained major public attention.
It has become part of everyday advertisement, newspaper articles and reports as well as it has diffused both
the researchers’ and the practitioners’ area. Chellappa
[1] first defined cloud computing as a “computing
paradigm where the boundaries of computing will be
determined by economic rationale rather than technical
limits alone”. Ramireddy et al. [2] describe cloud computing as an “important player in the field of IT infrastructure outsourcing” and Weiss [3] concludes that “it
is a buzzword almost designed to be vague, but cloud
computing is more than just a lot of fog”.
Forrester Research expects cloud computing to be a
$159.3 billion market by 2020. Gartner Research prognosticates a $150 billion business by 2014. The expectations of the business with cloud computing are high
and no competitor on the IT service market can ignore
the cloud computing paradigm.
By following Koehler et al. [4] analyzing that
“most of the research work so far focused on technical
issues of cloud computing” we understand cloud computing as a kind of technology which allows IT service
978-0-7695-4525-7/12 $26.00 © 2012 IEEE
DOI 10.1109/HICSS.2012.557
2.1. Finding a Cloud Computing Definition
Considerable controversy surrounds the definition
of the term cloud computing. This fact makes it necessary to analyze different findings in the research literature and aggregate them to new definition of cloud
computing. This new definition was put in front of the
survey to ensure that participants’ answers based on a
similar concept comprehension.
Vaquero et al. [6] derive a grounded definition of
cloud computing from a literature review. They define
cloud computing as aggregation of IT resources (hardware, platform and services), which can dynamically
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and private cloud.
From this detailed described and further definitions
([12], [13], [14], [15], [16], [17], [18], [19], [20], [21])
we derive eight possible key characteristics of a cloud
computing definition. Table 1 summarizes the key
characteristics of different cloud computing definitions. The seven characteristics service, hardware,
software, network, abstraction, on demand and scalability are most common. However the distinguishing
feature private and public in a cloud computing definition could be neglected. From this analysis we suggest
the following working definition of cloud computing:
match the requested demand. Further characteristics
are accounting models for this on-demand request and
Service Level Agreements (SLA), which affirm delivery of these flexible services. Plummer et al. [7] define cloud computing for the Gartner Group as an IT
service. This service is highly scalable and is delivered
through the internet to external customers. The idea of
a private cloud – which means delivering services, also
to internal customers (regarding to Plummer et al. [7])
– is excluded in their definition. Armbrust et al. [8]
also explicitly exclude the private cloud from their
definition of cloud computing. In their definition cloud
computing is a construct of the three layers hardware,
operation system and applications. Furthermore
Armbrust et al. [8] define cloud computing as an on
demand solution, which serves at the end of the applications’ layer to public customers. Mell & Grace [9]
define cloud computing for the National Institute of
Standards and Technology (NIST) as a model that
allows the comfortable, demand-based delivery of a
pool of configurable IT resources (network, server,
storage, applications and services). Foster et al. [10],
Ramireddy et al. [2] as well as Unal & Yates [11] developed similar definitions, which focused on the on
demand, scalable distribution of software, hardware
(also network infrastructure) as a service. In their definition they leave out the differentiation between public
The technology cloud computing contains abstract,
high scalable and managed on demand infrastructure
(server/computer, storage, networks) and on demand
software (operation systems, applications, middleware,
management and development tools), which can dynamically match the requirements and are paid per
usage.
2.2. Opportunities and Risks of Cloud Computing
For the understanding of the cloud computing domain it is assistant to describe opportunities and risks
vendors and customers associate with cloud compu-
Network
Scalability
Public/
Private
On demand
Software
Anandasivam & Weinhardt [12]
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Armbrust et al. [8]
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Buyya et al. [13]
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Ercan [14]
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Foster et al. [10]
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Grossmann et al. [15]
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Kim [16]
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Li [17]
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Marston et al. [18]
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Mei et al. [19]
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Mell & Grace [9]
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Mohammed et al. [20]
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Plummer et al. [7]
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Ramireddy et al. [1]
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Subashini & Kavitha [21]
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Unal & Yates [11]
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Vaquero et al. [6]
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Hardware
Key characteristics
Service
Abstraction
Table 1. Key characteristics of cloud computing definitions
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ting. Further research in this area can lead to new findings for the survey or the later done adaptation of
TAM.
Armbrust et al. [8] describe the economies of scale
from a vendors view as one positive characteristic of
cloud computing in huge computer centers. It is possible to reduce cost for network infrastructure, storage,
energy, administration and maintenance compared to
smaller computer centers. Even the guarantee of secure
cloud services and the protection of the infrastructure
are possible with lower costs. A certification process
for example is cheaper to realize for one huge computer center than for many medium-sized and small computer centers. After this Armbrust et al. [8] take the
view of a customer and describe the advantages of
cloud computing on demand model. This makes it
possible to calculate only with variable costs and not
with fixed costs for IT infrastructure. Also costs for
software licenses or the costs for the management of
licenses cancelled. Even the flexibility of the service
allocation is interesting for the customer and differ
cloud computing from normal IT outsourcing.
Ramireddy et al. [2] take the customers view and
describe the possible “loss of direct control of resources” as one negative key issue. Other issues in IT
security are mentioned by [9], for example trust, multitenancy, encryption and compliance.
Koehler et al. [4] describe the requirements of
cloud computing from customers view and conclude
that “reputation of the cloud service provider and the
use of standard data formats are more important than
those financial aspects such as cost reduction or pricing
tariff choice”.
Latest news from Amazon’s cloud crash and the
hack of the Sony Gaming Network sensitized customer
regarding to IT security and the availability of IT resources.
search literature, being it in high ranked scientific
journals [23] or proceedings of actual IS conferences,
see for example Kim & Zhang [24].
The basis of the original Technology Acceptance
Model 1 is the interaction of the variable Perceived
Usefulness, Perceived Ease of Use, Attitude Toward
Using and Behavioral Intention to Use, which joins
into the variable Actual System Use. Actual System
Use is a dummy variable whether a person does or
does not use a system. Perceived Usefulness is defined
as “as the prospective user's subjective probability that
using a specific application system will increase his or
her job performance within an organizational context”
[5]. Perceived Ease of Use is the degree to which a
person “expects the target system to be free of effort”
[5].
2.3. Basis of the Technology Acceptance Model
The model was extended in version 2 by new input
variables. Furthermore, the variables Attitude Toward
Using and Behavioral Intention to Use were merged
into the variable Intention to Use, which measures a
person’s intention to use or not use a technology. The
new input variables can be divided in variables of
social influence and cognitive instrumental process
[22]. The group of social influence variables includes
Subjective Norm, Image, Experience and Voluntariness. Subjective Norm is defined as a “person’s perception that most people who are important to him
think he should or should not perform the behavior in
question” [33]. Image is the “degree to which use of an
innovation is perceived to enhance one’s status in
one’s social system” [34]. So the variable Image is not
the image of the technology, but the image of the person that uses it. Further the variable Experience is the
Figure 1. Overview TAM 1 by Davis [5] and TAM 2
by Venkatesh & Davis [22]
We have chosen the proven theory model Technology Acceptance Model 2 (TAM 2; regarding to the
second version of the model by Davis [5] described by
Venkatesh & Davis [22]) to create an informationtheoretical background for the survey and thereby
increase the expressiveness of the results. Moreover, as
the model originates from the social sciences, it gave
us a less technical or monetary perspective than in
other recent cloud computing adoption research. The
Technology Acceptance Model validates statements
about why people use certain technologies. The model
can be used both for explanations and forecasts [5]. A
characteristic of the model is the high level of abstraction and the consequent low number of model variables. TAM 1 and TAM 2 is widely used in IS re-
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experience of a person with a system. It acts on the
outgoing effects of Subjective Norm. Voluntariness is
a dummy variable to separate voluntary and mandatory
usage settings.
The group of cognitive instrumental process variables includes Job Relevance, Output Quality and
Result Demonstrability. Job Relevance is defined as
“an individual’s perception regarding the degree to
which the target system is applicable to his or her job”
[22]. In addition to the question which tasks a system is
capable of performing, people might consider how well
it performs these tasks. We will refer to that as Output
Quality. Finally, Result Demonstrability is defined as
“the tangibility of the results of using the innovation”
[33]. Even systems that do perform well can fail to
reach a high level of user acceptance if users do not
attribute gains in their performance to the system.
In summary, TAM 2 is an extension of TAM 1 and
it contains the original model. For a visual comparison
see Figure 1.
Figure 2. Modified TAM 2 – a model for cloud computing adapted from Venkatesh & Davis [22]
2.4. Conceptual Research Framework
3. Methodology
For investigating the technology acceptance of
cloud computing we used the TAM 2, because it delivers more refined information on the input side. We also
adapted the model slightly. As the addressees of the
survey were only CIOs and employees in similar positions the voluntariness of a decision towards cloud
computing was assumed. As a consequence the variable Voluntariness was removed from the model. Furthermore the influence of Subjective Norm on Intention to Use in the original model is only assumed in
non-voluntary settings [22]. Therefore this relationship
is not part of our examination either. For our study we
investigated on the following eleven hypotheses (H1 –
H11) which were adopted from the original TAM 2
research paper [22]:
3.1. Questionnaire Design
H1.
H2.
H3.
H4.
H5.
H6.
H7.
H8.
Perceived Ease of Use will have a positive
effect on Perceived Usefulness.
H9. Perceived Ease of Use will have a positive
effect on Intention to Use.
H10. Perceived Usefulness will have a positive effect on Intention to Use.
H11. Intention to Use will have a positive effect
on Actual System Use.
A cross-sectional survey was conducted to evaluate
the proposed research model. The questionnaire was
divided into five sections. In the first section the participants were asked for their contact data and whether or
not they were interested in receiving the results of the
study and a biannual newsletter from the Chair of Information Management. This was done to increase the
response rate. The first section was marked as optional.
The second section contained the proposed short definition of cloud computing to give the participants a
consistent thought of our view on cloud computing.
The third section asked for general enterprise data such
as the amount of full time employees, amount of employees in the IT-department, industry sector and annual turnover to evaluate if the sample is a good reflection of the German enterprise landscape. In addition it
asked if the participating company already uses cloud
computing technology and if so, since when. The
fourth section investigated the Technology Acceptance
Model. For each variable of the model several items
were created. In a first step all items from the original
TAM 1 and TAM 2 research papers were translated
into German language to ensure item validity. Some
items could be used directly. Others had to be adapted
in a second step to better match the cloud computing
subject or to sound more natural to German participants. Furthermore, some new items were created. A
panel of two academic experts and several non experts
reviewed the questions to confirm that the constructs
Subjective Norm will have a positive effect
on Image.
Subjective Norm will have a positive effect
on Perceived Usefulness.
The positive effect of Subjective Norm on
Perceived Usefulness will attenuate with increased Experience.
Image will have a positive effect on Perceived Usefulness.
Job Relevance will have a positive effect on
Perceived Usefulness.
Output Quality will have a positive effect on
Perceived Usefulness.
Result Demonstrability will have a positive
effect on Perceived Usefulness.
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sets [28]. All variables were examined regarding their
usability for factor analysis. In addition, several tests
were included to confirm the quality of the sample.
Scientific literature has many views on whether or not
a sample size is adequate. Recent research on simulated data points out that sample size is not a concern if
factor loadings of at least 4 items are greater than 0.6.
The reliability of factor analysis and individual variables can be determined using the Kaiser-MeyerOlkin measure of sampling adequacy (KMO) [29].
KMO values around 0.5 are barely acceptable [29].
KMO values between 0.5 and 0.7 are mediocre, values
between 0.7 and 0.8 are good, values between 0.8 and
0.9 are great and values above 0.9 are superb [28].
When using factor analysis the consistency of the questionnaire should be checked using Cronbach’s α (CA),
which should be around 0.8 [28] or at least 0.7 [30].
When factor loadings, KMO and CA delivered reasonable results the extracted factor was then used as a
variable in our research model. To examine the Technology Acceptance Model, linear regression analysis
was applied. Regression analysis is a statistical method
to reveal quantitative dependency between variables
[31].
were adequately described by the item wording. All
items had a 5-point Likert scale ranging from “Disagree strongly” to “Agree strongly” [25]. The final version of the questionnaire contained three to five items
for every variable of the proposed research model. The
fifth and last section contained an open question where
the participants could enter any thoughts, comments or
criticism on the cloud computing matter.
3.2. Data Collection
A paper-based questionnaire was sent out via postal
mailing in December 21st, 2010. All letters contained a
hand signed covering letter to express appreciation to
the participants, the questionnaire itself and a selfaddressed envelope. Addressees were 567 CIOs, ITmanagers and other staff responsible for IT decisions
from companies listed in German stock indexes. The
participants were asked to return the questionnaire until
January 14th, 2011, giving a response time of almost
four weeks. Participants were not expected to respect
that timeframe, so an unofficial deadline was set to the
end of January 2011. In addition to the paper-based
survey an online survey was created via the surveygizmo.com platform. Items and item order were kept
intact in order to minimize any possible influence on
the outcome due to different media. A hyperlink to the
survey was published on various IT related bulletin
boards and groups on business networks such as
xing.com on January 5th, 2011. At the end of January
we received a total number of 97 postal replies,
amounting to a response rate of 97 ÷ 567 = 17.1 %.
This response rate is consistent with rates in similar
surveys in IS research (regarding to Mani et al. [26],
Poppo & Zanger [27]). In addition, we received three
online questionnaires, making a total sample of
N = 100.
4. Findings
4.1. Sample Profile
All findings are based on the sample profile shown
in Table 2. The participating enterprises belong to a
variety of industries, such as manufacturing (31%),
trade and commerce (23%), information and communication technologies (16%), financial services (9%),
construction industries (1%) and others (30%), which
consisted amongst others of public sector, media industries, health care and agriculture. The sample is representative for Germany. The annual turnover and the
number of employees indicate that large enterprises
dominate the sample. 55% of the participating enterprises already use cloud computing technology. Introduction years vary from 1990 to 2010, whereas 91% of
the using companies introduced it in 2004 or later. The
sample was divided in two groups based on chronological response date to test for late- or non-response bias.
There were no significant differences.
3.3. Data Analysis
We applied confirmatory factor analysis to reduce
the large item sets to their underlying variables from
the Technology Acceptance Model. In contrary to the
common exploratory factor analysis, its confirmatory
counterpart is an applied statistical method in the social
sciences to confirm proposed structures in variable
Table 2.Sample profile
Employees of the enterprise
Annual turnover 2009
in millions of Euros
Percentage
Frequency
Percentage
Employees of the IT department
Frequency
Percentage
Frequency
0-4
1.0%
1
1-99
9%
10
1-4
13%
13
5-9
6.3%
6
100-499
19%
19
5-9
10%
10
10-49
10.4%
10
500-999
11%
11
10-49
21%
21
50-499
28.1%
27
1,000-4.999
21%
35
50-499
14%
14
500+
54.2%
52
5,000+
40%
41
500+
42%
42
N = 96 (four missing)
N = 100
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N = 100
Table 3. Results of the factor analysis
Factor
Item
Loading
Subjective Norm
People who influence my behavior think that I should use cloud
computing.
.887
Experts who are important to me think that I should use cloud computing.
.606
People who are important for my career think that I should use
cloud computing.
.771
Job Relevance
Image
Output Quality
Result Demonstrability
Experience
Perceived Usefulness
Perceived Ease of
Use
Intention to Use
I am expected to use cloud computing.
.786
Usage of cloud computing is relevant for my job.
.806
For my future work in my company, cloud computing is important.
.833
In my job, usage of cloud computing is important.
.861
Usage of cloud computing improves my reputation in my company.
.981
I can level my profile by using cloud computing.
.906
IT decision makers using cloud computing have more prestige
.872
Cloud computing is a status symbol in an organization.
.752
Cloud computing makes sense for increasing our company’s output.
.818
I think IT resources can be used more effectively with cloud computing.
.750
The service level of our IT will increase with cloud computing.
.777
The quality of the output I get from cloud computing is high.
.869
IT costs will sink by using cloud computing.
.769
The results of using cloud computing are apparent to me.
.606
I believe I could communicate to others the consequences of using
cloud computing.
.771
I have no difficulty telling others about the results of using cloud
computing.
.742
I can describe the difference between the concepts of IT outsourcing and cloud computing.
.639
I have experience in using cloud computing.
.650
I know several cloud computing service providers and their services.
.771
I can distinct between SaaS, PaaS and IaaS.
.671
I expect additional benefits in my company by using cloud computing.
.885
Cloud computing improves my performance in my job.
.908
Cloud computing enhances my effectiveness in my job.
.895
I expect higher flexibility in our IT by using cloud computing.
.803
Using cloud computing would not lead to technical difficulties in my
company.
.705
Cloud computing integrates quite easily in our IT infrastructure.
.700
I find cloud computing easy to use.
.753
For our employees using cloud computing does/would not require a
lot of mental effort.
.708
Assuming I can decide, I intend to use cloud computing.
.872
Given that I have access to cloud computing, I predict that I would
use is.
.884
I intend to use cloud computing.
.808
We will start using cloud computing soon (or have started).
.886
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KMO
CA
.789
.872
.693
.775
.801
.886
.830
.852
.584
.479
.667
.586
.813
.898
.664
.689
.823
.881
4.2. Factor Analysis
The results of the factor analysis are shown in Table 3. The variables Result Demonstrability and Experience have mediocre KMO values and non satisfying
CAs while overall factor loadings are acceptable. Perceived Usefulness also has a mediocre KMO value, but
a more convenient CA. Subjective Norm and Job Relevance have good KMO and CA values. Image, Output Quality, Perceived Usefulness and Intention to Use
give the best results, with overall good to very good
KMO and CA. Factor loadings were > .6 in general,
with an exception in item 13, which with a loading of
.32 was removed from further analysis. The variable
Actual System Use was not part of the factor analysis,
it was derived directly from the questionnaire item
whether or not the participating company uses cloud
computing.
Figure 3. Results of regression analysis
Therefore we modified the model further in order to
find better relations. As H1 was supported by the data
we took Subjective Norm as an input variable for Image only in further research. With Subjective Norm not
being an input variable for perceived usefulness anymore we stopped examining H3. The high correlation
between Output Quality and Perceived Usefulness
suggests that both variables explaining similar things.
To validate this point we regressed Image, Job Relevance and Result Demonstrability with both Output
Quality and Perceived Usefulness. As can be seen in
Figure 4 the regression coefficients, R2 and significance are very similar. As a consequence we rearranged
the model so that Image, Job Relevance and Result
Demonstrability were input variables for Output Quality, leaving the latter as the only input variable for Perceived Usefulness. This way we could achieve a good
fit. The final model is shown in Figure 5.
4.3. Evaluation of the Conceptual Model
In the following section our proposed model is examined from right to left, beginning with H11. Using
binomial logistic regression it could be shown that
Intention to Use can explain 82.5 % of the variance in
Actual System Use with p < .001. Consistent with H9
and H10 Perceived Usefulness was a strong determinant of Intention to Use (β = .792, p < .001), leaving
Perceived Ease of Use as a weaker but still significant
determinant (β = .066, p < .05). Both variables explain
66.4 % of the variance in Intention to Use. As H9 –
H11 are strongly supported by the data, the suitability
of the original TAM 1, which is a part of our modified
model, can be assumed for cloud computing. In a
second step we examined the variables of our modified
TAM 2. H1 was supported by the data (β = .679,
p < .001). As can be seen in Figure 3 the suggested
effects from Subjective Norm, Image, Job Relevance,
Output Quality, Result Demonstrability and Perceived
Ease of Use on Perceived Usefulness are not supported
by the data, contradicting H2 and H4 – H8. While
Output Quality seems to have a huge and highly significant effect, the other variables do not. There could be
several reasons for this. While sample size was satisfactory and the items were proven valid in former
research, the model itself could be insufficient in describing cloud computing usage.
Figure 4. Comparison Perceived Usefulness and
Output Quality and relationship representation
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Subjective Norm
.679***
.896***
Image
Job Relevance
Perceived
Usefulness
.164*
.391***
.792***
82,5%***
Output Qualiy
Intention to Use
1
Actual System Use
.486***
Result Demonstrability
Perceived Ease of
Use
.066*
* p < 0,05; *** p < 0,001; explaining 82.5% in variance
methods impose specific limitations to the results. First
of all findings can only be representative only for
Germany as only IT departments of German companies
participated. Results may vary in different regions
however, due to different policies and regulations,
economics or social norms. Moreover, only enterprises
were asked, leaving technology acceptance of cloud
computing unanswered for other entities such as non
government organizations, schools & universities or
consumers. Limitations also derive from the application of factor analysis. Conclusions are restricted to the
sample collected and generalizations of the results can
be achieved only if analysis using different sample
reveals the same factor structure [28]. Furthermore,
dividing participants in chronological groups to test for
non-response bias is not necessarily sufficient, as late
responses may have other reasons than no responses [32].
Figure 5. Final model
Summarizing we can state that the technology acceptance of cloud computing can be explained by the
version 1 Technology Acceptance Model quite well. In
contrast we run into problems by extending the study
with the new input variables of our modified Technology Acceptance Model 2. We were able to find a good
fit by rearranging some variables however. As it turns
out Output Quality and Perceived Usefulness seem to
describe similar underlying aspects and should be the
focus of further research. Until then we propose the
model shown in Figure 5 for describing technology
acceptance of cloud computing.
5. Conclusion
From the results in the former sections it can be
concluded, with certain limitations, that technology
acceptance of cloud computing can be described by the
Technology Acceptance Model 1 and our rearranged
version of the Technology Acceptance Model 2.
Our initial research question “Which factors explain the adoption of cloud computing?” can be answered as follows: Actual System Use can be described
by Intention to Use which can be described by Perceived Usefulness and Perceived Ease of Use, former
being the stronger determinant. Perceived Usefulness
can be described well by Output Quality. This may
implicate that IT decision makers tend to find things
useful that generate good output. Output Quality had
several significant and highly significant determinants,
Image, Job Relevance and Result Demonstrability.
Finally Subjective Norm is a highly significant determinant of Image.
5.2. Implications
There are several practical implications of our findings. The results from our study can give cloud service
providers new structured determinants on when users
intend to use cloud computing for developing new
cloud services and bringing them to market. We could
show that in accordance with Koehler et al. [4] decisions towards the usage of cloud computing is not
necessarily made for monetary reasons. Social influences from the processes behind the variables Image
and Subjective Norm play an important role. Cloud
service providers have to focus on designing communicating campaigns to raise prestige and image of their
cloud computing services. The variables from the
group cognitive instrumental process also play an important role. Cloud services should be developed with
job relevance in mind. Practical interventions for increasing result demonstrability could be useful, such as
empirically demonstrating the users the comparative
effectiveness of cloud computing services relative to
5.1. Limitations
There are some limitations in our preliminary findings. The sample selection and the applied statistical
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their status quo systems. This should provide important
leverage for increasing user acceptance. From a user
perspective our final model can also help making decisions. Users can evaluate the job relevance, output
quality and usefulness of cloud computing services in
order to decide whether or not a service is useful for
them. This helps to get the attention away from monetary-only aspects and towards a more sustainable view.
Our research represents a contribution to the body
of knowledge in cloud computing usage and user acceptance in general. For the latter we can state that the
Technology Acceptance Model once more turned out
to be a valid model for explaining or predicting user
acceptance and usage behavior. We have shown that a
more abstract or transparent technology as cloud computing can be described by a model originally developed for explaining user behavior in end user software
systems with a direct user experience. Therefore we
broadened the research field where the Technology
Acceptance Model can be used for predicting and explaining user acceptance of technologies.
Concerning the research on cloud computing itself
we provide a new theoretical usage model. The less
detailed and more abstract TAM 1 works with cloud
computing, whereas TAM 2 had to be rearranged in
order to give a good fit. Our final model can be used
for further research on the cloud computing subject.
form of regulatory compliance on the other. Examples
are security, privacy or disclosure of data. The ultimate
goal of the research process would be a comprehensive
model of cloud computing adoption.
In conclusion, technology acceptance of cloud
computing remains a complex yet important phenomenon. With our final model however, we made significant progress towards unraveling some of its mysteries.
It thereby contributes to the body of knowledge and
can be used in further research.
6. References
[1] R.K. Chellappa, "Intermediaries in Cloud-Computing: A New
Computing Paradigm", in Proceeding of INFORMS, Dallas,
1997.
[2] S. Ramireddy, R. Chakraborthy, T.S. Raghu, H. Raghav Rao,
“Privacy and Security Practices in the Arena of Cloud Computing – A Research in Progress”, in Proceedings of the 16th
Americas Conference on Information Systems, Lima, Peru,
2010, Paper 574.
[3] A. Weiss, “Computing in the Clouds”, ACM Networker (11:4),
2007, pp. 18-25.
[4] P. Koehler, A. Anandasivam, M. Dan, “Cloud Services from a
Consumer Perspective“, in Proceedings of the 16th Americas
Conference on Information Systems, Lima, Peru, 2010, Paper
329.
[5] F.D. Davis, “Perceived Usefulness, Perceived Ease of Use, and
User Acceptance in Information Technology“, in MIS Quarterly (13:3), 1989, pp. 319-340.
[6] M. Vaquero, L. Redero-Merino, J. Caceres, M. Lindner, “A
break in the clouds: towards a cloud definition”, Computer
Communication Review (39:1), 2009, pp. 50 55.
[7] D. Plummer, T. Bittman, T. Austin, D. Clearly, D. Smith,
Cloud Computing: Defining and describing an emerging phenomenon, Technical report, Gartner Group, 2008.
[8] M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A.
Konwinski, G. Lee, D. Paterson, A. Rabkin, I. Stoica, M. Zaharia, Above the Clouds: A Berkeley View of Cloud Computing,
Technical Report UCB, EECS Department, University of California, Berkeley, 2009.
[9] P. Mell, T. Grance, The NIST Definition of Cloud Computing,
Technical report, National Institute of Standards and Technology, 2009.
[10] I.T. Foster, Y. Zhao, I. Raicu, S. Lu, “Cloud Computing and
Grid Computing 360-Degree Compared”, in Proceedings of the
Grid Computing Environments Workshop 2008, Austin, Texas,
2008, pp. 1 10.
[11] E. Unal, D. Yates, “Enterprise Fraud Management using Cloud
Computing: A Cost-Benefit Analysis Framework“, in Proceeding of the 18th European Conference on Information Systems,
2010, Paper 144.
[12] A. Anandasivam, C. Weinhardt, “Towards an Efficient Decision Policy for Cloud Service Providers”, in Proceeding of the
31st International Conference on Information Systems, St.
Louis, 2010, Paper 40.
[13] R. Buyya, C.S. Yeo, S. Yen, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as
Computing Utilities”, in Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and
Communications, 2008, pp. 5-13.
[14] T. Ercan, “Effective use of cloud computing in educational
institutions”, in Procedia Social and Behavioral Sciences (2),
2010, pp. 938–942.
5.3. Further research
For further research we suggest a two step process.
Step one concerns cloud computing, TAM and the
limitations of our survey. It should be replicated in
other countries to determine if the results are transferable to different social standards, laws & regulations or
economics. Other target groups should be examined,
including non government organizations or end users.
The outcomes should be discussed by conducting case
studies in order to get qualitative feedback in addition
to our quantitative analysis. Another important question concerns the underlying aspect of the variables
Output Quality and Perceived Usefulness. The high
correlation between these two and the similar regression coefficients from Image, Job Relevance and Result Demonstrability on these two variables suggest
that there could be one underlying variable that has to
be found and named. The second step of the further
research process involves finding new variables for
explaining cloud computing adoption. As has been
mentioned earlier on, TAM originates from social
sciences. Adoption variables deal with behavioral
elements such as attitude and intention. In a next step
we will examine new variables in the scope of influences from within companies like cost/benefit, risk or
usability on the one side and external influence in the
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[24] Y. Kim, P. Zhang, “Continued Use of Technology: Combining
Controlled and Automatic Processes”, in Proceedings of the
31st International Conference on Information Systems, St.
Louis, 2010, Paper 214.
[25] P.N. Hague, “Market research: a guide to planning, methodology & evaluation”, 3rd Ed., Kogan Page, London, 2002.
[26] D. Mani, A. Barua, A. Whiston, “An Empirical Analysis of the
Impact of Information Capabilities Design on Business Process
Outsourcing Performance”, in MIS Quarterly (34:1), 2010, pp.
39-62.
[27] L. Poppo, T. Zanger, “Do formal contracts and relational
governance function as substitutes of complements?”, in Strategic Management Journal (23), 2009, pp. 707-725.
[28] A. Field, “Discovering statistics using SPSS”, 3rd Ed., Sage,
Los Angeles et al., 2009.
[29] H.F. Kaiser, “A second generation little jiffy”, in Psychometrika (35:4), 1970, pp. 401-415.
[30] J. Nunnally, I. Bernstein, “Psychometric theory”, 3rd Ed.,
McGraw-Hill, New York, 1994.
[31] N. Draper, H. Smith, H, “Applied regression analysis”, 3rd Ed.,
Wiley, New York et al., 1998.
[32] J.T. Mentzer, D.J. Flint, “Validity in Logistics Research”, in
Journal of Business Logistics (18), 1997, pp. 199-216.
[33] M. Fishbein, I. Ajzen, “Belief, Attitude, Intention and Behavior. An Introduction to Theory and Research”, Reading, MA,
1975.
[34] G. Moore, C. I. Benbasat, “Development of an instrument to
measure the perceptions of adopting an information technology
innovation”, in Information Systems Research (2:3), 1991, pp.
192-222.
[15] R.L. Grossmann, Y. Gu, M. Sabala, W. Zhang, “Compute and
Storage Clouds Using Wide Area High Performance Networks”, in Future Generation Computer Systems (25:2) 2009,
pp. 179-183.
[16] W. Kim, “Cloud Computing: Today and Tomorrow”, in Journal of Object Technology (8:1), 2009, pp. 65-72.
[17] X. Li, “Cloud Computing: Introduction, Application”, in
International Journal of Computer Science and Network Security (11:5), 2011.
[18] S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, A. Ghalsasi,
“Cloud computing - The business perspective”, in Decision
Support Systems (51), 2011, pp. 176-189.
[19] L. Mei, W.K. Chan, T.H. Tse, “A Tale of Clouds: Paradigm
Comparisons and Some Thoughts on Research Issues”, in Proceedings of the 2008 IEEE Asia-Pacific Services Computing
Conference, Los Alamitos, CA, 2008.
[20] A.B. Mohammed, J. Altmann, J. Hwang, “Cloud Computing
Value Chains: Understanding Businesses and Value Creation in
the Cloud”, in Economic Models and Algorithms for Distributed Systems, Part III, 2010, pp. 187-208.
[21] S. Subashini, V. Kavitha, “A survey on security issues in
service delivery models of cloud computing”, in Journal of
Network and Computer Applications (34), 2011, pp. 1-11.
[22] V. Venkatesh, F.D. Davis, “A theoretical extension of the
technology acceptance model: Four longitudinal field studies”,
in Management Science (46:2), 2000, pp. 186-204.
[23] W.R. King, “A meta-analysis of the technology acceptance
model”, in Information & Management (43:6), 2006, pp. 740755.
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