Cloud Computing Technology Acceptance Model Literature Review Summary Read the literature review attached. Provide a summary of the literature review. Chap

Cloud Computing Technology Acceptance Model Literature Review Summary Read the literature review attached. Provide a summary of the literature review. Chapter II. LITERATURE REVIEW
Introduction
In order to have a clear picture of the pathway to the effective use of Cloud Computing, an
extensive study was performed. This thesis surveyed the existing literature on topics relating to
Cloud adoption including technology acceptance or adoption models, influential features, and
rate of adoption and implementation.
Background
Related literature demonstrates that technology acceptance and characteristic features of
Cloud Computing occupy most recent studies. A majority of studies on the adoption of
technology uses the Technology Acceptance Model (TAM) to illustrate user tendencies towards
the adoption of new technology.
In this study, we assessed the literature on Cloud adoption and identified the key factors
affecting Cloud adoption. Cloud security, privacy, standards and compliance issues are heavily
cited as factors impeding the adoption of the Cloud (Dillon, Wu, & Chang, 2010; Khan, 2012;
Lewis, 2013; Pant et al., n.d.; Ryan K. L. Ko, Stephen S. G. Lee, 2013; Sadiku, Musa, &
Momoh, 2014). Technology acceptance in information systems theory models how users accept
and use a technology, with a number of influential factors taken into consideration. Cloud
Computing adoption is directly related to technology acceptance since Cloud Computing is an
consolidation of multiple technologies.
– 31 –
Technology Acceptance Model
The evolution of IT in the past several decades has stirred a lot of research interest in the
area of technology adoption (Nan, Xunhua, & Guoqing, 2008; Venkatesh & Davis, 2000).
This research resulted in a number of theoretical models with the Technology Acceptance
Model heading the charts as the most popular. This model was derived from Fishbein and
Ajzen’s Theory of Reasoned Action (TRA), and emphasizes users’ beliefs, attitudes and
intentions in adopting technology (P. Lin, Lu, & Liu, 2013). The TAM has been cited to be
the only model which has captured the most attention of the Information Systems community
(Chuttur, n.d.-a). It was first proposed by Fred Davis in a thesis at the MIT Sloan School of
Management. He suggested that system use is dependent on the user’s motivation which is
directly influenced by the system’s features and capabilities. The user’s motivation is
influenced by three factors: Perceived ease-of-use (PEOU), Perceived usefulness (PU) and
attitudes towards usage (ATU). This model went through refinements and one of the most
stable versions is depicted in Figure 10 below.
Figure 10: Technology Acceptance Model
– 32 –
In general technology acceptance refers to an information systems theory that models
how users adopt and use a new technology. The TAM asserts that perceived ease-of-use
(that is, using the system will be free of effort) and perceived usefulness (an indicator that
gauges whether the technology helps the user perform their job better) are two important
determinants that influence the acceptance or rejection of a new technology (Davis & Davis,
1989). In a study by (Aceh, 2002) the model in Figure 10 was used to predict user intentions
to use internet banking. Table 3 shows the use of the model in other applications.
Related Models
Figure 11: Illustrations of (a) the Technology Acceptance Model (TAM), and related
theories, including (b) TAM2, (c) the Unified Theory of Acceptance and Use of
Technology (UTAUT), and (d) the Theory of Planned Behavior (TPB). Source: (Holden
& Karsh, 2010)
– 33 –
Figure 11 illustrates models related to the TAM; this includes the TAM, TAM2
(Technology Acceptance Model 2), UTAUT (Universal Theory of Acceptance and Use of
Technology), and the TPB (Theory of Planned Behavior).
IT leaders need a means or method for predicting and understanding technology adoption
and use. At minimum, the need for a theoretical framework to guide them make decisions on
technology adoption is vital. Some theoretical models employed to study user acceptance,
adoption, and usage behavior include the Technology-Organization-Environment (Oliveira,
Martins, & Lisboa, 2011), the Technology Acceptance Model (Aceh, 2002; Bharadwaj &
Lal, 2012; Chen, 2011; Chuttur, n.d.-a; Gu & Liu, 2011; Lederer, Maupin, Sena, & Zhuang,
2000; Maqueira & Bruque, 2006; Money & Ph, 2004; Partner & Teknoloji, n.d.; Turner,
Kitchenham, Brereton, Charters, & Budgen, 2010; H. Zheng, Li, & Jiang, 2012), the
Technology Trade Theory (Triple-T) model (Obeidat & Turgay, 2012b), the Cloud Maturity
Model (RightScale, 2013), the Rough Set Theory (Wu, 2011), the Unified Theory of
Acceptance and Use of Technology model (Im, Hong, & Kang, n.d.), and others.
In the literature available on technology acceptance, the Technology Acceptance Model
was the most cited. The TAM was developed in response to the need to understand and
predict user acceptance of technology (Davis & Davis, 1989). This framework consists of
two dependent variables: Behavioral Intention to Use and System Usage; and two
independent variables: Perceived Usefulness and Perceived Ease of Use. An individual’s
perception of the usefulness and ease of use determines their intention to use the system and
– 34 –
hence implies the actual system use. The model speculates that perceived ease of use also
affects perceived usefulness.
In a study by (Holden & Karsh, 2010), the authors examined the application of the TAM
to health care. The researchers reviewed 16 data sets analyzed in over 20 studies of clinicians
using health IT for patient care. This study confirms some of the limitations of the TAM,
stating that “certain TAM relationships were consistently found to be significant, whereas
others were inconsistent”.
The TAM has become quite popular in the academia that it has been used in diverse
applications. Other researchers believe that national culture also influences the actual
behavior and as such cultural factors should be considered in the development of adoption
models (Thowfeek & Jaafar, 2010). This suggests that an adaptable framework is needed to
fit the demands of multitude of applications.
Application of Existing Models
Other studies have used the TAM or adapted versions of it for different purposes. Table
3 below shows the application of the TAM or related models in areas ranging from mobile
commerce to Cloud Computing; this illustrates the popularity of the TAM. Though the TAM
portrays some usefulness, its shortcomings demand the emergence of a flexible and complete
framework compatible with today’s technologies.
Application
Education
Internet
Descriptions and references
Examines students’ behavioral intention to use an electronic portfolio
system (Shroff, Deneen, & Ng, 2011).
To evaluate the World Wide Web user behavior (Lederer et al., 2000).
– 35 –
Mobile
Commerce
Grid
Information
Technology
Knowledge
management
system
Cloud
Computing
(SaaS)
Cloud Security
Digital library
Cloud
Computing
Health IT
Health IT
Smart Card
Technology
E-Commerce
Cloud
Computing
Cloud
Computing
Social Media
Investigates consumers’ willingness to accept mobile commerce and
factors that influence their acceptance of mobile commerce (H. Zheng
et al., 2012).
Studies the influence of environmental, organizational and innovation
factors to the adoption of Grid IT (Maqueira & Bruque, 2006).
Studies user acceptance of a knowledge management information
system (Money & Ph, 2004).
Assesses the factors which affect the cognition and acceptance of
SaaS-based services (Gu & Liu, 2011).
Models and provide more understanding about security in the Cloud
(Partner & Teknoloji, n.d.).
Evaluates features of the digital library and determine whether
technology acceptance predicts the actual usage of the technology
(Turner et al., 2010).
Investigates the acceptance of Cloud Computing technology (Opitz,
Langkau, Schmidt, & Kolbe, 2012).
Predicts and explain health IT acceptance and use (Holden & Karsh,
2010).
Analyzes the features impacting the adoption of Assistive Technology
for people with dementia (Zhang et al., 2014).
Examines the factors that influence user acceptance of the technology
(Taherdoost & Masrom, 2009).
Assesses e-commerce adoption using TAM characteristics as essential
basis for verifying user acceptance of ecommerce/e-business
technology (Depickere, 2007).
Finds out whether the TAM can be applied as a viable model for
Cloud adoption (Tjikongo & Uys, 2013).
Explores the significant factors affecting the adoption of SaaS for
enterprise users (Wu, 2011).
Explores and add to the theoretical knowledge base pertaining to
adoption of newer technology such as Social Networking amongst the
business students (Dhume, Pattanshetti, Kamble, & Prasad, 2012).
Provides an integrated research model and explore how it can serve as
a basis for mediated communication adoption(C. Lin, 2003).
Uses TAM related constructs in evaluating the impact of fundamental
characteristics of a technology towards adoption (Im et al., n.d.).
Interactive
Communication
MP3 players
and Internet
banking
Table 3: Application of TAM and related models
– 36 –
Limitations of existing models
Though the TAM is popular and highly cited in academic works, it has some limitations
that have exposed the model to criticisms. Researchers have criticized the TAM as not
completely representing a model that captures all features necessary for technology
acceptance. (Chuttur, n.d.-b) states that researchers share mixed opinions regarding TAM
theoretical assumptions, and practical effectiveness.
The origin of the first version of the TAM dates back to 1985 when its author proposed
the model in his doctoral thesis at the MIT Sloan School of Management. This leaves the
model at a disadvantage because technology has gone through many revolutions since then
and the model has not been updated accordingly to keep up with the advances in modern
technology. In particular and most essentially, the model does not address the features of
Cloud Computing.
The TAM does not address the features or characteristics of modern technologies. Figure
12 shows some of the measurable characteristics of the TAM grouped into two key
categories namely usefulness and ease of use. In contrast, our proposed model (see Figure
13), the CCAUM extends the TAM and TAM2 to include five additional categories of
features namely technology features, economic factors, security and privacy, standards and
control. The TAM and its related features are inadequate to clearly and sufficiently represent
today’s modern technologies. (Holden & Karsh, 2010) suggests that the TAM may benefit
from several additions and modifications. The CCAUM represents a modern framework that
aligns with the innovations of recent times.
– 37 –
Figure 12: TAM Usefulness and Ease of Use characteristics
The CCAUM presents a flexible and extensive model that incorporates current Cloud
Computing features and allows for future updates to adapt to emerging technologies. A
mathematical representation of this model is demonstrated using equations (refer to Equation
3 – Equation 12) and experiments that help to predict successful adoption and use of
technology, particularly relating to Cloud Computing.
Proposed Model
In (Davis & Davis, 1989), the researchers considered only two behavioral factors (perceived
ease-of-use and perceived usefulness) as determinants of technology acceptance. Two
determining factors are not sufficient to assist potential Cloud adopters in making a reasonable
decision. People, process and technology are the three key elements for a successful
organizational transformation (Konrad, 1996; Ramakrishnan & Testani, 2011). We believe that
Cloud Computing adoption involves organizational transformation and should consider these key
elements for an effective implementation.
In order to bridge this gap, we take into consideration significantly more factors including the
TAM features. The following factors were used for the development of our Cloud Adoption and
– 38 –
Use Model which also provides for flexibility and adaptability to accommodate more features to
suit modern applications, see Figure 14 and the listing below.
I.
II.
III.
IV.
V.
VI.
Behavioral Features (illustrated by the TAM, see Figure 10)
1. Perceived Usefulness
2. Perceived Ease of Use
Technology Features
1. On-demand self-service
2. Heterogeneous or broad network access
3. Resource pooling
4. Rapid elasticity and scalability
5. Measured service – pay per use
6. Multi Tenacity
7. Availability and Continuity
8. Energy consumption – efficiency
9. Quality of Service (QoS)
10. Manageability – people, technology and process
11. Cost – TCO – capital expenditure (CapEx) + operational expenditure (OpEx)
12. Device and location independence
13. Performance and reliability
14. Maintenance
Economic Factors
1. Ease of adaption to new markets
2. Global access to a wide customer base due to the broad network access capability
3. Flexible cost model – pay-as-you-go
4. Cost Savings – OpEx is the New CapEx, TCO, ROI
Security and Privacy
Standards
Process and Control
– 39 –
Figure 13 below illustrates the CCAUM which is an extension of the TAM. This novel
model is divided into three phases namely phase A, phase B and phase C.
Figure 13: Proposed Cloud Computing Adoption and Use Model
Phase A: Essential Features (see Figure 13)
Phase A incorporates the features and mechanisms essential to determining Cloud
Computing adoption. Organizations or individuals contemplating the adoption of Cloud
Computing will perform an evaluation using the following algorithm:
Step 1:
From a table of technology essential features, identify or select those features that
apply to the particular business (1 = essential and 0 = not essential), see illustration in Figure 14 .
Step 2: Input the selected values into the adoption model described in Phase B.
– 40 –
Figure 14: Essential Technology Features
Step 3: Assign an appropriate weight, to each of the essential features to indicate the
level of importance of this feature for the organization in question. Some Cloud Computing
features could be very important to one organization or business but not to another. Take for
example; a grocery store may not pay as much importance to security as would a banking or
national defense organization.
Phase B: Cloud Adoption Framework (see Figure 13)
Step 4:
The CCAUM uses a mathematical function in phase B as shown in Figure 13 to
compute values that will help in determining whether a business is ready to adopt Cloud
– 41 –
Computing. This process is discussed in details in subsequent sections. The result of this step
will advise the user whether or not to adopt the Cloud.
Step 5: If the determination is for the user to adopt the Cloud then phase C is invoked,
otherwise the process ends and the user may reconsider restarting the whole process from the
beginning with a new set of input values for the Cloud features.
Phase C: Cloud Computing Roadmap and Use (see Figure 13)
Step 6: In phase C, a technology roadmap gives guidance and paves a path towards
implementing and integrating the Cloud. For an existing organization, this phase starts with a
clear understanding of the current or as-is environment (which is presumably non-Cloud). A
transition from the current environment will be accompanied by transition or modernization
projects which convert the current environment to the target or end state.
Phase C of the CCAUM (see Figure 13) deals with the actual use of the technology in
use. In our study, we use the essential characteristics of Cloud Computing (On-demand selfservice, broad network access, resource pooling, rapid elasticity and measured service) to
demonstrate its effectiveness and efficiency. Experiments to demonstrate the essential benefits of
adoption Cloud Computing are presented in Chapter IV.
– 42 –

Purchase answer to see full
attachment