cloud computing adoption by smes in australia -...
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Cloud Computing Adoption by SMEs in Australia
by
ISHAN RUWAN SENARATHNA
MBA, BSc., IM (Hons)
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
Deakin University
February 2016
Dedication
I dedicate this thesis to my beloved parents
You have successfully made me the person I am becoming
You will always be remembered
Acknowledgements
I would like to express my sincere gratitude to several individuals who helped me
throughout my PhD. My sincere appreciation goes to my principal supervisor, Professor
Matthew Warren, for his persistent support, consistent supervision, and continuous
encouragement throughout this long journey. The precious support he extended to me in
order to gain a scholarship is highly appreciated and this research would not have been
possible without his support. Undoubtedly, without his continuous guidance, insightful
comments, critical reviews, inspiration and patient help, finishing this thesis would not
have been possible. I am extremely thankful and indebted to my associate supervisors,
Doctor William Yeoh and Doctor Scott Salzman, for sharing their expertise, and their
sincere and valuable guidance and encouragement extended in the preparation of this
thesis.
I gratefully acknowledge the support of the Deakin University and NCAS Sri Lanka, who
contributed to the completion of this thesis by providing a scholarship and PhD grant for
the duration of my study. I am grateful to the market research company ‘Research Now’
who assisted me in data collection. This thesis could not have been completed without
the assistance of all these organisations.
I take this opportunity to express my gratitude to the staff of the Department of
Information Systems at the Faculty of Business and Law of Deakin University,
Melbourne, who supported me in my research. I must also acknowledge Ms Julie Asquith
from the Faculty of Business and Law for her administrative support throughout my PhD
study. I am also grateful to the doctoral students in the Faculty for their support and
encouragement.
My sincere appreciation goes to my family for the support they provided me throughout
my life, and in particular I must acknowledge my wife Eureka and two children, son
Vibudha and daughter Yashara, without whose loving support, encouragement and
understanding I would not have finished this thesis. I would also like to thank my parents-
in-law for looking after our kids and all other immense support they provided during my
studies.
I also place on record, my sense of gratitude to one and all who, directly or indirectly,
have lent their hand in this venture.
Related Publications
Refereed Journals
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2016)“Security and Privacy Concerns for Australian SMEs Cloud Adoption: Empirical Study of Metropolitan Vs Regional SMEs”, Australasian Journal of Information Systems, 20, pp 1-20.
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2014) “The Influence of Organisation Culture on E-Commerce Adoption”, Industrial Management & Data Systems, 114(7), pp. 1007-1021, Emerald Group.
Refereed Book Chapters
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2015) A Conceptual Model for Cloud Computing Adoption by SMEs in Australia,Delivery and Adoption of Cloud Computing Services in Contemporary Organizations, pp. 100-128, IGI-Global.
Refereed Conference Papers
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2014)“Security and Privacy Concerns for Australian SMEs Cloud Adoption”,Proceedings of the ICIS 2014 SIGSEC: Workshop on Information Security & Privacy 2014, Auckland University, New Zealand.
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2013) “An Empirical Study of the Influence of Different Organisation Cultures on E-Commerce Adoption Maturity”, Proceedings of the 24th Australasian Conference on Information Systems, ACIS 2013, pp. 1-10, RMIT, Melbourne, Vic.
Senarathna, Ishan; Warren, Matthew; Yeoh, William and Salzman, Scott (2013) “Ethical Issues of Cloud Computing Use by SMEs”, Proceedings of the 7th Australian Institute of Computer Ethics Conference, AiCE 2013, pp. 64-66, RMIT, Melbourne, Vic.
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Abstract
This research investigates the factors that influence small and medium-sized enterprises’
(SMEs) behavioural intention towards the adoption of Cloud computing within Australia.
The growing adoption of Cloud computing is changing the way of businesses
maintaining, selecting, updating and managing information and communication
technology. In particular, Cloud computing promises to improve the reliability and
scalability of IT systems, which allows SMEs to focus their limited resources on their
core business and strategy. In the SME context, technology adoption and usage decisions
are influenced by many factors. Despite the extensive literature, there is still limited
research related to the factors which impact on SMEs’ adoption of Cloud computing.
Therefore, investigating SMEs’ adoption of Cloud computing is an important issue for
the successful implementation of this system.
In this thesis, the diffusion of innovation theory (DOI) and the technological,
organisational and environmental framework (TOE) were combined to model the
relationship between factors hypothesised and Cloud computing adoption to increase the
probability that SMEs adopted Cloud computing successfully. It was hypothesised that
SMEs’ intention to adopt Cloud computing as a dependent variable was influenced by
six independent variables: Cloud relative advantages (CRA), Cloud flexibility (CF),
Quality of service (QoS), Cloud security (CS), Cloud privacy (CP) and Awareness of
Cloud (AWC).
To address the research objectives, a quantitative research design was applied using an
online questionnaire. A conceptual model of Cloud computing adoption by SMEs in
Australia has been developed. Several factors were identified as important for influencing
the likelihood that SMEs would adopt Cloud computing successfully. In particular,
AWC, CRA and QoS were found to be statistically significant contributory factors. The
variances of Cloud adoption among the varying sizes of organisations was found to differ
and be statistically significant towards adopting Cloud computing. Hence, this result is
important to owners and decision makers of SMEs, service providers, service consultants
and governments, to enable them to facilitate the adoption of Cloud computing by SMEs.
Further, this may help to establish strategies for SMEs to ensure a better adoption of
Cloud computing, thus stimulating the implementation process.
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Table of Contents
Dedication .................................................................................................................................. ivAcknowledgements .................................................................................................................... vRelated Publications ..................................................................................................................... viAbstract ....................................................................................................................................... viiTable of Contents ....................................................................................................................... viiiList of Tables .............................................................................................................................. xiiList of Figures ............................................................................................................................ xivList of Appendices ...................................................................................................................... xvList of Abbreviations ................................................................................................................. xvi
CHAPTER 1: INTRODUCTION ................................................................................................. 11.1 Introduction .............................................................................................................. 11.2 Problem Statement .................................................................................................... 41.3 Research Purpose ...................................................................................................... 61.4 Theoretical Framework............................................................................................. 71.5 Research Questions .................................................................................................. 81.6 Research Significance............................................................................................... 91.7 Thesis Structure ...................................................................................................... 10
CHAPTER 2: LITERATURE REVIEW .................................................................................... 132.1 Introduction ............................................................................................................ 132.1.1 Cloud Computing ................................................................................................ 132.1.1.1 Development of Cloud Computing ..................................................................... 132.1.1.2 Cloud Computing Definitions ............................................................................. 132.1.1.3 Cloud Computing Services ................................................................................. 232.1.1.4 Cloud Computing Deployment Models ..............................................................262.1.1.5 Cloud Computing Charactoristics .......................................................................282.1.1.6 Advantages of Cloud Computing ........................................................................302.1.1.7 Disadvantages of Cloud Computing ...................................................................32 2.1.1.8 Some Technical and Business Issues of Cloud Computing ................................ 132.1.1.9 Cloud Computing Adoption ................................................................................ 132.1.1.10 Current usage of Cloud Computing ....................................................................362.1.2 Small and Medium-Sized Enterprises (SMEs) .................................................... 372.1.3 SMEs and Cloud Computing .............................................................................. 402.1.4 Cloud Computing Adoption in SMEs ................................................................. 412.2 Theoretical Foundation ........................................................................................... 452.2.1 ICT Innovations Adoption .................................................................................. 452.2.2 Technology Organisation Environment .............................................................. 472.2.3 Diffusion of Innovations ..................................................................................... 522.2.4 TOE & DOI ......................................................................................................... 612.3 Chapter Summary ................................................................................................... 63
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CHAPTER 3: RESEARCH MODEL AND HYPOTHESIS DEVELOPMENT ........................ 653.1 Introduction ............................................................................................................ 653.2 Towards the Research Model ................................................................................. 653.3 Hypothesis Development ........................................................................................ 723.3.1 Cloud Relative Advantage (CRA)....................................................................... 723.3.2 Cloud Flexibility (CF) ......................................................................................... 743.3.3 Quality of Service (QoS) ..................................................................................... 763.3.4 Cloud Security (CS) ............................................................................................ 783.3.5 Cloud Privacy (CP) ............................................................................................. 803.3.6 Awareness of Cloud (AWC) ............................................................................... 823.4 Chapter Summary ................................................................................................... 87
CHAPTER 4: RESEARCH METHODOLOGY ........................................................................ 884.1 Introduction ............................................................................................................ 884.2 The Research Methods ........................................................................................... 884.3 Data Collection ....................................................................................................... 904.3.1 Questionnaire Design .......................................................................................... 914.3.2 Development of the Research Factors ................................................................. 944.3.2.1 ADOPT ............................................................................................................... 944.3.2.2 Cloud Relative Advantages............................................................................... 954.3.2.3 Cloud Flexibility ............................................................................................... 954.3.2.4 Quality of Service ............................................................................................. 964.3.2.5 Cloud Security ................................................................................................... 964.3.2.6 Cloud Privacy .................................................................................................... 974.3.2.7 Awareness of Cloud .......................................................................................... 974.3.3 Pre-testing the Survey ......................................................................................... 984.4 Sampling Design .................................................................................................... 994.4.1 Target Population ................................................................................................ 994.4.2 Sample Size and Sampling Method .................................................................... 994.4.3 Questionnaire Administration ........................................................................... 1004.5 Data Analysis ........................................................................................................ 1024.5.1 Data Screening .................................................................................................. 1024.5.1.1 Accuracy and Missing Data .............................................................................. 1024.5.1.2 Outliers .............................................................................................................. 1024.5.1.3 Normality .......................................................................................................... 1024.5.2 Descriptive Analysis ......................................................................................... 1034.5.3 Validation of the Measures ............................................................................... 1044.5.3.1 Reliability of the Measures ............................................................................... 1044.5.3.2 Validity of the Measures ................................................................................... 1044.5.4 Multiple Linear Regression ............................................................................... 1074.5.5 Assumptions of the MLR Analysis ................................................................... 1084.5.5.1 Sample Size ....................................................................................................... 1084.5.5.2 Normality .......................................................................................................... 1084.5.5.3 Linearity ............................................................................................................ 1084.5.5.4 Multicollinearity ................................................................................................ 1084.5.5.5 Outliers............................................................................................................ 1094.6 Chapter Summary ................................................................................................. 109
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CHAPTER 5: RESULTS .......................................................................................................... 1105.1 Introduction .......................................................................................................... 1105.2 Response Rate ...................................................................................................... 1105.3 Data Screening ...................................................................................................... 1105.4 Descriptive Analysis ............................................................................................. 1115.4.1 Demographic Classification of Respondent Organisations ............................... 1115.4.1.1 Current Position of the Respondent .................................................................. 1115.4.1.2 Organisation Type........................................................................................... 1125.4.1.3 Duration of the Organisation........................................................................... 1125.4.1.4 Industry Type .................................................................................................. 1135.4.1.5 State or Territory............................................................................................. 1145.4.2 Use of Cloud Computing .................................................................................. 1145.4.3 Descriptive Analysis of the Adoption Factors .................................................. 1145.4.3.1 ADOPT .......................................................................................................... . 115 5.4.3.2 CRA ............................................................................................................... . 115 5.4.3.3 CF................................................................................................................ .... 116 5.4.3.4 QoS.............................................................................................................. .... 117 5.4.3.5 CS................................................................................................................ .... 117 5.4.3.6 CP................................................................................................................. ... 118 5.4.3.7 AWC ............................................................................................................ ... 118 5.5 Reliability and Validity Assessment of the Measures .......................................... 1195.5.1 Reliability Assessment of the Measures............................................................ 1195.5.2 Validity Assessment of the Measures ............................................................... 1205.6 Hypotheses Testing .............................................................................................. 1245.6.1 Explaining the Adoption of Cloud Computing by SMEs ................................. 1245.6.2 Factors Influencing SMEs to Adopt Cloud Computing .................................... 1255.6.2.1 CRA .................................................................................................................. 125 5.6.2.2 CF............................................................................................................... ..... 126 5.6.2.3 QoS............................................................................................................... ... 126 5.6.2.4 CS................................................................................................................. ... 126 5.6.2.5 CP................................................................................................................ .... 126 5.6.2.6 AWC .......................................................................................................... ..... 127 5.7 Cloud Computing Adoption Based on Company Size ......................................... 1275.7.1 Explaining the Adoption of Cloud Computing by Micro Organisations .......... 1275.7.2 Explaining the Adoption of Cloud Computing by Small & Medium-sized
Organisations ................................................................................................... 1295.7.3 Micro vs Small & Medium Organisations ........................................................ 1305.8 Cloud Adoption Based on Organisation Type ...................................................... 1315.9 Summary of the Hypothesis testing ...................................................................... 1315.10 Chapter Summary ................................................................................................. 132
CHAPTER 6: DISCUSSION .................................................................................................... 1336.1 Introduction .......................................................................................................... 1336.2 Validity of the Research Instrument ..................................................................... 1336.3 The Research Model and SMEs’ Perception of Adopting Cloud Computing ...... 1336.4 Factors Influencing Adoption of Cloud Computing ............................................. 1346.4.1 CRA and ADOPT ............................................................................................. 1356.4.2 CF and ADOPT ................................................................................................. 136
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6.4.3 QoS and ADOPT...............................................................................................1386.4.4 CS and ADOPT.................................................................................................1386.4.5 CP and ADOPT.................................................................................................1406.4.6 AWC and ADOPT ............................................................................................1416.5 Cloud Computing Adoption Based on Company Size ......................................... 1436.6 Cloud Adoption Based on Organisation Type...................................................... 1436.7 The Revised Research Model ............................................................................... 1446.8 Chapter Summary................................................................................................. 145
CHAPTER 7: CONCLUSION..................................................................................................1467.1 Introduction .......................................................................................................... 1467.2 Overview of the Research..................................................................................... 1467.3 Summary of Findings ........................................................................................... 1487.3.1 The Research Model and SMEs to Adopt Cloud Computing ...........................1487.3.2 Factors Influencing SMEs to Adopt Cloud Computing ....................................1497.4 Implications .......................................................................................................... 1517.5 Contributions ........................................................................................................ 1537.5.1 Contribution to Theory......................................................................................1537.5.2 Contribution to Practice ....................................................................................1557.6 Limitations............................................................................................................ 1557.7 Future Research .................................................................................................... 1567.8 Conclusion............................................................................................................ 157
REFERENCES..........................................................................................................................159APPENDICES ..........................................................................................................................194
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List of Tables
Table 2.1: Distinguishing attributes of Cloud computing vs traditional computing (Adaptedfrom Melvin Greer 2009) ..........................................................................................17
Table 2.2: Cloud computing definitions......................................................................................22Table 2.3: Cloud computing service models (Adapted from Dillon et al. 2010) ........................25Table 2.4: Cloud computing deployment models (Adapted from Dillon et al. 2010).................27Table 2.5: Cloud computing essential characteristics (Adapted from Grance 2010;
Dillon et al. 2010) ......................................................................................................29Table 2.6: Advantages of Cloud computing ...............................................................................31Table 2.7: Disadvantages of Cloud computing ...........................................................................32Table 2.8: Technical and business issues of Cloud computing...................................................33Table 2.9: SME definition...........................................................................................................38Table 2.10: Analysis of Cloud computing constructs in the SME context .................................43Table 2.11: Theoretical frameworks used to examine IT adoption.............................................47Table 2.12: Examples of TOE-based studies ..............................................................................52Table 2.13: Examples of DOI-based studies...............................................................................61Table 2.14: Examples of studies combining TOE and DOI models ...........................................62Table 3.1: Examples of TOE & DOI usage in research ..............................................................68Table 3.2: Constructs used to examine Cloud computing adoption (Developed for this study).85Table 4.1: Research approach options (Adapted from Creswell 2003) ......................................90Table 4.2: Summary of the questionnaire instrument .................................................................90Table 5.1: Response rate ...........................................................................................................110Table 5.2: Skewness and Kurtosis values of the adoption factors ............................................111Table 5.3: Frequencies of respondents’ current position ..........................................................112Table 5.4: Frequencies of organisation type .............................................................................112Table 5.5: Frequencies of organisations’ length of operation...................................................113Table 5.6: Frequencies of organisations’ industry type ............................................................113Table 5.7: Frequencies of organisations’ state or territory........................................................114Table 5.8: Frequencies of Cloud computing usage by organisations........................................114Table 5.9: Frequency distribution of the ADOPT.....................................................................115Table 5.10: Frequency distribution of the CRA........................................................................116Table 5.11: Frequency distribution of the CF ...........................................................................116Table 5.12: Frequency distribution of the QoS.........................................................................117Table 5.13: Frequency distribution of the CS ...........................................................................118Table 5.14: Frequency distribution of the CP ...........................................................................118Table 5.15: Frequency distribution of the AWC.......................................................................119Table 5.16: Reliability of the measures.....................................................................................119Table 5.17: Results of KMO and Bartlett’s Test of Sphericity.................................................120Table 5.18: Total variance in the initial FA ..............................................................................121Table 5.19: Total variance in the FA for independent variables ...............................................121Table 5.20: Pattern matrix of the FA for the independent variables .........................................122Table 5.21: Total variance in the FA for the dependent variable..............................................122Table 5.22: ANOVAa (Significance of overall regression relationship) ...................................125Table 5.23: Coefficients of MLR (Factors explaining perceptions of SMEs) ..........................125Table 5.24: Organisation size and Cloud adoption ANOVA....................................................127Table 5.25: ANOVAa (Significance of overall regression relationship - Micro)......................128
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Table 5.26: Coefficients of MLR (Factors explaining perceptions of Micro organisations) ....129Table 5.27: ANOVAa (Significance of overall regression relationship – Small and Medium) 129Table 5.28: Coefficients of MLR (Factors explaining perceptions of Small and Medium
organisations) .......................................................................................................130Table 5.29: Independent sample t-test ......................................................................................131Table 5.30: Organisation type and Cloud adoption ANOVA ...................................................131Table 5.31: Summary of the Hypothesis testing .......................................................................132
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List of Figures
Figure 1.1: Flow chart of the thesis.............................................................................................12Figure 2.1: Traditional computing vs Cloud computing .............................................................17Figure 2.2: NIST Cloud computing definition (Adapted from Grance 2010).............................21Figure 2.3: Cloud computing systems (Adapted from Jeffrey & Neidecker-Lutz 2009)............30Figure 2.4: TOE framework........................................................................................................48Figure 2.5: Stages of adoption process .......................................................................................55Figure 2.6: Categories of adopters ..............................................................................................56Figure 2.7: Factors influencing innovation adoption in a social system.....................................58Figure 2.8: Diffusion of innovation in organisations ..................................................................59Figure 3.1: Conceptual framework for the adoption of Cloud computing..................................70Figure 3.2: Research model for SMEs Cloud computing adoption ............................................84Figure 5.1: Scree plot of the FA for ADOPT............................................................................123Figure 6.1: The research model with results of the MLR .........................................................134Figure 6.2: Research model for Australian SMEs Cloud computing adoption.........................144Figure 6.3: The Revised research model...................................................................................145
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List of Appendices
Appendix A: Assumptions of MLR Analysis......................................................................... 194Appendix B: The Questionnaire ............................................................................................. 196Appendix C: The Letter of Approval for Data Collection...................................................... 204
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List of Abbreviations
ABS: Australian Bureau of Statistics
ACCA: Asia Cloud Computing Association
ACMA: Australian Communications and Media Authority
ADOPT: Adoption
ANOVA: Analysis of Variance
APPs: Australian Privacy Principles
AWC: Awareness of Cloud
B2B: Business to Business
CAGR: Compound Annual Growth Rate
CapEx: Capital Expenditure
CARM: Cloud Adoption Reference Model
CCRM: Cloud Computing Reference Model
CF: Cloud Flexibility
CIO: Chief Information Officer
CIOS: Customer-based Inter-Organisational Systems
CP: Cloud Privacy
CPU: Computer Processing Unit
CRA: Cloud Relative Advantages
CRM: Customer Relationship Management
CS: Cloud Security
CSA: Cloud Security Alliances
CSC: City of Sydney Council
CSPs: Cloud Service Providers
DaaS: Database as a Service
DIISR: Department of Innovation, Industry, Science and Research
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DOI: Diffusion of Innovation
EC2: Elastic Compute Cloud
EDI: Electronic Data Interchange
ENISA: European Network and Information Security Agency
ERP: Enterprise Resource Planning
EU: European Union
FA: Factor Analysis
GDP: Gross Domestic Product
HEAG: Human Ethics Advisory Group
IaaS: Infrastructure as a Service
ICTs: Information and Communication Technologies
IDC: International Data Corporation
IS: Information System
IT: Information Technology
ITA: International Trade Administration
ITIIC: Information Technology Industry Innovation Council
KMO: Kaiser-Meyer-Olkin
MLR: Multiple Linear Regression
MRA: Multiple Regression Analysis
MRP: Material Requirement Planning
NBN: National Broadband Network
NIST: National Institute of Standards and Technology
NSW: New South Wales
NT: Northern Territory
OpEx: Operational Expenditure
PaaS: Platform as a Service
PC: Personal Computer
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PDA: Personal Digital Assistant
QLD: Queensland
QoS: Quality of Service
RAM: Random Access Memory
RBA: Reserve Bank of Australia
RFID: Radio Frequency Identification Devices
RMB: Renminbi
S3: Secure Storage Service
SA: South Australia
SaaS: Software as a Service
SEM: Structural Equation Modelling
SLA: Service Level Agreement
SMEs: Small and Medium-sized Enterprises
SOA: Service-Oriented Architecture
SPSS: Statistical Package for the Social Sciences
TAM: Technology Acceptance Model
TAS: Tasmania
TCO: Total Cost of Ownership
TOE: Technological, Organisational and Environmental
TPB: Theory of Planned Behaviour
TRA: Theory of Reasoned Action
URL: Uniform Resource Locator
UTAUT: Unified Theory of Acceptance and Use of Technology
VIC: Victoria
VPNs: Virtual Private Networks
WA: West Australia
WWW: World Wide Web
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1 CHAPTER 1: INTRODUCTION
1.1 Introduction
The use of Information and Communication Technologies (ICTs) can improve
business competitiveness, and can provide genuine advantages for small and
medium-sized enterprises (SMEs: Enterprises with fewer than 199 employees). A
key issue is that SMEs form an essential element of a country’s economy as they
are the main source of employment and technological development. SMEs are the
fastest growing sector of most economies around the world and represent a higher
proportion of all businesses and gross domestic product (GDP) (Paik 2011).
Similarly, SMEs in Australia account for 95 per cent of all businesses (MacGregor
& Kartiwi 2010). The importance of SMEs is widely recognised in relation to the
adoption and diffusion of ICT innovations because of their perceived creative,
innovative and adaptive capabilities (Ritchie & Brindley 2005). The technological
advances and the constant development of new innovative ICTs provide many
opportunities for SMEs to adopt and gain benefits from ICTs (Wang, Rashi &
Chuang 2011). Today, emerging businesses find themselves in an environment of
rapid technological change. Organisations face challenges for maintaining,
selecting, updating and managing information and communication technology,
particularly for SMEs. SMEs cannot afford to invest in planning, designing,
implementing, and managing increasingly complex software, hardware and
networking for in-house ICT requirements (Carroll, Helfert & Lynn 2014). Many
SMEs are seeking alternative solutions that can reduce the total costs of ownership
of their ICT systems in order to focus their limited time and resources on their core
business. This has led to increased interest in on-demand computing, which
provides scalable IT-related capabilities as a service through Internet technologies
to meet their business needs and reduce the operational costs of their IT systems
(Alshamaila, Papagiannidis & Li 2013).
The newest technology that has emerged central to the discussion on modern
business computing is the concept of Cloud computing. This was first mentioned
by Professor Ramnath Chellappa in 1997, that Cloud computing is going to be a
“new computing paradigm where the boundaries of computing will be determined
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by economic rationale rather than technical limits alone” (Chellappa 1997). Cloud
computing is a new ICT that helps organisations to use new IT development at
affordable costs (Sultan 2011) and it becomes an increasingly important area in the
development of business services. Cloud computing can be viewed as a way to
deliver IT-enabled services in the form of software, platform and infrastructure
using Internet technologies. These types of services are delivered under different
deployment models on demand, and use a pay-as-you-go method. The growing
adoption of Cloud computing is changing the way business information systems
are developed, scaled up, maintained and paid for. Cloud computing promises to
improve the reliability and scalability of IT systems, allowing in particular for
SMEs to focus their limited resources on their core business. Thus, SMEs have
been identified amongst the primary beneficiaries of Cloud computing
(Alshamaila, Papagiannidis & Li 2013). Cloud computing is a novel business
model which is particularly valuable for SMEs, as Cloud computing adoption can
be undertaken with limited capital investment (Mudge 2010). Cloud computing is
commercially viable for many SMEs due to its flexibility and pay-as-you-go cost
structure (Sultan 2011).
In general, companies obtain Cloud computing services (e.g. software as a service)
from a Cloud computing environment and they have the opportunity to continue to
take advantage of new developments in IT technologies at an affordable cost. The
main advantages of Cloud computing services include cost reductions and
scalability (Marston et al. 2011). Cloud computing offers economies of scale by
waiving the upfront cost for infrastructures acquisition, and hence leads to cost
savings (Shimba 2010). The associated scalability offered by Cloud services allows
SMEs to scale up or scale down their ICT requirements in accordance with demand
variations (Azarnik et al. 2013). Therefore, Cloud computing is a cost-effective IT
solution which can benefit SMEs and larger organisations as well as a country’s
government and public services. It provides shared computing resources, software,
storage, and information on demand. For example, economies of scale for data
centres’ (facility used to house computer systems and associated components) cost
savings can lead to a 5 to 7 time reduction in the total cost of computing (Armbrust
et al. 2010). This allows SMEs to focus on core business without involving IT
management processes such as system upgrading, licencing etc. This environment
2
best suits SMEs, which might have scarce resources in terms of money, time, and
expertise (Wymer & Regan 2005). In order to gain the maximum benefits from
Cloud computing services, organisations need to understand the surrounding
influencing issues of Cloud computing (Smith 2009). However, despite the
potential benefits, the adoption rate of Cloud computing is still relatively low in
Australia (Busch et al. 2014). According to the Australian Communications and
Media Authority (ACMA), less than half of the SMEs in Australia are currently
using Cloud services (Ericson 2015; NBNCO 2015).
Business professionals have shown real interest in using technologies that reduce
technology costs, streamline processes and improve efficiencies throughout the
enterprise (Anderson & Rainie 2010). Cloud computing has been introduced as a
solution to meet their needs, with flexibility and Internet accessibility as two of its
greatest assets, which is causing businesses to consider how best to deploy this
technology within their organisations (Thomas, Puttini & Mahmood 2013). Cloud
computing is a paradigm that enables organisations to leverage dynamic
provisioning capacity and adapt to changing business environments (Armbrust et
al. 2010). This paradigm has received significant attention from major IT
companies such as Amazon, Microsoft, Google and IBM (Buyya et al. 2009; Misra
& Mondal 2011). Industry professionals and scholars globally have started to take
an interest in the new research challenge raised by Cloud computing (Buyya, Yeo
& Venugopal 2008; Chang 2015; Gangwar, Date & Ramaswamy 2015; Zhao,
Scheruhn & von Rosing 2014).
Industry-based researchers have predicted that the global market for Cloud
computing services will grow rapidly in the next decade (ITIIC 2011). Researchers
believe that “Cloud computing” will revolutionise the entire ICT industry (Tuncay
2010). The actual size of the Cloud computing market is unknown. An industry
report estimates that the global Cloud computing market would show a continuous
rapid growth from 2011 reaching almost US$250 billion by 2017 (Li et al. 2015).
Most of the developed countries are moving quickly to ensure the rapid adoption
of Cloud computing (Mudge 2010). Herhalt and Cochrane reported that the
adoption of Cloud computing in Australian organisations lagged behind the same
levels of US adoption by a year or more (Herhalt & Cochrane 2012). A survey by
Frost & Sullivan suggests that, in 2011, 43 per cent of businesses in Australia were
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using some form of Cloud computing service, up from 35 per cent in 2010. Phil
Harpur, Senior Research Manager of Frost & Sullivan, says that the Australian
Cloud computing market is expected to grow strongly over the next five years from
US$1.23 billion in 2013 to US$4.55 billion by 2018 (Harper 2014).
This research focuses on SMEs’ adoption of Cloud computing in Australia. The
issues will be explored in this research with the aim of understanding the key factors
for Cloud computing adoption by investigating the relationship between related
adoption factors and SMEs wishing to adopt. This leads to the primary research
objective of designing a model for the adoption of Cloud computing by SMEs in
Australia by considering the factors that influence Cloud computing adoption.
Further, this study will pursue new areas for research in innovative Cloud service
adoption.
The remainder of this chapter contains separate sections of the problem statement
and the rationale for undertaking this research. Following this is the purpose of the
study, indicating the aim and objectives of this research, an overview of the
research theoretical framework and a list of research questions. The final sections
of this chapter include the nature and significance of the study and an outline of the
thesis.
1.2 Problem Statement
The use of the World Wide Web (WWW) increased in the mid-1990s and a new
Internet-based computing model emerged and transitioned technology beyond the
centralised client-server models of earlier decades (Weisinger 2013). A product of
the Internet-based computing model, Cloud computing, emerged as a solution for
all sectors to improve their businesses and reduce costs (Jackson 1994). The
widespread use of mobile and wireless computing, i.e. to become location
independent, has contributed to the growth of Cloud computing and is expected to
fuel future growth- enabled enterprises in all sectors to improve their businesses
and reduce costs (Philipson 2012). Despite the benefits, there is still reluctance from
organisations to adopt Cloud computing technology (Fishman 2012; Luftman &
Ben-Zvi 2010). The Victorian State Privacy Commissioner showed that Australian
organisations (in particular Victorian organisations) have a high level of interest in
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and concerns about Cloud computing (Anthony 2012). According to the Australian
Bureau of Statistics’ (ABS) first survey of Australian Cloud usage, the actual
adoption of Cloud computing by SMEs is less than 19 per cent (Cowan 2015).
Therefore, the focus of this research is on SMEs within an Australian context.
Understanding why and how SMEs are motivated to adopt Cloud computing will
enable the creation of a more favourable environment for greater adoption and will
help in developing strategies to promote the adoption process. When a new
innovation is introduced, a greater understanding of the factors influencing its
adoption will result in an increase in adoption. Similarly, careful consideration of
meta-factors that affect Cloud adoption is important to ensure that SMEs make
adoption decisions. Thus, the objective of this research is to understand the meta-
factors that influence Australian SMEs’ decisions to adopt Cloud computing, to
enhance the adoption process.
Moreover, while there are some prior studies on Cloud adoption in SMEs (Carcary,
Doherty & Conway 2014; Dahiru, Bass & Allison 2014a; Prasad et al. 2014a), there
is a lack of studies examining the adoption of Cloud computing especially
considering micro, small and medium-sized organisations separately. In addition,
the influencing factors can vary based on the context and surrounding environment
(Son & Lee 2011), therefore these issues should be studied in the context of their
own environments. Most past research merely considered Cloud computing as a
technology adoption issue, but it should be considered as a combination of
technology adoption and service adoption by giving greater weight to service
adoption from the business perspective.
The National Broadband Network (NBN) will give all Australians access to very
fast broadband over fixed lines, wireless or via satellite. This high capacity data
communication infrastructure across the whole of Australia has been one of the
most significant projects introduced by the Australian government (Matthew 2014).
This is a national initiative that has recently been made by the Australian
government to move towards a digital environment, in particular for Cloud
computing. This has resulted in attempts to increase technology usage among
Australian organisations especially SMEs. As part of this strategy, the Australian
Federal Government published a best practice guide to better understand how to
5
comply with privacy laws and regulations when choosing Cloud-based services
(IMO 2013), in order to enhance Cloud usage by organisations.
Therefore, there is a need for research to investigate the adoption of Cloud
computing by SMEs in an Australian context. Accordingly, this research would
provide significant information and useful guidelines to enhance SMEs’ adoption
of Cloud computing, thereby increasing their business profits and contributing
significantly to the Australian economy.
1.3 Research Purpose
It is essential for SMEs to understand how organisations perceive Cloud computing
and its importance when they are adopting or intending to adopt Cloud computing.
This study will enable them to disclose the crucial factors that might influence their
decisions on the adoption of Cloud computing; that is, why and how organisations
adopt or reject it. This information will be beneficial to consulting companies that
are assisting SMEs with Cloud computing implementation, and for the government
to assist with developing awareness, support programs and policies for SMEs to
adopt Cloud computing (Senarathna et al. 2015).
The main aim of this research is to explore the factors that influence SMEs’
decisions towards the adoption of Cloud computing. The following objectives were
established to specifically achieve the main aim of this research:
1. To empirically assess the conceptual model of SMEs towards the adoption
of Cloud computing;
2. To determine the meta-factors that influence SMEs towards Cloud
adoption;
3. To assess the controlling and influential effects of industry type and
organisation size on SMEs’ decision to adopt Cloud computing;
4. To provide clear understanding of issues related to Cloud adoption for
service providers, Cloud agents, governments and other policymakers to
accelerate the adoption of Cloud computing.
6
1.4 Theoretical Framework
The theoretical underpinning of this research is briefly outlined in this section. The
emergence of many adoption models and theories has provided many IT adoption
factors in different research contexts. However, integrating factors from different
adoption theories can enhance understanding of the potential influential factors of
IT adoption (Chong et al. 2009; Oliveira & Martins 2011). This research integrates
two adoption models in order to develop a theoretical model to address the research
issues identified. The theoretical model was principally built by integrating a
technological, organisational and environmental (TOE) framework (Tornatzky &
Fleischer 1990) and Diffusion of Innovation (DOI) theory (Rogers 2003).
However, to enable a broader perspective, this was extended to incorporate other
important influencing factors, particularly in Cloud computing adoption research.
Section 2.2 (Theoretical Foundation) provides more details in this regard.
The TOE framework is one of the most supported and applied adoption models in
a variety of disciplines and organisations. It has been used by many researchers to
analyse the adoption of a variety of information systems (IS) and IT innovations
(Chong et al. 2009; Oliveira & Martins 2011; Pan & Jang 2008). According to TOE,
three aspects are likely to influence an organisation’s process of adopting and
implementing technological innovations: technological, organisational and
environmental. The factors in the technological innovations aspect are relative
advantage, uncertainty, compatibility, complexity and trialability. Organisational
factors are top management, organisation structure, size and communication.
Environmental factors are competitors, government regulation and market scope.
The TOE framework has also been shown to be consistent with Rogers’ (2003)
diffusion of innovations theory because both theories share constructs related to
adopting innovation (Chong et al. 2009; Low, Chen & Wu 2011; Pan & Jang 2008).
The DOI theory has been applied extensively in various studies and has consistent
empirical support in the IT adoption domain. DOI theory seeks to explain how,
why, and at what rate new ideas and technology spread through cultures. According
to DOI, four primary elements were considered for technology adoption, which are:
an innovation, a communication channel, a time frame for adoption, and a social
structure that fosters technology innovation adoption.
7
This study contains discussion about a new theoretical model based on integrating
the TOE framework and DOI theory and used to determine the factors of Cloud
computing adoption by SMEs in Australia. This also includes flexibility, quality of
service, security and privacy in Cloud computing as an extended version of the
theory. The factors include: the relative advantage that Cloud computing adoption
brings to the organisation; the flexibility of Cloud computing use compared to other
similar computing systems; the quality of service provided by Cloud computing;
Cloud security matters related to adoption and use of Cloud computing resources;
Cloud privacy concerns related to adoption and use; and the awareness perceived
by organisations in terms of Cloud computing and its benefits.
Further, this research also examined the controlling effect of two variables:
organisation type and size. These were chosen because size and type of organisation
might control the relationship of influencing factors on SMEs’ decisions regarding
Cloud computing adoption.
In summary, the research is categorised into the broad theoretical field of IS
adoption. Based on the adoption research, factors that might influence the adoption
of IS are organisational, technological, environmental and individual
characteristics. These characteristics indicate a strong relationship with IS adoption
in previous research. Consequently, as presented in Chapter 3, it is important that
Cloud computing adoption research takes a comprehensive approach and includes
a broader range of factors that might influence SMEs’ decisions to adopt Cloud
computing. This research attempts to provide significant insights and useful
guidelines for SMEs to enhance the successful adoption of Cloud computing.
1.5 Research Questions
Even though Cloud computing is potentially a future key technology for SMEs
( ), Australian SMEs are still in the introductory stage
(Millar 2014) and face many obstacles in adopting Cloud. Based on this notion
drawn from the literature and conceptual framework, this motivates a specific
research question:
“What are the factors that influence the decision of SMEs to adopt Cloud
computing in Australia?”
8
The following subsidiary research questions are considered in order to answer this
research question:
Q1: Do relative advantage, Cloud flexibility, quality of service, Cloud security,
Cloud privacy and awareness of Cloud influence the perception of Australian
SMEs’ decision to adopt Cloud computing?
Q2: Does the industry type and size of the organisation control the influence of
these factors on Australian SMEs’ decision to adopt Cloud computing?
1.6 Research Significance
Australian business organisations, especially SMEs, face a number of daunting
challenges when it comes to adopting information technology. Cloud computing
has become increasingly important for Australian SMEs because one of the major
benefits that Cloud computing brings to SMEs is access to the IT applications that
previously only large companies could afford (NBNCO 2015). Further, Cloud
computing adoption is expected to provide several advantages that should result in
reduced costs for organisations, especially for SMEs, and to save money, become
more productive, accomplish tasks more quickly, and gain cost benefits.
The research findings that may be attained from this research extend the current
understanding of Cloud computing adoption by SMEs using a technology-based
service adoption framework, and also provide guidelines and a better understanding
of the potential benefits and risks associated with Cloud adoption by SMEs to
evaluate possible alternatives and realise which factors influence the adoption
process. This adoption framework is developed through the synthesis of a critical
literature review for investigating Cloud computing adoption by SMEs, and thus
bridges the research gap and contributes to Cloud computing adoption literature,
especially in the SME context. The outcome of this study may assist in strategic
planning from the perspectives of SMEs that are planning or are in the process of
implementing a review of their Cloud computing initiatives, and Cloud service
providers using Cloud technology to gain a competitive edge. The government can
reflect upon the Cloud computing adoption framework when developing policies
for SMEs.
9
10
1.7 Thesis Structure
The thesis is divided into seven chapters. A breakdown of the overall thesis is
depicted in Figure 1.1 and described in the following sections.
Chapter 1 sets out the introduction to the research, which covers the research
background, research problems and an outline of the research questions. It furnishes
the aims and objectives, a brief summary of the research approach and significance
of the study, and concludes with an outline of the structure of the thesis.
Chapter 2 presents a detailed literature review of Cloud computing covering a brief
general background, history and definitions of Cloud computing, services and
deployment models of Cloud, special features of Cloud computing and advantages.
It also describes the small and medium-sized enterprises (SMEs) in general and
specifically to the Australian context. Moreover, it provides an overview of
previous studies about Cloud computing adoption and Cloud adoption in a SME
context. Based on the existing literature on technology adoption, focusing on Cloud
computing and SMEs, Chapter 2 identifies a lack of available well-defined and
framed research on factors influencing SMEs towards the adoption of Cloud
computing, especially in Australia. Therefore, a broad theoretical perspective is
utilised in order to gain a comprehensive understanding of the issue. This
theoretical framework, which combines the TOE framework and DOI theory to
develop a comprehensive model, is described in Chapter 3.
Chapter 3 is dedicated to the conceptual framework. Based on the two theories
explained in Chapter 2, an integrated research model is developed. The factors that
are assumed to influence the adoption of Cloud computing are defined and included
in the research model. More specifically, it is posited that towards the adoption of
Cloud computing SMEs are influenced by six factors:
Cloud relative advantage compared to other technologies;
Cloud flexibility regarding the use of Cloud computing;
Quality of service regarding the use of Cloud computing;
Cloud security matters pertaining to Cloud computing;
Cloud privacy issues pertaining to Cloud computing;
Awareness of Cloud perceived by organisations.
Further, the relationships of these factors within the research model are developed.
The chapter also proposes that two control variables, i.e. industry sector and size of
the firm, control the relationships.
Chapter 4 describes and justifies the research method, approach and philosophy
used to undertake this research. A quantitative research method is considered the
most appropriate to discover the relationships and evaluate the acceptance of new
technologies. Quantitative questionnaire surveys are reportedly the most often used
data collection method in numerous studies based on diffusion of innovation in the
IS domain, and is therefore considered in this research. The detailed steps followed
in the data analysis are described in detail in this chapter.
Chapter 5 presents the findings of the questionnaire and the results of the various
statistical analyses used to assess the hypothesis developed in Chapter 3. At the
beginning it shows the usable responses received, followed by a depiction of the
survey as an acceptable instrument with sufficient level of validity. Then the results
of the multiple regression analysis (MRA) reveal that the model is statistically
significant in explaining the perception of SMEs towards the adoption of Cloud
computing.
Chapter 6 provides a discussion of the key findings based on the results in Chapter
5, regarding the factors influencing SMEs towards the adoption of Cloud
computing. The relationship between research factors and Cloud adoption are
discussed, referring to previous research. The effect of control variables is also
discussed. The proposed model of Cloud computing adoption has been revised by
excluding insignificant relationships.
Chapter 7 highlights the research key findings and details the implications and
theoretical and practical contributions of the research. The research limitations are
acknowledged and the chapter concludes with possible further research and
investigations. Figure 1.1 depicts the organisation of the thesis.
11
Figure 1.1: Flow chart of the thesis
Introduction
Literature Review
Results
Descriptive analysis Reliability & Validity Hypotheses testing
Small and Medium-sized Enterprises (SMEs)
Cloud Computing and SMEs
Cloud Computing
Conclusion
Summary of findings
Implications Contributions Limitations and Future research avenues
Cloud Computing Adoption Models and Technology Adoption Theories
TOE DOI
Discussion
Research Model and Hypotheses Development
Relative advantage Cloud flexibilityQuality of service Cloud securityCloud privacy Awareness of Cloud
Research Methodology
Data collection Sampling design Data analysis
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2 CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
Cloud computing is one of the recent Internet-related computing paradigms which
help SMEs become technologically closer to large businesses and makes it possible
for them to access sophisticated computing services over a network. Cloud
computing allows organisations, especially SMEs, to save money, become more
productive, increase their operational efficiencies and effectiveness, and
concentrate on their core businesses instead of non-core activities such as
maintaining and upgrading systems (Ross & Blumenstein 2015; Zhao, Scheruhn &
von Rosing 2014). However, Cloud computing adoption in Australia still fails to
rival the USA and Europe (Miller 2014). Thus, it is important to understand the
factors that encourage SMEs to adopt Cloud computing.
This chapter presents a literature review of the main issues in this research, and
focuses on Cloud computing and adoption models, SMEs, and the related services
provided. The chapter begins with an overview of Cloud computing that underpins
this research. The potential benefits of Cloud computing especially for SMEs and
issues related to Cloud computing are also presented. The SMEs overview and the
status and need for Cloud computing by SMEs are briefly discussed. To address
the literature gap identified, the theoretical framework for investigating the factors
that influence SMEs’ decisions to adopt Cloud computing is explained.
2.1.1 Cloud Computing
The revolution of Cloud computing, its advantages and adoption are explored in
this section.
2.1.1.1 Development of Cloud Computing
The inherent concept of Cloud computing was first introduced in the 1950s. During
that time large-scale mainframe computers were available but too costly to have a
separate mainframe for each user. Therefore a new architecture was developed;
users from different terminals were able to access the mainframe and share the
computer processing unit (CPU) time and power; thus, mainframe idle time was
13
reduced and return on investment increased. Later, in the 1960s, this phenomenon
became more popular after John McCarthy predicted that someday computation
would become a public utility (McCarthy 1960). Although the definition of Cloud
computing has emerged in recent years, Alali & Yeh (2012) argued that the origin
of Cloud computing began in 1960 when research into parallel computing,
distributed computing and virtualisation was initiated. In a study of parallel
computing, it was found that the Cloud model allows for easy allocation of
resources in minimum time (Opala 2012). Cloud computing is different from other
distributed computing mainly because of its apparent infinite flexibility of
resources (Petcu et al. 2012).
The idea of Cloud computing is more popular and Buyya et al. (2009) mentioned
that many people believe that in the future the basic level of computing will be
provided to people to meet their day-to-day needs, just like other types of utility
(water, electricity, gas and telephone). In the 1990s, telecommunication companies
began offering their services through Virtual Private Networks (VPNs). Cloud was
first used as a symbol to depict the link between providers and users, but later
scientists and technologists developed new algorithms to allocate computing
resources more efficiently, and it became more popular and capable of providing
the optimal use of computing resources such as software, platform and
infrastructure (Tehrani & Shirazi 2014). As a result, Cloud computing extends the
current use of IT as a service over the network especially through the Internet. Its
major goal is to reduce the cost of IT services while increasing efficiency,
flexibility, reliability, availability and processing.
Salesforce, which began operating in 1999, was the first company to deliver the
first actual Cloud computing service from its own website (Saleforce.com). In
2002, Amazon introduced Amazon Web Service to allow customers the ability to
store their data and also to allow many people to work collaboratively. Later in
2006, Elastic Compute Cloud (EC2) was launched by Amazon, enabling users to
run their applications on Cloud. In 2007, Salesforce.com launched a Platform as a
Service (PaaS). In 2009, Google launched Google Apps, which allows creating and
storing documents online. Later in 2010, a Cloud-based database was launched by
Salesforce to enable customers to develop applications on Cloud. These
14
applications, which are written in any language, can be used by any device (Tehrani
& Shirazi 2014).
Although the term Cloud computing is relatively new, this technology was based
on many other earlier computing methods. According to Buyya, Yeo and
Venugopal (2008), Cloud computing exhibited as one combined system which
contained distributed, grid and parallel resources from a group of connected and
virtual computers. Some of the precursor technologies, which fostered computing
advances to Cloud computing, included Service-Oriented Architecture (SOA),
distributed computing, virtualisation, and grid computing (Aymerich, Fenu &
Surcis 2008; Youseff, Butrico & Da Silva 2008). As mentioned by Gong et al.
(2010), Cloud computing is the integration of already known computing services
such as grid computing, utility computing and virtualisation. There are some
similarities and differences between Cloud computing and other computing
services. According to Foster et al. (2008), factors that differentiate Cloud
computing from other types are security, application, abstraction, business model,
data model, compute model and programming model. Other aspects of Cloud
computing which distinguish it from other types of computing are the ability to
provide an on-demand self-service, a scalable and flexible system, and an
autonomic computing system (Wang, Wang & Yang 2010). Erdogmus (2009)
mentioned that Cloud computing was being heavily promoted for mainstream
adoption as a result of the latest advances (at that time) in virtualisation
technologies, combined with an acute realisation of the increasing economic burden
of maintaining proprietary IT infrastructures. Buyya, Broberg and Goscinski (2011)
explained Cloud computing not as a completely novel concept, but rather as having
quite a long history based on the advancement of several technologies, especially
in hardware (virtualisation), Internet technologies (Web 2.0), and distributed
systems (cluster and grid computing). Unlike grid computing which combines
computer resources from various domains to reach a main objective, Cloud
computing uses virtualisation to achieve the required objectives. Virtualisation is
the basis of the technology that shapes Cloud computing (Chang, Walters & Wills
2014). It allows customers to access a pool of resources on an on-demand basis.
However, similar to grid computing, Cloud computing uses distributed computing
resources to accomplish the objective of the task (Zhang, Cheng & Boutaba 2010).
15
Both Cloud and utility computing are similar in that they provide an on-demand
service to customers and charge them only for the resources they have used (Zhang,
Cheng & Boutaba 2010). The main notion of these terms is time-sharing and
utilising computer resources (Alshamaila 2013). Cloud computing is being
perceived as a huge Internet data centre in which hardware and software resources
are virtualised, offering a variety of services to customers. Cloud computing has
been utilised from a service provider’s pool of capacity and Cloud computing
infrastructure on a pay-as-you-go basis as an alternative to customers managing
their own IT infrastructure (Lim et al. 2009; Sultan 2010). Cloud computing is also
similar to autonomic computing as it enables autonomic resource provisioning
(Zhang, Cheng & Boutaba 2010). Now the Internet environment has contributed
directly to the technology shift from traditional in-house computing to Cloud
computing. As explained by Melvin & Greer (2009), this transition is gradually
modifying the way information system services are developed, scaled, maintained
and paid for. A strong argument has been presented on how the traditional IT
service delivery model is being transformed into a utility which aligns IT with
services like electricity and water (Lacity & Hirschheim 2012). Businesses will
shift their focus from creating technology innovation to finding the lowest cost
providers when the traditional IT service delivery model is viewed as a utility
(Buyya et al. 2009). Navigant Research explained how traditional IT service
delivery was being changed by Cloud computing (NR 2011). The most obvious
chance is the reduction of IT services within the internal network and the user’s
personal computers being the only devices remaining in the internal network.
Figure 2.1 depicts an internal and external Cloud infrastructure. Baird Equity
Research Technology estimated that Cloud computing saved at least 75 per cent IT
expenses compared to traditional IT (Linthicum 2013). This potentially leads to a
decrease in maintaining and supporting traditional hardware, software, and possibly
IT staff. It also makes Cloud computing a strategic technology option for the future.
As can be seen in Table 2.1, Cloud computing reported improved attributes
compared to in-house computing in different ways.
16
Figure 2.1: Traditional computing vs Cloud computing
Table 2.1: Distinguishing attributes of Cloud computing vs traditional computing
Traditional computing Cloud computingAcquisitionmodel
Buy assets and build technicalArchitecture.
Buy service.
Business model Pay for fixed assets andAdministrative overhead.
Pay based on use.
Access model Over Internal network, to CorporateDesktop.
Over the Internet, to any device.
Technical model Single tenant, non-shared, static (often not shared).
Scalable, elastic, dynamic, multi-tenant.
(Adapted from Melvin and Greer 2009)
Buyya, Broberg and Goscinski (2011) described Cloud computing as a category of
sophisticated on-demand computing services initially offered by commercial
providers such as Amazon, Google and Microsoft. Brandic and Dustdar (2011)
stated that Cloud computing represents continuance in the development of
infrastructure for the provision of computational resources as utilities. Cloud
computing presents a business model for on-demand delivery of computing
17
services as a “pay-per-use” service, where clients pay only for the services they
used, and is similar to traditional public utility services such as water, gas,
electricity and the telephone (Buyya, Broberg & Goscinski 2011).
Over the past decade since 2005, Cloud computing has become a central tool for
changing IT operations in almost every aspect of the whole economy. According
to Budrien (2012), Cloud computing offers the flexibility of
providing agile computing services without the need for organisations to warehouse
all the hardware required to run their system. Cloud computing offers benefits such
as reduced IT overheads for the customers, greater flexibility, reduced Total Cost
of Ownership (TCO), on-demand services, and improved productivity (Wei et al.
2009). According to Erdogmus (2009), economic benefits, simplification and
convenience in the way computing services are delivered, seem to be the key
drivers to speed up the adoption of Cloud computing. Farah (2010) highlights
Cloud computing adoption as fast-tracking cost reductions, increasing efficiency
and, ultimately, creating a competitive advantage in any market.
There are many business areas where Cloud computing has been adopted including
in higher education (Suess & Morooney 2009; Sultan 2010; Wheeler & Waggener
2009), to provide solutions for human resources (Farah 2010), software testing
(Babcock 2009), data back-up or archive services (Treese 2008), Web 2.0 based
collaborative applications (Orr 2008), for storage capacity on demand (Kraska et
al. 2009), and for content distribution services (Fortino et al. 2009).
New IT approaches and services have taken advantage of Cloud computing, for
example market-oriented allocation of resources (Buyya et al. 2009), hard discrete
optimisation problems (Li et al. 2009), corporate fraud detection using intelligence
(Lodi et al. 2009), collaborative business intelligence (Chow et al. 2009), data
mining algorithms and predictive analytics (Guazzelli, Stathatos & Zeller 2009;
Zeller et al. 2009), software testing as a service (Ciortea et al. 2009), e-government
solutions (Cellary & Strykowski 2009), and architecture and implementation
courses at graduate level in Cloud computing (Holden et al. 2009).
18
2.1.1.2 Cloud Computing Definitions
Cloud computing has been defined differently by industry experts and researchers,
so the definition of Cloud computing is also unclear. As Madhavaiah, Bashir and
Shafi (2012) mentioned, there is a wide range of definitions that have been
proposed for the technology, and a number of definitions for Cloud computing too.
These various interpretations are based on the concept of Cloud computing. Cloud
computing definitions were simplified by using a common term to describe
“computing services provided via the Internet” (Katzan 2010).
Geelan attempted to seek a unified definition of Cloud computing for research and
he assembled 21 experts’ views (Geelan 2009). The experts postulated that Cloud
computing consists of an immense pool of configurable resources that can be
dynamically provisioned to a variable workload (Geelan 2009). In 1997, Cloud
computing was defined by Ramnath Chellapa as “a computing paradigm where the
boundaries of computing will be determined by rationale rather than technical”
(Chellappa 1997). This was the first academic definition of Cloud computing.
According to the European Network and Information Security Agency (ENISA),
Cloud computing was defined as an “on-demand service model for IT provision,
often based on virtualisation and distributed computing technologies” (Catteddu &
Hogben 2009, p. 14).
Another common academic definition of Cloud computing was proposed by Buyya
et al. (2009) as:
“a type of parallel and distributed system consisting of a collection of
interconnected and virtualised computers that are dynamically provisioned
and present as one or more unified computing resources based on service-
level agreements established through negotiation between service provider
and customer”.
Wang and Laszewski (2008) defined Cloud computing as “a set of network enabled
services, providing scalable, Quality of Service (QoS) guaranteed, normally
personalised, inexpensive computing platforms on demand, which could be
accessed in a simple and pervasive way”. Luis et al. (2009) propose the Cloud
computing definition as follows:
19
“Cloud computing is a large pool of easily usable and accessible virtualised
resources (such as hardware, development platforms and/or services). These
resources can be dynamically reconfigured to adjust a variable load (scale),
allowing also for an optimum resource utilisation. This pool of resources is
typically exploited by a pay-per-use model in which guarantees are offered
by the infrastructure provider by means of customised SLAs”.
The founder of Oracle, Larry Ellison, stated “we’ve redefined Cloud computing to
include everything that we already do...” (Farber 2008). Richard Stallman, founder
of the Free Software Foundation and creator of the operating system GNU, stated
“Cloud computing was simply a trap aimed at forcing more people to buy into
locked, proprietary systems that would cost them more and more over time… it’s
stupidity. It’s worse than stupidity: it’s a marketing hype campaign” (Johnson
2008).
The National Institute of Standards and Technology (NIST) proposed a definition
for Cloud computing as:
“a model for enabling convenient, on demand network access to a shared pool
of configurable computing resources (e.g. network, servers, storage,
applications and services) that can be rapidly provisioned and released with
minimal management effort or service provider interaction. This Cloud
computing model promotes availability and is composed of five essential
characteristics and three service models and four deployment models” (Mell
& Grance 2011, p. 2).
Figure 2.2 shows the framework of the NIST (2009) definition of Cloud computing.
This framework includes deployment models, service models, and essential
characteristics of Cloud computing and common characteristics. In line with other
Cloud computing researchers (e.g. Dillon, Wu & Chang 2010; Monika et al. 2010),
the NIST definition is used in most of the Cloud computing studies.
20
21
Figure 2.2: NIST Cloud computing definition (Adapted from Grance 2010)
According to Mell and Grance (2011), On demand self-service allows users to
increase the amount of computing resources without any human interaction with
the Cloud service provider. Broad network access allows users to access Cloud
services over the network especially through the Internet using any type of device
(e.g. laptop, desktop, tablet, mobile phone). Customers can access the pool of
computing resources through a multi-tenant model. This Resource pooling enables
service providers to serve multiple users through virtualisation. Rapid elasticity
allows customers to expand their resource usage based on their demands. Measured
usage is a special characteristic in Cloud computing that allows customers and
service providers to know their accurate usage of resources. This enable users and
service providers to access, monitor, control and repair their usage easily.
These different definitions show that the different stakeholders, such as
academicians, architects, consumers, developers, engineers and managers,
understand Cloud computing differently (CSA 2009). Table 2.2 provides Cloud
computing definitions that are currently available (Geelan 2009), as summarised
by Luis et al. (2009 p.52). Cloud computing is defined as ‘a model enabling
access to
22
a shared scalable and elastic computing resources as-a-service through Internet’ for
the context of this research.
Table 2.2: Cloud computing definitions
Author Year Definition/Excerpt
M. Klems 2008 “You can scale your infrastructure on demand within minutes or even seconds, instead of days or weeks, thereby avoiding under-utilisation (idle servers) and over-utilisation (blue screen) of in-house resources.”
P. Gaw 2008 “Refers to the bigger picture...basically, the broad concept of using the internet to allow people to access technology enabled services.”
R. Buyya 2008 “A type of parallel and distributed system consisting of a collection of interconnected and virtualised computers that are dynamically provisioned and present as one or more unified computing resources based on service-level agreements established through negotiation between service provider and customer.”
R. Cohen 2008 “For me the simplest explanation for Cloud computing is describing it as, ‘internet centric software’. This new Cloud computing software model is a shift from the traditional single tenant approach to software development to that of scalable, multi-tenant, multi-platform, multi-network, and global.”
J. Kaplan 2008 “A broad array of web-based services aimed at allowing users to obtain a wide range of functional capabilities on a ‘pay-as-you-go’ basis that previously required tremendous hardware/software investment and professional skills to acquire.”
D. Gourlay 2008 “Cloud will be the next transformation over the next several years, building off of the software models that virtualisation enabled.”
D. Edwards 2008 “...what is possible when you leverage web scale infrastructure (application and physical) in an on-demand way...anything as a service...all terms that couldn’t get it done. Call it ‘Cloud’ and everyone goes bonkers.”
B. De Haff 2008 “...there are really only three types of services that are Cloud-based: SaaS, PaaS, and Cloud computing Platforms.”
B. Keppes 2008 “Cloud computing is the infrastructural paradigm shift that enables the ascension of SaaS.”
K. Sheynkman 2008 “…the ‘cloud’ model initially focused on making the hardware layer consumable as on demand compute and storage capacity. ... to harness the power of the Cloud, complete application infrastructure needs to be easily configured, deployed, dynamically scaled and managed in these virtualised hardware environments.”
O. Sultan 2008 “...in a fully implemented Data Center 3.0 environment, you can decide if an app is run locally (cook at home), in someone else’s data center (take-out) and you can change your mind on the fly in case you are short on data center resources (pantry is empty) or you having environmental/facilities issues (too hot to cook).”
K. Harting 2008 “Cloud computing overlaps some of the concepts of distributed, grid and utility computing, however, it does have its own meaning if contextually used correctly. Cloud computing really is accessing resources and services needed to perform functions with dynamically changing needs.”
J. Pritzker 2008 “Cloud tends to be priced like utilities...I think it is a trend not a requirement.”
T. Doerksen 2008 “Cloud computing is...the user-friendly version of grid computing.”T. von Eicken 2008 “...outsourced, pay-as-you-go, on-demand, somewhere in the
Internet.”M. Sheedan 2008 “...‘Cloud pyramid’ to help differentiate the various Cloud
offerings out there...top: SaaS; middle: PaaS; bottom: IaaS.”A. Ricadela 2008 “...Cloud computing projects are more powerful and crash-proof
than Grid systems developed even in recent years.”I. WladawskyBerger
2008 “...the key thing we want to virtualise or hide from the user is complexity. ...with Cloud computing our expectation is that all that software will be virtualised or hidden from us and taken care of by systems and/or professionals that are somewhere else – out there in the Cloud.”
B. Martin 2008 “Cloud computing really comes into focus only when you think about what IT always needs: a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software.”
R. Bragg 2008 “…the key concept behind the Cloud is Web application...a more developed and reliable Cloud.”
G. Gruman and E. Knorr
2008 “Cloud is all about: SaaS...utility computing...Web services... PaaS...Internet integration...commerce platforms.”
P. McFedries 2008 “Cloud computing, in which not just our data but even our software resides within the Cloud, and we access everything not only through our PCs but also cloud-friendly devices, such as Smartphones, PDAs...the mega computer enabled by virtualisation and software as a service...this is the utility computing power by massive utility data center.”
Gartner 2008 “A style of computing where scalable and elastic IT-related capabilities are provided as-a-service using Internet technologies to multiple external customers.”
2.1.1.3 Cloud Computing Services
Three service models are extensively used by the Cloud computing community to
categorise the Cloud computing services (Ahuja & Rolli 2011; Dillon, Wu & Chang
2010; Feuerlicht & Govardhan 2010). Cloud computing provides software (SaaS),
platforms (PaaS), and infrastructure (IaaS) services on demand and pay-as-you-go.
Software as a service in Cloud computing eliminates the need to install and run an
application on the client’s computer (Marston et al. 2011). The software is installed
on providers’ servers and is delivered to customers through the Internet. Customers
can access the software anytime anywhere by using any device, as long as they
have access to the Internet (Fang & Yin 2010). In addition, it is not necessary to
worry about software licensing and upgrading to the latest versions. According to
Hu and Yan (2012), an increasing number of business organisations are attracted
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to the SaaS model to meet growth opportunities changing business and technology
requirements. However, customers do not have access to the infrastructure and
cannot customise as they need to (Khan et al. 2011). Usually, customers are able to
change only the basic features of the system such as system appearance. According
to Sullivan (2010), there are various types of services that come under Software as
a Service (SaaS), namely Customer relationship management (CRM), video
conferencing, IT service management, accounting, web analytics, web content
management, etc.
Similarly, application design, development, testing, deployment, and hosting are
services provided by Platform as a Service (PaaS). The development and
deployment of applications without the cost and complexity of buying and
managing the underlying hardware and software layers are facilitated by PaaS
(Marston et al. 2011). Customers have access to programming language, libraries
and other tools which are required to develop and deploy an application without
being concerned about the underlying infrastructure platform. PaaS provides
computing platforms which are able to establish applications tailored to the specific
needs of the organisation (Dutta, Peng & Choudhary 2013). They can do everything
through the Internet, but one potential problem with PaaS is that each service
provider offers its own programming language and thus it is difficult for the user to
switch to another service provider. For example, as explained in Windows Azure,
Python and Java are the programming languages used by Google’s PaaS
(AppEngine) and .Net, and PHP is used by Microsoft’s PaaS Cloud (Mather,
Kumaraswamy & Latif 2009).
Further, Sullivan (2010) explains that Infrastructure as a Service (IaaS) provides
services such as server space, network equipment, memory, storage space and
computing capabilities. This is the basic level of Cloud service that is delivering an
infrastructure service to customers over a network. IaaS is similar to hosting but
different in that the customer does not need to have a long-term contract with the
service provider and has the ability to provision resources on demand (Bhardwaj,
Jain & Jain 2010). In this model the service provider is responsible for maintaining
the servers, storage and network settings and the rest is part of the customer’s
responsibility (Armbrust et al. 2010; Buyya et al. 2009). Mell and Grance (2011)
mentioned that customers have control over the operating system, storage and
24
deployed applications, and are only required to pay for the amount of resources
used. According to Hwang and Li (2010), IaaS is the service model among the three
distinctive Cloud computing models that is of most interest to business and IT
executives of enterprises adopting Cloud computing technology because of its
secure properties. Elastic Compute Cloud (EC2) and Secure Storage Service (S3)
are two examples of IaaS.
Table 2.3 summarises the three main service models used in the Cloud computing
environment: SaaS, PaaS and IaaS. Some authors explain Database as a Service
(DaaS) as a different service model, but it can be seen as a special type of service
model under IaaS. Among these three models, IaaS has the highest control over the
service provider’s infrastructure compared with other service models. In
comparison to IaaS, PaaS has less control over the service provider’s infrastructure.
SaaS has very little control over the infrastructure in which the software is installed.
Managing and controlling the underlying infrastructure and platforms is part of the
Cloud service provider’s responsibility.
Table 2.3: Cloud computing service models
Services Description
Software as a Service (SaaS)
Cloud computing applications are released in a hosting environment, which can be accessed through networks from various clients (e.g. Web browser, PDA etc.). Cloud computing users do not have control over the Cloud computing infrastructure that often employs multi-tenancy system architecture to achieve economies of scale and optimisation. Examples of SaaS include Salesforce.com, Google Mail and Google Docs.
Platform as a Service (PaaS)
PaaS is a development platform supporting the full "Software Lifecycle" which allows to develop Cloud computing services and applications (e.g. SaaS) directly on the PaaS Cloud. Hence, PaaS offers a development platform that hosts both completed and in-progress Cloud computing applications. An example of PaaS is Google App Engine.
Infrastructure as a Service (IaaS)
Users can directly use IT infrastructures (processing, storage, networks, and other fundamental computing resources) provided in the IaaS Cloud. Virtualisation is extensively used in IaaS Cloud to integrate/decompose physical resources in an ad hoc manner to meet changingresource demands from Cloud computing users. An example of IaaS is Amazon's EC2.
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Database as a Service (DaaS)
DaaS could be seen as a special type of IaaS. DaaS allows consumers to pay for what they are actually using rather than the site license for the entire database. Examples of this kind of DaaS include Amazon S3, Google BigTable, and Apache HBase.
(Adapted from Dillon et al. 2010)
2.1.1.4 Cloud Computing Deployment Models
In reviewing the literature, services provided by Cloud computing can be
categorised according to the level of service and the way it is provided. Deployment
models are recorded based on these characteristics. More recently, four Cloud
computing deployment models have been defined in the Cloud computing
community and are summarised in Table 2.4 (Dillon, Wu & Chang 2010; Sasikala
2011b).
A public Cloud computing service is available from a third-party service provider
via the Internet. It is a cost-effective way to deploy IT solutions, and provides many
benefits such as being elastic and service-based. This is the commonly used model
and is suitable especially for SMEs because it provides almost immediate access to
hardware resources, with no upfront capital investment for users, leading to a faster
time to market in many businesses. This treats IT as an operational expense rather
than a capital expense (‘Opex’ as opposed to a ‘Capex’ model) (Marston et al.
2011). In this model, Cloud providers have the full ownership of the infrastructure
and they have their own policies, rules and pricing models. These providers’
customers can be academics, businesses and government organisations. Some well-
known public Cloud providers are Amazon, Google and Microsoft (Grossman
2009).
Private Cloud is a type of Cloud computing which offers computing services to one
particular organisation and is not available to the public. This deployment model
provides greater control over the Cloud computing infrastructure and can be owned
(or leased) and managed by the organisation itself or by a third party. Mell and
Grance (2011) mentioned that, depending on the company, the underlying
infrastructure of Cloud computing can be on-premises or off-premises. Therefore,
it is often suitable for large organisations as they are using larger installations
(Marston et al. 2011).
26
Hybrid Cloud computing is a combination of public and private Cloud computing
models which try to address the limitations of each (Zhang, Cheng & Boutaba
2010). According to Mell and Grance (2011), hybrid Cloud enables the portability
of data and applications between different types of Cloud. The main reason for
using hybrid Cloud is to enhance organisations’ core competencies. Organisations
are able to manage their core activities using their on-premises private Cloud, while
outsourcing their non-core activities to a public Cloud provider. Thus,
organisations can maintain their cost and security at a reasonable level (Grossman
2009).
The community Cloud computing infrastructure is controlled and shared by a group
of organisations and supports a specific community that has shared concerns such
as mission, security requirements, policy, and compliance considerations (Sasikala
2011a). The ownership, management and operation of the community Cloud can
be dedicated to one or more organisations, whereas in some cases it is outsourced
to a third party. This deployment model brings economies of scale and equilibrium
to the community (Dillon, Wu & Chang 2010). According to Lawrence et al. (2010)
the different business models are used differently in each deployment model.
Table 2.4: Cloud computing deployment models
Deployments Description
Public Cloud
The public Cloud is used by the general public Cloud consumers, and the Cloud service provider has the full ownership of the public Cloud with its own policy, value, profit, costing, and charging model. Many popular Cloud services are public Clouds including Amazon EC2, S3, Google AppEngine, and Force.com.
Private Cloud
The Cloud infrastructure is operated solely within a single organisation, and managed by the organisation or a third party regardless of whether it is located on-premises or off-premises.
Hybrid Cloud
The Cloud infrastructure is a combination of two or more Clouds (private, community, or public) that remain unique entities but are bound together by standardised or proprietary technology that enables data and application portability.
Community Cloud
Several organisations jointly construct and share the same Cloud infrastructure as well as policies, requirements, values, and concerns. The Cloud community forms into a degree of economic scalability and democratic equilibrium. The Cloud infrastructure could be hosted by a
27
third-party vendor or within one of the organisations in the community.
(Adapted from Dillon, Wu & Chang 2010)
2.1.1.5 Cloud Computing Characteristics
Cloud computing characteristics are more important in identifying how Cloud
computing differs from information technology. These characteristics can be
categorised into essential characteristics and common characteristics. According to
Plummer et al. (2009), five essential characteristics of Cloud computing were
identified by NIST (Dillon, Wu & Chang 2010; Grance 2010). The Cloud
computing essential characteristics are shown in Table 2.5. These five
characteristics are crucial in a Cloud computing environment.
The payment model is one of the characteristics of Cloud computing which
differentiates it from other types of computing, and is a utility-based payment
model. The organisations only pay for the amount of resources and services they
use, which is known as the pay-as-you-go method. This converts organisations’
capital expenditure (CapEx) into operational expenditure (OpEx), and involves
minimal initial investment (Creeger 2009).
On-demand self-service makes Cloud computing more flexible than other
computing paradigms. Cloud computing is location independent, and this enables
customers to access and use the services wherever they have access to the network.
Also, Cloud computing is device independent, which means the service is able to
function on a wide variety of devices. Thus, the on-demand self-service feature
increases the flexibility of Cloud computing in comparison to traditional forms of
computing (Tehrani & Shirazi 2014).
The demands of organisations vary over time, hence their need for computing
services also varies over time. Generally, most organisations tend to provide for
their peak demand when estimating the resources they need (Armbrust et al. 2010).
In this situation, most of the time, resources are not utilised to maximum capacity,
and is a waste of money. The scalability of Cloud computing significantly reduces
resources’ idle time and allows organisations to use only the amount of computing
resources they need. They can instantly scale resources up and down accordingly
when their demands increase and decrease. In Cloud, computing resources are
28
dynamically released to customers, with minimal human interaction (Marston et al.
2011).
Table 2.5: Cloud computing essential characteristics
Characteristics Description
On-demand self-service
A consumer with an immediate need at a particular time slot can access computing resources (e.g. CPU time, network storage, software use, and so forth) in an automatic (i.e., convenient, self-serve) fashion without resorting to human interactions with the providers of these resources.
Broad network access
These computing resources are delivered over the network (e.g. Internet) and used by various client applications with heterogeneous platforms (e.g.mobile phones, laptops, and PDAs) situated at a consumer’s site.
Resource pooling
A Cloud computing service provider’s computing resources are 'pooled' together in an effort to serve multiple consumers using either the multi-tenancy or the virtualisation model, "with different physical and virtual resources dynamically assigned and reassigned according to consumer demand". The motivation for setting up such a pool-based computing paradigm lies in two important factors:economies of scale and specialisation.
Rapid elasticity
For consumers, computing resources become immediate rather than persistent: there are no upfront commitments and contracts as they can use them to scale up whenever they want, and release them once they finish scaling down.
Measured service
Although computing resources are pooled and shared by multiple consumers (i.e., multi-tenancy), the Cloud computing infrastructure is able to use appropriate mechanisms to measure the usage of these resources for each individual consumer through its metering capabilities.
(Adapted from Grance 2010; Dillon et al. 2010)
Figure 2.3 shows a summarised view of the Cloud computing system, highlighting
its stakeholders, locality of hosting, modes of delivery, types of Cloud computing
offered, its features, and benefits. The essential features of Cloud computing
include elasticity, reliability and virtualisation. Stakeholders of the Cloud
computing services are providers, resellers, adopters and users. Major benefits are
cost reduction and ease of use (Gupta, Seetharaman & Raj 2013). Technologies
29
used in Cloud computing as listed in Figure 2.3 are service-oriented architecture,
grid and an Internet services.
Figure 2.3: Cloud computing systems (Adapted from Jeffrey & Neidecker-Lutz
2009)
2.1.1.6 Advantages of Cloud Computing
Cloud computing offers a number of benefits to businesses based on its different
deployment and delivery models (Andrei & Jain 2009; Buyya et al. 2009; Catteddu
& Hogben 2009; Miller 2008; Sasikala 2011b; Voona & Venkantaratna 2009).
According to Miller (2008), one of the major benefits expected by businesses using
Cloud services is cost-saving. Financial benefits are expected mainly because of
the pay-per-use pricing model. As Marston et al. (2011) explained, Cloud
computing provides almost direct access to shared computing resources, thus small
businesses can launch new operations quickly with little or no upfront capital
investment, enabling a faster time to market. Most organisations do not use more
than half of their total ICT resource capacity, and thereby incur more costs
unnecessarily (Leavitt 2009). Therefore, Cloud computing is more important for
them because the provider can simply increase the provision accordingly in order
to handle the increased business needs whenever the client needs additional
30
computing resources such as storage space. One of the major benefits of Cloud
computing is to scale resources up and down dynamically with minimal service
provider interaction (Marston et al. 2011). Ease of use and flexibility are two further
advantages of Cloud computing. They include instant software updates, latest
version availability, easier group collaboration and universal access from any
device (Miller 2008). Cloud applications look like browser web-based applications
or windows-based applications and tend to be intuitive and easy to use (Melvin &
Greer 2009). Leavitt (2009) stated that most Cloud computing suppliers offer more
flexible contract terms, which encourages organisations to implement Cloud
services as needed to expand their businesses.
Some time-consuming and costly procedures of maintaining internal data centres
and applications can be reduced or eliminated by using Cloud computing (Clark
2009). Cloud computing helps SMEs to use most of the state of the art technologies,
without having to worry about maintaining and upgrading the technology. It allows
SMEs to focus more on their core business and innovation (Ashford 2008), thus
increasing the efficiency and productivity of their companies. Cloud computing can
assist SMEs to have a wide market and broad horizontal company operations, such
as regional or international, to decrease external costs and make them less location
dependent. Some of the advantages of Cloud computing are listed in Table 2.6.
These advantages vary based on the different deployment and delivery models.
Table 2.6: Advantages of Cloud computing
Advantages Description
Cost-effectivenessAccording to the literature review, it is obvious that using Cloud computing to run applications, systems, and IT infrastructure saves on staff and financial resources.
Flexibility
Cloud computing allows organisations to start a project quickly without worrying about upfront costs. Computing resources such as disk storage, CPU, and RAM can be added when needed. In this case, the author started on a small scale by purchasing one node and added additional resources later.
Data safety
Organisations are able to purchase storage in data centres located thousands of miles away, increasing data safety in case of natural disasters or other factors. This strategy is very difficult to achieve with traditional off-site backup.
High availabilityCloud computing providers such as Microsoft, Google, and Amazon have better resources to provide more up-time than almost any other organisations and companies do.
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Ability to handle large amounts of data
Cloud computing has a pay-for-use business model that allows academic institutions to analyse terabytes of data using distributed computing over hundreds of computers for a short-time cost.
Reduced costs Cloud computing technology is paid incrementally, saving organisations money.
Increased storage Organisations can store more data than on private computer systems.
Highly automated IT personnel need not worry about keeping software up to date.
More mobility Employees can access information wherever they are, rather than having to remain at their desks.
Allows IT to shift focus
There is no longer a need to worry about constant server updates and other computing issues, and government organisations will be free to concentrate on innovation.
(Sources: Avram 2014; Marston et al. 2011; Tarmidi et al. 2014; Yan 2010)
2.1.1.7 Disadvantages of Cloud Computing
Cloud computing also has a number of disadvantages. Some researchers consider
the disadvantages to be: the requirement for a constant Internet connection, the
service can be slow in the case of slow Internet connections, limited features are
offered, security might not meet the organisation’s standards, and the danger of loss
of business in the case of data loss or the Cloud computing vendor filing for
bankruptcy (Jeffrey & Neidecker-Lutz 2009; Miller 2008; Ristenpart et al. 2009).
Some of the main issues with Cloud computing which have been discussed by
scholars and researchers are Cloud’s security, privacy, reliability and ownership of
data. The key disadvantages of Cloud computing that have been identified are listed
in Table 2.7.
Table 2.7: Disadvantages of Cloud computing
Disadvantages Source
Business continuity and service availability. Feuerlicht & Govardhan 2010;Hsu, Ray & Li-Hsieh 2014
Data confidentiality/auditability. Armbrust et al. 2010Requiring a constant Internet connection. Miller 2008Lack of security. Shaikh & Haider 2011
Privacy concerns. Oliveira, Thomas & Espadanal 2014
Hard to integrate with in-house IT system. Dillon, Wu & Chang 2010; Hsu, Ray & Li-Hsieh 2014
Not enough ability to customise Cloud computing applications.
Dillon, Wu & Chang 2010
Small number of suppliers. Dillon, Wu & Chang 2010
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Limited features. Miller 2008Danger of loss of business in the case of data loss or vendor disruption.
Shimba 2010
2.1.1.8 Some Technical and Business Issues of Cloud Computing
As Cloud computing is still a recent technology, current adoption is associated with
numerous technical and business challenges. Table 2.8 describes some of the issues,
such as the availability of a service, data confidentiality, data transfer bottlenecks,
and legal jurisdiction.
Table 2.8: Technical and business issues of Cloud computing
Issues Description
Data confidentiality
Most academic libraries have open-access data. This issue can be solved by encrypting data before moving to Cloud. In addition, licensing terms can be negotiated with providers regarding data safety and confidentiality.
Data transfer bottlenecks
Accessing digital collections requires considerable network bandwidth, and digital collections are usually optimised for customer access. Moving huge amounts of data (e.g. preserving digital images, audios, videos, and data sets) to data centres can be scheduled during off hours (e.g. 1–5a.m.), or data can be shipped on hard disks to the data centre.
Legal jurisdiction
Converting to Cloud computing involves legal restraints. For example, there are legal restrictions prohibiting the provider from transmitting data to outside of Australia without the prior approval of the agency (DFD 2011b). Since Cloud computing providers can be multinational, it is imperative that such providers are aware of and abide by national regulations where they do business.
(Sources: Armbrust et al. 2010; DFD 2011b; Yan 2010)
2.1.1.9 Cloud Computing Adoption
Some methods of fostering the rapid adoption of Cloud computing in the scientific
community were explored by Youseff, Butrico and Da Silva (2008). This trend
towards adopting Cloud computing has been perceived differently by various
prominent members of the computing community. For example, Microsoft did not
foresee the trend towards Cloud computing, which was being led by Amazon and
33
Google (Cusumano 2009). Even though many firms showed little early interest in
Cloud computing, with the maturation of virtualisation technology and the current
almost explosive increase in interest in Cloud computing, many firms are joining
the Cloud computing wave.
The Open Cloud Manifesto was signed by a group of 38 companies and academic
organisations, calling for open standards in Cloud computing (Merritt 2009). This
manifesto is an attempt to promote common standards for Cloud computing in areas
such as security, portability, interoperability, management, and monitoring. The
National Institute of Standards and Technology is also working on Cloud
computing standards (NIST 2009). If such standards are adopted by the majority of
Cloud computing vendors, this would make it easier to move applications from one
Cloud computing provider to another, which is currently not possible with some
vendors because of proprietary Cloud computing applications. Although many
major corporations, such as Advanced Micro Devices, Juniper, and IBM, along
with the Open Cloud Consortium, are backing this manifesto, some major Cloud
computing participants, namely Amazon, Microsoft, and Google, are
conspicuously absent (Merritt 2009). The Open Cloud Consortium, which includes
Cisco Systems, Yahoo, and several academic partners, runs a Cloud computing test
bed and has developed Cloud computing services benchmarks (Merritt 2009). This
movement towards Cloud computing standards and the conspicuous absence of
some major Cloud computing providers appears to be a battle between some early
major Cloud computing participants attempting to protect their initial market and
the others that want to make Cloud computing a more open, standardised
technology. Such common standards could also make it easier and more affordable
for potential Cloud computing customers to participate in Cloud computing.
Providers, both existing and planned, have a vested interest in the future of Cloud
computing (Weiss 2007).
There is currently widespread interest in Cloud computing and the growth in the
available options for using Cloud computing. Low, Chen and Wu (2011) found that
Cloud computing in the high-tech industry depends on the firm’s technological,
organisational and environmental contexts. There are many advantages, such as
economies of scale, the availability of large computing resources (Greenberg et al.
2008), and the ability to test their business plan quickly and increase business
34
agility (Wang, Rashi & Chuang 2011). In addition, Cloud computing providers can
maintain a very high level of availability, often with considerably less downtime
than individual organisations (Greenberg et al. 2008). Parthasarathy and
Bhattacherjee (1998) and also Rogers (1962) found that when clients were
displeased with a technology they had adopted, they tended to discontinue its use.
Because of this issue, it is important for a Cloud computing provider to maintain
customer satisfaction to retain its client base. Maintaining customer satisfaction
involves continuing to satisfy client needs, staying cost-competitive, maintaining
system reliability and availability, and ensuring information security and
confidentiality. One illustration of a process for running a successful Cloud
computing organisation is given by Kaliski (2008). In describing how to promote a
well-run Cloud computing entity, Kaliski says that the entity should run like a
container ship or cruise liner, with standardised products, set costs, and non-
interference with other customers’ products. This model could appeal to cost-
conscious, organised people. Various organisations are beginning to adopt Cloud
computing, ranging from individuals to small and larger organisations.
Although there is extensive current interest in Cloud computing, there can be a gap
between the promise of Cloud computing and market adoption. Greenberg et al.
(2008) anticipated that, while individuals are already adopting Cloud computing
for applications readily available, and small organisations will adopt Cloud
computing in the near term, it may take from fifteen to twenty years for larger
corporations to convert to Cloud computing. Aligning a company’s technology and
corporate strategy by addressing the needs of management, resource issues, and
external factors improves organisational functioning (Standing et al. 2008).
Adopting Cloud computing can meet the technology and corporate needs of
smaller, resource-poor organisations and individuals, while large organisations can
afford to purchase and maintain their own large computing resources. As a result,
larger organisations have less of an incentive to go to outside providers than SMEs
or individuals have. An example of the gap between the potential and the actual are
the recent survey results presented by Delahunty (2009), where the participant
responses showed that 11 per cent of their firms used Cloud computing for data and
information storage, with another 19 per cent considering using Cloud computing.
35
This leaves 70 per cent of the respondents showing little interest in Cloud
computing.
Even with the movement toward transitioning computing and storage applications
to Cloud computing, there are some applications that organisations are choosing
not to transition. These are especially in the area of mission critical applications,
which are expected to be retained by their owners rather than being transitioned to
Cloud computing (Greenberg et al. 2008). These applications are retained in-house
for reasons such as the criticality of response times or concerns about the
inadvertent release of very sensitive information.
User training can further an organisation’s adoption of Cloud computing by making
users more comfortable with using Cloud technology (Marshall 2008). The younger
and more technology savvy workers may adopt Cloud computing more easily than
those who are technology averse (Ross 2010). Even though some potential users
adopt new technologies more rapidly than others, any user when faced with the
ability to perform a job more easily, more completely, at lower cost, and faster, can
find Cloud computing attractive (Aljabre 2012).
2.1.1.10 Current usage of Cloud services
There are significant differences in how countries use Cloud computing services.
One clear finding which emerges from the research is that different regions appear
to be at significantly different stages in the Cloud computing deployment life cycle
and have distinct attitudes towards Cloud. The global Cloud computing market is
expected to grow from US$37.8 billion in 2010 to US$121.1 billion in 2015 at a
compound annual growth rate (CAGR) of 26.2% from 2010 to 2015 (Rohan 2014).
Forrester Research estimated that the Cloud computing market would grow from
US$732 million in 2011 to US$3.2 billion in 2020 (ACMA 2014a). The
International Trade Administration believes that businesses will spend about
US$191 billion on Cloud services by 2020, compared to US$72 billion in 2014
(ITA 2015). The International Data Corporation (IDC) predicts a 2017 market
worth US$107 billion, more than twice as much as its 2013 estimate of US$47.4
billion. In 2015, Europe is expected to have the highest growth in Cloud IT
infrastructure spending at 32%, followed by Latin America (23%), Japan (22%),
and the USA (21%). By 2019, IDC expects Cloud IT infrastructure spending to be
36
US$52 billion, or 45% of total IT infrastructure spend; public Cloud will represent
about US$32 billion of that amount, and private Cloud will account for the
remaining US$20 billion (IDC 2015). Gartner predicted that the public Cloud
services market would reach almost US$250 billion by 2017 with a CAGR of
17.4% continuous rapid growth from 2011 to 2017 (Anderson et al. 2013). For
instance, Parallels (2013) predicted that the Chinese SME Cloud service market
would reach (renminbi) RMB33.8 billion by 2016 with a growth of 26 per cent
every year from 2014 to 2016. Pieterse (2013) mentioned that SMEs are also
leading the local adoption of Cloud services in South Africa. Further, it was also
predicted that total revenue for Cloud services in South Africa will reach US$374
million in 2017.
2.1.2 Small and Medium-Sized Enterprises (SMEs)
SMEs are an important component in any economy. They are widely recognised as
a great contributor to national and international economic development (Smallbone
& Wyer 2000). SMEs are the main source of employment in most countries and are
recognised as a source of technological innovation.
A number of definitions for SMEs exist, many coming from various governmental
and official sources such as SME agencies, ministries, government institutions and
national statistical institutions and bureaus around the world. Business size has been
used by a lot of published research to define SMEs based on the number of
employees or annual turnover (Altenburg & Eckhardt 2006; Carter & Jones-Evans
2006; Verheugen 2003). The European Union defines SMEs thus: “micro
enterprises are enterprises with fewer than 10 employees and turnover not more
than two million euros; small enterprises have between 10 and 49 employees and
turnover not more than 10 million euros; medium-sized enterprises have greater
than 50 and fewer than 250 employees and turnover not more than 50 million euros”
(EU 2003). According to Carter and Jones-Evans (2006), the UK government
defines SMEs as: “micro 0-9 employees; small 0-49 employees (including micro)
and medium 50-250 employees”. In the USA, SMEs employ fewer than 500
employees, from 1-100 is considered as a small firm, and 101-500 as medium-sized
firms (Hammer et al. 2010). The Australian Bureau of Statistics (ABS) defines a
small business as having fewer than 19 employees, whereas micro businesses have
37
fewer than 4 employees. Medium-sized enterprises are defined as businesses with
from 20 to 199 employees (DIISR 2011). For this study the following criteria (see
Table 2.9) are considered to define SMEs in Australia.
Table 2.9: SME definitions
Micro Enterprises Micro enterprises have 0 to 4 employees.
Small Enterprises Small enterprises have 5 to 19 employees.
Medium-sized EnterprisesMedium-sized enterprises have greater than 20 and fewer than 199 employees.
(Source: DIISR 2011)
Some researchers have criticised these definitions for only using a simple
quantitative criterion such as the number of employees. Van Hoorn (1979)
proposed the five additional characteristics below to differentiate SMEs from larger
firms, rather than considering only the number of employees:
a) a comparatively limited number of products, technologies and know-how;
b) comparatively limited resources and capabilities;
c) less-developed management systems, administrative procedures and
techniques;
d) an unsystematic and informal management style;
e) senior management positions held by either the founders of the firm and/or
their relatives.
Prashantham and Birkinshaw (2008) reported that SMEs typically operate
regionally and have knowledge of local distribution channels and markets.
Operations in SMEs are typically limited and resources are often focused on a
narrow niche in a particular market. SMEs are often viewed as being deeply
embedded as an integral part of the community. They are specialised in specific
services or products and tend to have a smaller market scope, and they are always
attempting to reduce the cost of production (Simpson & Docherty 2004). Small
businesses usually focus on survival and independence rather than growth (Curran
et al. 1996), hence the growth in SMEs is relatively slow. SMEs tend to have a
small management team and centralised management style. The manager of an
SME is usually the owner of the business and has a strong influence on the
organisation’s decision making (Murphy, Trailer & Hill 1996). Therefore,
38
transforming or adopting new innovation mainly depends on the owners of SMEs.
Beynon (2002) stated that the IT infrastructure in SMEs is not well established
compared to large enterprises. The uneven financial supply and limited resources
are just two further attributes of these organisations (Pollard 2003; Reid & Jacobsen
1988). Tetteh and Burn (2001) described more features of SMEs such as centralised
power; small management team; multifunctional management; flexibility; limited
product range; inadequate organisational planning; and unsophisticated software or
IT applications. Storey and Sykes (1996) suggested that SMEs tended to be risky
and uncertain compared to large organisations, whereas Leyden and Link (2004)
pointed out that SMEs are more risk-averse compared to large businesses.
2.1.2.1 SMEs in Australia
SMEs account for 95 per cent of active businesses in Australia, employ 70 per cent
of the nation’s workforce (MacGregor & Kartiwi 2010), and contribute more than
AU$480 billion to the national economy (DIISR 2011); therefore, they are the
major component of the Australian economy. The Reserve Bank of Australia
(RBA) considers SMEs as the backbone of Australia’s national economy
(Connolly, Norman & West 2012). Globally, SMEs make a substantial contribution
to national economies and are estimated to account for 80 per cent of global
economic growth. In Australia they contribute over 33 per cent of Australia’s GDP
(ASMEA 2012). According to a Reserve Bank of Australia report, 96 per cent of
businesses in Australia are classified as small organisations, and medium-sized
organisations represent 4 per cent of Australian businesses (Connolly, Norman &
West 2012). Connolly, Norman and West (2012) mentioned that the large
organisations were only 0.3 per cent. In other words, SMEs perform a critical role
in the Australian economy, in particular as suppliers to large firms, as customers of
large firms, and as suppliers to end-user customers in their own right. Australia’s
SME sector plays a vital role in the new job creation venture, emerging export
markets, sustainable economic growth and business resilience (Wei 2010).
According to Connolly, Norman and West (2012), the Reserve Bank of Australia
reported that around 47 per cent of employees were working in small businesses
(including micro businesses), with 23 per cent in medium-sized organisations, and
some 30 per cent in large enterprises. This shows that approximately 70 per cent of
Australian employees are working in the SME sector, which plays a significant role
39
in the economy of Australia. SMEs are also a significant customer segment for
financial service providers (MacGregor & Kartiwi 2010).
Sydney is a major city of Australia, and the City of Sydney Council highlighted the
significant importance of small-sized organisations in Sydney by considering this
sector as the core of Sydney’s economy in 2014 (CSC 2014). They reported that
more than 80 per cent of all businesses in the city were small businesses which
contributed around 25 per cent of Sydney’s economic output. Australian SMEs use
a wide range of ICTs in their business operations with their use of Internet
technologies. The Australian Communications and Media Authority reported that
94 per cent of SMEs in Australia were estimated to be connected to some form of
Internet service (ACMA 2014b).
2.1.3 SMEs and Cloud Computing
SMEs mainly differ from large organisations in terms of size and structure of the
organisation, which gives some advantages including fast communication between
managers and employees and their ability to implement and execute decisions
rapidly. On the other hand, these organisations face many disadvantages. Most of
these challenges are due to SMEs’ lack of resources (Welsh & White 1981) which
mainly include financial and human resources. These limitations influence
financing, planning and control, training and development, and also IT
intemperately (Blili & Raymond 1993). The biggest challenge that SMEs face is
keeping the costs under control, therefore they are not able to spend a significant
amount of money on their IT.
The benefits of Cloud computing for SMEs are quite similar to those for any other
type of organisation; however, they are more significant for SMEs in some respects
because of their characteristic features (Armbrust et al. 2010). The principal
argument is that it cuts costs for SMEs mainly because investments in hardware
and maintenance can be reduced ( ). This also lowers the
amount of money bound in capital expenditure and allows SMEs access to software
that would otherwise be too expensive to purchase (Armbrust et al. 2010). Cloud
computing could potentially be a future technology for SMEs (
). Cloud computing services offer SMEs an advantage in terms of
access to services without paying higher costs. SMEs that do not have enough
40
resources to establish a full-scale IT department may be able to access specific IT
applications as a service from Cloud computing for a small fee. Cloud computing
enables SMEs to easily access IT services without hiring an onsite IT professional.
In addition, providing the typical Cloud characteristics such as elasticity and short-
term contracts, Cloud software can provide more flexibility for SMEs (Mell &
Grance 2009). Cloud computing may provide a first opportunity for many SMEs to
try IT services. Overall, Cloud computing could have a dramatic impact on SMEs
which alters the way SMEs conduct business (Mahesh et al. 2013).
2.1.4 Cloud Computing Adoption in SMEs
Carr (2005) suggested that, in many instances, using Cloud computing might
provide the first opportunity for SMEs to try new software approaches in a cost-
effective manner. Often SMEs are unable to afford their own dedicated IT but have
a sufficient IT budget to buy the bandwidth and pay according to their need and
usage (Monika et al. 2010). In a Cloud computing environment, SMEs can reduce
their capital expenditure for IT infrastructure and, instead, utilise and pay for the
resources and services provided by Cloud computing (Rittinghouse & Ransome
2009).
Marks and Lozano (2010) proposed a Cloud Computing Reference Model (CCRM)
that supports major business drivers. It consists of four supporting models: Cloud
Enablement Model, Cloud Deployment Model, Cloud Governance Model and
Cloud Ecosystem Model. The Cloud enablement model describes different tiers of
Cloud services from the Cloud business tier that can be selected according to the
user’s business necessity. However, such explanations tend to be awkward with the
Cloud Adoption Reference Model (CARM) introduced by Keung & Kwok (2012)
as it is completely based on confidential data.
As previously explained, there are various types of business models related to
Cloud computing adoption, and their application depends on the nature and size of
an enterprise (Handler, Barbier & Schottmiller 2012; Rahimli 2013). According to
Lawrence et al. (2010), all direct and indirect go-to-market models in Cloud
computing are able to cater for SMEs’ needs; however, they are not necessarily
suitable for large enterprises because of their scale and complexity. It has been
41
found that the current charging pattern and other aspects of Cloud computing make
it more suitable for SMEs than for larger organisations (Misra & Mondal 2011).
Further, the public Cloud service provides a more valuable service to micro/small
businesses (non-employee businesses and with 1-4 employees) as they require
many of the same business services provided to large organisations even though
they may have only a PC and an Internet connection (Handler, Barbier &
Schottmiller 2012).
In addition, the findings of Sultan (2011) and Bharadwaj and Lal (2012) suggest
that Cloud computing is likely to be a more attractive option for most SMEs
because of the flexible cost structures and scalability. Traditional in-house
enterprise resource planning (ERP) implementation incurs high costs for SMEs,
whereas by using the Cloud they can buy ERP components relevant to their
business and pay per component instead of buying a whole ERP suite (Sharif 2010).
Findings also show that SMEs can expand their usage and services easily using
Cloud computing. The Cloud services are more acceptable to SMEs due to the
relative advantage, flexibility and scalability features (Salleh, Teoh & Chan 2012).
Gorniak (2009) demonstrated the first three reasons behind the possible use of
Cloud computing by SMEs as:
1) avoiding capital expenditure in hardware, software and IT support, and
information security by outsourcing infrastructure/platforms/services;
2) flexibility and scalability of IT resources; and
3) business continuity and disaster recovery capabilities.
The Cloud computing constructs previously identified were analysed against the
requirements of the different SMEs types (Table 2.10). The factors affecting Cloud
computing adoption is investigated separately for micro, small, and medium
enterprises, filling the research gap identified from the literature on micro
organisations.
42
Table 2.10: Analysis of Cloud computing constructs in the SME context
ConstructSize of SME
References
Cloud Security
Micro Carcary, Doherty & Conway 2014; Oliveira et al. 2014; Minifie 2014; Browne & Lang 2014; Gupta et al. 2013
Small Monika et al. 2010; Kelly 2011; Sahandi et al.2012; Carcary, Doherty & Conway 2014;Stieninger & Nedbal 2014; Tehrani & Shirazi 2014; Zardari et al. 2014a; Li et al. 2015; Oliveira et al. 2014; Minifie 2014; Browne & Lang 2014; Gupta et al. 2013
Medium Monika et al. 2010; Kelly 2011; Sahandi et al.2012; Carcary, Doherty & Conway 2014;Stieninger & Nedbal 2014; Tehrani & Shirazi 2014; Zardari et al. 2014a; Li et al. 2015; Oliveira et al. 2014; Minifie 2014; Browne & Lang 2014; Gupta et al. 2013
Cloud Privacy
Micro Carcary, Doherty & Conway 2014; Gupta et al.2013
Small Sahandi et al. 2012; Tehrani & Shirazi 2014; Li et al. 2015; Gupta et al. 2013
Medium Sahandi et al. 2012; Tehrani & Shirazi 2014; Li et al. 2015; Gupta et al. 2013
Cloud Flexibility
Micro Carcary et al. 2014; Oliveira et al. 2014; Minifie 2014; Gupta et al. 2013
Small Brian et al. 2008; Monika et al. 2010; Sahandi et al. 2012; Salleh et al. 2012; Oliveira et al. 2014; Minifie 2014; Gupta et al. 2013
Medium Brian et al. 2008; Monika et al. 2010; Sahandi et al. 2012; Salleh et al. 2012; Oliveira et al. 2014; Minifie 2014; Gupta et al. 2013
Relative Advantage
Micro Carcary et al. 2014; Handler et al. 2012; Oliveira et al. 2014; Gupta et al. 2013
Small Monika et al. 2010; Salleh et al. 2012; Alshamaila et al. 2013; Stieninger & Nedbal 2014; Tehrani & Shirazi 2014; Oliveira et al. 2014; Gupta et al.2013
Medium Monika et al. 2010; Salleh et al. 2012; Alshamaila et al. 2013; Stieninger & Nedbal 2014; Tehrani & Shirazi 2014; Oliveira et al. 2014; Gupta et al.2013
43
Awareness of Cloud
Micro Handler et al. 2012; Rath et al. 2012; Carcary,Doherty & Conway 2014
Small Rath et al. 2012; Salleh et al. 2012; Surendro & Fardani 2012; Carcary, Doherty & Conway 2014; Dahiru, Bass & Allison 2014b; Tarmidi et al. 2014; Shetty & Kumar 2015
Medium Rath et al. 2012; Surendro & Fardani 2012;Carcary et al. 2014; Dahiru, Bass & Allison2014b; Tarmidi et al. 2014; Shetty & Kumar 2015
Quality of Service
Micro Carcary et al. 2014Small Brian et al. 2008; Monika et al. 2010; Keung &
Kwok 2012; Salleh et al. 2012Medium Brian et al. 2008; Monika et al. 2010; Keung &
Kwok 2012; Salleh et al. 2012
There are eight key objectives to achieve when using Cloud computing: e-industry
regulatory change, disaster recovery, improving capability and availability,
minimising IT investment in infrastructure, enhancing IT control, business agility,
mitigating IT maintenance and management, and improving IT productivity.
Therefore, Cloud computing can be considered as one of the business success
factors that could lead to improving business outcomes and a country’s national
economy (Mladenow et al. 2012). It can also act as one of the most important
success factors for business survival, improving and managing business processes
and innovating new business ideas (Isom & Holley 2012). Cloud computing can
help SMEs by providing most of the utilities needed, which consist of hardware
infrastructure to operating systems, software, file storage, databases and more, as a
means of renting online. This facilitates mitigation of the IT resource issues for
SMEs, improving their income and growth contribution to the Australian economy.
In order to gain maximum benefits, Cloud computing requires access to a fast and
reliable Internet infrastructure to guarantee quality of service and ensure 24/7
online availability. One of Australia’s most significant projects, the National
Broadband Network (NBN) is a national Internet network designed to provide
access to all the benefits of fast and reliable Internet services. It was introduced as
an investment in infrastructure to provide high-capacity data communications
across the nation (Busch et al. 2014). It aimed to reach 93 per cent of Australian
homes, schools and businesses that could access the NBN through optic fibre in
44
order to provide peak speeds of up to 25Mbps. The NBN system provides
broadband services over a mix of three technologies: optic fibre, fixed wireless, and
next-generation satellite which was proposed to work by 2015 (Matthew 2014).
The NBN could enable SMEs in Australia to further embrace Cloud computing
services such as SaaS and IaaS.
2.2 Theoretical Foundation
Adoption was referred to as “the organisational policies, strategies, processes, and
tasks employed, either explicitly or otherwise, by an organisation in its efforts to
identify, acquire, and diffuse appropriate information technology” (Huff & Munro
1985; Wang & Qualls 2007).
The adoption of a new technology such as Cloud computing occurs in stages; for
example, there is a staggered time frame for adoption, with some early adopters
embracing the technology before the mainstream users begin using it. Adoption
decisions are also vulnerable to many social influences, both inside and outside an
organisation, for example that the technology won’t work or is too expensive.
Negative social influences may have a more profound effect on a technology
adoption decision than positive social influences toward technology adoption, and
can also affect decisions to discontinue its adoption (Parthasarathy & Bhattacherjee
1998). Because of these social factors, it is critical for Cloud computing providers
to nurture a good reputation and maintain high customer satisfaction when
implemented.
2.2.1 ICT Innovations Adoption
The Technology Acceptance Model (TAM) was developed by Davis in 1986 to
model patterns of user-adoption of information systems (Davis, Bagozzi &
Warshaw 1989). TAM has been used extensively to educate individuals and
encourage organisations to adopt new technologies. An individual’s intention to
purchase a product or service is determined by two factors, namely perceived
usefulness and ease of use (Wang & Qualls 2007). Several models have been
proposed to enhance the power of TAM to explain adoption behaviour (Shih 2004;
Yang & Yoo 2004). When researchers use TAM, the user friendliness and value to
the user are also major factors when adopting a form of technology. In one study
45
by Wu and Lederer (2009), the ease of use and usefulness were found to have a
significant effect on technology adoption. Another key factor was the time that a
user had for making the decision. In another example, in adopting mobile data
services in China, ease of use and brand experience, or attitude towards the
technology, were given as prime drivers behind adopting this technology (Qi et al.
2009). These examples indicate that the factors influencing technology adoption
decisions can be complex and may vary from one situation to another.
Normally, when a person or organisation sees an advantage to accepting a new form
of technology, there is an incentive to accept it. An example is a study by Lease
(2005) that identifies the reasons why computer security managers adopted
biometric security technologies. Another example is where Glynn, Fitzgerald and
Exton (2005) used adoption theory to evaluate open source software adoption. They
expressed concern that prior adoption theory research was at the individual level
rather than at the organisational level. Rogers (2002) stated that prior experience
with a technology could influence technology adoption decisions, both positively
and negatively. Customer relationship management could also influence adoption
decisions (Richard & Thirkett 2007). Also, Sabherwal, Jeyaraj and Chowa (2006)
found that user-related and contextual issues were critical to information systems’
adoption success. Table 2.11 shows the theoretical framework used to examine IT
adoption by different authors. The table presents the theories used in IT adoption,
and the independent and dependent factors used in those theories.
46
Table 2.11: Theoretical frameworks used to examine IT adoption
Theory Independent factors Dependent factors SourcesTechnology Acceptance Model (TAM)
Perceived UsefulnessPerceived Ease of Use
Behavioural Intention to UseSystem Usage
Davis et al. 1989; Szajna 1996; Legris, Ingham & Collerette2003
Unified Theory of Acceptance and Use of Technology(UTAUT)
Performance ExpectancyEffort ExpectancySocial InfluenceFacilitating ConditionsGenderAgeExperienceVoluntariness of Use
Behavioural IntentionUsage Behaviour
Venkatesh et al. 2003; Oshlyansky 2007
Theory of Reasoned Action
Attitude toward BehaviourSubjective Norm
Behavioural IntentionBehaviour
Fishbein 1967; Ajzen & Fishbein 1973;Fishbein & Ajzen 1975
Theory of Planned Behaviour
Attitude toward BehaviourSubjective NormPerceived Behavioural Control
Behavioural IntentionBehaviour
Ajzen 1985; Ajzen 1991; Armitage &Corner 2001
Diffusion Innovation Theory (DOI)
Compatibility of TechnologyComplexity of TechnologyRelative Advantage (Perceived Need for Technology)
Implementation Success or Technology Adoption
Rogers 1962; Rogers & Shoemaker 1971;Rogers 1995
TOE framework
Organisational contextTechnological contextEnvironmental context
Intention to Adopt
Tornatzky & Fleischer 1990; Low et al. 2011
Iacovou, Benbasat &Dexter model
Perceived benefitsOrganisational readinessExternal pressure
EDI adoption and Integration
Iacovou et al. 1995; Lee & Cheung 2004
DeLone &McLean model
Intention to useUser satisfaction
Net benefits DeLone & McLean 2003; Wang & Liao2008
2.2.2 Technology Organisation Environment
The Technology Organisation Environment (TOE) framework developed by
Tornatzky and Fleischer (1990) identified three aspects that influence an
47
organisation’s process of adopting and implementing technological innovations:
technological, organisational and environmental. Figure 2.4 depicts the TOE
framework and its elements. Each of these elements are explained in detail below.
Figure 2.4: TOE framework
2.2.2.1 Technological context
The technological context refers to internal and external technologies applicable to
the organisation (Oliveira & Martins 2011; RUI 2007). According to the TOE
framework, technologies that are currently used by the organisation, and also
technologies that are available in the market, influence the adoption decision.
Technology that is currently being used by an organisation defines the scope and
limit of the technological change that the organisation can accept, whereas
technology that is available in the market indicates how organisations can evolve
by adopting new technologies. According to Tornatzky and Fleischer (1990),
technologies that are available outside an organisation’s boundaries create
incremental, synthetic or discontinuous changes to the existing technology.
Technologies that offer incremental changes add new features to the existing
technologies, which has the lowest amount of risk. Technologies that offer
synthetic changes which combine already existing technologies in a novel way have
48
a moderate risk. Innovations producing discontinuous changes are radically
different from existing technologies (Baker 2012). The technology factors are
discussed in detail below.
Relative advantage is considered as a central indicator for the adoption of new IS
innovation. The greater the perceived benefits of an innovation to an organisation,
the higher the probability that the organisation will adopt the innovation (Lee
2004). Uncertainty refers to the extent to which the results of using an innovation
can be assured (Fuchs 2005). Security risk and uncertainty have been considered as
key factors limiting the use of some ICT technologies (Kalakota & Whinston 1996).
Lack of knowledge about a particular innovation can lead to less predictable results,
thus the adoption decision and the associated changes may imply some risks.
Compatibility refers to the technical and procedural requirements of the innovation
to be compatible and consistent with the values and technological requirements of
the adopting organisation (Lertwongsatien & Wongpinunwatana 2003). According
to Tornatzky and Fleischer (1990), the perceived high compatibility of the
innovation with an organisation’s existing technologies could positively affect the
adoption process. Business owners are concerned whether the new innovation is
consistent with the values and technological needs of their organisations (Jungwoo
2004). Poor integration of new systems with existing technologies could result in a
problematic situation (Akbulut 2003). Complexity refers to the degree to which a
new innovation is perceived as relatively difficult to understand and use. New
technologies have to be user friendly and easy to use in order to increase the
adoption rate, whereas adoption is less likely if the innovation is considered more
challenging to use (Sahin 2006). Complexity is negatively linked with the adoption
decisions. Trialability refers to whether the innovation can be tried out for a limited
time period before an actual outlaying of the particular innovation. Trialability
reduces the perceived risk of purchasing (an unsuitable) new innovation and the
adoption rates will rise substantially. This is more significant for early adopters
rather than later adopters as the latter can get an indication of how the innovation
performs from previous adopters. Therefore, when it comes to exploring new
innovations, trialability is more significant for early adopters and innovators
(Rogers 1995).
49
2.2.2.2 Organisational context
This refers to factors such as organisation size and scope, human resources,
organisational structure and culture. Organisational characteristics affect the
adoption and implementation of innovation in many ways. Rogers (2003) stated
that the size of the organisation is one of the most critical determinants of the
innovator profile. It is often argued that larger organisations have more resources,
skills, experience and ability to survive a failure than small organisations, whereas
small organisations are flexible enough to adapt their actions to the environment
changes because of their small size (Oliveira & Martins 2011). IT adoption often
needs coordination, which may be relatively easier to achieve in small organisations
(Premkumar 2003). The behaviour of top management is another influencing
factor that can promote or inhibit innovation adoption. Top management support
can convey the importance of the innovation for the organisation to all stakeholders
and also ensure the availability of the necessary resources (Daylami et al. 2005).
The study by Jeyaraj, Rottman and Lacity (2006) found that top management
support is considered the main link between individual and organisational ICT
innovation adoption. Organisational structure is another factor that influences
innovation adoption. Researchers believe that centralised organisations are best
suited for the implementation stage of the innovation adoption process.
Communication is also another organisational factor that influences the adoption
of innovation (Baker 2012).
2.2.2.3 Environmental context
The external environment refers to the factors outside an organisation such as
competitors, government regulations and industry. Competitors is an
environmental factor that influences innovation adoption. Competitive pressure
refers to the external environment that can have a direct effect on an organisation’s
decision making. It is a strong incentive to adopt relevant new technologies
(Majumdar & Venkataraman 1993). Government regulations can either support or
limit the adoption of innovation (Baker 2012). The industry that an organisation
operates in can influence the ability to adopt new ICT innovation (Jeyaraj, Rottman
& Lacity 2006). The adoption rate is high in rapidly growing industries. Goode and
Stevens (2000) reported that the industry which an organisation belongs to might
50
have an effect on the organisation’s adoption of new technology. Market scope
indicates the nature of an organisation’s market domain (Zhu, Kraemer & Xu
2003). Businesses with a broader scope are usually more motivated to start and
implement a new innovation. Organisations may be more willing to try a new
innovation if they feel there is sufficient external support (Premkumar & Roberts
1999).
The TOE framework has been used by many researchers in the IS field to
investigate the process of innovation adoption. In the study by Chau and Tam
(1997): perceived benefits, perceived barriers, the importance of compliance to
standards, interoperability, and interconnectivity are part of the technological
context of the open system; complexity of the IT infrastructure, satisfaction with
existing systems, and formalisation of system development and management are
organisational factors; and market uncertainty are all part of the external
environment context that have been used in the TOE framework to investigate the
adoption process of open systems in Hong Kong. Zhu, Kraemer and Xu (2003)
developed a conceptual model based on the TOE framework to study the influence
of IT infrastructure, e-business know-how, firm scope, firm size, consumer
readiness, competitive pressure, and lack of trading partner readiness, on the
adoption of e-business by organisations from Germany, the UK, Denmark, Ireland,
France, Spain, Italy, and Finland. In another study, Zhu, Kraemer and Xu (2006)
investigated the influence of technology competence, size, international scope,
financial commitment, competitive pressure and regulatory support on the adoption
of e-business by organisations in both developed countries (Denmark, France,
Germany, Japan, Singapore, and the USA) and developing countries (Brazil, China,
Mexico and Taiwan). Teo, Ranganathan and Dhaliwal (2006) studied the
deployment of business-to-business (B2B) e-commerce by organisations using the
TOE framework. Liu and Fellow (2008) investigated the adoption of e-commerce
development as an innovative business process in organisations. The adoption
process of ERP systems was studied by Pan and Jang (2008) using the TOE
framework. In another study, the internal integration of e-business and the external
diffusion of using e-business among large organisations were investigated by Lin
and Lin (2008). Oliveira and Martins (2009) used the TOE framework to study the
adoption of website and e-commerce by organisations. Table 2.12 summarises a
51
few examples of TOE-based studies and outlines the variables considered for each
of the contexts.
Table 2.12: Examples of TOE-based studies
IS adoption & context
Research factors Authors
Open systemsHong Kong
Perceived benefits; perceived barriers; perceived importance of compliance to standards; interoperability; interconnectivity; complexity of IT infrastructure; satisfaction with existing systems; formalisation of system development and management; and market uncertainty.
Chau & Tam 1997
Communication technologiesSmall businesses inthe USA
Relative advantage; cost; complexity; compatibility; top management support; IT-expertise; size of business; competitive pressure; vertical linkages; and external support.
Premkumar &Roberts 1999
EDISmall businesses in Hong Kong
Perceived direct benefits; perceived indirect benefits; perceived financial cost; perceived technical competence; perceived industry pressure; and perceived government pressure.
Kuan & Chau 2001
e-business usageOrganisations in developed countries
Technology competence; size; international scope; financial commitment; competitive pressure; and regulatory support.
Zhu & Kraemer 2005
E-commercedevelopment levelOrganisations in China
Support from technology; human capital; potential support from technology; management level for information; firm size; user satisfaction; and e-commerce security.
Liu & Fellow 2008
Enterprise systemsSMEs in UK
Relative advantage; compatibility; complexity; trialability; observability; top management support; organisational readiness; IS experience; size; industry; market scope; competitive pressure; and external IS Support.
Ramdani &Kawalek 2007
2.2.3 Diffusion of Innovations
Rogers’ Diffusion of Innovations (DOI) theory seeks to explain how, why, and at
what rate new ideas and technology spread through cultures (Rogers 1962). The
theory of DOI originated from six different disciplines: early sociology, rural
sociology, industrial sociology, medical sociology, education and anthropology.
Numerous studies have attempted to update Rogers’ (1962) DOI theory which
forms the foundations for much of the current adoption theory (Lundblad 2003).
The current version of DOI theory tries to discover the factors that influence the
52
spread of a new technology or idea in a society (Rogers 2003). The objective of this
theory is to define the reasons why one innovation successfully diffuses in a society
while others do not. In 1962, Rogers mentioned three major factors for innovation
adoption by an individual: the actor’s identity and perception of the innovation, the
process, and the result; resulting in either adoption or rejection. In a subsequent
refinement of Rogers’ theory on innovation, in the fourth edition (Rogers 1995) as
cited in Lundblad (2003), four primary elements were considered by Rogers for
technology adoption. These elements include innovation, a communication
channel, a time frame for adoption, and a social structure that fosters technology
innovation adoption. Each of these elements is explained in detail below.
2.2.3.1 Innovation
There are various definitions for innovation but most commonly, as perceived by
individuals, innovation is considered as any new idea, process, technology, product
etc. Each innovation has different attributes which influence its diffusion in society
(Rogers 1983). There are mainly five key attributes of each innovation: relative
advantage, compatibility, complexity, trialability, and observability. Rogers (2003)
defines relative advantage as “the degree to which an innovation is perceived as
being better than the idea it supersedes”. In many instances, relative advantage has
a positive influence on the diffusion of innovation. According to Rogers (2003),
compatibility is the degree to which an innovation is perceived as consistent with
the existing values, experiences and needs of potential adopters. An innovation that
is compatible with the norms and values of individuals or with the norms of a social
system, positively influences the speed of adoption in a society. Rogers (2003)
mentioned that complexity is “the degree to which an innovation is perceived as
relatively difficult to understand and use”. Complexity has a negative influence on
diffusion where more complex innovation has less chance to be diffused
successfully in the society. Trialability is defined by Rogers (2003) as “the degree
to which an innovation may be experimented on a limited basis”. Observability is
defined as “the degree to which the results of an innovation are visible to others”
(Rogers 2003). Among all these five attributes, relative advantage, complexity and
compatibility are the factors that most significantly influence the adoption of
different innovations (Tornatzky & Klein 1982). For a new innovation such as
53
Cloud computing to be considered for adoption, it must offer potential benefits to
the users and be compatible with the users’ current technology (Lundblad 2003).
2.2.3.2 Communication channel
This is the second element of DOI theory, in which the innovation is transmitted
from an individual or firm that has the knowledge about the innovation, to an
individual or firm that does not have the knowledge and information about the
innovation. This refers to any type of tool or medium that is used by individuals to
transfer knowledge to someone else. This transfer of knowledge is done through a
communication channel. The main role of these channels is to increase awareness
about the innovation. Brown (1981) further explained that a communication or
learning process can foster innovation adoption. Adequate communication is
needed to educate and persuade potential users of the technology’s value. There are
two types of communication channel (Rogers 2003), interpersonal and mass media.
Mass media delivers the information about the innovation to the general public.
The interpersonal channel persuades people to make their adoption decisions. With
the popularity of Web 2.0, the Internet became both an interpersonal channel and
mass media (Tehrani & Shirazi 2014).
2.2.3.3 Time
Time is a key element of DOI theory. The adoption process does not occur instantly,
rather it takes place over a period of time through different stages. DOI involves
five different stages: knowledge, persuasion, decision, implementation and
confirmation, as shown in Figure 2.5.
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Figure 2.5: Stages of adoption processIndividuals come across an innovation during the knowledge stage but have only
limited information about the innovation. They become interested in the innovation
at the persuasion stage and try to find out more information and details about it.
During the decision stage they compare the advantages and disadvantages of the
innovation and decide whether to accept or reject it. If the innovation is accepted,
individuals start using the innovation at the implementation stage. At this stage,
individuals determine whether the innovation is useful or not, based on their
experience. They might look for more information if it is useful. The last stage of
the model is the confirmation stage: when individuals finalise their decision and
recognise the benefits of using the innovation they start to promote the innovation
to others.
Time also defines various categories of adopters in addition to stages of diffusion.
People have different degrees of willingness to adopt an innovation (Rogers 2003).
Rogers categorised adopters into five different groups: innovators, early adopters,
early majority, late majority and laggards. Innovators play an important role in
launching the new idea and bringing the innovation to the system’s boundaries.
They should have more awareness and technical knowledge to implement complex
innovations. Further, they should have the ability to deal with a high level of
uncertainty about the innovation adoption and sustainable financial resources to
face unprofitable innovations. Early adopters have the highest degree of opinion
Knowledge
Persuasion Research
Decision
Implementation
Confirmation
Accept Reject
55
leadership than any other group. Early majority is the most numerous adopter
category representing one-third of the members of a system. They act as the main
linkage between very early and relatively late adopters and they take longer periods
in making the innovation adoption decision compared to innovators and early
adopters. Late majority also represents one-third of the members in a system. They
need to be certain about their innovation adoption decision because of relatively
scarce resources availability. Many factors can influence their adoption decisions.
Laggards are the last in adopting a new innovation. They tend to be suspicious of
innovation and changing agents, and thus they lag far behind regarding knowledge
and awareness of a new innovation. Rogers (2003) assumed that the numbers of
individuals in different groups are normally distributed over time. These categories
are shown in Figure 2.6. Members of each group share some common
characteristics. The figure clearly shows that most adopters are in the early and late
majority groups.
Figure 2.6: Categories of adopters
2.2.3.4 Social system
In 1983, Rogers defined the social system as “a set of interrelated units that are
engaged in joint problem solving to accomplish a common goal”. The norms and
roles become meaningful within the social system. In a social system, diffusion of
innovation is influenced by some characteristics of the system, which are social
structure, social norms, opinion leaders, change agent, types of innovation
decisions, and consequences of an innovation.
According to Rogers (2003), social structure is defined as “the patterned
arrangements of the units (individuals, organisations, etc.) in a system”. Social
56
norms are the established behaviour patterns among the members of a social system
(Rogers 2003). If the social norms are compatible with the innovation, it has a high
chance of being diffused in the system. Individuals who have the potential to
influence other members of the social system are the opinion leaders. A change
agent is an individual or an entity that can control the diffusion of innovation by
influencing the other members’ behaviour. In general they support the diffusion of
innovation but in some cases they prevent the diffusion of innovation. For instance,
governments can stimulate a diffusion of innovation by offering incentives,
whereas they can slow down a diffusion of innovation by enforcing laws and
regulations (Rogers 2003). Champions within an organisation and change agents
from outside an organisation can promote the adoption of new technology
(Lundblad 2003).
Types of innovation decisions depend on two factors, which are the degree of
voluntariness and the person’s responsibility for decision making. Consequences of
innovation is another factor that influences the diffusion of an innovation in the
social system. Individuals are more likely to adopt an innovation if they receive any
short-term benefits by adopting. All the elements of DOI are depicted in Figure 2.7.
57
Figure 2.7: Factors influencing innovation adoption in a social system
DOI theory is applicable at both individual level and organisation level innovation
adoption, but with a somewhat different story at each level. Decision making at the
organisation level is more complex as it involves more than one individual making
innovation decisions. Therefore, Rogers (1995) proposed another model to explain
the level of innovativeness specific to the organisation level. The DOI theory at
organisation level is depicted in Figure 2.8. This model shows that organisational
innovativeness depends on the leader’s characteristics, the internal characteristics
of the organisational structure, and the external characteristics of the organisation
(Rogers 1995). Innovativeness is defined by Rogers (1995) as “the degree to which
an individual or other unit of adoption is relatively earlier in adopting new ideas
than the other members of a system”.
58
Figure 2.8: Diffusion of innovation in organisations
Leader characteristics describes attitudes towards change which influence the level
of their innovativeness. The internal characteristics of the organisation, which are
centralisation, complexity, formalisation, interconnectedness, organisational slack
and size of the organisation, influence the level of organisational innovativeness.
Based on his model, Rogers (1995) explained centralisation as “the degree to which
power and control in a system are concentrated in the hands of a relatively few
individuals”; complexity as “the degree to which an organisation’s members
possess a relatively high level of knowledge and expertise”; formalisation as “the
degree to which an organisation emphasises its members’ following rules and
procedures”; interconnectedness as “the degree to which the units in a social system
are linked by interpersonal networks”; organisational slack as “the degree to which
uncommitted resources are available to an organisation”; and size as “the number
of employees of the organisation”. He also explained system openness as the
external characteristics of the organisation which influence organisational
innovativeness.
Rogers’ DOI theory is used extensively by many researchers in different fields,
especially to confirm the validity of the model. The variances in adoption of
innovations are mainly explained by the five attributes of the innovation: relative
advantage, compatibility, complexity, trialability and observability. Cooper and
Zmud (1990) used Roger’s DOI theory to build their adoption model to investigate
59
the influence of managerial tasks on the implementation of the Material
Requirement Planning (MRP) system. In 1999, Thong used DOI to develop and
validate a new IS adoption model to investigate the influence of decision makers’
characteristics, information systems, organisational characteristics and
environmental characteristics in the context of the small business. Another study
used DOI to develop a model to study the influence of top management support,
organisational structure, organisation size, IT infrastructure, IS structure and
earliness of adoption on the diffusion of the intranet in organisations (Eder &
Igbaria 2001). Beatty, Shim and Jones (2001) investigated the influence of
perceived benefits, organisational compatibility, technical compatibility, top
management support and complexity on the adoption of websites by organisations.
Roger’s DOI was used in another study to investigate the influence of innovative
characteristics, organisational characteristics and environmental characteristics on
the adoption of an ERP system (Pan & Jang 2008).
Many researchers modified the DOI constructs and used it to propose a new model.
Tan et al. (2009) added some constructs to Rogers’ DOI five influencing factors
and proposed a new model to study the factors that influence the adoption of
Internet-based ICT in SMEs in the southern region of Malaysia. They found that
security was also a significant factor in addition to factors in the original DOI
model. DOI is mainly used in quantitative studies but also in qualitative studies.
Doshi and Gollakota (2011) used DOI to perform a qualitative study investigating
the diffusion of telecentres in rural areas in developing countries. Table 2.13
summarises a few examples of DOI-based studies and outlines the main variables
considered for each of the contexts.
60
Table 2.13: Examples of DOI-based studies
IS adoption & Context Research factors Authors
Customer-based interorganisational systems (CIOS)
Structure; strategic planning; implementation planning; infrastructure; technology policy; role of IT; industry; customer power; top management support; compatibility; relative advantage; and complexity.
Grover 1993
EDI in small organisations
Perceived benefits; organisational readiness; and external pressure.
Iacovou et al. 1995
IT pre- and post-adoption
Behavioural beliefs about adopting the IT; normative beliefs about adopting the IT; behavioural beliefs about using the IT; and normative beliefs about using the IT.
Karahanna et al. 1999
Expert systems in forecasting
Complexity; communicability; product risk; psychological risk; relative advantage; divisability; and compatibility.
Armstrong &Yokum 2001
ERP in organisations Relative advantage; compatibility; and complexity. Bradford &Florin 2003
2.2.4 TOE & DOI
In many instances the TOE framework is combined with DOI theory to study the
diffusion of innovation in organisations. Chau and Tam (1997) mentioned that to
better understand the organisational decisions related to the adoption of
technological innovation, the context of the study should be comprehensive and the
variables tailored to the specificity of the innovation. In many ways, the TOE
perspectives overlap with the innovation characteristics identified by Rogers
(1995). DOI theory’s internal and external organisational characteristics include the
same measures as TOE’s organisational context, and the technological context is
implicitly the same idea as that of Rogers (Hsu, Kraemer & Dunkle 2006). There
are also important differences between the two theories. TOE does not specify the
role of individual characteristics whereas the DOI theory suggests the inclusion of
top management support in the organisational context. Similarly, DOI does not
consider the impact of the environmental context, but the TOE framework helps to
provide a more comprehensive perspective for understanding IT adoption by
including the technological, organisational, and environmental contexts, thus the
theories meaningfully complement each other (Oliveira & Martins 2011).
In 1999, Thong combined TOE and DOI to develop a conceptual model to study
the influence of CEOs’ innovativeness, CEOs’ IS knowledge, the relative
advantages of IS, the compatibility and complexity of IS, business size, employees’
IS knowledge, information intensity, and competition, on the adoption of
61
information systems by small organisations. Another study, Zhu et al. (2006)
combined the TOE framework and DOI theory to develop a conceptual model to
study the usage and impact of e-business on organisations. They investigated the
influence of relative advantage, compatibility, costs, security concerns, technology
competence, organisation size, competitive pressure and partner readiness, on the
adoption of e-business by organisations in Europe. Chong et al. researched the
adoption of collaborative commerce (c-commerce) combining TOE and DOI
theory (Chong et al. 2009). They studied the influence of relative advantage,
compatibility, complexity, expectations of market trends, competitive pressure,
trust, information distribution, information interpretation, top management support,
feasibility, and project champion characteristics, on the decisions to adopt c-
commerce. TOE and DOI theories were combined by Wang, Wang and Yang
(2010) to form a new conceptual model to investigate the influence of relative
advantage, complexity, compatibility, top management support, firm size,
technology competence, competitive pressure, trading partner pressure, and
information intensity, on the adoption of Radio Frequency Identification Devices
(RFID) by manufacturing organisations. Table 2.14 summarises a few examples of
studies that combined TOE and DOI theories and outlines the variables considered
for each of the contexts.
Table 2.14: Examples of studies combining TOE and DOI models
Theoretical model Research factors Authors
TOE and DOI information systems
CEO’s innovativeness; CEO’s IS knowledge; relative advantage of IS; compatibility of IS; complexity of IS; business size; employees’ IS knowledge; information intensity; and competition.
Thong 1999
TOE and DOI e-business
Relative advantage; compatibility; costs; security concerns; technology competence; organisation size; competitive pressure; and partner readiness.
Zhu et al. 2006
TOE and DOI e-commerce
Relative advantage; compatibility; technology readiness; international scope; firm size; cost savings; security concerns; competitive pressure; and regularity support.
Leinbach 2008
TOE and DOI c-commerce
Relative advantage; compatibility; complexity; expectations of market trends; competitive pressure; trust; information distribution; information interpretation; top management support; feasibility; and project champion characteristics.
Chong et al. 2009
62
TOE and DOIbenchmarking
Relative advantage and compatibility. Azadegan &Teich 2010
TOE and DOI RFID
Relative advantage; complexity; compatibility; top management support; firm size; technology competence; competitive pressure; trading partner pressure; and information intensity.
Wang et al. 2010
TOE and DOI Cloud computing
Relative advantage; complexity; compatibility; top management support; firm size; technology readiness; competitive pressure; and trading partner pressure.
Low et al. 2011
TOE and DOI Cloud computing
Relative advantage; uncertainty; compatibility; complexity; trialability; size; top management support; innovativeness; prior IT experience; competitive pressure; industry; market scope; supplier effort; and external computing support.
Alshamaila 2013
TOE and DOI Cloud computing
External support; competitive pressure; decision-maker’s innovativeness; decision maker’s Cloud knowledge; employee’s Cloud knowledge; firm’s information intensity; relative advantage; complexity; compatibility; security and privacy; trialability; and cost.
Tehrani & Shirazi 2014
2.3 Chapter Summary
This chapter has presented an overview of Cloud computing and small and
medium-sized enterprises. The literature review indicates that over the last decade
since 2005, Cloud computing has become a central tool for redefining IT operations
in almost every aspect of the whole economy. The adoption of Cloud computing
by organisations is increasing because of its significant benefits especially for
SMEs. However, the actual adoption of Cloud computing by SMEs in Australia is
less compared to other developed countries. The literature review indicates that
little research has been undertaken to look into the adoption of Cloud computing
by SMEs, especially considering micro, small and medium-sized organisations
separately. Consequently, there is a need for more research on this issue so that
SMEs can better adopt Cloud computing, thereby increasing their business profits
and contributing significantly to their country’s economy.
This chapter has also provided an overview of the theoretical basis to understanding
the adoption of Cloud computing by SMEs. This research model builds on two
well-known theories that have been validated and found useful for explaining
organisations’ adoption of IT. The literature shows that these two theories have
63
dominantly been used by researchers to study the adoption of innovations by
organisations. The first model was Rogers’ DOI theory, which seeks to explain
how, why, and at what rate new ideas and technology spread through cultures. The
theory proposes mainly the factor relative advantage that influences organisations’
intentions towards the adoption and use of Cloud computing. This also proposes
flexibility, quality of service, security and privacy in Cloud computing as an
extended version of the theory. However, it is limited to some potential influential
factors, such as awareness of the organisation, size and type of organisation. Thus,
the current research draws on the TOE framework, which proposes the
organisational factors which are awareness of Cloud, organisation size and type of
organisation.
Therefore, it can be argued that integrating the notion of the aforementioned two
models can allow examination of broader factors that may influence SMEs towards
the adoption of Cloud computing. The above-mentioned factors are grouped
together to form this research model. The model, its constructs and related
hypotheses are discussed in more detail in the following chapter.
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3 CHAPTER 3: RESEARCH MODEL AND HYPOTHESIS
DEVELOPMENT
3.1 Introduction
This chapter builds on the theoretical foundations established in the previous
chapter. Its purpose is to develop the conceptual framework and related hypotheses
based on previous literature. These will be used as a theoretical basis for studying
the factors that influence SMEs’ adoption of Cloud computing. The chapter will
first explain the integration of the diffusion of innovation theory and the
Technology-Organisation-Environment (TOE) framework and then propose a
research model for SMEs’ adoption of Cloud computing. The next section presents
and discusses the meta-factors comprising the research model. Lastly, the
developed hypotheses are presented.
3.2 Towards the Research Model
Practitioners and researchers agree that diffusion of an innovation depends on
different factors. Over the last few decades, scholars have tried to determine the
factors that influence the diffusion process of different technologies (Alshamaila,
Papagiannidis & Li 2013; Liang et al. 2007; Woodside et al. 2005; Zhu & Kraemer
2005). Many different theories and models have been proposed to study the process
of adopting new technologies. The major theories used in this field are Theory of
Reasoned Action (TRA) (Ajzen & Fishbein 1980), the Technology Acceptance
Model (TAM) (Davis, Bagozzi & Warshaw 1989), the Theory of Planned
Behaviour (TPB) (Ajzen 1985; Ajzen 1991) and Diffusion of Innovations (DOI)
(Rogers 1995). Among these theories, DOI is one of the most commonly used
theories that try to explain and predict the adoption of innovations. The majority of
these theories explain and predict the adoption decision, based on factors that are
related to the technology itself. However, technology-related constructs are not the
only factors that influence the adoption of technologies. There are other factors
(such as organisational factors) that influence the decision to adopt an innovation.
Technology-Organisation-Environment (TOE) (Tornatzky & Fleischer 1990) is
another theoretical framework that overcomes this drawback. DOI and TOE are the
65
two theories commonly used in innovation diffusion and adoption studies in
organisations (Chang et al. 2013; Espadanal & Oliveira 2012; Oliveira, Thomas &
Espadanal 2014).
Rogers’ DOI has been used extensively by scholars from different fields. The
majority of the studies aimed to either confirm the validity of the model or use the
constructs to interrogate the diffusion of an innovation. One of the leading studies
in the field of diffusion of innovation was conducted by Cooper and Zmud (1990).
Their findings emphasise the importance of properly positioning managerial
rationality and are very important in explaining the diffusion of innovation,
although the findings are not very explanatory in describing the infusion of the
innovation. Eder and Igbaria (2001) studied the diffusion of intranets in
organisations. They proposed a model defining earliness of adoption, top
management support, organisational structure, organisational size, IT infrastructure
and IS structure to be influential in the diffusion and infusion of innovations.
Another study which investigated the adoption of a technology was conducted by
Beatty, Shim and Jones (2001). Bradford and Florin (2003) used Rogers’ DOI to
investigate the adoption of ERP systems. They developed and tested a model based
on DOI and IS success theories.
As stated by Rogers (2003), the variance in the adoption of innovations is mainly
explained by the following five attributes of the innovation:
1. Relative advantage – the extent to which an innovation is better than the previous
generation’s;
2. Compatibility – the degree to which an innovation can be assimilated into the
existing business processes, practices, and value systems;
3. Complexity – how difficult it is to use the innovation;
4. Observability – the extent to which the innovation is visible to others; and
5. Trialability – the ease of experimenting with the innovation.
Many researchers not only used DOI constructs, but also in many cases modified
the model to propose a new model. Tan et al. (2009) added some constructs to
Rogers’ five influential factors and proposed a new model. They used this model
to study the factors that influence the adoption of Internet-based ICT in SMEs. The
application of DOI in adoption studies is not limited to quantitative studies. Doshi
66
and Gollakota (2011) used DOI to perform a qualitative study to investigate the
diffusion of rural telecentres in the developing world. DOI does not take into
account the environmental aspects of the context; therefore there is a need to use
another theory that does consider the environmental aspects as well. The TOE
framework is another theoretical framework which studies the factors that influence
the adoption of new technologies.
The TOE framework, originally an organisational psychology theory, was
developed by Tornatzky and Fleischer (1990). However, it has been used
extensively by IS researchers. According to the TOE framework, the decision to
adopt an innovation at firm level is influenced by three aspects of the enterprising
context. The decision to adopt a technological innovation is influenced by the
technological, organisational and environmental aspects of the enterprise. The TOE
framework explains the entire process of innovation at firm level, unlike DOI which
explains innovation adoption at both individual and firm level. DOI’s ability to
explain the intra-firm innovation diffusion is improved by TOE (Hsu, Kraemer &
Dunkle 2006).
Many researchers in the IS field have used the TOE framework to study the process
of adopting new technologies. The TOE framework has been used as the only
theoretical framework to investigate the adoption process in many studies, while
for many other researchers the theory is combined with other theoretical
frameworks to investigate the adoption process. The findings of Tehrani and Shirazi
(2014) suggest that companies tend to be more concerned about their ability to
adopt rather than the benefits they gain by adopting. Kuan and Chau (2001) used
the TOE framework to study the adoption of an Electronic Data Interchange (EDI)
system in small businesses in Hong Kong. Another study conducted by Zhu,
Kraemer and Xu (2003) investigated the adoption of e-business by organisations
applying the TOE framework. Son and Lee (2011) proposed a Cloud computing
adoption model which was based on the TOE framework. Zhu et al. (2006) used
the TOE framework to develop a model which not only focused on the adoption of
innovation but also their stages of innovation assimilation. Teo, Ranganathan and
Dhaliwal (2006) studied the deployment of B2B e-commerce by organisations
using the TOE framework. Research published by Liu and Fellow (2008) studied
the adoption of e-commerce development as an innovative business process. The
67
adoption process of ERP systems was studied by Pan and Jang (2008) in Taiwan’s
communications industry. Yu-hui (2008) studied the factors that influence the
adoption of electronic procurement in Chinese manufacturing enterprises using the
TOE framework. The internal integration of e-business and external diffusion using
e-business were investigated by Lin and Lin (2008) using the TOE framework.
Oliveira and Martins (2009) used the framework to study the adoption of websites
in Portuguese firms. Some examples of TOE and DOI usage in national and
international research are shown in Table 3.1.
Table 3.1: Examples of TOE & DOI usage in research
Description InternationalDeterminants of information technology in Portugal. Oliveira & Martins 2009The adoption process of ERP systems. Pan & Jang 2008Cloud adoption by SMEs. Tehrani & Shirazi 2014Support of Cloud in supply chain information system infrastructure.
Wu et al. 2013
Information technology adoption models at firm level. Oliveira & Martins 2011 Conceptual model to investigate adoption of Radio frequency identification.
Wang et al. 2010
Many researchers have searched for approaches that combine more than one
theoretical perspective to understand the adoption of innovative new technologies
(Lyytinen & Damsgaard 2001; Oliveira & Martins 2011; Wu et al. 2013). The TOE
framework is combined with other theoretical frameworks in many instances such
as DOI and institutional theory. TOE and DOI have been used extensively in IT
adoption studies, and have enjoyed consistent empirical support. Chang et al.
(2011) mentioned that to better understand the organisational decisions related to
the adoption of technological innovation, the context of the study should be
comprehensive and the variables tailored to the pertinence of the innovation. The
TOE perspectives overlap with the innovation characteristics of DOI theory.
Therefore, researchers (Chang et al. 2011; Oliveira & Martins 2011; Wu et al. 2013)
well recognized the value of incorporating the TOE contexts to strengthen the DOI
theory. Rogers (2003) mentioned that the technological context is implicitly the
same idea as that of his DOI theory. Hsu, Kraemer and Dunkle (2006) argued that
DOI’s internal and external organisational characteristics include the same
measures as TOE’s organisational context. There are also important differences
68
between the two theories. Hsu, Kraemer and Dunkle (2006) mentioned that DOI
does not consider the impact of the environmental context. TOE does not specify
the role of individual characteristics such as top management support. Here, the
DOI theory suggests the inclusion of top management support in the organisational
context. TOE and DOI were combined and used by Thong (1999) to develop a
conceptual model in order to study the adoption of information systems by SMEs.
In the study by Zhu et al. (2006), TOE and DOI were combined to study the usage
and impact of e-business on firms in Europe. In other research, Chong et al. (2009)
combined two theories to study the adoption of e-commerce by organisations.
Wang, Wang and Yang (2010) combined TOE and DOI theories to form a new
conceptual model to investigate the adoption of radio frequency identification
devices (RFID) by manufacturing firms.
According to the Cloud computing principles defined by Zhang, Cheng & Boutaba
(2010), it would have been more persuasive to consider factors such as security and
privacy, rather than solely considering the general IT infrastructure (Marston et al.
2011). There is an inconsistency with the factor named “competitive pressure” in
Low et al.’s (2011) model, as Cloud computing is a service which anybody can
access globally. Further, Cloud computing adoption is not simply a technological
adoption, as it is mainly undertaken in addition to technology adoption in the real
environment.
According to Oliveira and Martins (2011), combining different theories helps to
better understand technology adoption. Other popular theories are not considered
in this research because they pertain to an individual’s choice. According to the
literature, two theories are found to have been dominantly used by researchers to
study the adoption of innovations. To investigate this study’s research questions
and identify meta-factors that relate to and may influence SMEs’ perception in
adopting Cloud computing, Rogers’ DOI theory and Tornatzky and Fleischer’s
(1990) TOE framework were appropriate underlying theoretical lenses.
Also according to the literature, the attributes of innovation and change agent
characteristics are variables which are most dominantly used in this context.
Security, privacy and relative advantage have the most significant influence on
Cloud adoption decisions. In addition to these three factors, awareness, flexibility
69
and quality of service are also important factors in the context of Cloud computing.
In order to study the adoption of Cloud computing by SMEs, a new conceptual
model was developed combining two different theoretical models. According to
this model, six variables influence the SMEs’ decision to adopt Cloud computing:
1. Cloud Security;
2. Cloud Privacy;
3. Cloud Flexibility;
4. Relative Advantage of Cloud;
5. Awareness of Cloud computing; and
6. Quality of Service of Cloud computing.
Figure 3.1 depicts the proposed conceptual model.
Figure 3.1: Conceptual framework for the adoption of Cloud computing
The industry sector that an organisation operates within may influence its ability to
adopt new ICT innovation (Forman 2005; Jeyaraj, Rottman & Lacity 2006;
Levenburg, Magal & Kosalge 2006). Levy, Powell and Yetton (2001) suggested
that the sector in which a firm operates has little influence on IS innovation
70
adoption. Levenburg, Magal and Kosalge (2006) point out that firms in different
industry sectors have different needs; it appears that some businesses in certain
sectors are more likely to adopt new ICT technologies than others in different
sectors. In the context of Cloud computing, many researchers (Ifinedo 2011; Prasad
et al. 2014a) illustrated how certain sectors are adopting Cloud computing services
more than others. Thus, the industry sector is considered as a control variable in
this study.
Organisational size is one of the most critical determinants of the innovator profile
(Rogers 2003). It is considered to be an important predictor of ICT innovation
adoption (Buonanno et al. 2005; Levenburg, Magal & Kosalge 2006). However,
empirical results from Lee and Xia (2006) showed that the correlation between
them are mixed. Some studies reported a positive correlation (Belso-Martinez
2010; Kamal 2006; Ramdani & Kawalek 2007) and others reported a negative
correlation (Goode and Stevens 2000). It is often argued that larger firms have more
resources, skills, experience and ability to adopt new innovation than smaller firms.
On the other hand, because of their size, small firms can be more innovative, and
they have more business agility compared to larger firms (Jambekar & Pelc 2002).
IT adoption often needs coordination, which may be relatively easier to achieve in
small firms (Premkumar 2003) than larger firms, as complex organisational
structures slow down the decision-making process (Oliveira & Martins 2011).
Therefore, organisation size is focused on three categories – micro, small and
medium – and is considered as a control variable within the study.
To conclude, it is postulated that SMEs’ consideration of adopting Cloud
computing is influenced by six factors:
1. CRA compared to other technologies;
2. CF regarding the use of Cloud computing;
3. QoS regarding the use of Cloud computing;
4. CS matters pertaining to Cloud computing;
5. CP issues pertaining to Cloud computing;
6. AWC perceived by organisations.
Further, this research proposed that if these six factors influenced the adoption of
Cloud computing by SMEs, this influence would vary based on organisation size
71
and industry sector type. The hypotheses development is presented in the next
section.
3.3 Hypothesis Development
This section incorporates the conceptual adoption research to develop a set of
hypotheses regarding the adoption of Cloud computing. This addresses the research
questions based on a group of hypothesised relationships developed from the
research model. Based on past literature, the proposed relationships were addressed
and developed as follows.
3.3.1 Cloud Relative Advantage (CRA)
Relative advantage refers to the degree to which potential adopters, such as IT
managers, perceive the innovation to provide many benefits and to be superior to
its predecessor (Rogers 2003). The impact of relative advantage on technology
adoption has been widely investigated in previous studies (Gibbs & Kraemer 2004;
Iacovou, Benbasat & Dexter 1995; Lee 2004; Ramdani & Kawalek 2007; Thong
1999). Alkhater, Wills and Walters (2014) mentioned that relative advantage is an
element of the DOI model that refers to the level of benefit to an organisation if it
decides to move into Cloud computing. Prior studies have presented relative
advantage as one of the most important attributes of innovation adoption (Oliveira
& Martins 2011) and IT adoption (Lee 2004; Thong 1999; Yang & Yoo 2004).
Stieninger and Nedbal (2014), in their study conducted among SMEs, showed that
Cloud computing solutions provided several relative advantages including removal
of hardware maintenance, simple administration, collaboration opportunities,
potential cost savings, and increased automation. According to Sokolov (2009),
from an ICT capabilities perspective, the relative advantages of Cloud computing
are almost manifest. Cloud computing provides instant access to hardware
resources, and small businesses would have faster time to market with no upfront
capital investment (Marston et al. 2011). Wu et al. (2013) suggest that compatibility
and application functionality reflect pertinent relative advantages of Cloud
computing, which may influence an organisation’s propensity to adopt the
technology. Relative advantage is refined as a benefit to SMEs in terms of wider
market coverage, lower business costs, and the importance of doing business on the
72
Internet in the future (Kendall et al. 2001). This has been described in economic
terms as economic profitability, reduced costs, and savings in time and effort
(Cragg & King 1993). Dong, Neufeld and Higgins (2009) highlight the ability to
reduce costs, provide more flexibility, reduce development time, and allow for
scalability and centralised data storage as some of the more significant gains in
Cloud adoption. The Australian government traces key drivers as basically the
value for money for organisations adopting Cloud computing, such as a reduction
in duplication and costs, leveraging economies of scale, increased savings through
virtualisation, pay-as-you-use, and reduced energy use (DFD 2011d). Further, with
the characteristics of scalability and elasticity of services in Cloud, relative
advantages are more easily achieved. In addition, SMEs can purchase their IT
requirements at a relatively low cost and in a short time due to the immediate access
to hardware resources, and without first having to make an upfront capital
investment (Feuerlicht & Govardhan 2010; Marston et al. 2011).
The benefits when entering new businesses or markets are: no upfront capital
investment, lower operational costs which leads to faster profitability, the ability to
pay-as-you-go (Hutley 2012), and the availability of services as relative advantages
with Cloud adoption for SMEs. In Cloud, the pay-per-use model eliminates the cost
of software licensing and installation (Chunye et al. 2010). Moreover, being able
to use applications that are developed by IT giants and are highly expensive, such
as ERP, and that are available over the Internet, is a benefit for companies that lack
qualified IT developers (Gupta, Seetharaman & Raj 2013; Misra & Mondal 2011).
Another advantage pointed out by Price (2011) is that customers do not need to
manage the underlying infrastructure such as servers, operating systems and
storage, and the applications are accessible from various client devices. As
mentioned by Alam (2009), organisations adopt a technology as long as it brings
them relative advantage. According to Feuerlicht, Burkon and Sebesta (2011),
Cloud computing offering rented services on a pay-as-you-use basis leads to
adjusting the level of usage according to the current needs of the organisation. As
the requirements for Cloud computing change, the Cloud user should be able to
scale up their resources and infrastructure to satisfy the new requirements in terms
of storage, number of servers, processing, and connection bandwidth (Benlian &
Hess 2011; Kim et al. 2008;). Marsten et al. (2011) mentioned that companies spend
73
a big percentage of their finances on IT infrastructure while actual utilisation is less
than 10 per cent, which results in expenses that could be avoided using Cloud
computing. However, due to the clear similarity between perceived ease of use and
perceived usefulness in TAM to relative advantage in DOI (Nedbal et al. 2014),
these two factors are of particular interest in Cloud computing and therefore are
being considered as a “relative advantage” factor within this study. This prior
research suggests that companies which perceive greater relative advantage in
online services tend to be more likely to adopt Cloud-based services. Thus, the
argument leads to the following hypothesis:
H1: Relative advantages of Cloud computing are positively associated with SMEs’
decision to adopt Cloud computing.
3.3.2 Cloud Flexibility (CF)
A number of researchers have pointed out that flexibility is a vital factor in the
adoption of new technologies in SMEs (MacGregor & Kartiwi 2010; Mudge 2010).
IT flexibility is defined as the fast deployment of technology components as
facilitated through a company’s IT infrastructure (Ness 2005). Mell and Grance
(2011) mentioned that IT flexibility is an essential characteristic involving the
ability of an organisation to rapidly and easily adapt to new or changing
requirements to support business processes. Chebrolu (2010) suggested that the
adoption of Cloud computing is a surrogate for IT flexibility. Previous researchers
have noted IT flexibility as a core element that has addressed certain IT adoptions
(Chebrolu 2010; Ness 2005). Byrd and Turner (2000) described flexibility as the
ability of an organisation to implement processes that can maximise the growth of
the organisation. Fairchild (2014) mentioned that Cloud-based software provides
far greater flexibility than on-premises solutions that need to be installed and
maintained, especially for SMEs without the resources for a dedicated IT staff.
According to Dillon and Vossen (2014), flexibility was a more important aspect to
the adoption of SaaS in SMEs than topics such as security, cost and capability.
Salleh, Teoh and Chan (2012) reported that the flexibility and capability of Cloud
computing enabled Enterprise Systems to be delivered via the Internet and
accessible to a wide variety of users at much lower costs. Sahandi, Alkhalil and
Justice (2012) noted that Cloud is capable of providing a degree of flexibility for
74
IT resources which would allow organisations to adapt to the changing demands of
their business needs.
Benlian, Hess and Buxmann (2009) mentioned that SaaS technology can bring
organisations the benefit of cost saving and flexibility in using IT resources via
Cloud computing. Zhao, Scheruhn and von Rosing (2014) indicated that flexibility
is important when new IT systems are implemented. Frequently, SMEs are not able
to invest large amounts in IT infrastructure (Foster et al. 2008) compared to larger
organisations. However, a KPMG report in 2009 on Australian lessons and
experiences showed that using Cloud computing allowed SMEs to adopt innovative
IT technologies quickly without paying upfront for capital investment (McCabe &
Hancock 2009). Even a small client operating on a small scale can use Cloud
services easily and access computing resources such as disk storage and CPUs.
RAM can be added and services can be expanded, where necessary, with the
dynamic resource-scaling features. Tehrani and Shirazi (2014) indicated that being
location and device independent increases the flexibility of Cloud computing in
comparison to traditional forms of computing such as on-premises deployment.
This flexibility, in turn, increases the productivity and efficiency of companies by
letting them perform their work remotely. The business agility feature in Cloud
computing leads to achieving greater flexibility. Minifie (2014) mentioned in their
paper that the flexibility and innovation made possible by Cloud may become more
important over time. In their analysis of the experiences of Australian business and
industry, McCabe and Hancock reported that the use of Cloud computing directly
translated to a more responsive, adaptive and competitive business (McCabe &
Hancock 2009). Further, Cloud computing provides greater flexibility to
encompass the innovations of Australian government and industry (Mudge 2010).
Previous studies found that Cloud computing adoption leads in many cases to major
operational improvements through strategic flexibility (Armbrust et al. 2010;
Cusumano 2010; Whitten, Chakrabarty & Wakefield 2010). Some studies (Etro
2009, 2011; Hogan et al. 2013) quantify the value of increased flexibility and new
business creation derived from the Cloud estimate benefits of more than 1 per cent
of GDP in the European Union (Minifie 2014). Therefore, this study hypothesises
that a high level of flexibility, as perceived by organisation leaders, also motivates
them to adopt Cloud computing-based services.
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H2: The flexibility of Cloud computing positively influences SMEs’ decision to
adopt Cloud computing.
3.3.3 Quality of Service (QoS)
Carroll, Helfert and Lynn (2014) mentioned QoS as the capability to meet specific
requirements using specific metrics to analyse the level of quality (e.g. response
time, throughput). Several studies have found that Cloud computing is a service
process where availability and reliability are bundled with ongoing service updates
(ITIIC 2011; Lippert & Govindarajulu 2006). Fairchild (2014) mentioned that two
major challenges in Cloud computing are scalability and consistent achievement of
QoS standards set by the consumers. Luis et al. (2009) reported that QoS is an
inherent feature of many Clouds; for example, Amazon has already included a
rough attempt to provide a certain QoS by means of basic Service Level
Agreements (SLAs) (e.g. 99.9 per cent infrastructure uptime). According to the
findings of Uusitalo et al. (2010), 30 per cent of their interviewees think that
transparent and reliable Cloud services will compromise the trustworthiness that is
linked to their intentions regarding Cloud adoption. Habib et al. (2012) showed that
several Cloud providers promise high availability such as 99.99 or 100 per cent
availability of service. ITIIC (2011) reported that high QoS options provided by
Cloud service providers, such as availability, reliability and dynamic resource
scaling based on demand, are a driving force behind the growth in demand for
Cloud computing. The National Broadband Network (NBN) in Australia will
provide the bandwidth, connectivity, reliability and network QoS for Cloud (ITIIC
2011; Matthew 2014). The right QoS level can be guaranteed via the SLA between
the SME and the Cloud service providers (Kloch, Petersen & Madsen 2011).
Armbrust et al. (2010) have identified that business continuity and service
availability are significant factors in considering Cloud adoption. According to an
Australian KPMG report in 2012, Herhalt and Cochrane showed that 21 per cent of
respondents found availability of service was a challenge in Cloud computing
adoption in Australia (Herhalt & Cochrane 2012). This implies that more than 70
per cent consider availability is an enabler for Cloud adoption in Australia. Buyya,
Yeo and Venugopal (2008) highlighted that Amazon EC2 has introduced the
concept of “availability zones” (i.e., a set of resources that have a specific
76
geographic location) which appears to have set a trend regarding Cloud storage.
Reliability is another thought-provoking feature of Cloud adoption. Jeffrey and
Neidecker-Lutz (2009) mentioned that reliability is a particular QoS aspect which
forms a specific quality requirement. For example, customers of Salesforce lost
their connection on February 2008 for 6 hours (Sultan 2010). Similar problems
have occurred with Google’s Gmail, Amazon’s S3, EC2 and Google Docs.
However, these were considered temporary problems for the Cloud users. One of
the most welcome characteristics of Cloud computing compared with traditional IT
provision is ongoing service updates. Busch et al. (2014) highlighted that Cloud
computing is able to provide a high level of QoS at a competitive cost by sharing
the tasks of IT management and administration, with a high commitment to service
efficiency. As mentioned by Buyya, Yeo and Venugopal (2008), because
consumers rely on Cloud providers to supply all their computing needs, they will
require specific QoS to be maintained by their providers in order to meet their
objectives and sustain their operations. Further, Buyya et al. (2009) mentioned that
in the case of Cloud as a commercial offering to enable crucial business operations
of companies, there are critical QoS parameters to consider in a service request,
such as time, cost, and reliability. Interestingly, decision makers in organisations
no longer need to worry about constant server updates and other computing issues,
and organisations will be free to concentrate on innovation. The debate continues
on the QoS as an important characteristic in Cloud adoption with the combination
of availability, reliability and ongoing service updates. According to Prasad et al.
(2014b), an important factor for the SMEs is to decide which is the most favourable
Cloud provider. This depends on the QoS that a Cloud provider is offering and the
cost that the provider is charging for their services. There will be a trade-off
between the costs and the QoS of hosting a particular application, and this situation
needs to be balanced. Rath et al. (2012) identified quality of service as a major
factor influencing Cloud computing adoption. Therefore, this study hypothesises
that a high level of QoS in Cloud computing-based services, as perceived by
organisation leaders, also motivates them to adopt Cloud computing-based
services.
H3: The quality of service of Cloud computing is positively associated with SMEs’
decision to adopt Cloud computing.
77
3.3.4 Cloud Security (CS)
Security is defined as “both the perception, or judgment, and fear of safeguarding
mechanisms for the movement and storage of information through electronic
databases and transmission media” (Lippert & Govindarajulu 2006, p. 151). The
information security was defined as “Preservation of confidentiality, integrity and
availability of information; in addition, other properties such as authenticity,
accountability, non-repudiation and reliability can also be involved” (Pearson 2012,
p. 14). Edson et al. (2008) mentioned that information security is always associated
with prevention and detection of unauthorised activities in the networks or systems,
and requires businesses to institute relevant strategic policies. The foundations of
information security are based upon the confidentiality, integrity, availability,
accountability, assurance and resilience of information (Friedman & West 2010).
Kalakota and Whinston (1996) identified security risk as a key factor hindering the
use of some ICT technologies. For Cloud computing, security is a typical concern
that businesses may have (Aziz 2010). Security is about protecting data from
unauthorised access (Alshamaila 2013). Major issues pertaining to data security in
the Cloud computing environment are: data location and data transmission, data
availability, data security (Mahmood 2011), malicious insiders, outside attacks, and
service disruptions (Behl 2011). The biggest challenge with security in Cloud
computing is the delegation of confidentiality, availability and integrity of data to
a third party. The security of Cloud computing is complicated because of the multi-
tenancy of the virtualised resources (Opala 2012). Cloud users may think that Cloud
computing simplifies security issues for users by outsourcing the responsibility to
another party that is presumed to be highly skilled at dealing with them (Anthes
2010). Industry practitioners (Chakraborty et al. 2010; McCabe & Hancock 2009)
have reported that security was a critical concern in the initial stages of Cloud
computing adoption. Bhayal (2011) states that Cloud security is the most important
concern amongst Cloud clients since the data owner does not know where the data
is stored and data hosts cannot be considered as completely reliable. According to
Benlian and Hess (2011), research regarding the adoption of Cloud computing has
considered the security as a barrier to adopt Cloud computing. Feuerlicht, Burkon
and Sebesta (2011) also identified security as a potential risk and challenge in
Cloud computing adoption. The security issues of Cloud computing related to third
78
79
parties’ involvement also pose challenges (Subashini & Kavitha 2011). According
to Katzan (2010), Cloud computing security is not just about authenticity,
authorisation, and accountability, it is more concerned with data protection, disaster
recovery, and business continuity. The architecture of Cloud computing also leads
to many new security issues such as data leakage, virtualisation vulnerability and
hypervisor vulnerability (Gonzalez et al. 2012). Garg and Stiller (2014) mentioned
that Cloud security risks also include contractual loopholes, confidentiality,
information security, and service outages.
Zardari, Bahsoon and Ekárt (2014) mentioned that measuring the data security and
availability of Cloud computing is very difficult and time-consuming because there
is still no proper simulation tool to measure Cloud security. A survey of chief
information officers and IT executives by the International Data Corporation (IDC)
rated security as their main Cloud computing concern, and almost 75 per cent of
respondents were worried about security (Sultan 2010). Dillon and Vossen (2014)
also found that security issues were the most limiting factor for Cloud adoption.
Security is one of the concerns about Cloud computing that is delaying its adoption
(Jamwal, Sambyal & Sambyal 2011). Sarwar and Khan (2013) found that security
is the biggest issue in Cloud computing as, while utilising storage service in a
remote location, the consumers are generally unaware of what happens to their data.
According to Forrester research, Sahandi, Alkhalil and Justice (2012) reported that
security concerns are the most commonly cited reason why enterprises are not
interested in SaaS. In recent studies, security concerns has been cited as the most
significant barrier to Cloud adoption (Armbrust et al. 2010; Kshetri 2013; Xiao &
Xiao 2013). Security issues in Cloud computing were discussed in general by many
researchers (Jamil & Zaki 2011; Kshetri 2013; Zissis & Lekkas 2012) and with
specific reference to SMEs (Adam 2014; Alshamaila 2013; Gupta, Seetharaman &
Raj 2013). Further, several other studies have shown security as one of the major
challenges that is keeping end-users away from Cloud computing (Gens et al. 2009;
Shaikh & Haider 2011). Therefore, this study hypothesises that higher levels of
security in the Cloud computing environment, as perceived by organisation leaders,
may motivate them to adopt Cloud computing-based services.
H4: The security of the Cloud computing environment may positively associate
SMEs’ decision to adopt Cloud computing.
3.3.5 Cloud Privacy (CP)
At the broadest level, privacy is considered a fundamental human right in Europe,
whereas in America it has been viewed more in avoiding harm to people (Pearson
2012). However, privacy is not an absolute right. It always needs to be balanced
against societal needs (Anthony 2012). Privacy is an important issue in
technological innovations, particularly when it has an online interaction. In the
Cloud environment, privacy reflects a consumer’s concerns about information being
stored in Cloud and accessed by other individuals anywhere in the world (Vanessa
2014). In other words, input data for Cloud services are uploaded by a user to the
Cloud that the user does not own or control. As Abadi (2009, pp. 2-3) points out,
“Computer power is elastic, but only if the workload is parallelisable… data is
stored at an un-trusted host… data is replicated, often across large geographic
distances”, which are some of the Cloud characteristics that make Cloud a risk.
Privacy risk was defined by Featherman and Pavlou (2003, p. 455) as “Potential loss
of control over personal information, such as when information about you is used
without your knowledge or permission”. They recognised privacy as a key factor
hindering the use of some ICT technologies due to the open nature of the Internet.
Privacy risk is among the typical concerns businesses may have regarding Cloud
computing (Aziz 2010). Privacy is one of the longest standing and most significant
concerns with Cloud computing (Hailu 2012). According to a survey carried out
among Chief Information Officers (CIOs) in Europe, approximately 70 per cent of
CIOs were prevented from launching Cloud computing solutions because of privacy
and security fears (Wijesiri 2010). In particular, lack of transparency creates legal
issues that are affected by Cloud’s physical location, which creates difficulties in
determining jurisdiction. Because of this key issue, the Australian Federal
Government is extremely concerned about the location of outsourced personal data
storage and there is a strong desire for Cloud services to be located only within
Australia’s borders (Hutley 2012). For example, in other parts of the world the
European Union (EU) has privacy regulations that prohibit the transmission of some
types of personal data outside the EU (Sultan 2010). Since privacy is a leading cause
of not adopting Cloud solutions, Pearson (2009) has defined a top six privacy
practices:
80
minimise personal information sent to and stored in Cloud;
protect personal information in Cloud;
maximise user control;
allow user choice;
specify and limit the purpose of data usage; and
provide feedback.
Taking these requirements into consideration it can be said that poor user control,
loss of trustworthiness and lack of transparency create most of the privacy issues.
In a survey conducted by Tang and Liu (2015), the respondents indicated that data
privacy issues are their top concerns for adopting Cloud computing. According to
Gupta, Seetharaman and Raj (2013), privacy is the top concern of 50 per cent of
organisations in terms of Cloud computing adoption decision-making. A study by
Safari, Safari and Hasanzadeh (2015) showed that privacy is a significant
determinant for adopting Cloud computing. Xu and Gupta (2009) included privacy
concerns as a way of evaluating the potential adoption of location-based services.
Xiao and Xiao (2013) reviewed the privacy issues in Cloud computing. According
to Chen, Paxon & Katz (2010), concern about privacy has emerged as one of the
most significant disadvantages of joining a Cloud computing group. According to
Alkhater, Wills and Walters (2014), privacy is the main concern for organisations
thinking about Cloud computing because they cannot fully control the information
stored on Cloud-based servers. Tancock, Pearson and Charlesworth (2013)
highlighted that the major hurdles in large-scale acceptance of Cloud computing
are mostly due to privacy issues. Trust in privacy has also been shown as one
primary determinant of IT innovation acceptance and diffusion in organisations (Li,
Hess & Valacich 2008; Schoorman, Mayer & Davis 2007). Trust is a purely abstract
and subjective term; therefore, it is ordinarily difficult to tangibly measure and
effectively manage (Sarwar & Khan 2013). Trust arrangements between Cloud
computing service providers and users need to consider new and additional
elements to cover all critical interactions (Mudge 2010).
Transferring personal data to a third party without privacy policies in place creates
huge risks of data loss, data theft, data damage, and data misuse. Even with policies
in place, risks are still apparent (Ko et al. 2011). The Victorian State Government
has published the legal issues, such as information privacy related to Cloud services,
81
82
considering the Australian Privacy Principles (APPs) (Anthony 2012). The
Australian Federal Government, Department of Finance and Deregulation in
Australia published a “Best Practice Guide” to help agencies to navigate typical
legal issues in Cloud computing agreements, with the intention of emphasising the
privacy concerns when adopting Cloud services (DFD 2011b). Further, the
Information Management Office of the Australian Federal Government published a
best practice guide “Privacy and Cloud Computing for Australian Government
Agencies” to better understand how to comply with privacy laws and regulations
when choosing Cloud- based services (IMO 2013). This is another reason for
emphasising the importance of privacy in Cloud adoption in Australia. Therefore,
this study hypothesises that higher levels of privacy in the Cloud computing
environment, as perceived by organisation leaders, may motivate them to adopt
Cloud computing-based services.
H5: The privacy of the Cloud computing environment may positively associate
SMEs’ decision to adopt Cloud computing.
3.3.6 Awareness of Cloud (AWC)
Recent developments in IT in relation to Cloud computing provide more benefits
for SMEs. The Information Technology Industry Innovation Council (ITIIC) in
Australia has published information on the importance of educating Australian
businesses and consumers on how best to harness the benefits and manage the
potential risks of adopting Cloud computing solutions (ITIIC 2011). They indicate
that knowledge about Cloud computing and its benefits for SMEs could be
increased among the Australian business community; and similar statistics are
indicated in the readiness index published by the Asia Cloud Computing
Association (ACCA 2012). Hadjimanolis (1999) mentioned that lack of awareness
of technology comprised the factors for barriers in Cloud adoption. The research
model by Shetty and Kumar (2015) suggested that awareness of Cloud computing
influences adoption. Tan et al. (2009) considered whether organisations have the
awareness of Cloud computing to adopt. The early stage of Cloud adoption reflects
the recognition and awareness of an innovation (Vanessa 2014). Tarmidi et al.
(2014) explored the level of awareness and adoption of Cloud computing among
small and medium enterprises (SMEs) in Malaysia.
The findings of Tehrani and Shirazi (2014) revealed that knowledge about Cloud
computing is the primary reason that influences SMEs’ decision to adopt Cloud
computing. They mentioned that awareness of Cloud computing can be increased
if providers use other social media such as Facebook, Twitter, LinkedIn, etc. to
increase general knowledge about Cloud computing. Prasad et al. (2014a)
suggested that awareness of Cloud would ensure that their use of the Cloud
computing services is sustainable. Minifie (2014) considered knowledge and
awareness as the main constraints for Cloud computing adoption. Knowledge about
Cloud computing and what benefits their companies stand to gain by adopting this
technology are important in determining the adoption of Cloud computing (Dahiru,
Bass & Allison 2014a). The findings of Carcary et al. (2014) indicated that lack of
awareness of the benefits of Cloud computing limited its adoption. Adam (2014)
discussed that the main issue for adopting Cloud computing by African SMEs was
the awareness of Cloud. As mentioned by Hinde and Van Belle (2012), creating
awareness by Cloud service providers has led to an increase in Cloud adoption by
SMEs in Ghana. Alshamaila, Papagiannidis and Stamati (2013) stated that
awareness and understanding of Cloud computing advantages are important for the
adoption decision. Further, the survey by KPMG in 2012 shows that the Australian
Cloud market is still in the early stages of adoption; that is, many organisations
were not using or ready to use Cloud-based services (Hutley 2012). Central to the
opportunities provided by Cloud computing adoption is awareness of the green
agenda (Singh, Bhisikar & Singh 2013). This could potentially be a significant
factor for Cloud adoption in SMEs. Rath et al. (2012) used awareness of Cloud
computing as a construct in their study. Therefore, this study hypothesises that
awareness of Cloud computing-based services also motivates organisations’
leaders to adopt Cloud computing-based services.
H6: Awareness of Cloud computing is positively associated with the SMEs’
decision to adopt Cloud computing.
Figure 3.2 depicts the proposed research model with two control variables.
83
Figure 3.2: Research model for SMEs Cloud computing adoption
The constructs used to examine Cloud computing adoption in this study are
explored in Table 3.2 using theoretical, practitioner and government underpinnings.
The academic, government and practitioner literature for each construct was
reviewed and listed separately in this table.
84
Tabl
e 3.
2: C
onst
ruct
s use
d to
exa
min
e C
loud
com
putin
g ad
optio
n (D
evel
oped
for t
his s
tudy
)
Con
stru
cts
Aca
dem
icG
over
nmen
tPr
actit
ione
r
Clo
ud S
ecur
ity
Saha
ndi e
t al.
2012
; Ksh
etri
2013
; Sar
war
& K
han
2013
; Ada
m 2
014;
Avr
am 2
014;
Bus
ch e
t al.
2014
; C
arca
ry e
t al.
2014
, 201
4b; C
hang
et a
l.20
14;
Dah
iru, B
ass &
Alli
son
2014
a, 2
014b
; Fai
rchi
ld
2014
; Fak
ieh,
Blo
unt &
Bus
ch20
14;J
ohan
sson
et a
l.20
14; K
auff
man
et a
l.20
14;L
oske
et a
l.20
14;
Mah
moo
d et
al.
2014
; Ned
bal e
t al.
2014
;Oliv
eira
et
al.2
014;
Pra
sad
et a
l.20
14b;
Stie
ning
er &
Ned
bal
2014
;Tar
mid
i et a
l.20
14; T
ehra
ni &
Shi
razi
201
4;
Zard
ari e
t al.
2014
b; Z
hao
et a
l.20
14; G
angw
ar e
t al.
2015
; Li e
t al.
2015
; Ros
s & B
lum
enst
ein
2015
; Sa
fari
et a
l.20
15; T
ang
&Li
u 20
15
DFD
201
1a, 2
011b
,20
11c;
ITIIC
201
1;
Jans
en &
Gra
nce
2011
; Ant
hony
201
2;IM
O 2
012,
201
3;SW
I 201
2; D
BC
DE
2013
; DO
C20
15
Mat
her e
t al.
2009
; McC
abe
& H
anco
ck 2
009;
D
odda
vula
& G
awan
de20
09; C
hakr
abor
ty e
t al
.201
0; F
riedm
an &
Wes
t 201
0; G
oodb
urn
&H
ill 2
010;
Mar
tin 2
010;
Mud
ge 2
010;
Sul
livan
20
10;T
akab
i et a
l.20
10; D
eter
man
n 20
11;
Fros
t & S
ulliv
an 2
011;
Gro
baue
r et a
l.20
11;
HB
RA
S 20
11; K
o et
al.
2011
; LEM
T 20
11;
Mar
k 20
11; T
elst
ra 2
011;
Dav
e20
12; H
andl
er
et a
l.20
12; H
erha
lt &
Coc
hran
e 20
12; H
utle
y 20
12; P
ears
on 2
012;
Ren
et a
l.20
12;A
CC
A20
14; B
en 2
014;
Min
ifie
2014
Clo
ud P
riva
cy
Gao
et a
l.20
12; S
ahan
di e
t al.
2012
;Ksh
etri
2013
; Sa
rwar
& K
han
2013
; Tan
cock
et a
l.20
13; A
dam
20
14; A
vram
201
4; B
usch
et a
l.20
14; C
arca
ry e
t al.
2014
; Cha
ng e
t al.
2014
; Dah
iru, B
ass &
Alli
son
2014
a, 2
014b
; Fai
rchi
ld 2
014;
Fak
ieh,
Blo
unt &
B
usch
2014
; Kau
ffm
an e
t al.
2014
;Mah
moo
d et
al.
2014
; Ned
bal e
t al.
2014
;Stie
ning
er &
Ned
bal 2
014;
Tehr
ani &
Shira
zi 2
014;
Van
essa
201
4; Z
arda
ri et
al.
2014
b; G
angw
ar e
t al.
2015
; Ros
s & B
lum
enst
ein
2015
; Saf
ari e
t al.
2015
; Tan
g &
Liu
201
5
DFD
201
1b, 2
011c
, 20
11d;
ITIIC
201
1;
Jans
en &
Gra
nce
2011
; Ant
hony
201
2;IM
O 2
012,
201
3;D
BC
DE
2013
; DO
C20
15
Mat
her e
t al.
2009
; Dod
davu
la &
Gaw
ande
2009
; Cha
krab
orty
et a
l.20
10; F
riedm
an &
W
est 2
010;
Goo
dbur
n &
Hill
201
0; Jo
anna
&
Chi
emi 2
010;
Mar
tin 2
010;
Mud
ge 2
010;
Ta
kabi
et a
l.20
10; D
eter
man
n 20
11; G
roba
uer
et a
l.20
11; K
o et
al.
2011
; LEM
T 20
11; M
ark
2011
; Tel
stra
201
1; D
ave
2012
; Her
halt
&C
ochr
ane
2012
;Hut
ley
2012
;Pea
rson
201
2;R
en e
t al.
2012
; AC
CA
201
4; B
en 2
014;
M
inifi
e 20
14
Clo
ud F
lexi
bilit
yM
aria
n &
Ham
burg
201
2;O
pala
201
2;Jo
hans
son
et
al.2
014;
Ned
bal e
t al.
2014
; Oliv
eira
eta
l. 20
14;
Tehr
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3.4 Chapter Summary
This chapter looked in-depth at the factors that comprise the research model. This model
included six independent variables: CS, CP, CF, CRA, AWC and QoS that were assumed
to influence SMEs’ Cloud computing adoption in Australia. The model also included two
control variables: industry sector and size of the firm, as controllers of the relationship
between independent and dependent variables, as proposed. Hypotheses related to each
factor of the model were developed accordingly. It was expected that this new model
would enable better investigation of the meta-factors that might influence the adoption of
Cloud computing by SMEs. The methodology adopted to test these hypotheses will be
described in further detail in the next chapter.
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4 CHAPTER 4: RESEARCH METHODOLOGY
4.1 Introduction
Accurate methodological assumptions lead to the identification of research methods and
techniques that are considered to be appropriate for gathering valid empirical evidence.
Therefore, the cornerstone for undertaking a successful research study depends on
making the correct methodological assumptions (Myers & Avison 2002). Correct
assumptions shape “how we conduct research and how we use the results… the
philosophy of science, which seeks to understand the underlying assumptions associated
with different approaches” (Polonsky & Waller 2011, p. 4). As utilised by prior
researchers, a survey is the most widely used methodology in IT adoption research
(Borgman et al. 2013; Lawal 2014; Marian & Hamburg 2012; Moghavvemi, Hakimian
& Feissal 2012). Therefore, a quantitative design using an online survey questionnaire
was used to collect data to develop a model on the meta-factors that might influence the
perception of SMEs towards the adoption of Cloud computing. This chapter provides a
discussion and justification of the method, approach and philosophy used to undertake
this research. It commences with a description of the research method adopted followed
by the description of procedures used to design and develop the online questionnaire as
a data collection tool. Then sampling design and survey administration are discussed.
Eventually, an outline is provided of data analysis procedures used to test the hypotheses
and address the research questions.
4.2 The Research Methods
This research followed a deductive approach to examine the adoption of Cloud
computing by Australian SMEs. This is because deductive research strategy tries to find
an explanation for an association between two concepts by proposing a theory (Blaikie
2009), and because technology adoption research is well defined. As a result, a number
of theories and models have been developed and validated to examine the adoption of
new technologies (Lawal 2014; Rogers 2003; Tehrani & Shirazi 2014; Tornatzky &
Fleischer 1990). As adopted by Alshamaila, Papagiannidis and Li (2013), this study base
was theoretically grounded in the TOE framework for developing the conceptual model
and formulating the research hypotheses that are presented in Chapter 3.
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According to Guba & Lincoln (1994), two approaches or methods, quantitative and
qualitative, are available to researchers. A qualitative method looks for understanding
using a more in-depth enquiry than a quantitative method. The researcher who uses a
qualitative method seeks to study the phenomena from the inside, which often requires
case studies and in-depth interviews without specific questions or alternative answers.
Creswell (2003) discusses three commonly accepted research approaches: quantitative,
qualitative and mixed method. Each approach can provide valid research results although
the methods used to obtain the results are quite different. Creswell (2003) provides an
overview of each of the strategies, methods and expected outcomes (see Table 4.1).
Swanson and Holton (2005, p. 33) suggest that “Quantitative research can be exploratory;
it is used to discover relationships, interpretations, and characteristics of subjects that
suggest new theory and define new problems”. A considerable amount of literature has
shown that the quantitative survey method is best used to evaluate the acceptance of new
technologies (Flick 2009; Jahangir & Begum 2007; Lease 2005). A quantitative research
method will, therefore, be applied in this research.
Quantitative research is most often used in studies with clearly stated hypotheses that can
be tested. It focuses on well-defined, narrow studies. A quantitative method discusses the
problem from a broader perspective, often by providing a survey with specific answer
alternatives (Merriam 1998). It was decided that the best method to adopt for this
investigation is a survey, therefore this research will be performed using a survey data
collection method, and data will be collected by IT managers or decision makers in the
IT sections of selected SMEs.
A longitudinal design would not have been appropriate to answer the research questions
in a timely manner since such research requires years to complete. Interviews and direct
observations would have been costly, difficult to schedule and time-consuming.
According to a literature review by Tornatzky and Klein (1982), most of the studies in
the field of innovation adoption collected data using surveys. An in-depth examination
of previous research found that quantitative survey methodology had been successfully
applied within each research study (Gupta, Seetharaman & Raj 2013; Rahimli 2013;
Zardari, Jung & Zakaria 2014). Therefore, the survey method is chosen as an efficient
way to reach larger numbers of SMEs quickly while protecting their anonymity.
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There are three phases in the research design. In phase one, academic and practitioner
literature on technology adoption, Cloud computing and SMEs will be studied to identify
the key factors for successful Cloud computing adoption by SMEs. In phase two, a
structured questionnaire will be used to collect the quantitative data from Australian
SMEs. The questionnaire is the major instrument designed for this study to collect data
from SMEs. The data analysis, model verification and modifications will be conducted
in phase three.
Table 4.1: Research approach options (Adapted from Creswell 2003)
Research approach
Strategies Methods Outcomes
Quantitative Surveys and Experiments.
Closed-ended questionnaireNumeric data collectionAnalysis of existing research dataUse of deduction.
Statistical hypothesis evaluation based on numerical standards.
Qualitative Grounded theory, case study, focus group, narrative and phenomenology.
Open-ended questionnaire, expert opinions, field observation.
Understanding of the interrelationships and the phenomena.
Mixed Methods
Hybrid of quantitative and qualitative methods.
Quantitative techniques combined with qualitative techniques with the purpose of improving the quality of the results.
Integration of both qualitative and quantitative research to inform the research.
4.3 Data Collection
An online survey questionnaire was used to collect the relevant data. The questionnaire
survey approach was also considered because many studies based on diffusion of
innovation in the IS domain have used this approach to identify the factors influencing
the adoption rate of Cloud computing (Premkumar & Roberts 1999; Teo, Ranganathan
& Dhaliwal 2006; Thong 1999). This section presents the questionnaire design,
development and pre-testing processes.
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4.3.1 Questionnaire Design
There is no standard method to create a questionnaire but defining the appropriate
question style, designing the questions and piloting and then revising the questionnaire
can create a useful questionnaire (Nardi 2006). This approach was selected as it is
inexpensive, less time-consuming, and has the ability to provide qualitative data and a
reasonably large sample (Cornford & Smithson 2006). The developed questionnaire
aimed to capture respondents’ opinions about Cloud computing and other factors that
may influence their decision to adopt Cloud computing.
Mainly a closed-ended question style was employed that allowed respondents to select
the answer that best fit their opinion. This type of question style was selected because it
is easy to answer and can be completed within a short period of time (Saunders et al.
2011). Further, it is easy to code and analyse. Open-ended questions allow participants
to respond to questions exactly as they would like to answer them (Wilson 1996),
however, they are time-consuming and difficult to code and interpret. To overcome some
of the limitations of closed questions, three open-ended questions were included at the
end of the questionnaire asking respondents to express top drivers or initiatives, main
issues or any influences other than the listed research factors, regarding the adoption of
Cloud computing. The questions and items were composed in a simple manner in order
to motivate and increase the response rate (Deutskens et al. 2004; Dillman, Smyth &
Christian 2014; Saunders et al. 2011).
The questionnaire was divided into three sections. The first section included questions
relating to the demographic information of each respondent’s organisation. It consisted
of 7 questions related to organisation size, the length of time the organisation has been
operating, the organisation’s primary business, how their market is comprised, and the
location of the organisation. Apart from the location, these were measured with single
questions based on categorical scales. Location was measured using three questions
related to region, state or territory and postcode. Participants were asked to select the
subject, classification or characteristic most appropriate to their organisation. A nominal
scale was used to measure these demographic variables.
The second section included questions regarding the awareness and use of Cloud
computing. In this section, participants were asked to indicate whether their organisation
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was familiar with Cloud computing or was already a user of any type of Cloud
application. A nominal scale was also used to measure these questions.
The third section contained the items used to study the factors concerning the adoption
of Cloud computing by SMEs. It contained 28 items addressing:
CRA towards the adoption of Cloud computing (6 items);
CF towards Cloud adoption (4 items);
QoS of Cloud computing towards Cloud adoption (4 items);
CS regarding adopting Cloud (3 items);
CP regarding adopting Cloud (3 items);
AWC towards Cloud adoption (4 items); and
ADOPT regarding intention to adopt Cloud computing (4 items).
All these items were grouped and presented in three separate blocks. The survey was
constructed to ensure that respondents answered only the block of questions that
pertained specifically to them, thus tailoring the survey. This eliminated respondent
confusion, because complicated instructions were not needed. Thus, the relevant specific
block was displayed for the respondent based on previous response: whether they use
Cloud computing or not even if they use Cloud computing without being aware. These
28 items were developed by merging previous instruments which adequately reflected
the underlying theoretical factors (Alshamaila, Papagiannidis & Li 2013; Dillon &
Vossen 2014; Lawal 2014; Oliveira, Thomas & Espadanal 2014; Tehrani & Shirazi 2014;
Vanessa 2014). These items, after modifying them to address a specific context, have
been adopted many times in IS and IT adoption research (Alshamaila, Papagiannidis &
Stamati 2013; Oliveira, Thomas & Espadanal 2014; Safari, Safari & Hasanzadeh 2015;
Shetty & Kumar 2015; Vanessa 2014).
All items were measured using a form of numerical scaling. The Likert-scale type
question is probably the most widely used response scale featured in surveys and is often
used to measure attitudes and other factors. Hair et al. (2010) recommended that Likert-
scales are the best designs when using self-administered surveys or online survey
methods to collect the data. The Likert-scale is an interval scale that is used to ask
respondents to indicate whether they agree or disagree about a given subject by rating a
series of mental belief or behavioural belief statements (Hair et al. 2010). This type of
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response scale has been widely used in related research (Tehrani & Shirazi 2014;
Gangwar, Date & Ramaswamy 2015; Li et al. 2015; Safari, Safari & Hasanzadeh 2015).
It makes responses easy to manage and code, and increases the statistical methods that
can be used (Pallant 2013).
The most frequently used scale in survey instruments is a one-to-five scale. However,
this scale has some limitations. With only five points, two at both ends and one mid-
point, the scale suffers its own bounded parameters. Many respondents are reluctant to
select extreme values, which leads to a restricted set of scores, making it difficult to
measure differences. The seven-point Likert-scale avoids the limitations of the five-point
scale and provides more flexibility such as a larger array of options (Carey 2010).
Therefore, an online survey questionnaire with a seven-point Likert-scale will be
administered in this research. Based on this scale, participants were asked to select one
of seven numbers that ranged: (7) ‘strongly agree’, (6) ‘agree’, (5) ‘somewhat agree’, (4)
‘neither agree nor disagree’, (3) ‘somewhat disagree’, (2) ‘disagree’, (1) ‘strongly
disagree’. The methodology used in this study is consistent with previous researchers
(Chebrolu 2010; Ifinedo 2011; Lee & Kwon 2014; LI, Zhao & Yu 2015), that is, a seven-
point Likert-scale was used as the foundation for data collection and analysis.
Finally, as Dillman, Smyth and Christian (2014) suggest, the main features of the
introduction and closing of the questionnaire were taken into consideration to maximise
the response rate. A cover page was provided with the questionnaire in order to explain
clearly to the participants and in detail the aim of the study, mainly because the
questionnaire was self-administered, which made the cover page essential for the
respondents’ cognisance. In an attempt to attract the interest of the respondent, a clear,
unbiased, unambiguous title for the topic and other parts of the questionnaire was
presented. Moreover, it was clearly stated that the information collected would be kept
strictly confidential, and respondents were assured that the information they provided
would only be used for the purpose of this research. Further, the researcher’s full contact
details were clearly provided in order to enhance authenticity. Also, the supervisor’s
contact details were added to the questionnaire as a contact point for further information
and improved authenticity.
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4.3.2 Development of the Research Factors
A set of questionnaire items (statements) is widely used by researchers to gather
information about organisations’ behaviour and attitudes towards the new innovation
adoption. Thus, the factors under investigation were measured using a set of statements
for each factor. By reviewing the literature in IT/IS adoption (Gangwar, Date &
Ramaswamy 2015; Loske et al. 2014; Oliveira, Thomas & Espadanal 2014; Safari, Safari
& Hasanzadeh 2015; Stieninger & Nedbal 2014), well established and validated items
were adopted to measure these factors. These items are considered more applicable to
examining the behavioural intentions of organisations towards the adoption of new
innovations. In this section, the questionnaire items used are described in more detail and
a copy of the questionnaire is attached in Appendix B. A summary of the measurement
items used for each factor and their related key literature sources is presented in Table
4.2.
Table 4.2: Summary of the questionnaire instrument
Factor Scales used
Type of Scale
No. of Items Key sources used
ADOPT 7-point Likert
Ordinal 4 Oliveira et al. 2014; Hailu 2012; Opala 2012; Ross 2010; Tweel 2012
CRA 7-point Likert
Ordinal 6 Tehrani & Shirazi 2014; Oliveira et al. 2014; Alshamaila et al. 2013a, 2013b; Tweel 2012; Ghobakhloo et al. 2011; Ifinedo 2011
CF 7-point Likert
Ordinal 4 Tehrani & Shirazi 2014; Opala 2012
QoS 7-point Likert
Ordinal 4 Lawal 2014; Tweel 2012; Ross 2010
CS 7-point Likert
Ordinal 3 Gupta et al. 2013; Opala 2012; CSA 2009
CP 7-point Likert
Ordinal 3 Oliveira et al. 2014; Tehrani & Shirazi 2014; Vanessa 2014; ENISA 2009
AWC 7-point Likert
Ordinal 4 Dillon & Vossen 2014; Tehrani & Shirazi 2014; Martin 2010; Nir 2010; Kerr & Bryant 2009
4.3.2.1 ADOPT
This factor sought to measure the perceptions of SMEs’ decisions towards the adoption
of Cloud computing. Four items adopted from previous researchers (Hailu 2012; Oliveira,
Thomas & Espadanal 2014; Opala 2012; Ross 2010; Tweel 2012) were used to measure
this variable using a seven-point Likert scale ranging from (1) ‘strongly disagree’ to (7)
‘strongly agree’. These items were:
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ADOPT1: ‘Our organisation intends to adopt Cloud computing’;
ADOPT2: ‘Our organisation feels that the organisation’s needs can be met by
Cloud computing’;
ADOPT3: ‘Our organisation will take steps to adopt Cloud computing in the
future’;
ADOPT4: ‘Our organisation will adopt Cloud computing within the next 12
months’.
4.3.2.2 Cloud Relative Advantages
This factor sought to measure the SMEs’ perceptions of the relative advantages of Cloud
computing towards the decision about Cloud computing adoption. Six items adopted
from previous researchers (Alshamaila, Papagiannidis & Stamati 2013; Ghobakhloo,
Arias-Aranda & Benitez-Amado 2011; Ifinedo 2011; Oliveira, Thomas & Espadanal
2014; Tehrani & Shirazi 2014; Tweel 2012) were used to measure this variable. The
respondents were asked to indicate the extent to which they agreed or disagreed with
these six items using a seven-point Likert scale ranging from (1) ‘strongly disagree’ to
(7) ‘strongly agree’. These items were:
CRA1: ‘Adopting Cloud computing increases the profitability of our
organisation’;
CRA2: ‘Adopting Cloud computing allows for reduced operational costs’;
CRA3: ‘Adopting Cloud computing allows us to enter into new businesses or
markets’;
CRA4: ‘Adopting Cloud computing allows better communication with our
suppliers and customers’;
CRA5: ‘Adopting Cloud computing requires no upfront capital investment’;
CRA6: ‘Adopting Cloud computing provides dynamic and high service
availability’.
4.3.2.3 Cloud Flexibility
This factor sought to measure the flexibility of Cloud computing perceived by SMEs
towards the decision about Cloud computing adoption. Four items adopted from previous
researchers (Tehrani & Shirazi 2014; Opala 2012) were used to measure this variable.
The respondents were asked to indicate the extent to which they agreed or disagreed with
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these four items using a seven-point Likert scale ranging from (1) ‘strongly disagree’ to
(7) ‘strongly agree’. These items were:
CF1: ‘Adopting Cloud computing creates a flexible service-oriented
environment’;
CF2: ‘Adopting Cloud computing provides dynamic resource scaling’;
CF3: ‘Adopting Cloud computing provides access to Cloud services from various
client devices’;
CF4: ‘Adopting Cloud computing increases business agility’.
4.3.2.4 Quality of Service
This factor sought to measure the quality of service of Cloud computing perceived by
SMEs towards the decision about Cloud computing adoption. Four items adopted from
previous researchers (Lawal 2014; Ross 2010; Tweel 2012) were used to measure this
variable. The respondents were asked to indicate the extent to which they agreed or
disagreed with these four items using a seven-point Likert scale ranging from (1)
‘strongly disagree’ to (7) ‘strongly agree’. These items were:
QoS1: ‘Our organisation sees Cloud computing as a reliable service’;
QoS2: ‘Our organisation realises that the service availability of Cloud computing
is high’;
QoS3: ‘Our organisation believes that Cloud computing provides an up to date
service’;
QoS4: ‘Our organisation thinks that Cloud computing responds quickly’.
4.3.2.5 Cloud Security
This factor sought to measure the security of Cloud computing perceived by SMEs
towards the decision about Cloud computing adoption. Three items adopted from
previous researchers (CSA 2009; Gupta, Seetharaman & Raj 2013; Opala 2012) were
used to measure this variable. The respondents were asked to indicate the extent to which
they agreed or disagreed with these three items using a seven-point Likert scale ranging
from (1) ‘strongly disagree’ to (7) ‘strongly agree’. These items were:
CS1: ‘Our organisation has concerns about the security of the technology used in
Cloud computing services such as virtualisation, SaaS, and PaaS’;
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CS2: ‘Our organisation considers that Cloud computing technology is more
secure than traditional enterprise networks methods’;
CS3: ‘Our organisation is willing to use Cloud computing to host sensitive
information’.
4.3.2.6 Cloud Privacy
This factor sought to measure the privacy of Cloud computing perceived by SMEs
towards the decision about Cloud computing adoption. Three items adopted from
previous researchers (ENISA 2009; Oliveira, Thomas & Espadanal 2014; Shimba 2010;
Tehrani & Shirazi 2014; Vanessa 2014) were used to measure this variable. The
respondents were asked to indicate the extent to which they agreed or disagreed with
these three items using a seven-point Likert scale ranging from (1) ‘strongly disagree’ to
(7) ‘strongly agree’. These items were:
CP1: ‘Our organisation feels secure storing data in Cloud if the data centre is
located in Australia’;
CP2: ‘Our organisation feels a loss of privacy if the Cloud services run from a
different country, as different privacy legislation applies’;
CP3: ‘Our organisation feels that Cloud computing is unreliable and can’t be
trusted’.
4.3.2.7 Awareness of Cloud (AWC)
This factor sought to measure the awareness of Cloud computing perceived by SMEs
towards the decision about Cloud computing adoption. Four items adopted from previous
researchers (Dillon & Vossen 2014; Kerr & Bryant 2009; Martin 2010; Nir 2010; Tehrani
& Shirazi 2014) were used to measure this variable. The respondents were asked to
indicate the extent to which they agreed or disagreed with these four items using a seven-
point Likert scale ranging from (1) ‘strongly disagree’ to (7) ‘strongly agree’. These items
were:
AWC1: ‘Our organisation is aware of Cloud computing services’;
AWC2: ‘Our organisation understands the difference between SaaS, PaaS, and
IaaS’;
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AWC3: ‘Our organisation is aware that Cloud computing is linked with other
applications’;
AWC4: ‘Our organisation realises the difference between Public, Private, and
Hybrid Cloud services’.
4.3.3 Pre-testing the Survey
It is noted that pre-testing questionnaires let the researcher learn whether questions make
sense, are logically ordered, have biased wording, or will provide the desired information
(Gaddis 1998).
4.3.3.1 Pre-test
The original survey instrument was revised several times by the supervisory team.
Afterwards, the link of the online questionnaire was sent by email to three colleagues
from the School of Information and Business Analytics and the School of Accounting
and Finance at Deakin University. They were requested to fill out the online questionnaire
and provide comments. The pre-test revealed that some change to the questions was
required. To improve the questionnaire, minor wording changes were made in some
questions. For example, the phrase “business agility” was explained in greater detail. The
pre-test identified the possible need for open-ended questions in order to possibly
improve response rate.
4.3.3.2 Pilot-test
After the pre-testing and questionnaire redevelopment, a pilot study was carried out. Ten
SMEs were contacted via email. The purpose of the study was explained and they were
invited to participate in the pilot survey. These SMEs were asked to provide feedback to
all the questions based on their perceptions of Cloud computing adoption with additional
comments related to the survey. The pilot study revealed that the ranges (7-point scale)
provided for each measure in the questionnaire were well balanced, the length of time
given to answer the questions was reasonable (less than 20 minutes), and the instrument
was easy to complete. Overall, the pilot study participants indicated that the questionnaire
was easily understood. During the pilot study, the reliability of the questions was
measured. A pilot study can be used to test the reliability and validity of the instrument
(Straub 1989). Furthermore, Burgess (2001) pointed out that running a pilot study can
98
contribute to maximising the response rate and minimising the error rate on answers.
Saunders et al. (2011) argued that a pilot-test was important to improve the questions and
to test respondents’ comprehension and clarity before the actual survey was administered.
In order to increase the reliability and validity of the questions, some of the items were
removed from the questionnaire, as suggested by the SMEs that were targeted in this pilot
study.
4.4 Sampling Design
This section describes the target population of this study, how the research sample was
identified and selected, and the way the survey was conducted.
4.4.1 Target Population
The unit of analysis for the current research are SMEs in Australia. This includes all states
and territories in Australia to accommodate a wide geographical distribution of these
companies.
This research focuses on the adoption of Cloud computing by SMEs all over Australia.
Thus, the target population for this quantitative study consisted of decision makers (IT
executives, CIOs, IT managers, and other IT personnel) at SMEs from various industries
in Australia, who are authorised and responsible for both technology acquisition and
policy decisions for their organisation.
4.4.2 Sample Size and Sampling Method
The common intention of survey research is to select a proper sample so that any results
obtained can be used inferentially. According to Saunders et al. (2011), the sampling
technique and sample size used in research are usually influenced by the availability of
the resources; in particular, for a PhD researcher the research is related to the time and
financial support available. According to Hair et al. (2010), the minimum sample size
required in order to perform a factor analysis is 50. The acceptable ratio is ten
observations for each construct (Tehrani & Shirazi 2014). Thorndike (1978) proposed a
more rigorous method to determine minimum sample size requirement, in which N is a
function of the square of the number of variables V plus a modifier of 50 to 100 to be
added in order to ensure reliability when the sample sizes are small. This approach yields
the following:
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+ 50Using Thorndike's (1978) methodology for this study (seven variables and a small sample
modifier of 100) invokes minimum responses of the following magnitude:
N=72 +100 =149
Our sample consists of 152 Australian SMEs, thus meeting the necessary conditions for
using factor analysis.
Mainly there are two types of sampling techniques, probability sampling and non-
probability sampling. Probability sampling (representative sampling) offers an equal
chance for each unit in the population to be selected. The researcher in this case is able
to answer the research questions and meet the objectives that allow the characteristics of
the population to be estimated from the sample. In order to identify all possible
respondents, Chambers of Commerce in Australia and the Yellow Pages website were
chosen. It is important to note that there were no updated exacted numbers of SMEs
operating throughout Australia. A sampling frame for this study was acquired from the
market research company Research Now, which comprised of the current list of
registered members with the company. Hence, a stratified random sampling technique
was used to select participants according to states and territories. Using this sampling
strategy, SMEs are chosen from each strata to represent the entire population. Within
each SME, the owner or manager in IT was chosen as the point of contact, as they were
considered to be in the best position to answer these research questions.
4.4.3 Questionnaire Administration
Online surveys have numerous strengths compared to other survey methods (Evans &
Mathur 2005). They are quite flexible and can be delivered in several formats, such as by
email with a link to a survey URL, or by email with an embedded survey. Surveys can
easily be tailored to customer demographics or their experiences by presenting only their
respective relevant sections. Then each respondent can see only the pertinent questions.
Online surveys can be administered in a timely manner, minimising the time taken in the
field and for data collection (Kannan, Chang & Whinston 1998). As a result of self-
administered surveys, costs can also be kept down as postage or interviews are not
required (Evans & Mathur 2005). Further, online surveys can attract a larger sample.
Based on the research questions and with a high level of Internet usage by SMEs (ACMA
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2010), an online survey tool is considered to be the best choice to collect data for this
study, especially in Australia.
Online surveys have come a long way due to the evolving nature of technologies. Surveys
can be accessed by respondents by clicking on a URL sent by email and be transferred to
a feature-rich web survey tool (Mullarkey 2004). Tingling, Parent and Wade (2003)
explained that online surveys not only provide greater control and flexibility but can also
be used to reduce possible bias by randomly rotating choices to each question, thus
providing more complex and fully randomised displays. This is also very convenient for
respondents. They can answer at a convenient time to themselves. It is relatively simple
for respondents to complete online surveys and the researcher immediately receives all
the data to a database (Wilson & Laskey 2003). Online surveys require the respondent to
answer questions in the order intended by the researcher to prohibit the respondent from
looking ahead to later questions. This might appear to have a seemingly endless number
of questions but a graphical progress indicator can be quite informative for respondents.
Online surveys can be constructed to ensure that respondents answer only the questions
that pertain specifically to them; this eliminates respondent confusion and makes the
process simpler for taking the survey.
Qualtrics software was used to design and develop the online survey. It allows to create
surveys, develop and share them with collaborators, distribute to participants, monitor
the response progress, and visualise the result as well as download in a different format
for more detailed analysis. This is one of the most commonly used online questionnaire
tools in both the academic and industrial environments. Since convincing the decision
makers in SMEs to participate in a study was very difficult as a contact list of SMEs was
not accessible, it was decided to hire a market research company to distribute the survey
to SMEs. Research Now is an online sampling and data collection company that collects
visitors’ digital data by tracking their social networks and referring sites. The company
is a member of the Research Now Group, Inc. which operates all over the world. The
company had an extensive list of SMEs representing all states and territories across
Australia. In the pre-questionnaire delivery stage, the questionnaire was reviewed by an
expert team in the company and received good comments about the structure and logical
flow of the questionnaire. Based on those comments, the questionnaire was modified and
the final version is exhibited in Appendix B of this thesis.
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The link to the survey was sent to a total of 1179 SMEs using a stratified random sampling
technique for participants across the whole of Australia. The distribution of the survey
link took place in mid-June 2014. The participants were given seven days to complete the
survey and the survey was deactivated after this time. In the email cover letter,
participants were informed that participation was voluntary and their responses would
not be identifiable. As discussed by Bryman and Bell (2015), ethics approval was
obtained from the Faculty Human Ethics Advisory Group (HEAG) at Deakin University.
This approval was received in 2013 from the HEAG Secretariat, reference number: BL-
EC 32-13 (see Appendix C).
4.5 Data Analysis
In order to test the hypothesis developed and answer the research questions, several
procedures and analyses were used. This section starts with the data screening process
prior to the data analysis, including accuracy and missing data, outliers and an assessment
of normality. It outlines the statistical analytical techniques used, as well as the rationale
for selecting them. Then, descriptive analysis and validation of the measures (reliability
and validity) were undertaken, after which MLR analysis was carried out (with related
assumptions considered). IBM SPSS (Statistical Package for the Social Sciences)
Statistics version 22 was used for all data transformation and subsequent analysis.
4.5.1 Data Screening
The data collected with the online survey tool, Qualtrics, were downloaded into SPSS
files to analyse. The properties (e.g. name, label) of each variable were updated to enable
ease of analysis. These were coded with a numeric value based on the number of
categories. Numbers from one to seven were used to represent each scale. The 28 items
were measured based on a seven-point Likert scale and coded from the lowest value (one
for ‘strongly disagree’) to the highest value (seven for ‘strongly agree’). To fulfil the
requirement for multivariate analysis, the data were screened to check missing data,
outliers and normality (Pallant 2013; Tabachnick & Fidell 2007).
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4.5.1.1 Accuracy and Missing Data
Survey data was checked to ensure accuracy and to locate missing data. Missing values
were assessed using the SPSS missing value analysis tool (Pallant 2005). Further, the
data were checked to ensure completeness.
4.5.1.2 Outliers
Outliers are data points that are very different from the rest of the data. Outliers can be
identified from a univariate, bivariate, or multivariate perspective. Tharenou, Donohue
and Cooper (2007) explained the outliers as extreme data points that can have a
disproportionate influence on the conclusions drawn from most statistical techniques.
The detection of univariate outliers is outlined in this section. Under the assumptions of
multiple linear regression (MLR), the multivariate outliers will be discussed. According
to Tabachnick and Fidell (2007), any standardised score of a variable that exceeded ±3.29
was considered a potential outlier.
4.5.1.3 Normality
Normality is one of the assumptions required for several statistical tests (Hair et al. 2010;
Tabachnick & Fidell 2007). The normality distribution for all the research factors was
checked using skewness and kurtosis values. According to Hair et al. (2010), skewness
and kurtosis values between -1.96 and +1.96 are considered to approximate normal
distributions.
4.5.2 Descriptive Analysis
Descriptive analysis will then be performed to summarise the data set and understand
them clearly for further analysis.
A descriptive analysis was executed for the demographic variables and research factors.
This was undertaken to describe the characteristics of SMEs to simply summarise the
sample. The analysis was undertaken using mean and standard deviation to depict
research factors. This analysis involved examining the central tendency and frequency
distribution of each item and the variable. The mean value and standard deviation were
calculated for each item representing each factor.
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4.5.3 Validation of the Measures
The main elements of data quality are validity, reliability, and generalisability (Lancaster
2007). Validity and reliability were assessed to ensure that subsequent modelling could
be undertaken. It was necessary to assess the internal consistency of research factors to
check whether the items that made up the scale were all measuring the same underlying
construct. The validity and reliability of the items were assessed based on the accepted
guidelines (Pallant 2005; Lancaster 2007).
4.5.3.1 Reliability of the Measures
Reliability is the ability of a measurement instrument to provide the same error-free
results consistently. In other words, reliability is concerned with how much random error
researchers (Hair et al. 2012; Lee & Kwon 2014; Prasad et al. 2014a; Tehrani & Shirazi
2014; Zhao, Scheruhn & von Rosing 2014) in this area and is recommended as a measure
of internal consistency. It measures how closely a set of items is related to each other as
a group (Straub 1989). Hinton (2004) suggested four cut-off points for reliability:
0.90 and above – excellent reliability;
0.70 to 0.89 – high reliability;
0.50 to 0.69 – moderate reliability;
below 0.50 – low reliability.
A reliability coefficient of 0.70 or higher is considered acceptable in most social science
research (Cohen, Mou & Trope 2014; Gupta, Seetharaman & Raj 2013; Hailu 2012;
Tehrani & Shirazi 2014)
-off point items for each of the research factors.
4.5.3.2 Validity of the Measures
Validity was tested to examine the extent to which an instrument measures what it is
intended to measure (Bryman & Hardy 2004; Pallant 2005). According to Swanson &
Holton (2005), there are two common types of validity: content validity and construct
validity. In this study, the content validity of the research instrument was established
through the theoretical literature review and the extensive process of item selection and
refinement when developing the questionnaire. The separate question items were used to
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measure the research factors (ADOPT, CRA, CF, QoS, CP, CS and AWC) adopted from
prior empirical research, after modifying them to suit the context of this research. In
addition, the questionnaire was reviewed for content validity by supervisors and three
academics from Deakin University. The separate question items of the questionnaire were
also reviewed by supervisors and three academics from Deakin University and were
indicated to be relevant from the academics’ perspectives, thus the questionnaire was
accepted as possessing content validity.
The construct validity comprised of two other components: 1. convergent validity, and 2.
discriminant validity. The construct validity of the measures was verified by means of
factor analysis (FA) (Low, Chen & Wu 2011). The purpose of FA was to evaluate
whether all the items intended to measure each construct loaded highly onto their
expected theoretical constructs (Cohen, Mou & Trope 2014). Convergent validity
assesses whether items purportedly measuring the construct cluster together to form that
construct. Discriminant validity defines whether a construct is different to another
construct (Bagozzi, Yi & Phillips 1991). Discriminant validity is considered by reviewing
the cross-loading of the separate question items. For example, existence of cross-loading
for an item indicates that that item does not actually align with one factor, and may in
fact be cross-loading with more than one factor. Confirmatory factor analysis using
principle axis factoring extraction was performed on all 28 separate question items used
in the questionnaire to verify the construct validity. Two separate FAs were conducted:
one for the items used to measure the independent variables together, and another for the
items used to measure the dependent variable.
According to Pallant (2005), three main steps were followed in order to conduct the FA
properly. This process began with the assessment of suitability of the data for factor
analysis. There are two main areas to focus on in determining whether a particular data
set is suitable for factor analysis: sample size and the strength of the relationship among
the items. Two statistical methods were used to assess the factorability of the data: the
Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s test of
sphericity. The KMO is a summary statistic that ranges from 0.0 to 1.0 where higher
values indicate an adequate sample size suitable for FA (Pallant 2005). Based on
Tabachnick & Fidell’s (2007) recommendation, a minimum value of 0.6 was used as a
cut-off point to assess the suitability of the sample size. The significance level of
Bartlett’s test of sphericity shows that there are adequate relationships between items,
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and that factor analysis is appropriate. Pallant (2005) also mentioned that Bartlett’s test
of sphericity should be statistically significant (p<.05), and thus considered appropriate
for FA. Further, the correlation matrix of the items was inspected to check the strength
of the intercorrelations among the items. As recommended by Tabachnick & Fidell
(2007), the evidence of coefficients should be greater than 0.3.
The next step was factor extraction. Although, there are several extraction methods, for
example principal components, principal factors and maximum likelihood factoring, the
most commonly used approach is principal components. Factor analyses with the
principal axis factoring method were performed because it can help minimise cross-
loadings (Tabachnick & Fidell, 2007).
A number of techniques can be used to assist in determining the number of factors to be
retained for FA. The most suitable techniques were Kaiser’s criterion or the eigenvalue
rule and scree plot test (Hsu, Ray & Li-Hsieh 2014; Pallant 2005; Saffu, Walker &
Mazurek 2012; Shareef et al. 2011; Tehrani & Shirazi 2014). Based on Kaiser’s criterion,
eigenvalues that are more than 1 are considered significant and were retained for further
analysis, while those that have eigenvalues of less than 1 are considered insignificant and
therefore were disregarded (Low, Chen & Wu 2011; Shareef et al. 2011; Tehrani &
Shirazi 2014). Another popular approach is the scree test, which involves the visual
exploration of a graphical representation of the eigenvalues (Cattell 1966). The logic
behind this method is that this point divides the major factors from the trivial factors.
According to Osborne and Costello (2009), an examination of the scree plot is the easiest
way to determine the number of factors for retention.
After the number of usable factors was identified, they were rotated to present the pattern
of loadings in a way that is easier to interpret (Pallant 2005). Orthogonal factor solution
with the Varimax rotation method was selected because it provides easier interpretation
of these factors. This method attempts to minimise the number of variables that have high
loadings on each factor (Pallant 2005). The higher loading items on each of the
components were checked and a loading of 0.4 was used as the cut-off point for
interpreting the factor loading (Hair et al. 2010). However, if an item had a cross-loading
with more than one factor or did not load properly on the intended factor, it was dropped
and the analysis was run again (Gangwar, Date & Ramaswamy 2015; Saffu, Walker &
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Mazurek 2012). Following this, the components were labelled based on the theoretical
base of that item represented (Tabachnick & Fidell 2007).
After verifying that the constructs were reliable and valid, the results of this analysis were
used to create values representing the research factors (ADOPT, CRA, CF, QoS, CP, CS
and AWC). In other words, the extracted factor scores were saved as variables (within
the SPSS data matrix). These factor scores were used as input values for the multiple
linear regression (MRL) analysis to test the developed hypotheses.
4.5.4 Multiple Linear Regression
The final step of the analysis was to use multivariate statistical analysis to estimate the
relationship between the dependent variable and independent variables. Due to its
advantages in analysing complex models, the structural equation modelling (SEM)
approach using AMOS 22.0 was considered to empirically assess the research model.
However, the sample size was not large enough to be considered useful. It was restricted
to obtain larger sample because of limited available resources. MLR analysis was
therefore the most appropriate analytical technique available.
The direct relationships between the independent variables (CRA, CF, QoS, CP, CS and
AWC) and the dependent variable (ADOPT) towards the adoption of Cloud computing
by SMEs were examined using multiple regression. A simultaneous entry method was
chosen to perform this analysis, since all the independent variables were considered to be
equally important (Pallant 2005). The relative contribution of each independent variable
was assessed based on this method. This approach also showed how much unique
variance in the dependent variable each of the independent variables was able to explain
(Pallant 2005).
To test any effect of the control variables (Organisation size and Organisation type) on
the relationships of the model, an ANOVA was used, specifically to determine if Cloud
adoption varied with the size of the organisation and the type of the organisation. The
organisation size was re-categorised into two groups: micro SMEs, and small & medium
SMEs. In order to test for differences between groups, a One-Way ANOVA was used.
Then MLR analysis was performed on each group separately, if there existed any
significant differences among the groups to assess the relative contribution of the
independent variables for all groups.
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4.5.5 Assumptions of the MLR Analysis
All assumptions of the regression analysis were checked, including: sufficient sample
size, multicollinearity, outliers, normality, linearity and homoscedasticity (Pallant 2005).
4.5.5.1 Sample Size
A suitable number of cases required for the MLR analysis was determined based on the
formula presented by Tabachnick & Fidell (2007):
where m = number of independent variables; and n = number of cases required. Since
there were six independent variables, a minimum 98 cases were required.
4.5.5.2 Normality
Normality imposes that each individual variable must be normally distributed. Data were
examined for normality by visually inspecting histograms of standardised residuals. This
method, as a graphical method, is considered one of the simplest and most adequate to
assess normality (Tabachnick & Fidell 2007).
4.5.5.3 Linearity
Linearity is the assumption that the relationship between the variables is linear (Pallant
2005). Tabachnick & Fidell (2007) suggest a ‘spot check’ of some combination of
variables. It assumes that the residuals should have a straight-line relationship with
predicted dependent variable scores (Pallant 2005). The linearity of the relationships was
examined by testing the standardised residuals and the scatterplot (Lawal 2014; Opala
2012).
4.5.5.4 Multicollinearity
Multicollinearity indicates the high correlation that may exist among independent
variables (Pallant 2005). The Pearson correlation matrix was used to check
multicollinearity. Correlation between independent variables below the threshold value
of 0.7 was considered to present no multicollinearity violations.
4.5.5.5 Outliers
The presence of outliers can be detected from the scatterplot (Pallant 2005). Tabachnick
& Fidell (2007) define outliers as cases that have a standardised residual greater than 3
or less than -3. Outliers can also be checked by inspecting three criteria: Mahalanobis
distance, Cook's distance and Centered Leverage Value analyses (Pallant 2005;
Tabachnick & Fidell 2007).
4.6 Chapter Summary
This chapter focused on the research methodology utilised and outlines the research
method in detail. A cross-sectional quantitative research design was used to validate the
research model. The data collection method based on the online survey questionnaire was
adapted by already validated instruments from well-established models and theories. This
was used to investigate the intention to adopt Cloud computing by Australian SMEs and
to detect the influence of the independent variables and their hypothesised relationships
on the perceptions of SMEs to adopt Cloud computing. This chapter also described how
the questionnaire was developed and consisted of previously developed and validated
items to measure each of the research factors (ADOPT, CRA, CF, QoS, CP, CS and
AWC). The population of interest in this study was SMEs representing various Australian
industries. The services of Research Now were used to contact the random sample of
1179 potential respondents, pointing them to a web page containing the online
instrument, and inviting them to take part in the survey. The survey responses were
downloaded from Qualtrics website in the form of SPSS to analyse. In order to answer
the three research questions, Pearson’s correlation and multiple regression analyses were
used to explore the relationship among research factors and the intention to adopt Cloud
computing by SMEs. The findings of the questionnaire and the test results of the
hypotheses are presented in the next chapter.
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5 CHAPTER 5: RESULTS
5.1 Introduction
This chapter presents the results of the various statistical methods used to explore the
influence of the factors on SMEs to adopt Cloud computing. The statistical analysis
comprised three stages. The first stage focused on the questionnaire response rate, data
screening and descriptive statistical analysis. The second stage examined measurement
validation, including reliability analysis and factor analysis. Based on satisfactory results
from stage two, multiple regression analysis (MRA) was used in the third stage to test the
proposed research model and assess the overarching research hypotheses.
5.2 Response Rate
Over seven days, 191 (out of 1179) responses were received, giving a 16% response rate.
Of these responses, 39 cases were excluded because of incomplete responses, thereby
reducing the final sample to 152 usable responses (an effective response rate of 13%).
This exceeded the suitable minimum level in a large population (Harrigan, Rosenthal &
Scherer 2008). Research Now distributed the survey link to respondents. Table 5.1 shows
the responses rate.
Table 5.1: Response rate
Description ValueTotal number of questionnaire links distributed 1179Total number of responses received 191Incomplete responses 39Total number of usable responses 152Response rate 13%
5.3 Data Screening
The data screening process was done in two stages, in terms of case screening and
variable screening. In case screening the data set was analysed for missing values in
single responses. Based on a case-screening test, incompletes responses were detected
with many missing values and were excluded from the data set (39). Subsequently, the
data were analysed for any unengaged responses. Based on an unengaged responses
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analysis test, few unengaged responses were detected and were removed from the data
set (3). Furthermore, all the items were checked for outliers and three items were removed
due to assumption violations. In variable screening, the data set was analysed for missing
values. No missing values were detected. The results presented in Table 5.2 indicate that
all the adoption factors can be assumed to be normally distributed (Opala 2012). The
skewness values range from 1.4186 to 0.5866, while the kurtosis values range from
0.2788 to 1.7615.
Table 5.2: Skewness and kurtosis values of the adoption factors
Factors Skewness KurtosisAWC -1.4175 1.7615
CF -1.4186 1.5626QoS 0.4827 0.3894CRA -0.6985 0.8510CP -0.2737 0.2788CS 0.5866 0.9448
5.4 Descriptive Analysis
Descriptive statistics are used to summarise the basic features of data. These summary
measures include measurements expressing location, and dispersion. With descriptive
analysis, the raw data is transformed into a form that makes it easy to understand and
interpret (Zikmund 1994). A descriptive analysis was conducted to describe the
characteristics of SMEs and how they viewed the factors assumed to be affected in Cloud
computing adoption.
5.4.1 Demographic Classification of Respondent Organisations
Characteristics of the sample were described using descriptive analytical summary
methods.
5.4.1.1 Current Position of the Respondent
Table 5.3 shows that the majority of the respondents were owners of SMEs (67.8% of the
sample); 6.7% of respondents were IT managers, with the CEO (2.7%) and IT executives
(1.3%).
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Table 5.3: Frequencies of respondents’ current position
Frequency Per cent Valid Percent
Cumulative Per cent
Valid Owner 101 67.8 67.8 67.8CEO 4 2.7 2.7 70.5IT Manager 10 6.7 6.7 77.2IT Executive 2 1.3 1.3 78.5Other 32 21.5 21.5 100.0Total 149 100.0 100.0
5.4.1.2 Organisation Type
According to the ABS, small businesses are represented with fewer than 20 employees,
whereas micro businesses represent fewer than 5 employees. Medium-sized enterprises
are defined as businesses with fewer than 200 yet with 20 or more employees (DIISR
2011). As shown in Table 5.4, the highest responding organisations were micro (61.8%;
n=92), whereas small businesses represented 20.8% (n=31), followed by medium with
17.4% (n=26).
Table 5.4: Frequencies of organisation type
Frequency Per cent Valid Percent
Cumulative Per cent
Valid Micro 92 61.8 61.8 61.8Small 31 20.8 20.8 82.6Medium 26 17.4 17.4 100.0Total 149 100.0 100.0
5.4.1.3 Duration of the organisation
Table 5.5 represents the number of years the organisations had been operating. The
majority of organisations had been operating for more than five years (73.8%; n=110),
while 22.8% had been operating for one to five years and only a few (3.4%) had been
operating for less than a year.
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Table 5.5: Frequencies of organisations’ length of operation
Frequency Per cent Valid Percent
Cumulative Per cent
Valid Less than a year 5 3.4 3.4 3.41 – 5 years 34 22.8 22.8 26.2More than 5 years 110 73.8 73.8 100.0Total 149 100.0 100.0
5.4.1.4 Industry Type
As shown in Table 5.6, the highest percentage of organisations (20.8%; n=31) were
Professional, Scientific and Technical Services, followed by retail trade (10.8%) and
construction (7.4%). The rest of the organisations were Information, Media and
Telecommunications, Health Care and Social Assistance, Administrative and Support
Services, etc.
Table 5.6: Frequencies of organisations’ industry type
Frequency Per cent Valid Per cent
CumulativePer cent
Valid Agriculture ̧Forestry and Fishing 7 4.7 4.7 4.7Mining and Quarrying 2 1.3 1.3 6.0Manufacturing 3 2.0 2.0 8.0Electricity, Gas, Water & Waste Services 3 2.0 2.0 10.0
Construction 11 7.4 7.4 17.4Wholesale Trade 3 2.0 2.0 19.4Retail Trade 16 10.8 10.8 30.2Accommodation, Hospitality & Food/Beverage Services 4 2.7 2.7 32.9
Transport, Postal and Warehousing 4 2.7 2.7 35.6Information, Media and Telecommunication 8 5.4 5.4 41.0
Banking and Insurance Services 6 4.0 4.0 45.0Rental, Hiring and PropertyServices 1 .6 .6 45.6
Professional, Scientific and Technical Services 31 20.8 20.8 66.4
Administrative and Support Services 7 4.7 4.7 71.1
Public Administration and Safety 1 .7 .7 71.8Education and Training 5 3.4 3.4 75.2Health Care and Social Assistance 9 6.0 6.0 81.2Arts and Recreation Services 4 2.7 2.7 83.9Other Services 24 16.1 16.1 100.0Total 149 100.0 100.0
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5.4.1.5 State or Territory
As can be seen in Table 5.7, the majority of organisations were from Queensland (QLD)
(27.5%; n=41), New South Wales (NSW) (20.1%; n=30) and Victoria (VIC) (19.5%;
n=29). The remaining organisations came from West Australia (WA) (12.8%), South
Australia (SA) (11.4%), Tasmania (TAS) (6.7%) and the Nothern Territory (NT) (2%).
Table 5.7: Frequencies of organisations’ state or territory
Frequency Per cent Valid Percent
Cumulative Per cent
Valid VIC 29 19.5 19.5 19.5NSW 30 20.1 20.1 39.6QLD 41 27.5 27.5 67.1WA 19 12.8 12.8 79.9SA 17 11.4 11.4 91.3TAS 10 6.7 6.7 98.0NT 3 2.0 2.0 100.0Total 149 100.0 100.0
5.4.2 Use of Cloud Computing
The number of SMEs which did not use Cloud computing is high (Table 5.8). More than
two-thirds (72.5%; n=108) of responding SMEs were identified as non-users of Cloud
computing and were still using traditional computing methods. Twenty seven and a half
per cent were identified as current users of some form of Cloud computing service.
Table 5.8: Frequencies of Cloud computing usage by organisations
Frequency Per cent Valid Percent
Cumulative Per cent
Valid Yes 41 27.5 27.5 27.5No 108 72.5 72.5 100.0Total 149 100.0 100.0
5.4.3 Descriptive Analysis of the Adoption Factors
This section represents the descriptive analysis of all the multi-item factors used in the
conceptual framework that was used to examine the hypotheses.
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5.4.3.1 ADOPT
Table 5.9 shows the distribution of the separate question items used to measure the
perceptions of SMEs to adopt Cloud computing. The means of the items of ADOPT
ranged from = 5.46 (stdev. 1.742) to = 5.58 (stdev. 1.560). The highest mean was for
item ADOPT2 – ‘Our organisation feels that an organisation’s needs can be met by Cloud
computing’ ( = 5.58; stdev. 1.560). The lowest mean was for item ADOPT4 – ‘Our
organisation will adopt Cloud computing within the next 12 months’ ( = 5.46; stdev.
1.742). The overall mean of ADOPT was = 5.53, which indicates a high degree of
agreement in SMEs’ perception to adopt Cloud computing. The majority of the
respondents agreed or strongly agreed that they were feeling, planning, predicting and
intending to adopt Cloud computing in the near future.
Table 5.9: Frequency distribution of the ADOPT
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre % Fre % Fre % Fre % Fre % Fre % Fre %ADOPT1 8 5.4 6 4.0 3 2.0 17 11.4 5 3.4 69 46.3 41 27.5 5.52 1.650ADOPT2 7 4.7 4 2.7 3 2.0 18 12.1 7 4.7 69 46.3 41 27.5 5.58 1.560ADOPT3 7 4.7 6 4.0 5 3.4 15 10.1 6 4.0 69 46.3 41 27.5 5.54 1.625ADOPT4 10 6.7 6 4.0 4 2.7 17 11.4 2 1.3 69 46.3 41 27.5 5.46 1.742
Overall mean 5.53
5.4.3.2 CRA
Table 5.10 shows the frequency distribution of the items of CRA. The means of the items
ranged from = 3.99 (stdev. 1.020) to = 4.68 (stdev. 1.028). The highest mean was for
item CRA6 – ‘Adopting Cloud computing provides dynamic and high service
availabilityy’ ( = 4.68; stdev. 1.028). The lowest mean was recorded for item CRA1 –
‘Adopting Cloud computing increases the profitability of our organisation’ ( = 3.99;
stdev. 1.020). The overall mean of CRA was = 4.33, which indicates a degree of
agreement in SMEs’ perception of CRA to adopt Cloud computing.
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Table 5.10: Frequency distribution of the CRA
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre % Fre % Fre % Fre % Fre % Fre % Fre %CRA1 5 3.4 7 4.7 14 9.4 93 62.4 21 14.1 7 4.7 2 1.3 3.99 1.020CRA2 7 4.7 8 5.4 14 9.4 50 33.6 40 26.8 26 17.4 4 2.7 4.36 1.351CRA3 8 5.4 10 6.7 14 9.4 75 50.3 30 20.1 8 5.4 4 2.7 4.00 1.230CRA4 5 3.4 6 4.0 3 2.0 62 41.6 45 30.2 25 16.8 3 2.0 4.50 1.183CRA5 3 2.0 7 4.7 13 8.7 60 40.3 33 22.1 29 19.5 4 2.7 4.45 1.227CRA6 4 2.7 0 0.0 7 4.7 46 30.9 67 45.0 22 14.8 3 2.0 4.68 1.028
Overall mean 4.33
5.4.3.3 CF
Table 5.11 indicates the frequency distribution of the items of CF. The means of the items
ranged from = 4.38 (std. 1.154) to = 4.97 (stdev. 1.191). The highest mean was for
item CF3 – ‘Adopting Cloud computing provides access to Cloud services from various
client devices’ ( = 4.97; stdev. 1.191). The lowest mean was for item CF4 – ‘Adopting
Cloud computing increases the ability of a business to adapt rapidly and cost efficiently
in response to changes in the business environment’ ( = 4.38; stdev. 1.154). The overall
mean of CF was = 4.63, which indicates a degree of agreement in SMEs’ perception
of CF to adopt Cloud computing.
Table 5.11: Frequency distribution of the CF
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre. % Fre % Fre % Fre % Fre % Fre % Fre %CF1 4 2.7 2 1.3 3 2.0 52 34.9 53 35.6 32 21.5 3 2.0 4.72 1.097CF2 4 2.7 3 2.0 4 2.7 72 48.3 43 28.9 23 15.4 0 0.0 4.45 1.030CF3 4 2.7 2 1.3 4 2.7 37 24.8 45 30.2 51 34.2 6 4.0 4.97 1.191CF4 6 4.0 1 .7 10 6.7 74 49.7 34 22.8 20 13.4 4 2.7 4.38 1.154
Overall mean 4.63
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5.4.3.4 QoS
Table 5.12 presents the frequency distribution of the items of QoS. The means of the
items ranged from = 4.48 (stdev. 0.859) to = 4.92 (stdev. 0.889). The highest mean
was for item QoS3 – ‘Cloud computing provides an up to date service’ ( = 4.92; stdev.
0.889). The lowest mean was for item QoS4 – ‘Cloud computing responds quickly to
customers’ requests’ ( = 4.48; stdev. 0.859). The overall mean of QoS was = 4.72,
which indicates a degree of agreement in SMEs’ perception of QoS to adopt Cloud
computing.
Table 5.12: Frequency distribution of the QoS
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre. % Fre % Fre % Fre % Fre % Fre % Fre %QoS1 3 2.0 2 1.3 9 6.0 63 42.3 41 27.5 24 16.1 7 4.7 4.59 1.139QoS2 1 0.7 0 0.0 4 2.7 46 30.9 65 43.6 27 18.1 6 4.0 4.87 .925QoS3 0 0.0 0 0.0 3 2.0 50 33.6 58 38.9 32 21.5 6 4.0 4.92 .889QoS4 0 0.0 0 0.0 8 5.4 85 57.0 36 24.2 16 10.7 4 2.7 4.48 .859
Overall mean 4.72
5.4.3.5 CS
Table 5.13 displays the frequency distribution of the items of CS. The means of the items
ranged from = 4.30 (stdev. 0.826) to = 4.93 (stdev. 0.798). The highest mean was for
item CS1 – ‘Our organisation is concerned about the security of Cloud computing’ ( =
4.93; stdev. 0.798). This was followed by item CS3 – ‘Our organisation uses Cloud
computing to host sensitive information’ ( = 4.58; stdev. 1.021). The lowest mean was
for item CS2 – ‘Our organisation considers Cloud computing is more secure than
traditional computing’ ( = 4.30; stdev. 0.826). The overall mean of CS was = 4.60,
which indicates a degree of agreement in SMEs’ perception of CS to adopt Cloud
computing.
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Table 5.13: Frequency distribution of the CS
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre. % Fre % Fre % Fre % Fre % Fre % Fre %CS1 0 0.0 0 0.0 5 3.4 35 23.5 78 52.3 28 18.8 3 2.0 4.93 .798CS2 0 0.0 0 0.0 20 13.4 79 53.0 37 24.8 12 8.1 1 .7 4.30 .826CS3 0 0.0 1 0.7 16 10.7 60 40.3 48 32.2 16 10.7 8 5.4 4.58 1.021
Overall mean 4.60
5.4.3.6 CP
Table 5.14 illustrates the frequency distribution of the items of CP. The means of the
items ranged from = 3.19 (stdev. 1.161) to = 3.56 (stdev. 1.188). The highest mean
was for item CP3 – ‘Our organisation feels that Cloud computing can’t be trusted’ ( =
3.56; stdev. 1.188). This was followed by item CP2 – ‘Our organisation feels a loss of
privacy if the Cloud services are run from a different country’ ( = 3.24; stdev. 1.160).
The lowest mean was for item CP1 – ‘Our organisation prefers to store data in Cloud if
the data centre is located in Australia’ ( = 3.19; stdev. 1.161). The overall mean of CP
was = 3.33, which indicates neither agreement nor disagreement in SMEs’ perception
of CP to adopt Cloud computing.
Table 5.14: Frequency distribution of the CP
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre. % Fre % Fre % Fre % Fre % Fre % Fre %CP1 8 5.4 36 24.2 47 31.5 40 26.8 13 8.7 5 3.4 0 0.0 3.19 1.161CP2 11 7.4 29 19.5 42 28.2 51 34.2 12 8.1 4 2.7 0 0.0 3.24 1.160CP3 9 6.0 18 12.1 36 24.2 61 40.9 17 11.4 8 5.4 0 0.0 3.56 1.188
Overall mean 3.33
5.4.3.7 AWC
Table 5.15 shows the frequency distribution of the items of AWC. The means of the items
ranged from = 5.00 (stdev. 1.619) to = 5.70 (stdev. 1.523). The highest mean was for
item AWC1 – ‘Our organisation is aware of Cloud computing services’ ( = 5.70; stdev.
1.523). The lowest mean was for item AWC3 – ‘Our organisation is aware that Cloud
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computing is linked with other applications’ ( = 5.00; stdev. 1.619). The overall mean
of AWC was = 5.42, which indicates quite a high degree of agreement in SMEs’
perception of AWC to adopt Cloud computing.
Table 5.15: Frequency distribution of the AWC
Item
SMEs responses
Mean Std. dev.Strongly
Disagree Disagree Somewhat Disagree
Neither Agree nor Disagree
Somewhat Agree Agree Strongly
Agree
Fre. % Fre % Fre % Fre % Fre % Fre % Fre %AWC1 9 6.0 0 0.0 4 2.7 11 7.4 9 6.0 73 49.0 43 28.9 5.70 1.523AWC2 17 11.4 4 2.7 3 2.0 10 6.7 4 2.7 69 46.3 42 28.2 5.38 1.919AWC3 8 5.4 1 .7 4 2.7 60 40.3 9 6.0 31 20.8 36 24.2 5.00 1.619AWC4 12 8.1 2 1.3 6 4.0 13 8.7 5 3.4 55 36.9 56 37.6 5.59 1.790
Overall mean 5.42
5.5 Reliability and Validity Assessment of the Measures
This section presents the results of the reliability and validity assessment of the items
used to measure the research factors.
5.5.1 Reliability Assessment of the Measures
Table 5.16 shows Cronbach’s alpha values for all the research factors. All the alpha
values exceeded the recommended threshold of 0.70. The values ranged from 0.709 for
CS to 0.992 for ADOPT. This implies that all the items developed to measure the factors
were considered internally consistent and measured using an acceptable scale.
Table 5.16: Reliability of the measures
Factors No. of Items
Alpha if item deleted
Item for deletion
Cronbach’s alpha
ADOPT
ADOPT1
4
0.988
None 0.992ADOPT2 0.992ADOPT3 0.988ADOPT4 0.991
CRA
CRA1
6
0.728
None 0.771
CRA2 0.734CRA3 0.691CRA4 0.700CRA5 0.788CRA6 0.769
CF CF1 4 0.737 None 0.820
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CF2 0.766CF3 0.799CF4 0.789
QoS
QoS1
4
0.739
None 0.797QoS2 0.723QoS3 0.715QoS4 0.801
CSCS1
30.629
None 0.709CS2 0.661CS3 0.552
CPCP1
30.763
None 0.746CP2 0.542CP3 0.664
AWC
AWC1
4
0.911
None 0.930AWC2 0.896AWC3 0.944AWC4 0.876
5.5.2 Validity Assessment of the Measures
This section demonstrates the results of the factor analysis for both independent and
dependent variables. As shown in Table 5.17, the KMO values for both the independent
and dependent variables exceed the thresold value of 0.6. Bartlett’s test of sphericity
indicates statistical significance (p < 0.001). These preliminary results indicate that the
FA can be used.
Table 5.17: Results of KMO and Bartlett’s test of sphericity
Independent variable
Dependent variable
KMO Measure of Sampling Adequacy 0.783 0.844Bartlett's Test of Sphericity
Approx. Chi-Square 1923.516 1461.352df 276 6Sig. 0.000 0.000
The results of the initial FA for the independent variables are presented in Table 5.18.
Six factors with eignenvalues greater than one were extracted. These 6 factors explained
57% of the variance.
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Table 5.18: Total variance in the initial FA
FactorInitial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared Loadings
Total % of Variance
Cumulative% Total % of
VarianceCumulative
% Total
1 6.376 26.565 26.565 5.958 24.824 24.824 3.2392 3.218 13.409 39.974 2.960 12.333 37.157 2.8243 2.121 8.839 48.813 1.735 7.231 44.388 2.1864 1.900 7.915 56.729 1.406 5.859 50.247 2.1795 1.437 5.987 62.715 1.018 4.241 54.487 1.6886 1.070 4.459 67.175 0.606 2.526 57.013 1.567
Extraction Method: Principal Axis Factoring.
To provide easier interpretation of these extracted factors, orthogonal varimax rotation
was undertaken. This produces factor structures that are uncorrelated. However, the
rotation solution revealed that the grouping of some items was not as expected: some
items did not clearly load with their predetermined construct. To gain a better solution,
the FA was run several times, with the removal of the items that cross-loaded. Finally,
results of the FA indicated a clear solution with six factors with eigenvalues greater than
one (see Table 5.19). Three items – QoS4, CRA5 and CRA6 – were dropped because of
cross-loading. The factor explained 61.16% of the variance.
Table 5.19: Total variance in the FA for independent variables
FactorInitial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared Loadings
Total % of Variance
Cumulative% Total % of
VarianceCumulative
% Total
AWC 5.814 27.688 27.688 5.435 25.882 25.882 3.231CF 3.089 14.711 42.399 2.838 13.514 39.396 2.435CRA 2.080 9.906 52.305 1.707 8.128 47.524 2.137QoS 1.799 8.568 60.873 1.329 6.330 53.854 1.850CP 1.353 6.445 67.318 0.951 4.529 58.383 1.685CS 0.987 4.698 72.016 0.583 2.777 61.160 1.506Extraction Method: Principal Axis Factoring.
The review of the pattern matrix (see Table 5.20) shows that all the items are loaded
highly on their respective factors (CRA, CF, QoS, CS, CP and AWC). All items’ loadings
were significant and higher than 0.5, except for item CF4 with structure coefficient of
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0.464. however the loading of this item is still within the threshold of 0.4 (Hair et al.
2010). Therefore, this result was considered adequate and a satisfactory valid solution.
Table 5.20: Pattern matrix of the FA for the independent variables
Items FactorCRA CF QoS CS CP AWC
CRA1 .678 .189 .167 .160 -.017 .064CRA2 .617 .183 .100 -.117 -.062 .173CRA3 .704 .266 .119 .140 -.018 .062CRA4 .588 .255 .153 .045 -.118 .044CF1 .330 .733 .203 .075 -.081 .135CF2 .313 .693 .139 .096 -.034 .135CF3 .244 .564 .252 .081 -.133 -.123CF4 .255 .464 .249 .064 -.004 .014QoS1 .264 .158 .728 .137 .083 .163QoS2 .142 .136 .740 .044 .053 .148QoS3 .105 .321 .627 .077 -.006 .133CS1 -.104 .112 -.001 .685 -.166 -.005CS2 .266 .000 .072 .622 .032 -.064CS3 .050 .078 .130 .722 -.013 .109CP1 -.215 -.159 -.008 -.060 .551 -.056CP2 .092 -.132 -.070 .001 .879 .057CP3 -.019 .095 .175 -.085 .724 .023AWC1 .121 .062 .160 .007 -.018 .846AWC2 .106 -.039 .127 .002 .013 .914AWC3 -.012 .131 .028 .056 .040 .752AWC4 .112 -.011 .131 -.014 -.018 .966Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalisation.Rotation converged in 7 iterations.
The results of the FA for the dependent variable are shown in Table 5.21. The principal
axis FA through Kaiser’s criterion revealed the presence of one factor with an eigenvalue
greater than one, explaining the high value 97.4% of the variance. A review of the scree
plot (see Figure 5.1) indicated a clear one factor. Further, the inspection of the factor
matrix revealed that all items’ loading were significant and higher than 0.95.
Table 5.21: Total variance in the FA for the dependent variable
FactorInitial Eigenvalues Extraction Sums of Squared
Loadings
Total % of Variance
Cumulative % Total % of
VarianceCumulative
%ADOPT 3.921 98.025 98.025 3.895 97.371 97.371Extraction Method: Principal Axis Factoring.
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Figure 5.1: Scree plot of the FA for ADOPT
All the factors were then identified according to the similarities of the items that were
related to each factor. These factors were then labelled based on the theoretical factors
that were designed and investigated in the research model. The summary of these factors
is as follows:
ADOPT consisted of four items with a factor structure coefficient (Goodwyn 2012)
ranging from 0.952 to 0.990 (where ADOPT1 =0.986, ADOPT2 =0.952, ADOPT3
=0.990, and ADOPT1 =0.967);
CRA consisted of four items with a factor structure coefficient ranging from
0.588 to 0.704;
CF consisted of four items with a factor structure coefficient ranging from
0.464 to 0.733;
QoS consisted of three items with a factor structure coefficient ranging from
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0.627 to 0.740;
CS consisted of three items with a factor structure coefficient ranging from
0.622 to 0.722;
CP consisted of three items with a factor structure coefficient ranging from
0.551 to 0.879; and
AWC consisted of four items with a factor structure coefficient ranging from
0.752 to 0.966.
Cronbach’s alpha for CRA and QoS also supported the removal of three items from the
FA. Cronbach’s alpha for CRA improved from 0.771 to 0.807 after removal of CRA5
and CRA6. Cronbach’s alpha for QoS improved from 0.797 to 0.801 after removal of
QoS4. In addition to the improved factor loadings, these improvements demonstrated that
the removal of these items from the analysis was appropriate.
Consequently, all the items were shown to be internally consistent and demonstrated a
sufficient level of validity. Therefore, the measures were viewed as sufficient for further
analysis. As a result, hypothesis testing was conducted as follows.
5.6 Hypotheses Testing
This section presents the results of the Multiple Regression Analysis (MRA) that was
used to test research hypotheses. It presents an assessment of the possible linear
relationships that might exist between the independent variables (CRA, CF, QoS, CS, CP
and AWC) and the dependent variable (ADOPT). The results of regression analysis
performed separately for each independent variable on the dependent variable to test
whether the model was influenced by control variables (organisation size and industry
type).
5.6.1 Explaining the Adoption of Cloud Computing by SMEs
Multiple regression analysis using the entry method was conducted to assess whether the
research model with its six research factors significantly explained the perception of
SMEs to adopt Cloud computing. Underlying assumptions of regression were first
checked to ensure model suitability (see Appendix A). All assumptions were met.
MRA indicated that the model was statistically significant [F (6,142) 19.834 and p <
0.001]. The R2 was 0.840, indicating that 84% of the variance in the perception of SMEs
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towards Cloud computing adoption was explained. The adjusted R2 (0.833) was similar
to R2.
Table 5.22: ANOVAa (Significance of overall regression relationship)
Source Sum of Squares df Mean Square F R2 Adj. R2 Sig.
RegressionResidualTotal
119.00422.667
141.671
6142148
19.834.160
124.250 .840 .833 .000b
a. Dependent Variable: ADOPTb. Predictors: (Constant), CS, CRA, AWC, CP, CF and QoS.
5.6.2 Factors Influencing SMEs to Adopt Cloud Computing
Presented in Tables 5.22 and 5.23, are the results of the post hoc tests done on the
regressed independent factors. This was done to determine which of the independent
variables significantly influenced the perception of SMEs to adopt Cloud computing.
Table 5.23: Coefficients of MLR (Factors explaining perceptions of SMEs)
Model B Beta t Sig. Tolerance VIF(Constant) 0.038 1.157 0.249CRA 0.187 0.190 5.635 0.000 0.994 1.006CF -0.002 -0.002 -0.047 0.962 0.991 1.009QoS 0.193 0.186 5.497 0.000 0.987 1.013CS -0.018 -0.019 -0.564 0.574 0.999 1.001CP 0.006 0.006 0.188 0.851 0.998 1.002AWC 0.853 0.873 25.969 0.000 0.998 1.002
Note: R2 = 0.840; Adj.R2 = 0.833; F (6, 142) = 19.834; P <0.001
5.6.2.1 CRA
This study sought to determine whether CRA influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
H1: CRA and ADOPT. It was hypothesized that the relative advantages of Cloud
computing would have a significant effect on SMEs’ decision to adopt Cloud computing.
This hypothesis was supported. The result of the MLR showed a positive and significant
relationship between CRA and ADOPT (see Table 5.23), with 95% of CI ( 0.190, t
5.635, p-value < 0.000). The inspection of the beta weights indicated that, for every unit
increase in the CRA scores, ADOPT increased, on average, by 0.190 units, demonstrating
a small effect.
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5.6.2.2 CF
This study sought to determine whether CF influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
H2: CF and ADOPT. It was hypothesized that Cloud flexibility would have a significant
effect on SMEs’ decision to adopt Cloud computing. This hypothesis was not supported.
The result of the MLR showed a non-significant relationship between CF and ADOPT
(see Table 5.23) with ( -0.002, t -0.047, p-value > 0.05).
5.6.2.3 QoS
This study sought to determine whether QoS influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
H3: QoS and ADOPT. It was hypothesised that the quality of service of Cloud
computing would have a significant effect on SMEs’ decision to adopt Cloud computing.
This hypothesis was supported. The result of the MLR showed a positive and significant
relationship between QoS and ADOPT (see Table 5.23), with 95% of CI ( 0.186, t 5.497,
p-value < 0.000). The inspection of the beta weights indicated that, for every unit increase
in the QoS scores, ADOPT increased, on average, by 0.186 units, demonstrating a small
effect.
5.6.2.4 CS
This study sought to determine whether CS influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
H4: CS and ADOPT. It was hypothesised that Cloud security would have a significant
effect on SMEs’ decision to adopt Cloud computing. This hypothesis was not supported.
The result of the MLR showed a non-significant relationship between CS and ADOPT
(see Table 5.23) with ( -0.019, t -0.564, p-value > 0.05).
5.6.2.5 CP
This study sought to determine whether CP influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
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H5: CP and ADOPT. It was hypothesised that Cloud privacy would have a significant
effect on SMEs’ decision to adopt Cloud computing. This hypothesis was not supported.
The result of the MLR showed a non-significant relationship between CP and ADOPT
(see Table 5.23) with ( 0.006, t 0.188, p-value > 0.05).
5.6.2.6 AWC
This study sought to determine whether AWC influences the perception of SMEs to adopt
Cloud computing. To explore this, the following hypothesis was tested.
H6: AWC and ADOPT. It was hypothesised that awareness of Cloud computing would
have a significant effect on SMEs’ decision to adopt Cloud computing. This hypothesis
was supported. The result of the MLR showed a strong positive and significant
relationship between AWC and ADOPT (see Table 5.23), with 95% of CI ( 0.873, t
25.969, p-value < 0.000). The inspection of the beta weights indicated that, for every unit
increase in the AWC scores, ADOPT increased, on average, by 0.873 units,
demonstrating a large effect.
5.7 Cloud Computing Adoption Based on Company Size
The ANOVA analysis was conducted to assess whether Cloud adoption varies with the
size of the organisation. As can be seen in Table 5.24, the ANOVA analysis indicated
that the result was statistically significant. The ANOVA between these groups indicated
that the variances of Cloud adoption among the size of the organisations were different.
The results imply that ADOPT is affected by an organisations’ size (see Table 5.24).
Table 5.24: Organisation size and Cloud adoption ANOVA
Sum of Squares df Mean Square F Sig.Between GroupsWithin GroupsTotal
6.239135.432141.671
2146148
3.120.928
3.363 .037
5.7.1 Explaining the Adoption of Cloud Computing by Micro Organisations
Sixty two per cent of responding organisations were Micro organisations. MRA using the
entry method was conducted to assess whether the research model with its six research
factors significantly explained the perception of micro organisations to adopt Cloud
computing.
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MRA indicated that the model was statistically significant (Table 5.25). The R2 was
0.832, indicating that 83.2% of the variance in perception of micro organisations towards
Cloud computing adoption might be explained by the model.
Table 5.25: ANOVAa (Significance of overall regression relationship - Micro)
Source Sum of Squares df Mean Square F R2 Adj. R2 Sig.RegressionResidualTotal
81.17416.36097.534
68591
13.529.192
70.289 .832 .820 .000b
a. Dependent variable: ADOPTb. Predictors: (Constant), CS, CRA, AWC, CP, CF and QoS.
Summary statistics of the MRA are shown in the Table 5.25.
CRA and ADOPT: MLR revealed a strong positive and significant relationship between
CRA and ADOPT (see Table 5.26). With 95% of CI ( 0.171, t 3.650, p-value < 0.000).
For every unit increase in the CRA scores, ADOPT increased, on average, by 0.171 units.
CF and ADOPT: MLR revealed a non-significant relationship between CF and ADOPT
(see Table 5.26). With 95% of CI ( 0.001, t 0.023, p-value > 0.05).
QoS and ADOPT: MLR revealed a strong positive and significant relationship between
QoS and ADOPT (see Table 5.26). With 95% of CI ( 0.144, t 2.965, p-value < 0.01).
For every unit increase in the QoS scores, ADOPT increased, on average, by 0.144 units.
CS and ADOPT: MLR revealed a non-significant relationship between CS and ADOPT
(see Table 5.26). With 95% of CI ( -0.007, t -0.166, p-value > 0.05).
CP and ADOPT: MLR revealed a non-significant relationship between CP and ADOPT
(see Table 5.26). With 95% of CI ( 0.026, t 0.574, p-value > 0.05).
AWC and ADOPT: MLR revealed a strong positive and significant relationship between
AWC and ADOPT (see Table 5.26). With 95% of CI ( 0.833, t 19.584, p-value < 0.000).
For every unit increase in the AWC scores, ADOPT increased, on average, by 0.883 units.
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Table 5.26: Coefficients of MLR (Factors explaining perceptions of micro organisations)
Model B Beta t Sig. Tolerance VIF(Constant) 0.059 1.230 0.222CRA 0.177 0.171 3.650 0.000 0.902 1.109CF 0.001 0.001 0.023 0.982 0.845 1.183QoS 0.161 0.144 2.965 0.004 0.833 1.201CS -0.008 -0.007 -0.166 0.869 0.997 1.003CP 0.026 0.026 0.574 0.567 0.972 1.029AWC 0.856 0.883 19.584 0.000 0.972 1.029
Note: R2 = 0.832; Adj.R2 = 0.820; F (6, 85) = 13.529; P <0.001
5.7.2 Explaining the Adoption of Cloud Computing by Small & Medium-sized
Organisations
Small and medium organisations represent 57 out of the 149 sample. MRA using the
entry method was conducted to assess whether the research model with its six research
factors significantly explained the perception of small and medium organisations to adopt
Cloud computing.
As shown in Table 5.27, MRA indicated that the model was statistically significant [F (6,
50) 5.433 and p < 0.001]. The R2 was 0.848, indicating that 84.8% of the variance in
perception of small and medium organisations towards Cloud computing adoption was
explained by the model.
Table 5.27: ANOVAa (Significance of overall regression relationship – small & medium)
Source Sum of Squares df Mean Square F R2 Adj. R2 Sig.
RegressionResidualTotal
32.6005.837
38.436
65056
5.433.117
46.543 .848 .830 .000b
a. Dependent Variable: ADOPTb. Predictors: (Constant), CS, CRA, AWC, CP, CF and QoS.
Table 5.28 shows the detailed results of the MRA. This is done to determine which of the
independent variables influenced the perception of small and medium organisations to
adopt Cloud computing.
CRA and ADOPT: MLR revealed a strong positive and significant relationship between
CRA and ADOPT (see Table 5.28). With 95% of CI ( 0.235, t 4.151, p-value < 0.000).
For every unit increase in the CRA scores, ADOPT increased, on average, by 0.235 units.
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CF and ADOPT: MLR revealed a non-significant relationship between CF and ADOPT
(see Table 5.28). With 95% of CI ( 0.033, t 0.575, p-value > 0.05).
QoS and ADOPT: MLR revealed a strong positive and significant relationship between
QoS and ADOPT (see Table 5.28). With 95% of CI ( 0.272, t 4.815, p-value < 0.000).
For every unit increase in the QoS scores, ADOPT increased, on average, by 0.272 units.
CS and ADOPT: MLR revealed a non-significant relationship between CS and ADOPT
(see Table 5.28). With 95% of CI ( -0.058, t -1.043, p-value > 0.05).
CP and ADOPT: MLR revealed a non-significant relationship between CP and ADOPT
(see Table 5.28). With 95% of CI ( -0.050, t -0.879, p-value > 0.05).
AWC and ADOPT: MLR revealed a strong positive and significant relationship between
AWC and ADOPT (see Table 5.28). With 95% of CI ( 0.848, t 14.577, p-value < 0.000).
For every unit increase in the AWC scores, ADOPT increased, on average, by 0.848 units.
Table 5.28: Coefficients of MLR (Factors explaining perceptions of small & medium organisations)
Model B Beta t Sig. Tolerance VIF(Constant) 0.010 0.196 0.845CRA 0.195 0.235 4.151 0.000 0.950 1.052CF 0.022 0.033 0.575 0.568 0.928 1.077QoS 0.237 0.272 4.815 0.000 0.948 1.054CS -0.048 -0.058 -1.043 0.302 0.969 1.032CP -0.045 -0.050 -0.879 0.384 0.922 1.084AWC 0.909 0.848 14.577 0.000 0.898 1.114
Note: R2 = 0.848; Adj.R2 = 0.830; F (6, 50) = 5.433; P <0.001
5.7.3 Micro vs Small & Medium Organisations
Comparison between two groups (micro and small & medium organisations) was made
using independent sample t-tests. Table 5.29 shows the comparison results of two groups
regarding Cloud adoption and other influencing factors.
ADOPT: According to the t-test, the mean value of Cloud adoption between two groups
was not equal (see Table 5.29). With 95% of CI (t 2.482, p-value < 0.05).
AWC: According to the t-test, the mean value of AWC between two groups was not
equal (see Table 5.29). With 95% of CI (t 3.435, p-value < 0.05).
No other significant differences were detected (Table 5.29).
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Table 5.29: Independent sample t-test
Factors NMicro
Mean Micro
NSmall & Med
Mean Small & Med t value Sig.
ADOPT 92 -0.13 57 0.27 2.482 0.014CRA 92 -0.03 57 0.07 0.626 0.532CF 92 0.00 57 -0.03 -0.153 0.879QoS 92 0.13 57 -0.16 -1.843 0.067CS 92 0.03 57 -0.01 -0.225 0.822CP 92 -0.07 57 0.16 1.439 0.152AWC 92 -0.23 57 0.33 3.435 0.001
5.8 Cloud Adoption Based on Organisation Type
ANOVA was conducted to assess whether Cloud adoption varied between the different
types of organisation. No significant differences were detected (Table 5.30). That is, it is
concluded that the variances of Cloud adoption among the types of organisations are
similar.
Table 5.30: Organisation type and Cloud adoption ANOVA
Sum of Squares df Mean Square F Sig.Between GroupsWithin GroupsTotal
10.914130.757141.671
18130148
.6061.006
.603 .892
5.9 Summary of the Hypothesis testing
The results of this analysis indicate that Cloud adoption by SMEs is determined by
different factors, these being CRA, QoS and AWC. The summary of the results is
presented in Table 5.31. The interpretation of the results and the relationship between the
independent and dependent variables are discussed in detail in the next chapter.
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Table 5.31: Summary of the Hypothesis testing
Relationship Hypothesis Description Beta t Outcome
CRA ADOPT H1
CRA has a significant positive effect on SMEs to adopt Cloud computing.
0.190 5.635 Supported
CF ADOPT H2
CF does not have asignificant effect on SMEs to adopt Cloud computing.
-0.002 -0.047 Unsupported
QoS ADOPT H3
QoS has a significant positive effect on SMEs to adopt Cloud computing.
0.186 5.497 Supported
CS ADOPT H4
CS does not have a significant effect onSMEs to adopt Cloud computing.
-0.019 -0.564 Unsupported
CP ADOPT H5
CP does not have a significant effect onSMEs to adopt Cloud computing.
0.006 0.188 Unsupported
AWC ADOPT H6
AWC has a significant positive effect on SMEs to adopt Cloud computing.
0.873 25.969 Supported
5.10 Chapter Summary
In Chapter 5, the data collected from the online survey questionnaire on items affecting
SMEs’ perception to adopt Cloud computing, were assessed using descriptive statistics,
reliability and validity analyses, factor analysis and multiple level regression analysis.
Initially, the sample and how respondent organisations perceived the research factors
were described. Next, the reliability and validity of the survey questionnaire’s
measurements were assessed using Cronbach’s alpha and factor analysis. The
questionnaire demonstrated a high level of internal reliability and construct validity.
Then, the research model was assessed, and the hypothesised relationships developed in
Chapter 3 were tested using several multiple level regression analyses. Out of six
hypotheses, H1, H3 and H6 were supported. Finally, ANOVA tests were used to test for
differences between organisation type and organisation size.
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6 CHAPTER 6: DISCUSSION
6.1 Introduction
This chapter presents a discussion of the results that emerged from assessing the research
model, as presented in the previous chapter. The research questions and hypotheses are
addressed as a discussion of results that reflects on the findings from the existing related
literature presented in Chapter 2. Based on the results, the model was revised by
excluding insignificant relationships.
6.2 Validity of the Research Instrument
Cronbach’s alpha and factor analysis was utilised to determine the adequate reliability
and validity of measures, as multiple items were used to measure each of the main
research factors. Cronbach’s alpha scores were all above 0.7, which is above the
acceptable threshold for reliability (Hair et al. 2010). This suggests that the level of
internal consistency with the instrument is high. Further, the factor analysis indicated that
all the extracted factors had an eigenvalue of one, the items loaded highly on their
associated factors (loadings of > 0.5), and items were not cross-loaded, hence
discriminant validity was established. This implied that the instrument provided a valid
measure of all the theoretical factors incorporated into the model.
6.3 The Research Model and SMEs’ Perception of Adopting Cloud Computing
The findings revealed that the integrated adoption model successfully assisted in
explaining the perception of SMEs towards the adoption of Cloud computing in Australia.
The research model with the results of the analysis is shown in Figure 6.1. The results of
the MRA indicated that the proposed model was statistically significant [F (6, 142) =
19.834; p < 0.001] for explaining the perceptions of SMEs to adopt Cloud computing.
The six factors explained 84% (Adj R2 = 0.833) of the variance in perceptions. Thus,
these results demonstrate that the model explains a great amount of variance for the
adoption of Cloud computing compared to other similar studies, such as the 39.9% by
Tehrani & Shirazi (2014), who combined the TOE and DOI theories; and 38.1% by
Oliveira, Thomas and Espadanal (2014), who combined the DOI and TOE theories. These
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results support the applicability of the TOE and DOI theories together to explain the
perception of SMEs towards Cloud computing adoption in Australia.
The findings also revealed that SMEs have positive intentions to adopt Cloud computing.
This behavioural intention was evident from the overall mean of 5.53 out of seven (std.
1.6458) in the ADOPT scale. This indicates that the trend of SMEs towards the adoption
of Cloud computing is high, showing that there is a high level of intention by SMEs to
adopt this operational model. The majority of SMEs emphasised that their organisations’
needs can be met by Cloud computing. They agreed or strongly agreed with the statement
that their organisation will take steps to adopt Cloud computing in the near future. These
results can be considered important, given that the Cloud Service Providers (CSPs)
continue to develop services specifically for SMEs. However, some factors have been
found to influence SMEs’ intentions to adopt Cloud computing.
Figure 6.1: The research model with results of the MLR
6.4 Factors Influencing Adoption of Cloud Computing
For the sub-research questions about the factors influencing SMEs, the literature on IT
and Cloud computing adoption were relied upon. A set of critical factors were expected
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to influence the perceptions of SMEs towards adopting Cloud computing. Based on the
analysis, three factors out of six were found to influence SMEs. The following section
explains these findings.
6.4.1 CRA and ADOPT
Many researchers have suggested that CRA could influence Cloud adoption, as discussed
in Chapter 3 (Ammurathavalli & Ramesh 2014; Borgman et al. 2013; Nedbal et al. 2014;
Ning 2013; Oliveira, Thomas & Espadanal 2014; Tehrani & Shirazi 2014). Relative
advantages identified by the study include increasing profitability, enabling better
communication with suppliers and customers, reducing operational costs, allowing
access anywhere anytime and providing new business opportunities. This study produced
results which corroborate the findings of Armbrust et al. (2010) that the relative
advantages of Cloud computing implementation is that it could improve speed of business
communications, efficiency of coordination among firms, customer communications, and
access to market information mobilisation. In the current study, relative advantage is
found to have a positive significant (p < 0.001) influence on SMEs to adopt Cloud
computing. This result supported the related hypothesis (H1), which means that CRA is
a determining factor in the perceptions of SMEs’ decisions towards the adoption of Cloud
computing.
This finding is consistent with previous studies (Alshamaila, Papagiannidis & Li 2013;
Bharadwaj & Lal 2012; Oliveira, Thomas & Espadanal 2014) which state that CRA is a
significant antecedent of organisations’ intention to adopt Cloud computing. For
example, Borgman et al. (2013) found relative advantage to be significant among global
enterprises, whereas Bharadwaj and Lal (2012) suggest that the Cloud adoption decision
depends on relative advantage among Indian organisations. In 2013, Alshamaila,
Papagiannidis and Li (2013) identified relative advantage as a main factor playing a
significant role in the adoption of Cloud services by SMEs in the North East of England.
These findings are also consistent with a study that focussed on the perceived benefits of
SaaS to help organisations in the adoption of SaaS (Wu 2011).
This finding is consistent with similar studies reported in the literature (Ifinedo 2011; Tan
et al. 2009; To & Ngai 2006; Wang 2010; Wang, Wang & Yang 2010). Previous studies
found relative advantage as the best predictor of adoption and usage (Plouffe, Hulland &
Vandenbosch 2001). For example, Lu, Quan and Cao (2009) found relative advantage to
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be an important determinant of Wi-Fi technology adoption among University staff
members. When organisations perceive that a particular innovation is offering a relative
advantage, then it is more likely that they will adopt that innovation (Lee 2004). A study
by Alam (2009) indicated that IT adoption had also supported the effect of relative
advantage.
However, the finding is inconsistent with some other studies (Lin & Chen 2012; Low,
Chen & Wu 2011; Tehrani & Shirazi 2014). Low, Chen and Wu (2011) found it to be an
inhibitor of Cloud computing adoption by firms in the high-tech industry, and mentioned
one possible reason was that Cloud computing is a new technology that has a complex
charging mechanism, and firms may consider trading off the relative advantage and
charging service costs. Tehrani & Shirazi (2014) found that CRA was not significantly
influencing the adoption decisions about Cloud computing by SMEs in North America.
Teo, Lin and Lai (2009) found that relative advantage has a significantly negative effect
on Cloud adoption as the cost of the systems can be comparatively high and often
represent a major barrier to their adoption. In contrast, a study by Lian, Yen and Wang
(2014) implied that the relative advantage of Cloud computing technology is unimportant
to hospitals.
To summarise, the results showed that the CRA perceived by SMEs influence their
decision to adopt Cloud computing. According to Carr (2005), it seems possible that these
results are due to Cloud computing possibly providing the first opportunity for SMEs to
try new software approaches in a cost-effective manner. SMEs can reduce their capital
expenditure on IT infrastructure and instead utilise and pay for the resources and services
provided by Cloud computing (Rittinghouse & Ransome 2009). Often SMEs are unable
to afford their own dedicated IT but have a sufficient IT budget to buy the bandwidth and
pay according to their need and usage (Monika et al. 2010).
6.4.2 CF and ADOPT
As discussed in the literature review, a number of researchers have pointed out that
flexibility is a vital factor for SMEs in the adoption of new technologies (Amini et al.
2014; Lawal 2014; MacGregor & Kartiwi 2010; Mudge 2010). Cloud flexibility
identified by the study includes a flexible environment to operate in, dynamic resource
scaling, access from various client devices, and increased business agility. The findings
revealed that Cloud flexibility is a negative insignificant factor (p > 0.05) for SMEs
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towards the adoption of Cloud computing. This finding was unexpected and suggests that
CF is not a determining factor of perception in SMEs’ decisions towards the adoption of
Cloud computing, which means that this result did not support the related hypothesis
(H2).
This finding is inconsistent with other previous studies (Chebrolu 2010; Ness 2005;
Sahandi, Alkhalil & Justice 2012). The results of their surveys showed that SMEs are
highly interested in Cloud computing enabling them to improve flexibility and scalability.
Being location and device independent increases the flexibility of Cloud computing in
comparison to traditional forms of computing such as on-premises deployment (Tehrani
& Shirazi 2014). Dillon and Vossen (2014) stated that flexibility was a more important
aspect in the adoption of SaaS by SMEs than other factors. The present findings seem to
be inconsistent with other research which found that simplification and convenience in
the way computing-related services are delivered seem to be among the main drivers of
Cloud computing (Erdogmus 2009). Sahandi, Alkhalil & Justice (2012) indicated that
flexibility was one of the main factors in SMEs perception towards adopting Cloud
computing.
Shea (2007) recognised through factor analysis that flexibility unfolds as a leading factor
towards online adoption and that inadequate compensation is the leading barrier. Yan
(2010) identified flexibility as a leading factor in Cloud computing that allowed
organisations to start a project quickly without worrying about upfront costs. Bajenaru
(2010) identified flexibility as an important factor for European SMEs to adopt Cloud
computing. Monika et al. (2010) found flexibility to be an adoption factor for Cloud
computing services by SMEs in India compared to traditional ERP systems. Alshamaila,
Papagiannidis and Li (2013) identified compatibility and trialability each as a means of
flexibility in Cloud computing adoption by SMEs in the north east of England. Lawal
(2014) suggested that a combination of the three dimensions of IT flexibility of Cloud
computing significantly predicted IT effectiveness for SMEs in the USA. It is interesting
to note that no consistent results from prior research were found.
To summarise, the results showed that the CF perceived by SMEs does not influence their
decision to adopt Cloud computing. However, these results were not very encouraging.
A possible explanation for this might be less awareness of this newest operational model
Cloud computing and its specific features by Australian SMEs.
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6.4.3 QoS and ADOPT
Prior studies have noted the importance of quality of service in Cloud computing adoption
(Buyya et al. 2009; Chang et al. 2013; Kloch, Petersen & Madsen 2011; Marston et al.
2011; Takabi, Joshi & Ahn 2010). The quality of service identified by the study includes
providing an up-to-date reliable service, responding quickly to customer requests, and a
highly available service. The results of this study indicate that QoS is a positive
significant factor (p < 0.001) for SMEs towards adopting Cloud computing. This result
supported the related hypothesis (H3), which means that QoS is a determining factor in
SMEs’ perceptions and decisions to adopt Cloud computing.
These findings further support Buyya, Yeo & Venugopal’s (2008) idea that QoS is a
critical parameter – while other parameters have to be considered as service requests – in
the context of Cloud computing adoption. Rahimli (2013) showed that QoS (reliability)
had a positive and significant effect on organisations’ decision to adopt Cloud computing.
ITIIC (2011) identified that monitoring and enforcing QoS requirements was a key
challenge to fulfilling Service Level Agreements (SLAs) between the Cloud application
owner and the customer. Armbrust et al. (2010) identified that business continuity and
service availability were significant factors in considering Cloud adoption. It is somewhat
surprising that no inconsistency results were found in the literature.
To summarise, the results showed that the QoS perceived by SMEs influences the
decision to adopt Cloud computing. This result may be explained by the fact that
Australian SMEs prefer to acquire new technology if it provides a highly available, up to
date and reliable service.
6.4.4 CS and ADOPT
Many researchers suggested that Cloud security was one of the main influences for Cloud
adoption (Gupta, Seetharaman & Raj 2013; Rahimli 2013; Sarwar & Khan 2013;
Tancock, Pearson & Charlesworth 2013; Tang & Liu 2015; Trigueros-Preciado, Pérez-
González & Solana-González 2013). The study perceived organisations to be concerned
more about secure computing than traditional computing and preferring to host sensitive
information. However, the current study findings revealed that Cloud security is not a
significant factor (p > 0.05) for SMEs towards adopting Cloud computing. Contrary to
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expectations, this study did not find Cloud security to be a significant influence for SMEs
towards adopting Cloud computing. This result did not support the hypothesis (H4).
This finding is consistent with Oliveira, Thomas & Espadanal’s (2014) study that security
concerns were not found to inhibit the adoption of Cloud computing in either the full
sample or the industry-specific sub-samples. A possible explanation is recent advances
in the security of data in the Cloud environment (Wang 2010).
The findings of the current study do not support previous research (Rahimli 2013) that
security effectiveness has a positive and significant effect on organisations’ decision to
adopt Cloud computing. The results of Dillon and Vossen (2014) showed that the number
one concern for SMEs to adopt SaaS Cloud computing was Cloud security. Tang and Liu
(2015) presented a FAGI model as an objective and effective process to save
organisations’ time, effort and grief regarding the selection of a qualified and trusted
Cloud provider. Tancock, Pearson & Charlesworth (2013) identified that there are
amplified Cloud security problems and specific Cloud security problems which need to
be addressed to prove compliance with IT security best practices and data protection laws.
The findings of Sarwar and Khan (2013) suggested that establishing trust is the way to
overcome the security issues as it establishes a relationship quickly and safely. Kwofie
(2013) found that the most challenging issue in Cloud computing is security. Kshetri
(2013) emphasised that as the width and depth of Cloud increased, it may become a more
attractive cybercrime target, which would mean that the importance of Cloud security
would further increase. The results of Gupta, Seetharaman and Raj (2013) indicated that
Cloud security strongly influences adoption because the better the security of Cloud, the
greater the usage and adoption of Cloud. The study results of Opala (2012) determined
that managements’ perception of Cloud security significantly influences the decisions to
adopt Cloud computing. Hailu (2012) found that the perceptions of security effectiveness
correlate positively with a willingness by IT leaders in developing countries to
recommend Cloud computing technologies. Shaikh and Haider (2011) identified that
security is the biggest hurdle in achieving wide acceptance of Cloud computing. An
investigation of the influencing factors in the acceptance of Cloud computing among
SMEs in Austria revealed the importance of perceived security and safety in the adoption
process (Nedbal et al. 2014).
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To summarise, the results showed that CS is not associated with SMEs’ perception
towards adopting Cloud computing. Therefore, it seems that Australian SMEs are not
significantly concerned about information security, do not consider they have information
worth protecting, or are simply not aware of the security risks they face. A possible
explanation for this might be that they are not fully aware of Cloud computing and its
nature. A fair conclusion therefore is that SMEs are typically less concerned about
security threats, in part because they don’t have dedicated IT staff and the associated
internal knowledge that goes with that.
6.4.5 CP and ADOPT
As discussed in Chapter 3, many researchers have suggested that CP could influence the
adoption of Cloud computing (Abadi 2009; Alshamaila, Papagiannidis & Li 2013;
Chang, Walters & Wills 2013; Hutley 2012; Kshetri 2013; Mark 2011; Oliveira, Thomas
& Espadanal 2014; Pearson 2009; Sarwar & Khan 2013; Tancock, Pearson &
Charlesworth 2013; Tang & Liu 2015; Vanessa 2014; Zissis & Lekkas 2012). Cloud
privacy was identified by these studies as perceived by organisations to include concerns
that Cloud cannot be trusted, a loss of privacy when moving to Cloud, and they would
prefer to store information in the Cloud data centre located in Australia. Contrary to
expectations, the current study findings revealed that Cloud privacy is not a significant
factor (p > 0.05) in SMEs’ decision towards adopting Cloud computing. This result did
not support the hypothesis (H5).
This finding is consistent with Vanessa (2014) who suggested that privacy did not affect
an individual’s decision to adopt a technological innovation. The results may mean that
as Cloud computing is still in its infancy people do not have privacy concerns as they
take adequate safety precautions. The findings of the current study are consistent with
those of Tehrani and Shirazi (2014) who found that a higher perception about Cloud
computing privacy has no positive influence on Cloud adoption.
The findings of the current study do not support the previous research (Chen & Chang
2013; Gupta, Seetharaman & Raj 2013). Gupta, Seetharaman & Raj (2013) showed that
the greater and better the privacy regulations of the Cloud, the higher the usage and
adoption of Cloud. The results of Sahandi, Alkhalil & Justice’s (2012) study showed data
protection and privacy as the combined number one reason for not considering Cloud-
based IT as a service. Ren, Wang and Wang (2012) identified Cloud privacy as a primary
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obstacle to its wide adoption. Although these results differ from some published studies
(Horrigan 2008; Salmon 2008), identified loss of privacy is a significant barrier to the
adoption of Cloud services. Pearson (2012) stressed the importance of Cloud privacy as
a requirement. Gatewood (2009) indicated that privacy is one of the longest-standing and
most significant concerns with Cloud computing.
This finding is inconsistent with similar studies by several researchers (Angst & Agarwal
2009; Shareef, Kumar & Kumar 2008; Yoo & Donthu 2001), who conducted empirical
studies regarding the acceptance of the online environment, and observed that perceived
privacy is a major concern for Internet customers during interaction with websites. This
result is discrepant with a previous study of SaaS adoption (Wu 2011). Inconsistent with
the results, Safari, Safari and Hasanzadeh (2015) showed privacy as being one of the top
influencing factors in Cloud adoption. Alkhater, Wills and Walters (2014) showed a
statistically significant result for the importance of privacy in adopting Cloud computing.
According to Alshamaila, Papagiannidis & Li (2013), some SMEs might show no
tolerance regarding this issue.
To summarise, the results showed that the CP perceived by SMEs does not influence their
decision to adopt Cloud computing. This rather contradictory result may be due to a lack
of awareness of this newest technology Cloud computing, by Australian SMEs, and its
specific features.
6.4.6 AWC and ADOPT
The initial (pre-adoption) stage reflects recognition and awareness about the new
innovation Cloud computing (Vanessa 2014). Many researchers have pointed out that
awareness of Cloud is a vital factor in the adoption of new technologies in SMEs
(Ammurathavalli & Ramesh 2014; Dillon & Vossen 2014; Gupta, Seetharaman & Raj
2013; Jansen & Grance 2011; Ning 2013; Oliveira, Thomas & Espadanal 2014; Shareef
et al. 2011; Tancock, Pearson & Charlesworth 2013). The level of awareness of Cloud
identified by the study as perceived by organisations includes awareness of Cloud
computing services, understanding the difference between SaaS, PaaS and IaaS, realising
the difference between public, private and hybrid Cloud services, and awareness that
Cloud computing is linked with other applications. The current study findings revealed
that awareness of Cloud computing is a significant factor (p < 0.001) for SMEs towards
adopting Cloud computing. This result supported the related hypothesis (H6), which
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means that AWC is a determining factor in the perceptions of SMEs’ decisions towards
adopting Cloud computing.
The finding is consistent with Tehrani and Shirazi’s (2014) who showed that increasing
the awareness about various aspects of Cloud computing has a direct, positive influence
on Cloud computing. Kwofie (2013) showed that by creating Cloud awareness, Cloud
service providers have seen an increase in Cloud adoption by SMEs in Ghana.
Alshamaila, Papagiannidis and Li (2013) indicated that awareness and understanding of
the advantages of Cloud computing are important for the adoption decision. Rath et al.
(2012) found awareness of Cloud computing to be a significant factor in Cloud adoption.
The main implication of Low, Chen and Wu’s (2011) finding is that increasing user
awareness of the benefits of Cloud computing positively affects its efficient use and
diffusion. The present findings seem to be consistent with other research which found
that a lack of awareness of Cloud computing has an impact on its adoption (Martin 2010).
The findings of Prasad et al. (2014a) revealed that an awareness of the surrounding
environment will assist in establishing a trajectory towards adopting Cloud computing
services. They mentioned that improving SMEs’ awareness of the Cloud opportunity and
improving their skills can help speed Cloud adoption.
To summarise, the results showed that the AWC perceived by SMEs influences their
decision on the adoption of Cloud computing. There are several possible explanations for
this result. Awareness of Cloud computing can be increased if providers use other social
media such as Facebook, Twitter, LinkedIn, etc. to increase general knowledge about
Cloud computing (Ammurathavalli & Ramesh 2014). Given the potential offered by
Cloud to the SME sector, efforts by government and industry need to be made in
promoting awareness of these benefits alongside advice on how to circumvent any pitfalls
(ITIIC 2011). The diffusion of Cloud computing is facilitated if its benefits and structure
become widely recognised among SMEs (Tehrani & Shirazi 2014). Prasad et al. (2014b)
pointed out that government should allow the private sector, including providers, industry
associations, consumer advocacy bodies and brokers, to lead in engaging with SMEs on
the value of Cloud computing. Further, greater awareness of the adoption process and
step-by-step guidance may encourage SMEs to leverage the power of available Cloud
computing developments (Carcary et al. 2014). A study by Kwofie (2013) also
recommended that more awareness needs to be created by all stakeholders, for SMEs to
know the benefits that they can derive from adopting Cloud computing. Alshamaila,
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Papagiannidis and Stamati (2013) recommended that giving SMEs the chance to try the
products before actual use would increase awareness of Cloud services. ITIIC (2011)
believed that both industry associations and government should provide education and
awareness for existing Australian ICT service provider organisations of the benefits and
opportunities of Cloud computing and how to make the transition to providing Cloud-
based solutions.
6.5 Cloud Computing Adoption Based on Company Size
According to Rogers' (1995) DOI theory, organisation size is found to be positively
related to an organisation’s trend to adopt innovations. A number of previous studies
indicated that company size is considered one of the fundamental variables in explaining
organisations’ behaviour (Alvarez & Barney 2001; Chen & Hambrick 1995; Redondo &
Fierro 2007; Wincent 2005). Paik (2011) indicated that company size is a leading factor
for company performance. The size of the organisation is a factor commonly used to set
boundaries between segments of companies (Tornatzky & Fleischer 1990). In this study,
the findings revealed the variation in Cloud adoption among the different sizes of
organisations, which is statistically significant (p < 0.05) in relation to adopting Cloud
computing. This finding is consistent with similar studies (Chakraborty et al. 2010). For
example, Jeyaraj, Rottman and Lacity (2006) suggested that organisation size is a
predictor of IT adoption by organisations. A study by Zhu, Kraemer and Xu (2003) found
that larger firms are more willing to adopt the Internet for business use than smaller firms.
Teo, Tan and Buk (1997) found that firm size has a significantly positive relationship
with Internet adoption. Further, the TOE framework also suggests that organisation size
positively influences organisational adoption of IT innovation (Yoon 2009).
To summarise, the results showed that there are variances of Cloud adoption among the
different sizes of organisation. One possible explanation for the significant relationship
between firm size and Cloud adoption is that, in general, larger organisations have greater
resources (e.g. financial, technical and human resources) to allocate to the adoption of
new technology (Montazemi 1988).
6.6 Cloud Adoption Based on Organisation Type
The findings of this study revealed that the variances in Cloud adoption among the
different types of organisations are not statistically significant (p > 0.05) towards the
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adoption of Cloud computing. It is difficult to explain this result, but it might be related
to an early stage of the Cloud adoption life cycle.
6.7 The Revised Research Model
Based on the results of the MLR analysis, the research model confirmed some findings
from previous technology adoption studies in identifying critical factors affecting
perceptions of SMEs decisions towards adopting Cloud computing. Especially, the
research model was found to be significant regarding three factors, excluding CS, CP and
CF. The research model explains 83.3% of Cloud computing adoption. Thus, to answer
the main research question, three factors (CRA, QoS and AWC) were statistically
significant factors that influenced SMEs to adopt Cloud computing. Consequently, the
proposed model of Cloud computing adoption by SMEs as presented in Figure 6.2 has
been revised and is shown in Figure 6.3. The new model is a combination of AWC, QoS
and CRA. The findings indicate that the research model is significant in explaining the
adoption of Cloud computing by SMEs.
Figure 6.2: Research model for Australian SMEs Cloud computing adoption
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Figure 6.3: The Revised research model
6.8 Chapter Summary
This research is mainly based upon Rogers’ Diffusion of Innovations Theory (Rogers
2003) and Tornatzky and Fleischer’s Technology, Organisation, and Environment (TOE)
framework (Tornatzky & Fleischer 1990), and attempted to explain the adoption of Cloud
computing by Australian SMEs. This chapter discussed the results of the analysis
presented in Chapter 5 in order to address the research questions and verify the research
model. Some of the hypotheses proposed were statistically significant, providing
substantially greater support for the research model. The results support the applicability
of DOI and the TOE framework in explaining the perceptions of SMEs towards the
adoption of Cloud computing. All research questions have been answered and the
proposed model was found to be statistically significant. This explained approximately
83.3% of the variance in Cloud computing adoption by SMEs. Further, the results
revealed that three factors (CRA, QoS and AWC) out of six play an important role in
forming the Cloud adoption model. The implications of the findings on Cloud computing
adoption by Australian SMEs are discussed in Chapter 7 and concludes the research.
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7 CHAPTER 7: CONCLUSION
7.1 Introduction
The main objective of this research was to empirically assess a theoretical model which
was developed based on the TOE framework and DOI theory to identify the factors
influencing the decisions of SMEs to adopt Cloud computing in Australia. The findings
indicated that the model is statistically significant and the results of the analysis indicated
that three factors (CRA, QoS and AWC) which encompass technological, organisational
and process aspects could significantly influence the adoption decision of SMEs.
This chapter begins with an overview of the research, re-emphasising the objective and
research questions identified. The theoretical model and design adopted to conduct this
research are designated next. Following this, the main findings drawn from the research
are highlighted in relation to the factors influencing the decisions of SMEs towards the
adoption of Cloud computing. Some implications are provided based on the findings. A
discussion of the key theoretical and practical contributions of the research are presented.
This is followed by discussion of the limitations of this research along with suggestions
for further research and development.
7.2 Overview of the Research
As was explained in Chapter 1, the adoption of Cloud computing by organisations is
increasing because of its significant benefits especially for SMEs. However, the actual
adoption of Cloud computing by SMEs in Australia is less compared to other developed
countries. As Cloud computing integrates precursory computing advanced Service-
Oriented Architecture (SOA), distributed computing, virtualisation, and grid computing
(Aymerich, Fenu & Surcis 2008; Youseff, Butrico & Da Silva 2008), it emerges that
some issues with Cloud computing that had been discussed by scholars and researchers,
which are Cloud’s security, privacy, reliability and ownership of data (Jeffrey &
Neideckerlutz 2009; Miller 2008; Ristenpart et al. 2009), may limit actual adoption.
Studies have found that the attributes of innovation and change agent characteristics are
important factors in the successful adoption of that innovation (Chau & Tam 1997;
Lundblad 2003; Oliveira & Martins 2011; Rogers 2003). Therefore, it was considered
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important to explain this situation by investigating perceptions of SMEs towards the
adoption of Cloud computing.
The literature review revealed that although there has been considerable research into the
adoption of Cloud computing, adoption by SMEs has received much less attention,
especially from micro organisations. Further, the literature review also uncovered that
there is a paucity of empirical research to explain less adoption of Cloud computing by
Australian SMEs. Thus, the motivation behind this research was to theoretically study to
explain Cloud computing adoption in particular by SMEs.
In order to investigate the factors that influence SMEs’ decisions to adopt Cloud
computing, particularly in Australia, the proposed model postulated that SMEs’ intention
to adopt Cloud computing as a dependent variable is influenced by six independent
variables:
CRA compared to other technologies;
CF regarding the use of Cloud computing;
QoS regarding the use of Cloud computing;
CS matters pertaining to Cloud computing;
CP issues pertaining to Cloud computing;
AWC perceived by organisations.
In addition, the model proposed two controlling variables: organisation size and industry
sector type that would vary the relationships between these independent variables and the
dependent variable (ADOPT) as shown in the model. Accordingly, a number of
hypotheses were developed to test these relationships.
To undertake this research, a quantitative research method was applied as this strategy
tries to find an explanation for an association between two concepts by proposing a theory
(Blaikie 2009). The theoretical model was tested empirically using data collected from
IT managers or decision makers in the IT sections of selected SMEs by using an online
survey questionnaire developed using Qualtrics software. The measure items for each
factor were adopted from an extensive review of literature. These items were developed
by consolidating existing, well established and validated instruments from previous
related IT/IS adoption research after modifying them to address Cloud computing
(Gangwar, Date & Ramaswamy 2015; Loske et al. 2014; Oliveira, Thomas & Espadanal
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2014; Safari, Safari & Hasanzadeh 2015; Stieninger & Nedbal 2014). The link to the
survey was distributed to a total of 1179 SMEs, and a total of 152 usable responses were
obtained (an effective response rate of 13%).
The data were analysed using SPSS version 22 as it is a powerful analytic tool that allows
for Statistics data transformation, hypothesis testing and reporting capabilities. Then the
data were screened to check for accuracy and missing values, outliers and normality.
Descriptive analysis was performed to summarise the data. The validity and reliability of
the measurements were determined through utilising Cronbach’s alpha coefficient and
factor analysis. Multiple linear regression was used to test the proposed hypotheses and
answer the research questions. Based on the results, the main findings of the research
addressing the research questions is presented in next section.
7.3 Summary of Findings
The findings of the study are summarised in this section.
7.3.1 The Research Model and SMEs to Adopt Cloud Computing
Based on the analysis conducted, the integrated proposed model with six independent
variables was found to be statistically significant comprehending 84% of the variance in
perceptions of SMEs to adopt Cloud computing. This result demonstrates that the model
explains a great amount of variance in the adoption of Cloud compared to other similar
studies (such as 39.9% by Tehrani & Shirazi 2014; 38.1% by Oliveira, Thomas &
Espadanal 2014). These findings largely support the combination of the TOE framework
and DOI theory to explain the perception of SMEs towards Cloud computing adoption in
Australia.
The analysis indicates that SMEs have positive intentions towards adopting Cloud
computing. It shows that the trend of SMEs towards adopting Cloud is high, and that
there is a high level of intention by SMEs to adopt this operational model. However, a
certain set of factors has been found to influence their intention to adopt. The next section
presents these factors.
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7.3.2 Factors Influencing SMEs to Adopt Cloud Computing
The findings indicated that the six independent variables were significantly correlated to
influencing SMEs. However, the regression analysis showed that only three were
significant in influencing SMEs to adopt Cloud computing. In addition, the results also
showed that control variables controlled the relationship between the independent
variables and the dependent variable. The conclusion about these independent variables
and control variable is presented in the following section.
CRA was found to be the second most significant factor influencing SMEs to adopt Cloud
computing. It seems possible that these results are due to Cloud computing possibly
providing relative advantages such as no upfront capital investment, lower operational
costs, the ability to scale up their resources and infrastructure to satisfy the new
requirements, wider market coverage, allowing access anywhere anytime, and the ability
to pay-per-use model, which eliminates payments for the cost of software licensing and
installation (Gong et al. 2010). This provides the first opportunity for SMEs to try new
software approaches in a cost-effective manner.
Given the results of prior research on innovation adoption (Chebrolu 2010; Sahandi,
Alkhalil & Justice 2012; Dillon & Vossen 2014), it may be unexpected to report that CF
was found to be an insignificant factor in the adoption of Cloud computing by SMEs.
This inconsistent result may be due to the early stage of Cloud adoption by SMEs in
Australia, as it might be that there is less awareness of this newest operational model
Cloud computing, and its specific features, by Australian SMEs.
QoS has been found to be the lowest significant factor influencing SMEs to adopt Cloud
computing. Prior studies showed that QoS has been considered a significant factor in
organisations’ decision on innovation adoption (Rahimli 2013). This implies that
Australian SMEs prefer to acquire new technology if it provides a highly available, up to
date, reliable service.
Contrary to expectations, CS was found to be an insignificant factor not influencing
SMEs’ decisions to adopt Cloud computing. This is unexpected as prior researchers have
indicated that CS is one of the main influences for Cloud adoption (Sarwar & Khan 2013;
Tancock, Pearson & Charlesworth 2013; Tang & Liu 2015). This inconsistent result may
be due to the early stage of Cloud adoption by SMEs in Australia, and may also be due
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to the stronger influence of other important factors, such as AWC. A possible explanation
for this might be that they are not fully aware of Cloud computing and its nature. A fair
conclusion therefore is that SMEs are typically less concerned about security threats, due
to less awareness of this newest operational model and its specific features.
Prior studies have suggested that CP could influence the adoption of Cloud computing
(Alshamaila, Papagiannidis & Li 2013; Chang et al. 2013; Oliveira, Thomas & Espadanal
2014; Tang & Liu 2015; Vanessa 2014) and organisations’ concern that Cloud cannot be
trusted, loss of privacy when moving to Cloud, and would prefer to store information in
the Cloud data centre located in Australia. Contrary to expectations, CP was also found
to be an insignificant factor not influencing SMEs’ decision to adopt Cloud computing in
Australia. This may be due to Cloud computing still being in its infancy; people do not
have privacy concerns due to low usage of Cloud computing. This rather contradictory
result may also be due to a lack of awareness of this newest technology by Australian
SMEs and its specific features.
AWC was found to be the most significant factor influencing SMEs to adopt Cloud
computing. This can be considered as a dominating factor among the other factors in the
model, as the other factors had no consideration without having AWC. Many previous
studies have pointed out AWC as a vital factor in the adoption of new technologies in
SMEs (Ammurathavalli & Ramesh 2014; Dillon & Vossen 2014; Gupta, Seetharaman &
Raj 2013; Oliveira, Thomas & Espadanal 2014). There are several possible explanations
for this result. Social media can play a major role in increasing general knowledge about
Cloud computing, thereby increasing awareness of Cloud among SMEs. Providing
opportunities for SMEs to try the products before actual use would increase their
awareness of Cloud services (Alshamaila, Papagiannidis & Li 2013). Efforts to introduce
Cloud to the SME sector need to be made to promote awareness of these benefits,
alongside advice on how to circumvent any pitfalls (ITIIC 2011). Further, the government
should allow the private sector, including providers, industry associations, consumer
advocacy bodies and brokers, to lead in engaging with SMEs on the value of Cloud
computing. Both industry associations and government should provide education and
awareness for existing Australian ICT service provider organisations of the benefits and
opportunities of Cloud computing and how to make the transition to providing Cloud-
based solutions.
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Previous studies indicated that company size is considered one of the fundamental
variables in explaining organisations’ behaviour (Alvarez & Barney 2001; Redondo &
Fierro 2007; Wincent 2005). The variances in Cloud adoption among the different sizes
of organisations was found to be statistically significant towards adoption of Cloud
computing. This is similar to the findings by Chakraborty et al. (2010), who stated that
organisation size influenced the adoption of Cloud computing. This may be due to, in
general, larger organisations having greater resources (e.g. financial, technical and human
resources) to allocate to the adoption of new technology (Montazemi 1988).
To conclude, the model which is a combination of the three influential factors: CRA, QoS
and AWC, explains the successful adoption of Cloud computing by SMEs in Australia.
These findings indicate that some factors were found to be significant in previous
research, but nevertheless were not significant in this research and were contrary. These
findings support the research argument in the stated context, which is seen as important,
but might not be so in other contexts. Some implications are provided in the following
section based on these findings.
7.4 Implications
Taking into account the importance of Cloud computing and its low adoption rate by
SMEs in Australia, there is a great need for better understanding of what factors are
important to focus on in the adoption of Cloud computing. The proposed adoption model
which represents theoretically grounded framework, attempts to examine these factors in
detail. The findings have a number of important implications that may assist the owner
or decision maker of SMEs, service providers, service consultants and governments to
facilitate the adoption of Cloud computing by SMEs, thus stimulating the implementation
process of this newest operational model.
A rapidly changing environment represents a key challenge for business leaders to make
decisions. The proposed model for the successful adoption of Cloud computing could be
set as guidelines to help decision makers to evaluate possible alternatives and understand
which factors influence the adoption process. As shown by the empirical analysis, relative
advantage is an important factor in SMEs’ adoption of Cloud computing, and implies that
decision makers should evaluate the potential benefits of Cloud computing to increase
their awareness of these services. The decision makers could use the information about
relative advantage and the instrument explained in this research to evaluate and plan their
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technology adoption and implementation, that is, it can be identified how Cloud
computing can enable them to accomplish tasks more quickly, increase productivity, and
gain cost benefits. SMEs can use the measurement that is developed in this research to
identify the strengths and weaknesses of potential services providers. Moreover, they
need to gain technical and organisational support from service providers to promote a
favourable environment to infuse Cloud computing around technical and organisational
changes. To ensure a better adoption, SMEs need to attempt to gain all possible support
from the service provider during the different adoption stages until the full
implementation stage.
SMEs are an important component in any economy and consequently represent an
important market segment for software vendors and service providers. Therefore, service
providers should be more focused on identifying specific problems these SMEs face,
understanding organisational characteristics and taking a more proactive approach to
promoting the successful diffusion of Cloud computing in these organisations.
Understanding factors that influence SMEs’ adoption of Cloud computing will enable
technology consultants to design strategies for the widespread adoption of Cloud
computing. SMEs are generally not capable of spending a significant amount of money
on their IT. Therefore, it is essential for software vendors to devise a strategy to cater for
this category, offering components of a system as a module instead of offering a system
as a whole (e.g. the components of an ERP system). Cloud computing providers may
need to improve their interaction with SMEs who are involved in the Cloud computing
experience and expand their awareness of this innovation to others. Therefore, it is
essential for service providers to devise a strategy that actively communicates the benefits
of Cloud computing through promotional tactics such as social media. It is essential for
service providers to reduce the feeling of uncertainty regarding Cloud computing
adoption. Providers need to work on providing reliable and secure environments in the
most scalable, cost-effective, and reliable manner. Service providers should provide 24/7
technical support for Cloud services, thereby reducing the uncertainty.
The study shows that the regulatory environment is an important factor that influences
SMEs’ perceptions and decisions to adopt Cloud computing. Cloud computing poses a
range of privacy issues which will need to be addressed and mitigated with appropriate
legal, contractual and operational procedures, as the Cloud service provider assumes
responsibility for hosting the information. Cloud computing run from a different country
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is a major concern, as it would lead to a loss of privacy for clients’ data, due to different
privacy legislation being applied. Thus, governments may need to consider revising this
legislation related to data protection. The Australian government published a best practice
guide “Privacy and Cloud computing for Australian Government Agencies” to better
understand privacy laws and regulations when choosing Cloud-based services (IMO
2013). Because of legal issues that are affected by Cloud’s physical location, which
creates difficulties in determining jurisdiction, the Australian government is extremely
concerned about the location of outsourced personal data storage and there is a strong
desire for Cloud services for Australia to be only located within Australia’s borders, as
an impact of the Privacy Act (Hutley 2012). Cloud computing growth and development
could involve government reviewing its policies and incentives in promoting the adoption
of technology among SMEs. As Cloud computing adoption is vary among different sizes
of SMEs, the government should introduce polocy focusing on smaller firms to enable to
get Cloud benefits. The research model can help maximise the potential benefits of the
Australian government’s ICT implementation effort by providing an understanding of the
factors that influence the adoption and implementation of Cloud computing. Therefore,
Cloud computing can be seen as a chance to enable SMEs to reduce the cost of their IT
operations.
7.5 Contributions
This is one of the few studies investigating the adoption of Cloud computing by SMEs in
Australia, especially looking at micro, small and medium-sized organisations separately.
This study has attempted to explore and develop an SME Cloud computing adoption
model that is theoretically grounded combining the TOE framework and DOI theory.
Accordingly, a validated conceptual model was developed to examine the influence of
six contextual factors on Cloud computing adoption by SMEs in Australia. This study
can be regarded as making theoretical and practical contributions in this field of research
and are discussed below.
7.5.1 Contribution to Theory
This research makes several theoretical contributions to the ICT adoption literature by
studying Cloud computing adoption in SMEs. The study extends the current
understanding of Cloud computing adoption by SMEs using a technology-based service
adoption framework. In this epoch of rapid development of new technologies, by looking
at SMEs’ adoption of new IS innovations this can help to enrich the knowledge and
understanding of the innovation adoption process. This model builds on the existing
literature on IS innovation adoption and diffusion. The model adds new insights to the
literature as the decision to adopt Cloud computing is a function of a variety of factors.
To the best of our knowledge, the model proposed in this study is unique and has never
been used in other studies. As Cloud computing transcends boundaries and regional ICT
infrastructure, it is recommended that other researchers use the same model to investigate
the adoption of Cloud computing in different contexts. The revised model itself also a
significant contribution. A methodological contribution of the study is the synthesis of
the existing literature to define forms of measurement and operationalisation. A
multidimensional view is also offered to assess Cloud computing adoption factors by
considering innovation factors, in addition to organisational and technological factors.
The model comprehends several factors related to technological, organisational and
innovation aspects. Consequently, this research adds to a growing body of literature on
organisational innovation adoption by examining the above three elements on distinct
dimensions of Cloud computing adoption. Based on the findings, this model consists of
a dependable variable, SME, and three independent variables:
CRA compared to other technologies;
QoS regarding the use of Cloud computing;
AWC perceived by organisations.
Although this proposed theoretical model is grounded in the specific behaviour of Cloud
computing adoption, the model can also be modified and used to study the other
innovations.
Further, this research contributes to the field of IS and innovation adoption in particular
by integrating the TOE framework and DOI theory and extends it by incorporating Cloud
computing specific constructs that represent unique characteristics of Cloud such as
privacy and security. The research studied the applicability of this model to the adoption
of Cloud computing among micro, small and medium-sized organisations separately,
which has received less attention in previous research.
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The literature review revealed that although there has been considerable research into the
adoption of Cloud computing, its adoption by SMEs has received much less attention
especially for micro organisations. Micro organisations are specially considered
separately for Cloud adoption and this research contributed to the literature in relation to
innovation adoption in micro organisations. Therefore, it bridges the research gap and
contributes to the Cloud computing adoption literature, especially in the SME context.
7.5.2 Contribution to Practice
From a practical perspective, this research has contributed to a better understanding of
issues related to the adoption of Cloud computing in Australia. The results indicate that
some factors (awareness, quality of service and relative advantage) are important and
greatly influence the adoption of Cloud computing by Australian SMEs. The findings
may help establish strategies for SMEs to ensure a better adoption of Cloud. They can
help SMEs to evaluate possible alternatives, understand the factors that influence the
adoption decision and identify how Cloud computing enables them to accomplish tasks
efficiently, more effectively and finally gain cost benefits.
Further, the Cloud providers can use the results of this study to increase the rate of
adoption among Australian SMEs. Based on the results, awareness is the key factor in
the diffusion of Cloud computing. Thus, Cloud providers can use various mass media
such as Facebook, LinkedIn and Twitter to increase awareness of Cloud. Similarly, the
Australian Federal Government can reflect upon the Cloud computing framework when
developing policies for SMEs to facilitate them to reduce the cost of their IT operations
and reach globalisation.
7.6 Limitations
This study expands our knowledge about ICT innovation adoption among SMEs through
the development of a comprehensive framework, employing reliable and valid measures
of study variables, and analysing the data using appropriate and robust statistical
techniques. However, although this study has fulfilled its aim and objectives, as with any
research work the research had some limitations that should be considered when
interpreting its findings.
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Due to source limitation in terms of time and money, as outlined in Chapter 4
Methodology, the first limitation of this study is its sample size. A larger sample may be
able to present a clearer picture of SMEs in an Australian context. Further, the
methodology that has been chosen to achieve the research objectives was limited to the
cross-sectional research design (online questionnaire survey). The response rate could be
enhanced if other design methods were used other than an online survey. This research
included only six important aspects for investigation. This could be further explored if it
extended to other important aspects.
Another limitation of this study was that the research focused only from the perspective
of SMEs within Australia, excluding SMEs from other countries. Therefore, the findings
of this study are limited to an extent and should be undertaken carefully as they are not
applicable to SMEs from different parts of the world. This research helped to increase
cognisance towards Cloud adoption and implementation by SMEs; however, the study
would have benefited more by including the perspectives of SMEs from other countries.
In addition, this study is also limited in terms of its qualitative data. Exploratory research
used to gain an understanding of underline reasons and provides insights into the
problem. It would be probably help to understand some of the survey findings, such as
why flexibility, privacy and security are not considered so important for Cloud adoption
in Australian context. Therefore, further research is required to gain a solid understanding
of this phenomenon. These limitations raise the need for further research work to be
undertaken on the adoption of Cloud computing in the Australian context and in other
countries with a wider and clearer picture to generalise the findings globally.
7.7 Future Research
The findings and the limitations of this research suggest the way for possible further
research and investigation. Several recommendations and future research directions are
presented as follows.
Cloud computing adoption by SMEs in Australia is still in its initial stage, and further
detailed research incorporating important aspects in this area is required. Flexibility is
considered as a characteristics of the product/service. This could be considered in terms
of being tied to a particular Cloud provider/supplier in future research. Further, although
this study includes important variables, other studies could focus on variables such as the
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cultural implications for Cloud computing, the differences in legal systems in the use of
technology across different countries, and the management role in Cloud computing
implementation support. Organisational culture could be strongly associated with
technology adoption in general, and Cloud computing in particular. The social, cultural
and religious aspects of technology adoption and SME performance also need further
investigation. Also, a qualitative method as an inductive research could be used to explore
research factors more in-depth for understanding the problem in detail.
Notwithstanding the fact that this research has focused mainly on Cloud computing
adoption among SMEs in Australia, the conceptual model could be applied by other
researchers and may also provide strong theoretical foundations for further studies on IS
innovation adoption, exploring factors influencing the adoption of specific Cloud
services such as IaaS, PaaS, and SaaS by SMEs. As this study analysed Cloud computing
adoption by SMEs in Australia, which is a developed nation, another recommendation is
that studies could be extended to include SMEs in developed countries or less developed
nations globally. Also, Cloud computing is relatively new, future studies should
incorporate this measure when the number of decision makers/SMEs familiar to Cloud
computing reaches a critical mass.
Our online survey investigation of the model of Cloud computing adoption provided
findings on adoption among SMEs, but further research is needed to comprehend the
adoption. A longitudinal research with Large-scale would be preferable for addressing
this issue. Further, the factors influencing the adoption of Cloud computing at a particular
phase may change over time through other phases. It would also be interesting to look at
organisations’ performance pre/post-adoption of Cloud innovations in future as dynamic
modelling approaches. In addition, the research model could be examined further by
applying SEM, as this is a more robust statistical technique. As mentioned by Tabachnick
and Fidell (2007), regarding the advantages in analysing complex models, SEM examines
relationships that are free from measurement error.
7.8 Conclusion
Cloud computing adoption by SMEs in the Australian context has become of great
importance, and this research model can help maximise the potential benefits of the
Australian government's ICT implementation effort and support. In line with this, NBN
could enable SMEs in Australia to further embrace Cloud computing services. In order
to achieve successful implementation of Cloud computing, organisations must consider
the potential factors that might affect the implementation process.
This research attempted to enhance understanding of the factors influencing SMEs’
decisions towards adopting Cloud computing, where Cloud adoption is still in its initial
stage of implementation in Australia. The results of this research provide theoretical and
practical guidelines and contributions regarding the factors that facilitate the adoption of
Cloud computing by SMEs, specifically considering micro, small and medium-sized
organisations separately. However, based on the findings of this research, several factors
related to technological, organisational and innovation aspects should be considered
simultaneously to enable successful adoption. In addition, the successful adoption of
Cloud computing requires adequate government support in the form of revising the
legislation related to data protection and reviewing policies and incentives in promoting
the adoption.
The findings also highlighted that awareness is the key factor in the diffusion of Cloud
computing. Thus, knowledge of Cloud computing can be enhanced through various mass
media such as Facebook, LinkedIn and Twitter. It would have been more beneficial if it
had included the perspectives of SMEs from other countries, to generalise the findings
globally. However, the study’s narrow scope provides a clear focus and the study
justification provides a clear rationale for in-depth analysis within the Australian context.
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APPENDICES
Appendix A: Assumptions of MLR Analysis
Histogram of normality distribution. Scatter plot for linearity test.
Scatter plot for testing homogeneity of variance test.
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Pearson Correlations Matrix of the research factors
Correlations ADOPT AWC CF QoS CRA CP CS
Cloud Adoption (ADOPT) 1Awareness of Cloud (AWC) .880** 1Cloud Flexibility (CF) .004 -.018 1Quality of Service (QoS) .196* .026 .084 1Cloud Relative Advantage (CRA) .189* .013 .029 -.065 1Cloud Privacy (CP) .015 .020 -.006 -.023 -.023 1Cloud Security (CS) -.011 .013 .012 -.012 -.005 -.009 1
** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).
Residuals statistics to detect outlier
Test Minimum Maximum Mean Std. Deviation N
Mahal. Distance .123 21.820 5.960 4.583 149Cook's Distance .000 .159 .008 .022 149Centered Leverage Value .001 .147 .040 .031 149
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Appendix B: The Questionnaire
196
197
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If Cloud computing is being used, Directed to:
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If Cloud computing is being used without being aware, Directed to:
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If Cloud computing is not being used, Directed to:
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Appendix C: The Letter of Approval for Data Collection
31 October 2013
Dear Ishan,
BL-EC 32-13: Cloud Computing Adoption Factors by SMEs in Australia
Thank you for submitting the above project for consideration by the Faculty Human Ethics Advisory Group (HEAG). The HEAG recognised that the project complies with the National Statement on Ethical Conduct in Research Involving Humans (2007) and has approved it. You may commence the project upon receipt of this communication.
The approval period is for four years. It is your responsibility to contact the Faculty HEAG immediately should any of the following occur:
Serious or unexpected adverse effects on the participantsAny proposed changes in the protocol, including extensions of timeAny changes to the research team or changes to contact detailsAny events which might affect the continuing ethical acceptability of the projectThe project is discontinued before the expected date of completion.
You will be required to submit an annual report giving details of the progress of your research. Failure to do so may result in the termination of the project. Once the project is completed, you will be required to submit a final report informing the HEAG of its completion.
Please ensure that the Deakin logo is on the Plain Language Statement and Consent Forms. You should also ensure that the project ID is inserted in the complaints clause on the Plain Language Statement, and be reminded that the project number must always be quoted in any communication with the HEAG to avoid delays. All communication should be directed to [email protected]
The Faculty HEAG and/or Deakin University Human Research Ethics Committee (HREC) may need to audit this project as part of the requirements for monitoring set out in the National Statement on Ethical Conduct in Research Involving Humans (2007).
If you have any queries in the future, please do not hesitate to contact me.
We wish you well with your research.
Kind regards,
Katrina FlemingBL-HEAG Secretariat
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