the usage and adoption of cloud computing by small and medium businesses

14
International Journal of Information Management 33 (2013) 861–874 Contents lists available at ScienceDirect International Journal of Information Management j ourna l ho me pa ge: www.elsevier.com/locate/ijinfomgt The usage and adoption of cloud computing by small and medium businesses Prashant Gupta a , A. Seetharaman a , John Rudolph Raj b,a S P Jain School of Global Management, 10, Hyderabad Road, Singapore 119579, Singapore b Faculty of Management, Multimedia University, Persiaran Multimedia, 63000 Cyberjaya, Selangor Darul Ehsan, Malaysia a r t i c l e i n f o Article history: Keywords: Cloud computing Software-as-a-Service (SaaS) Platform-as-a-Service (PaaS) Infrastructure-as-a-Service (IaaS) Small and medium enterprises (SMEs’) Small and medium businesses (SMBs’) a b s t r a c t Cloud computing has become the buzzword in the industry today. Though, it is not an entirely new concept but in today’s digital age, it has become ubiquitous due to the proliferation of Internet, broadband, mobile devices, better bandwidth and mobility requirements for end-users (be it consumers, SMEs or enterprises). In this paper, the focus is on the perceived inclination of micro and small businesses (SMEs or SMBs) toward cloud computing and the benefits reaped by them. This paper presents five factors influencing the cloud usage by this business community, whose needs and business requirements are very different from large enterprises. Firstly, ease of use and convenience is the biggest favorable factor followed by security and privacy and then comes the cost reduction. The fourth factor reliability is ignored as SMEs do not consider cloud as reliable. Lastly but not the least, SMEs do not want to use cloud for sharing and collaboration and prefer their old conventional methods for sharing and collaborating with their stakeholders. © 2013 Elsevier Ltd. All rights reserved. 1. Introduction Cloud computing has created the same paradigm shift as what was analogous to replacement of individual generators by the cen- tralized electricity grid (Etro, 2011; Li, Wang, Wu, Li, & Wang, 2011). This is exactly how cloud differs from typical IT/IS, wherein the pro- ducers and consumers (of information) do not necessarily reside within the same physical proximity. Large enterprises have quickly adopted this cloud computing bandwagon (Klie, 2011; Li et al., 2011; Mahesh, Landry, Sridhar, & Walsh, 2011). However, many micro businesses and SMBs are still sitting on the fence and are contemplating whether to move to or not to move to the cloud com- puting trend, as highlighted by mindSHIFT (USA based company). In this research study, an attempt has been made to bring clarity to this paradigm shift affecting the local environment in Asia pre- dominantly. With respect to the local Singapore context, SPRING Singapore is the statutory board in charge of promoting the growth of SMEs in Singapore. Maybank Singapore and SPRING Singapore define micro business (registered and incorporated in Singapore) as a business with 10 or less employees or annual sales not exceeding $1 million and a minimum 30% equity (local shareholding). SPRING Singapore defines SMEs as businesses with annual sales turnover of not more than $100 million or employing no more than 200 staff. As per this definition, there are 154,000 SMEs in Singapore, Corresponding author. Tel.: +60 3 8312 5681; fax: +60 3 8312 5590. E-mail address: [email protected] (J.R. Raj). which means that 99.3% enterprises in Singapore are SMEs. They contribute 46% to Singapore’s GDP (Gross Domestic Product) and employ 63% of the workforce (Low, 2005; Yeo, 2007). In OECD countries (Organization for Economic Cooperation and Develop- ment, Paris) more than 95% of the enterprises are SMEs. These SMEs provide 60–70% of jobs. Two thirds of all the EU (European Union) jobs are provided by SMEs. They provide 78% of the jobs in Japan (Bernroider, 2002). India has about 3 million SMEs accounting for 50% of its industrial output. SMEs are the 2nd largest employer after agriculture and contribute to 40% of exports. Indian government initiatives include setting up of MSMEs (Micro Small and Medium Enterprises) Development Act 2006. For the purpose of this study, ‘Micro businesses’ are defined as SOHO (Small Office Home Office) SMBs having 1–10 employees and ‘Small businesses’ are defined as SMBs having 11–99 employees. ‘Medium businesses’ are defined as SMEs having 100–200 employ- ees. The literature review reveals that many studies were (and cur- rently are being) conducted on the use of cloud computing by large scale enterprises primarily on their perceptions about cost reduction, ease of use and convenience, reliability, sharing and collaboration and lastly but not the least, security and privacy. The major contribution of this paper is to identify new factors as well as to develop a sense of the relative weight of existing fac- tors like cost reduction, ease of use and convenience, reliability, sharing and collaboration, security and privacy on SMEs approach toward usage and adoption of cloud computing for their busi- nesses. 0268-4012/$ see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijinfomgt.2013.07.001

Upload: john-rudolph

Post on 18-Dec-2016

236 views

Category:

Documents


2 download

TRANSCRIPT

Page 1: The usage and adoption of cloud computing by small and medium businesses

Tb

Pa

b

a

A

KCSPISS

1

wtTdwa2mcpItdSoda$Sos

0h

International Journal of Information Management 33 (2013) 861– 874

Contents lists available at ScienceDirect

International Journal of Information Management

j ourna l ho me pa ge: www.elsev ier .com/ locate / i j in fomgt

he usage and adoption of cloud computing by small and mediumusinesses

rashant Guptaa, A. Seetharamana, John Rudolph Rajb,∗

S P Jain School of Global Management, 10, Hyderabad Road, Singapore 119579, SingaporeFaculty of Management, Multimedia University, Persiaran Multimedia, 63000 Cyberjaya, Selangor Darul Ehsan, Malaysia

r t i c l e i n f o

rticle history:

eywords:loud computingoftware-as-a-Service (SaaS)latform-as-a-Service (PaaS)

a b s t r a c t

Cloud computing has become the buzzword in the industry today. Though, it is not an entirely newconcept but in today’s digital age, it has become ubiquitous due to the proliferation of Internet, broadband,mobile devices, better bandwidth and mobility requirements for end-users (be it consumers, SMEs orenterprises). In this paper, the focus is on the perceived inclination of micro and small businesses (SMEsor SMBs) toward cloud computing and the benefits reaped by them. This paper presents five factors

nfrastructure-as-a-Service (IaaS)mall and medium enterprises (SMEs’)mall and medium businesses (SMBs’)

influencing the cloud usage by this business community, whose needs and business requirements arevery different from large enterprises. Firstly, ease of use and convenience is the biggest favorable factorfollowed by security and privacy and then comes the cost reduction. The fourth factor reliability is ignoredas SMEs do not consider cloud as reliable. Lastly but not the least, SMEs do not want to use cloud forsharing and collaboration and prefer their old conventional methods for sharing and collaborating with

their stakeholders.

. Introduction

Cloud computing has created the same paradigm shift as whatas analogous to replacement of individual generators by the cen-

ralized electricity grid (Etro, 2011; Li, Wang, Wu, Li, & Wang, 2011).his is exactly how cloud differs from typical IT/IS, wherein the pro-ucers and consumers (of information) do not necessarily resideithin the same physical proximity. Large enterprises have quickly

dopted this cloud computing bandwagon (Klie, 2011; Li et al.,011; Mahesh, Landry, Sridhar, & Walsh, 2011). However, manyicro businesses and SMBs are still sitting on the fence and are

ontemplating whether to move to or not to move to the cloud com-uting trend, as highlighted by mindSHIFT (USA based company).

n this research study, an attempt has been made to bring clarityo this paradigm shift affecting the local environment in Asia pre-ominantly. With respect to the local Singapore context, SPRINGingapore is the statutory board in charge of promoting the growthf SMEs in Singapore. Maybank Singapore and SPRING Singaporeefine micro business (registered and incorporated in Singapore) as

business with 10 or less employees or annual sales not exceeding1 million and a minimum 30% equity (local shareholding). SPRING

ingapore defines SMEs as businesses with annual sales turnoverf not more than $100 million or employing no more than 200taff. As per this definition, there are 154,000 SMEs in Singapore,

∗ Corresponding author. Tel.: +60 3 8312 5681; fax: +60 3 8312 5590.E-mail address: [email protected] (J.R. Raj).

268-4012/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ijinfomgt.2013.07.001

© 2013 Elsevier Ltd. All rights reserved.

which means that 99.3% enterprises in Singapore are SMEs. Theycontribute 46% to Singapore’s GDP (Gross Domestic Product) andemploy 63% of the workforce (Low, 2005; Yeo, 2007). In OECDcountries (Organization for Economic Cooperation and Develop-ment, Paris) more than 95% of the enterprises are SMEs. These SMEsprovide 60–70% of jobs. Two thirds of all the EU (European Union)jobs are provided by SMEs. They provide 78% of the jobs in Japan(Bernroider, 2002). India has about 3 million SMEs accounting for50% of its industrial output. SMEs are the 2nd largest employer afteragriculture and contribute to 40% of exports. Indian governmentinitiatives include setting up of MSMEs (Micro Small and MediumEnterprises) Development Act 2006.

For the purpose of this study, ‘Micro businesses’ are defined asSOHO (Small Office Home Office) SMBs having 1–10 employees and‘Small businesses’ are defined as SMBs having 11–99 employees.‘Medium businesses’ are defined as SMEs having 100–200 employ-ees.

The literature review reveals that many studies were (and cur-rently are being) conducted on the use of cloud computing bylarge scale enterprises primarily on their perceptions about costreduction, ease of use and convenience, reliability, sharing andcollaboration and lastly but not the least, security and privacy.The major contribution of this paper is to identify new factors aswell as to develop a sense of the relative weight of existing fac-

tors like cost reduction, ease of use and convenience, reliability,sharing and collaboration, security and privacy on SMEs approachtoward usage and adoption of cloud computing for their busi-nesses.
Page 2: The usage and adoption of cloud computing by small and medium businesses

862 P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874

Table 1Comparison of empirical studies on the usage and adoption of cloud computing by SMEs or SMBs.

Literature details inchronological order

Inference on SMEsadoption of cloudcomputing

Importance of cloud computingparameters on SMEs adoption

Detailed discussion on featuresof each parameter

Expectations of SMEs fromfuture usage and adoption ofcloud computing

Rising above the din(Ferguson, 2008)

Alternate ways andSaaS model forrevenues by DELL,focusing on providingservices to SMBs, anuntapped market

Software and services needs ofSMBs to help manage their emails,licensing of software, other assetsetc.

No Not analyzed

Small businessesmoving to cloudcomputing services(King, 2008)

Availability of securedIT infrastructure,minimal up-frontinvestment, disasterrecovery, softwareupgrades

Cost reduction, avoiding naturaldisaster mishaps, better securitybut lack of reliability in using cloudcomputing are the most importantparameters

Yes – emphasized on SMEscomfort with cloud computing

SMEs are showing positiveinclination toward cloud

SMEs can benefit mostfrom the cloud(Clark, 2009)

Security, reliability,trust, cost reduction,online collaborationare the majorinfluences for cloudcomputing usage

Trust in cloud providers,incremental cost and reliability arethe most important parameters

Yes – the success of the cloudcomputing adoption and itsimage in the mind of SMEsdiscussed in detail

SMEs can explore cloudcomputing with relatively littlerisk

Untitled (Grant, 2009) DELL launched threecloud based servicesfor SMEs

30% IT cost reduction for SMEs,customized services like storageand minimizing their email outage,security breaches and servicedisruption are the most importantfactors

No Major inclination toward SMEsby a big corporate like DELL,offering cloud services

Should You Move YourBusiness to theCloud? (Martin,2010)

Security and privacyare top concerns of 51%SMBs, availabilityversus suddendowntime, migrationacross cloud services

The importance of moving to cloudstep by step is recommended usingcouple of tips for SMEs. Moving tocloud is emphasized

Yes – with specific focus onprivacy, availability, data loss,data mobility and ownership,tool robustness

Strongly emphasized for smallbusinesses

Untitled (Grant, 2011) SMEs prefer to buyfrom a local cloudprovider and arewilling to pay

Collaboration, data storage,backup, scalable, pay as you go arethe primary factors

No Awareness, acceptance, usageand adoption of cloud by SMEsis on the rise

This research paper Core variables havebeen focused andstudied in detail

SMEs have shown stronginclination for three out of the fivecore variables for using andadopting cloud, as per this research

Yes – future recommendationsfor the remaining twoparameters have beendiscussed in detail that wouldhelp in designing the cloudframework for improved usage

Five existing core factors andfew new factors are identifiedand inference is drawn, usingextensive quantitative surveyacross the APAC region usingstructural equation modeling

yA

trqwrrc

bl

cscattffr

In addition, very limited research or literature has been foundet on this research topic in any developed country as well as inPAC (Asia-Pacific) region.

This research study reveals the perceptions as well as the inten-ions of the SMEs toward factors like cost reduction, ease of use,eliability, sharing and collaboration, security and privacy in auantitative manner, which are quite different as perceived by theorldwide cloud community especially in large enterprises. This

esearch captured the actual decisions taken by the respondentsather than merely the eagerness and intention to adopt cloudomputing.

For practical reasons, this study focuses on the micro and smallusinesses (SMEs or SMBs) in Singapore and neighboring countries

ike Malaysia, India.The rest of this paper is organized as follows: Section 2 dis-

usses the related literature reviewed for this research study; theubsequent sub-sections outlines the research methodology; dis-uss the empirical findings and presents the conceptual modelnd experimental hypothesis on which the model is based; Sec-ion 5 describes the analysis of the data to validate the model and

he final Section 9 concludes the paper’s results. The implicationsor industry as well as for research and limitations and scope foruture research have been discussed in last Sections 6, 7 and 8espectively.

and adoption of cloudcomputing by SMEs

(SEM)

2. Survey of literature and theoretical development

The literature review has been grouped under the variables(both independent and dependent) considered for this researchstudy. The dependent variable has been identified as ‘Cloud com-puting adoption by micro and small businesses (SMEs or SMBs)’.The various independent variables (factors influencing dependentvariable) have been identified as, (1) Cost reduction (economical) interms of data storage, subscription, low upfront cost (capital expen-diture), cost control via scalability, elasticity of resources (scaleup and down in a very short time); (2) Convenience, easy to useimplies simple in the form of accessibility and availability fromanywhere and anytime; (3) Reliability indicates dependability touse it whenever needed (backup and least outage); (4) Sharing andcollaboration; and (5) Security and privacy

Table 1 illustrates the flow of research over the years and theadditional value this research paper would add in helping SMEswith their existing and forthcoming usage and adoption of cloudcomputing to enhance their ROI (return on investment).

This table captures how SMEs/SMBs got onto the cloud com-

puting bandwagon over the period of couple of years as cloudcomputing evolved. This table clearly shows the increasing trendof the value being captured by small businesses in getting moreinclined toward cloud computing.
Page 3: The usage and adoption of cloud computing by small and medium businesses

P. Gupta et al. / International Journal of Inform

lSip

2

hHut

tbl

suscm

2

b

ipaepITgi2ItiaeaCc

Fig. 1. Research framework.

This table also signifies the gradual shift in the attention ofarge enterprise like DELL, to start focusing on the needs of theMEs/SMBs and how to fulfill them via cloud, rather than provid-ng the custom-made licensed solutions and services, within theremises of small business.

.1. Research methodology

After the extensive literature survey, the research methodologyas been centered on the already identified existing core variables.ence a simple direct relationship of these core variables has beensed to create the research model to understand which of them ishe most dominant.

To further quantify, a detailed questionnaire was used to gatherhe formal data (primary data) from the various micro and smallusinesses (SMEs/SMBs), primarily based in APAC region. The col-

ected sample size was 211 during the first half of 2012.Finally, data collected from the final survey was analyzed. For

tatistical analysis, SmartPLS (a structural model based tool) wassed to build, run and validate the process model. Partial leastquare (PLS) regression techniques were used to analyze the latentonstructs. SmartPLS exhibits both the measurement model (outerodel) and the structural model (inner model).

.2. Research framework and hypotheses definition

Fig. 1 is the research framework on which this research study isuilt upon.

Cloud computing (dependent variable) is similar to an electric-ty grid, where resources like hardware, software, information areooled and shared with the end-user via the internet, which is useds a medium of exchange (Li et al., 2011). Users do not know thexact location of their digital data (McAfee, 2011). The frameworkrovided by cloud computing is in the form of high quality leased

T resources instead of building the IT infrastructure from scratch.hus, the in-house versus cloud computing comparison is, analo-ous to the make or buy decision faced by SMBs. Cloud computings analogous to outsourcing data center operations (Mahesh et al.,011). This approach also typically implies renting software via thenternet instead of employing an in-house software developmenteam (Payton, 2010). As a result, SMEs in-house IT parts are min-mal (Li et al., 2011). Mahesh et al. (2011), Sultan (2011), Truongnd Dustdar (2011), Ojala and Tyrvainen (2011), Creeger (2009), Lit al. (2011), Durkee (2010), Marston, Li, Bandyopadhyay, Zhang,

nd Ghalsasi (2011), Karadsheh (2012), Rath (2012), Neves, Marta,orreia, and de Castro (2011) and McAfee (2011) observe that cloudomputing comprises three services:

ation Management 33 (2013) 861– 874 863

(1) Software-as-a-Service (SaaS): Instead of installing software onthe client’s machine and updating it with regular patches, fre-quent version upgrades etc., applications like Word processing,CRM (Customer Relationship Management), ERP (EnterpriseResource Planning) are made available (hosted) over the inter-net for the consumption of the end-user. It can achieveeconomies of scale. This is the biggest and most maturecloud model. Commercial vendors are Yahoo Mail, Gmail, Hot-mail, TurboTax Online, Facebook, Twitter, Microsoft Office Live,Google Apps, Salesforce.com, Cisco WebEx web conferencing,antivirus, SuccessFactors (HRM tool) etc.,

(2) Platform-as-a-Service (PaaS): Instead of buying the softwarelicenses for platforms like operating systems, databases andmiddleware, these platforms and the software developmentkits (SDKs) and tools (like Java, .NET, Python, Ruby on Rails) aremade available over the Internet. Commercial vendors includeMicrosoft Azure Services, Amazon Web Services (AWS), Sales-force’s Force.com, Google App Engine platform, IBM Cloudburst,Amazon’s relational database services, Rackspace cloud sites,

(3) Infrastructure-as-a-Service (IaaS): This refers to the tangi-ble physical devices (raw computing) like virtual computers,servers, storage devices, network transfer, which are physi-cally located in one central place (data center) but they can beaccessed and used over the internet using the login authen-tication systems and passwords from any dumb terminal ordevice. Commercial vendors include Amazon EC2 (Elastic Com-pute Cloud), Elastic Block Storage (EBS) and Simple StorageService (S3), Rackspace cloud servers, Joyent and Terremark.

One of the biggest advantages of moving to cloud computing isthe opportunity cost of freeing up some of the IT administrativetime, which can now be applied to the business aspects of growingthe core business of SMBs (Creeger, 2009). Due to cloud computing,innovation is nurtured as the entry barrier (in terms of cost) getslowered. Now, startups and small firms can use cloud computingresulting in introduction of these types of online applications andsocial-media services such as Facebook, YouTube, TripIT (travel),Mint (personal finance) (Marston, Li, Bandyopadhyay, Zhang, &Ghalsasi, 2011).

There are four different cloud deployment models within orga-nizations namely (Neves et al., 2011; Marston et al., 2011; Rath,2012):

(1) Public cloud: It is available from a third party service providervia Internet and is very cost effective for SMBs to deploy ITsolutions. For example, Google Apps.

(2) Private cloud: It is managed within an organization and is suit-able for large enterprises (managed within the walls of theenterprises). For example, the US government cloud product isin a segregated environment, both physically and logically. It iscertified by FISMA (Federal Information Security ManagementAct) and is being handled by a third party provider, Google. Pri-vate clouds provide the advantages of public clouds but stillincur capital expenditures.

(3) Community cloud: It is used and controlled by a group ofenterprises, which have shared interests. For example, theUS federal government using community cloud (built on Ter-remark’s Enterprise cloud platform) for forms.gov, flu.gov,cars.gov, USA.gov, Apps.gov.

(4) Hybrid cloud: It is a combination of public and private cloud.

The following steps are recommended regarding the adoption

of cloud computing (Etro, 2011): (a) Data portability and free flowof data across geographical borders should be favored by interna-tional agreements; (b) A minimum set of standards and processesshould be agreed upon by EU (European Union) and other global
Page 4: The usage and adoption of cloud computing by small and medium businesses

8 Inform

aFoas

2

sibovtfcclmSihsvclei

asC2btetueito

tl29AU(huebetitctbun

s

64 P. Gupta et al. / International Journal of

uthorities to promote data security, privacy and portability; (c)iscal incentives and promotions should be provided for adoptionf cloud computing by governments etc. by partly bearing the vari-ble cost; and (d) To reallocate the employment within the IT sectorhould get public support.

The discussion of the independent variables follows:

.2.1. Cost reductionDue to the subscription model, there is a huge cost savings for

mall firms (Ankeny, 2011). The entry cost for small firms utiliz-ng business analytics, which needs lots of computing power, haseen lowered (Marston et al., 2011). A 70% cost reduction has beenbserved since adopting AWS (Amazon Web Services) as the cloudendor (CC 2011). AWS has also reduced their prices a couple ofime, in the past three years, in spite of the absence of competitiveorces (McAfee, 2011). European SMEs, who are more risk averse,ompared to USA SMEs, appreciate this reduction of fixed IT assetsost as well reduction of maintenance costs of IT assets, resulting inowering the entry barrier (Etro, 2011). Due to the per user revenue

odel, small businesses could afford enterprise applications likealesforce.com (CRM tool) (Klie, 2011; Mahesh et al., 2011). Thiss in line with the trend of software becoming a commodity (likeardware) due to stiff competition and availability of open sourceoftware. Downward pricing pressures have resulted in cloud ser-ices being used as a commodity now, hence large scale adoption ofloud computing has to be ensured, similar to volume sales but at aower price (Durkee, 2010). Computing power is nowadays consid-red as a commodity due to the entry of various players providingt at an affordable cost (Marston et al., 2011).

Startups and small businesses can now afford applications suchs ERP (Enterprise Resource Planning), CRM (Customer Relation-hip Management), SFA (Sales Force Automation) and SCM (Supplyhain Management) due to economical subscription fees (Krell,011). For example, by adopting Google Apps, a European smallusiness has saved 80,000 Euros annually (Payton, 2010). Reduc-ion of operating and maintenance costs and improvements infficiency by SMBs has been observed too (Li et al., 2011). Reduc-ion of data center costs has also been cited (Swartz, 2011). Hugepfront investments can be reduced especially for SMEs (Marstont al., 2011; Stoller, 2011). Immediate access to hardware resourcess available with no upfront capital investments resulting in fasterime to market, with IT becoming an operational expense (insteadf capital expense model) (Marston et al., 2011; Karadsheh, 2012).

For the price of one cup of coffee/tea, small firms can now getheir latest office applications from a reputed branded companyike Microsoft (US$ 6 per user per month for up to 50 users) (Blum,011a, 2011b). This is an affordable price for small business (NZ$.25 per user per month) (Kevany, 2011). On the other hand, Googlepps has small business pricing of US$ 5 per user per month orS$ 50 per user per year with no restrictions on number of users

Wenzel, 2011). Amazon (computing infrastructure provider) onlyas cost determining tools related to machine, storage and networksage (Truong & Dustdar, 2011). Adoption of IaaS reduces capitalxpenses and IT costs (Voith, Oberle, & Stein, 2012). Besides smallusiness, massive cost reduction for the public sector (healthcare,ducation) has been observed, such as the 20% cost reduction byhe Swedish Red Cross (Etro, 2011). Regarding cost effective scal-ng and elasticity, small businesses can move their components tohe cloud step by step instead of in one single go and growth in theloud happens at the pace of the business (Ankeny, 2011). Attrac-ion of on-demand processing power and storage (dynamic scaling)y CFOs is becoming a reality (Swartz, 2011). Elasticity in ramping

p (scalable infrastructure) and disposing of cloud capacity whenot needed, is extremely budget friendly (Durkee, 2010).

For risky business models, if the demand rises sharply athort notice, scalability of resources provided by cloud providers

ation Management 33 (2013) 861– 874

(operational excellence) becomes a huge competitive advantage(Mahesh et al., 2011). For example, a US startup has ramped upfrom 50 to 3500 Amazon cloud servers. Nowadays, adding comput-ing capacity has become as simple as adding building blocks to anexisting grid. Another example is Smugmug (online photo website)whose workload increases five times during December/January,and is well handled by cloud computing (Marston et al., 2011).

The hypotheses follow from the above discussion:

Hypothesis 1 (H1). Cost reduction (resulting from scalability ofresources and avoiding physical hardware setup) has a positive effectin terms of ease of use and convenience for SMEs.

Hypothesis 2 (H2). Cost reduction achieved using digital files anddocuments, which can be delivered online using cloud (compared tophysical assets) has a positive effect in terms of easier sharing andcollaboration.

Hypothesis 3 (H3). Cost reduction or cost saving achieved usingcloud (by paying only for what is needed and thereby avoiding upfrontcosts for various resources) has a positive effect on the SMEs usage andadoption of cloud computing.

2.2.2. Ease of use and convenienceSmall business employees often work outside the actual office

location and hence having easy access to their data (using theirmobile devices) is a big plus (Ankeny, 2011; Jain, 2011). This needfor employees to have access from remote locations as well as theincreasing number of online transactions necessitates a cloud com-puting solution (Mahesh et al., 2011). Accounting and finance workhas been outsourced to the cloud, leaving more time for small busi-nesses executives to spend on strategic work and initiatives (Krell,2011). Canadian SMEs are moving from PC-based accounting pack-ages to cloud based ones (Stoller, 2011). This avoids continuoushardware upgrades by a small business thereby preventing main-tenance woes for utilizing different machines (Mahesh et al., 2011).Accountants are using cloud technologies for their SMEs clientsfor a convenient monthly fee (Kevany, 2011). This ability helpsto manage SOX (Sarbanes-Oxley) and other regulatory changes inbilling in a flexible manner, as well as helps them manage theirrevenues better while using dynamic business models (Swartz,2011). Replacement of FTP (file transfer protocol) by uploads to acloud environment (like box.net) is easy (Devaki, 2011; Jain, 2011;McAfee, 2011). The Cloud approach helps eliminate administrativeoverhead and permits access from any geographical location, anydevice, and from any organization (McAfee, 2011). Less powerfuldevices (smartphones, netbooks) are able to make the most of thecompany’s backend IT systems via a simple web-based interfacelike AWS Management console (Marston et al., 2011).

These hypotheses follow from the above discussion:

Hypothesis 4 (H4). The ease of use and convenience in using thecloud infrastructure (via a simple and intuitive user-interface andround the clock accessibility) is positively related to improved sharingand collaboration.

Hypothesis 5 (H5). The ease of use and convenience in using cloudsolutions is positively related to SMEs use and adoption of cloud com-puting (as it makes them more productive and efficient).

2.2.3. ReliabilitySince the cloud is available round the clock, it is more reliable.

Employees can even call up the cloud center (if needed) insteadof depending on the in-house IT staff (Ankeny, 2011). Data redun-

dancy is built-in by cloud storage solutions so that the files arealways available, even during times of power failures, networkdowntime etc. (Devaki, 2011). This built-in redundancy helped Net-flix to stay afloat online, in spite of AWS failure in 2011 (McAfee,
Page 5: The usage and adoption of cloud computing by small and medium businesses

Inform

23On2pitisrwAelupev

Hobe

HpS

He

Hr

2

doa(rGt2Cisbeati2

Hta

2

ccg(e

P. Gupta et al. / International Journal of

011). Even in 2010, Gmail had an uptime of 99.984%, which is2 times more reliable than a typical widely used email system.n the contrary, for SMEs, the reliability of cloud services is defi-itely important but not as crucial as for large companies (Sultan,011). Sultan adds that portability of end-user data to another cloudrovider (in case of failure of the primary provider) is extremely

mportant. Lack of interoperability is an issue prevailing acrosshe cloud computing landscape (Rath, 2012). Also, reliability getsmpacted because of the downtime of various commercial cloudolutions like Salesforce.com, Amazon, Gmail and Google Docs,esulting in setting up of failsafe cloud systems. Efforts are under-ay by the FTC (Federal Trade Commission) and the Cloud Securitylliance to improve the reliability of these cloud providers (Mahesht al., 2011). Needed reliability level has to be observed inspite ofow prices of cloud services (Durkee, 2010). To provide 99.999%ptime, n + 1 redundancy is needed. He further states that quickhone support is needed under guaranteed SLAs by commercialnterprises. Providing automatic disaster recovery and back up pro-ides confidence.

The following hypotheses are based on the above discussion:

ypothesis 6 (H6). Better reliability or improvement in reliabilityf a cloud solution has a positive effect on cost cutting (as the smallusiness owners can convert their capital expenditures to operatingxpenditures).

ypothesis 7 (H7). A higher degree of reliability in using cloud isositively related to the ease of use and convenience proposition forMEs (as they can access their data anytime, anywhere via any device).

ypothesis 8 (H8). The reliability of cloud solutions has a positiveffect on sharing and collaboration by SMEs.

ypothesis 9 (H9). The reliability of cloud providers is positivelyelated to the usage and adoption of cloud by SMEs.

.2.4. Sharing and collaborationWith the proliferation of social media and smart phones (mobile

evices), startups and small companies have improved collab-ration within their companies (Krell, 2011). Cloud file storagellows various SMBs stakeholders to share information and datavia emails, shared web-links, IM-instant messengers), store andetrieve information with each other (Devaki, 2011; Jain, 2011).oogle Apps, box and Jive are very good examples of sharing con-

ent and collaboration among stakeholders (McAfee, 2011; Sultan,011). Large data are being shared and collaboration with otherSE (Computational Science and Engineering) research groups

s enabled (Truong & Dustdar, 2011). Exact same test scenariosetups can be easily reproduced using the cloud. Collaborationecomes easier with IMs (instant messaging) and video confer-ncing, enabled via the cloud (Payton, 2010). Document sharingnd editing of the same document by several people at the sameime (via Google Docs) and collaboration (via Skype, Google chat)s compelling for users to adopt cloud computing (Marston et al.,011).

Hypothesis 10 follows from the above discussion:

ypothesis 10 (H10). The need for sharing and collaboration inoday’s highly competitive world has a positive effect on using anddopting the cloud computing by SMEs.

.2.5. Security and privacyOrganizations talking about cloud security are actually more

oncerned about having their own control (something like a private

loud) than any other serious issue (Payton, 2010). Cloud security isood as risk gets minimized due to authentication and encryptionJain, 2011; Mahesh et al., 2011). Security is heightened by, forxample, monitoring activities, tracking transactions, providing

ation Management 33 (2013) 861– 874 865

selective access to users, and utilizing strong password. Sultanreported that 75% of the CIOs reporting in his study are concernedabout cloud security and argues that Google does not encrypt dataon their servers (Sultan, 2011). On the other hand, Sultan also statesthat 66% of USB drives are lost; hence the cloud is more secure.Installation of security patches can be avoided thereby savingdays and months. There may be some flexibility depending on thecloud solution chosen, for example, Google Apps allows certainusers to specify the location of data storage to meet the Federalguidelines (Mahesh et al., 2011). Enhanced security is possible dueto economies of scale as well as affordability of excellent securityexperts (Neves et al., 2011). Though data security is the main issuefor SMBs, they are still adopting public clouds because a publiccloud provides standard services at affordable cost (Li et al., 2011).

The availability of secure e-banking (online banking) function-ality is driving the growth of e-banking as it is very easy to usenow by typical consumers, enhancing their convenience of gettingtheir routine financial chores done from home rather than visitingan ATM (Featherman, Miyazaki, & Sprott, 2010; Jahangir & Begum,2007; Lallmahamood, 2007). Online shopping through internet isgaining attention due to high security and ease of use (Islam &Daud, 2011). For international travelers, due to their confidenceabout security, internet is extremely easy to use, while traveling todifferent countries, thereby improving their accessibility to enter-tainment needs (Ryan & Rao, 2008). The individual motivation forbringing your own device (BYOD) into the workplace is arising dueto privacy and data security. But, at the same time, it is enhancingthe convenience of accessing the office emails on their own devices,rather than using the officially provided cumbersome laptops. Thisincreases the performance of the employees (Chigona, Robertson,& Mimbi, 2012).

Security directly contributes to the reliability of the system. Areliable software system is a system with reliable security. Hence,designing a highly secure cloud system is very important (Burtescu,2010; Hanmer, McBride, & Mendiratta, 2007).

The following hypotheses are based on the above discussion:

Hypothesis 11 (H11). Security and Privacy improvements are pos-itively related to cost reduction or cost savings by SMEs.

Hypothesis 12 (H12). A better secured and privacy protected cloudsolution is positively related to the ease of use and provides enhancedconvenience to the SMEs (who can use this solution without worrying).

Hypothesis 13 (H13). Security and Privacy is positively related toa better and solid reliability of the cloud solution adopted by smallbusiness. The higher is the cloud security and privacy, the higher is theprobability of the cloud being reliable and available all the time.

Hypothesis 14 (H14). A secured and reliable cloud solution has apositive effect on sharing and collaboration efforts (by keeping themminimal, simpler as well as trustworthy for small business).

Hypothesis 15 (H15). The security and privacy is positively relatedto the usage and adoption of cloud by SMEs.

3. Methods

The secondary data had been obtained from the literature sur-vey, resulting in five core variables. A detailed discussion hasalready been captured in the literature survey section earlier. Sub-sequently, a questionnaire was used to collect the primary datafrom 211 respondents.

3.1. Data collection

A pilot survey, using personal interviews with 30 respondents,was conducted to obtain holistic feedback about SMEs/SMBs usage

Page 6: The usage and adoption of cloud computing by small and medium businesses

8 Information Management 33 (2013) 861– 874

abiqoTtqttciaqaaasdptit22tffataaarawbmdg

4

fsEfutpF

4

tsaswmams

Table 2Demographic characteristics of respondents.

Demographic characteristics of respondents

Survey participants (n = 211)

No. of employees5 or less 27 12.8%6 to 10 17 8.1%11 to 20 25 11.8%21 to 99 34 16.1%100 to 200 108 51.2%

No. of IT staffNo IT staff 42 19.9%1 to 2 38 18.0%3 to 5 31 14.7%6 and more 100 47.4%

Country in which business is registeredIndia 76 36.0%Singapore/Malaysia 111 52.6%USA 24 11.4%

Annual revenue (turnover)Less than USD 40,000 19 9.0%USD 40,000 – USD 1 million 38 18.0%USD 1 million – USD 2 million 20 9.5%USD 2 million – USD 8 million 13 6.2%More than USD 8 million 64 30.3%Confidential (cannot disclose) 34 16.1%Do not know 23 10.9%

Broadband (Internet) connectionYes 208 98.6%No 3 1.4%

Cloud computing layer usageIaaS 54 25.6%PaaS 13 6.1%SaaS 51 24.2%Do not know 93 44.1%

Payment modePay for each transaction 31 14.7%Pay for the time duration for which I am

using the cloud solution44 20.9%

Pay per user license 29 13.7%Pay a fixed amount by subscription

(monthly, yearly etc.)55 26.1%

Single one-time payment for unlimited users 23 10.9%Others (not applicable) 29 13.7%

66 P. Gupta et al. / International Journal of

nd adoption of cloud computing. This pilot survey was developedy framing relevant questions under each of the 5 core variables

dentified from the literature survey. The survey included bothualitative and quantitative questions for latent constructs. Basedn the feedback, the final survey questionnaire was formulated.his final survey was administered only to those small businesseshat were well aware of the cloud. A note at the beginning of theuestionnaire explained the purpose of this research and statedhat the confidentiality of the data would be maintained. The ques-ionnaire was divided into two parts. The first part of the surveyaptured influence, usage and adoption of cloud computing, assum-ng that the respondents do have the necessary awareness andcceptance of the cloud. For each latent construct, three to fiveuestions (indicators) were formulated capturing the usage anddoption by SMEs. All the reflective indicators were measured on

5-point Likert scale using scales from “Not at all (strongly dis-gree)” to “Very often (strongly agree)”. The second part of theurvey captured the demographic details of the respondents. Theata were collected through online survey (via Google Docs) andersonal interviews (using hardcopy prints). This final survey ques-ionnaire was sent to about 1100 participants (SMEs/SMBs) locatedn various countries primarily from APAC. Out of 1100 requests, aotal of about 230 responded back positively. After editing, only11 responses were found useful. The response rate was about0% and the participants were recruited from the SMEs/SMBs inhe APAC region. This survey was administered to them is dif-erent forms like email requests, hardcopy (paper-based) forms,ace-to-face interviews. There was no incomplete response becausell the Likert scale questions were mandatory but adding addi-ional comments was optional. Finally, there were 211 completend usable responses. Table 2 summarizes the demographic char-cteristics of the respondents. The respondents for this researchre SMEs/SMBs in developed countries as well as from Asia-Pacificegion. The responses were compared based on demographic vari-bles, including employees’ strength, IT staff strength, country inhich SMEs/SMBs are registered, annual turnover, availability of

roadband (Internet) connection, usage of specific cloud layers, andode of payment, to evaluate the response bias. For the 211 respon-

ents, the demographics characteristics are described as per Table 2iven below.

. Statistical techniques/tools for data analysis

SEM (Structural Equation(s) Modeling) is a statistical techniqueor simultaneously testing and estimating causal relation-hips among multiple independent and dependent constructs.xploratory factor analysis (EFA) using Smart PLS has been usedor the initial set of 30 respondents. EFA was used to uncover thenderlying structure of the five core variables. The assumption washat any independent factor could be more associated. There is norior theory in EFA. Later, for 211 respondents, CFA (Confirmatoryactor Analysis) has been used.

.1. Data analysis

PLS (Partial Least Square) technique has been used to validatehe measurements and to test hypotheses using SmartPLS 2.0M3oftware (Ringle, Wende, & Will, 2005). The PLS technique employs

component-based approach for model estimation and is bestuited for testing complex structural models. The PLS techniqueas selected because it does not impose any normality require-

ents on the data. A two-step approach has been used to first

ssess the quality of measures (as per this research study) using theeasurement model (outer model), and then to test the hypothe-

es using the structural model (inner model) (SEM stage, Joreskog

& Sorbom, 1993) as recommended by Segars and Grover (1993).SmartPLS 2.0 M3 software http://smartpls.com (Ringle et al., 2005)is used for path modeling with latent variables. The tool is usedto measure the validity and reliability of the constructs. BesidesPLS Algorithm, Bootstrapping is used with 211 cases (sample size)and 170 samples (resamples) to generate the standard error of theestimate and t-values. SmartPLS uses the PLS technique to simul-taneously examine theory and measures (Hulland, 1999).

5. Discussion, analysis and findings

An important conclusion of this research is that cost savings andcost reduction are not the most important factor for small business(SMEs or SMBs) to adopt cloud. ‘Ease of Use and convenience’ and‘Security and privacy’ are considered to be the top two priorities forthem to adopt cloud, followed by cost reduction or cost savings. Thisindicates that SMEs/SMBs are happy to adopt cloud due to its easyuse, convenience and better security and privacy besides reducingtheir investment.

A confirmatory factor analysis (CFA) has been conducted toassess reliability, convergent validity and discriminant validity of

the scales, as given below:
Page 7: The usage and adoption of cloud computing by small and medium businesses

P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874 867

Table 3Reliability Validation for Latent Constructs.

Overview Ave Compositereliability

Cronbach’s alpha R square LV indexvalues

Cost savings 0.633 0.873 0.808 0.316 3.793Ease of use 0.536 0.822 0.711 0.464 3.829Reliability 0.663 0.907 0.872 0.197 4.030

5

acsB&tNoTaa

5

uNc01eTm

ctc

5

wini(tc1ct0cssm

5

bs

Security and privacy 0.776 0.912

Sharing and collaboration 0.633 0.873

Usage and adoption of cloud computing 0.685 0.867

.1. Measurement validation and reliability

The reliability of these research measurements has been evalu-ted using Cronbach’s alpha and composite reliability scores. Theonstructs are considered adequate when the Cronbach’s alphacores are above the minimum recommended value of 0.6 (Hair,lack, Babin, & Anderson, 2010; Malhotra, 2010; Robinson, Shaver,

Wrightsman, 1991) and composite reliability scores are abovehe recommended cut-off of 0.7 (Gefen, Straub, & Boudreau, 2000;unnally, 1978). Composite reliability is considered a more rig-rous estimate for reliability (Chin & Gopal, 1995). As shown inable 3, the composite reliability scores exceed 0.8 and Cronbach’slpha values exceed 0.7. Thus the model can be considered as reli-ble.

.2. Convergent validity

For testing the convergent validity, each item’s loading on itsnderlying construct should be above 0.70 (Chin, Marcolin, &ewsted, 2003). Also, the average variance extracted (AVE) for eachonstruct should be above the minimum recommended value of.50 (Bagozzi & Yi, 1988; Dillon & Goldstein, 1984; Fornell & Larcker,981). As observed in Table 3, the AVE values are above 0.53. Also,ach item’s loading constructs are above 0.7, as shown in Table 4.hese two tests prove the convergent validity is satisfactory for theeasurement model.Also, as shown in Table 5 the item-to-construct correlation vs.

orrelations with other constructs, shows that the indicators arehe part of the highlighted constructs only and are not part of otheronstructs.

.3. Discriminant validity

Discriminant validity was investigated to indicate the extent tohich the measures in the model are different from other measures

n the same model. In the PLS context, the criterion for discrimi-ant validity is that a construct should share more variance with

ts measures than it shares with other constructs in the given modelHulland, 1999). The discriminant validity was examined by testinghe correlations between the measures of potentially overlappingonstructs and must be different from unity (Anderson & Gerbing,988). Also, as shown in Table 5, the correlation between any twoonstructs is greater than 0.7. The highest correlation between anywo constructs should have a minimum recommended value of.60. Next, as shown in Table 6, the square root of the AVE of eachonstruct is larger than all the cross-correlations between the con-truct and other constructs (Fornell & Larcker, 1981). These testsuggest that discriminant validity is satisfactory for the measure-ent model.

.4. Assessment of the structural model

Next, the hypotheses generated out of this research was testedy examining the structural model using SmartPLS software. Thetructural model includes estimating the path coefficients, which

0.856 0.000 3.6060.809 0.409 3.5900.768 0.480 3.679

indicates the strength of the relationships between the indepen-dent variables and dependent variable and R-square value (varianceexplained by the independent variables). A bootstrapping re-sampling procedure (Davison & Hinkley, 1997; Efron & Tibshirani,1993) of 170 samples was used to determine the significance levelof the paths defined within the structural model (Chatelin, Vinzi,& Tenenhaus, 2002; Chin & Gopal, 1995). Bootstrapping results ina larger sample which is claimed to model the unknown popula-tion (Henderson, 2005). The corresponding t-values show the levelof significance using the magnitude of the standardized parameterestimates between the constructs. A 5% significance level (p < 0.05)is used as a statistical decision criterion (Fisher, 1925; Cowles &Davis, 1982). The results of the structural model are summarizedin Table 7.

Out of the fifteen hypotheses, eleven are supported. The varianceexplained ranges from 0.19 to 0.48.

As observed, Hypothesis H1 is supported because the pathfrom cost reduction to ease of use and convenience is significant(b = 0.178, p < 0.01). This is further supported by the following quotefrom SMEs who took this survey, “Benefits of auto backup and savedand managed centrally”.

Hypothesis H2 is supported (b = 0.174, p < 0.01). This is becausethe greater is the cost reduction due to the usage of cloud tools; thebetter is the sharing and collaboration among stakeholders.

Hypothesis H3 is supported indicating cost reduction is oneof the primary reasons for SMEs to use and adopt (b = 0.194,p < 0.05) cloud. This is further supported by the following quotesfrom SMEs who took this research survey, “Hiring an employeewho can handle the IT matters and can also take up operationalresponsibility, so to best utilize the manpower.”, “Eliminationof data sharing overheads”, “Data storage and backup costs”,“Cut the costs on printed material, motivate employees andclients to be environmental friendly”, “Cost efficiency in hardwarecost”.

Hypothesis H4 (b = 0.274, p < 0.01) is supported because higherthe ease of use and convenience in using the cloud, the higher isthe sharing and collaborations aspects with the stakeholders.

Hypothesis H5 (b = 0.417, p < 0.01) is strongly supported becausethe ease of use and convenience (like easy access while on themove) achieved by SMEs while using the cloud is the primaryreason for driving them to use and adopt cloud. This is furthersupported by these quotes from SMEs, “Convenient when visitingclients and when we participate in various activities beyond thefour walls of the office”, “When a new process is adopted with lesscost in terms of time and money”, “For a new startup, the advantagewith cloud is greater”, “Independent of the office physical loca-tion for decentralized operations”, “Seamless data access”, “Contactthe employees anytime at anywhere or sharing in house knowl-edge on issues”, “Accessing the data or other documents from anymachine anywhere (cloud services are especially useful for techni-cal personnel in the field)”, “Data can be reviewed anytime and

anywhere as the cloud office has no office time. And you don’tneed assistant to take out the document files”, “Various online toolsto manage the cloud instances”, “Increase in efficiency, especiallywhen you are on the move”, “Everything is online and there is no
Page 8: The usage and adoption of cloud computing by small and medium businesses

868 P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874

Table 4Item loading for indicators of latent constructs.

Construct Item definition Loadings Ave Compositereliability

Cronbach’salpha

R square

Usage AND adoption of cloud computing 2. Its ease of use 0.776 0.685 0.867 0.768 0.4803. Its reliability 0.88265. Security and privacy 0.8203

Cost savings 8. Reduction of the operating costs ofmy organization by using cloudcomputing tools and techniques

0.8032 0.633 0.873 0.808 0.316

9. Using cloud infrastructure instead ofbuying and deploying physicalmachines and software

0.8692

10. Elimination of hiring expensive ITexpertise in-house

0.7281

11. Improvement of the scalability of ITinfrastructure (ramp up or ramp downat will)

0.7749

Ease of use 12. Negligible learning time for allemployees

0.7269 0.536 0.822 0.711 0.464

13. The ability to use and access cloudtools as well as my data anywhere

0.71

14. Increased focus of our energy andtime on other more critical issues

0.7528

15. Good internet connection speed ofcloud services

0.7378

Reliability 17. Provision of excellent ‘backup’ formy organization’s data againsthard-disk crash

0.8582 0.663 0.907 0.872 0.197

18. Better and reliable ‘storage’solution for my office data instead ofthumb drive (USB) or portable harddisk

0.7722

19. Provision of excellent disasterrecovery (in-case of an unforeseenevent) with uninterrupted access

0.86

20. The ability of the cloud computingservice provider to backup my officedata safely even if it gets corrupted dueto spam/malware

0.8276

21. High uptime and availability of thecloud services round the clock24x7x365

0.7459

Sharing and collaboration 22. Sharing my work (company data orfiles) with other supply chain partners(like customers

0.7677 0.633 0.873 0.809 0.409

23. Usage of the same set of data ordocuments with other partners

0.8114

24. Cutting business travel (bothdomestic and international) due toeasy sharing

0.7702

25. Easy tracking 0.832

Security and privacy 26. No loss or manipulation of mycompany’s data by online criminals orpredators

0.902 0.776 0.912 0.856 0.000

27. Non-usage of my official data fortheir own commercial benefits by

0.8649

ntoscv

io

rc

cloud providers28. Better security

eed to maintain multiple database”, “Data backup”, “Synchroniza-ion/compatibility of the hardware with my systems”, “Integrationf information is smooth”, “Lack of need to have a dedicated supporttaff”, “Functionality or diversity of features”, “It’s effectiveness toonnect and contact employees and clients”, “Globally accessibleia any device”.

Hypothesis H6 (b = 0.572, p < 0.01) is supported becausemprovements in reliability of cloud would increase the confidence

f SMEs to move to cloud resulting in obvious cost savings.

Hypothesis H7 (b = 0.473, p < 0.01) is supported because bettereliability in cloud usage improves the ease of use and is highlyonvenient for SMEs, who are always hard-pressed for managing

0.8748

their time and can now access their business data from anywhere,anytime.

Hypothesis H8 (b = 0.146, p > 0.1) is not supported because cur-rently the SMEs do not find the cloud as reliable as it should be,hence these SMEs are not willing to share and collaborate usingcloud.

Hypothesis H9 (b = 0.025, p > 0.1) is not supported from reli-ability to usage and adoption of cloud because SMEs perception

about cloud reliability is extremely low resulting in non-usage andnon-adoption of cloud. This is further supported by these quotesfrom SMEs, “I would prefer to have a backup of the data in myown server”, “Bandwidth: see how much longer now Google takes
Page 9: The usage and adoption of cloud computing by small and medium businesses

P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874 869

Table 5Item-to-construct correlation vs. correlations with other constructs.

Construct Item definition Costreduction

Ease ofuse

Reliability Sharingand collab-oration

Security &Privacy

Usage &Adoption ofCloudComputing

Cost reduction 8. Reduction of the operating costs ofmy organization by using cloudcomputing tools and techniques

0.8032 0.4605 0.5361 0.4345 0.2435 0.4177

9. Using cloud infrastructure instead ofbuying and deploying physicalmachines and software

0.8692 0.3987 0.48 0.317 0.1575 0.3773

10. Elimination of hiring expensive ITexpertise in-house

0.7281 0.3568 0.3871 0.3505 0.2005 0.3005

11. Improvement of the scalability of ITinfrastructure (ramp up or ramp downat will)

0.7749 0.2818 0.3397 0.2719 0.1059 0.3083

Ease of use 12. Negligible learning time for allemployees

0.2666 0.7269 0.3914 0.3832 0.372 0.4799

13. The ability to use and access cloudtools as well as my data anywhere

0.4892 0.71 0.5387 0.4033 0.2099 0.358

14. Increased focus of our energy andtime on other more critical issues

0.4225 0.7528 0.5234 0.4237 0.251 0.4251

15. Good internet connection speed ofcloud services

0.2366 0.7378 0.4434 0.3998 0.3987 0.5397

Reliability 17. Provision of excellent ‘backup’ formy organization’s data againsthard-disk crash

0.495 0.5733 0.8582 0.4247 0.3513 0.4301

18. Better and reliable ‘storage’solution for my office data instead ofthumb drive (USB) or portable harddisk

0.4676 0.4973 0.7722 0.3814 0.3129 0.4244

19. Provision of excellent disasterrecovery (in-case of an unforeseenevent) with uninterrupted access

0.4531 0.5526 0.86 0.4404 0.4382 0.431

20. The ability of the cloud computingservice provider to backup my officedata safely even if it gets corrupted dueto spam/malware

0.4534 0.5374 0.8276 0.4903 0.42 0.4062

21. High uptime and availability of thecloud services round the clock24x7x365

0.4152 0.4721 0.7459 0.391 0.2673 0.3497

Sharing and collaboration 22. Sharing my work (company data orfiles) with other supply chain partners(like customers

0.2799 0.4309 0.3779 0.7677 0.2401 0.2329

23. Usage of the same set of data ordocuments with other partners

0.3371 0.4363 0.392 0.8114 0.2926 0.2955

24. Cutting business travel (bothdomestic and international) due toeasy sharing

0.3176 0.3969 0.3629 0.7702 0.366 0.2931

25. Easy tracking 0.4381 0.4796 0.5079 0.832 0.49 0.4107

Security and privacy 26. No loss or manipulation of mycompany’s data by online criminals orpredators

0.1537 0.3913 0.4086 0.3911 0.902 0.411

27. Non-usage of my official data fortheir own commercial benefits bycloud providers

0.2075 0.3383 0.3364 0.3775 0.8649 0.3993

28. Better security 0.2429 0.3795 0.4207 0.4224 0.8748 0.4992

Usage and adoption of cloudcomputing

2. Its ease of use 0.363 0.5531 0.3781 0.3376 0.2978 0.7763. Its reliability 0.4056 0.549 0.4698 0.2905 0.4088 0.88265. Security and privacy 0.3444 0.4286 0.3954 0.3652 0.5303 0.8203

Note: The highlighted boldface numbers are the item loadings on the constructs.

Table 6Reliability and inter-construct correlations for reflective scales.

LV construct Costsavings

Ease ofuse

Reliability Security andprivacy

Sharing andcollaboration

Usage and adoption ofcloud computing

Cost savings 0.795Ease of use 0.482 0.732Reliability 0.562 0.648 0.814Security and privacy 0.230 0.421 0.444 0.881Sharing and collaboration 0.441 0.550 0.524 0.452 0.796Usage and adoption of cloud computing 0.449 0.617 0.503 0.499 0.398 0.827

Note: Value on the diagonal is the square root of AVE.

Page 10: The usage and adoption of cloud computing by small and medium businesses

870 P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874

Table 7Summary of hypotheses tests (path coefficients and hypotheses testing).

Significance values p < 0.1 1.652p < 0.05 1.971p < 0.01 2.599

Hypothesis No. Hypothesis (direction) Path coefficient T-value Significance (one-tailed) Supported?

H1 Cost reduction → ease of use 0.178 2.764 p < 0.01 YesH2 Cost reduction → sharing and collaboration 0.174 2.974 p < 0.01 YesH3 Cost reduction → usage and adoption of CC 0.194 2.476 p < 0.05 YesH4 Ease of use → sharing and collaboration 0.274 3.082 p < 0.01 YesH5 Ease of use → usage and adoption of CC 0.417 5.135 p < 0.01 YesH6 Reliability → cost reduction 0.572 8.848 p < 0.01 YesH7 Reliability → ease of use 0.473 6.005 p < 0.01 YesH8 Reliability → sharing and collaboration 0.146 1.458 n.s. NoH9 Reliability → usage and adoption of CC 0.025 0.326 n.s. NoH10 Sharing and collaboration → usage and adoption of C -C0.064 0.917 n.s. NoH11 Security and privacy → cost reduction −0.024 0.457 n.s. NoH12 Security and privacy → ease of use 0.170 2.303 p < 0.05 YesH13 Security and privacy → reliability 0.444 6.128 p < 0.01 Yes

twipcluiii

ifmiSetra

StTSis

ttp

ac

ius

boTniP

H14 Security and privacy → sharing and collaboration

H15 Security and privacy → usage and adoption of CC

o load”, “High availability is still a question”, “In one scenario,e dropped the cloud computing due to bandwidth unavailabil-

ty”, “Business continuity and availability of the right software,ackaged or otherwise on the cloud”, “Downtime and SLAs on theloud”, “All SaaS are focusing on fat bandwidth nations, huge prob-em from developing world locations”, “Testimonials from othersers is expected”, “It is more time consuming”, “Bandwidth hogs,

t does not work in Africa”. In other words, a reliable cloud providermproves the chances of small business moving to, using and adopt-ng cloud computing.

Hypothesis H10 (b = −0.064, p > 0.1) is not supported from shar-ng and collaboration to usage and adoption of cloud because of theollowing quotes from SMEs, “Ability for version control for docu-

ents on the cloud is needed”, “IP protection, correct segregation ofnformation from different organizations using the same CSP (Cloudervice Provider) are also important considerations”, “Not ready toxplore geographical independence access at this point in time dueo data protection and confidentiality issues”. So, SMEs are deter-ent to share and collaborate via cloud unless their above concernsre addressed, as indicated by the negative path coefficient too.

Hypothesis H11 (b = −0.024, p > 0.1) is not supported becauseMEs are aware of the face that the higher the security and privacyhey expect from the cloud, the higher is the price they have to pay.his is also indicated by the negative path coefficient depicting thatMEs are fine to adopt cloud, even if it does not provide the bestn class level of security and privacy, compensated by higher costavings.

Hypothesis H12 (b = 0.170, p < 0.05) is supported because a bet-er secured and privacy controlled cloud environment enhanceshe ease of use and convenience for SMEs, as they could be moreroductive, free from worry and more efficient now.

Hypothesis H13 (b = 0.444, p < 0.01) is strongly supported since secured and privacy friendly cloud increase the reliability of theloud multiple times.

Hypothesis H14 (b = 0.232, p < 0.01) is supported because anmprovement in security and privacy of the cloud solutions buildsp the trust and strong faith in the minds of SMEs so that they canhare and collaborate more with their stakeholders.

Hypothesis H15 (b = 0.297, p < 0.01) is strongly supportedecause the higher and better the security and privacy regulationsf the cloud, the higher is the usage and adoption of the cloud.

his is also as indicated by the following quotes of SMEs, “Data isot corrupted and adequate backup is created”, “Cloud security

s a joke right now”, “Customizable access control”, “Security andrivacy is still a grave concern of cloud computing”, “Don’t forget,

0.232 2.802 p < 0.01 Yes0.297 4.045 p < 0.01 Yes

my hard-disk won’t be sold to marketers or FBI/CIA etc.”, “Stillclients are not confident if their data is really secured”, “Cloudcomputing is proved to be a next level in computing, but thesecurity and privacy are the concerns for now.”, “Security andwill the cloud provider stick to the SLAs on downtime so thatbusiness is not affected?”, “Reason not to adopt cloud computingis user is unsure about the security of clients data which cannotbe breached”, “I look for the best security provided so that mydata does not leak to unauthorized user”. As of today, SMEs arequite satisfied with the security and privacy of the cloud resultingin faster adoption of cloud. Inspite of the above comments, thisresearch study reveals that SMEs are very much satisfied withthe existing security and privacy provided by the cloud and hencehave accepted and adopted cloud to a larger extent. This is verymuch in contrast to the general belief in the industry about cloudsecurity concerns, which is mostly shared by large enterprises.

The following figures exhibit the findings using PLS structuralmodeling (Figs. 2–4):

5.5. Assessment of fit

The goodness-of-fit (GoF) measure has been conducted forassessment of this research PLS path modeling (Amato, EspositoVinzi, & Tenenhaus, 2004). GoF is suggested as a global fit measurefor PLS path modeling (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005).GoF (0 < GoF < 1) is defined as the geometric mean of the averagecommunality/AVE and average R2 (for endogenous constructs).

GoF =√

AVE ∗ R2

Following the guidelines of Wetzels, Odekerken-Schröder, and vanOppen (2009), the GoF value has been calculated, which validatesthe PLS model of this research study. The GoF value for this researchmodel is 0.450 (geometric mean of average communality/AVE was0.650 and average of R2 was 0.311). The GoF value for the modelexceeds the minimum cut-off value of 0.36 for large effect sizesof R2. The GoF value provides adequate support to validate thePLS model (Wetzels et al., 2009). The baseline values for validat-ing the PLS model globally are GoFsmall = 0.1, GoFmedium = 0.25and GoFlarge = 0.36 (Akter, D’Ambra, & Ray, 2011).

6. Implications for the industry

This study focused on the core variables in detail. Contrary tothe generic belief, cost reduction (or cost savings) is not the top

Page 11: The usage and adoption of cloud computing by small and medium businesses

P. Gupta et al. / International Journal of Information Management 33 (2013) 861– 874 871

Fig. 2. Results of PLS structural model analysis (SmartPLS snapshot).

Fig. 3. The stars represent those four hypotheses which are not supported.

Fig. 4. Results of PLS structural model analysis Note: Sig

nificant relation (→), Insignificant relation ( ).
Page 12: The usage and adoption of cloud computing by small and medium businesses

8 Inform

ttm(asrubsfSsrThcfSiuhdcmRssiapop

7

cpa(ecSydmiTlppSi

eeposFiSts

72 P. Gupta et al. / International Journal of

wo factors for SMEs to move to cloud. However, it is definitely thehird crucial factor forcing the move to cloud by SMEs. The other

ajor findings of this study are that for SMEs, security and privacysecond major factor) of the existing cloud solutions is acceptablend they are more than willing to move to cloud. This is a muchtronger proposition for SMEs to move to cloud compared to costeduction (third major factor). The ease of use and convenience insing the cloud scores the topmost slot. This is primarily fueledy the exponential growth of tablets and smartphones. The cloudervice providers should focus on improving the reliability (fourthactor) of the cloud, which would expedite the cloud adoption byMEs. A better reliable cloud would also increase the chances ofharing and collaboration (last fifth factor which has a negativeelationship with cloud adoption) among the stakeholders by SMEs.he industry players providing cloud computing should focus onigh uptime and availability of cloud, make the cloud the defaulthoice to SMEs for storage and backup as well as for SMEs to lookorward to, during times of disaster. It is more about providingMEs worry-free days and nights that their business data is keptn a highly reliable place accessible at any time. The forthcomingsage and adoption of cloud by SMEs is very much dependent onow the cloud providers are able to build the trust, faith, confi-ence and reliability of their cloud services for SMEs to positivelyontribute toward sharing and collaboration via cloud tools. A lotore emphasis is needed on this aspect by the industry players.

egarding mode of payment, SMEs are most comfortable paying asubscription fees or for the time duration during which the cloudervices are being used for. Making a one-time lump sum payments not favored by SMEs. About 25% of the micro & SMEs using IaaSs the cloud layer indicate use of the bare metal and computingower (available at an affordable price) as well as the availabilityf IT-savvy developers with the right skillsets to harness this rawower themselves.

. Implications for research

The findings of this research paper are multifold. Firstly, it indi-ates few variables (ease of use and convenience, security andrivacy, cost reduction) which are intuitively in favor of SMEs usingnd adopting cloud. Secondly, this research indicates one variablereliability) that needs immediate attention by the industry lead-rs. This is like a catalyst for the cloud providers, which if improved,an result in immediate usage and faster adoption of cloud byMEs. This is further supported by the fact that in the next fewears, European Union is proposing data protection regulations forata processing, cloud computing service provider security require-ents and mandatory notifications of data breaches. This would

mprove the reliability of cloud services for SMEs (Tarzey, 2012).hirdly, this research indicates a specific variable (sharing and col-aboration) which is counter-intuitive to the generic understandingrevalent in the market today. Lastly but not the least, this researchoints to several other new variables which are also promptingMEs to use and adopt cloud besides the core variables discussedn this paper.

This research proves that various other inter-relationships doxist between the five core variables, which are significant, asxplained by the various hypotheses above. So, even though therimary latent variable relationship with usage and adoptionf cloud is insignificant but that same latent variable has verytrong relationship and significance with other latent variables.or example, the ease of use and convenience in using cloud

s positively related to the sharing and collaboration with otherMEs, even though sharing and collaboration is not the main fac-or resulting in usage and adoption of cloud, as per this researchtudy.

ation Management 33 (2013) 861– 874

8. Limitations and scope for further research

This research has been primarily conducted in Singapore,Malaysia and India and may not be representative of the entireAPAC (Asia Pacific) region. This research is limited to in-depth studyof only five (core) latent variables, supported by the existing liter-ature survey.

During the survey, other variables and observations are men-tioned by SMEs to use and adopt cloud at the present time but theyhave not been covered in this study. These other factors are, Inter-nal testing being done using cloud by SMEs before releasing theirsolution to their customers; Trial-run (alpha and beta releases) oftheir solutions or services deployed in the cloud, being used bytheir customers; Duration of cloud implementation; Smooth inte-gration of information and integration with other services shouldbe simpler; Use of latest original software; Data synchronization;Attracting talent – top developers want to work in the cloud today;Tools on the cloud are usually general and therefore, customizingthem to suit the organization’s needs is a problem (especially truefor CAD editing applications); For generic tools, usage of cloud isacceptable. As for specific requirements, customization may not bepossible and therefore, difficult to be relied on; (1). Business shouldrun unhindered also on the cloud as it would run if the software istotally in control of in-house IT. (2). Are enough business readysoftware based packages available on the cloud? For example, saySAP and its modules available on the cloud? Are CAD/CAM/PLM(Computer Aided Design/Computer Aided Manufacturing/ProductLifecycle Management) packages available on the cloud? Will theperformance be just as good? If any software crashes on the cloudhow do we recover and restart the application? (3). Are enoughintegration methods available on the cloud? For example, integra-tion buses like TIBCO, MQ should also be available on the cloud.Are all BI (business intelligence) packages available on the cloud?;Conflict resolution, every country wanted to host servers to gainthe latency advantage. Also, at times, IT does not want to go extramiles to host servers as they think they will have to bear the angerof business if things are not working, internal organization politicsetc.

The above details signify that there is tremendous scope for fur-ther research in this area which includes further investigation intothese new variables.

9. Conclusions

Cloud computing is definitely making waves with micro as wellas SMBs or SMEs and is slowly creeping into their business strategyformulation and implementation now and in the near future. SMBsor SMEs are not hesitant to incorporate cloud into their businessstrategy inspite of the few concerns being cited by industry pun-dits. On similar lines, Desmond (2012) cites that ‘cloud is reallyjust for SMBs’ is a myth. The perceptions of the small business(SMEs or SMBs) in different geographies are different. For exam-ple, European businesses have different behavior compared to USbusinesses as well as Asian businesses. As per TCS (Tata Consul-tancy Services, India) cloud study, USA and Europe lag behind therest of the world in cloud computing adoption. On the other hand,Latin American and Asia-Pacific companies are the most aggres-sive adopters of cloud computing. As per this research study, theease of use and convenience is the biggest factor cited by SMEs toadopt cloud. The second factor to use and adopt cloud is improvedsecurity and privacy. The third factor for the usage and adoption of

cloud is the cost reduction. This means that SMEs or SMBs find thecloud easy to use, convenient, adequately secured for their busi-ness, their business privacy is well protected and lastly but notthe least is that the cloud helps SMEs to bring down their cost in
Page 13: The usage and adoption of cloud computing by small and medium businesses

Inform

atRvw(r‘prV(aS(tcttfscdrKotvbcffpoP

bslcBeCapvticmli

ppaSs

pS

R

A

P. Gupta et al. / International Journal of

significant way. This observation is supported by and builds onhe arguments of Jain (2011), Mahesh et al. (2011), Krell (2011),obuck (2011) and Murphy (2012) stating ease of use and con-enience. Regarding security and privacy, this observation agreesith Sultan (2011), Blum (2011a, 2011b), Wenzel (2011), Bennett

2012) and Marks (2012) but is in contrast to the generic IaaS secu-ity risks explored by Karadsheh (2012). As per Desmond (2012),cloud services aren’t secure’ is a myth. This has been quantitativelyroved and supported by this research paper too. Regarding costeduction, it is in line with reduction of operational expenses byoith et al. (2012), Sultan (2011), Mahesh et al. (2011), Harnish

2011), Devaki (2011), Kevany (2011), Wang (2011), Shivakumarnd Raju (2010), Narayanan (2010), Rash (2011), Ohlhorst (2012),avitz and Vogels (2012), Hawser (2009), Coughlin (2011), Lamont2011) and Kuhl (2012) etc. but is in contrast with the observa-ions shared by Marks (2012). According to Marks, in 2012, theloud is not yet a viable option for most small businesses becausehe rent is still very high but might become affordable in the nexthree to five years. The fourth factor, reliability is not an importantactor for SMEs to adopt and use cloud because SMEs do not con-ider cloud as reliable as it should be for their business. SMEs areoncerned about cloud downtime and rely more on their physicalevices within their physical proximity for backup, storage etc. Thiseliability factor is in-line with Mahesh et al. (2011), Sultan (2011),evany (2011), Blum (2011b), Durkee (2010), Kelly (2011). AWSutage for four days (due to human error) in April, 2011 leadingo 0.07% data getting lost permanently is in synch with this obser-ation (Butler, 2012). But, this is also against the thoughts sharedy Rash (2011). The fifth and the last negative factor is sharing andollaboration which indicates that SMEs who have a higher needor sharing and collaboration do not go for cloud, instead they pre-er face to face meetings, phone calls, business travel, possessinghysical devices etc. for their business needs. This is against thebservations of Creeger (2009), Li et al. (2011), Wenzel (2011) andortsmouth (2010).

Few other positive factors cited for using and adopting cloudy SMEs/SMBs are: Easy to use, cost efficient, no physical officepace is needed, no need to carry storage devices; Cloud is an excel-ent choice for small applications; Scalability of service and fasterontent delivery; Crowd sourcing and multiple revenue models;randing effort to keep up with technology with intangible ben-fits such as confidence and trust from investors and customers;loud is clearly the technology of the future; the faster we adaptnd accept this the better positioned we are; Lesser trained man-ower and hassle free; Scalability and reliability; Cheap, easy accessia any OS (operating system); any machine, any continent; Timeo deploy solution and conflict resolutions when you are presentn multiple countries; Piloting is essential for gaining customeronfidence; Ability of the cloud to achieve clients trust and commit-ent by proving the effectiveness of the company; Cloud provides

ean startup principles; Low licensing fee and payments be maden installments – which plays very critical for a small IT company.

The actionable items for a manager for adopting cloud com-uting is to make the best use of cloud computing as it is beingrovided by various cloud vendors (both local and global) in a reli-ble and affordable way. Especially with micro & SMEs based iningapore, where broadband connectivity well-established, acces-ing the cloud should become second nature for these managers.

Based on this research we foresee the adoption of cloud com-uting to grow exponentially and provide huge benefits to micro &MEs in the days to come.

eferences

kter, S., D’Ambra, J., & Ray, P. (2011). Trustworthiness in mHealth Information Ser-vices: An Assessment of a Hierarchical Model with Mediating and Moderating

ation Management 33 (2013) 861– 874 873

Effects Using Partial Least Squares (PLS). Journal of The American Society for Infor-mation Science and Technology, 62(1), 100–116.

Amato, S., Esposito Vinzi, V., & Tenenhaus, M. (2004, March). A global goodness-of-fitindex for PLS structural equation modeling. France: HEC School of Management(Oral Communication to PLS Club)

Anderson, J. C., & Gerbing, S. W. (1988). Structural equation modeling in practice:A review and recommended two-step approach. Psychological Bulletin, 103(3),411–423.

Ankeny, J. (2011, March). Heads in the cloud. Entrepreneur, 39(10), 50–51.Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal

of the Academy of Marketing Science, 16(1), 74–94.Bennett, B. (2012, May). Protecting Your Information Assets. NZ Business, 26(4),

38–41.Bernroider, E. (2002, October). Factors in SWOT analysis applied to micro, small-

to-medium, and large software enterprises: An Austrian Study. EuropeanManagement Journal, 20(5), 562–573.

Blum, J. (2011, September). All around the cloud. Entrepreneur, 39(9), 54.Blum, J. (2011, December). Backup plans. Entrepreneur, 39(12), 46–48, 1p.Burtescu, E. (2010). Reliability and security – convergence or divergence. Informatica

Economica, 14(4), 68–77.Butler, B. (2012, April 27). www.cio.com/article/705193/Amazon Outage One Year

Later Are We Safer ?taxonomyId=3089Chatelin, Y. M., Vinzi, V. E., & Tenenhaus, M. (2002). State-of-art on PLS modeling

through the available software. Jouy-en-Josas: HEC Business School.Chigona, W., Robertson, B., & Mimbi, L. (2012, June). Synchronised smart phones:

The collision of personal privacy and organisational data security. South AfricanJournal of Business Management, 43(2), 31–40.

Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: Relative importance ofbeliefs. DATA BASE, 26(2/3), 42–64.

Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latentvariable modeling approach for measuring interaction effects: Results from aMonte Carlo simulation study and voice mail emotion/adoption study. Informa-tion Systems Research, 14(2), 189–217.

Clark, L. (2009). SMEs can benefit most from the cloud. Computer Weekly, 19.Coughlin, T. (2011, May). A ‘cloudy’ future. Computer Graphics World, 34(4), 22–23.Cowles, M., & Davis, C. (1982, May). On the origins of the.05 level of statistical

significance. American Psychologist, 37(5), 553–558.Creeger, M. (2009, August). CTO roundtable: Cloud computing. Communications of

the ACM, 52(8), 50–56.Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application.

Cambridge: Cambridge University Press.Desmond, P. (2012, February). www.nttcom.tv/2012/02/16/jeff-kaplan-founder-and

-managing-director-of-thinkstrategies/Devaki, S. (2011, August). File storage trends in cloud computing era. Siliconindia,

14(8), 34–35.Dillon, W. R., & Goldstein, M. (1984). Multivariate analysis: Methods and applications.

New York: Wiley.Durkee, D. (2010, May). Why cloud computing will never be free. Communications

of the ACM, 53(5), 62–69.Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York:

Chapman Hall.Etro, F. (2011, May). The economics of cloud computing. IUP Journal of Managerial

Economics, 9(2), 7–22.Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy

risk to facilitate e-service adoption: The influence of perceived ease of use andcorporate credibility. Journal of Services Marketing, 24(3), 219–229.

Ferguson, S. (2008). Rising above the din. eWeek, 25(17), 29.Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh: Oliver and

Boyd.Fornell, C., & Larcker, D. F. (1981). Evaluating structural equitation models with

unobservable variables and measurement errors. Journal of Marketing Research,18, 39–50.

Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling tech-niques and regression: Guidelines for research practice. Communications of theAssociation for Information Systems, 7(7), 1–78.

Grant, I. (2009). [Untitled]. Computer Weekly, 18.Grant, I. (2011). [Untitled]. Computer Weekly, (13), 42.Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis.

A global perspective. Upper Saddle River: Pearson.Hanmer, R. S., McBride, D. T., & Mendiratta, V. B. (2007). Comparing reliability and

security: Concepts, requirements, and techniques. Bell Labs Technical Journal,12((3) Fall), 65–78.

Harnish, V. (2011). Five creative money-saving strategies. Fortune, 164(5), 54.Hawser, A. (2009, December). Cloud control: Businesses looking for cost effective

data and IT infrastructure solutions are increasingly finding the answer is in thecloud. Global Finance, 23(11), 59–61.

Henderson, A. R. (2005). The bootstrap: A technique for data-driven statistics. Usingcomputer-intensive analyses to explore experimental data. Clinica Chimica Acta,359, 1–26.

Hulland, J. (1999). Use of partial least squares (PLS), Strategic management research:A review of four recent studies. Strategic Management Journal, 20(4), 195–204.

Islam, Md., & Daud, A. Ku. A. (2011, February). Factors that influence customers’buying intention on shopping online. International Journal of Marketing Studies,3(1), 128–139.

Jahangir, N., & Begum, N. (2007, December). Effect of perceived usefulness,ease of use, security and privacy on customer attitude and adaptation

Page 14: The usage and adoption of cloud computing by small and medium businesses

8 Inform

J

JK

KKK

K

KKL

L

L

L

M

M

MM

M

M

MN

N

NOO

PP

R

and Emerald.

John Rudolph Raj is a lecturer at the Faculty of Management, Multimedia Univer-

74 P. Gupta et al. / International Journal of

in the context of E-banking. Journal of Management Research, 7(3), 147–157.

ain, V. (2011, October). How the cloud resonates with business today. Siliconindia,14(10), 22–23.

oreskog, K., & Sorbom, D. (1993). LISREL. Chicago, IL: VIII Scientific Software.aradsheh, L. (2012, May). Applying security policies and service level agreement

to IaaS service model to enhance security and transition. Computers & Security,31(3), 315–326.

elly, L. (2011). The security threats facing SMEs. Computer Weekly, 11–12.evany, K. (2011, September). Cloud cover. NZ Business, 25(8), 56–59.ing, R. (2008). Cloud computing: Small companies take flight. BusinessWeek Online,

4.lie, L. (2011, December). SMB hosted CRM market set to triple by 2015. CRM Mag-

azine, 15(12), 16.rell, E. (2011). The state of small business. Baylor Business Review, 30((1) Fall), 4–9.uhl, C. (2012, January). Cable’s SMB forecast: Cloudy and bright. CED, 38(1), 27–28.allmahamood, M. (2007, December). An examination of individual’s perceived

security and privacy of the Internet in Malaysia and the influence of this on theirintention to use E-commerce: Using an extension of the technology acceptancemodel. Journal of Internet Banking & Commerce, 12(3), 1–26.

amont, J. (2011, January). Cloud computing: It can work for you. KM World, 20(1),12–13.

i, Q., Wang, C., Wu, J., Li, J., & Wang, Z.-Y. (2011, November). Towards thebusiness-information technology alignment in cloud computing environment:An approach based on collaboration points and agents. International Journal ofComputer Integrated Manufacturing, 24(11), 1038–1057.

ow, L. (2005, February). Entrepreneurship development in Ireland and Singapore.Journal of the Asia Pacific Economy, 10(1), 116–138.

arks, G. (2012, March). For small business, the rent in the cloud is still too d*@nhigh!. Accounting Today, 26(3), 30–31.

ahesh, S., Landry, B. J. L., Sridhar, T., & Walsh, K. R. (2011, July–September). Adecision table for the cloud computing decision in small business. InformationResources Management Journal, 24(3), 9–25.

alhotra, N. (2010). Marketing research: An applied orientation. Boston: Pearson.arston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011, April). Cloud

computing—The business perspective. Decision Support Systems, 51(1), 176–189.artin, J. A. (2010, April). Should You move your business to the cloud? PC World,

28(4), 29–30.cAfee, A. (2011, November). What every CEO needs to know about the cloud.

Harvard Business Review, 89(11), 124–132.urphy, R. M. (2012). How intuit rules. Fortune, 165(5), 22.arayanan, C. V. (2010, July). Testing, the ‘cloud’ testing, the way. Siliconindia, 13(7),

36–38.eves, F. T., Marta, F. C., Correia, A. M. R., & de Castro, N. (2011). The adoption of

cloud computing by SMEs: Identifying and coping with external factors. In Paperpresented at 11a Conferência da Associac ão Portuguesa de Sistemas de Informac ão(CAPSI 2011) – A Gestão de Informac ão na era da Cloud Computing, Lisboa, ISEG/IUL-ISCTE/ 19–21 October, 2011,

unnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.hlhorst, F. J. (2012). 2012: A cloudy year for big data. eWeek, 29(1), 7.jala, A., & Tyrvainen, P. (2011, July). Developing cloud business models: A case

study on cloud gaming. IEEE Software, 28(4), 42–47.

ayton, S. (2010). Fluffy logic. Financial Management, 22–25 (14719185).ortsmouth, I. (2010, November). Perpetual obstacles, unlimited opportunity. Profit,

29(5), 25–32.ash, W. (2011). 1&1 Internet builds green data center for cloud services. eWeek,

28(19), 24–25, 2p.

ation Management 33 (2013) 861– 874

Rath, A. (2012). Cloud computing: Facing the reality. Bhubaneswar (India): Batoi.Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3 (beta). University of

Hamburg. www.smartpls.deRobinson, J. P., Shaver, P. R., & Wrightsman, L. S. (1991). Criteria for scale selection

and evaluation. Measures of personality social psychology attitudes. San Diego:Academic Press.

Robuck, M. (2011, June). Forecast sunny for cloud-based commercial services. CED,37(5), 16–20.

Ryan, C., & Rao, U. (2008, July/August). Holiday users of the Internet—ease ofuse, functionality and novelty. International Journal of Tourism Research, 10(4),329–339.

Savitz, E., & Vogels, W. (2012). How the cloud changes businesses big and small.Forbes.com, 14.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness:A confirmatory factor analysis. MIS Quarterly, 17(4), 517–529.

Shivakumar, B. L., & Raju, T. (2010). Emerging role of cloud computing in redefiningbusiness operations. Global Management Review, 4(4), 48–52.

Stoller, J. (2011, May). Tech’s renewable resource. Profit, 30(2), 27–30.Sultan, N. A. (2011, June). Reaching for the “cloud”: How SMEs can manage. Interna-

tional Journal of Information Management, 31(3), 272–278.Swartz, S. (2011, October). Monetizing in the cloud: Five questions CFOs should ask.

Financial Executive, 27(8), 67–68, 2p.Tarzey, B. (2012). Adapting to new data rules. Computer Weekly, 12.Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling.

Computational Statistics and Data Analysis, 48(1), 159–205.Truong, H.-L., & Dustdar, S. (2011, Jane). Cloud computing for small research groups

in computational science and engineering: Current status and outlook. Comput-ing, 91(1), 75–91.

Voith, T., Oberle, K., & Stein, M. (2012, March). Quality of service provisioning fordistributed data center inter-connectivity enabled by network virtualization.Future Generation Computer Systems, 28(3), 554–562.

Wang, J. (2011, November). A terrible host. Entrepreneur, 39(11), 50.Wenzel, E. (2011, July). Productivity apps’ cloud showdown. PC World, 29(7), 28–29.Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS path mod-

eling for assessing hierarchical construct models: Guidelines and empiricalillustration. MIS Quarterly, 33(1), 177–195.

Yeo, P. (2007, August). Why SMEs are like nuts. Straits Time.

Prashant Gupta works as a program/project manager for Hewlett Packard atSingapore. He has more than 17 years of work experience in the corporate world,having worked at Borland, Autodesk, and Intergraph in various managerial positions.His research interests include cloud computing, mobility, 3D printing, agile practicesand cutting edge IT trends affecting software solutions, services and applications aswell as how the technology acts as an enabler to achieve business objectives.

A. Seetharaman is currently serving as Dean Academic Affairs at S P Jain School ofGlobal Management at Singapore, and its branch campuses in Dubai and Sydney.His specialisation is in the area of Strategy, Finance and Accounting InformationSystems. He had supervised more than 25 PhD candidates, and had published morethan 100 research articles listed in data bases such as ISI Thomson, Scopus, Cabells,

sity, Malaysia. His research interests are in quantitative analysis, business statistics,project management and strategic information systems.