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Cloud computing adoption and usage in communitycollegesTara S. Behrend a , Eric N. Wiebe b , Jennifer E. London b & Emily C. Johnson ca Department of Organizational Sciences & Communication , The George WashingtonUniversity , Washington, DC, 20052, USAb Friday Institute for Educational Innovation, North Carolina State University , Raleigh,North Carolina, USAc Federal Management Partners, Inc. , Alexandria, Virginia, USAPublished online: 15 Jul 2010.

To cite this article: Tara S. Behrend , Eric N. Wiebe , Jennifer E. London & Emily C. Johnson (2011) Cloudcomputing adoption and usage in community colleges, Behaviour & Information Technology, 30:2, 231-240, DOI:10.1080/0144929X.2010.489118

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Cloud computing adoption and usage in community colleges

Tara S. Behrenda*, Eric N. Wiebeb, Jennifer E. Londonb and Emily C. Johnsonc

aDepartment of Organizational Sciences & Communication, The George Washington University, Washington DC 20052, USA;bFriday Institute for Educational Innovation, North Carolina State University, Raleigh, North Carolina, USA; cFederal

Management Partners, Inc., Alexandria, Virginia, USA

(Received 15 September 2009; final version received 21 April 2010)

Cloud computing is gaining popularity in higher education settings, but the costs and benefits of this tool have gonelargely unexplored. The purpose of this study was to examine the factors that lead to technology adoption in ahigher education setting. Specifically, we examined a range of predictors and outcomes relating to the acceptance ofa cloud computing platform in rural and urban community colleges. Drawing from the Technology AcceptanceModel 3 (TAM3) (Venkatesh, V. and Bala, H., 2008. Technology Acceptance Model 3 and a research agenda oninterventions. Decision Sciences, 39 (2), 273–315), we build on the literature by examining both the actual usage andfuture intentions; further, we test the direct and indirect effects of a range of predictors on these outcomes.Approximately 750 community college students enrolled in basic computing skills courses participated in this study;findings demonstrated that background characteristics such as the student’s ability to travel to campus hadinfluenced the usefulness perceptions, while ease of use was largely determined by first-hand experiences with theplatform, and instructor support. We offer recommendations for community college administrators and others whoseek to incorporate cloud computing in higher education settings.

Keywords: cloud computing; Technology Acceptance Model; higher education; community college; educationaltechnology

1. Introduction

Cloud computing is becoming increasingly popular asa way to deliver technology to secondary and post-secondary education environments and other organi-sations. Industry leaders estimate that revenues fromcloud computing enterprises will reach $160 billion,and define cloud computing as ‘an emerging ITdevelopment, deployment and delivery model, en-abling real-time delivery of products, services andsolutions over the Internet’ (Fowler and Worthen2009). With its emphasis on the delivery of low-cost orfree applications anywhere on the Internet, cloudcomputing is a promising prospect for educationalinstitutions faced with budget restrictions and mobilestudent population. Commercial providers are eager toencourage educational adoption of cloud computing;for example, Google has created a special educationedition of their cloud-based Google Apps and touts ontheir website the number of educational institutionsthat have adopted this suite (Google 2009). Successfulimplementation of cloud computing in educationalsettings, however, requires careful attention to anumber of factors from both the student and school’sperspective. This study examines the implementation

of a cloud computing initiative in a community collegesetting with the goal of identifying the factors that leadto successful implementation.

1.1. Cloud computing

The term cloud computing describes the softwareapplications or other resources that exist online andare available to multiple users via the Internet, ratherthan being installed on a particular user’s localcomputer. Users can access these applications fromany computer with a high speed Internet connectionwhile having no other connection to the hardware thatholds the source software (Gruman 2008). Becausecomputation takes place on a remote server, the user’shardware requirements are much lower than theywould be otherwise, reducing both cost and main-tenance requirements (Erenben 2009). For this reason,cloud computing holds appeal for school adminis-trators who seek to reduce information technology(IT) budgets (Behrend et al. 2008a). For many schoolsystems, cloud computing allows students to accesssoftware that was previously unavailable either due tocost or due to capability limitations in the local

*Corresponding author. Email: behrend@gwu.edu

Behaviour & Information Technology

Vol. 30, No. 2, March–April 2011, 231–240

ISSN 0144-929X print/ISSN 1362-3001 online

� 2011 Taylor & Francis

DOI: 10.1080/0144929X.2010.489118

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computer hardware (Jerald and Orlofsky 1999). Alter-nately, applications can be offered through cloudsolutions because they are easier to maintain ascentralised services. Some well-known providers ofsuch applications are email service providers such asGmail or Yahoo. Similarly, Google provides GoogleDocs (2007), a popular, free program that allows a userto upload a document and specify other users who canhave access to it.

Cloud computing can be highly beneficial ineducational settings. Among the possible benefits isthe enhanced usefulness of the existing technology.Because processing is done outside a user’s computer,older computers can remain useful for longer periodsof time. In addition, installing software or repairingerrors can be done centrally at the server level by theschool IT staff, as opposed to that at the individualcomputer level, which can result in less time expendedon these tasks (Chen 2004, Erenben 2009).

Community colleges have begun to offer moredistance learning courses in the hopes that with agreater flexibility to complete their coursework, morestudents will be able to enrol. In this scenario, cloudcomputing offers a way to ensure that students are ableto access and run the required course software,regardless of their location or local computer proces-sing power. Without cloud computing, the potential ofdistance learning is often not realised for technology-intensive courses, as students would need to travel tocampus to access the software they need in schoollaboratories.

Rural students may have the most to gain fromcloud computing initiatives. Students who attend ruralschools are typically dispersed and have to commutelonger distances to get to campus. Cloud computingcan meet these students’ needs by providing a commoninterface to a class or a school and by providing richcontent that allows the students to engage in learningregardless of location. The prospect of completingtechnology assignments from home instead of stayingafter school to use laboratory computers may mean thedifference between staying enrolled and dropping out,as high transportation costs can become unmanageablefor many low-resource students (Sander 2008).

Along with the substantial benefits of cloudcomputing, though, come potential pitfalls that canimpede usefulness and cause substantial frustration.One concern is the prospect of uncontrollable down-time, which will vary by provider, and can occur asserver maintenance is performed or as unforeseenoutages occur (Stone 2008). Because software isaccessed remotely, there may be a perceived or actuallack of control over when it will be available for use.Another concern is the lack of training and support forkey stakeholders, such as instructors, administrators or

IT professionals. Unless instructors both understandand endorse cloud computing as a means of softwaredelivery, students will probably not understand thebenefits from the system (Behrend et al. 2008b).

1.2. Cloud computing in community collegeeducation

Many community college students attend courses whilebalancing other roles and obligations, such as a full-time job or a family, making them more likely thantraditional college students to drop out before com-pleting degree requirements (Conklin 1997, Medvedand Heisler 2002). Finances can also be a factor indecisions to pursue higher education, for instancewhen gas prices make long commutes to campusprohibitive (Sander 2008) or when courses requireexpensive software. Perhaps in part due to these issues,college students tend to appreciate learning tools thatallow them greater flexibility to do their work whenand where they want (Beyth-Marom et al. 2003, Selim2007).

Given that rural community colleges are relativelyunderfunded and tend to serve a dispersed studentbody where long commutes require even largerinvestments of time (Yudko et al. 2006), cloudcomputing could be a technological innovation thatboth reduces IT costs for the college and eliminatesmany of the time-related constraints for students,making learning tools accessible for a larger number ofstudents. It is clear that careful, strategic integration ofcloud computing applications into courses is necessaryif students are to accept and use them; research on thistopic, however, does not yet exist to guide colleges.Thus, it is important to understand the factors thatlead students to embrace this technology, from both aninstructional and administrative/planning perspective(Venkatesh and Bala 2008). The purpose of the currentstudy is to address this goal by examining studentcharacteristics and experiences that lead to successfuladoption of a cloud computing platform in a commu-nity college setting. The following sections outline thetheoretical frameworks we draw from to form ourpredictions.

1.3. Theoretical background

Successful implementation and adoption of ITs such ascloud computing depend on technical factors of IT,characteristics of the organisation that introduces thetechnology and the response of individuals within theorganisation to the new technological tools. Central tothis research has been the influential TechnologyAcceptance Model (TAM) (Davis 1989, Davis 1993,Venkatesh and Davis 2000, Venkatesh et al. 2003,

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Venkatesh and Bala 2008). Though the TAM wasinitially developed to predict individual adoption anduse of new technologies in a business setting, theframework has also been used in educational settings.We draw from the TAM in developing hypothesesabout the factors that lead community college studentsto adopt cloud computing resources offered by theircollege. The initial and subsequent TAM refinementshave garnered extensive empirical support (e.g. Al-Gahtani and King 1999, Venkatesh et al. 2003, Kingand He 2006, Karahanna et al. 2006, Schepers andWetzels 2007, Venkatesh et al. 2007) and provide arobust framework that is well aligned with the cloudcomputing IT acceptance context being studied.

The most recent TAM, i.e. TAM3 (Figure 1), canbe best understood by exploring the determinants toperceived usefulness and perceived ease of use. TheTAM posits that individuals’ behavioural intention touse IT is determined by two beliefs: perceivedusefulness and perceived ease of use (Venkatesh andBala 2008). Perceived usefulness is defined as the extentto which a person believes that using an IT willenhance their job performance, while perceived ease ofuse is defined as the degree to which a person believesthat using an IT will be free of effort. These two beliefsare driven by a number of other external factors (e.g.specific characteristics of the IT system, organisationor the individual user).

Individual differences are important determinantsof the construct of ease of use. Among the identifiedfactors are computer self-efficacy, computer anxietyand computer playfulness. Computer self-efficacy is thedegree to which an individual believes that they havethe ability to perform a specific task or job using acomputer or a related technology (Compeau andHiggins 1995). Computer anxiety, on the other hand,is the degree of ‘an individual’s apprehension, or evenfear, when she/he is faced with the possibility of usingcomputers’ (Venkatesh 2000, p. 349). Finally, computerplayfulness is ‘. . . the degree of cognitive spontaneity inmicrocomputer interactions . . .[where] those higher in

microcomputer playfulness tend to be more sponta-neous, inventive, and imaginative in their microcom-puter interactions’ (Webster and Martocchio 1992, p.204).

Perception of external control, a facilitating condi-tion, is also a determinant of perceived ease of use.Perception of external control is defined as the degreeto which an individual believes that there areorganisational and technical resources available tosupport his/her use of IT system (Venkatesh et al.2003). Finally, there are two system characteristics thatare the determinants of perceived ease of use: perceivedenjoyment is the extent to which the system is perceivedto be enjoyable in its own right, regardless as towhether its use impacts job performance; objectiveusability is a comparison of systems (e.g. the currentlyused system versus the new system) on the actual levelof effort required to use the computer for a specifictask (Venkatesh 2000).

Determinants of perceived usefulness are groupedunder social influence and system characteristics, andrelate to the degree to which a user believes that the ITsystem will further their workplace goals. An impor-tant social influence, subjective norm, is the degree towhich a user perceives that other individuals of highstatus (or otherwise influential) believe that theyshould use the new IT (Fishbein and Ajzen 1975,Venkatesh et al. 2003). Related to this is the constructof image: the degree to which an individual perceivesthat the use of the IT will enhance his/her organisa-tional status (Moore and Benbasat 1991). This view ofcompliance is further supported by the technologyacceptance research, which demonstrates that thesesocial influence factors have the highest impact whenIT system use is considered mandatory (Sun andZhang 2006, Schepers and Wetzels 2007).

Three system characteristics are considered as thedeterminants of perceived usefulness: job relevance isthe degree to which the user believes the IT system isapplicable to his/her job duties. It follows that thedeterminant of output quality relates to the user’s belief

Figure 1. Technology Acceptance Model 3 (Venkatesh and Bala 2008).

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that the IT system performs its tasks well. A relateddeterminant is that of result demonstrability, which isdefined as the degree to which an individual believesthe results of using the IT system are tangible,observable and communicable (Moore and Benbasat1991). All of these factors are related to performanceexpectancy, the degree to which a user believes the ITsystem will help with his/her job performance.

Under the current TAM3, perceived usefulness isalso influenced by perceived ease of use, though this ismoderated by experience. That is, more experiencesolidifies one’s perception of ease of use and increasesits effect on perceived usefulness (Venkatesh and Bala2008). Together, perceived ease of use and usefulnessare then used to formulate a behavioural intention touse the system that, in turn, drives the actual use of thesystem. The expectation is that behavioural intentionwill have a significant positive influence on technologyusage (Venkatesh et al. 2003). It is also important tonote that another key goal of TAM3 is to operatio-nalise the model into actionable strategies to enhancethe use of IT system. Organisational interventions suchas training and user support are important compo-nents of this model.

Much of the research on and validation of the TAMmodels have been done using mainstream businessorganisations and IT systems. For example, Holdenand Karsh (2009) used TAM2 to help develop atestable, theoretical multilevel model of health IT usagebehaviour. With more and more computing resourcesbeing located on the Internet (i.e. cloud computing), itis appropriate that a greater proportion of recentstudies involving the TAM models has used Internet orintranet-based (i.e. network-based but within a singleorganisation) IT. Lee and Kim (2009), using the TAM/UTAUT model (Venkatesh et al. 2003), examinedbusiness users of a company intranet, finding taskcharacteristics, technical support and perceived ease ofuse all to be significant factors influencing usage.

In an educational context, Yuen and Ma (2008)and Teo et al. (2008) used TAM models to explore pre-service and in-service teachers’ acceptance of instruc-tional technologies such as e-Learning systems. Ger-mane to the research reported here is whether theTAM3 model can be applied as a model of useracceptance and usage for cloud computing technolo-gies in the community college system. For example,while teacher usage may be considered as essentially aprofessional work application of the IT, would studentuse of IT for learning create a different user context?Also, would the use of IT in a community collegesetting be a different context from a more mainstreambusiness setting?

To summarise the TAM theoretical framework forthe current study, a great deal of research has been

conducted on the relationship between two importantmediators, usefulness and ease of use, in predicting theindividual intentions to use technological innovations.Less is known, however, about the factors that lead tousefulness and ease of use, especially with regard to acommunity college student population using a cloudcomputing technology. Further, a variety of outcomemeasures can and should be considered in thispopulation, beyond simple usage intentions. Thus, weinvestigate a variety of factors that are expected toinfluence ease of use and usefulness perceptions forcommunity college students. We group these predic-tors according to student attitudes, situational factorsand student experiences with the technology.

1.4. Hypotheses

Hypothesis 1: Usefulness perceptions (1a) andease-of-use perceptions (1b) will affect the studentusage of the cloud computing tool.

Hypothesis 2: Usefulness perceptions (2a) andease-of-use perceptions (2b) will affect studentbeliefs about future utility of the cloud computingtool.

Hypothesis 3: Usefulness perceptions (3a) andease-of-use perceptions (3b) will affect studentintentions for future use of cloud computing tools.

Hypothesis 4: Student attitudes about technologywill affect usefulness (4a) and ease of use (4b).

Hypothesis 5: Situational factors will affect useful-ness (5a) and ease of use (5b).

Hypothesis 6: Student experiences with the cloudcomputing tool will affect usefulness (6a) and easeof use (6b).

2. Method

2.1. Study context

The cloud computing platform used in this study wasthe Virtual Computing Lab (VCL 2007), which wasdeveloped by a team of IT professionals for thepurpose of better deploying software for universitystudent and faculty use. The VCL provides authorisedusers with a virtual desktop through which they canaccess software licensed by the educational institution.Users can access this software from any computer ordevice with a high-speed Internet connection. The VCLdiffers from web-based e-Learning tools such as coursemanagement software (e.g. Moodle, Blackboard) orapplets that run within a web browser. Instead, the

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VCL provides a remote connection window into acomplete computer desktop running on a remoteserver. This creates, in essence, a computer within astudent’s local computer on which a student can runcomputer applications supported by the VCL’s remoteservers, but not available on the student’s localcomputer (e.g. expensive, difficult to obtain or mem-ory-intensive applications). These remote connectionsare managed by a web-based reservation system, wherestudents check on the availability and status of remotedesktop servers and make reservations. Measurementof the VCL’s perceived ease of use and usefulnessconcerns the functioning of the remote connection, theweb-based reservation system and the management ofmultiple drives on a student’s computer (e.g. both thestudent’s hard drive and the virtual hard drive).

Because the services provided by the VCL wereexpected to fulfil a need in the community collegesystem, a partnership was established with the statecommunity college system to pilot the system at selectcommunity college campuses. The community collegesparticipating in the pilot were responsible for introdu-cing the VCL into the classroom and providing basictechnical support. The current pilot study was con-ducted with students enrolled in an introductorycomputing course required for most majors, coveringbasic topics such as file management and wordprocessing. The VCL provided a virtual desktop withthe specific version of the Windows OS and MicrosoftOffice software suite used in the course. Use of theVCL was required for students in participating sectionsof the course.

2.2. Design and procedure

Data were collected at four time points over the courseof two semesters in 2008 (i.e. at the beginning and endof each of the two semesters). Instructors were asked todirect their students to an online questionnaire, whichwas designed to assess the various study variables. Itconsisted of 112 questions (see Section 2.4) and tookapproximately 20 min to complete.

Additionally, qualitative information was obtainedthrough focus groups with the instructors and inter-views with other stakeholders (e.g. college adminis-trators and IT support staff) as part of the larger pilotproject associated with this study. However, these datawere used solely to inform our interpretation of theresults of study analyses.

2.3. Participants

Participants in this study were students at twocommunity colleges in the Southeastern USA, onerural (N ¼ 142) and one urban (N ¼ 618). Fifty-two

per cent of the participants were females. Withrespect to age, 53% were 25 years old or younger,27% were 26–35, 11% were 36–45, 5% were 46–55 and1%were over 55 years of age. Most of the students weretraditional full-time degree seekers (63%) or part-timedegree seekers (20%), but part-time non-degree seekers(3%) and other types (5%) were also represented.Students took the class in an online format (19%), atraditional seated format (79%) or in a hybrid format(2%) format that combined aspects of the two.

2.4. Measures

Access to personal copies of software (a ¼ 0.80) wasmeasured with two items, e.g. ‘I have a personal copyof the software I need for this course’. Responses wereon a 5-point Likert scale with anchors strongly disagreeto strongly agree, with higher values representing moreaccess to personal copies of software.

Ease of travel (a ¼ 0.47) to campus was measuredwith two items, e.g. ‘Restraints on my transportationarrangements make it difficult to commute to campus’.These items were 5-point Likert scales anchoredstrongly disagree to strongly agree with agreementindicating more difficulty getting to campus.

Personal innovativeness in the domain of informationtechnology (a ¼ 0.87) was measured with three itemscreated by Agarwal and Prasad (1998) and includeitems such as ‘If I heard about a new informationtechnology, I would look for ways to experiment withit’. Responses were given on a 5-point Likert scaleanchored strongly disagree to strongly agree, withhigher values representing higher levels of personalinnovativeness.

Anxiety towards technology (a ¼ 0.81) was mea-sured with a three-item scale from Venkatesh et al.(2003), e.g. ‘Computer technology often interferes withmy learning’. The items were on a 5-point Likert scaleanchored strongly disagree to strongly agree, withhigher levels representing more anxiety towardstechnology.

Instructor support (a ¼ 0.85) was measured by afive-item scale based on Venkatesh et al. (2003) thatincluded items such as ‘I am encouraged by myinstructor to use the Virtual Computing Lab’. Theitems were 5-point Likert scales anchored stronglydisagree to strongly agree, with higher levels represent-ing more instructor support.

Reliability (a ¼ 0.76) was measured with two items,e.g. ‘The Virtual Computing Lab is reliable’. Re-sponses were on a 9-point scale with anchors never toalways, with higher values representing greaterreliability.

Perceived usefulness (a ¼ 0.78) was measured withtwo items from Venkatesh et al. (2003), e.g. ‘The

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Virtual Computing Lab has made it easier for me tocomplete assignments’. Responses were on a 5-pointLikert scale anchored strongly disagree to stronglyagree, with higher values representing higher perceivedusefulness.

Ease of use (a ¼ 0.93) was measured with fouritems from Venkatesh et al. (2003), e.g. ‘I thought theVirtual Computing Lab was easy to use’. Responseswere given on a 5-point Likert scale anchored stronglydisagree to strongly agree, with higher values repre-senting greater ease of use.

Actual use was measured with one item thatcombined the time spent on six activities, e.g. ‘Howmany minutes per week did you spend using theVirtual Computing Lab at school?’ Responses wereopen-ended.

Intentions for future use (a ¼ 0.81) was measuredwith items, e.g. ‘I think that I would like to use theVirtual Computing Lab frequently’. Responses wereon a 5-point Likert scale anchored strongly disagree tostrongly agree, with higher values representing higherintention to use the VCL in the future.

Future usefulness perceptions (a ¼ 0.92) were mea-sured with three items, e.g. ‘I think I will find theVirtual Computing Lab useful in my courses’. Re-sponses were on a 5-point Likert scale anchoredstrongly disagree to strongly agree, with higher valuesrepresenting higher likelihood of future use.

2.5. Analyses

In order to assess the influence of the various studyvariables on the likelihood of adopting cloud comput-ing technology (i.e. the VCL), a path-analytic modelwas developed and tested. Path analysis (cf. Cohenet al. 2003) allows researchers to test the directand indirect effects of multiple continuous indepen-dent variables on multiple continuous dependentvariables, as illustrated in Figure 2. In this way, wewere able to test (and thus control) all study variablessimultaneously.

3. Results

Inter-correlations and descriptive statistics for allmeasures are presented in Table 1. To test ourhypotheses, a path-analytic model was tested usingthe MPLUS software program (see Figure 2 for apresentation of all standardised path coefficients). Onthe predictor side, the model included the followingvariables: alternative access to software, ease of travelto campus, personal technology innovativeness, tech-nology anxiety and instructor support and reliability.Three dependent variables (actual use, future useful-ness perceptions and intentions to use in the future) aswell as two mediating variables (usefulness and ease ofuse) were included.

Figure 2. Path-analytic model results. Note: Only significant (p 5 0.05) standardised path coefficients are displayed. Non-significant paths appear as grey, dashed arrows.

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3.1. Model fit

Fit indices were calculated to determine the suitabilityof the model. The standardised solution for the modelis shown in Table 2.

3.2. Student usage of cloud computing

Hypothesis 1 predicted that usefulness (1a) and ease ofuse (1b) affect the student usage of the VirtualComputing Lab. While usefulness showed no signifi-cant relationship with student usage, ease of use wasfound to be significantly related to it (see Figure 1 forpath weights). Thus, Hypothesis 1a was not supported,and Hypothesis 1b was supported.

3.3. Student beliefs about future utility

Hypothesis 2 predicted that usefulness perceptions andease-of-use perceptions would affect student beliefsabout future utility. The path-analytic results indicatedfull support for this hypothesis, with significantpositive relationships from both usefulness and ease-of-use perceptions to beliefs about the future utility.

3.4. Intentions for future use

Hypothesis 3, which predicted that usefulness percep-tions and ease-of-use perceptions would affect inten-tions for future cloud computing use, was also fullysupported. Both usefulness and ease of use were foundto positively influence the students’ intentions forfuture cloud computing use.

3.5. Student attitudes about technology

Hypothesis 4 investigated the relationship betweenstudents’ attitudes towards technology and usefulness(4a) and ease of use (4b). We found that anxiety aboutthe technology had a significant, negative effect onperceived usefulness (i.e. students who were moreanxious about the technology perceived cloud

Table

1.

Item

descriptives

andintercorrelations.

MSD

12

34

56

78

910

11

12

1.Homeaccessto

software

3.81

1.19

2.Ease

oftravel

tocampus

70.01

0.82

70.01

3.Personalinnovativeness

3.36

0.87

0.09

0.00

4.Technologyanxiety

2.42

0.99

70.12

0.17**

70.11**

5.Instructorsupport

70.01

0.80

0.05

70.03

0.19**

70.12*

6.Reliabilityperceptions

4.84

1.26

70.05

70.35**

0.12

70.18*

0.31**

7.Usefulness

3.78

1.20

70.13

70.22*

0.04

70.26**

0.04

0.24**

8.Ease

ofuse

3.63

1.01

70.05

70.22*

0.28**

70.08

0.54**

0.54**

0.20**

9.Actualweekly

use

(minutes)

100.99

163.26

70.28**

70.10

0.18*

70.10

0.16*

0.33**

0.20**

0.32**

10.Intentionsforfuture

use

70.01

0.89

70.11

70.08

0.25**

70.13**

0.43**

0.47**

0.31**

0.54**

0.30**

11.Future

usefulnessperceptions

3.37

1.12

70.14

70.17

0.24**

0.04

0.39**

0.40**

0.20**

0.62**

0.17*

0.48**

12.Agegroup

––

70.15*

70.04

70.18*

70.01

70.08

0.04

70.06

70.06

0.10

0.05

0.06

13.Gender

––

70.05

70.07

0.26**

70.05

70.05

0.02

0.01

0.07

0.18*

70.01

70.02

0.10

*p5

0.05

**p5

0.01.

Note:N

varies

from

cellto

cellduedopairwisedeletionofmissingdata.

Table 2. Model fit.

w2/df ratio CFI TLI RMSEA SRMR

Model 2.96 0.934 0.827 0.087 0.036

Note: Interpretation of Indices is as follows: w2/df ratio: a valuelarger than 2 indicates an inadequate fit; Comparative Fit Index(CFI) and Tucker Lewis Index (TLI): a value between 0.90 and 0.95indicates acceptable fit and a value above 0.95 indicates good fit;Root Mean Square Error of Approximation (RMSEA): 0.05 or lessindicates good fit, greater than 0.10 indicates poor fit; StandardisedRoot Mean Square Residual (SRMR): a value less than 0.08indicates good fit.

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computing to be less useful), but anxiety did not have asignificant effect on the ease of use. Personal innova-tiveness did not have significant effects on ease-of-useor usefulness perceptions. Thus, Hypothesis 4a waspartially supported, and Hypothesis 4b was notsupported.

3.6. Situational factors

To test Hypothesis 5, we examined the relationshipsbetween usefulness (5a) and ease of use (5b) with twosituational factors, software access and ease of travelto campus. We found that both of these factors hadsignificant, negative effects on the usefulness and easeof use. Further, alternative access to the software had adirect and negative effect on the actual use of the VCL,and ease of travel to campus had a direct and negativeeffect on the future usefulness perceptions. Thus,Hypotheses 5a and 5b were fully supported.

3.7. Student experiences

To test Hypothesis 6, we examined the relationshipsbetween two situational factors, instructor support andreliability and usefulness (6a) and ease of use (6b). Wefound that while reliability was significantly, positivelyrelated to both ease of use and usefulness, instructorsupport was only significantly related to ease of use.This lends partial support to Hypotheses 6a and 6b.

4. Discussion

4.1. Summary of results

In this study, we examined factors that led communitycollege students to adopt cloud computing technology.We found that overall, students’ use of cloud com-puting was a function of ease-of-use perceptions, aswell as the students’ access to alternative tools.Usefulness was not a factor in predicting actual usage;however, usefulness did predict the student’s intentionto use cloud computing technologies like VCL in thefuture. Beliefs about future usefulness, like actualusage, were predicted by ease of use and access toalternative tools.

Given that both usefulness and ease of use wereimportant in understanding the usage patterns, weexamined the factors that led to usefulness and ease-of-use perceptions. Usefulness was a function of situa-tional factors, attitudes and experience factors; tech-nology reliability was the strongest predictor. Ease ofuse was also predicted by a number of factors, thestrongest being instructor support.

As a whole, the findings from this study providenew insight into the factors that leads to the adoptionof cloud computing technology. One important finding

is the discrepancy between the actual usage during agiven semester and the intention to use the tools atsome point in the future. It appears that students inthis setting form impressions about the technologybased on their immediate needs, rather than theanticipated future needs. This may be a function, inpart, of the relatively higher number of non-degreestudents, and students who enrolled for part-timecourses, whose primary focus is on their currentcourses. This points to an important distinctionbetween the community college context and businesscontexts where the TAMs have been more typicallyvalidated – at least a portion of the community collegepopulation should be seen as much more transient,with a different relationship to the IT being providedby the institution.

We found that the ease-of-use perception was amuch stronger predictor of adoption than the useful-ness perception. This indicates that students mayacknowledge the utility of a tool, but lack themotivation to use it if it is not user friendly. Giventhat these students often balance multiple roles, andlack advanced technology skills, they may not wish toinvest the time needed to learn a new tool even if it is auseful one. With regard to cloud computing specifi-cally, it appears that the students can acknowledge theefficacy of tool in enabling them to complete their workwith greater flexibility and speed, but still choose notto use it if it requires effort to learn to use. This findingis counter to previous research with 4-year universitystudents, which had typically shown a stronger effectof usefulness compared to ease of use (e.g. Lee et al.2005, Martinez-Torres et al. 2008). These authorsasserted that college students have above-averageexposure to IT, increasing their willingness to trynew tools. The current study indicates that perhapscommunity college students are different from 4-yearuniversity students and their needs and relationship toIT should be planned for accordingly.

This study makes a number of unique contribu-tions. This is the first study to empirically test thedeterminants of cloud computing technology usage ina higher education setting. We examined distalpredictors as well as the mediating influences ofusefulness and ease of use, as they are related to anumber of outcomes. We examined factors at thestudent, instructor and school levels in order to gain afuller picture of why students used cloud computing, toenable the administrators and curriculum specialists tobetter plan for technology courses. Finally, weexamined these factors in a community college setting.Community colleges are poised to fill an essential rolein quickly and efficiently providing job training formany technical professions. Understanding the ways inwhich these students are related to and use the

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technology, then, is an important goal for botheducational planners and researchers.

4.2. Limitations and future research

This study has a number of limitations that should benoted. First, we were not able to include a number offactors that have been shown to influence technologyacceptance. For example, technology use varies de-pending on whether use is mandatory or optional(Venkatesh et al. 2003). Predictors of use also varyover time as users gain more familiarity and experience(Venkatesh et al. 2003). However, this study yieldsimportant information about usage within a manda-tory use, initial experience context. Future researchwill be needed to examine longitudinal changes inpatterns and predictors of use.

Second, this study focused on a single level ofanalysis, namely student behaviour. Successful imple-mentation of cloud computing technology in commu-nity colleges also depends on teacher beliefs andbehaviours, as well as those of the administrationand IT staff. Our interviews with teachers showed cleardifferences in the acceptance and understanding ofcloud computing; it will be of interest to determinehow these differences translate into student usagepatterns.

4.3. Practical implications

College IT planners must be informed about thepossible risks and benefits of cloud computingtechnologies before engaging in wide-scale implemen-tation. The results of this study showed that studentswho had difficulty in travelling to the campus weremore likely to find cloud computing useful; ruralcolleges can expect to have a higher proportion ofstudents travelling a long distance and, by extension,more students who may be interested in using cloudcomputing. It is clear, however, that students will notuse these tools unless they have a positive in-classintroduction, with an instructor who is able to explainthe benefits and demonstrate its use. Thus, college-levelcost analysis has to go hand-in-hand with organisa-tional ‘fit’ of the tool. That is, ‘cost-effectiveness’ andactual utilisation of the tool have to be likely takeninto account when judging the success of a project.Instructor buy-in is critical; the pay for the instructorand IT support training costs should be included incost projections.

4.4. Conclusions

Community colleges educate a rapidly growing num-ber of students (Moltz 2008), often with underfunded

IT resources. IT administrators are always seekingways to deliver IT while keeping budgets manage-able, with cloud computing promising to be aneffective tool towards this goal. Careful planning,however, is needed to ensure that cloud computinginvestments do not go to waste. Unless students feelthat they are supported in the use of cloudcomputing, and that this tool is an easy and reliablealternative, they will not use it and hence the collegewill not be benefited.

Acknowledgements

The authors thank the VCL Project Team at North CarolinaState University for providing the cloud computing resourcesand helping to facilitate the data collection. In particular, theauthors thank Sarah Stein, Sam Averitt, Henry Schaffer,Aaron Peeler and Josh Thompson. This work was supportedin part by funding from the State of North Carolina throughthe NC Community College System and from an IBMFaculty Award to the second author.

References

Agarwal, R. and Prasad, J., 1998. A conceptual andoperational definition of personal innovativeness in thedomain of information technology. Information SystemsResearch, , 9 (2), 204–215.

Al-Gahtani, S.S. and King, M., 1999. Attitudes, satisfactionand usage: factors contributing to each in the acceptanceof information technology. Behaviour and InformationTechnology, 18 (4), 277–297.

Behrend, T., Wiebe, E., and Sharek, D., 2008a. Implementa-tion of virtual computing lab at Wake Technical Commu-nity College. Raleigh, NC: The Friday Institute forEducational Innovation.

Behrend, T., et al., 2008b. Evaluation of virtual computing labimplementation in community colleges. Raleigh, NC: TheFriday Institute for Educational Innovation.

Beyth-Marom, R., et al., 2003. Internet-assisted versus tradi-tional distance learning environments: factors affectingstudents’ preferences. Computers and Education, 41 (1), 65.

Chen, A., 2004. Public school district thinks thin. EWeek21(49). Available from: http://www.eweek.com/c/a/IT-In-frastructure/Public-School-District-Thinks-Thin/ [Accessed September 2009].

Cohen, J., et al., 2003. Applied multiple regression/correlationanalysis for the behavioral sciences, 3rd ed. Mahwah, NJ:Lawrence Erlbaum.

Compeau, D.R. and Higgins, C.A., 1995. Computer self-efficacy: development of a measure and initial test. MISQuarterly, 19, 189–211.

Conklin, K.A., 1997. Course attrition: a 5-year perspectiveon why students drop classes. Community College Journalof Research and Practice, 21 (8), 753.

Davis, F.D., 1989. Perceived usefulness, perceived ease ofuse, and user acceptance of information technology. MISQuarterly, 13 (3), 319–339.

Davis, F.D., 1993. User acceptance of information technol-ogy – system characteristics, user perceptions andbehavioral impacts. International Journal of Man-Ma-chine Studies, 38 (3), 475–487.

Erenben, C., 2009. Cloud computing: the economic impera-tive. ESchool News, 12 (3), 13–19.

Behaviour & Information Technology 239

Dow

nloa

ded

by [

Uni

vers

ity o

f H

aifa

Lib

rary

] at

09:

20 2

2 O

ctob

er 2

013

Fishbein, M. and Ajzen, I., 1975. Belief, attitude, intentionand behavior: an introduction to theory and research.Reading, MA: Addison-Wesley.

Fowler, G.A. and Worthen, B., 2009. The internet industry ison a cloud – whatever that may mean. Wall StreetJournal – Eastern Edition, 253 (70), A1–A10.

Google, 2009. Google apps education edition – customers[online]. Available from: http://www.google.com/a/help/intl/en/edu/customers.html [Accessed 11 June 2009].

Google docs, 2007. Computer software [online]. Availablefrom: http://docs.google.com/ [Accessed 11 June 2009].

Gruman, G., 2008. What cloud computing really means.InfoWorld [online]. Available from: http://www.info-world.com/print/34031 [Accessed 11 June 2009]

Holden, R. and Karsh, B.T., 2009. A theoretical model ofhealth information technology usage behaviour withimplications for patient safety. Behaviour and InformationTechnology, 28 (1), 21–38.

Jerald, C.D. and Orlofsky, G.F., 1999. Raising the bar onschool technology. Education Week, 19 (4), 58.

Karahanna, E., Agarwal, R., and Angst, C.M., 2006.Reconceptualizing compatibility beliefs in technologyacceptance research. Mis Quarterly, 30 (4), 781–804.

King, W.R. and He, J., 2006. A meta-analysis of theTechnology Acceptance Model. Information and Man-agement, 43 (6), 740–755.

Lee, M.K.O., Cheung, C.M.K., and Chen, Z., 2005.Acceptance of Internet-based learning medium: the roleof extrinsic and intrinsic motivation. Information andManagement, 42 (8), 1095–1104.

Lee, S. and Kim, B.G., 2009. Factors affecting the usage ofintranet: a confirmatory study. Computers in HumanBehavior, 25 (1), 191–201.

Martinez-Torres, M.R., et al., 2008. A technologicalacceptance of e-learning tools used in practical andlaboratory teaching, according to the European highereducation area. Behaviour and Information Technology,27 (6), 495–505.

Medved, C.E. and Heisler, J., 2002. A negotiated orderexploration of critical student-faculty interactions: Stu-dent-parents manage multiple roles. CommunicationEducation, 51 (2), 105–120.

Moltz, D., 2008. The Community college enrollment boom.Inside Higher Ed [online]. Available from: http://www.insidehighered.com/news/2008/08/22/growth [Ac-cessed 22 August 2008].

Moodle, 1999. Computer software [online]. Available from:http://moodle.org/ [Accessed 11 June 2009].

Moore, G.C. and Benbasat, I., 1991. Development of aninstrument to measure the perceptions of adopting aninformation technology innovation. Information SystemsResearch, 2, 192–222.

Sander, L., 2008. Rising cost of gasoline pinches students atrural community colleges. Chronicle of Higher Education,54 (41), A17–A17.

Selim, H.M., 2007. Critical success factors for e-learningacceptance: confirmatory factor models. Computers andEducation, 49 (2), 396–413.

Schepers, J. and Wetzels, M., 2007. A meta-analysis of theTechnology Acceptance Model: investigating subjectivenorm and moderation effects. Information and Manage-ment, 44 (1), 90–103.

Stone, A., 2008. The holistic model for blended learning: anew model for K-12 district-level cyber schools. Interna-tional Journal of Information and Communication Tech-nology Education, 4 (1), 56–71.

Sun, H.S. and Zhang, P., 2006. The role of moderatingfactors in user technology acceptance. InternationalJournal of Human-Computer Studies, 64 (2), 53–78.

Teo, T., Luan, W.S., and Sing, C.C., 2008. A cross-culturalexamination of the intention to use technology betweenSingaporean and Malaysian pre-service teachers: anapplication of the Technology Acceptance Model(TAM). Educational Technology and Society, 11 (4),265–280.

VCL, 2007. Virtual computer lab [online]. Available from:http://vcl.ncsu.edu/ [Accessed 11 June 2009].

Venkatesh, V., 2000. Determinants of perceived ease of use:integrating perceived behavioral control, computer anxi-ety and enjoyment into the Technology AcceptanceModel. Information Systems Research, 11, 342–365.

Venkatesh, V. and Bala, H., 2008. Technology AcceptanceModel 3 and a research agenda on interventions.Decision Sciences, 39 (2), 273–315.

Venkatesh, V. and Davis, F.D., 2000. A theoretical extensionof the Technology Acceptance Model: four longitudinalfield studies. Management Science, 46 (2), 186–204.

Venkatesh, V., Davis, F.D., and Morris, M.G., 2007. Deador alive? The development, trajectory and future oftechnology adoption research. Journal of the Associationfor Information Systems, 8 (4), 267–286.

Venkatesh, V., et al., 2003. User acceptance of informationtechnology: toward a unified view.MIS Quarterly, 27 (3),425–478.

Webster, J. and Martocchio, J.J., 1992. Microcomputerplayfulness: development of a measure with workplaceimplications. MIS Quarterly, 16, 201–226.

Yuen, A.H.K. and Ma, W.W.K., 2008. Exploring teacheracceptance of e-learning technology. Asia-Pacific Journalof Teacher Education, 36 (3), 229–243.

Yudko, E., Hirokawa, R., and Chi, R., 2006. Attitudes,beliefs, and attendance in a hybrid course. Computers andEducation, 50 (4), 1217–1227.

240 T.S. Behrend et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f H

aifa

Lib

rary

] at

09:

20 2

2 O

ctob

er 2

013