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Computers in Human Behavior 45 (2015) 93–98
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Computers in Human Behavior
journal homepage: www.elsevier .com/locate /comphumbeh
Effects of security and privacy concerns on educational use of cloudservices
http://dx.doi.org/10.1016/j.chb.2014.11.0750747-5632/� 2014 Elsevier Ltd. All rights reserved.
⇑ Corresponding author.E-mail addresses: [email protected] (I. Arpaci), [email protected].
tr (K. Kilicer), [email protected] (S. Bardakci).
Ibrahim Arpaci ⇑, Kerem Kilicer, Salih BardakciGaziosmanpasa University, Faculty of Education, Department of Computer Education and Instructional Technology, Turkey
a r t i c l e i n f o a b s t r a c t
Article history:
Keywords:Cloud servicesPrivacySecurityEducational technology
Cloud computing, which offers software, platform, and infrastructure services, focuses on effective use ofshared resources to achieve economies of scale and coherence. This paper aims to understand the effectsof security and privacy concerns on educational use of cloud services. We have proposed a researchmodel based on Ajzen’s (1991) Theory of Planned Behaviour (TPB). Following the TPB, we developed aresearch model, which posits that student attitudes predicted by security and privacy perceptions andbehavioural intentions are predicted by attitudes towards using cloud services. A Structural EquationModel was used to assess the model based on the data collected by means of survey questionnaires from200 pre-service teachers. Results supported the proposed model, validating the predictive power of theTPB. The results also indicate that security and privacy have a strongly significant influence on the stu-dents’ attitudes towards using cloud services in educational settings. Implications of these findings arediscussed.
� 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Educational use of cloud services is desirable as these servicescan provide several advantages. Possible advantages such as any-time/anywhere access to documents and files, synchronization ofdata across devices, easy to share data, data redundancy, bespokelearning with no up-front capital investment and maintenanceresponsibility (Cenka & Hasibuan, 2013; Leipert & Simandl, 2012).Given the arrival of cloud services the limitations such as interoper-ability and compatibility issues, low storage capacity, installation,backup, and recovery overheads have been largely resolved(Lacity & Reynolds, 2014; Rizzardini, Linares, Mikroyannidis, &Schmitz, 2013). However, some challenges still need to beaddressed such as security and privacy issues (Dillon, Wu, &Chang, 2010; Park & Kim, 2014).
Cloud computing refers to expandable and on demand servicesthat are served via the Internet from specialized data centres(Johnson, Brown, Cummins, & Estrada, 2012). These services havea potential to enable and facilitate both formal and informal learn-ing by allowing students and academics share learning resources,interact and brainstorm solutions, elaborate reports, and create con-ceptual designs (Rizzardini et al., 2013). In this study, cloud services
refer to the Internet based applications that provide different ser-vices such as social networking, distributed file systems, and struc-tured storage systems such as Google Drive, Dropbox (allow users tostore, synchronize, and share files) and Evernote (allows users tocreate text, audio, and video memos). An investigation into thedeterminants of educational use of cloud services was studied aim-ing to identify the impact of security and privacy concerns on usebehaviour. The results of this study may lead to successful under-standing for the use of cloud services in educational settings.
The paper proceeds as follows. In the next section, we reviewthe literature on studies of cloud computing adoption and use, fol-lowed by the research methods and the results of data analysis.Finally, discussion of the research findings and their implicationsare provided along with the limitations of the study.
2. Literature review
Cloud computing has recently received increasing attention ininformation systems and computer science disciplines (Armbrustet al., 2010; Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011;Pallis, 2010; Zhang, Ma, Wu, Ordonez de Pablos, & Wang, 2014).Recently, Lin, Wen, Jou, and Wu (2014) proposed a cloud-basedreflective learning environment to enhance student reflection moti-vation and performance. Results of the experimental study verifythat the proposed learning environment is able to effectively assiststudents and instructors in administering and conducting reflective
94 I. Arpaci et al. / Computers in Human Behavior 45 (2015) 93–98
learning activities during and after a class. In a similar study, Alamriet al. (2014) proposed a cloud-based game that monitor healthconditions of obese people. The proposed game enables ubiquitousand real-time access of health data by the therapists and supportstherapist-mediated dynamic change of game level and recommen-dation. A sample of 150 undergraduate obese students played thegame and filled a questionnaire after game-play. Results show thatthey were self-aware and motivated to play the game for weightloss. In another study, Schepman, Rodway, Beattie, and Lambert(2012) investigated use of a multi-platform cloud based note takingsoftware (Evernote) to provide mobile support to students’ learn-ing. Results demonstrate that the students mostly used Evernotein their independent study behaviours, including informationacquisition, organization, and management.
Jou and Wang (2013) compared college students with highschool and vocational high school backgrounds in terms of learningattitudes and academic performances induced by the utilization ofresources driven by cloud computing technologies. They found nocognition differences between academic or vocational students.However, vocational students were better motivated. In anotherstudy, Park and Ryoo (2013) used two-factor theory to investigateswitching behaviour to cloud services. Results demonstrate thatkey switching enablers are omnipresence and collaboration sup-port, while switching inhibitors are satisfaction with incumbentIT and breadth use of incumbent IT. The results also show thatpersonal innovativeness and social influence positively moderatedthe relationship between positive perception on switching to cloudas well as negative perception on switching to cloud and intentionto switch.
Stantchev, Colomo-Palacios, Soto-Acosta, and Misra (2014)investigated the motivations that lead higher education studentsto use Learning Management Systems (LMS) services or cloud ser-vices for information sharing and collaboration. Based on the Tech-nology Acceptance Model (TAM; Davis, 1989), they conducted aquestionnaire survey with a sample of 121 students. Results showthat the perceived ease of use of cloud services is above that of LMSservices. In addition, cloud services presented higher levels of per-ceived usefulness than LMS services and attitude towards usingcloud services is well above that of using LMS services. In a similarstudy, Park and Kim (2014) investigated factors affecting user per-ceptions of and attitude towards mobile cloud computing servicesbased on the TAM. They found that perceived mobility, security,connectedness, satisfaction, and quality of service have a signifi-cant effect on user acceptance of mobile cloud services.
Ratten (2012) investigated how ethics influence individuals’decision to adopt cloud computing based on social cognitive the-ory. Results show that ethics and marketing are important deter-minants of individuals’ behavioural intention towards technologyinnovations. However, entrepreneurial orientation, learning, andoutcome expectancy have no effect on their intention to adopt thistechnology. In another study, Bharadwaj and Lal (2012) used a casestudy approach to explore the cloud computing adoption driversand its impact on organizational flexibility. Their results suggestthat decision to adopt cloud computing depends on factors likeperceived usefulness, relative advantage, perceived ease of use,vendor credibility, and attitude towards using technology.
Low, Chen, and Wu (2011) investigated the factors that affectthe adoption of cloud computing based on a questionnaire basedsurvey data collected from 111 firms belonging to the high-techindustry in Taiwan. Their findings show that relative advantage,top management support, firm size, competitive pressure, andtrading partner pressure have a significant effect on the adoption.In another study, Behrend, Wiebe, London, and Johnson (2011)examined the factors that lead to cloud computing adoption andusage in a higher education setting. They found that backgroundcharacteristics such as the student’s ability to travel to campus
have an effect on the usefulness perceptions, while ease of use islargely determined by first-hand experiences with the platformand instructor support.
Lin and Chen (2012) conducted a survey by interview approachto understand IT professionals’ understandings and concerns aboutcloud computing. Their findings suggest that while the benefits ofcloud computing such as its computational power and ability tohelp companies save costs, the primary concerns that IT managersand software engineers have are compatibility of the cloud withcompanies’ policy, IS development environment, and businessneeds. The findings also suggest that most IT companies in Taiwanwill not adopt cloud computing until the challenges of cloud com-puting, including security and standardization are reduced. Inanother study, Dillon et al. (2010) identified the challenges andissues of cloud computing from the adoption perspective. Theyfound that the security issue has played the most important rolein hindering cloud computing. They claimed that security issuessuch as data loss, phishing, and botnet pose serious threats tosensitive data.
Overall, the studies reviewed here suggest that cloud servicesmay have advantages not only for organizations but also individualsas these services provide the opportunity for students and academ-ics ubiquitous and interactive access to various applications andresources. This automatically reduces the cost of licensing, installa-tion, and maintenance while offering more powerful functionalcapabilities such as recovery and scalability. However, there is agap in research investigating the key determinants of educationaluse of cloud services. In this study, we aim to fill that gap by exam-ining the effects of security and privacy concerns on educational useof cloud services.
3. Research model
Theory of Planned Behaviour (TPB) suggests that behaviour isdetermined by intention. Intention, which refers to individuals’plans and motivations to commit a specific act, is predicted by atti-tude towards the behaviour, subjective norms, and perceivedbehavioural control (Ajzen, 1985). Attitude directly affects theintention to perform a behaviour and may directly affect behaviourin situations where an individual intends to perform the behaviour(Ajzen, 1991).
In TPB, beliefs that are specific to each situation are antecedentto attitude. The theory does not assume that beliefs that apply inone context also apply in other contexts. Likewise, TPB taps theimportant control variables for each situation independently andis more likely to capture situation specific factors (Mathieson,1991). Compatible with this theory, we consider security andprivacy perceptions of students are significant predictors of atti-tude towards using the cloud services in an educational context(see Fig. 1).
4. Constructs and associated hypotheses
4.1. Security and privacy
Security refers to the degree to which students believe thatcloud services are secure platforms for storing and sharing sensi-tive data. Security is relevant to introduce to our model, becausewhen one uses cloud services, there is a perception of risk involvedin transmitting sensitive information. One important aspect thatcan affect use of cloud services is the security of wireless datatransfer and cloud applications. The perception of a low level ofsecurity may affect students’ attitudes towards using such services.Students with low tolerance for technological risks may defer theiruse of these services. On the other hand, privacy refers to the
Attitude Behavioral Intention
Educational use of cloud servicesSecurity&Privacy
Fig. 1. Research model.
I. Arpaci et al. / Computers in Human Behavior 45 (2015) 93–98 95
degree to which students believe that cloud services are safe andprotects their sensitive information. When a student uses theseservices, there is a risk of recording or monitoring his/her personalinformation. Therefore, similar to security, privacy concerns mayimpede attitude towards educational use of cloud services. In linewith this discussion, the following hypothesis is formulated:
H1. Perceived security and privacy will have a positive influenceon attitude.
4.2. Attitude
Attitude towards using technology is defined as students’ overallaffective reaction to using cloud services (Davis, Bagozzi, & Warshaw,1989; Venkatesh, Morris, Davis, & Davis, 2003). Students’ positive ornegative feelings about cloud services may affect their behaviouralintentions. Therefore, the following hypothesis is formulated:
H2. Attitude will have a significant positive influence on behav-ioural intention.
4.3. Behavioural intention
Behavioural intention refers to the degree to which a studenthas formulated conscious plans to use or not use cloud servicesin the future (Warshaw & Davis, 1985, p. 214). Consistent withthe technology acceptance and use models, we expect that behav-ioural intention will have a significant positive influence on tech-nology usage. Thus, the following hypothesis is formulated:
H3. Behavioural intention will have a significant positive influenceon educational use of cloud services.
Table 1Demographics of the participants.
Frequency Percent Mean S.D.
Gendera
Female 137 68.5Male 62 31Mobile device ownership 1.57 .70Smartphone 136Tablet 23Laptop 128Other 27Cloud applications 1.35 .69Evernote 53 19.8Dropbox 62 23.1Google drive 114 42.5Other 39 14.6Age 21.19 1.86Duration of use of mobile devices (year) 4.15 2.94
a Has a missing value.
5. Methodology
5.1. Measurements
This study involves the preparation and administration of a sur-vey instrument, based on the literature. In an attempt to ensurecontent validity and face validity, the survey instrument was devel-oped based on questionnaire items that had been successfully usedin prior studies: security (Salisbury, Pearson, Pearson, & Miller,2001), attitude (Venkatesh et al., 2003), and behavioural intention(Venkatesh & Bala, 2008). Also, three academics experienced incloud services use took part in the expert panel to test the contentand face validity of the measurement. Based on the feedback fromexpert panel, modifications were done on the survey instrument.The instrument has total 16 Likert items, including 5 items for secu-rity-privacy, 4 items for use, 4 items for behavioural intention, and3 items for attitude. Participants were asked to indicate their levelof agreement using a five-point scale ranging from ‘‘strongly agree’’to ‘‘strongly disagree.’’
5.2. Participants
In total, 301 randomly selected undergraduate students of Fac-ulty of Education, whose ages ranged from 18 to 31 years, wererecruited for the study. However, 101 questionnaires were discarded
from data set; 6 invalid or incomplete questionnaires, 73 question-naires filled by subjects who never use any type of cloud applica-tions, and 22 outliers having z scores that fall within �3 and +3range. This left the study with 200 usable questionnaires for dataanalysis; a usable response rate of 66.45%. Table 1 presents thedetailed information on the participants involved in this study.Overall, 75% of the 200 participants reported that they use at leastone cloud application, while 25% of them use more than oneapplication.
5.3. Data analysis
5.3.1. Validity and reliabilityPrior to conducting factor analysis, data set was checked for
suitability for factor analysis. Table 2 shows the suitability of thedata for factor analysis. In addition to Kaiser–Meyer–Olkin (KMO)tests, the results of Bartlett’s test of Sphericity verified samplingadequacy of the data set for factorability.
After the data set was checked for factorability, 19 items weresubjected to factor analysis. We conducted an exploratory factoranalysis using principal components extraction to test constructvalidity of the scale. Based on the results of the exploratory factoranalysis, 3 questionnaire items were omitted from the scale. TheBartlett’s test of Sphericity indicated that the measures for fourlatent constructs were interdependent and the KMO measure ofsampling adequacy was well above the minimally accepted levelof .50 (Leech, Barrett, & Morgan, 2005). On the basis of a scree plotof eigenvalues, a one-factor solution appeared to be most appropri-ate for each measurement. For each measurement, total varianceexplained ranged between 49.74 and 74.95, which are all muchhigher than acceptable value of .40 for measures with one factor(Scherer, Wiebe, Luther, & Adams, 1988). Moreover, each measure-ment item has a factor loading above .71 and communality valueabove .50. Factor loadings, which are all much higher than accept-able value of .40 (Field, 2005), ensure that factor structures arerobust. The corrected item-total correlation coefficients ranged from.49 to .80, indicating acceptable to high homogeneity of the items(more than .50 is considered ideal according to Hair, Tatham,Anderson, & Black, 2006). Item analysis using the item discrimina-tion index also indicated that the measurement items can reliablydiscriminate the subjects.
Table 2The suitability of the data for factor analysis.
Privacy-security Attitude Behavioural intention Use
KMO .768 .684 .793 .832Chi-square 206.239 184.234 320.572 441.168Sig. .000 .000 .000 .000
96 I. Arpaci et al. / Computers in Human Behavior 45 (2015) 93–98
Reliability analysis results suggest that the instrument hasstrong internal consistency with the Cronbach’s alpha values ran-ged from .75 to .89, indicating acceptable to good internal consis-tency (Creswell, 2005). The results of principal componentanalysis (total variance explained, factor loadings, communalityvalues) and internal consistency reliability measures (item dis-crimination indices (t), corrected item-total correlations, and Cron-bach’s a values) are shown in Table 3.
5.3.2. Model fitWe then employed a structural equation modelling (SEM)
approach and estimated the model via maximum likelihood usingLISREL v.8.71 (Jöreskog & Sörbom, 2004) to assess the researchmodel. The model produced good fit indices as shown in Table 4.
The value of Chi-square/df is 2.01 a ratio of less than two is good(Kline, 2005). The GFI, an absolute fit index, is .89. The AGFI, a par-simony fit index, is .85. The NNFI is .97, which exceeds the thresh-old (P.95) for good fit. The CFI, an incremental fit index, is .98,which exceeds the threshold (P.95) for good fit. The NFI has avalue of .96 and the IFI has a value of .98, both exceed the threshold(P.95) for good fit. Lastly, the RMSEA, an absolute fit index, is .071.This value, which is also called a Badness of Fit index, is well belowthe acceptable level of .08. All together, the model fits the data rea-sonably well.
Table 3Reliability and validity.
Construct and items Total varianceexplained
Factorload
Com(com
Security-Privacy 49.74S1: Cloud applications have a strong security policy
to protect sensitive information.77 .59
S3 .78 .61S4 .71 .50P5: I store my personal information and documents
in cloud applications.75 .56
P7 .83 .70
Use 74.95U10 .86 .73U11: I use cloud applications for educational
purposes.87 .76
U12 .89 .79U13 .84 .71
Behavioural Intention 67.49BI14 .76 .58BI15 .80 .63BI16 .87 .76BI17: I intend to use cloud applications for
educational purposes in the future.86 .73
Attitude 70.48A18: Using the cloud applications for educational
purposes is a good idea.78 .61
A19 .87 .76A20 .86 .75
*** p < .001.
5.3.3. Path analysisThe SEM analysis was employed to test hypothesized relation-
ships. Consistent with the hypotheses, the results show that allproposed paths among the latent variables are statistically signifi-cant. Fig. 2 provides the results of the SEM analysis, including thepath coefficients with significance levels.
Structural equations for each variable are given in Table 5. Sum-mary of the hypotheses testing are listed below:
H1. Security and privacy have a significant effect on attitude atthe .001 level (b = .69; t = 8.08; p < .001).H2. Attitude has a significant effect on behavioural intention atthe .001 level (b = .98; t = 8.94; p < .001).H3. Behavioural intention has a significant effect on educationaluse at the .001 level (b = .83; t = 8.70; p < .001).
6. Discussion
This paper extends Ajzen’s (1991) Theory of Planned Behaviour(TPB) to explain and predict educational use of cloud services bypre-service teachers. Following the TPB, we developed a researchmodel, which posits that student attitudes predicted by securityand privacy perceptions and behavioural intentions are predictedby attitudes towards using cloud services. Our findings supportedthe proposed model, validating the predictive power of the TPB.Our findings stressed the importance of security and privacy vari-able, which add to the explanatory and predictive power of themodel, in predicting attitudes towards using cloud services, justify-ing the integration of this variable within the TPB framework.
Previously, Picazo-Vela, Chou, Melcher, and Pearson (2010)extended the TPB with Big-Five personality framework factors toidentify factors that influence an individual’s intention to providean online review. Similarly, Baker and White (2010) extended theTPB with group norm and self-esteem factors to predict frequent
mon variancemunality)
Itemdiscriminationindices (t)
Corrected item-totalcorrelation coefficients
Cronbach’sa
.7513.38*** .55
15.18*** .6012.24*** .5112.63*** .49
20.70*** .60
.8919.80*** .7416.08*** .77
17.99*** .7913.59*** .72
.8410.86*** .6014.38*** .6414.96*** .7416.40*** .72
.7914.51*** .55
15.34*** .6715.41*** .67
Table 4Model fit indices.
Model Acceptable fit values
v2 202.98p value <.001 .05 6 p 6 1.00 (Hoyle, 1995)v2/df 2.01 <3 (Kline, 2005)GFI .89 P.90 (Hair, Anderson, Tahtam, & Black, 1998)AGFI .85 P.80 (Marsh, Balla, & McDonald, 1988)SRMR .054 6.10 (Kline, 2005)RMR .036 <.05 (McDonald & Moon-Ho, 2002)RMSEA .071 <.08 (Hair et al., 1998)NFI .96 P.90 (Hair et al., 1998)NNFI .97 P.90 (Bentler & Bonett, 1980)CFI .98 P.90 (Bentler, 1990)IFI .98 P.90 (Bollen, 1989)PNFI .80
Table 5Structural equations for each variable.
Variable Equation Error R2
Attitude .69 ⁄ Security-Privacy .52 .48Behavioural intention 1.05 ⁄ Attitude .45 .95Educational use 0.87 ⁄ Intention .31 .69
I. Arpaci et al. / Computers in Human Behavior 45 (2015) 93–98 97
social networking sites use. In another study, Crespo and RodríguezDel Bosque (2008) proposed a model of electronic commerce adop-tion that adds personal innovativeness to the traditional formula-tion of the TPB.
We hypothesized that security and privacy have a positivelysignificant influence on students’ attitudes towards using cloudservices. Our results, along with previous studies, support thishypothesis. This study has shown that security and privacy percep-tions have a significant influence on attitude. Security variable hasbeen found in prior studies as a significant predictor of adoptionbehaviour. Salisbury et al. (2001) found that perceived security isa strong predictor of intention to purchase online. Similarly, Zhu,Kraemer, and Xu (2006) found that security perception has a sig-nificant effect on e-business adoption. In another study, Liu(2008) found that security is a significant determinant of e-com-merce adoption. This variable has been found in prior studies asa significant predictor of use of cloud computing. Dillon et al.(2010) found that security issues have an important role in hinder-ing cloud computing. Lin and Chen (2012) argued that challengesof cloud computing are security and standardization. Morerecently, Park and Kim (2014) found that user acceptance of mobilecloud services is largely affected by security, perceived mobility,quality of service, connectedness, and satisfaction.
Lian and Lin (2008) suggested that perceived web security andpersonal privacy concerns have significant effect on attitudestowards online shopping in the context of different product types.In another study, Wu, Huang, Yen, and Popova (2012) investigatedtrust and privacy concerns related to the willingness to provide per-sonal information online based on Privacy–Trust–BehaviouralIntention model. Their findings indicated a significant relationshipbetween the privacy concern and trust, content of privacy policiesand privacy concern/trust, and willingness to provide personalinformation and privacy concern/trust. More recently, Fogel andNehmad (2013) studied risk taking, trust, and privacy concerns withregard to social networking websites among 205 college students.They discovered that general privacy concerns and identity informa-tion disclosure concerns are of greater concern to women than men.
Finally, it is important to note that most of the participantsbelieve that cloud applications have a strong security policy to pro-tect sensitive information. Moreover, most of them believe thatsensitive information or documents stored in cloud applicationscannot be lost or end up in the hands of unauthorized parties.
AttitudeSecurity&Privacy .69** .98**
Chi-Square = 202.98, df = 101, P-v
Fig. 2. Casual model of pre-service teache
Consequently, most of the participants reported that they usecloud applications in educational settings to store or share sensi-tive information and documents.
7. Conclusion and future directions
7.1. Practical implications
The fact that security and privacy were found to be importantdeterminants of attitude suggests that students’ intention to usecloud services is positively related to their security and privacy per-ception. This suggests that higher levels of security and privacy per-ception positively influence the actual usage. Therefore, cloudservice providers should fortify both application and network levelsecurity in order to protect the privacy of the users and the intellec-tual property of them as these services collect and compile anincreasing amount of sensitive information. By this way, the serviceproviders may increase security and privacy perceptions of theusers.
Universities, which demand diffusion of cloud services amongtheir students, may also promote use of these services by their stu-dents providing such services working in the intranet with secureaccess. On the other hand, universities may collaborate with ser-vice provider companies to provide cloud services. Thus, universi-ties may decrease costs of implementation of these services andthe companies may increase number of customers. Academics alsoneed to equip with the acquired literacy and skills regarding thesetechnologies. Therefore, universities should support educationaluse of cloud services by providing free services and trainings onhow to use these services.
Governments should extend regulations to protect sensitiveinformation of citizens using cloud services. Thus, these serviceswill be diffused and commonly used among citizens as theirawareness and security-privacy perceptions were increased.
7.2. Research implications
The results of the study supported assumptions of Theory ofPlanned Behaviour that we based our study on. This suggests thatthe assumptions of this theory can be used to explain the natureof educational use of similar technologies. The present study mayalso contribute to the literature since the study on educationaluse of cloud services is limited. The security and privacy variableswere found to be a strong determinant of educational use of cloudservices. Therefore, these variables should be taken into account infuture researches on use of similar technologies. Moreover, theresults demonstrated that attitude has a stronger effect on educa-tional use of cloud services than behavioural intention. Thus, futurestudies may focus on the effect of attitude on educational use ofsuch services to develop a more parsimony model that efficientlypredicts usage.
Behavioral Intention
Educational use of cloud services .83**
alue = .000, RMSEA = .071
rs’ educational use of cloud services.
98 I. Arpaci et al. / Computers in Human Behavior 45 (2015) 93–98
7.3. Limitations and directions for future research
Several limitations of our study should be addressed by futureresearch. First, the study focused on cloud services; therefore, theresults should be applied to other services with caution. Second,since we investigate educational use of cloud services, the studywas delimited from the examination of the subjects who neveruse such services. Third, the study examines the use of cloud ser-vices by individuals; therefore, the results should not be general-ized to use of these services at organizational level. Forth, westudied security and privacy constructs as one variable, however,these constructs can be investigated separately by future research.Furthermore, the proposed model can be enhanced by additionalvariables such as subjective norms and perceived control. Finally,qualitative data should be included to provide a better understand-ing of why and how the factors have influence. This investigation ofdeterminants of use cloud services by universities is a potentialavenue of future research.
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