Keywords: Cloud Computing, Cloud Computing Acceptance, Saudi Arabia, Technology Acceptance Model (TAM), Users
INTRODUCTION
In 1955, a new concept was proposed, aim-ing to provide computaim-ing services for public utilities, such as gas, water and electricity was proposed (Parkhill, 1966). This concept remained a dream for approximately 40 years. In a trial to make this dream real, grid comput-ing was proposed in the early 1990s as way to make supercomputer capabilities available to all who need them (Foster & Kesselman, 2004).
Currently, cloud computing has emerged as an important topic that differs from grid computing in providing Software as a Service (SaaS) and Platform as a Service (PaaS). By taking a look into the literature of cloud computing, it can be ascertained that researchers have focused on finding the most appropriate definition of cloud computing, technical and economic potential, and so on. Researchers also have proposed various tools and models of cloud computing that help in solving different problem domains. However, the literature lacks user studies in cloud computing in spite of the fact that users
Users’ Acceptance of Cloud
Computing in Saudi Arabia:
An Extension of Technology
Acceptance Model
Saad T. Alharbi, Taibah University, Saudi Arabia
ABSTRACT
Cloud computing has become a popular topic in the research community because of its ability to transform computer software, platforms, and infrastructure as a service. However, cloud computing literature currently lacks user studies despite the fact that users play a crucial role in the success and failure of emerging tech-nologies. This paper presents a study aimed at investigating users’ acceptance of cloud computing in Saudi Arabia. As a baseline, it utilizes the Technology Acceptance Model (TAM) along with five additional factors believed to affect users’ acceptance of new technology in the region in order to achieve the study goals. These factors are gender, age, education level, job domain, and nationality. The results demonstrated a high level of acceptance of cloud computing and a valid TAM in its standard form. The results also indicated that age, education, job domain, and nationality have a significant effect on users’ attitudes toward the adoption of cloud computing. However, no difference was found in the attitude toward the adoption of cloud computing between male and female employees.
play an important role in the success and failure of technologies.
Therefore, this paper presents a study aimed at investigating users’ acceptance of cloud computing. This case study was applied to employees of information technology orga-nizations in the kingdom of Saudi Arabia. The Technology Acceptance Model (TAM) (Davis, 1989) is one of the dominant models currently used in determining users’ intentions to use a new technology. It assumes that acceptance of new technology can basically be predicted based on two principles: Perceived Usefulness (PU) and Perceived Ease of Use (PEU). These principles are believed to significantly affect users’ attitudes toward using the technology. Moreover, Behavioral Intention (BI) to use a technology, as posited in the model, can be determined by users’ attitude together with per-ceived usefulness. As such, behavioral intention defines Actual Use (AS) of this technology.
Previous studies have shown that TAM is an effective indicator for predicting the acceptance and usage of new technologies in different domains, such as information security (Fahad Al-Harby, Qahwaji, & Kamala, 2010; Boswell & Reithel, 2006; Lai & Li, 2005; Polatoglu & Ekin, 2001; Wang, Wang, Lin, & Tang, 2003), learning technologies (Sek, Lau, Teoh, Law, & Parumo, 2010), gender differ-ences (Al-Harby, Qahwaji, & Kamala, 2009; Gefen & Straub, 1997) and new systems and interfaces (Amoako-Gyampah & Salam, 2004). Therefore, the Technology Acceptance Model (TAM) was used as baseline to predict users’ acceptance of cloud computing in Saudi orga-nizations. It was extended with five factors: gender, age, education level, job domain (i.e., profession) and nationality. These factors were anticipated to affect users’ acceptance of new technologies in the region. The results of this study can be used as a basis for researchers and IT practitioners in the region who would like to adopt cloud computing effectively.
The paper begins with a brief description about cloud computing in terms of definitions, classifications, architecture, economic values and benefits, and current cloud services. The
paper then describes the adopted research model and presents the hypotheses. Then, the experimental instrument used to collect the data is described prior to discussion of the obtained results in terms of validation tests, sample profile and hypotheses testing. The paper concludes by interpreting experimental results into recommendations and guidelines that would help IT practitioners to adopt cloud computing effectively in Saudi Arabia.
CLOUD COMPUTING
Various efforts were made in order to find an appropriate definition for cloud computing. For instance, Hays defined it as on-demand computing, software as services or the Internet as a platform (Hayes, 2008). However, this definition seems to be general and does not give a comprehensive technical view. In an effort to give a more descriptive definition, Armbrust and colleagues defined cloud computing as ap-plications that deliver services over the Internet where the hardware and software systems in the datacenter provide these services (Armbrust et al., 2009). In this definition, cloud refers to the hardware and software in the datacenters, and the applications can be defined as software as a service (SAAS). Based on these definitions, it can be noticed that cloud computing helps in adopting IT services without considering the infrastructure and hardware required running these services.
There exist various technical terms that need to be understood when speaking about cloud computing. SaaS, for example, is an important term related to cloud computing and is defined as the delivery of applications to users through the Internet-based infrastruc-ture in different locations (Knorr & Gruman, 2008). Platform as a Service (PaaS) is also a term related to cloud computing, which is the delivery of development environments and software systems as a service (Knorr & Gru-man, 2008; Luis, Luis, Juan, & Maik, 2008). Buyya, Yeo and Venugopal (2008), on the other hand, defined cloud computing
differ-ently based on their expectations of what cloud computing promises to become. From their point of view, Cloud “is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumer” (Buyya et al., 2008). As mentioned previously, there are various definitions for cloud computing in the literature (Bragg, 2008; Geelan, 2008; Hwang, Wu, Yang, & Xu, 2008; McFedries, 2008; Milojicic, 2008). Luis and colleagues claim that cloud computing has not been defined completely in the literature yet (Luis et al., 2008). Therefore, they proposed a new definition: Clouds “are a large pool of eas-ily usable and accessible virtualized resources, such as hardware, platforms and services where these resources can be dynamically reconfigured to adjust to a variable load allowing also for optimum resource utilization” (p. 51).
Cloud computing also can be classified into different categories. For instance, Armbrust and colleagues classified clouds into public and pri-vate categories (Armbrust et al., 2009). A public cloud refers to the services made available in the form of pay per usage to the public (i.e., utility computing). A private cloud, on the other hand, includes organizations’ internal data centers where services are not available to the public. Grossman also categorized clouds differently: specifically, he differentiated between clouds that provide computing instances on demand and clouds that provide computing capacity on demand (Grossman, 2009). The first type of clouds can use the instances for providing software as a service (SaaS) and platforms as a service (PaaS).
One of the crucial factors for adopting cloud computing successfully in organiza-tions is the understanding of its architecture. The architecture of cloud computing basically can be depicted in three layers: down to top cloud provider, SaaS provider and SaaS user (Armbrust et al., 2009; Michael et al., 2010). However, several efforts have been carried out
in order to enhance this architecture. Buyya and colleagues, for instance, extended this basic architecture to support market-oriented resource allocation in data centers (Buyya, Ranjan, & Calheiros, 2009; Buyya et al., 2008). This proposed model consisted of four layers, namely users/brokers, SLA source allocation, virtual machines and physical machines. SLA source allocation works as the interface between the cloud provider and the user, which requires a service request examiner, pricing, and ac-counting, VM monitoring and service request monitoring mechanism.
In the literature, researchers have docu-mented numerous benefits and potentials for cloud computing. One of the main benefits of cloud computing is a reduction in running costs for data centers. Michael and colleagues, for instance, have pointed out that pay by usage (i.e., resources and services) leads to significant cost saving even if rent rates are higher owing to the resources (Michael et al., 2010). In an-other work, Armburust and colleagues raised an important question: “Is it more economical to move my datacenter-hosted service to the cloud, or to keep it in a datacenter?” (Arm-brust et al., 2009). They updated Gray’s 2003 cost data to 2008 in order to track the rate of change of technologies for cloud computing in the previous five years. They estimated that pay-as-you-go services could reduce the cost of resources and applications by half. Buyya and colleagues highlighted the fact that the economic potential of cloud computing was recognized by various market research firms, such as IDC (International Data Corporation), which estimated that international spending on cloud services will increase from $16 billion in 2008 to $42 billion in 2012 (Rajkumar Buyya et al., 2009). Nevertheless, several challenges and technical risks were highlighted in the literature, such as data lock-in and availability licensing.
Additionally, security and privacy are key risks about which researchers were concerned when speaking about cloud computing. Buyya and colleagues pointed out that some organi-zations and specific types of users, such as banks, would not accept their data being saved
in clouds (Rajkumar Buyya et al., 2009). This reluctance occurs because the physical locations of the datacenters would not be anywhere in the world and could be unknown to them. Forming a Service Level Agreement (SLA) that includes clear policies regarding how data is going to be dealt with as well as including resource pricing and billing has been proposed as one of the methods for increasing the trust of cloud providers (Rajkumar Buyya et al., 2009; Ryan, 2011). However, Pearson, Shen and Mowbray (2009) highlighted the fact that privacy laws vary according to many factors, such as the datacenter’s region or country. Therefore, they developed a tool called Privacy Manager, which assists users managing the privacy of their data in the cloud. The idea of handling data privacy on a technology called obfuscated where the data is sent to the cloud in an encrypted way and processing are performed on the original data. Privacy Manger also allows users to set their preferences for personal data handling by the cloud provider.
Currently, top technology companies have started investing in cloud computing. For in-stance, Amazon launched a service called Elastic Compute Cloud (EC2) (Amazon Web Services, n.d.) which provides resizable compute capac-ity. Service users pay only for what they use, and no minimum is required. Google launched a service called Google App Engine (Google, n.d.), which allows users to build and host web applications written in different programming languages on the same system that powers Google applications. Microsoft also provides its users such a service, called Live Mesh (http:// explore.live.com/home); it stores applications in a centralized location. Cloud computing also is widely employed to support scientific purposes (Rajkumar Buyya et al., 2009; Hoffa et al., 2008; Wang et al., 2008).
RESEARCH MODEL
The main aim of the study is to investigate the extent to which employees in a Saudi Arabian organization would accept cloud computing.
The study also aimed to investigate the effect of various factors like gender, age, education, job domain and nationality on the acceptance of cloud computing. TAM was used as a baseline to verify the aims of the study. Figure 1 shows the research model of the study. It shows that the model consists of TAM main constructs with five external factors (i.e., gender, age, education level, job domain and nationality). The basic assumption of the model is that these factors directly link with users’ attitudes towards cloud computing. Thus, the following hypotheses are tested in the study:
H1: The perceived usefulness of cloud comput-ing in Saudi organizations has a positive influence on the attitude towards cloud computing.
H2: The perceived ease of use of cloud comput-ing in Saudi organizations has a positive influence on the attitude towards cloud computing.
H3: The perceived ease of use of cloud com-puting in Saudi organizations has a posi-tive influence on the usefulness of cloud computing.
H4: The perceived usefulness of cloud comput-ing in Saudi organizations has a positive influence on the behavioral intention to use cloud computing.
H5: The attitude towards adopting cloud com-puting in Saudi organizations has a positive influence on the behavioral intention to use cloud computing.
H6: Gender has a significant effect on the at-titude towards cloud computing in Saudi organizations.
H7: Age has a significant effect on the attitude towards cloud computing in Saudi organi-zations. The younger employees are more likely to accept cloud computing.
H8: Education has a significant effect on the attitude towards cloud computing in Saudi organizations. Employees with higher education are more likely to accept cloud computing.
H9: Job domain has a significant effect on the attitude towards cloud computing in Saudi
organizations. The higher employees are in the organizational hierarchy, the more likely they are to accept cloud computing.
H10: Nationality has a significant effect on the attitude towards cloud computing in Saudi organizations. Saudi employees are more likely to accept cloud computing than non-Saudis.
METHOD
In order to achieve the main aim of the study and to test the previously mentioned hypotheses, an online questionnaire was designed as an instrument to measure the acceptance of cloud computing amongst employees in Saudi organi-zations. This questionnaire consisted of twenty-eight questions categorized into five sections: personal information (six questions), perceived usefulness (ten questions), perceived ease of use (five questions), attitude (four questions) and
behavioral intention to use (three questions). A review on the surveys performed in previous studies (Amoako-Gyampah & Salam, 2004; Davis, 1989; Koufaris, 2003; Sek et al., 2010; Wixom & Todd, 2005) was carried out in order to identify existing questions measuring TAM constructs. These questions were adopted and modified to suit the study framework. The first section of the survey (i.e., PI) aimed to collect information about respondents, such as gender, age and professions where all questions were multi-items questions. In the remaining sections, respondents were asked to rate their agreement on each statement using a 1-to-5 Likert scale, where one indicated strongly disagree and five indicated strongly agree.
The survey was distributed via e-mail to employees working in IT organizations in the kingdom of Saudi Arabia. It was available online and accessible to the public using Google forms for two months starting on 1st March 2011. A total of 171 completed responses were collected. Figure 1. Research model
These responses came from IT organizations in public sectors, such as IT centers in universities, ministries and institutions as well as from major IT companies in the Kingdom. Respondents also were kindly asked to resend the survey hyperlink to their colleagues working in the IT sector. Further details about the study sample are presented in the sample section.
RESULTS
Validation Tests
Before commencing with the data analysis process, several validation tests were car-ried out in order to ensure the reliability and discriminant validity of the instruments. For instance, Cronbach alpha test was performed on the items of each construct to ensure the validity of the questionnaire and its internal consistency. The reliability of items in each construct ranged from 0.74 for behavioral in-tention to 0.92 for perceived ease of use (Table 1), which exceeded the standard suggested by Nunnally and Bernstein (1994). Therefore, the internal consistency of the questionnaire was within an acceptable range.
Furthermore, factor analysis was carried out in order to test the convergent and dis-criminant validity of the instrument (question-naire). A total of 28 items were analyzed at the level of four factors (i.e., perceived usefulness, perceived ease of use, attitude and behavioral intention). Table 2 shows the correlation matrix for the factor analysis using principal component analysis as an extraction method and Varimax rotation. It can clearly be noticed that each item scored higher (i.e., higher than 0.50) in its
construct than any other construct. According to Fornell and Larcker (1981), this result rep-resents a satisfactory level of discriminate validity and convergence.
Sample
As mentioned previously, the questionnaire was distributed by e-mail to people who work in information technology organizations in the Kingdom of Saudi Arabia. A total of 171 completed surveys were received and used for analysis. Seventy-three percent of respondents were male and the majority of the respondents were aged between 20 and 35 years old. Table 3 shows a descriptive analysis of sample char-acteristics.
Hypotheses Testing
The first five hypotheses (i.e., hypothesis 1–5) tested the relationships amongst the regular TAM constructs (PU, PEU, AT and BI) while the rest of the hypotheses (i.e., hypothesis 6–10) tested the effect of the external variables (age, gender, job domain, education and nationality) on attitudes towards cloud computing in Saudi organizations. In order to test these hypotheses, linear regression was applied on the obtained data. The results are presented in Table 4. Hy-potheses 1 and 2 tested the effects of PU and PEU on the AT of cloud computing in Saudi or-ganizations respectively. Moreover, hypotheses 6, 7, 8, 9 and 10 tested the effect of gender, age, education, job domain and nationality on the AT of cloud computing in Saudi organizations, respectively. Entering all variables in one block, the results presented a significant percentage of variance 62% (R2= 0.65, F (7,163) = 37.92, Table 1. Descriptive statistics and Cronbach’s Reliability
Construct Mean SD Cronbach’s α
PU 3.74 0.88 0.89
PEU 3.93 1.03 0.92
AT 3.86 0.87 0.86
p<0.01). The results also demonstrated that PU (β= 0.47, t= 7.66, p<0.01) and PEU (β= 0.37, t= 7.67, p<0.01) are significantly related with AT. However, the results indicated that gender had no effect on AT of cloud computing in Saudi organizations (β= -0.16, t= 1.32, p>0.05). On the other hand, significant effects were found for age (β= -0.36, t=- 2.18, p<0.05), education (β= 0.19, t= 2.65, p<0.05), job domain (β= -0.14, t= -2.18, p<0.05) and nationality (β= -0.44, t= -3.14, p<0.05). Thus, hypotheses 1, 2, 7, 8, 9 and 10 were supported, whereas hypothesis 6 was rejected.
Hypothesis 3 tested the effect of PEU on PU of cloud computing in Saudi organizations.
The results demonstrated 15% (R2= 0.15, F (1,169) = 30.50, p<0.01). The results also in-dicated that PEU is significantly related with PU (β= 0.34, t= 5.53, p<0.01). Thus, hypoth-esis 3 was supported. Hypotheses 4 and 5 tested the effect of AT and PU on BI of cloud computing in Saudi organizations respectively. Entering all data in a single block, the results showed 73% (R2= 0.73, F (2,168) = 229.70, p<0.01) of variance. Also, the results indicated that PU (β= 0.34, t= 7.31, p<0.01) and AT (β= 0.65, t= 11.98, p<0.01) are significantly related with BI; therefore, hypotheses 4 and 5 were supported.
Table 2. Factor analysis where 1:PU, 2:PEU, 3: AT and 4:BI
Construct 1 2 3 4 PU1 .547 .079 .255 .362 PU2 .516 .082 .296 .386 PU3 .559 .249 .266 .410 PU4 .574 .289 .332 .459 PU5 .557 .144 .366 .342 PU6 .598 .237 .266 .294 PU7 .581 .341 .494 .551 PU8 .503 .310 .303 .391 PU9 .567 .401 .543 .560 PU10 .560 .317 .488 .454 PEU1 .298 .661 .441 .526 PEU2 .198 .748 .449 .514 PEU3 .169 .706 .455 .516 PEU4 .297 .739 .526 .636 PEU5 .262 .769 .558 .594 AT1 .399 .546 .584 .540 AT2 .372 .555 .696 .640 AT3 .364 .392 .638 .580 AT4 .308 .450 .610 .547 BI1 .297 .526 .495 .775 BI2 .372 .555 .522 .741 BI3 .494 .341 .469 .595
DISCUSSION
The obtained results demonstrated an overall high level of users’ acceptance of cloud com-puting in Saudi organizations. The results also showed a valid TAM model in its casual form. For instance, it was found that users’ attitude towards adopting cloud computing in Saudi organizations was significantly affected by perceived usefulness and ease of use. Further-more, behavioral intention was found signifi-cantly to be affected by perceived usefulness and attitudes towards the adoption of cloud computing. Thus, increasing the awareness of users towards the usefulness and ease of use of adopting cloud computing will most likely increase the acceptance of such technology in Saudi organizations.
The TAM model was extended in this study by linking five external variables (gender, age, education, job domain and nationality) with the attitude of users towards cloud computing. The results indicated that gender has no direct effect on the attitude towards the adoption of cloud computing. Such results may help deci-sion makers minimize the efforts that come
from differentiating between male and female. It was also found that age significantly affects users’ attitudes towards cloud computing. Specifically, it was found that younger users would likely accept cloud computing more than senior employees. Therefore, recruiting younger staff in Saudi organizations would make the adoption of such technology easier. The results also demonstrated that education is an important factor in the acceptance of cloud computing. In fact, it was found that the higher the degree obtained by users, the more likely they will be to accept cloud computing. Moreover, it was found that users’ profession (job domain) significantly affects their at-titude towards cloud computing. Employees in managerial positions, such as directors and head of managers, had more positive attitudes than those who perform technical hardware and software tasks. Therefore, information technol-ogy managers play an important role in the successful implementation of cloud computing in Saudi organizations. The obtained results also indicate that Saudi employees working in the IT sector are more likely to accept cloud computing than their non-Saudi partners. The Table 3. Descriptive statistics of sample characteristics
Measure Items Frequency Percent
Gender Male 124 72.5 Female 47 27.5 Nationality Saudi 149 87.1 Non-Saudi 22 12.9 Education Diploma 21 12.3 Bachelor 114 66.7 Master 18 10.5 PhD 18 10.5
Job domain Hardware 44 25.7
Software 75 43.9
Management 52 30
Age group Young 147 86
Middle 22 12.9
reasons behind such findings could be the fear of losing their job or reduction in salaries as such employees usually are employed through short-term contracts by third party contractors (i.e., outsourcing).
CONCLUSION
Studies related to cloud computing have fo-cused heavily on the theoretical definitions of clouds and studying the technical side; less attention has been given to user acceptance of this emergent technology. In an effort to fill this gap in the literature, this paper presented a study aimed at investigating users’ acceptance of cloud computing. The study was applied in the Kingdom of Saudi Arabia as a case study using the Technology Acceptance Model (TAM) along with five additional factors: gender, age, education level, job domain and nationality. The results showed a high level of acceptance of cloud computing while presenting a valid TAM in its regular form. Furthermore, the results highlighted a significant influence of age, education, job domain and nationality on attitudes towards cloud computing. In contrast, male and female employees were found to
have almost the same attitude towards cloud computing. The effect of several factors, such as trust, privacy and cost on the perceived use-fulness and attitude towards cloud computing is an important topic that should be considered further in the future.
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