Received 19 May 2016; accepted 11 July 2016; published 14 July 2016
Cloud computing is a general term that provides services over the Internet. Security and privacy are the two main issues that cloud computing providers consider.  argues that security issues play a vital role in cloud computing. The cloud services which allow people accessing to their documents whenever they are concerned the ways in which that services will be secured. Also, the cloud providers may use customer data, the trade-off between extensibility and security responsibility, virtualization, and the different approaches to provide security and privacy may generate integration challenges  -  . According to  , the revenue of cloud service providers has been increased year by year as companies all around the world is connected through open networks, and these networks are used to transmit information electronically. Consequently, it grows fast.
In addition, organizations consider IT governance to be one of the top influential factors for creating business value and gaining competitive advantage   . In 2009, a research group proposed a theoretical model for IT governance and IT business alignment  . The proposed model was based on the suggestion by  that the cores of IT governance mechanism were the strategic alignment and the operational alignment. The authors show theoretically from governance prospective that combining the key finding from IT governance, IT business and IT value research is the role of top executive support and operational alignment. Executive support derived and strongly correlated with strategy alignment, operational alignment, and IT governance tools and indirectly with business process performance. Moreover, the authors indicate empirically the IT governance is mediated the by strategic and operational alignment.
Several researchers (e.g.  -  ) emphasized that one of the most essential advantages offered by cloud computing was the reduced cost. Cloud computing radically lowers the cost of entry especially for smaller firms trying to benefit from compute-intensive business analytics that are solely available to the largest ones.  which defined cloud computing as the process of allowing users to access available services through the internet, tried to help small businesses to decide whether or not cloud computing was efficient for their operation. They focused on the benefits that would be gained if small businesses used the system and they had mentioned the special characteristics of such computing. A survey was developed to collect and analyze data. The results show that 59% of the small businesses that use cloud computing are satisfied. 34% determined that cloud computing was a solid information management facility, and 55% agreed that was cost effective.
According to  , cloud is a parallel and distributed computing system consisting of a collection of inter- connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established throughout cooperation between the service provider and consumers.  suggests that cloud computing and virtualization are new but indispensable components of computer engineering and information systems. The study related to this article indicated that teaching cloud computing reduced overall IT costs through the consolidation of systems besides results in reduced loads and energy savings in terms of the power and cooling infrastructure. Indeed,  stated that users of cloud services themselves relying on some internet information resources which lie on some nodes like computing resources, software resources, data resources and management resources. These services should be allocated based on a demand driven, user dominant, on-demand services, no centralized control, and users do not care where the server. Thus, the parallel computing and virtualization technology has become the core support technology after the concept of cloud computing was proposed.
However, potential challenges related to cloud computing occur such as security, service availability, lack of interoperability standards, complex structure and compatibility issues, cloud computing adoption, and competitiveness of cloud computing and trading partner support for cloud services   -  . Furthermore, since both of TAM model and TOE framework are considered widely in the literature related to technology adoption studies for successfully predicting and explaining users’ intentions to adopt existing and new technologies, this research used them to examine the determinants of cloud computing adoption. Indeed, TAM and TOE are considered as commonly used models for measuring acceptance level of customer for emerging systems (for detailed information regarding TAM and TOE, please refer to  ). Although many studies examine the applicability and convergence of TAM in the literature review and meta-analysis, there is no study attempt to use TAM in a co-citation analysis. Therefore,  used a co-citation analysis to study its intellectual structure and determine the main trends of TAM. Based on certain statistical analyses which include factor analysis, multidimensional scaling, and clustering analysis; three main trends were identified: task-related system, e-commerce systems, and hedonic systems. The authors expected that these findings may offer benefits for both academic and practical fields and considered as benchmarks for future research of TAM field to classify the emerging new technologies to which trend are followed.
The current research examined several technological, organizational, and environmental variables which will impact the adoption of cloud computing in firms by implementing both TAM and TOE models. Moreover, since the Internet usage and its applications such as the implementation of cloud computing tools smooth the progress of intra communication to lessen trade and investment impediments and boost intra-region trade systems  ; this study examined factors impacting cloud computing perceived importance in several Arab firms, exclusively in Jordan, Saudi Arabia and United Arab Emirates. In doing so, the study will assist policy makers, and business managers to realize the resources and conditions necessary to comprehend the potential values of their IT investments in the existence of cloud computing capabilities, which in turn will facilitate the trade systems among the Arab countries.
This paper is structured as follows. It begins with the research model regarding cloud computing and its hypothesis. Then, the research methodology used for the study is provided. Next, data analysis techniques and the conclusions are then addressed.
2. Research Model
In general, executives are unable to influence each other very well  . CIOs (Chief Information Officers) are the head of technology and the source of technology innovation. A study was conducted to understand how Irish CIOs persuade the technology innovation and IT-business alignment within an organization  . The results proved that most CIOs persuade other executives to maintain technology innovation that improve IT-Business alignment. On the other hand, some other CIOs were unable to do so, which increase the gap between CIOs and executives. Based on the results of their study, the authors suggested some important behaviors and practices for CIOs to decrease and increase the connection. They recommend CIOs to encourage technological innovations and improve IT-business alignment with other executives by maintaining partner wisely, gathering the right data, and soliciting others to influence and communicating appropriately. Consequently, top management support is crucial when adopting new technologies such as the use of cloud computing applications. Cloud computing is an attractive model to companies; it eliminates the cost and the required small resources to start with.  defined cloud computing as the applications delivered as services over the Internet and the hardware and system in the data centers that supply these services in which the services themselves referred to as Software as a Service (SaaS). According to the National institute of standards and technology (NIST), the definition of cloud computing has been recognized as a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction   .
According to   and  , cloud computing offers many services such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The capability provided to the customer of IaaS is raw storage space, computing, or network resources with which he/she can run and implement an operating system, applications, or any software that they select. In PaaS, the cloud providers supply the hardware and a toolkit in which a number of supported programming languages help to build higher level services. In the case of SaaS, the applications are usually easily reached through a thin client interface, such as a web browser.  stated that cloud computing types include private, public, hybrid, and community. The private cloud is devoted to a single organization in providing the different services necessary for its working. Cloud stack, Rackspace, Red Hat cloud are examples of private cloud providers. The public cloud is meant for organizations sharing their resources or diverse infrastructure, softwares and platforms with the public, besides sharing of resources and storage could take place over the internet. Some of the public cloud service providers include Bluelock, Microsoft, Google, HP and Dell Inc. Hybrid cloud model arranges the use of both public and private cloud, which includes scaling across various clouds. The exploitation of hybrid cloud may well necessitate the need for an on-premise and off-premise resources. The hybrid cloud providers include Voxen, VMware and Western Digital. Community cloud is defined as a subclass of public cloud in which various resources and services like softwares, platforms and infrastructures can be shared among various users. Intel Corporation and Cisco are some of the service providers for community cloud.
According to cloud computing characteristics, cloud computing has been adopted for many purposes  -  . For instance, managing the labs to assist maintenance and network management. Thus,  presented the possibilities of using cloud computing as a solution to expand work efficiency at Taiz University Computer Center and Information Technology labs which equipped with hardware and software resources; in the hope that their results could be the guideline for all universities in Yemen to use the cloud computing. The researchers found that cloud computing impacts managing the resources for Taiz University in numerous areas such as daily education operations, supporting staff, and distributed management system which in turn can considerably reduce load, leveraging efficiencies. In addition, students can work on the cloud, cooperate with their team members, share knowledge, and access their homework anywhere, at home or from the labs.
Another cloud computing application suggested the development of information technology helped to improve and facilitate the water distribution system and here the researchers (i.e.  ) focused on the use of cloud computing to support decision making in water distribution system. The characteristics of cloud computing was the main factoring in development the water distribution system. In addition to the wide range of benefits, cloud computing provides four deployment models to the water companies, and they can select the model that better suits their requirements. Private cloud: the cloud infrastructure is provisioned for exclusive single organization; Community cloud: Cloud infrastructure provisioned for exclusive specific community of consumers. Public cloud: Infrastructure provisioned for open use by the general public. Hybrid cloud: Composition of two or more distinct cloud infrastructures (private, community, or public). For the case of the water industry, hybrid cloud approach has several advantages among others because of the flexibility it provides to keep information inside the proprietary IT infrastructure of companies besides using some public infrastructure.
 and  argued that with the broad development in mobile applications and advancements in cloud computing, few years later a new growth happened in the form of mobile cloud computing by providing a platform where mobile users make use of cloud services on mobile devices. However, the users of mobile cloud computing are still less than expectations because of the associated risks in terms of security and privacy issues such as data theft risk; privacy of data belongs to customers; violation of privacy rights; loss of physical security; handling of encryption and decryption keys; security and auditing issues of virtual machines; lack of standard to ensure data integrity; and services incompatibility because of different vendors involvement. Also, there are some attacks which are probable at end user mobile device such as device data theft; virus and malware attacks via wireless devices; and misuse of access rights. Nevertheless, some common information security issues of cloud computing could be used as a protection methods including system security of server and database; networking security; user authentication; data protection; and system and storage protection techniques.
However, as the current research aims to examine the factors impacting cloud computing perceived importance in some Arabian firms, exclusively Jordan, Saudi Arabia and United Arab Emirates by using the integration of Technology Acceptance Model (TAM) model and Technology-Organizational-Environmental (TOE) framework as adapted from  (Figure 1 displays the research model), the hypotheses are formalized as below:
H1: The higher the level of relative advantage, the greater influence on perceived usefulness.
H2: The higher the level of relative advantage, the greater influence on perceived ease of use.
H3: The higher the level of compatibility, the greater influence on perceived usefulness.
H4: The higher the level of compatibility, the greater influence on perceived ease of use.
H5: The lower the level of complexity, the greater influence on perceived usefulness.
H6: The lower the level of complexity, the greater influence on perceived ease of use.
H7: The higher the level of organizational competency, the greater influence on perceived usefulness.
H8: The higher the level of organizational competency, the greater influence on perceived ease of use.
H9: The higher the level of top management support, the greater influence on perceived usefulness.
H10: The higher the level of top management support, the greater influence on perceived ease of use.
H11: The higher the level of training and education, the greater influence on perceived usefulness.
H12: The higher the level of training and education, the greater influence on perceived ease of use.
H13: The higher the level of ease of use, the greater influence on perceived usefulness.
H14: The higher the level of ease of use, the greater influence on cloud computing perceived importance.
H15: The higher the level of perceived usefulness, the greater influence on cloud computing perceived importance.
3. Research Methodology
Indeed, the goal of the research is to examine factors impacting cloud computing adoption by implementing the integration of TAM model and TOE framework as adapted from  and  . Nine constructs were measured
Figure 1. Research model.
using closed-end five-point Likert scale items, with scales ranging from 1 = “strongly disagree” through 3 = “neither agree nor disagree” to 5 = “strongly agree”. Table 1 shows the measured constructs and the items measuring each construct.
Indeed, online and offline survey questionnaires were used for data gathering from the firms those have implemented cloud computing, specifically from top and middle-level IT managers, and IT employees from the operational level as well. Thus, judgmental sampling technique was used in this research. Before implementing the survey, the instrument was reviewed by five lecturers who are specialized in the Management Information Systems (MIS) discipline in order to identify problems with wording, content, and question ambiguity. After some changes were made based on their suggestions, the modified questionnaire was piloted on four employees who are working in AL Moasron Trading firm, in Jordan. Based on the feedback of this pilot study, minor edits were introduced to the survey questions, and the questionnaires were distributed to the participants. As per ethics policies, all potential participants were briefed about the nature of the work and were requested to provide explicit approval. Three Arab firms that operate in the telecommunication industry, which use cloud computing services, approved to participate in the study; and two of them assured that their identities will not be identified, based on their requests, for confidentiality purposes. AL Moasron Trading firm from Jordan, which responds offline to the surveys, and firms X, and Y from Saudi Arabia and United Arab Emirates respectively, which filled the surveys online. These three firms were chosen as they use cloud computing since few firms exploit such new services in the Arab world, and most important they accepted to participate in the study.
As showed in Table 2, 66.2% of the respondents were males; 63.5% of them more than 30 years old; and most of them experienced with 10 years and more. Consequently, their answers to the survey items are experienced and further trusted.
Table 1. Constructs and measurement items.
Table 2. Demographic data for respondents.
4. Research Results
4.1. Descriptive Statistics
All the 41 items were tested for their means, standard deviations, skewness, and kurtosis. Indeed, in order to describe the responses and thus the attitude of the respondents toward each question they were asked in the survey, the mean and the standard deviation were estimated. While the mean shows the central tendency of the data, the standard deviation measures the dispersion which offers an index of the spread or variability in the data  . In other words, a small standard deviation for a set of values reveals that these values are clustered closely about the mean or located close to it; a large standard deviation indicates the opposite. The descriptive statistics presented below in Table 3 indicate a positive disposition towards the items. While the standard deviation (SD) values ranged from 0.44442 to 1.03572, these values indicate a narrow spread around the mean. Also, the mean values of all items were greater than the midpoint (2.5) and ranged from 1.9483 (OC1) to 4.1581 (PE2). However, after careful assessment by using skewness and kurtosis, the data were found to be normally distributed. Indeed, skewness and kurtosis were normally distributed since all of the values were inside the adequate ranges for normality (i.e. −1.0 to +1.0) for skewness, and less than 10 for kurtosis   . Furthermore, the ordering of the items in terms of their means values, and their ranks based on three ranges (i.e. 1 - 2.33 low; 2.34 - 3.67 medium; and 3.68 - 5 high) are provided.
As noticed from Table 3, relative advantage of using cloud computing services has different scores, ranged from RA1 “By using cloud computing, we pay only for what we use” to the lowest score of RA4 “By using cloud computing, we do not need to administer our IT infrastructure”, thus, firms still think to manage their IT infrastructure. Also, CM1 “In case of any incompatibility issue, we ask cloud service provider to recommend integrated services” found in a medium level, and OC1 “My company hires highly specialized or knowledgeable personnel for cloud computing” got a low score. Consequently, firms should hire more cloud computing specialists. TM4 “My top management is willing to take risks involved in the adoption of cloud computing”, and TE2 “My company provided me complete training in using cloud computing” were implemented moderately, thus need more attention from the decision makers. However, results showed that respondents highly agree of cloud computing perceived usefulness, perceived ease of use, and perceived importance.
Structural Equation Modeling (SEM) analysis was employed to estimate the SEM parameters by using the maximum likelihood method. Table 4 shows different types of goodness of fit indices in assessing this study initial specified model. It demonstrates that the research constructs fits the data according to the absolute, incremental, and parsimonious model fit measures, comprising chi-square per degree of freedom ratio (x²/df), Incremental Fit Index (IFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). The researchers examined the standardized regression weights for the research’s indicators and found that all indicators had a high loading towards the latent variables. Moreover, since all of these items did meet the minimum recommended value of factor loadings of 0.50; and RMSEA less than 0.10
Table 3. Mean standard deviation of scale items.
Table 4. Measurement model fit indices.
 -  , they were all included for further analysis, except RA4, RA7, CM6, and OC4 which has a loading of 0.271, 0.321, 0.311, and 0.411 respectively, thus excluded from further analysis. Therefore, the measurement model showed a better fit to the data (as shown in Table 4). For instance, x²/df was 1.335, the IFI = 0.87, TLI = 0.86, CFI = 0.87; and RMSEA 0.046 indicated better fit to the data considering all loading items.
4.2. Measurement Model
Confirmatory factor analysis (CFA) was conducted to check the properties of the instrument items. Indeed, prior to analyzing the structural model, a CFA based on AMOS 20.0 was conducted to first consider the measurement model fit and then assess the reliability, convergent validity and discriminant validity of the constructs  . The outcomes of the measurement model are presented in Table 5, which encapsulates the standardized factor loadings, measures of reliabilities and validity for the final measurement model.
Unidimensionality is the extent to which the study indicators deviate from their latent variable. An examination of the unidimensionality of the research constructs is essential and is an important prerequisite for establishing construct reliability and validity analysis  . Moreover, in line with  and  , this research assessed unidimensionality using the factor loading of items of their respective constructs. Table 5 shows solid evidence for the unidimensionality of all the constructs that were specified in the measurement model. All loadings were above 0.50, except RA4, RA7, CM6, and OC4, which is the criterion value recommended by  . These loadings confirmed that 37 items were loaded satisfactory on their constructs.
Reliability analysis is related to the assessment of the degree of consistency between multiple measurements of a variable, and could be measured by Cronbach alpha coefficient and composite reliability  . Some scholars such as  suggested that the values of all indicators or dimensional scales should be above the recommended value of 0.60. Table 5 indicates that all Cronbach Alpha values for the nine variables exceeded the recommended value of 0.60  demonstrating that the instrument is reliable. Furthermore, as shown in Table 5, composite reliability values ranged from 0.85 to 0.93, and were all greater than the recommended value of more than 0.60  or greater than 0.70 as suggested by  . Consequently, according to the above two tests, all the research constructs in this study are considered reliable.
As shown above, since the measurement model has a good fit; convergent validity and discriminant validity can now be assessed in order to evaluate if the psychometric properties of the measurement model are adequate.
4.2.3. Content, Convergent, and Discriminant Validity
Although reliability is considered as a necessary condition of the test of goodness of the measure used in research, it is not sufficient   , thus validity is another condition used to measure the goodness of a measure. Validity refers to which an instrument measures is expected to measure or what the researcher wishes to measure  . Indeed, the items selected to measure the nine variables were validated and reused from previous researches. Therefore, the researchers relied upon in enhancing the validity of the scale was to benefit from a pre- used scale that is developed from other researchers. In addition, the questionnaire items were reviewed by five instructors of the Business Faculty at the University of Jordan. The feedback from the chosen group for the pre- test contributed to enhanced content validity of the instrument. Moreover, in order to enhance the content validity of the instrument, four employees were asked to give their feedback about the questionnaire, thus confirming that the knowledge presented in the content of each question was relevant to the studied topic.
Furthermore, as convergent validity test is necessary in the measurement model to determine if the indicators in a scale load together on a single construct; discriminant validity test is another main one to verify if the items developed to measure different constructs are actually evaluating those constructs  . As shown in Table 5, all items were significant and had loadings more than 0.50 on their underlying constructs. Moreover, the standard errors for the items ranged from 0.089 to 0.126 and all the item loadings were more than twice their standard error. Discriminant validity was considered using several tests. First, it could be examined in the measurement model by investigating the shared average variance extracted (AVE) by the latent constructs. The correlations among the research constructs could be used to assess discriminant validity by examining if there were any extreme large correlations among them which would imply that the model has a problem of discriminant validity.
Table 5. Properties of the final measurement model.
If the AVE for each construct exceeds the square correlation between that construct and any other constructs then discriminant validity is occurred  . As shown in Table 5, this study showed that the AVEs of all the constructs were above the suggested level of 0.50, implying that all the constructs that ranged from 0.61 to 0.73 were responsible for more than 50 percent of the variance in their respected measurement items, which met the recommendation that AVE values should be at least 0.50 for each construct   . Furthermore, as shown in Table 6, discriminant validity was confirmed as the AVE values were more than the squared correlations for each set of constructs. Thus, the measures significantly discriminate between the constructs.
4.3. Structural Model and Hypotheses Testing
Following the two-phase SEM technique, the measurement model results were used to test the structural model, including paths representing the proposed associations among research constructs. Further, in order to examine the structural model it is essential to investigate the statistical significance of the standardized regression weights (i.e. t-value) of the research hypotheses (see Table 7); and the coefficient of determination (R²) for the research
Table 6. AVE and square of correlations between constructs.
Note: Diagonal elements are the average variance extracted for each of the nine constructs. Off-diagonal elements are the squared correlations between constructs.
Table 7. Summary of proposed results for the theoretical model.
endogenous variables as well. The coefficient of determination for perceived usefulness, perceived ease of use, and perceived importance were 0.61, 0.63, and 0.74 respectively, which indicates that the model does account for the variation of the proposed model.
This research aimed to test several factors impacting cloud computing perceived importance in several Arab firms, exclusively in Jordan, Saudi Arabia and United Arab Emirates. The study used two main statistical tools to analyze the survey data. Regarding the survey analysis methods, a general descriptive analysis was conducted by applying SPSS version 22, to obtain a summary about the respondents’ demographic characteristics by using the response means, frequencies, and standard deviations, alongside initial data examination such as reliability tests. Then, the data were analyzed by using the Structural Equation Modeling (SEM) method, with AMOS software version 20, which involved confirmatory factor analysis (CFA) and structural model analysis.
Fifteen hypotheses were all supported and consistent with the findings of  . Indeed, relative advantage, compatibility, complexity, organizational competency, top management support, and training and education impacted cloud computing perceived usefulness as well as perceived ease of use. In addition, perceived ease of use has an impact on perceived usefulness. Thus, hypothesis 1 - 13 were supported. This is to say that managers and policy makers need to focus on relative advantages and technological resources to enable cloud computing adoption. Also, the study finds that cloud computing adoption is not complex, and a firm should make necessary contribution to making cloud computing system compatible with the firm’s processes. Further, in line with  findings, top management support is crucial in convincing their workers toward adopting cloud computing services, besides offering them the appropriate trainings on knowing how the technical and non-technical perceptions of cloud computing implementations are. Moreover, as found in  study, the current research confirmed that perceived ease of use impacted perceived importance; and perceived usefulness had an impact on perceived importance as well. Therefore, hypothesis 14 and 15 were supported. Therefore, the more understandable and easier to use cloud computing, then the more the cooperation and coordination between business partners has. Also, the more usefulness to use cloud computing by managing the firms’ operations efficiently and the like, the more the trade systems between the business partners facilitate. Consequently, cloud computing adopters support the formation of networks with other partners along with the sharing of organizational resources. This is by allowing sharing specialized documents, tracking their supply chain management systems and enabling electronic collaborations.
However, results reported in this study were based on three firms each in Jordan, Saudi Arabia and United Arab Emirates, and in turn were applicable exclusively to this context. Thus, this raises inquiries regarding the generalisability to other cultures and different contexts. Consequently, further research is needed with regards to different sectors and industries and in different countries as well in order to show the difference between adopting cloud computing in the Arab world in comparison to the western countries. Also, for the current research, data were gathered from three firms which implemented cloud computing from top, middle-level IT managers, and IT employees from the operational level as well. Then, it is worthy to conduct further research to discriminate the adopting of cloud computing from the top-middle-operational level. In addition, since researchers (i.e.  -  ) considered the IT and its flexibility as an enabler to achieve the desired competitive advantages, then further research should examine how IT systems with their KM strategies and processes could exploit cloud computing services in order to survive in their highly competitive business environments.
 Gangwar, H., Date, H. and Ramaswamy, R. (2015) Understanding Determinants of Cloud Computing Adoption Using an Integrated TAM-TOE Model. Journal of Enterprise Information Management, 28, 107-130.
 Saravana, N., Rajya, G.V. and Balamurugan, B. (2015) Enhanced Attribute Based Encryption for Cloud Computing. Procedia Computer Science, 46, 689-696.
 Lemoudden, M., Bouazza, N., Ouahidi, B. and Bourget, D. (2013) A Survey of Cloud Computing Security Overview of Attack Vectors and Defense Mechanisms. Journal of Theoretical and Applied Information Technology, 54, 325-330.
 Sari, R. and Kurniawan, Y. (2015) Cloud Computing Technology Infrastructure to Support the Knowledge Management Process (a Case Study Approach). Journal of Theoretical and Applied Information Technology, 73, 377-382.
 Shahzad, F. (2014) State-of-the-Art Survey on Cloud Computing Security Challenges, Approaches and Solutions. Procedia Computer Science, 37, 357-362.
 Siani, P. (2011) Toward Accountability in the Cloud. IEEE Internet Computing, 15, 64-69.
 Virgile, K. and Yu, H. (2015) Securing Cloud Emails Using Two Factor Authentication Based on Password/Apps in Cloud Computing. International Journal of Security and Its Applications, 9, 121-130.
 Altamony, H., Masa’deh, R., Alshurideh, M. and Obeidat, B. (2012) Information Systems for Competitive Advantage: Implementation of an Organisational Strategic Management Process. Proceedings of the 18th IBIMA Conference on Innovation and Sustainable Economic Competitive Advantage: From Regional Development to World Economic, Istanbul, 9-10 May 2012, 583-592.
 Beimborn, D., Schlosser, F. and Weitzel, T. (2009) Proposing a Theoretical Model for IT Governance and IT Business Alignment. Proceedings of the 42nd Hawaii International Conference on System Sciences, Washington DC, 1-11.
 Van Grembergen, W., De Haes, S. and Guldentops, E. (2004) Structures, Processes and Relational Mechanisms for IT Governance. In: Van Grembergen, W., Ed., Strategies for Information Technology Governance, Idea Group Publishing, Belgium, 1-37.
 Furuichi, T. and Yamada, K. (2014) Next Generation of Embedded System on Cloud Computing. Procedia Computer Science, 35, 1605-1614.
 Lombardi, F. and Dipietro, R. (2011) Secure Virtualization for Cloud Computing. Journal of Network and Computer Applications, 34, 1113-1122.
 Marston, S., Li, Z., Bandyopadhyay, S., Juheng, Z. and Anand, G. (2011) Cloud Computing: The Business Perspective. Decision Support Systems, 51, 176-189.
 Olaru, M. (2014) Advantages and Challenges of Adopting Cloud Computing from an Enterprise Perspective. Procedia Technology, 12, 529-534.
 Sinjilawi, Y., AL-Nabhan, M. and Abu-Shanab, E. (2014) Addressing Security and Privacy Issues in Cloud Computing. Journal of Emerging Technologies in Web Intelligence, 6, 192-199.
 Weinhardt, C., Anandasivam, W., Blau, B., Borissov, N., Meinl, T., Michalk, W. and StoBer, J. (2009) Cloud Computing—A Classification, Business Models, and Research Directions. Business & Information Systems Engineering, 5, 391-399.
 Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. (2009) Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems, 25, 599-616.
 Guo, C. and Zheng, X. (2015) The Research of Data Security Mechanism Based on Cloud Computing. International Journal of Security and Its Applications, 9, 363-370.
 Alshamaila, Y., Papagiannidis, S. and Li, F. (2013) Cloud Computing Adoption by SMEs in the North East of England: A Multi-Perspective Framework. Journal of Enterprise Information Management, 26, 250-275.
 Lin, A. and Chen, N.C. (2012) Cloud Computing as an Innovation: Perception, Attitude, and Adoption. International Journal of Information Management, 32, 533-540.
 Low, C., Chen, Y. and Wu, M. (2011) Understanding the Determinants of Cloud Computing Adoption. Industrial Management & Data Systems, 111, 1006-1023.
 Hsiao, C. and Yang, C. (2011) The Intellectual Development of the Technology Acceptance Model: A Co-Citation Analysis. International Journal of Information Management, 31, 128-136.
 Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R. and Konwinski, A. (2010) A View of Cloud Computing. ACM Communications, 53, 50-58.
 Subashini, S. and Kavitha, V. (2011) A Survey on Security Issues in Service Delivery Models of Cloud Computing. Journal of Network and Computer Applications, 34, 1-11.
 Karajeh, H., Maqableh, M. and Masa’deh, R. (2014) Security of Cloud Computing Environment. Proceedings of the 23rd IBIMA Conference on Vision 2020: Sustainable Growth, Economic Development, and Global Competitiveness, Valencia, 13-14 May 2014, 2202-2215.
 Joseph, A., Kathrine, J. and Vijayan, R. (2014) Cloud Security Mechanisms for Data Protection: A Survey. International Journal of Multimedia and Ubiquitous Engineering, 9, 81-90.
 Kim, S. and Lee, I. (2015) Study on User Authority Management for Safe Data Protection in Cloud Computing Environments. Symmetry, 7, 269-283.
 Sandholm, T. and Lee, D. (2014) Notes on Cloud Computing Principles. Journal of Cloud Computing: Advances, Systems and Applications, 3, 1-10.
 Sehgal, N., Sohoni, S., Xiong, Y., Fritz, D., Mulia, W. and Acken, J. (2011) Cross Section of the Issues and Research Activities Related to Both Information Security and Cloud Computing. IETE Technical Review, 28, 279-291.
 Varalakshmi, A., Haleema, P., Subalakshmi, R. and Iyengar, N. (2014) Quality Based Solution for Adaptable and Scalable Access Control in Cloud Computing. International Journal of Grid Distribution Computing, 7, 137-148.
 Ibrahim, M. and Ameen, A. (2015) Exploring the Feasibility of Adopting Cloud Computing in Computer Center Taiz university. International Journal of Advanced Networking and Applications, 6, 2359-2366.
 Arango, I., Izquierdo, J., Campbell, E. and Pérez-García, R. (2014) Cloud-Based Decision Making in Water Distribution Systems. Procedia Engineering, 89, 488-494.
 Shahzad, A. and Hussain, M. (2013) Security Issues and Challenges of Mobile Cloud Computing. International Journal of Grid and Distributed Computing, 6, 37-50.
 Terzis, V., Moridis, C. and Economides, A. (2013) Continuance Acceptance of Computer Based Assessment through the Integration of User’s Expectations and Perceptions. Computers & Education, 62, 50-61.
 Newkirk, H. and Lederer, A. (2006) The Effectiveness of Strategic Information Systems Planning under Environmental Uncertainty. Information & Management, 43, 481-501.
 Chou, T., Chang, P., Cheng, Y. and Tasi, C. (2007) A Path Model Linking Organizational Knowledge Attributes, Information Processing Capabilities and Perceived Usability. Information & Management, 44, 408-417.
 Bagozzi, R. and Yi, Y. (1988) On the Evaluation of Structural Evaluation Models. Journal of the Academy of Marketing Science, 16, 74-94.
 Gefen, D., Straub, D.W. and Boudreau, M.C. (2000) Structural Equation Modelling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4, 1-79.
 Fronell, C. and Larcker, D. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50.
 Masa’deh, R. (2012) The Impact of Management Information Systems (MIS) on Quality Assurance (QA): A Case Study in Jordan. International Journal of Information, Business and Management, 4, 93-110.
 Masa’deh, R. (2013) The Impact of Information Technology Infrastructure Flexibility on Firm Performance: An Empirical Study of Jordanian Public Shareholding Firms. Jordan Journal of Business Administration, 9, 204-224.
 Masa’deh, R., Maqableh, M. and Karajeh, H. (2014) A Theoretical Perspective on the Relationship between Leadership Development, Knowledge Management Capability, and Firm Performance. Asian Social Science, 10, 128-137.
 Almajali, D., Mansour, K., Masa’deh, R. and Maqableh, M. (2016) The Impact of Electronic Supply Chain Management Usage on Firm’s Performance. International Journal of Communications, Network and System Sciences, 9, 280-293.