Assessment of the status and factors influencing the adoption of cloud computing in knowledge-based companies Case Study: Kerman Science and Technology Park

Soleimani Nejad, Adel; Doroudi, Fariborz; Kamyab, Rahil

doi:10.22034/ijism.2023.1977670.0

Assessment of the status and factors influencing the adoption of cloud computing in knowledge-based companies Case Study: Kerman Science and Technology Park

Document Type : Articles

Authors

adel soleimani nejad ¹

Fariborz Doroudi ²

Rahil Kamyab ³

¹ Assistant professor, Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran.

² Assistant professor / Iranian Research Institute for Information Science and Technology (Irandoc)

³ M.A. of Knowledge and Information Science, Kerman Branch, Islamic Azad University, Kerman, Iran

https://doi.org/10.22034/ijism.2023.1977670.0

Abstract

Cloud computing is one of the most important topics in knowledge-based companies. Small and medium-sized enterprises with a low budget and few human resources are one of the major groups tending to use cloud computing to benefit from this technology. Several components affect the adoption of cloud in these companies, which should be evaluated before making the decision. This study aimed to identify these components and determine how much each component impacts the adoption of cloud in small and medium-sized companies. Accordingly, based on the diffusion of innovation theory and technology-organization-environment (TOE) framework as well as the previous studies, a conceptual model with twelve components was presented. Data were collected via a questionnaire using the descriptive survey method from 59 knowledge-based companies of Kerman Science and Technology Park. In this study, the “need” factor was selected as the desired state and “use” as the current state; then, the mean of the other components was compared with the mean of these two factors. The results of this study showed that based on the gap between the desired state and the current state, the employees’ knowledge of cloud computing, compatibility, complexity, and security and privacy require more attention. Innovation factors, decision makers’ knowledge of cloud computing, benefits, and costs have a better position than other components. Finally, factors effective in the compliance of knowledge-based companies of Kerman Science and Technology Park with cloud computing were ranked using the Vikor method. The need factor (information need), decision makers’ innovation, and benefits were ranked first to third, respectively, and the complexity factor was ranked last among the indicators. Therefore, identifying the current state (not using cloud computing based on the needs or not matching with cloud) and the desired state (using cloud computing based on the needs or matching with the cloud) in knowledge-based companies, based on the criteria or factors whose usefulness was investigated in this study, can be an important step in joining these companies into the cloud, and thus bringing the benefits of this new technology to knowledge-based companies.

Keywords

Cloud Computing

Small and Medium-Sized Enterprises

Knowledge-Based Companies

Kerman Science and Technology Park

20.1001.1.20088302.2024.22.1.2.9

Agrawal, N. & Tapaswi, S. (2019). Defense mechanisms against DDoS attacks in a cloud computing environment: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 21(4), 3769-3795. https://doi.org/10.1109/COMST.2019.2934468

Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), 250-275. https://doi.org/10.1108/17410391311325225

Amini, M. (2014). The factors that influence on adoption of cloud computing for small and medium enterprises. Master Thesis, Universiti Teknologi Malaysia, Faculty of Computing.

Awaysheh, F. M., Aladwan, M. N., Alazab, M., Alawadi, S., Cabaleiro, J. C. & Pena, T. F. (2021). Security by design for big data frameworks over cloud computing. IEEE Transactions on Engineering Management, 69(6), 3676-3693. http://dx.doi.org/10.1109/TEM.2020.3045661

Caldarelli, A., Ferri, L. & Maffei, M. (2017). Expected benefits and perceived risks of cloud computing: An investigation within an Italian setting. Technology Analysis & Strategic Management, 29(2), 167-180. https://doi.org/10.1080/09537325.2016.1210786

Chatzithanasis, G. & Michalakelis, C. (2018). The benefits of cloud computing: Evidence from Greece. International Journal of Technology Diffusion (IJTD), 9(2), 61-73. http://dx.doi.org/10.4018/IJTD.2018040104

Elsherbiny, S., Eldaydamony, E., Alrahmawy, M. & Reyad, A. E. (2018). An extended intelligent water drops algorithm for workflow scheduling in cloud computing environment. Egyptian Informatics Journal, 19(1), 33-55. https://doi.org/10.1016/j.eij.2017.07.001

Eskandari, E. (2013). Investigating the business values of computing and its services based on the views of information technology experts in Tehran province. Master Thesis. Faculty of Social and Economic Sciences. Al-Zahra University. [in Persian]

Fathi Kiadehi, E. (2013). Feasibility study of implementing cloud computing in Iranian organizations and industries: opportunities, threats. Master Thesis. Faculty of Industrial Engineering. Khajeh Nasir al-Din Tusi University of Technology. [in Persian]

Friedrich-Baasner, G., Fischer, M. & Winkelmann, A. (2018). Cloud computing in SMEs: A qualitative approach to identify and evaluate influential factors. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 4681-4690). Retrieved from https://pdfs.semanticscholar.org/77a6/3a81d9734885e760870e9c44f6035f5920e2.pdf

Halkiopoulos, C., Giotopoulos, K., Antonopoulou, H. & Theodorakopoulos, L. (2020). E-business and cloud computing services in Greek companies during economic recession. International Journal of Business and Management Review, 8(2), 66-75. https://dx.doi.org/10.2139/ssrn.4153968

Kaushik, S. & Gandhi, C. (2019). Fog vs. cloud computing architecture. In Advancing Consumer-Centric Fog Computing Architectures (pp. 87-110). IGI Global.

Khajeh‐Hosseini, A., Greenwood, D., Smith, J. W. & Sommerville, I. (2012). The cloud adoption toolkit: Supporting cloud adoption decisions in the enterprise. Software: Practice and Experience, 42(4), 447-465. https://doi.org/10.1002/spe.1072

Kollolu, R. (2020). Infrastructural constraints of cloud computing. international Journal of Management, Technology and Engineering, 10(12), 255-260. https://dx.doi.org/10.2139/ssrn.3912456

Krishnadoss, P. & Jacob, P. (2018). OCSA: task scheduling algorithm in cloud computing environment. International Journal of Intelligent Engineering and Systems, 11(3), 271-279. https://doi.org/10.22266/IJIES2018.0630.29

Low, C., Chen, Y. & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems. 111(7), 1006-1023. https://doi.org/10.1108/02635571111161262

Lynn, T., Liang, X., Gourinovitch, A., Morrison, J. P., Fox, G. & Rosati, P. (2018, January). Understanding the determinants of cloud computing adoption for high performance computing. In 51st Hawaii International Conference on System Sciences (HICSS-51) (pp. 3894-3903). University of Hawai'i at Manoa

Mohammadi, V. (2013). Security challenges cloud computing privacy in e-commerce applications. Master Thesis. Faculty of Engineering. University of Guilan. [in Persian]

Namasudra, S., Chakraborty, R., Majumder, A. & Moparthi, N. R. (2020). Securing multimedia by using DNA-based encryption in the cloud computing environment. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(3s), 1-19. https://doi.org/10.1145/3392665

Namasudra, S., Devi, D., Kadry, S., Sundarasekar, R. & Shanthini, A. (2020). Towards DNA based data security in the cloud computing environment. Computer Communications, 151, 539-547. https://doi.org/10.1016/j.comcom.2019.12.041

Oliveira, T. & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110-121. Retrieved from https://academic-publishing.org/index.php/ejise/article/view/389/352

Palade, A., Kazmi, A. & Clarke, S. (2019). An evaluation of open source serverless computing frameworks support at the edge. In 2019 IEEE World Congress on Services (SERVICES) (Vol. 2642, pp. 206-211). IEEE. https://doi.org/10.1109/SERVICES.2019.00057

Priya, A., & Saradha, D. S. (2020). An overview on cloud computing frameworks and review on cloud security schemes. Journal. Critical. Reviews, 7(17), 3303-3308. https://doi.org/10.31838/jcr.07.17.415

Rahman, M. N. & Iqbal, B. A. (2019). Public policies for providing cloud computing services to SMEs of Latin America. In Advanced Methodologies and Technologies in Government and Society (pp. 365-376). IGI Global. https://doi.org/10.4018/978-1-5225-7661-7.ch029

Ramezani Tehrani, S. (2013). Factors Influencing the Adoption of Cloud Computing by Small and Medium-Sized Enterprises (SMEs). Master Thesis, Toronto Metropolitan University. https://doi.org/10.32920/ryerson.14661393.v1

Rogers, E.M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications. In: Stoetzer, MW., Mahler, A. (eds) Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des Wissenschaftlichen Instituts für Kommunikationsdienste, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79868-9_2

Safari, F. (2013). Developing a cloud computing adoption model in it small and medium size enterprises. Master Thesis. Faculty of Management and Economics. Tarbiat Modares University. [in Persian]

Shahidinejad, A., Ghobaei-Arani, M. & Esmaeili, L. (2020). An elastic controller using Colored Petri Nets in cloud computing environment. Cluster Computing, 23(2), 1045-1071. https://doi.org/10.1007/s10586-019-02972-8

Subramanian, N. & Jeyaraj, A. (2018). Recent security challenges in cloud computing. Computers & Electrical Engineering, 71, 28-42. https://doi.org/10.1016/j.compeleceng.2018.06.006

Sunyaev, A. (2020). Cloud computing. In Internet computing (pp. 195-236). Springer, Cham. https://doi.org/10.1007/978-3-030-34957-8

Tong, Z., Chen, H., Deng, X., Li, K. & Li, K. (2020). A scheduling scheme in the cloud computing environment using deep Q-learning. Information Sciences, 512, 1170-1191. https://doi.org/10.1016/j.ins.2019.10.035

Tornatzky, L. & Fleischer, M. (1990). The process of technology innovation. Lexington, MA, Lexington Books.

Ullah, S., Awan, M. D. & Sikander Hayat Khiyal, M. (2018). Big Data in cloud computing: a resource management perspective. Scientific Programming. 5418679. https://doi.org/10.1155/2018/5418679