ARTÍCULO
TITULO

A Decision-Making Model for Adopting a Cloud Computing System

Seok-Keun Yoo and Bo-Young Kim    

Resumen

The use of big data, artificial intelligence, and new information and communication technologies has led to sustainable developments and improved business competitiveness. Until recently cloud services were classified as having special system requirements for a business organization, and was represented by different cloud computing architecture layers like infrastructure, platform, or software as a service. However, as the environment of IT services undergoes successive changes, companies have been required to reconsider their business models and consider adopting a cloud computing system, which can bring on business achievements and development. Regarding a decision-making model for adopting a cloud computing system, this paper analyzes critical variables in a hierarchical structure of decision areas: technology, organization, and environment, as well as seven factors and 23 attributes based on underlying decision factors of cloud computing adoption by AHP (Analytic Hierarchy Process) and Delphi analysis. Furthermore, this research explores a comparative analysis between demanders and providers of cloud computing adoption. Resultantly, this study suggests several important factors for adopting a cloud computing system: top management support, competitive pressure, and compatibility. From the demander side, the high priority factor was compatibility and competitive pressure; in contrast, related advantage and top management support were regarded as priority factors for providers to service their cloud computing systems.

 Artículos similares

       
 
Abdullah F. Al-Aboosi, Aldo Jonathan Muñoz Vazquez, Fadhil Y. Al-Aboosi, Mahmoud El-Halwagi and Wei Zhan    
Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intellig... ver más

 
Yee Sye Lee, Ali Rashidi, Amin Talei and Daniel Kong    
In recent years, mixed reality (MR) technology has gained popularity in construction management due to its real-time visualisation capability to facilitate on-site decision-making tasks. The semantic segmentation of building components provides an attrac... ver más
Revista: Buildings

 
Jiale Li, Jiayin Guo, Bo Li and Lingxin Meng    
The deep learning method has been widely used in the engineering field. The availability of the training dataset is one of the most important limitations of the deep learning method. Accurate prediction of pavement performance plays a vital role in road ... ver más
Revista: Buildings

 
Wei Wang, Huanhuan Feng, Yanzong Li, Quanwei You and Xu Zhou    
At present, the determination of tunnel parameters mainly rely on engineering experience and human judgment, which leads to the subjective decision of parameters and an increased construction risk. Machine learning algorithms could provide an objective t... ver más
Revista: Buildings

 
Wahhaj Ahmed, Baqer Al-Ramadan, Muhammad Asif and Zulfikar Adamu    
Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon... ver más
Revista: Buildings