Abstract

Cloud computing services such as file storage and big data analytics offer cost-effective, secure, flexible, and sustainable services to their users. Despite their benefits, the adoption of many cloud services is still limited, and many organizations are hesitant to adopt cloud technologies for several reasons. Researchers have used innovation adoption theories to explore the factors influencing users’ decisions toward accepting and using a new information system. This study presents a systematic review of the factors influencing organizational decisions concerning the acceptance of cloud-based technologies using the technology-organization-environment (TOE) framework. We analyze, integrate, and classify these factors and show that much of the literature has emphasized the technical aspects of technology adoption, such as cloud security. We further show factors like top management support, relative advantage, cloud complexity, and competitive pressure are the most critical factors affecting organizational attitude toward cloud technology adoption.

Introduction

The National Institute of Standards and Technology (NIST) defines cloud computing as “a model for enabling convenient, on demand network access to a shared pool of configurable computing resources (e.g., network, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [24]. The NIST definition reveals the main advantages of cloud computing, shareability, scalability, and cost reduction. It also reveals some disadvantages associated with using cloud computing, like access control and resource ownership. For example, clients become unable to migrate their data from a cloud service provider to another due to a lack of standards (Ali et al., 2019). As a result, cloud computing usage is accelerating among individual and organizational users, and its market size is expected to exceed 623.3 billion dollars by 2023 (GCCM, [35]). Furthermore, cloud computing disadvantages have become an important research agenda among researchers in recent years who strive to search for new solutions to these problems (Chen et al., 2012).

Cloud computing services are offered through three service models: cloud infrastructure-as-a-service (IaaS), cloud platform-as-a-service (PaaS), and software-as-a-service (SaaS) ([24]; Gill et al., 2020; Beheshti-Atashgah et al., 2020). In the IaaS service model, the cloud provider enables the user to access and use managed hardware like hosting servers, storage, or firewalls and can install customized operating software like virtualized operating systems. In the PaaS model, where the cloud provider enables the user to access and use pre-prepared operating systems with development platforms or data management systems. In the SaaS model, the cloud provider allows the user to use online applications like accounting software, a file-sharing service, or a learning management system.

Cloud computing is divided into four known deployment models: private, community, public, and hybrid [24]. In the private deployment model, the cloud computing services are offered through either an internet connection or a private connection and only to private users instead of the general public. This model is also called the corporate cloud and may reside inside the using organization. The community deployment model is different, as its services are accessible by a limited number of people or organizations who share its governance and share specific goals or missions, like research groups. The public deployment model offers its services over the internet and makes them available to anyone who wants to use or purchase them. Finally, the hybrid deployment model is a cloud environment that can operate a mix of the public and private deployment models. For example, this deployment model is useful for some organizations like travel agencies, where an application in the public cloud can access other applications in the firm’s private cloud to add or update records.

The cloud service or deployment models are designed to enable on-demand service delivery, which is a big step forward in using computational resources. Thus, the cloud makes computing resources, whether applications or hardware, essential assets for any computer user as mobile phones or electricity [7]. Much of the literature emphasizes the on-demand feature of the cloud, which leads to reduced cost in maintenance services compared to operating or maintaining similar computing services on the organizations’ premises, for example, costs associated with electricity consumption and the need for hardware [11,26]. Other advantages associated with operating cloud computing include the minimal need for human interaction [5], which is useful for organizations experiencing low demands for their services [26,39].

The rapid development of information technology has increased market competitiveness. Consequently, consumers’ habits also changed, to which organizations need to be efficient and deliver services at a reduced cost. This explains a significant portion of the continuous need to adopt an innovative technology (Trigueros-Preciado et al., 2013). The cloud, with its features, has attracted many organizations, even at a varying adoption rate from public to private organizations (Ali et al., 2015). For example, many companies are already using an advanced SaaS service like cloud-based big data analytics (CBDA) to generate revenues, driven by the cloud’s flexibility and cost-effectiveness (Hadwer et al., [14]). However, the adoption rate of the same technology among public organizations is low. At the same time, research shows that public organizations are still hesitant to adopt them, struggling with factors related to technical challenges like security and fear of data control loss [17], or low usage compared to high investment [23]. Additionally, other common adoption challenges also face educational organizations, like the uncertainty of value gained from cloud computing use [34] or cultural influence on the workplace [1].

Therefore, understanding the factors that motivated the private sector to adopt cloud technologies, whether technical or organizational, is essential to improve how emerging cloud technologies like cloud analytics can be adopted in the public sector (i.e., healthcare, education). To this end, innovation diffusion studies are pivotal to understand how cloud-based technologies, such as innovation, can reach optimum usage in organizations.

Cloud adoption studies fall under innovation diffusion research, which focuses on how innovation is communicated among members of a social system through specific channels (Rogers, 2003). This is usually built on several rational models, known in the literature as adoption frameworks or models. These models describe the factors that make users, whether an organization or an individual, decide to accept and use innovation, i.e., a cloud-based technology. While they form the theoretical foundation in adoption studies, they are used as instruments to identify the factors that impact users’ decision to accept new technologies and foresee the significant factors that affect the adoption decision (i.e., replacing a traditional technology with a new one).

Among many adoption frameworks, the technology-organization-environment (TOE) framework by Tornatzky and Fleischer (1990) is chosen for this research because it focuses on the organizational factors instead of individual ones. Other frameworks, such as the innovation diffusion model (Rogers, 2003), technology acceptance model (Davis et al., 1989; Venkatesh et al., 2000, 2003, 2008), human-organization-technology model (Yusof et al., 2008), decomposed theory of planned behavior (Taylor and Todd, 1995), technology task fit model (Goodhue and Thompson, 1995), and inter-organizational system model (Iacovou et al., 1995), are not considered in this research because their focus is on individual intention and behavior toward using innovation.

The TOE framework categorizes the factors influencing an organization to adopt an innovation into three categories, including (1) technology (i.e., systems security and complexity), (2) organization (i.e., organization size and top-management support of replacing functioning systems), and (3) environment (i.e., market uncertainty, governmental or competitors’ pressure). The TOE is a suitable holistic instrument for organizational-level research. It overarches the technical aspects of cloud technology and the external and internal organizational factors that make successful use of cloud-based technology [26]. Yet, the TOE framework and the remaining technology adoption frameworks belong to the pre-cloud era. They have been used in the past with traditional computing systems to measure and facilitate their organizational acceptance. Limited research has employed adoption frameworks to understand decisions of using cloud technologies in organizations. No single research known so far has been conducted to predict public organization’s intention to use cloud-based technology. We conduct a literature review to investigate the factors influencing cloud adoption within the TOE context with the following research question: What factors are influential on the organization’s decision to adopt a cloud computing technology?

The following objectives are formulated to answer this question:

• a)defining cloud computing, its models and services, and the associated opportunities and challenges surrounding its adoption,
• b)understanding how stand-alone or integrated TOE is used in the literature to investigate the different contexts of generic cloud adoption by organizations in the public or private sectors, and
• c)listing and classifying technical and non-technical factors influencing the cloud adoption process decisions and their significance.

The remainder of this paper is organized as follows. In Section 2, we present the methodology adopted for this study. In Section 3, we present our results. In Section 4, we further discuss our findings, and in Section 5, we conclude with our conclusions.