Multiple Perspective of Cloud Computing Adoption Determinants in Higher Education a Systematic Review

Multiple Perspective of Cloud Computing Adoption Determinants in Higher Education a Systematic Review

Mohammed Banu Ali
Copyright: © 2019 |Pages: 21
DOI: 10.4018/IJCAC.2019070106
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Abstract

Cloud computing has become a major talking point in recent times. An innovation such as cloud computing for higher education institutions (HEI) can be a cost effective means to operate their IT systems effectively without having to spend vast amounts of money on developing their IT infrastructure. HEIs also face the burden of several challenges e.g. limited infrastructure resources and IT budget, as well as limited teaching staff, technical experts, and IT skilled personnel. With support from a systematic literature review approach, this article identifies the key determinants of cloud adoption from a technological, organisational, environmental and personal perspectives. A total of 17 cloud adoption studies in the HEI context and their respected models from the period of 2012 to 2017 are reviewed and discussed. The findings suggest a lack of cloud adoption studies in the HEI domain from multiple perspectives, particularly in relation to the wider socio-technical concerns related to cloud adoption and future studies related to this research gap are deliberated.
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1. Introduction

Cloud computing is a highly prevalent technology for the delivery of IT-driven services and infrastructure owing to developments in a number of technologies, including the internet, hardware and distributed computing (Bhowmik, 2017). Recently, organisations have begun to shift from traditional ICTs to a cloud solution owing to the flexibility, scalability and agility of the technology, as well as the economic benefits the cloud can bring by adopting the technology (Armbrust et al., 2010). The National Institute of Standards and Technology (NIST) defines cloud computing as a ubiquitous and flexible data centralising tool that comprises of three service models: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) and four deployment models: private cloud, public cloud, community cloud, and hybrid cloud (Mell & Grance, 2011). Recently, cloud adoption has been the center of attention in the education domain, particularly in Higher Education Intuitions (HEIs).

In HEIs, cloud computing has become a highly popular paradigm. In view of Sultan (2011), cloud computing allows academic and non-academic staff, such as teachers and researchers in HEIs to access a wide pool of services and resources offered by the cloud service provider. HEIs that adopt the cloud can allow for virtualisation of IT resources, such as operating systems, servers, storage devices, or network resources (Odeh et al., 2017).

Several studies highlight the adoption of an educational cloud and associated cloud applications (Mircea & Andreescu, 2011; Sabi et al., 2016; Sultan, 2010). The aim of an educational cloud is to harness the power of multiple networked computers that enable collaboration among researchers and students. Cloud computing comprises of various unique and interesting characteristics to encourage cloud computing adoption by HEIs. Lian et al. (2014) outlines such characteristics as follows:

Computing resource virtualisation: Remote usage of computing resources so end users can access computing resources from various devices anytime and anywhere. High performance: Super computing power capable of massive data analysis capability, and large data storage capacity provision; Reduced cost: Acquisition and maintaining required resources or services onsite thereby reducing management costs in the long run; and Scalability: Dynamic scalability based on demand, thus requiring HEIs to only invest in required infrastructure and services (Alharthi et al., 2015; Mokhtar et al., 2014; Mokhtar et al., 2016; Rao & Selvamani, 2015).

Currently, HEIs face the burden of several challenges based on the provision of quality education. These challenges are summarised as follows:

  • Existing learning and teaching resources are distributed across education institutions not on the basis of their needs but on personal reasons or rationales, thus leading to unbalanced development among HEIs;

  • Limited funding to support HEIs’ education and training needs in terms of infrastructure and accessibility of placements for prospective students;

  • Paucity of technical expertise to support, maintain, and operate existing infrastructure in particular educational institutions;

  • Qualified educators are currently located in a few specific areas; thus, imbalanced distribution of expertise among the institutions occurs.

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