Abstract
Cloud computing has become prevalent in many sectors today, including higher-education. The study is premised on the assumption that despite the popularity of cloud computing in higher education, research within this context remains limited. The study, which is qualitative and exploratory in nature, involved an innovative methodological approach, drawing on interviews with three groups of participants: (a) members of a global, Fortune 100 technology company supplying cloud solutions; (b) members of a selected UK university's IT department implementing cloud solutions; and (c) students from the same UK university using cloud solutions. The findings improve understanding around cloud solutions in the higher education context by unpacking—through a qualitative thematic analysis approach—relevant themes that inform the extant information systems literature. Finally, the study provides recommendations for future researchers, cloud suppliers, universities, and students.
TopBackground
CC is a relatively new phenomenon in the technology industry, developed from traditional hosting, which is becoming pervasive in numerous sectors (Armbrust, 2010). Mell and Grance (2011) consider it “a model for enabling ubiquitous, 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” (p.2). It is not clear when the term CC was officially coined, because it has been used for many years in network diagrams to depict an area of uncertainty (Stevenson, 2009). It is believed the term was coined in 2006 when large companies such as Google and Amazon Web Services started naming their hosting services as CC (Regalado, 2011). CC has been viewed as a disruptive technology; as Krikos (2011) puts it, “CC has all the markings of a disruptive technology—those that change the game as it’s currently played both by traditional software licensing companies and by private, on-premises datacenters” (p.2). A disruptive technology often begins by only satisfying a niche segment of the market—for example CC being best suited to businesses ‘born on CC’—and later expanding to other sectors (Danneels, 2004).
From a technical perspective, the extant CC literature posits different CC service models:
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Infrastructure as a Service (IaaS): When the customer is able to provision processing, storage, networks, and other fundamental computing resources.
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Software as a Service (SaaS): Which allows the consumer to access the provider’s applications running on a cloud solutions (CS) but cannot manage the underlying infrastructure.
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Platform as a Service (PaaS): Where the customer can control the applications but not the infrastructure (e.g. Mell and Grance, 2011).
Further, CC types may also vary; from ‘Private Cloud’ (a high cost option but also the most secure, often preferred by banks and governments), through to ‘Community’ (shared by multiple organizations), ‘Public’ (allowing pay-per-use) and finally ‘Hybrid’ (a mix of the previous) (Chou, 2015).
Key Terms in this Chapter
Cloud Solutions: The delivery of IT services, retrieved through Internet-based tools and applications rather than via a direct server connection.
Data Protection: Legal control over access to, and use of, data in computers.
Information technology (IT): The application of computers to store, retrieve, transmit and manipulate data.
Higher Education (HE): Tertiary education at a university leading to a recognized degree.
License: Agreement that stipulates the terms of use for an application and defines the rights of the software producer and of the end-user.
Cloud Computing: A model enabling, convenient, on-demand network access to a shared pool of computing resources.
Virtually: Not physically existing, but made by software to appear to do so.
IT Security: Protecting information from theft or damage, either via hardware or software and the information stored on them.
Datacenter: A group of networked computer servers used for remote storage, processing, or distribution of large amounts of data.