Abstract:
Big data analytics have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in house. The collection, analysis, and visualization of such big data presents a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the decision-making surrounding the adoption of big data analytics can be difficult. It requires the consideration and evaluation of a wide range of technical and organizational aspects that need to be taken into account to ensure informed decisions are made. Many previous studies have been conducted to explore the factors that influence the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated model based on the DOI and the technology-organization-environment (TOE) framework is proposed. The model is then evaluated using structural equation modeling. The statistical analysis shows the level of impact of the identified variables on the decision to adopt big data analytics. Eleven determinants are confirmed to have influence on the decision to adopt big data analytics in HES. The result is expected to contribute on-going research that attempts to address the complex and multidimensional challenge of big data analytics implementation in HES.
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