College of Computing and Digital Media Dissertations

Date of Award

Spring 6-30-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

School

School of Computing

First Advisor

Theresa Steinbach, PhD

Second Advisor

Raffaella Settimi-Woods, PhD

Third Advisor

Olayele Adelakun, PhD

Fourth Advisor

Ioan Raicu, PhD

Fifth Advisor

Zarreen Farooqi, PhD

Sixth Advisor

David Rudden, MA

Abstract

Many organizations have on-premises data storage systems. Data storage systems are evolving in multiple ways. One way is the adoption of Big Data. Big Data is a data storage system with the ability to analyze large volumes, velocity, and a variety of data. Per the Economist, data is now the most valuable resource (Parkins, 2017). Big Data holds the promise of unlocking a substantial value of data stored. Yet many organizations are not implementing Big Data. There is a need to identify key factors affecting adoption for such organizations. The literature review revealed multiple gaps in studied adoption factors (un-studied or under-studied) such as data storage latency, ability to compute, data storage interface compatibilities, open-source software, enterprise sourced software, cost, perceived industry pressure, legislation barriers, and market turbulence. These factors are studied in this research using The Diffusion of Innovation (DOI) theory and Technology-Organization-Environment (TOE) framework with qualitative (semi-structured interviews, Interpretive Phenomenological Analysis (IPA), and structured interviews) and quantitative (survey) methods. Quantitative analysis is based on Partial Least Squares – Structural Equation Model (PLS-SEM) analysis. This analysis revealed that six of the nine studied factors are significant. Industry pressure, enterprise-sourced software, storage interface compatibility, market turbulence, open-source software, and cost are significant factors positively correlated to Big Data adoption.

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Available for download on Tuesday, October 03, 2023

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