CEC Theses and Dissertations

Date of Award

2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

College of Engineering and Computing

Advisor

Ling Wang

Committee Member

Paul Souren

Committee Member

Maxine Cohen

Abstract

The central question was how the relationship between trust-in-technology and intent-to-use Big Data Analytics in an organization is mediated by both Perceived Risk and Perceived Usefulness. Big Data Analytics is quickly becoming a critically important driver for business success. Many organizations are increasing their Information Technology budgets on Big Data Analytics capabilities. Technology Acceptance Model stands out as a critical theoretical lens primarily due to its assessment approach and predictive explanatory capacity to explain individual behaviors in the adoption of technology. Big Data Analytics use in this study was considered a voluntary act, therefore, well aligned with the Theory of Reasoned Action and the Technology Acceptance Model. Both theories have validated the relationships between beliefs, attitudes, intentions and usage behavior. Predicting intent-to-use Big Data Analytics is a broad phenomenon covering multiple disciplines in literature. Therefore, a robust methodology was employed to explore the richness of the topic. A deterministic philosophical approach was applied using a survey method approach as an exploratory study which is a variant of the mixed methods sequential exploratory design. The research approach consisted of two phases: instrument development and quantitative. The instrument development phase was anchored with a systemic literature review to develop an instrument and ended with a pilot study. The pilot study was instrumental in improving the tool and switching from a planned covariance-based SEM approach to PLS-SEM for data analysis. A total of 277 valid observations were collected. PLS-SEM was leveraged for data analysis because of the prediction focus of the study and the requirement to assess both reflective and formative measures in the same research model. The measurement and structural models were tested using the PLS algorithm. R2, f2, and Q2 were used as the basis for the acceptable fit measurement. Based on the valid structural model and after running the bootstrapping procedure, Perceived Risk has no mediating effect on Trust-in-Technology on Intent-to-Use. Perceived Usefulness has a full mediating effect. Level of education, training, experience and the perceived capability of analytics within an organization are good predictors of Trust-in-Technology.

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