CEC Theses and Dissertations

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Date of Award


Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Information Systems (DISS)


Graduate School of Computer and Information Sciences


Steven D Zink

Committee Member

Yair Levy

Committee Member

Maryln K Littman


When measuring Information Systems (IS) success, it is important to know the type of IS being measured, the systems objectives, and the unit of analysis. As organizations invest in technology to help achieve strategic goals, they need to be able to measure IS success. Measuring the effectiveness of IS from an organizational perspective is the effect it has on achieving organizational goals. The effectiveness of information systems is a measure of net benefits. This empirical study investigated IS success in terms of the DeLone and McLean IS success model variable, net benefits. In order to measure IS success, the context of the investigation and the unit of analysis are as important as what is being measured. This investigation, in the context of a building automation system (BAS), evaluated the net benefits success measurement with the organization as the unit of analysis.

Two hypothesis testing studies were conducted. The first study was a predictive investigation, which researched the relationship among the independent variables, kilowatt hours, kilowatt demand, and the dependent variable, the cost of energy. A multiple regression analysis was conducted to understand to what extent the independent variables could predict the dependent variable. The second study was a correlational investigation. This study was conducted to ascertain whether a building automation system affects the cost of energy. A Point Biserial Correlation Coefficient test was conducted to understand the relationship between the cost of energy and stores with a building automation system and those without. A t-test was conducted to understand the level of significance. The results of the analysis showed that the relationship between the cost of energy and a BAS is statistically significant and that the variables kilowatt hours and kilowatt demand are statistically significant as predictors of the cost of energy.

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