CCE Theses and Dissertations

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Graduate School of Computer and Information Sciences


John A. Scigliano

Committee Member

Sumitra Mukherjee

Committee Member

Steven R. Terrell


The primary purpose of the researcher this study was to determine what, if any, unexpected benefits, spin-offs, and other "serendipitous" events occurred as a result of the year 2000 remediation process undertaken by businesses, industry, and other organizations. This was measured through the use of an online survey instrument comprised of a series of questions designed to measure and discover those benefits. The survey was sent to approximately 50 different businesses/organizations during the pilot stage of this study and 250 during the main stage of this study. These businesses/organizations were randomly selected from the sampling frame using random sampling techniques. The random samples that were used included the areas of financial services, health care, non-computer manufacturing, telecommunications, transportation, and utilities. The survey responses were analyzed in an effort to determine what benefits were common to these businesses as well as to discover possible unique benefits some business may have experienced. Of particular interest were any benefits that companies indicate were totally unexpected or serendipitous. Final analysis of the data was accomplished through the use of canonical correlation. This statistical procedure was chosen because of its usefulness in determining correlations between a set of independent variables and a set of dependent variables. The findings of the pilot study were, for the most part, inconclusive due to the small number of responses. The analysis of the data from the main study resulted in one significant canonical function. The canonical correlation between the computer generated variates created by this function was reported at approximately .86. This indicates a high degree of correlation between criterion variate I that represented the independent variables, and is the predictor variate I that represented the dependent variables. The variables PCSYS, NTSYS, MFSYS had the highest correlation with the Predictor I variate. The variables ITISS, A W AREMIA, and A WAREBF had the highest correlation with the Criterion I variate. Therefore, the independent variables PCSYS, NTSYS, and MFSYS were most predictive of the dependent variables ITISS, AWAREMIA, and AWAREBF. To determine if there were unexpected benefits in those companies surveyed, a multiple regression was performed using the measured variable "SEREDIP" as the dependent variable and the measured variables Y2KEFFORT, ORGSP, and ORGHM as the independent variables. The null hypothesis was not rejected indicating that among the companies surveyed, the occurrences of serendipitous events were not statistically significant. However, the literature indicated that serendipitous events have occurred, although these occurrences were not universal.

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