George Heilman

Validating the End-User Computing Satisfaction Survey Instrument in Mexico

This study assesses the psychometric properties of a Spanish translation of Doll and Torkzadeh’s End-User Computing Satisfaction (EUCS) survey instrument. The study provides evidence that the EUCS Spanish version can be used as a valid and reliable measure of computing satisfaction among computer users in Mexico. The study also adds support to the use of the EUCS instrument in the investigation of the perceptions of computer users in countries other than the United States (U.S.) and in languages other than English.

1. DIFFERENCES IN FACEBOOK ASSIMILATION AND RELATIONSHIP MANAGEMENT BETWEEN TRADITIONAL AND NONTRADITIONAL UNDERGRADUATE ACCOUNTING STUDENTS

Since its founding in 2004, Facebook has become the object of many investigations exploring a diverse set of issues. In this study, conducted at an HBCU, we examine Facebook use by undergraduate accounting students. We investigate how Facebook is assimilated into the lives of accounting students, how they use Facebook to satisfy social needs, and how these uses differ between traditional and nontraditional students. We find significant differences between traditional and nontraditional accounting students in terms of Facebook assimilation and in the use of Facebook for social outreach and social maintenance.

Predicting Voluntary Participation in a Public Health Program Using a Neural Network

Researchers increasingly use Artificial Neural Networks (ANNs) to predict outcomes across a broad range of applications. They frequently find the predictive power of ANNs to be as good as or better than conventional discrete choice models. This article demonstrates the use of an ANN to model a consumer’s choice to participate in North Carolina’s Maternity Care Coordination (MCC) program, a state sponsored voluntary public health service initiative. Maternal and infant Medicaid claims data and birth certificate data were collected for 59,999 births in North Carolina during the years 2000-2002. Part of this sample was used to train and test an ANN that predicts voluntary enrollment in MCC. When tested against a holdout production sample, the ANN model correctly predicted 99.69% of those choosing to participate and 100% of those choosing not to participate in the MCC program.