Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R 2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar

This article presents a genetic algorithm-based approach for obtaining models in explicit consideration of this mailing depth. Issues related to overfitting, common in application of machine learning techniques, are examined, and experiments are based on a real-life data set

Effort estimation is a valuable asset to managers in planning maintenance activities and performing cost/benefits analysis. Early estimates and accurate evaluations permit to significantly reduce project risks and to improve resource scheduling. In this paper we present an approach for an early effort estimation based on the knowledge of a fraction of the programs composing the work-packet.

Data Mining is the application of algorithms for extracting valuable information from large databases in order to make important business decisions. This study explores a new technique for data mining -- Latent Semantic Indexing (LSI). LSI is an efficient information retrieval method for textual documents. By determining the singular value decomposition (SVD) of a large sparse term-bydocument matrix, LSI constructs an approximate vector space model which represents important associative relationships between terms and documents that are not evident in individual documents. This thesis explores the applicability of the LSI model to numerical databases, especially consumer product data. By properly chosing attributes of data records as terms or documents, a term-by-document incidence matrix is built and then a distribution-based indexing scheme is employed to construct a correlated distribution matrix. Hence a similar LSI vector space model can be generated to detect useful or hidden pa...

ABSTRACT We survey the methodological advances in DEA over the last 25 years and discuss the necessary conditions for a sound empirical application. We hope this survey will contribute to the further dissemination of DEA, the knowledge of its relative strengths and weaknesses, and the tools currently available for exploiting its full potential. Our main points are illustrated by the case of the DEA study used by the regulatory office of the Dutch electricity sector (Dienst Toezicht Elektriciteitswet; Dte) for setting price caps. The paper forms part of a research program on the fundamentals of the nonparametric methodology. A detailed description of the research program is available at

The coda Q of the early coda of shear waves was determined from a network of seismic stations deployed in the Mono Craters area during the summer of 1982. Coda amplitudes determined from a moving window analysis were fit to single scattering theory with an L1 norm for center frequencies from 1.5 to 12.0 Hz. We find that station-to-station variations in coda Q, observed for frequencies between 6 and 12 Hz, are well correlated with near-site geology. Coda Q for stations on crystalline rocks is larger and more frequency dependent than for stations sited on Bishop Tuff adjacent to Mono Craters. Coda Q from sites on Bishop Tuff away from Mono Craters is still lower and less frequency dependent. These observations suggest that site effects dominate the early coda in the Mono Craters region, and that the single scattering model is inappropriate. The mech-anisms generating these site effects are inferred to be: 1) multiple scattering from resonant structures such as sedimentary basins, volcanic edifices, variable basement topography, and surface topography; and 2) near-site attenuation.

Classification is an important task in the real-world. It is important to develop techniques for the comparison of classifiers. This has to be done statistically and in terms of well-defined criteria. This is a major goal of this thesis. This thesis discusses such techniques specifically in the context of comparing algorithmic classifiers (algorithms) and expert system classifiers (expert systems). The basic criterion used in this thesis is L, the weighted loss of misclassification. L is a well-established criterion in the statistical and pattern-recognition literature. A relatively unknown criterion by Brennan, kappa base ( b ) that adjusts for agreement attainable due to chance, is introduced from the biomedical and psychological literature. b is extended to b;w for the case when the mis-classifications are of varying degrees of severity. The importance of using statistical techniques such as hypothesis testing and confidence intervals is stressed. The use of cross-validation, th...

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