Information Systems Success: A Comparative Case Study

For as long as the information systems (IS) field has existed, the answer to one question in particular has eluded IS researchers: which conditions are necessary for successful implementation of information systems

A NEW AMORPHOPHALLUS FROM THAILAND

AmorpknphoMus dixenii is described and illustrated; this new species is assigned to section Cimdaritm and key to Asiatic species of this section is presented. Chromosome number of this species is found to be 2n =28

The Non-Classical Boltzmann Equation, and Diffusion-Based Approximations to the Boltzmann Equation

We show that several diffusion-based approximations (classical diffusion or SP1, SP2, SP3) to the linear Boltzmann equation can (for an infinite, homogeneous medium) be represented exactly by a non-classical transport equation. As a consequence, we indicate a method to solve diffusion-based approximations to the Boltzmann equation via Monte Carlo, with only statistical errors - no truncation errors.Comment: 16 pages, 3 figure

Adaptive Regularization in Neural Network Modeling

. In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme is an extended version of the recently presented algorithm [24]. The idea is to minimize an empirical estimate -- like the cross-validation estimate -- of the generalization error with respect to regularization parameters. This is done by employing a simple iterative gradient descent scheme using virtually no additional programming overhead compared to standard training. Experiments with feed-forward neural network models for time series prediction and classification tasks showed the viability and robustness of the algorithm. Moreover, we provided some simple theoretical examples in order to illustrate the potential and limitations of the proposed regularization framework. 1 Introduction Neural networks are flexible tools for time series processing and pattern recognition. By increasing the number of hidden neurons in a 2-layer architec..

Modes of Theory Integration

IS, among other social sciences, have moved from a relative paucity of theories about social phenomenon to a a state of multiple, overlapping, and overly narrow theories. We offer three Modes for theory Integration that will enable researchers to better integrate theories and processes into internally coherent models within theories, across theories and between fields. The basis for integration are semantic similarity, nomological congruence and physical/functional/causal overlap. We develop a framework that will justify propositions for theory integration that can subsequently be tested for correspondence to real world phenomenon

Establishing Nomological Networks for Behavioral Science: a Natural Language Processing Based Approach

As the accumulated research base of the behavioral sciences have grown, the amount of actual knowledge discovery has not kept pace as evidenced by an increasing number of disconnected theories and the related problem of construct proliferation. Therefore, integrating social and behavioral sciences across research areas or even disciplines in a meaningful way is imperative. Despite the information systems (IS) discipline’s leadership on creating nomological networks and inter-nomological networks for research integration, a quantitative approach to automatically establish nomological networks from large-scale data is missing. Based on the design science paradigm, we therefore propose a novel natural language processing based approach bringing together these two previous research endeavors. We used a dataset consisting of all the relevant behavioral studies from two tops journal in the IS and psychology fields to evaluate our approach in comparison to human decisions. Finally, the limitations and possible extensions of our approach are critically discussed