Analyzing E-Learning Adoption via Recursive Partitioning

The paper analyzes factors that influence the adoption of e-learning and gives an example of how to forecast technology adoption based on a post-hoc predictive segmentation using a classification and regression tree (CART). We find strong evidence for the existence of technological interdependencies and organizational learning effects. Furthermore, we find different paths to elearning adoption. The results of the analysis suggest a growing "digital divide" among firms. We use cross-sectional data from a European survey about e-business in June 2002, covering almost 6,000 enterprises in 15 industry sectors and 4 countries. Comparing the predictive quality of CART, we find that CART outperforms a traditional logistic regression. The results are more parsimonious, i. e. CARTs use less explanatory variables, better interpretable since different paths of adoption are detected, and from a statistical standpoint, because interactions between the covariates are taken into account.Technology Adoption, Path Dependence, Interaction between Different Technologies, Regression Trees, Predictive Segmentation, Logistic Regression, E-Learning, E-Business

Endogenous Acceleration of Technological Change

Our study shows that the technological development of a firm can be subject to an endogenous acceleration mechanism. The more advanced a firm is in using a particular set of technologies, the more likely will it adopt additional, related technologies. This acceleration mechanism implies that marginal differences in early adoption decisions lead to substantial differences in technology endowment later. This hypothesis is tested in a dataset that records the adoption times of various e-business technologies in a sample of 7,302 firms from 10 different industry sectors and 25 European countries. Estimation is carried out with a semi-parametric hazard rate model that controls for unobserved heterogeneity. The results show that the probability to adopt strictly increases with the number of previously adopted e-business technologies. Evidence for a growing digital divide among the companies in the sample is demonstrated for the period from 1994-2002. The endogenous acceleration mechanism is a possible source of early mover advantages, if technological uncertainty and technological improvements over time are not very large and if the price of the new technologies remains roughly constant.Technology adoption, Technological competition, Complementarity, Hazard rate models, IT

Transport functions of nitrogen up to 26,000 K

The current field strength characteristic, E(1), and a large number of radial temperature distributions, T(r,I), measured in a 5 mm N2 cascade arc at normal pressure are used to evaluate the transport properties of nitrogen up to 26,000 K. The electrical conductivity sigma (T) and the Coulomb cross section are determined directly from the E(I) and several T(r,I) curves. Between 10,000 and 15,000 K the radiative energy flux for different arc current, the thermal conductivity, and from this the charge exchange cross section are determined in a good approximation utilizing the large number of measured temperature distributions. It turns out, that at the highest measured arc current, i.e., 570 A, in the axial region of the arc about 95% of the supplied energy is carried off by radiation

Adoption of e-business: patterns and consequences of network externalities

The paper analyzes the adoption of various e-business technologies. Strong empirical evidence is found for the existence of increasing returns to adoption due to indirect network externalities between related technologies. If a company is close to the technological frontier, its probability of adoption increases. The empirical analysis is based on more than 5,000 observations from a cross-sectional European enterprise survey conducted in June 2002. A classification and regression tree (CART) is used to illustrate technological complementarities and their effect for the adoption probability of a firm. --Technology Adoption,Path Dependence,Classification Trees

From Reports to Maps

In this paper, we will sketch a project in progress. The project aims at an application of a command and control system. The application is meant to process military reports written in natural language. It exploits computer linguistic techniques, especially information extraction and ontological augmentation. A prototype has already be completed. A real world application of report processing has to go beyond pure syntactic parsing. Semantic analysis is needed and the meaning of the report has to be constructed. Even more, the meaning has to be represented in a format such that it can be visualized within the so called ``common operational picture'' (COP). The COP is an interactive map displaying information. COP standards are provided by NATO. Since military operations of our days -- war operations as well as peace-keeping and nation building ones -- involve forces of many nations, the COP serves as main tool for synchronizing actions and plans. The paper at hand will provide some insights what kind of problems come along if language processing has to result in map visualization. It also will describe some solutions to overcome these problems