Web 2.0 Use and Organizational Innovation: A Knowledge Transfer Enabling Perspective

Over the last several years, a variety of Web 2.0 applications has been widely adopted by individual users and recently has received great attention from organizations. While an increasing number of organizations have started utilizing Web 2.0 applications in hopes of boosting collaboration and driving innovations, only a small number of different theoretical perspectives are available in the literature that facilitate a further understanding of the phenomenon of organizational adoption of Web 2.0 to drive innovation. In this paper, we propose a theoretical model explicating this phenomenon from the perspective that Web 2.0 use enhances knowledge transfer by fostering the emergence of informal networks, weak ties, boundary spanners and social capital. This model conceptualizes the process through which organizations drive innovations by utilizing Web 2.0 applications. Based on this perspective, suggestions for organizations to facilitate this process are also provided

Web 2.0 Use and Knowledge Transfer: How Social Media Technologies Can Lead to Organizational Innovation

The concept of Web 2.0 has gained widespread prominence in recent years. The use of Web 2.0 applications on an individual level is currently extensive, and such applications have begun to be implemented by organizations in hopes of boosting collaboration and driving innovation. Despite this growing trend, only a small number of theoretical perspectives are available in the literature that discuss how such applications could be utilized to assist in innovation. In this paper, we propose a theoretical model explicating this phenomenon. We argue that organizational Web 2.0 use fosters the emergence and enhancement of informal networks, weak ties, boundary spanners, organizational absorptive capacity, which are reflected in three dimensions of social capital, structural, relational, and cognitive. The generation of social capital enables organizational knowledge transfer, which in turn leads to organizational innovation. Based on this model, suggestions for organizations to facilitate this process are also provided, and theoretical implications are discussed

Boosting Additive Models using Component-wise P-Splines

We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous from a computational point of view. In particular, we give a detailed analysis on the effect of various P-spline hyper-parameters on the boosting fit. In addition, we derive a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates

Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression

Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects and, as a consequence, focus on average properties of the response. Analyzing childhood malnutrition in developing or transition countries based on such a regression model implies that the estimated effects describe the average nutritional status. However, it is of even larger interest to analyze quantiles of the response distribution such as the 5% or 10% quantile that relate to the risk of children for extreme malnutrition. In this paper, we analyze data on childhood malnutrition collected in the 2005/2006 India Demographic and Health Survey based on a semiparametric extension of quantile regression models where nonlinear effects are included in the model equation, leading to additive quantile regression. The variable selection and model choice problems associated with estimating an additive quantile regression model are addressed by a novel boosting approach. Based on this rather general class of statistical learning procedures for empirical risk minimization, we develop, evaluate and apply a boosting algorithm for quantile regression. Our proposal allows for data-driven determination of the amount of smoothness required for the nonlinear effects and combines model selection with an automatic variable selection property. The results of our empirical evaluation suggest that boosting is an appropriate tool for estimation in linear and additive quantile regression models and helps to identify yet unknown risk factors for childhood malnutrition

Physics Of Eclipsing Binaries. II. Towards the Increased Model Fidelity

The precision of photometric and spectroscopic observations has been systematically improved in the last decade, mostly thanks to space-borne photometric missions and ground-based spectrographs dedicated to finding exoplanets. The field of eclipsing binary stars strongly benefited from this development. Eclipsing binaries serve as critical tools for determining fundamental stellar properties (masses, radii, temperatures and luminosities), yet the models are not capable of reproducing observed data well either because of the missing physics or because of insufficient precision. This led to a predicament where radiative and dynamical effects, insofar buried in noise, started showing up routinely in the data, but were not accounted for in the models. PHOEBE (PHysics Of Eclipsing BinariEs; http://phoebe-project.org) is an open source modeling code for computing theoretical light and radial velocity curves that addresses both problems by incorporating missing physics and by increasing the computational fidelity. In particular, we discuss triangulation as a superior surface discretization algorithm, meshing of rotating single stars, light time travel effect, advanced phase computation, volume conservation in eccentric orbits, and improved computation of local intensity across the stellar surfaces that includes photon-weighted mode, enhanced limb darkening treatment, better reflection treatment and Doppler boosting. Here we present the concepts on which PHOEBE is built on and proofs of concept that demonstrate the increased model fidelity.Comment: 60 pages, 15 figures, published in ApJS; accompanied by the release of PHOEBE 2.0 on http://phoebe-project.or

GAMLSS for high-dimensional data – a flexible approach based on boosting

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs

Nonparametric Estimation of the Link Function Including Variable Selection

Nonparametric methods for the estimation of the link function in generalized linear models are able to avoid bias in the regression parameters. But for the estimation of the link typically the full model, which includes all predictors, has been used. When the number of predictors is large these methods fail since the full model can not be estimated. In the present article a boosting type method is proposed that simultaneously selects predictors and estimates the link function. The method performs quite well in simulations and real data examples