Call number: LD2668 .T4 1952 M32 / Master of Science

Call number: LD2668 .T4 1952 U5 / Master of Science

No description available.

As the field of Social Enterprises has strongly grown over two decades, the importance of Social Enterprises for the society has also grown and still developing, more and more to a third economic segment between the private and public economy. Within this study we explain how “Small and Medium sized Social Enterprises” (SMSEs) can face the increasing pressure to measure and demonstrate their impact to society and their stakeholders. We especially address the limitations in skill and resource endowment, which SMSEs most commonly face. We execute this research based on a practical and outcome-oriented method and therefore apply an overall pragmatic research philosophy. The research is further designed around a narrative strategy, with an exploratory mono method cross-sectional study. For the collection of empirical data, six semi-structured interviews were conducted with (Social) Entrepreneurs and their stakeholders. This study finds a language barrier between SMSEs and their commercial stakeholders, caused by low willingness from the Social Entrepreneurs’ side to adapt to a commercial business language. The findings of this study further imply that SMSEs are capable of using surveys to measure their performance quantitatively, in terms of in- and output. More importantly, our results suggest that these measurements, completed with additional “soft” measurements to “qualified narratives”, are sufficient to prove the SMSEs’ performance to their stakeholders. Finally, this study takes up an existing contingency model and enriches it with the revealed discoveries, to a more comprehensive measurement approach for SMSEs. It thereby contributes to the field of Social Enterprises and lays valuable foundations for further research.

No description available.

Models used as part of quantitative studies of vadose zone processes are becoming increasingly complex. However, even the most elaborate models can not capture the complex interactions between spatially distributed water, plant, and atmospheric components of the unsaturated flow system. These processes will always need to be approximated by relatively simple mathematical expressions with limited parameterization. Because of this, there is an ever increasing awareness among hydrologists of the need to describe and quantify these uncertainties to better understand the utility of model predictions and inform decisions concerning model development and data collection. Significant developments in the most recent generation of parameter estimation codes have facilitated the estimation of parameters and quantification of the associated uncertainty in the parameter estimates and model structure; however, these codes are computationally expensive. To facilitate the proposed analysis of more computationally efficient models are required.Computationally efficient models do not necessarily imply over simplified models In the appropriate context, simplifications are possible that reduce the complexity of the model but do not reduce the complexity of the system being represented by the model. I investigate a series of approaches to reduce the computational load of models, facilitating inverse analysis with readily available computing facilities.In light of the improvements to the methodology of parameter estimation, the success of the analysis still depends on the observed response to which the model is compared; the data and the information contained in the data. Given limited resources (both cost and technology) it is important to identify those data that will provide the greatest information about a system. To this end, the investigations presented here also investigate methods to identify informative data and to extract information from data effectively.

No description available.

A Monte Carlo study was conducted to investigate the effect of truncation of score distributions on systematic bias and random error of correlation coefficient distributions. The findings were twofold: Correlation decreases systematically due to increasing truncation; and the standard error of the correlation coefficient, which is a measure of random error, increases due to increasing truncation.

No description available.

No description available.