El clima laboral como variable estratégica para contribuir al éxito de las organizaciones: estudio del caso de la Fundación Mariana de Jesús (Quito)

En la actualidad todas las organizaciones se ven enmarcadas en un contexto competitivo al cual hay que responder con una serie de estrategias orientadas a satisfacer de mejor manera las expectativas cada vez más marcadas y selectivas de los clientes. Dentro de esta óptica la generación de valor agregado en los diferentes procesos productivos donde el factor humano es vital, es necesario partir de la premisa: “que el contar con Recurso Humano motivado devengará positivamente sus esfuerzos hacia la empresa traduciéndose en eficiencia y eficacia en los procesos productivos”. Es por esta razón que se ha buscado analizar la situación de los colaboradores de la Fundación Mariana de Jesús realizando un Diagnóstico del Clima Laboral que nos permita determinar la influencia ambiental en la motivación de los mismos y conocer cual es la situación actual del clima laboral según una serie de factores definidos para este caso. Además en base a estos resultados se han elaborado recomendaciones y líneas de acción de las debilidades encontradas en la institución mencionada.

La cultura organizacional como variable estratégica para el cambio empresarial: caso Óptica los Andes Cía. Ltda.

Para muchas empresas la cultura organizacional se considera como una estrategia, que puede ser efectiva a la hora de mejorar las condiciones actuales y futuras de las organizaciones, especialmente si éstas se están enfrentando a grandes cambios. Por lo expuesto, el presente trabajo de investigación sobre la cultura organizacional puede convertirse en una herramienta administrativa muy útil para los directivos de la organización. El desarrollo de la investigación tiene cuatro momentos identificados claramente: en el primer capítulo se realiza un análisis teórico, donde se hablará sobre las organizaciones, tipos de estructuras, la cadena de valor y la administración estratégica. En el segundo capítulo hablaremos sobre la cultura organizacional, tipos de culturas y especialmente analizaremos cómo se crea y mantiene una cultura. En el tercer capítulo se aborda el tema sobre el cambio organizacional y finalmente en el cuarto capítulo, se realiza el análisis de las encuestas aplicadas en la empresa Óptica los Andes de la cultura organizacional y estilos de dirección. En el último capítulo se detallan las conclusiones y las recomendaciones para llegar a la cultura organizacional deseada.

La asociación estratégica Unión Europea-América Latina: ¿unas relaciones birregionales con geometría variable? (Tema Central)

Tras quince años de la “asociación estratégica UE-ALC”, los múltiples y profundos cambios en el sistema internacional han tenido gran impacto sobre estas relaciones birregionales. Por ello hay que replantear cuáles son los cambios en la percepciones mutuas de las dos regiones, y observar el nuevo contexto geopolítico, tomando nota de la pérdida de influencia de los EEUU en la región y el auge de Asia, especialmente China. La “asociación estratégica” no ha conducido ni a una convergencia de intereses ni a una reconocible compatibilidad normativa. Además, la UE ha perdido su papel de “modelo” para los procesos de integración, en una región que tiene aún más heterogeneidad por sus diferentes modelos de desarrollo. Por ello se hace necesario asumir una geometría variable para la adaptación de las relaciones birregionales a la realidad del siglo XXI.

A regime-dependent balanced control variable based on potential vorticity

In this paper it is argued that rotational wind is not the best choice of leading control variable for variational data assimilation, and an alternative is suggested and tested. A rotational wind parameter is used in most global variational assimilation systems as a pragmatic way of approximately representing the balanced component of the assimilation increments. In effect, rotational wind is treated as a proxy for potential vorticity, but one that it is potentially not a good choice in flow regimes characterised by small Burger number. This paper reports on an alternative set of control variables which are based around potential vorticity. This gives rise to a new formulation of the background error covariances for the Met Office's variational assimilation system, which leads to flow dependency. It uses similar balance relationships to traditional schemes, but recognises the existence of unbalanced rotational wind which is used with a new anti-balance relationship. The new scheme is described and its performance is evaluated and compared to a traditional scheme using a sample of diagnostics.

Sampling the soil in long-term forest plots: The implications of spatial variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.

Spatial analysis of the error in a model of soil nitrogen

Models of the dynamics of nitrogen in soil (soil-N) can be used to aid the fertilizer management of a crop. The predictions of soil-N models can be validated by comparison with observed data. Validation generally involves calculating non-spatial statistics of the observations and predictions, such as their means, their mean squared-difference, and their correlation. However, when the model predictions are spatially distributed across a landscape the model requires validation with spatial statistics. There are three reasons for this: (i) the model may be more or less successful at reproducing the variance of the observations at different spatial scales; (ii) the correlation of the predictions with the observations may be different at different spatial scales; (iii) the spatial pattern of model error may be informative. In this study we used a model, parameterized with spatially variable input information about the soil, to predict the mineral-N content of soil in an arable field, and compared the results with observed data. We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood. This novel approach allowed us to describe the joint variation of the observations and predictions as: (i) independent random variation that occurred at a fine spatial scale; (ii) correlated random variation that occurred at a coarse spatial scale; (iii) systematic variation associated with a spatial trend. The linear mixed model revealed that, in general, the performance of the N model changed depending on the spatial scale of interest. At the scales associated with random variation, the N model underestimated the variance of the observations, and the predictions were correlated poorly with the observations. At the scale of the trend, the predictions and observations shared a common surface. The spatial pattern of the error of the N model suggested that the observations were affected by the local soil condition, but this was not accounted for by the N model. In summary, the N model would be well-suited to field-scale management of soil nitrogen, but suited poorly to management at finer spatial scales. This information was not apparent with a non-spatial validation. (c),2007 Elsevier B.V. All rights reserved.

Sampling uncertainty properties of cloud fraction estimates from random transect observations

[1] Cloud cover is conventionally estimated from satellite images as the observed fraction of cloudy pixels. Active instruments such as radar and Lidar observe in narrow transects that sample only a small percentage of the area over which the cloud fraction is estimated. As a consequence, the fraction estimate has an associated sampling uncertainty, which usually remains unspecified. This paper extends a Bayesian method of cloud fraction estimation, which also provides an analytical estimate of the sampling error. This method is applied to test the sensitivity of this error to sampling characteristics, such as the number of observed transects and the variability of the underlying cloud field. The dependence of the uncertainty on these characteristics is investigated using synthetic data simulated to have properties closely resembling observations of the spaceborne Lidar NASA-LITE mission. Results suggest that the variance of the cloud fraction is greatest for medium cloud cover and least when conditions are mostly cloudy or clear. However, there is a bias in the estimation, which is greatest around 25% and 75% cloud cover. The sampling uncertainty is also affected by the mean lengths of clouds and of clear intervals; shorter lengths decrease uncertainty, primarily because there are more cloud observations in a transect of a given length. Uncertainty also falls with increasing number of transects. Therefore a sampling strategy aimed at minimizing the uncertainty in transect derived cloud fraction will have to take into account both the cloud and clear sky length distributions as well as the cloud fraction of the observed field. These conclusions have implications for the design of future satellite missions. This paper describes the first integrated methodology for the analytical assessment of sampling uncertainty in cloud fraction observations from forthcoming spaceborne radar and Lidar missions such as NASA's Calipso and CloudSat.