988 resultados para plant organization level
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This study discusses the nature of informal learning process in business organizations, and the importance of different organization-level factors in this process. The purpose of this study is to understand the role of organization-level factors on informal learning process with three subquestions: how informal learning process takes place in business organizations, what organization-level factors affects informal learning process, and how informal learning process is affected by organizational-level factors. The theoretical background of this study includes literatures on the concept of informal learning, its process, and organization-level factors that can affect informal learning process. The empirical research has been conducted in this study by face-to-face interviews. The interviews were conducted between June and August 2015 in Dhaka, Bangladesh. Thirteen interviews were made with the employees from different hierarchical levels from four freight forwarding MNCs in Bangladesh. Constant comparative analysis has been used to process the collected data until reaching a level of saturation. The empirical research found that all the phases in an informal learning process are not linear and sequential, and the role of organization-level factors on each phase varies with the degree and nature of each factor. In addition, the results also revealed that all the organization-level factors do not interact with each other while playing their role on informal learning process. The findings of this study considerably extend our understanding of the important role of HRD, manager, colleague, culture, and work structure on informal learning process in the workplace. However, future research in different organizational contexts is required to generalize the findings of this study.
Resumo:
This study discusses the nature of informal learning process in business organizations, and the importance of different organization-level factors in this process. The purpose of this study is to understand the role of organization-level factors on informal learning process with three subquestions: how informal learning process takes place in business organizations, what organizationlevel factors affects informal learning process, and how informal learning process is affected by organizational-level factors. The theoretical background of this study includes literatures on the concept of informal learning, its process, and organization-level factors that can affect informal learning process. The empirical research has been conducted in this study by face-to-face interviews. The interviews were conducted between June and August 2015 in Dhaka, Bangladesh. Thirteen interviews were made with the employees from different hierarchical levels from four freight forwarding MNCs in Bangladesh. Constant comparative analysis has been used to process the collected data until reaching a level of saturation. The empirical research found that all the phases in an informal learning process are not linear and sequential, and the role of organization-level factors on each phase varies with the degree and nature of each factor. In addition, the results also revealed that all the organization-level factors do not interact with each other while playing their role on informal learning process. The findings of this study considerably extend our understanding of the important role of HRD, manager, colleague, culture, and work structure on informal learning process in the workplace. However, future research in different organizational contexts is required to generalize the findings of this study.
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The purpose of this study was to understand the scope of breast cancer disparities within the Texas Medical Center. The goal was to increase the awareness of breast cancer disparities at the health care organization level, and to foster the development of organizational interventions to reduce breast cancer disparities. The study seeks to answer the following questions: 1. Are hospitals in the Texas Medical Center implementing interventions to reduce breast cancer disparities? 2. What are their interventions for reducing the effects of non clinical factors on breast cancer treatment disparities? 3. What are their measures for monitoring, continuously improving, and evaluating the success of their interventions? ^ This research project was designed as a mixed methods case study. Quantitative breast cancer data for the years 2000-2009 was obtained from the Texas Cancer Registry (TCR). Qualitative data collection and analysis was done by conducting a total of 20 semi-structured interviews of administrators, physicians and nurses at five hospitals (A, B, C, D and E) in the Texas Medical Center (TMC). For quantitative analysis, the study was limited to early stage breast cancer patients: local and regional. The dependent variable was receipt of standard treatment: Surgery (Yes/No), BCS vs Mastectomy, Chemotherapy (Yes/No) and Radiation after BCS (Yes/No). The main independent variable was race: non-Hispanic White (NHW) , non-Hispanic Black (NHB), and Hispanic. Other covariates included age at diagnosis, diagnosis date, percent poverty, grade, stage, and regional nodes. Multivariate logistic regression was used to test the adjusted association between receipt of standard care and race. Qualitative data was analyzed with the Atlas.ti7 software (ATLAS.ti GmbH, Berlin). ^ Though there were significant differences by race for all dependent variables when the data was analyzed as a single group of all hospitals; at the level of the individual hospitals the results were not consistent by race/ethnicity across all dependent variables for hospitals A, B, and E. There were no racial differences in adjusted analysis for receipt of chemotherapy for the individual hospitals of interest in this study. For hospitals C and D, no racial disparities in treatment was observed in adjusted multivariable analysis. All organizations in this study were aware of the body of research which shows that there are disparities in breast cancer outcomes for patient population groups. However, qualitative data analysis found that there were differences in interest among hospitals in addressing breast cancer disparities in their patient population groups. Some organizations were actively implementing directed measures to reduce the breast cancer disparity gap in outcomes for patients, and others were not. Despite the differences in levels of interest, quantitative data analysis showed that organizations in the Texas Medical Center were making progress in reducing the burden of breast cancer disparities in the patient populations being served.^
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In this study, we examine the effects of tariff reduction on firms' quality upgrading by employing an Indonesian plant-product-level panel dataset matched with a plant-level dataset. We explore the effects of lower output and input tariffs separately, by focusing on the apparel industry. By estimating the Berry-type demand function, we derive product-quality indicators based on the Khandelwal (Review of Economic Studies, 2010) methodology, which enables us to isolate quality upgrading from changes in prices. Our findings are as follows. First, a reduction in output tariffs does not affect product quality upgrading. Second, a reduction in input tariffs boosts quality upgrading in general. In particular, this impact is greater for import firms, which is consistent with the fact that the source of the boost is the import of high-quality foreign inputs.
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Peer-reviewed
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2009, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2009, in addition to the four community level cover estimates, cover of the moss layer was estimated.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2010, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2013, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2008, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2002, vegetation cover was estimated only once in Septemper just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2002, cover on the community level was only estimated for the sown plant community, weed plant community and bare soil. In contrast to later years, cover of dead plant material was not estimated.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2003, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2003, cover on the community level was only estimated for the sown plant community, weed plant community and bare soil. In contrast to later years, cover of dead plant material was not estimated.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2005, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2005, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2006, vegetation cover was estimated twice in June and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2006, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.
Resumo:
This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2007, vegetation cover was estimated twice in June and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2007, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.