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Process Outcomes (process + outcome)
Selected AbstractsThe behaviour of soil process models of ammonia volatilization at contrasting spatial scalesEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 6 2008R. Corstanje Summary Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic. [source] Complementing Mass Customization Toolkits with User Communities: How Peer Input Improves Customer Self-Design,THE JOURNAL OF PRODUCT INNOVATION MANAGEMENT, Issue 6 2008Nikolaus Franke In this paper, the authors propose that the canonical customer,toolkit dyad in mass customization (MC) should be complemented with user communities. Many companies in various industries have begun to offer their customers the opportunity to design their own products online. The companies provide Web-based MC toolkits that allow customers who prefer individualized products to tailor items such as sneakers, personal computers (PCs), cars, kitchens, cereals, or skis to their specific preferences. Most existing MC toolkits are based on the underlying concept of an isolated, dyadic interaction process between the individual customer and the MC toolkit. Information from external sources is not provided. As a result, most academic research on MC toolkits has focused on this dyadic perspective. The main premise of this paper is that novice MC toolkit users in particular might largely benefit from information given by other customers. Pioneering research shows that customers in the computer gaming and digital music instruments industries are willing to support each other for the sake of efficient toolkit use (e.g., how certain toolkit functions work). Expanding on their work, the present paper provides evidence that peer assistance appears also extremely useful in the two other major phases of the customer's individual self-design process, namely, the development of an initial idea and the evaluation of a preliminary design solution. Two controlled experiments were conducted in which 191 subjects used an MC toolkit to design their own individual skis. The authors found that during the phase of developing an initial idea, having access to other users' designs as potential starting points stimulates the integration of existing solution chunks into the problem-solving process, which indicates more systematic problem-solving behavior. Peer customer input also turned out to have positive effects on the evaluation of preliminary design solutions. Providing other customers' opinions on interim design solutions stimulated favorable problem-solving behavior, namely, the integration of external feedback. The use of these two problem-solving heuristics in turn leads to an improved process outcome,that is, self-designed products that meet the preferences of the customers more effectively (measured in terms of perceived preference fit, purchase intention, and willingness to pay). These findings have important theoretical and managerial implications. [source] Production of lovastatin examined by an integrated approach based on chemometrics and DOSY-NMRBIOTECHNOLOGY & BIOENGINEERING, Issue 5 2002Silvia Bradamante Abstract Microbial secondary metabolites are one of the sources of therapeutic molecules in the pharmaceutical industry. Product quality and high yields of secondary metabolites are the main goals for the commercial success of a fermentation process. Our novel approach was based on the decision-tree algorithm to determine the key variables correlated with the process outcome and on DOSY-NMR to identify both co-metabolites and impurities, and it improves fermentation systems and speeds up bioprocess development. The approach has been validated in the case of lovastatin production from Aspergillus terreus. © 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 80: 589,593, 2002. [source] The behaviour of soil process models of ammonia volatilization at contrasting spatial scalesEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 6 2008R. Corstanje Summary Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic. [source] Creating the conditions for growth: a collaborative practice development programme for clinical nurse leadersJOURNAL OF NURSING MANAGEMENT, Issue 6 2010CHRISTINE A. BOOMER RGN, PG Cert. boomer c.a.& mccormack b. (2010) Journal of Nursing Management 18, 633,644 Creating the conditions for growth: a collaborative practice development programme for clinical nurse leaders Aim, To evaluate a 3-year practice development (PD) programme for clinical nurse leaders. Background, The development of effective leaders is a key objective to progress the modernization agenda. This programme aimed to develop the participants alongside development of the culture and context of care. Methods, Programme evaluation methodology to determine the ,worth' of the programme, inform the experience of the participation, effect on workplace cultures and determine effectiveness of the process used. Results, Created the conditions for growth under two broad themes: process outcomes demonstrating growth as leaders contributing to cultural shifts; and general outcomes demonstrating practice changes. Conclusions, Developing communities of reflective leaders are required to meet demands within contemporary healthcare. PD provides a model to develop leaders to achieve sustainable changes and transform practice. Implications for nursing management, Active collaboration and participation of managers is crucial in the facilitation of and sustainability of cultural change. Approaches adopted to develop and sustain the transformation of practice need to focus on developing the skills and attributes of leaders and managers as facilitators. [source] Paths to deutero-learning through successive process simulations: a case studyKNOWLEDGE AND PROCESS MANAGEMENT: THE JOURNAL OF CORPORATE TRANSFORMATION, Issue 4 2004Päivi Haho This paper discusses the dynamic interaction between organizational learning processes and their outcomes in the context of innovative business process development and change projects in a pharmaceutical company. Through the answers to the research questions, I wish to demonstrate the paths to deutero-learning, which seldom can be empirically identified in an organization. The paper uses notions of strategic, operational and cultural outcomes,including their intangible and tangible manifestations,to explain different results in organizational learning processes. From 1998 to 1999, the pharmaceutical case company applied an evolutionary, process simulation-based business process development method. This method was used to invent and implement business process innovations in the New Product Development process, to shorten the time-to-market of its new medical entities. Successive process simulations guided and focused the business process development and actions on the strategically most valuable areas. The process simulations prepared the organization for the change, and promoted the implementation of the process outcomes. The successive simulations have triggered and thereafter sustained individual and organizational learning. Thus, they have accelerated organizational learning processes and the development of knowledge and innovations. The case demonstrates efficient deutero-learning, enabled through empowered successive process simulations. The results indicate that development projects are more successful, if there are intangible learning outcomes and systemic process learning at the early stages of the project. This also supports double-loop learning in the business process development project and assists changes in norms to occur. Copyright © 2004 John Wiley & Sons, Ltd. [source] The Impact of Input and Output Factors on Emergency Department ThroughputACADEMIC EMERGENCY MEDICINE, Issue 3 2007Phillip V. Asaro MD Objectives: To quantify the impact of input and output factors on emergency department (ED) process outcomes while controlling for patient-level variables. Methods: Using patient- and system-level data from multiple sources, multivariate linear regression models were constructed with length of stay (LOS), wait time, treatment time, and boarding time as dependent variables. The products of the 20th to 80th percentile ranges of the input and output factor variables and their regression coefficients demonstrate the actual impact (in minutes) of each of these factors on throughput outcomes. Results: An increase from the 20th to the 80th percentile in ED arrivals resulted in increases of 42 minutes in wait time, 49 minutes in LOS (admitted patients), and 24 minutes in ED boarding time (admitted patients). For admit percentage (20th to 80th percentile), the increases were 12 minutes in wait time, 15 minutes in LOS, and 1 minute in boarding time. For inpatient bed utilization as of 7 am (20th to 80th percentile), the increases were 4 minutes in wait time, 19 minutes in LOS, and 16 minutes in boarding time. For admitted patients boarded in the ED as of 7 am (20th to 80th percentile), the increases were 35 minutes in wait time, 94 minutes in LOS, and 75 minutes in boarding time. Conclusions: Achieving significant improvement in ED throughput is unlikely without determining the most important factors on process outcomes and taking measures to address variations in ED input and bottlenecks in the ED output stream. [source] | |||||||||||||