Development of a conceptual model of the soil-moisture-plant sub-system of the hydrological cycle

The soil-plant-moisture subsystem is an important component of the hydrological cycle. Over the last 20 or so years a number of computer models of varying complexity have represented this subsystem with differing degrees of success. The aim of this present work has been to improve and extend an existing model. The new model is less site specific thus allowing for the simulation of a wide range of soil types and profiles. Several processes, not included in the original model, are simulated by the inclusion of new algorithms, including: macropore flow; hysteresis and plant growth. Changes have also been made to the infiltration, water uptake and water flow algorithms. Using field data from various sources, regression equations have been derived which relate parameters in the suction-conductivity-moisture content relationships to easily measured soil properties such as particle-size distribution data. Independent tests have been performed on laboratory data produced by Hedges (1989). The parameters found by regression for the suction relationships were then used in equations describing the infiltration and macropore processes. An extensive literature review produced a new model for calculating plant growth from actual transpiration, which was itself partly determined by the root densities and leaf area indices derived by the plant growth model. The new infiltration model uses intensity/duration curves to disaggregate daily rainfall inputs into hourly amounts. The final model has been calibrated and tested against field data, and its performance compared to that of the original model. Simulations have also been carried out to investigate the effects of various parameters on infiltration, macropore flow, actual transpiration and plant growth. Qualitatively comparisons have been made between these results and data given in the literature.

Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

The role of biases in on-line learning of two-layer networks

The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase which has often been predominant in the original model all but disappears for a non-degenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g. attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Brand extensions in emerging markets:theory development and testing in China

Brand extensions are increasingly used by multinational corporations in emerging markets such as China. However, understanding how consumers in the emerging markets evaluate brand extensions is hampered by a lack of research in the emerging markets contexts. To address the knowledge void, we built on an established brand extension evaluation framework in the West, namely Aaker and Keller (1990)1. Aaker , D. A. and Keller , K. L. 1990 . Consumer evaluations of brand extensions . Journal of Marketing , 54 ( 1 ) : 27 – 41 . [CrossRef], [Web of Science ®] View all references, and extended the model by incorporating two new factors: perceived fit based on brand image consistency and competition intensity in the brand extension category. The additions of two factors are made in recognition of the uniqueness of the considerations of consumers in the emerging markets in their brand extension evaluations. The extended model was tested by an empirical experiment using consumers in China. The results partly validated the Aaker and Keller model, and evidence that both newly added factors were significant in influencing consumers' evaluation of brand extensions was also found. More important, one new factor proposed, namely, consumer-perceived fit based on brand image consistency, was found to be more significant than all the factors in Aaker and Keller's original model, suggesting that the Aaker and Keller model may be limited in explaining how consumers in the emerging markets evaluate brand extensions. Further research implications and limitations are discussed in the paper.

By writers for writers: developing a writer-centred model of the writing process

This thesis, set within an Action Research framework, details the development and validation of a writer-centred model of the writing process. The model was synthesised within the boundaries of a writers’ group for MA students. The initial data collected, and analysed using the principles of grounded theory, were retrospective descriptions of group members’ writing processes. After initial analysis, additional data, from group members’ writing, and from audio recordings, were used for further analysis, and to form a model of the writing process. To ascertain whether the model had value outside the specific context in which it was made, it was validated from three different perspectives. Firstly, the retrospective descriptions of other writers were collected and analysed, using the model as a framework. Secondly, the model was presented at academic conferences; comments about the model, made by members of the audience, were collected and analysed. Finally, the model was used in writing courses for PhD students. Comments from these students, along with questionnaire responses, were collected and the content analysed. Upon examination of all data sources, the model was updated to reflect additional insights arising from the analysis. Analysis of the data also indicated that the model is useable outside its original context. Potential uses for the model are 1) raising awareness of the process of writing, 2) putting writers at ease, 3) serving as a starting point for individuals or groups to design their own models of the writing process, and 4) as a tool to help writers take control of their writing processes.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

Effects of syllable structure in aphasic errors:implications for a new model of speech production

Current models of word production assume that words are stored as linear sequences of phonemes which are structured into syllables only at the moment of production. This is because syllable structure is always recoverable from the sequence of phonemes. In contrast, we present theoretical and empirical evidence that syllable structure is lexically represented. Storing syllable structure would have the advantage of making representations more stable and resistant to damage. On the other hand, re-syllabifications affect only a minimal part of phonological representations and occur only in some languages and depending on speech register. Evidence for these claims comes from analyses of aphasic errors which not only respect phonotactic constraints, but also avoid transformations which move the syllabic structure of the word further away from the original structure, even when equating for segmental complexity. This is true across tasks, types of errors, and, crucially, types of patients. The same syllabic effects are shown by apraxic patients and by phonological patients who have more central difficulties in retrieving phonological representations. If syllable structure was only computed after phoneme retrieval, it would have no way to influence the errors of phonological patients. Our results have implications for psycholinguistic and computational models of language as well as for clinical and educational practices.

Formative variables and unreal variables:why the formative MIMIC model is invalid

In this rejoinder, we provide a response to the three commentaries written by Diamantopoulos, Howell, and Rigdon (all this issue) on our paper The MIMIC Model and Formative Variables: Problems and Solutions (also this issue). We contrast the approach taken in the latter paper (where we focus on clarifying the assumptions required to reject the formative MIMIC model) by spending time discussing what assumptions would be necessary to accept the use of the formative MIMIC model as a viable approach. Importantly, we clarify the implications of entity realism and show how it is entirely logical that some theoretical constructs can be considered to have real existence independent of their indicators, and some cannot. We show how the formative model only logically holds when considering these ‘unreal’ entities. In doing so, we provide important counter-arguments for much of the criticisms made in Diamantopoulos’ commentary, and the distinction also helps clarify a number of issues in the commentaries of Howell and Rigdon (both of which in general agree with our original paper). We draw together these various threads to provide a set of conceptual tools researchers can use when thinking about the entities in their theoretical models.

The effectiveness of workplace coaching:A meta-analysis of learning and performance outcomes; scale development; theoretical model of individual differences and longitudinal study

The extant literature on workplace coaching is characterised by a lack of theoretical and empirical understanding regarding the effectiveness of coaching as a learning and development tool; the types of outcomes one can expect from coaching; the tools that can be used to measure coaching outcomes; the underlying processes that explain why and how coaching works and the factors that may impact on coaching effectiveness. This thesis sought to address these substantial gaps in the literature with three linked studies. Firstly, a meta-analysis of workplace coaching effectiveness (k = 17), synthesizing the existing research was presented. A framework of coaching outcomes was developed and utilised to code the studies. Analysis indicated that coaching had positive effects on all outcomes. Next, the framework of outcomes was utilised as the deductive start-point to the development of the scale measuring perceived coaching effectiveness. Utilising a multi-stage approach (n = 201), the analysis indicated that perceived coaching effectiveness may be organised into a six factor structure: career clarity; team performance; work well-being; performance; planning and organizing and personal effectiveness and adaptability. The final study was a longitudinal field experiment to test a theoretical model of individual differences and coaching effectiveness developed in this thesis. An organizational sample of 84 employees each participated in a coaching intervention, completed self-report surveys, and had their job performance rated by peers, direct reports and supervisors (a total of 352 employees provided data on participant performance). The results demonstrate that compared to a control group, the coaching intervention generated a number of positive outcomes. The analysis indicated that coachees’ enthusiasm, intellect and orderliness influenced the impact of coaching on outcomes. Mediation analysis suggested that mastery goal orientation, performance goal orientation and approach motivation in the form of behavioural activation system (BAS) drive, were significant mediators between personality and outcomes. Overall, the findings of this thesis make an original contribution to the understanding of the types of outcomes that can be expected from coaching, and the magnitude of impact coaching has on outcomes. The thesis also provides a tool for reliably measuring coaching effectiveness and a theoretical model to understand the influence of coachee individual differences on coaching outcomes.