916 resultados para asset pricing model


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It has been recognised that brands play a role in industrial markets, but to date a comprehensive model of business-to-business (B2B) branding does not exist, nor has there been an empirical study of the applicability of a full brand equity model in a B2B context. This paper is the first to begin to address these issues. The paper introduces the Customer- Based Brand Equity (CBBE) model by Kevin Keller (1993; 2001; 2003), and empirically tests its applicability in the market of electronic tracking systems for waste management. While Keller claims that the CBBE pyramid can be applied in a B2B context, this research highlights challenges of such an application, and suggests changes to the model are required. Assessing the equity of manufacturers’ brand names is more appropriate than measuring the equity of individual product brands as suggested by Keller. Secondly, the building blocks of Keller’s model appear useful in an organisational context, although differences in the subdimensions are required. Brand feelings appear to lack relevance in the industrial market investigated, and the pinnacle of Keller’s pyramid, resonance, needs serious modifications. Finally, company representatives play a role in building brand equity, indicating a need for this human element to be recognised in a B2B model.

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Purpose – The importance of branding in industrial contexts has increased, yet a comprehensive model of business-to-business (B2B) branding does not exist, nor has there been a thoroughempirical study of the applicability of a full brand equitymodel in a B2B context. This paper aims to discuss the suitability and limitations of Keller’s customer-based brand equity model and tests its applicability in a B2B market. Design/methodology/approach – The study involved the use of semi-structured interviews with senior buyers of technology for electronic tracking of waste management. Findings – Findings suggest that amongst organisational buyers there is a much greater emphasis on the selling organisation, including its corporate brand, credibility and staff, than on individual brands and their associated dimensions. Research limitations/implications – The study investigates real brands with real potential buyers, so there is a risk that the results may represent industry-specific factors that are not representative of all B2B markets. Future research that validates the importance of the Keller elements in other industrial marketing contexts would be beneficial. Practical implications – The findings are relevant for marketing practitioners, researchers and managers as a starting-point for their B2B brand equity research. Originality/value – Detailed insights and key lessons from the field with regard to how B2B brand equity should be conceptualised and measured are offered. A revised brand equity model for B2B application is also presented.

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The current study aims to investigate the non-linear relationship between the JD-R model and work engagement. Previous research has identified linear relationships between these constructs; however there are strong theoretical arguments for testing curvilinear relationships (e.g., Warr, 1987). Data were collected via a self-report online survey from officers of one Australian police service (N = 2,626). Results demonstrated a curvilinear relationship between job demands and job resources and engagement. Gender (as a control variable) was also found to be a significant predictor of work engagement. The results indicated that male police officers experienced significantly higher job demands and colleague support than female officers. However, female police officers reported significantly higher levels of work engagement than male officers. This study emphasises the need to test curvilinear relationships, as well as simple linear associations, when measuring psychological health.

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Sexually transmitted chlamydial infection initially establishes in the endocervix in females, but if the infection ascends the genital tract, significant disease, including infertility, can result. Many of the mechanisms associated with chlamydial infection kinetics and disease ascension are unknown. We attempt to elucidate some of these processes by developing a novel mathematical model, using a cellular automata–partial differential equation model. We matched our model outputs to experimental data of chlamydial infection of the guinea-pig cervix and carried out sensitivity analyses to determine the relative influence of model parameters. We found that the rate of recruitment and action of innate immune cells to clear extracellular chlamydial particles and the rate of passive movement of chlamydial particles are the dominant factors in determining the early course of infection, magnitude of the peak chlamydial time course and the time of the peak. The rate of passive movement was found to be the most important factor in determining whether infection would ascend to the upper genital tract. This study highlights the importance of early innate immunity in the control of chlamydial infection and the significance of motility-diffusive properties and the adaptive immune response in the magnitude of infection and in its ascension.

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We develop and test a theoretically-based integrative model of organizational innovation adoption. Confirmatory factor analyses using responses from 134 organizations showed that the hypothesized second-order model was a better fit to the data than the traditional model of independent factors. Furthermore, although not all elements were significant, the hypothesized model fit adoption better than the traditional model.

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This appendix describes the Order Fulfillment process followed by a fictitious company named Genko Oil. The process is freely inspired by the VICS (Voluntary Inter-industry Commerce Solutions) reference model1 and provides a demonstration of YAWL’s capabilities in modelling complex control-flow, data and resourcing requirements.

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The current policy decision making in Australia regarding non-health public investments (for example, transport/housing/social welfare programmes) does not quantify health benefits and costs systematically. To address this knowledge gap, this study proposes an economic model for quantifying health impacts of public policies in terms of dollar value. The intention is to enable policy-makers in conducting economic evaluation of health effects of non-health policies and in implementing policies those reduce health inequalities as well as enhance positive health gains of the target population. Health Impact Assessment (HIA) provides an appropriate framework for this study since HIA assesses the beneficial and adverse effects of a programme/policy on public health and on health inequalities through the distribution of those effects. However, HIA usually tries to influence the decision making process using its scientific findings, mostly epidemiological and toxicological evidence. In reality, this evidence can not establish causal links between policy and health impacts since it can not explain how an individual or a community reacts to changing circumstances. The proposed economic model addresses this health-policy linkage using a consumer choice approach that can explain changes in group and individual behaviour in a given economic set up. The economic model suggested in this paper links epidemiological findings with economic analysis to estimate the health costs and benefits of public investment policies. That is, estimating dollar impacts when health status of the exposed population group changes by public programmes – for example, transport initiatives to reduce congestion by building new roads/ highways/ tunnels etc. or by imposing congestion taxes. For policy evaluation purposes, the model is incorporated in the HIA framework by establishing association among identified factors, which drive changes in the behaviour of target population group and in turn, in the health outcomes. The economic variables identified to estimate the health inequality and health costs are levels of income, unemployment, education, age groups, disadvantaged population groups, mortality/morbidity etc. However, though the model validation using case studies and/or available database from Australian non-health policy (say, transport) arena is in the future tasks agenda, it is beyond the scope of this current paper.

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Historically, asset management focused primarily on the reliability and maintainability of assets; organisations have since then accepted the notion that a much larger array of processes govern the life and use of an asset. With this, asset management’s new paradigm seeks a holistic, multi-disciplinary approach to the management of physical assets. A growing number of organisations now seek to develop integrated asset management frameworks and bodies of knowledge. This research seeks to complement existing outputs of the mentioned organisations through the development of an asset management ontology. Ontologies define a common vocabulary for both researchers and practitioners who need to share information in a chosen domain. A by-product of ontology development is the realisation of a process architecture, of which there is also no evidence in published literature. To develop the ontology and subsequent asset management process architecture, a standard knowledge-engineering methodology is followed. This involves text analysis, definition and classification of terms and visualisation through an appropriate tool (in this case, the Protégé application was used). The result of this research is the first attempt at developing an asset management ontology and process architecture.

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With increasingly complex engineering assets and tight economic requirements, asset reliability becomes more crucial in Engineering Asset Management (EAM). Improving the reliability of systems has always been a major aim of EAM. Reliability assessment using degradation data has become a significant approach to evaluate the reliability and safety of critical systems. Degradation data often provide more information than failure time data for assessing reliability and predicting the remnant life of systems. In general, degradation is the reduction in performance, reliability, and life span of assets. Many failure mechanisms can be traced to an underlying degradation process. Degradation phenomenon is a kind of stochastic process; therefore, it could be modelled in several approaches. Degradation modelling techniques have generated a great amount of research in reliability field. While degradation models play a significant role in reliability analysis, there are few review papers on that. This paper presents a review of the existing literature on commonly used degradation models in reliability analysis. The current research and developments in degradation models are reviewed and summarised in this paper. This study synthesises these models and classifies them in certain groups. Additionally, it attempts to identify the merits, limitations, and applications of each model. It provides potential applications of these degradation models in asset health and reliability prediction.

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Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semi-parametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.

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This report presents the current state and approach in Building Information Modelling (BIM). The report is focussed at providing a desktop audit of the current state and capabilities of the products and applications supporting BIM. This includes discussion on BIM model servers as well as discipline specific applications, for which the distinction is explained below. The report presented here is aimed at giving a broad overview of the tools and applications with respect to their BIM capabilities and in no way claims to be an exhaustive report for individual tools. Chapter 4 of the report includes the research and development agendas pertaining to the BIM approach based on the observations and analysis from the desktop audit.

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This paper examines consumers self-referencing as a mechanism for explaining ethnicity effects in advertising. Data was collected from a 2 (model ethnicity: Asian, white) x 2 (product stereotypicality: stereotypical, non-stereotypical) experiment. Measured independent variables included participant ethnicity and self-referencing. Results shows that (1) Asian exhibit greater self-referencing of Asian models than whites do; (2) self-referencing mediates ethnicity effects on attitude ( ie, attitude towards the model, attitude toward the add, brand attitude, and purchase intentions); (3) high self-referencing Asian have more favourable attitude towards the add and purchase intentions than low self referencing Asians; and (4) Asian models advertising atypical products generate more self-referencing and more favourable attitudes toward the model, A, and purchase intentions for both Asians and whites.

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In a university context how should colour be taught in order to engage students? Entwistle states, ‘What we learn depends on how we learn, and why we have to learn it.’ Therefore, there is a need to address the accumulating evidence that highlights the effects of learning environments on the quality of student learning when considering colour education. It is necessary to embrace the contextual demands while ensuring that the student knowledge of colour and the joy of discovering its characteristics in practice are enhanced. Institutional policy is forcing educators to re-evaluate traditional studio’s effectiveness and the intensive 'hands-on' interactive approach that is embedded in such an approach. As curriculum development involves not only theory and project work, the classroom culture and physical environment also need to be addressed. The increase in student numbers impacting the number of academic staff/student ratio, availability of teaching support as well as increasing variety of student age, work commitments, learning styles and attitudes have called for positive changes to how we teach. The Queensland University of Technology’s restructure in 2005 was a great opportunity to re-evaluate and redesign the approach to teaching within the design units of Interior Design undergraduate program –including colour. The resultant approach “encapsulates a mode of delivery, studio structure, as well as the learning context in which students and staff interact to facilitate learning”1 with a potential “to be integrated into a range of Interior Design units as it provides an adaptive educational framework rather than a prescriptive set of rules”.

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This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.