Search results

Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive performance evidenced from both techniques, which contradicts some of the earlier studies which support a position of artificial neural network superiority. Demonstrates that at least 18 per cent of the “normal” property predictions and over 70 per cent of the “outlier” property predictions contained valuation errors greater than 15 per cent of the actual sales price. The combination of these substantial errors and the model‐optimization costs incurred motivate a message of caution before artificial neural networks are adopted by the real estate valuation and/or lending industries.

There is a growing academic interest in the examination and exploration of work intensification in a wide range of healthcare settings. The purpose of this paper is to explore the differing staff perceptions in emergency ambulance services in the UK. It provides evidence on the challenges for the paramedic professionalisation agenda and managing operational demands and work intensity in emotionally challenging circumstances, with significant implications for patient safety.

Drawing on the evidence from an empirical study in a large National Health Service ambulance trust in England, this paper examines the challenges and differing staff perceptions of the changing scope and practice of ambulance personnel in the UK. Amidst the progress on the professionalisation of the paramedic agenda, individual trusts are facing challenges in form of staff attitudes towards meeting performance targets, coupled with rising demand, fear of loss of contracts and private competition.

Research findings highlight differing perceptions from various sub-cultural groups and lack of clarity over the core values which are reinforced by cultural and management differences. Need for greater management to explore the relationship between high sickness levels and implications for patient safety including the need for policy and research attention follows from this study. The implications of work intensity on gender equality within the ambulance settings are also discussed.

Ambulance services around the world are witnessing a strain on their operational budgets with increasing demand for their services. Study evidence support inconclusive evidence for patent safety despite the growing specialist paramedic roles. Organisational implications of high staff sickness rates have been largely overlooked in the management literature. This study makes an original contribution while building upon the earlier conceptions of work intensification.

The study findings have significant implications for the ambulance services for better understanding of the staff perceptions on work intensity and implications for patient safety, high sickness absence rates amidst increasing ambulance demand. Study findings will help prepare the organisational policies and design appropriate response.

Societal understanding about the organisational implications of the work intensity in an important emergency response service will encourage further debate and discussion.

This study makes an original contribution by providing insights into the intra-organisational dynamics in an unusual organisational setting of the emergency ambulance services. Study findings have implications for further research inquiry into staff illness, patient safety and gender issues in ambulance services. Evidence cited in the paper has further relevance to ambulance services globally.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

Peer-to-peer (P2P) mentorship has been identified as an important component of professional identity formation in higher education (HE). This may be especially true for education-focused or teaching stream (TS) faculty to thrive in times of changing organizational structures and work environments. The purpose of this paper is to present a critical reflection on the experiences in a faculty P2P mentoring for teaching program and considers the ways in which such programs can influence professional identity formation among TS academics.

In this paper, a matched faculty mentorship pair from Nursing and Chemistry disciplines uses critical reflection as a process of inquiry to interpret their experiences of building and sustaining an effective mentoring relationship as part of the P2P program, and to consider implications for professional identity formation and the Scholarship of Teaching and Learning.

Through the P2P program, the authors discovered that establishment of clear goals, a commitment to teaching and mentoring processes, and a mutual desire to build a relationship based on authenticity and reciprocity resulted in positive short- and long-term impacts on instructional practices. Professional identity was strengthened through intentional engagement and the opportunity to connect with like-minded peers, contributing to a renewed sense of confidence and commitment.

Interest in examining professional identity formation in HE has been growing over the past decade. This paper is novel in the critical reflection on a structured peer mentorship initiative through the lens of professional identity formation, with implications for planning and executing mentoring programs for TS faculty.

WORK Study has know‐how in its bones. The astonishing results, from textiles down to warehousing, stems from experience. We entered the field with the Bedaux trained engineers, and with this flying start we have been able to attain the results of to‐day, even though most firms have not insisted on Bedaux training and many have accepted staff under a system of training that can only be described as slipshod.

The purpose of this paper is to estimate long‐run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory variables available for use such as land price, total substructure area, material price, etc. Moreover, another goal of this paper is to propose a new approach to deal with problems which arise due to a lack of proper data.

The data set is gathered from “The Municipality of Tehran” and “The Central Bank of Islamic Republic of Iran (CBI)”. One‐way fixed effects and one‐way random effects approaches (which are panel data approaches) are applied to model housing price forecasting function in Tehran's 20 different zones. Results are compared with ordinary least squares approach which is a common approach in this field. Finally, outcomes of the preferred approach are discussed and analyzed with regard to the economic point of view.

Results show that one‐way fixed effects approach provides more accurate forecasts and can be considered as a suitable tool to deal with housing price forecasting problems in environments which are: uncertain, complex, and faced with a lack of proper data. Moreover, it is found that land price is the most effective factor that has impact on total housing cost in Tehran, i.e. the main portion of house prices in Tehran is affected by land price, so appropriate policies have to be made by the government to control fluctuations of this factor.

The proposed approach will supply policy makers with improved estimations with decreased errors in uncertain and complex environments which are faced with a lack of proper data, and it extracts valuable information which enables policy makers for handling non‐linearity, complexity, as well as uncertainty that may exist in actual data sets with respect to housing price forecasting. Moreover, the proposed approach can be applied to similar housing price case studies to obtain more accurate and more reliable outcomes.

Applying panel data approach for estimation of housing prices is relatively new in the field of housing economics. Moreover, this is the first study which employs panel data approach for analyzing the housing market in Tehran.

The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.

The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.

The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.

A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.

This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.

The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.

Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.

The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.

This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.

The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.

The primary goal of this longitudinal study was to examine whether technology use predicts substance use and/or whether substance use predicts technology use during adolescence.

The sample included 1,031 10th and 11th grade students from the Mid-Atlantic United States. The students completed surveys in school in the spring of 2007 and 2008.

Gender differences in technology use were observed with girls texting, e-mailing/instant messaging, and working on the computer more than boys, and boys playing video games more than girls. Technology use also predicted later substance use for boys and girls. Importantly, technology use was observed to have both negative and positive effects on youth. Substance use also predicted later technology use for girls.

The sample only included adolescents from the Mid-Atlantic United States. In addition, the measures were based on self-reports. Nevertheless, results from this study highlight the importance of considering both negative and positive effects of technology on adolescents. Of note, social types of technology (texting and e-mailing) predicted more substance use for both boys and girls. As such, substance use prevention programs should target these types of technology.

Findings from this study underscore the importance of examining both directions of influence between technology use and adolescent adjustment.

‘Shear‐constraints’ can be used to produce efficient Mindlin/Reissner or ‘discrete Kirchhoff’ bending elements. The paper shows that ‘selective shear‐constraints’ can be used to produce an effective formulation for folded‐plated structures.