Can Target Costing and Whole Life Costing be Applied in the Construction Industry?: Evidence from Two Case Studies

Building on the results of a far‐reaching action research project we discuss an attempt to introduce target costing in the UK construction industry. After examining some of the issues facing the UK construction industry, we examine the case for using target costing as a way of supporting supply‐chain integration in view of an improvement of the level of profitability and quality of the industry. After presenting evidence from two pilot projects we propose some considerations on target costing and its applicability to the UK construction sector and derive directions for future research.     

A note on determinants of household saving behavior

By using cross-sectional interview data form 429 randomly selected households, four different saving estimates, namely reported bank savings, repayments of debts, total savings and a liquidity estimate, are computed and analyzed separately. Both socioeconomic characteristics and “softer” variables, such as attitudes and expectations, are used as explanatory variables.In a regression analysis the predictors used, fail to explain bank savings, while the traditional socioeconomic variables are found to influence the debt measure. The behavioral predictors are more successful in explaining the liquidity measure. When the regression result with the liquidity measure as the dependant variable (with the highest R² value) is decomposed using a hypothetical causal model and path analysis, it appears that the effects of household income and educational level of the family head are substantial. However, the intervening behavioral variables are still significant.     

Multi-professional vocational rehabilitation group meetings with female clients: a qualitative study

Many women require vocational rehabilitation before they can return to work. The objective with the present study was to describe female clients' situation during the rehabilitation process, as it became apparent during the rehabilitation meetings with the various actors involved. The clients' diagnoses varied, but the majority was affected with musculoskeletal disorders. The meetings were audio-taped and transcribed verbatim, after which they were analysed by qualitative content analysis. The following themes emerged: Adaptation to the rehabilitation group; client's health status; the workplace's significance for rehabilitation; and the client's decision-point. Conflict between health and work was immensely important for rehabilitation. Differences in attitude were apparent during the rehabilitation meetings, as some clients were passive and exercised less influence on the planning, than the other more active clients did.     

Finite mixtures of quantile and M-quantile regression models

In this paper we define a finite mixture of quantile and M-quantile regression models for heterogeneous and /or for dependent/clustered data. Components of the finite mixture represent clusters of individuals with homogeneous values of model parameters. For its flexibility and ease of estimation, the proposed approaches can be extended to random coefficients with a higher dimension than the simple random intercept case. Estimation of model parameters is obtained through maximum likelihood, by implementing an EM-type algorithm. The standard error estimates for model parameters are obtained using the inverse of the observed information matrix, derived through the Oakes (J R Stat Soc Ser B 61:479–482, 1999) formula in the M-quantile setting, and through nonparametric bootstrap in the quantile case. We present a large scale simulation study to analyse the practical behaviour of the proposed model and to evaluate the empirical performance of the proposed standard error estimates for model parameters. We considered a variety of empirical settings in both the random intercept and the random coefficient case. The proposed modelling approaches are also applied to two well-known datasets which give further insights on their empirical behaviour.     

Random coefficient models for binary longitudinal responses with attrition

We extend the approach introduced by Aitkin and Alfò (1998, Statistics and Computing, 4, pp. 289–307) to the general framework of random coefficient models and propose a class of conditional models to deal with binary longitudinal responses, including unknown sources of heterogeneity in the regression parameters as well as serial dependence of Markovian form.Furthermore, we discuss the extension of the proposed approach to the analysis of informative drop-outs, which represent a central problem in longitudinal studies, and define, as suggested by Follmann and Wu (1995, Biometrics, 51, pp. 151–168), a conditional specification of the full shared parameter model for the primary response and the missingness indicator. The model is applied to a dataset from a methadone maintenance treatment programme held in Sydney in 1986 and previously analysed by Chan et al. (1998, Australian & New Zealand Journal of Statistics, 40, pp. 1–10).All of the proposed models are estimated by means of an EM algorithm for nonparametric maximum likelihood, without assuming any specific parametric distribution for the random coefficients and for the drop-out process.A small scale simulation work is described to explore the behaviour of the extended approach in a number of different situations where informative drop-outs are present.     

Finite mixtures approach to ecological regression

During past few years great attention has been devoted to the analysis of disease incidence and mortality rates, with an explicit focus on modelling geographical variation of rates observed in spatially adjacent regions. The general aim of these contributes has been both to highlight clusters of regions with homogeneous relative risk and to determine the effects of observed and unobserved risk factors related to the analyzed disease. Most of the proposed modelling approaches can be derived as alternative specifications of the components of a general convolution model (Molliè, 1996). In this paper, we consider the semiparametric approach discussed by Schlattmann and Böhning (1993); in particular, we focus on models with an explicit spatially structured component (see Biggeri et al., 2000), and propose alternative choices for the structure of the spatial component.     

Variance component models for longitudinal count data with baseline information: epilepsy data revisited

Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity. Various approaches have been proposed to extend mixed models for repeated count data to include dependence on baseline counts. Dependence between baseline counts and individual-specific random effects result in a complex form of the (conditional) likelihood. An approximate solution can be achieved ignoring this dependence, but this approach could result in biased parameter estimates and in wrong inferences. We propose a computationally feasible approach to overcome this problem, leaving the random effect distribution unspecified. In this context, we show how the EM algorithm for nonparametric maximum likelihood (NPML) can be extended to deal with dependence of repeated measures on baseline counts.     

Social Movements Research and the 'Movement of Movements': Studying Resistance to Neoliberal Globalisation

This article explores the state of research on the 'movement of movements' against neoliberal globalisation. Starting from a general consideration of the significance of the movement and the difficulties inherent in studying it, it discusses the literature on the movement from within social movement studies, and argues that the response from social movement researchers falls short of what could be expected in terms of adequacy to the movement and its own knowledge production. It explores some effects of this failure and locates the reasons for it in the unacknowledged relationship between social movements theorising and activist theorising. The article then discusses the possible contributions that can be made by Marxist and other engaged academic writers, as well as the significance of the extensive theoretical literature generated by activists within the movement. It concludes by stating the importance of dialogue between activist and academic theorising and research in attempting to understand the movement.