Predicting intentions to purchase organic food: The role of affective and moral attitudes in the Theory of Planned Behaviour

This study examined the usefulness of integrating measures of affective and moral attitudes into the Theory of Planned Behaviour (TPB)-model in predicting purchase intentions or organic foods. Moral attitude was operationalised Lis positive self-rewarding feelings of doing the right thing. Questionnaire data were gathered in three countries: Italy (N = 202), Finland (N = 270) and UK (N = 200) in March 2004. Questions focussed on intentions to purchase organic apples and organic ready-to-cook pizza instead of their conventional alternatives. Data were analysed using Structural Equation Modelling by simultaneous multi-group analysis of the three Countries. Along with attitudes, moral attitude and subjective norms explained considerable shares of variances in intentions. The relative influences of these variables varied between the Countries, such that in the UK and Italy moral attitude rather than subjective norms had stronger explanatory power. In Finland it was other way around. Inclusion of moral attitude improved the model fit and predictive ability of the model, although only marginally in Finland. Thus the results partially Support the usefulness of incorporating moral measures as well as affective items for attitude into the framework of TPB. (c) 2007 Elsevier Ltd. All rights reserved.

Ultra-fast processing of gigapixel Tissue MicroArray images using high performance computing.

BACKGROUND:
tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.
METHODS:
a High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.
RESULTS:
the automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.
CONCLUSIONS:
the methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.