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Risk Anal ; 27(6): 1565-80, 2007 Dec.
Article in English | MEDLINE | ID: mdl-18093053

ABSTRACT

In developing and implementing appropriate food risk management strategies, it is important to understand how consumers evaluate the quality of food risk management practices. The aim of this study is to model the underlying psychological factors influencing consumer evaluations of food risk management quality using structural equation modeling techniques (SEM), and to examine the extent to which the influence of these factors is country-specific (comparing respondents from Denmark, Germany, Greece, Slovenia, and the United Kingdom). A survey was developed to model the factors that drive consumer evaluations of food risk management practices and their relative importance (n= 2,533 total respondents). The measurement scales included in the structural model were configurally and metrically invariant across countries. Results show that some factors appear to drive perceptions of effective food risk management in all the countries studied, such as proactive consumer protection, which was positively related to consumers' evaluation of food risk management quality, while opaque and reactive risk management was negatively related to perceived food risk management quality. Other factors appeared to apply only in certain countries. For example, skepticism in risk assessment and communication practices was negatively related to food risk management quality, particularly so in the UK. Expertise of food risk managers appeared to be a key factor in consumers' evaluation of food risk management quality in some countries. However, trust in the honesty of food risk managers did not have a significant effect on food risk management quality. From the results, policy implications for food risk management are discussed and important directions for future research are identified.


Subject(s)
Food , Risk Management , Animals , Community Participation , Europe , Food/adverse effects , Food/standards , Food Supply/standards , Humans , Models, Statistical , Regression Analysis , Risk Management/standards , Risk Management/statistics & numerical data , Safety , Trust
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