ABSTRACT
In the area of risk assessment associated with ecotoxicological and plant protection products, probabilistic risk assessment (PRA) methodologies have been developed that enable quantification of variability and uncertainty. Despite the potential advantages of these new methodologies, end-user and regulatory uptake has not been, to date, extensive. A case study, utilizing the Theory of Planned Behavior, was conducted in order to identify potential determinants of end-user adoption of probabilistic risk assessments associated with the ecotoxicological impact of pesticides. Seventy potential end-users, drawn from academia, government, industry, and consultancy organizations, were included in the study. The results indicated that end-user intention to adopt PRA varied across the different end-user groups. The regulatory acceptance of PRA was contingent on social acceptance across the regulatory community regarding the reliability and utility of the outputs. Training in interpretation of outputs is therefore highly relevant to regulatory acceptance. In other end-user sectors, a positive attitude toward PRA, “hands on” experience, and perceived capability of actually performing PRA is an important determinant of end-user intention to adopt PRA. It is concluded that training programs targeted to the specific needs of different end-user sectors should be developed if end-user adoption of PRA is to be increased.
ACKNOWLEDGMENT
The research presented in this article is supported by the European Commission's 5th Framework Programme (www.cordis.lu), contract number QLK5-CT 2002 01346.
This document is the responsibility of its publishers and in no way represents the views of the European Commission or its services.
Notes
1But see the U.S. Environmental Protection Agency's (USEPA) ECOFRAM Project, launched in 1997: http://www.epa.gov/oppefed1/ecorisk/index.htm.
2All measured on a 7-point scale from 1 to 7 with 1 being an indicator of very low frequency/agreement and 7 an indicator of very high agreement; and 4 being the neutral scale center.
a R2 for the multiple regression model.
b R2 based on zero-order correlation effects.
a R2 for the multiple regression model.
b R2 based on zero-order correlation effects.
a R2 based on zero-order correlation effects;
b R2 for the multiple regression model.
nsnot-significant,
a R2 based on zero-order correlation effects;
b R2 for the multiple regression model.
nsnot-significant,
3The results for participants working in academia may not be significant because of the very small sample involved (n = 8).
nsnot-significant,
+ p < .05 one-sided. Other effects significant at p < .05 two-sided.