Eraldo Francesco Nicotra Paola Grassi

Severity of the dreaded risk of genetic modification of biological systems in living organisms: comparison between perceptions of experienced and naive subjects

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Abstract

The core aim of this study is to evaluate the degree of acceptability/ rejection of risks associated with the use of genetically modified organisms (GMOs). The role of scientific knowledge («Expertise») about different types of biotechnologies was investigated. The «Psychometric paradigm» introduced by Fischhoff, Slovic, Lichtenstein, Read & Combs (1978) was used to create a questionnaire investigating sixteen qualitative risk characteristics associated with seven biotechnology applications: food-related vs. medical-pharmaceutical. The questionnaire was administered to a sample of 114 «experts» in biotechnology and 162 randomly selected «non experts». Both groups rated, on a 11-points scale, a total number of 16 × 7 items. Data were analyzed at the individual level. Nevertheless statistical analysis for aggregate data, using mean scores, were reported too. The mean correlation matrix of individuals has been submitted to multi-sample confirmatory factorial analysis (MS-CFA) to investigate differences about the latent structure of the mental representation of hazards, respectively for the expert and non expert groups. The results reported here indicate that (1) a common cognitive map of risks exists for both groups, and it involves three latent dimensions: «dread», «benefits» and «exposition»; (2) individuals shown little variation in their rating of risk characteristics on the same biotechnology application; (3) the role of the expertise does not appear to be a significant modulator factor to represent differences about perceived risks across experts and non experts groups' aggregation. Lastly, a structural equation model (SEM) has been implemented to test the causal statistical dependence of the «dread» and «benefits» from «exposition», separately for each group of participants.

Keywords

  • Risk Perception
  • Gmos
  • Biotechnology
  • Structural Equation Models
  • Multi-Sample Analysis
  • Measurement Invariance
  • Affect Heuristic

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