Determinants of reactions to gene technology: a generic approach

Abstract

This paper examines the reactions to gene technology (the intention to buy gene-tech food, worry about abuse, and the public's desire that different actors be able to influence decisions) in a sample of the Dutch population (n = 1010) and studies the relationship between these reactions and perception, trust, experience, knowledge and personal interest. The survey reveals that large parts of the public are concerned about the abuse of gene technology, are not willing to buy gene-tech products, and want actors to have an influence on legislation and enforcement. Path analysis shows that these reactions can be well explained using a generic model. Trust in authorities, personal interest in gene technology, and perception of gene technology are important predictors of people's reactions, whereas experience and knowledge are less important.

Keywords:

• perception
• trust
• gene technology
• cognitive and affective factors

Acknowledgements

This article is based on research funded by the Centre for Society and Genomics, The Netherlands, a national centre funded by the Netherlands Genomics Initiative.

Notes

Note: ^a RMSEA = 0.020; chi square = 17.98, df = 14, p = 0.21; CFI  =  0.989; SRMR  =  0.028. ^# p ≤ 0.05; ^## p ≤ 0.001; ^### p extremely small (6.0 ≤ t).

The authors particularly refrained from identifying the trust determinant included in our model as either strictly cognitive or affective, but referred to the broad discussion on the role and character of trust (Frewer et al. 2003, Tanaka 2004).

Overall, no major differences for the variables reported in this study were found between 2002 and 2005 (IBT Marktonderzoek 2002).

The net response of completed and usable interviews for statistical analysis with Amos was 725. The dropout can be attributed to randomly missing data. List-wise deletion led to excluding 285 incomplete cases. The demographic make-up of the 725 is comparable to the 1010 participants.

Many different indices can be used to assess model fit. Following Kline (2005), we reported four indices: RMSEA, which has been recognized as one of the most informative criteria (Byrne 2001), chi square, CFI and SRMR. The following criteria were used: RMSEA: when RMSEA is below 0.08, the model fit is acceptable and when RMSEA is below 0.05, the fit is good; CFI: when CFI is higher than 0.90, the model is acceptable; SRMR: values of less than 0.10 are favorable; and chi square: when the significance level of chi square is larger than 0.05, the model is acceptable. It should be noted that there are problems attached to the use of chi square, and that the main reason for reporting the coefficient is because many other fit statistics depend on it (Kline 2005).

RMSEA = 0.020; chi square = 17.94, df = 14, p = 0.21; CFI = 0.989; SRMR = 0.028.

RMSEA = 0.024; chi square = 20.06, df = 14, p = 0.13; CFI = 0.983; SRMR = 0.029.