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Academic discipline and risk perception of technologies: An empirical study

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  • Weisenfeld, Ursula
  • Ott, Ingrid
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    Abstract

    This article brings together two areas of research: studies on risk perception of technologies and studies on vocational/career choice. This is an important link since decisions concerning technologies are influenced by decision makers' risk perceptions and these in turn may be related to educational and career paths. We analyze students of different academic disciplines with regard to their risk perception of four technologies. The aim is to find out whether there is a relationship between area of study (as a precursor of vocational and career choice) and risk perception of technologies regarding health, environment and society. The four technologies under study are renewable energies, genetic engineering, nanotechnology and information and communication technologies (ICT). Key results are: irrespective of academic discipline risk of genetic engineering on average is rated highest and renewable energies lowest. This holds for all the risks studied (environmental, health, societal risks). On average, students from different academic disciplines differ in their risk perception. Factor analyses show that common dimensions of risk are the technologies and not the kind of risk. Regression analyses show that the variables influencing perceived risks vary between the technological fields.

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    Bibliographic Info

    Article provided by Elsevier in its journal Research Policy.

    Volume (Year): 40 (2011)
    Issue (Month): 3 (April)
    Pages: 487-499

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    Handle: RePEc:eee:respol:v:40:y:2011:i:3:p:487-499

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    Web page: http://www.elsevier.com/locate/respol

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    Keywords: Technologies Risk perception Career choice Academic disciplines Self-selection Socialization;

    References

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    1. Mark Elchardus & Bram Spruyt, 2009. "The Culture of Academic Disciplines and the Sociopolitical Attitudes of Students: A Test of Selection and Socialization Effects," Social Science Quarterly, Southwestern Social Science Association, vol. 90(2), pages 446-460.
    2. Kim, Jerry W. & Higgins, Monica C., 2007. "Where do alliances come from?: The effects of upper echelons on alliance formation," Research Policy, Elsevier, vol. 36(4), pages 499-514, May.
    3. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    4. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    5. Alba, Joseph W & Hutchinson, J Wesley, 2000. " Knowledge Calibration: What Consumers Know and What They Think They Know," Journal of Consumer Research, University of Chicago Press, vol. 27(2), pages 123-56, September.
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    Cited by:
    1. Adanu, Kwami & Hoehn, John P. & Norris, Patricia & Iglesias, Emma, 2012. "Voter decisions on eminent domain and police power reforms," Journal of Housing Economics, Elsevier, vol. 21(2), pages 187-194.

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