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Self-selection, socialization, and risk perception of technologies: An empirical study

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  • Ingrid Ott
  • Ursula Weisenfeld
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    Abstract

    We analyze students’ knowledge and risk perception of four technologies. The aim is to find out whether there is a relationship between area of study (self-selection) and progress of study (socialization) on the one hand and risk perception of technologies regarding health, environment and society on the other. The four technology fields under study are renewable energies, genetic engineering, nanotechnology and information and communication technologies (ICT). Key results are: Irrespective of study area, study progress and gender, genetic engineering has the highest perceived risk and renewable energies has the lowest. This holds for all the risks studied (environmental, health, societal risks). For most risk perception variables, advanced students perceive lower risks than beginners, and students in a technical study area perceive lower risks than students in a non-technical area. Factor analyses show that common dimensions of risk are the technological areas and not the type of risk. Regression analyses show that the variables influencing perceived risks vary between the technological fields

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    Bibliographic Info

    Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1555.

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    Length: 39 pages
    Date of creation: Sep 2009
    Date of revision:
    Handle: RePEc:kie:kieliw:1555

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    Keywords: technologies; risk perception; self-selection; socialization;

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    1. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, INFORMS, vol. 36(9), pages 1057-1079, September.
    2. Douglas Robinson & Arie Rip & Vincent Mangematin, 2007. "Technological agglomeration and the emergence of clusters and networks in nanotechnology," Grenoble Ecole de Management (Post-Print), HAL hal-00424519, HAL.
    3. Bresnahan, Timothy F. & Trajtenberg, M., 1995. "General purpose technologies 'Engines of growth'?," Journal of Econometrics, Elsevier, Elsevier, vol. 65(1), pages 83-108, January.
    4. Kim, Jerry W. & Higgins, Monica C., 2007. "Where do alliances come from?: The effects of upper echelons on alliance formation," Research Policy, Elsevier, Elsevier, vol. 36(4), pages 499-514, May.
    5. Geels, Frank W. & Schot, Johan, 2007. "Typology of sociotechnical transition pathways," Research Policy, Elsevier, Elsevier, vol. 36(3), pages 399-417, April.
    6. 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, University of Chicago Press, vol. 27(2), pages 123-56, September.
    7. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, INFORMS, vol. 34(2), pages 167-185, February.
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