The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
How to define and measure risk perceptions
Open accessAbstract
Research on risk perceptions within the tourism domain suffers from two closely related problems: diverging conceptual and measurement definitions. The lack of precision and standardization is hindering comparisons of findings across studies as well as systematic accumulation of knowledge. The present paper gives some examples of how diverging definitions of risk may constitute a serious problem. It also shows how measures of perceived risk are influenced by various heuristics and biases via item wording. Lacking awareness of the effects of these biases may lead to erroneous conclusions regarding the relative risk of various hazards. The paper concludes with specific suggestions for how some of these problems can be tackled, and should be of practical use for tourism scholars researching risk perceptions.
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Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. (2013) · Encyclopedia of Social Work · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,519
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
Prospect theory: An analysis of decision under risk
Kahneman, D., & Tversky, A. (1988) · Decision, Probability and Utility · doi:10.1017/cbo9780511609220.014 · Cited by 32,474
Availability: A heuristic for judging frequency and probability
Tversky, A., & Kahneman, D. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90033-9 · Cited by 9,984 · cited 1×
“B) The availability heuristic: with limited information from abroad dramatic and catastrophic events are the likeliest to be recalled (Tversky & Kahneman, 1973).”
BackgroundDiagnostic and Statistical Manual of Mental Disorders
Siegel, L. J., & Fletcher‐Janzem, E. (2008) · Encyclopedia of Special Education · doi:10.1002/9780470373699.speced0655 · Cited by 8,477
Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.2.267 · Cited by 5,955
Unrealistic optimism about future life events
Weinstein, N. D. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.39.5.806 · Cited by 4,999
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002) · Heuristics and Biases · doi:10.1017/cbo9780511808098.025 · Cited by 2,349
Freeston, M. H., Rhéaume, J., Letarte, H., Dugas, M. J., & Ladouceur, R. (1994) · Personality and Individual Differences · doi:10.1016/0191-8869(94)90048-5 · Cited by 1,731
Immune neglect: A source of durability bias in affective forecasting.
Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.75.3.617 · Cited by 1,547
Cited by
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
Chien, J., & Danks, D. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658946 · Cited by 22
Metadata
- Authors
- Wolff, K., Larsen, S., & Øgaard, T.
- Venue
- Annals of Tourism Research
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 79
- Pages
- 102759
- ISSN
- 0160-7383 (ISSN-L)
- OpenAlex
- W2969468216
- Cited by
- 253
- References
- 94
- FWCI
- 53.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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