Thesis

Measuring risk perception and risk-taking behaviour when driven by automatic cognitive processing : the development of new methods

Creator
Awarding institution
  • University of Strathclyde
Date of award
  • 2017
Thesis identifier
  • T14572
Person Identifier (Local)
  • 201281163
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • Risk perception and risk-taking behaviour, are often driven by automatic (implicit) thinking, either in isolation or in combination with reasoned, deliberative thinking. This presents research challenges as measuring automatic thinking requires the use of specialized methods. The main original contributions of this thesis are the development of novel methods that aim to measure risk perception and risk-taking behaviour when driven by automatic thought processing.Drawing on the MODE model and dual process theories of thinking, the thesis presents the development and demonstration of a risk version of the Evaluative Priming Task. This provides an implicit risk attitude measure that can be used as a novel research tool.In order to measure risk-taking behaviour a modification of a current risk behaviour method (the Balloon Analogue Risk Task) was developed. This version includes a priming component, thus allowing for measurement of changes in risk-taking behaviour based on automatic thought processing. This is the first method that can fulfil this aim.The final main contribution of this thesis is a demonstration of the 'affect heuristic' at an automatic (implicit) level of processing. This was achieved by using the new methods. Using these methods facilitated such investigation in a more direct manner than has previously been possible.The development of both methods provides novel research tools for investigation in implicit risk perception and behaviour. Currently, there are limited options for such investigation. The utility of the methods is discussed, including how they could be used in various contexts, such as recruitment or work evaluation. Limitations of the research, suggestions for future research, and the next steps for the further refinement of the methods are discussed.
Resource Type
DOI
Date Created
  • 2017
Former identifier
  • 9912550993502996

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