PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 21, 2005Journal of Risk Research1,017 citations

Personality and domain‐specific risk taking

View Full Paper
NNNigel NicholsonESEmma SoaneMFMark Fenton‐O’Creevy

Key Points

Key points are not available for this paper at this time.

Abstract

The concept of risk propensity has been the subject of both theoretical and empirical investigation, but with little consensus about its definition and measurement. To address this need, a new scale assessing overall risk propensity in terms of reported frequency of risk behaviours in six domains was developed and applied: recreation, health, career, finance, safety and social. The paper describes the properties of the scale and its correlates: demographic variables, biographical self‐reports, and the NEO PI‐R, a Five Factor personality inventory (N = 2041). There are three main results. First, risk propensity has clear links with age and sex, and with objective measures of career‐related risk taking (changing jobs and setting up a business). Second, the data show risk propensity to be strongly rooted in personality. A clear Big Five pattern emerges for overall risk propensity, combining high extraversion and openness with low neuroticism, agreeableness, and conscientiousness. At the subscale level, sensation‐seeking surfaces as a key important component of risk propensity. Third, risk propensity differs markedly in its distribution across job types and business sectors. These findings are interpreted as indicating that risk takers are of three non‐exclusive types: stimulation seekers, goal achievers, and risk adapters. Only the first group is truly risk seeking, the others are more correctly viewed as risk bearers. The implications for risk research and management are discussed.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Nicholson et al. (2005) studied this question.

synapsesocial.com/papers/69febf0c581c6e761e77403ahttps://doi.org/10.1080/1366987032000123856
Ask AI
Helpful
Bookmark
Share
View Full Paper