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Patterns of Risk Behaviors and Their Value: A Latent Class Growth Modeling Approach

The current body of individual risk behavior research has been mainly driven by two streams of literature. Stable risk researchers propose individuals tend to display similar risk behaviors across time and situations due to individuals' underlying propensity to either engage in risk averse or risk seeking behaviors. Changeable risk researchers have sought out to examine variability in risk behaviors due to factors such as personality and contextual characteristics. However, might it be the case that there are subgroups of individuals who may be more prone to display static risk behaviors and other subgroup whose risk behaviors are more amendable? To better address this question, the current study integrates literature on risk behavior, psychological safety, and personality to investigate the existence of these potential risk behavior classes. Furthermore, supervisor rated performance is captured in order to better understand the potential organizational value of these various risk behavioral classes. Latent class growth modeling revealed five different risk classes: stable risk seeking, stable risk averse, highly adaptive, moderately adaptive, and mixed risk behaviors. Individual personality traits were shown to be significant predictors of class membership, although the pattern of results was somewhat in contrast to predicted relationships. Furthermore, in support of proposed hypothesis, individuals in the highly adaptive risk behavior class received the highest ratings of supervisor rated performance. I discuss results in terms of risk research and organizational practice, and specifically advocate for the increased examination of risk behavior within organizational research.

Identiferoai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1021
Date01 January 2023
CreatorsNaranjo, Anthony
PublisherSTARS
Source SetsUniversity of Central Florida
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate Thesis and Dissertation 2023-2024

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