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Original Articles

An explanatory analysis of perceived risk decision weights (perceived-risk attitudes) and perceived benefit decision weights (perceived-benefit attitudes) in risk-value models

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Pages 739-761 | Received 15 Oct 2016, Accepted 19 Apr 2019, Published online: 02 Jul 2019
 

Abstract

All decision making requires a trade-off between risks and values. While Markowitz defined risk as the variance of returns (thus reasoning that investors should consider it as undesirable), the more general risk–value framework allows risk to be defined as a person’s subjective judgments. Psychological risk–return models go further, decomposing observed behavior (risk taking) into two processes: (1) a judgment of benefits and risks and (2) a trade-off between perceived benefits and perceived risks, with a person-specific willingness to trade-off units of returns (benefits) for units of risk, conceptualized as attitude toward perceived risk (PRA) and attitude toward perceived benefits (PBA). PRA and PBA describe the degree to which people find perceived risks and benefits attractive, all other things being equal, and are assumed to be relatively stable across situations and domains. We test this assumption in an empirical study, checking the temporal stability of PRA and PBA (using the a Domain-Specific Risk-Taking [DOSPERT] scale ) and the cross-task stability of PBA (performing comparisons between the DOSPERT and the Columbia Card Task[CCT]). Finally, we explain both PRA and PBA using the Big Five personality dimensions and Stimulating–Instrumental Risk Inventory (SIRI), showing that PBA weights increase with openness to experience, while the negative effect of perceived risk on risk taking (PRA) increases with conscientiousness and decreases with stimulating risk taking. The results show that PBA and PRA can be treated as traits which, in some instances at least, are stable across time and tasks, and which can be partially explained by personality, providing a link to the idea of a personality dependent ‘ideal point’ for risk preference.

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Acknowledgments

All computations were performed in the R environment (R Core Team 2017). Tables were prepared with the ‘sjPlot’ package (Lüdecke Citation2018), and graphs with the ‘ggplot’ package (Wickham Citation2009). We thank our students (Katarzyna Kuchno, Ewa Hałasa, Paweł Kozłowski, Łukasz Lakowski, Grzegorz Kloss) for their help at the fieldwork stage of the study. We also thank our colleague Maciej Stolarski, PhD for his comments. We thank John Charlton, PhD. for manuscript proofreading.

Author contributions

The first author developed the study concept and design, created the study materials, analyzed the data, drafted a first version of the manuscript and prepared the manuscript resubmission. The second author was responsible for data collection and drafted part of the manuscript. The third author analyzed part of the data set and drafted part of the manuscript. The fourth author was responsible for creating the study materials and data collection. All authors revised the manuscript and approved the final manuscript for submission.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 For purposes unrelated to the current paper: (1) during the laboratory study electrodermal activity (EDA) electrodes were attached to the index finger and middle finger of the non-dominant hand of each participant; (2) after completion of the study participants were asked for a saliva sample for genetic testing.

2 However gender’s influence on DOSPERT-related measures should be treated with caution as Zhang et al. (Citation2019) have shown that the underlying factor structure of the DOSPERT differs between men and women.

3 Markiewicz, Kubinska, and Tyszka (Citation2015) argued that CCT HOT measure may confound dynamic risk taking propensity with a momentum prognostic strategy.

Additional information

Funding

This work was supported by the National Science Centre, Poland (NCN) under Grant 2015/17/D/HS6/02684.

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