1 Introduction
2 Core self-evaluation and executives’ risk behavior
2.1 The tenets of prospect theory
2.2 The effect of hyper-core self-evaluation on risk behavior
2.2.1 Hyper-CSE and the value of risky prospects
Hypothesis 1: Higher levels of core self-evaluation are associated with lower levels of loss aversion among executives.
2.2.2 Hyper-CSE and decision weights in the domain of gains
Hypothesis 2: Higher levels of core self-evaluation are associated with higher decision weights in the domain of gains.
2.2.3 Hyper-CSE and decision weights in the domain of gains
Hypothesis 3: Higher levels of core self-evaluation are associated with lower decision weights in the domain of losses.
3 Research design
3.1 Pretest: validation of our risk behavior choice experiment
3.2 Main study: CSE and risk behavior choice experiment
3.2.1 Sample
3.2.2 Procedure
4 Results
4.1 Does core self-evaluation affect the value function and loss aversion?
Model | \( v\left( x \right) = ~\left\{ {\begin{array}{ll} {x^{\alpha } ,~~{\text{if}}~x~ \ge ~0} \hfill \\ { - \lambda ~ \times \left( { - x} \right)^{\beta } ,~~{\text{if}}~x~ < ~0} \hfill \\ \end{array} } \right. \) | |||
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Parameters | Estimate | Std. error | t value | p value |
Loss aversion (λ) | 2.623 | 0.669 | 3.920 | < 0.001 |
Diminishing sensitivity (α = β) | 0.949 | 0.053 | 18.810 | < 0.001 |
Residual standard error: 78.53 on 386 DoF |
Model | \( v\left( {x,{\text{cse}}} \right) = ~\left\{ {\begin{array}{ll} {x^{\alpha } ,~~{\text{if}}~x~ \ge ~0} \\ { - \left( {\lambda + \pi \times {\text{cse}}} \right) \times \left( { - x} \right)^{\beta } ,~~{\text{if}}~x~ < ~0} \\ \end{array} } \right. \) | |||
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Parameters | Estimate | Std. error | t value | p value |
Loss aversion (λ) | 5.266 | 1.328 | 3.966 | < 0.001 |
Diminishing sensitivity (α = β) | 0.947 | 0.050 | 18.848 | < 0.001 |
Core self-evaluation (\(\pi \)) | − 0.481 | 0.137 | − 3.498 | < 0.001 |
Residual standard error: 74.74 on 385 DoF |
4.2 Does core self-evaluation affect decision weights for gains and losses?
Model | \(w^{ + } \left( p \right) = \frac{{p^{\gamma } }}{{(p^{\gamma } + \left( {1 - p} \right)^{\gamma } )^{{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 \gamma }}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$\gamma $}}}} }}\) | |||
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Parameters | Estimate | Std. error | t value | p value |
γ | 0.775 | 0.010 | 79.050 | < 0.001 |
Residual standard error: 0.112 on 1454 DoF |
Model | \(w^{ + } \left( {p,{\text{cse}}} \right) = \frac{{\varepsilon^{ + } \times {\text{cse}} \times p^{\gamma } }}{{\varepsilon^{ + } \times {\text{cse}} \times p^{\gamma } + \left( {1 - p} \right)^{\gamma } }}\) | |||
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Parameters | Estimate | Std. error | t value | p value |
γ | 0.791 | 0.015 | 53.570 | < 0.001 |
Core self-evaluation (\({\varepsilon }^{+}\)) | 0.157 | 0.003 | 51.790 | < 0.001 |
Residual standard error: 0.112 on 1453 DoF |
Model | \(w^{ - } \left( p \right) = \frac{{p^{\delta } }}{{(p^{\delta } + \left( {1 - p} \right)^{\delta } )^{{{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 \delta }}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$\delta $}}}} }}\) | |||
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Parameters | Estimate | Std. error | t value | p value |
δ | 0.638 | 0.007 | 87.600 | < 0.001 |
Residual standard error: 0.137 on 1454 DoF |
Model | \(w^{ - } \left( {p,{\text{cse}}} \right) = \frac{{\varepsilon^{ - } \times {\text{cse}} \times p^{\delta } }}{{\varepsilon^{ - } \times {\text{cse}} \times p^{\delta } + \left( {1 - p} \right)^{\delta } }}\) | |||
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Parameters | Estimate | Std. error | t value | p value |
δ | 0.657 | 0.015 | 45.29 | < 0.001 |
Core self-evaluation (\({\varepsilon }^{-}\)) | 0.278 | 0.007 | 41.97 | < 0.001 |
Residual standard error: 0.143 on 1453 DoF |