Mediating role of psychosocial maturity on age - risk relationship

whether age leads to a decrease in implicit risk through psychosocial maturity – in this case, impulsiveness and conscientiousness. Considering that the older and younger age groups are presumed to differ in psychosocial maturity and this theo-retical assumption was supported by the data³ in this study, the correlations among age, psychosocial maturity, and risk variables are explored by age groups in Table 4.

Older age group. Age was found to be significantly correlated with implicit

= .12. Overall, these findings provided preliminary support for the expectation that the older adult participants’ implicit evaluations of risk decrease with age through psychological maturity.

Younger age group. As with the older age group, age was found to be significant-ly correlated with psychosocial maturity variables, r = -.29, p = 10^-6 for the BIS and r

= .20, p = 10^-3 for the NEO FFI-C, but not with implicit measures of risk, r = -.03, p

= .68 for the IRT-G and r = -.09, p = .16 for the IRT-U. Furthermore, both psychoso-cial maturity variables did not correlate significantly with implicit measures of risk, r = .04, p = .53 for the BIS – the IRT-G, r = -.04, p = .53 for the BIS – the IRT-U, r = -.04, p = .60 for the NEO FFI-C – the IRT-G, and r = .07, p = .32 for the NEO FFI-C – the IRT-U. Overall, these findings did not provide preliminary support for the expecta-tion that psychosocial maturity plays a mediating role of age – risk relaexpecta-tion in the younger age group.

Impulsiveness as a mediator. Using the data for the older adult participants, I first estimated Model 1 that constrained the indirect path coefficients (i.e., age  impul-siveness  implicit risk) to be zero to look at whether the inverse age – risk relation

3 The older adult participants scored significantly lower than the younger adult participants on the BIS (impulsiveness), t(267) = 3.8, p = 10^-5, d = .69, and scored significantly higher on the NEO FFI – C

that has been reported from previous studies using self-report risk measures is rep-licated in the implicit measures of risk (see Figure 4). As expected, the results showed a significant total effect of age on the latent implicit risk variable, β = -.42, p

= .01, and the fit indices of Model 1 exhibited a poor fit to the data (χ² = 10.81, df = 3, CFI = .77, TLI = .24, NFI = .76, RMSEA = .21). Based on Model 1, Model 2 is devel-oped by releasing the indirect path coefficients (i.e., age  impulsiveness  implic-it risk) to test whether impulsiveness functions as a mediator of the relationship between age and implicit risk. The fit indices of Model 2 revealed a good overall fit to the data, χ² = .057, df = 1, CFI = 1.00, TLI = 1.28, NFI = .99, RMSEA = .00, with a marginally significant direct effect of age on the latent implicit risk variable, β = -.29, p = .08. In addition, a chi-square test of significance for the difference between the two nested models revealed that the Model 2 had significantly better fit than the Model 1, χ2diff(2, N = 62) = 10.75 (p < .05). The findings suggest that impulsiveness partially mediated the relationship between age and implicit risk.

Figure 4. The partially mediated model among the older age group

Note. Numbers inside brackets refer to standardized coefficients in Model 1.

Numbers outside brackets refer to standardized coefficients in Model 2.

Conscientiousness as a mediator. The same analyses as used in impulsiveness were employed to examine whether conscientiousness acts as a mediator of the relation-ship between age and implicit risk in the older adult participants. The results showed a significant total effect of age on the latent implicit risk variable when the indirect path coefficients (i.e., age  conscientiousness  implicit risk) were con-strained to be zero (Model 1, see Figure 5), β = .42, p = .01. Also, the fit indices of Model 1 provided a poor fit to the data, χ² = 16.90, df = 3, CFI = .66, TLI = -.15, NFI

= .66, RMSEA = .28. However, Model 2 that released the indirect path coefficients (i.e., age  conscientiousness  implicit risk) exhibited a non-significant direct effect of age on the latent implicit risk variable, β = .15, p = .30, with a good overall fit to the data, χ² = 2.26, df = 2, CFI = .99, TLI = .97, NFI = .96, RMSEA = .05.

In addition, a chi-square test of significance for the difference between the two nested models revealed a better fit of Model 2 relative to Model 1, χ^2diff(1, N = 62) =

14.64 (p < .05). The findings suggest that the relationship between age and implicit risk was fully mediated by conscientiousness in the older adult samples.

Figure 5. The fully mediated model for the older age group

Note. Numbers inside brackets refer to standardized coefficients in Model 1.

Numbers outside brackets refer to standardized coefficients in Model 2.

3.4 DISCUSSION

Consistent with the findings observed from the Korean younger adult sample in Study 1, I replicated construct divergence between explicit and implicit measures of risk in a sample of Korean older adults, suggesting the coexistence of explicit and implicit aspects of risk construct in a person’s mental representation. In addition, the implicit measures of risk appeared to perform better than the corresponding explicit risk measures in capturing age differences in risk, based on its relatively large effect size. These findings suggest that implicit evaluations of risk reflect accumulated socio-cultural experiences regarding risk-taking that can drive each cohort group to be homogenous in their attitudes toward risk (Rudolph & Zacher, 2015), thereby engendering marked age differences in risk that cannot be accounted for by explicit risk measures. Furthermore, a consideration of psychosocial maturity capacities (i.e., impulsiveness and conscientiousness) associated with risk-taking helps to explain why the marked age differences were seen only in the implicit measures of risk.

Another possible account for the observed age differences in this study was derived from a biochemical perspective. It relates to the hormonal changes during the aging process. More specifically, age is positively correlated with the levels of monoamine oxidase (MAO) that functions to degrade the three neurotransmitters – in this case, dopamine, norepinephrine, and serotonin. In fact, individuals who scored high on sensation seeking and impulsivity tend to have lower-than-average levels of MAO. As a person gets older, the increased levels of MAO are more likely to lower his or her willingness to take risk through degrading the three neurotransmitters that positively affect risk-taking behavior (Harlow & Brown, 1990). One final account from a behavioral perspective arises from the social

influence that adults are least affected by the presence of peers in their risk taking, followed by college-age persons and teenagers (Gardner & Steinberg, 2005). These alternative explanations are in line with the results of this study.

Besides cross-national and inter-generational differences in psychological outcomes of risk (i.e., attitudes) that have been investigated through Study 1 and 2, it is also important to examine whether implicit measures of risk are capable of capturing individual differences in risk in a follow-up study (i.e., Study 3), with a particular emphasis on predictive power for individual risk-taking behavior in a financial context. understanding customers' attitude toward financial risk by asking them questions (e.g., preference for investment option) to pursue investor protection from investment solicitation (Park, 2011). One important challenge facing them relates to the fact that the FICs relied exclusively on self-report questionnaires (Min & Song, 2014), which may ultimately impede a full understanding of financial risk attitudes on the basis of excessive focus on explicit (conscious) processes of assessing overall evaluation of financial risk. However, significant evidence from social cognition suggests that introspectively inaccessible (implicit or automatic) processes occurring outside of conscious awareness are also involved in complex matters such as financial judgment and investment decision (Dijksterhuis & Nordgren, 2006; Thorsteinson & Withrow, 2009). As a result, the pursuit of integrating implicit as well as explicit aspects of financial risk taking as a personality trait into investment decision-making processes can allow us to explain more variance in criterion behavior – in this case, the rate of return on stock investment, which may ultimately advance understanding of individual differences in behavioral outcomes involving financial risk.

4.2 INTEGRATING EXPLICIT AND IMPLICIT RISK TAKING IN STOCK INVESTMENT

This expectation appears to be supported by a transcendent model from behavioral finance on the links between a complementary interaction of unconscious thought with conscious thought and financial decision-making (Cheng, 2010). The author defined conscious thought as a person’s cognition-based thought process that