Menu
Home
Log in / Register
 
Home arrow Business & Finance arrow Chronic Regulatory Focus and Financial Decision-Making
< Prev   CONTENTS   Next >

5.3 Further Hypotheses

This section will explore the additional hypotheses, concerning gender, education and age. Only significant results are stated.


5.3.1 Validity of Hα

Research indicates that gender is associated with risk preferences, with female investors exhibiting more risk aversion than male investors (Barsky et al. 1997). Regulatory focus is known to be associated with risk preferences as well (Kirmani and Zhu 2007). Gender and regulatory focus may thus affect risk preferences in tandem. In this book, risk preferences are modelled by financial allocation decisions.

To explore the hypothesis, participants were divided into groups based on their gender. A chi-squared analysis was then conducted to determine the association between the selections made and the regulatory focus. Significant results were only obtained for females in association between regulatory focus and eye tracker—asset selection, regulatory focus and self-report—asset selection, regulatory focus and self-report—portfolio selection. Significant results were not obtained for the asso- ciation between regulatory focus and eye tracker—portfolio selections. The rela- tionship between gender and regulatory focus on the eye tracker selections will first be explored, followed by the selections on the self-report.

5.3.1.1 Validity of Hα: Eye Tracker

This section explores Hα, the relationship between regulatory focus, eye tracker— asset selections and gender. As indicated, significant results were not observed for the relationship between regulatory focus, eye tracker—portfolio selections and gender. Table 5.12 indicates that when only female participants are included, a chi-square test for the association between regulatory focus and eye tracker—asset selection, yields a main effect, but at the p = 0.10 level (n = 52, χ2 = 1.853, p < 0.10). Table 5.12 states that 88 % of the females with a chronic promotion focus selected the promotion asset on the eye tracker. 70.4 % of females with a chronic prevention focus selected the prevention asset.

To gain further understanding into the relationship between gender, regulatory focus and the eye tracker selections, a logit model was made to run with eye tracker— asset selection is the dependent variable; age, education, ethnicity, marital status, regulatory focus, financial literacy and gender as the independent variables. The logit model with eye tracker—asset selection as the dependent variable is on the following page, Table 5.13.

Table 5.12 Basic Pearson chi-squared test and cross tabulation for regulatory focus

* eye tracker—

asset allocation (female) Table 5.13 Logit model for eye tracker—asset selection against age, education, ethnicity, marital status, regulatory focus, financial literacy, gender

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Eye tracker—asset selection

96.998

0.114

0.166

Variable

β

S.E.

Sig.

Age

−0.297

0.918

0.373

Education

−0.660

1.003

0.256

Ethnicity

0.937

1.202

0.218

Gender

1.027

0.542

0.029

Marital status

0.812

0.764

0.144

Regulatory focus

−1.166

0.532

0.014

Financial literacy

−0.899

0.539

0.048

Constant

1.491

0.58

0.005

Gender (β = 1.027, p < 0.10) is significant. This indicates that females are more likely to look at prevention assets than males. An interaction term, regulatory focus*gender was then added to gain further insight into the relationship between regulatory focus and gender, but did not yield significant results.

A logistic regression was then performed with eye tracker—portfolio selection as

the dependent variable and age, education, ethnicity, marital status, regulatory focus, financial literacy and gender as the independent variables. However, as indicated in Sect. 5.2.3 no significant results were obtained for gender. An interaction term was then added, regulatory focus * gender, comprised of the regulatory focus and gender variables. This was to gain greater understanding of the relationship between regu- latory focus and gender. No significant results were observed. The following section will explore the relationship between the selections on the self-report and gender.

5.3.1.2 Validity of Hα: Self-report

This section explores the relationship between chronic regulatory focus, the self-report selections (asset, portfolio) and gender. A chi-squared analysis was first conducted for the association between regulatory focus and asset selections on the self-report, for both genders. Table 5.14 indicates that when only female partici- pants are included, a chi-squared test for the association between regulatory focus and self-report—asset selection, yielded a main effect, but at the p = 0.10 level (n = 52, χ2 = 1.853, p < 0.10). Table 5.14 also states that 66.7 % of the females with a chronic prevention focus selected the prevention asset. No significant results were obtained for males.

To gain greater understanding into the effect of regulatory focus and gender on asset allocation (self-report), a logit regression was performed. Self-report—asset selection was the dependent variable, and age, education, ethnicity, marital status, Table 5.14 Basic Pearson chi-squared test and cross tabulation for regulatory focus

* self-report—asset (female)

regulatory focus, financial literacy, gender and regulatory focus * gender were elicited as independent variables. No significant results were observed. The fol- lowing section focuses on the relationship between portfolio selection on the self-report, regulatory focus and gender.

A chi-squared test of association was then performed to test the relationship between the portfolio selections on the self-report, and regulatory focus, examining participants based on their gender. No significant results were observed for males. Table 5.15 indicates the cross tabulations and chi-squared results regarding regu- latory focus and self-report—portfolio selection, for females.

Table 5.15 indicates that when only female participants are included, a chi-squared test for the association between regulatory focus and self-report— portfolio selection, yields a main effect (n = 52, χ2 = 2.920, p < 0.05). Table 5.15 states that 68 % of the females with a chronic promotion focus select the promotion portfolio. No significant results were obtained for males.

To gain greater insight into the effects of regulatory focus and gender on port- folio selection on the self-report, a logistic regression was performed with self-report—portfolio selection as the dependent variable, and age, education, ethnicity, marital status, regulatory focus, financial literacy, gender and regulatory focus * gender as independent variables. The interaction term, regulatory focus * gender, comprised of the regulatory focus and gender variables was added to obtain further understanding into the effect of gender and regulatory focus on portfolio allocation. Table 5.16, indicates the results in the following page.

As in Table 5.16a, the interaction term, regulatory focus * gender was significant (β = 1.736, p < 0.10) as was gender (β = −1.480, p < 0.10). Females are less likely to select a prevention portfolio. However, the relationship is weaker for females

Table 5.15 Basic pearson chi-squared test and cross tabulation for Regulatory focus * self-report—portfolio (female) Table 5.16 Logit model for self-report—portfolio selection against age, education, ethnicity, marital status, regulatory focus, financial literacy, gender, regulatory focus * gender

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Self-report—portfolio selection

118.407

0.104

0.139

Variable

β

S.E.

Sig.

Age

0.464

0.819

0.286

Education

−0.688

0.868

0.214

Ethnicity

−0.34

1.015

0.369

Marital status

−0.873

0.691

0.103

Regulatory focus

−0.792

0.684

0.124

Financial literacy

0.054

0.500

0.457

Gender

−1.480

0.653

0.012

Regulatory focus * gender

1.736

0.918

0.030

Constant

0.956

0.564

0.045

Table 5.16a Age groups of participants

with a chronic prevention focus. This result may indicate the relative strength of the prevention focus over the promotion focus.

The results state that females more likely look at prevention assets than males.

Also, females are less likely to select the prevention portfolio on the self-report, but this effect is weaker for females who are chronic prevention-focused. The following section states the analysis into Hβ.

5.3.2 Validity of Hβ

Insight into Hβ was obtained by dividing participants based on their age (20–30, 31–40, 41–50, 51–60) and then testing for association between regulatory focus and selections made on both measures. The logit models indicate no significant results between different age groups and the asset and portfolio selections. Hβ is thus unsupported.


5.3.3 Validity of Hγ

Research indicates that education is associated with risk preferences, although with mixed findings (Watson and McNaughton 2007). As regulatory focus is also associated with risk preferences (Kirmani and Zhu 2007), education, regulatory focus and risk preferences may be related. In the context of this book, risk pref- erences are modelled by financial allocation decisions. To explore Hγ, participants are examined based on their education levels (basic degree, higher degree) and then testing for the association between regulatory focus and selections made on both measures. Significant results are observed for the association between regulatory focus and eye tracker—portfolio, for the higher degree group, but not for the basic degree group. No significant results were obtained for the association between regulatory focus and eye tracker—asset selection, self-report—asset selection or self-report—portfolio selection.

5.3.3.1 Validity of Hγ: Eye Tracker

Table 5.17 indicates that when only participants with a higher degree are included, a chi-square test for the association between regulatory focus and eye tracker— portfolio selection, yields a main effect (n = 39, χ2 = 4.837, p < 0.05). Table 5.17 states that 86.7 % of those with a chronic prevention focus spend proportionately more time on the prevention portfolio scenario. For chronic promotion focused participants, there was no observable difference. No significant results were obtained for the basic degree group.

To obtain further insight into the relationship between regulatory focus, eye tracker—portfolio selection and education, a logistic regression was conducted. Eye tracker—portfolio selection was the dependent variable, and age, education, eth- nicity, marital status, regulatory focus, financial literacy, gender and regulatory focus * education were the independent variables. The interaction term, regulatory focus * education was added to gain further understanding into the relationship between regulatory focus and gender. Table 5.18, indicates the results.

Table 5.18 indicates that while education is not significant, regulatory focus * education is significant (β = 2.924, p < 0.10). This indicates that those who are prevention-focused and possess a higher degree are more likely to look at the prevention portfolio for a proportionately longer time.

Table 5.17 Basic Pearson chi-squared test and cross tabulation for regulatory focus

* eye tracker—portfolio

(higher degree) Table 5.18 Logit model for eye tracker—portfolio selection against age, education, ethnicity, marital status, regulatory focus, financial literacy, gender, regulatory focus * education

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Eye tracker—portfolio selection

102.925

0.153

0.213

Variable

β

S.E.

Sig.

Age

−1.013

0.911

0.133

Education

−0.619

1.041

0.276

Ethnicity

−0.941

1.026

0.180

Marital status

0.831

0.771

0.141

Regulatory focus

−1.021

0.657

0.060

Financial literacy

−1.121

0.533

0.018

Gender

0.781

0.521

0.067

Regulatory focus * education

2.924

1.121

0.005

Constant

1.222

0.605

0.022

5.3.4 Validity of Further Hypotheses: Summary

Hα is supported by the data, indicated by the associations between regulatory focus and eye tracker—asset selection, regulatory focus and self-report—asset selection, regulatory focus and self-report—portfolio selection. Hβ, however, is unsupported, with no relationship observed between regulatory focus, selections and age. Regarding Hγ, significant results are demonstrated for the association between regulatory focus and eye tracker—portfolio, for the higher degree group, but not for the basic degree group. The section that follows is concerned with additional analyses apart from the hypotheses.

 
Found a mistake? Please highlight the word and press Shift + Enter  
< Prev   CONTENTS   Next >
 
Subjects
Accounting
Business & Finance
Communication
Computer Science
Economics
Education
Engineering
Environment
Geography
Health
History
Language & Literature
Law
Management
Marketing
Philosophy
Political science
Psychology
Religion
Sociology
Travel