A conceptual model of the influence of South African investor well-being on risk tolerance

Figures & data

Table 1. Concepts related to financial well-being

Concept	Definition	Source
Financial well-being	Objective: The effective management of an individual’s economic life. Also referred to as economic well-being.	Rath et al. (2010)
	Subjective: An individual’s happiness or general satisfaction with his/her financial situation. Also referred to as financial satisfaction.	Joo (2008)
	Combination: An individual’s sense of his/her financial situation is formulated in terms of objective and perceived attributes. These attributes are criticised against standards of comparison to develop evaluated attributes of that financial situation.	Cox et al. (2009), Porter (1990)
Financial wellness	A multidimensional concept that involves financial satisfaction, as well as the objective status of financial situation, financial attitudes and financial behaviour. Also known as a measure of financial health.	Joo (2008), Norvilitis and MacLean (2010), Spann (2014)
Financial satisfaction	A sub-aspect of life satisfaction and focusses on an individual’s contentment with, or freedom from worry regarding his/her financial situation. Corresponds with the subjective side of financial well-being.	Joo and Grable (2004), Xiao et al. (2009)
Financial distress	A situation of physical or mental strain which includes stress relating to financial matters. Financial distress is a consequence of a lack of financial well-being. Also referred to as financial worry or economic distress.	Jansen van Vuren (2015), Prawitz et al. (2006)
Debt stress	An individual’s worry about his/her amount of debt. Specifically, the individual’s viewpoint of stress created by personal debt and worry about his/her ability to settle debt.	Drentea (2000)

Table 2. Advantages and disadvantages of SDA

Advantages	Disadvantages
Avoids the process of data collection which results in the researcher saving costs, time and inputs (Neuman, 2003).	The researcher must spend time examining the dataset in order to understand the data (Heaton, 2003). The dataset may be in an unaccustomed format that the researcher needs to adapt to.
Multidisciplinary as the same dataset can be analysed by different disciplines and observed from different perspectives to gain a better understanding of social issues (Strydom & Delport, 2011).	There may be prejudices in the existing documents based on the dataset (Strydom & Delport, 2011). The researcher should ensure to understand the dataset and eliminate any prejudices.
The data are verified and the findings of the SDA can be complementary to, or contrasted with other research to confirm or reject previous findings and identify trends (Ritchie & Lewis, 2003; Royse, 2004).	The fact that a different researcher is working with the dataset may mean that the confidentiality and anonymity aspect thereof could possibly be compromised (Strydom & Delport, 2011).
An SDA allows a researcher to study past events and issues in retrospect (Strydom & Delport, 2011).	The sampling procedure or sample size of the dataset may result in certain subgroups not to be included for analysis (Heaton, 2003). As such, it is possible that the sample size is not a depiction of the national population.
An SDA is an independent procedure that can be conducted on an exploratory, descriptive and an exploratory level (Alston & Bowles, 2003).	There is a limitation to using the available data as the researcher can only work with what data are available on the dataset (Alston & Bowles, 2003).

Table 3. Summary of the demographic profile of respondents

Demographic factor	Description	Frequency (f)	Percentage (%)
Age	50+ years	479	45
Gender	Female	596	56
Race	White	715	67.1
Marital status	Married	577	54.2
Income	R100 001—R200 000	190	17.8
Education	Undergraduate degree	373	35
Place of origin	Gauteng	439	41.2

Table 4. Descriptive statistics

Variable	Mean	Std deviation	Skewness	Kurtosis
GLRTS	2.055	0.398	0.278	−0.634
IFDFW	4.842	2.317	0.190	−0.840
SWLS	3.638	1.351	−0.150	−1.098
IPAQ	2.757	1.037	0.400	−0.387

Source: Author compilation.

Table 5. KMO and Bartlett’s test of sphericity

KMO and Bartlett’s Test		GLRTS	IFDFW	SWLS	IPAQ
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.820	0.946	0.875	0.752
Bartlett’s Test of Sphericity	Approx. Chi-Square	1287.976	8613.709	3133.415	2230.204
	df	66	28	10	45
	Sig.	0.000	0.000	0.000	0.000

Source: Author compilation.

Table 6. Correlation between investor well-being and risk tolerance

Scale(s)	Spearman Correlation	GLRTS	FWB	SWLS	IPAQ
GLRTS	Correlation Coefficient	1.000
	Sig. (2-tailed)	-
IFDFW	Correlation Coefficient	.197**	1.000
	Sig. (2-tailed)	0.000	-
SWLS	Correlation Coefficient	.104**	.610**	1.000
	Sig. (2-tailed)	0.001	0.000	-
IPAQ	Correlation Coefficient	.097**	0.031	0.008	1.000
	Sig. (2-tailed)	0.001	0.311	0.783	-

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

N = 1 065.

Source: Author compilation.

Figure 1. SEM of investor well-being’s influence on risk tolerance

Source: Author compilation

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