institutions and lowered borrowing rates from banks which in turn ... the bank or credit company and this charge represents the ...... Chevy Chase, Md. Vol. 108 ...
Credit Card Use Patterns among Working Adult in Batswana Maitumelo Pebe1, Uchenna Cyril Eze2, Jian Ai Yeow1, Kwee Pheng Lim1 1
Faculty of Business and Law, Multimedia University, Jalan Ayer Keroh Lama, Melaka, Malaysia. 2 School of Business, Monash University, Sunway Campus, Malaysia
Abstract- This study was designed to determine the causes of credit card debts among working adult in Batswana. The specific objective of this study is to examine the credit card use patterns among working Batswana. Questionnaires were used to gather the data for the research in line with literature review of past research. Despite, the size of sample is 310 but a totaled of 200 reliable respondents from various working places. For having more reliability in the study, the scope of workers broadened to include all individuals either owning or not owning a credit card. This scope is chosen to obtain a perception of individuals irrespective of whether they are credit card owners or not. Likewise, the selection of participants was not confined to any particular government segment, nor was it to any private sector. These results indicated that the only statistically significant variables for the study were concluded to be Financial Wellbeing, Financial Literacy and Interest Rate as they fell below the chosen significance level of the study that marked the range for the hypothesis statements that could be supported by the survey results as well as those that had not been statistically significant according to the survey findings. This meant that the remaining hypothesis statements that implied the influence of the other independent variables on the dependent variable were as a matter of fact, not a statistically significant factor in influencing changes to Debt levels. Keywords- Financial Wellbeing, Attitude towards Credit, Financial Literacy, Financial Confidence, Consumer Spending, Interest Rate
I.
INTRODUCTION
The purpose of this study was to identify the perception on the main factors that contributed to credit card debt, mainly among working adult in Batswana. In the modern context of society, economic growth and sustainability are considered the main point of success in every respect. It is a desired state to be in, which is sought from an individual level to a national level. This research is motivated by this very fact, the need for growth and prosperity of a nation, which in this regard stems from eradicating the current recession on all societal levels There is very limited academic literature on developing nations’ financial contribution to the world’s economy. Unlike many developed countries, there has been little evidence of research on the factors influencing individual levels of debt due to use of credit cards. These factors that contribute to credit card debt in developed nations have not been proven to generalize to third world nations, like Botswana, which have completely dissimilar cultures from those of the developed nations, and even have different practices on cultivating their economic growth and stability. Moreover, past research showed evidence of profound failure in the efforts of developing nations to curb their national levels of debt as well as their citizens’ individual levels of debt. This has been carried out through years and years of research, even until the current
financial crisis. Credit card debt has been studied throughout many countries, majority of which are first world nations, and these studies point out the fact that there has been a rise in credit card use because of its increased popularity throughout the previous decades. Despite their popularity, researchers had explained that excessive use of credit cards led to the potential to contribute to elevated stress levels, potential job losses, bankruptcy, and even loss of family members. Through this research, only can the true causes of personal debt among developing societies be revealed. These factors can only be obtained by studying these very nations to establish their patterns of credit behavior and practices. Nonetheless, some studies show a similarity among credit card use among different nations. There is room for further studies relating to perceived credit card usage and relative debt among individuals from third world nations. These individuals portray different behaviors when it comes to financial practices, from their savings practices and even their spending behaviors. This study can provide the platform to understanding such phenomenon, and provide more relevant solutions to the current problem. This research can better describe the situation in the context of developing nations, which can then be appropriately compared to developed nations. Throughout the research, the main factor important to this study is the perceived individual factors that contribute to credit card debt
among society. From this, an overall understanding of the debt pattern can be established and more importantly, an appropriate strategy can be formulated to address the debt problem starting with an individual perspective to a national perspective.
ii CONCEPTUAL FRAMEWORK The concept of credit has existed for many centuries and despite this fact has only reached magnitudes in recent decades. The credit card market has penetrated the society and even in status quo continues to expand rapidly. The borrowing institutions have secured a stream of infinite gain as more economies are shifting to the credit culture than maintaining the old saving culture. Many factors contributed to the use of credit and credit cards due to the advantages and conveniences of their use. However, there were detrimental effects associated with such ‘culture’ even though the loaning institutions do little or nothing to inform their market about them. As such, many are lured into the credit stream and with little knowledge and awareness coupled with living constraints and household needs, most of society has fallen prey to the reliance on credit and poor management of finances in both household and national levels. Therefore, in this research it consists of six independent variables and the dependent variable is credit card debt. The next section includes the hypotheses development.
FIGURE 1: CONCEPTUAL FRAMEWORK OF THE CAUSES OF CREDIT CARD DEBT
1.
Financial Wellbeing
2.
Attitude towards Credit
3.
Financial Literacy
4.
Financial Confidence
5.
Consumer Spending
6.
Interest Rate
Credit Card Debt
Hypotheses Development Financial Wellbeing: Financial wellbeing is the inclination of households to search for improvements in managing their economic resources as well as developing plans for strengthening their future financial position (Prather, 1990).The financial wellbeing of many households was described to be negatively affected by high debt levels because of aggressive use of credit for consumption and by borrowing disproportionately to earnings (Georgarakos, Lojschova, & Ward-Warmedinger, 2010). An improved state in financial wellbeing was associated with lower household debt whereby households felt comfortable with their household income, their credit card and mortgage debt as well as ability to make regular bill payments. High financial concerns or distress was described to be positively related to high debt burdens as these financial burdens become cumulative. These included unpaid bills, increasing credit card balances as well as receiving overdue notices from creditors and collection agencies (Sorhaindo, Kim & Garman, 2006). Furthermore, higher financial distress or poor financial wellbeing has been described to better explain the rising household debt levels through established higher credit card debt to income ratio (Drentea, 2000). Poor financial wellbeing was shown to have a positive relationship with increasing debt levels whereby the financial pressures and discomforts increased because debt accumulation increased beyond periods of obtaining and repaying minimum credit card balances (Rao & Barber, 2005). Similarly, increasing indebtedness from using credit and other forms of unsecured debt was considered the main factor accounting for greater tendency to report debt problems (Del-Rio & Young, 2005). Hypothesis 1: Low financial wellbeing will positively influence credit card debt level. Attitude towards Credit: There are three forms of financial attitudes that can be outlined as guidelines for categorizing consumers and households (Devaney, 2001). An individual may have a tolerant attitude, risk averse attitude, and or risk seeking attitude toward money and forms of credit. Furthermore, research revealed that consumers may hold different spending and consumption behaviours which were all in relation to the type of attitude they had and even these attitudes may change within one individual (Davies & Lea et. al 1995). That is, a consumer who was tolerant to debt may change and avoid such behaviour to become risk averse (Hayhoe et. al, 1999). On the other hand, researchers proved that another individual may start off by avoiding risks associated with soliciting credit to having tolerance towards such behaviours and practices that increase the chances of debt and or credit liabilities (Bell et. al 2001). Most of those who tolerated risk were identified in previous research as the youth and carried on with this form of financial attitude up to their middle life (Boddington & Kemp, 1999). Such attitude left many of these young adults with enormous credit card debts when they graduated from their higher learning institutions while on the contrary, older consumers, such as retirees and those reaching
retirement age, had a negative attitude towards credit and debt due to the need to save and secure funds for life after retirement. The economic conditions also affected the attitudes of consumers towards risk profiles wherein most households avoided risky investments and consumption behaviours during uncertain economic periods as a result of lower confidence in the market and economy as a whole (McKenna, Hyllegard, and Linder, 2003). Hypothesis 2: High positive attitude towards credit will positively influence credit card debt level. Financial Literacy: The ability to make sound saving choices and financial decisions relies on the availability and exposure to financial education. Prior research revealed a great relationship between levels of financial literacy and debt whereby the less knowledgeable had a tendency to incur more debt than those who were financially literate (Lusardi & Tufano, 2009). Having such literacy on financial concepts allowed an individual to make better judgments on retirement planning, borrowing behaviour, and participation in the stock market (Selejio, Onesimo, Mduma, & John, 2005). In contrast, poor financial literacy exposed individuals to poor decisions on investments and borrowing practices as well as saving behaviour (Worthington, 2006). There was abundant and increasingly sophisticated financial instruments in the market which many consumers had to choose from and with financial ignorance or lack of financial knowledge, there was a greater degree of falling into financial indebtedness from using such instruments like the credit card. Financial literacy can better improve borrowing habits of consumers, their savings behaviour and overall better financial decision making (Lusardi, 2008). Moreover, the link between financial literacy and wellbeing entail that the financially literate have positive financial behaviours which cushion them against heavy debt. They are better off since most literate consumers can make simple interest compounding calculations to assess how much debt they may fall into. Such knowledge allows the consumer to even plan for retirement than those who are financially illiterate (0steen, 2007). With relevance to credit card debt, those who are financially literate will be able to calculate the compound interest associated with relying on their use unlike those on the other end of the literary spectrum who may not be aware of the heavy interest charges associated with continuous reliance for consumption expenditure on credit cards and failing to save for retirement as well as contingency saving (Lusardi and Mitchell, 2007). Hypothesis 3: Low financial literacy will positively influence credit card debt levels. Financial Confidence: Financial confidence is a measure of the subjective expectations of individuals towards the current and expected economic conditions (Kershoff, 2000). This consumer confidence can be further extended to describe the degree of confidence consumers feel about their personal financial state and highlighted to determine the level of spending that consumers engage in (Investopedia, 2010). Evidence on influence of consumer
confidence on debt level indicated that consumer pessimism highly influences a slowdown in output whereby borrowing on credit or spending on credit is reduced (Matsusaka & Sbordone, 2007). Furthermore, there was qualitatively significant causal relationship established between consumer confidence and future changes in household spending activities which could include higher credit spending and higher credit borrowing patterns based on the optimism or pessimism towards future personal financial positions (Howrey, 2001). High degrees of financial confidence were seen to contribute to escalating financial vulnerability from the increased availability of consumer debt by credit card institutions and lowered borrowing rates from banks which in turn increased household spending beyond income levels (Moseley, 2002). On the contrary, evidence of decreased household debt was associated with lower financial confidence wherein consumers focused on repaying their already existing high debt balances from bank loans as well as monthly minimum repayments for credit cards (First National Bank, 2010). In addition, as consumer confidence declined, the effect was proved to be an increase in savings together with a decline in borrowing which in the end reduced the risks of increased household debt (Pettinger, 2010). Unwillingness of banks and other credit institutions to reduce the costs of borrowing reduced the consumer confidence in future financial positions and in effect focused their extra income on saving than spending which in effect reduced the levels of household debt associated with repaying bank loans and credit card charges (Pettinger, 2010). Hypothesis 4: High financial confidence will positively influence credit card debt levels Consumer Spending: Consumer spending deals with household consumption of goods and services that form their basket of necessities and wants for sustainment (Yee & Ee, 2007). Consumer spending on consumption also forms a large part of economic growth and in recent decades has increased, contributing to the increased living standards of many households (Amoateng et. al 2002). Consequently, the lower income bracket households have had major challenges in such economies though increased inequalities and new risks. This was indicated to be largely due to the fact that they also faced increased consumption needs for their households in contrast to their low incomes (Feldstein et. al 2006). The rising consumption aspirations and necessities had contributed to the high vulnerability to income changes and escalating levels of household debt (Institute for Public Policy and Research, 2009). There was an established constant relationship between credit card use, increased consumer spending, borrowing and debt whereby there is a significant link between credit card spending and consumer debt in contrast to credit card spending and credit card debt (et. al Mann, 2006). The relationship between spending and credit availability was further explained in which the forms of credit available were largely in the form of credit cards. Consumer spending was indicated to fall (rise) with respect to the increase
(decrease) in credit availability. Large changes in consumer spending were also linked to a greater need or significant importance in credit availability (Natoli, 2007). It was vital to consider credit availability when determining or forecasting consumer spending in the sense that there was an established relationship between the two whereby large changes in credit availability affected the consumption spending decisions of households. The periods of the large changes in credit availability was also proven to be largely associated with periods of high economic uncertainty (Beaton, 2009). Hypothesis 5: High consumer spending will positively influence credit card debt level Interest Rate: From a personal financial standpoint, the interest rate refers to the monetary amount paid out whenever the individual borrows from an investor such as the bank or credit company and this charge represents the cost of borrowing (Groves, 2010). In relation to unsecured debt such as credit card debt, the interest rate charge is followed by the compounding interest which forms the additional interest charged on the preexisting charge for using credit (Scottish Law Commission, 2005). Credit card interest rates may be influenced to increase or decrease whenever the central bank rates such as the Federal Fund Rate increases or declines (Carty, 2010). The interest rates charged on credit card usages was found to be a contributor to household debt in the sense that the actual amounts were hidden and fluctuated over time periods (Greenspan ,2005). The lowered rate of interest charged on credit cards was proven to influence a greater usage pattern of credit cards and in effect raising the level of indebtedness among households (Dey, Djoudad & Terajima, 2008). As compared to earlier generations, the recently lower repayment rates on credit cards fuelled the heavy borrowing patterns among the young generation and resulted in a relatively higher credit card debt burden which was suggested to have prolonged effects during later life periods (Jiang, 2006). Furthermore, high interest rates were shown to add up rapidly and as a result led to credit card trouble as making minimum payments became difficult with the high credit card rates (Frost, 2010).When the demand for consumer debt was high and there was a subsequent rise in interest rates, the level of household debt was observed to rise due to additional monetary amounts required to cover the increased costs of using credit cards (Kim, 2010). The compounding nature of interest rates charged on credit card usage was also identified as an influencing factor towards elevated debt levels (Marquit, 2008). The increase in interest charge as a penalty charge for late credit card payment was described to influence the debt level of the individual in that the finance charges increase and make it more expensive to carry a balance (Simon, 2010). Hypothesis 6: High interest rates will positively influence credit card debt level. II.
Research Methodology
For the purpose of the research, hypothesis testing was applied in order to determine whether there was an impact of the identified independent variables against the dependant variable. The influence of the identified independent variables can be shown by the use of hypothesis testing and the variations or the degree of influence may also be found by using hypothesis testing (Slowiaczek, Klayman, Sherman, & Skov, 1992). In respect to these qualities being satisfied, it was justified to approach the research with the stated method to facilitate more analysis on the variables at hand. For the purpose of this research, both primary and secondary sources of data were used. Questionnaires were used as the primary means of obtaining data from respondents while data necessary for literature purposes was obtained from secondary sources including journals and the internet. a.
Questionnaire Design
The questionnaire used in the research was mail questionnaires and in order to increase response rates, the respondents were notified in advance. In terms of the structure there were two sections (Mandell & Klein, 2009), whereby the first part of the questionnaire focused on the socio-demographic variables identified in the literature review which allowed for testing relationship and influence of the entities on the dependent variable. These variables were included in the questionnaire in the form of similar statements on the relevant variables. The other part under the second section was devoted to statements on the dependant variable which was compared with the independent variables to establish statistical variations and relationships among them and the dependant variable. The Likert scale was designed in such a manner that it can examine how strongly a subject agrees or disagrees with a given statement. That is, the scale allows the identification of a respondent’s attitude towards the given statement. The response was measured on a 5-point scale ranging from 1 for strongly disagree to 5 for strongly agree (Kotzé & Smit, 2008). b.
Pilot Study
A pilot study, or feasibility study, is described to be an experiment constructed to test logistics and gather information before the actual or larger study is conducted (Galloway, 1997). In order to ensure efficiency of the actual study, a pilot study was conducted first, within working individuals in Malaysia so that if there was need for improvement in the actual study, the amendments could be made to obtain better results from the larger study and to obtain verification that participants in the larger study would be able to understand the requirements of the study questions posed in the questionnaire.
c.
Data Analysis Techniques
The basic objectives of data analysis were to get a feel for the data, to test the goodness of the data, as well as testing the developed hypothesis. After checking the central tendencies and dispersion, frequency distributions were obtained and displayed in the form of histograms and charts through the use of SPSS (Statistical Package for Social Science). The following object that required to be fulfilled was the goodness of data through testing the reliability and validity of the measures. Reliability may be explained as the consistency of measurement or the repeatability of one’s measurement. On the other hand, validity was previously explained as “best available approximation to the truth or falsity of a given inference, proposition or conclusion" (Cook and Campbell, et. al 1979). result and finding a.
Demographic Profile
A total of 200 reliable respondents out of 310 distributed questionnaires were sent out for research and the data analysis tests were carried out including descriptive analysis, mean value of variables, reliability test, normality test, correlation analysis, regression analysis, and finally analysis of variance (ANOVA). TABLE 4.1: DEMOGRAPHIC PROFILE Demographic Profile
Frequency
Percentage (%)
Cumulative Percentage (%)
Gender Male Female
83 117
41.5 58.5
41.5 100.0
Age 20-25 years 26-30 years 31-35 years 36-40 years 41-45 years 46-50 years 51-55 years 56 and above
21 59 45 25 14 19 13 4
10.5 29.5 22.5 12.5 7.0 9.5 6.5 2.0
10.5 40.0 62.5 75.0 82.0 91.5 98.0 100.0
Marital Status Single Married Divorced Widowed
124 72 1 3
62.0 36.0 .5 1.5
62.0 98.0 98.5 100.0
Income Less than P5000 P5000-P9999 P10000-P14999 P15000-P19999 P20000-P24999 P25000-P29999 P30000 and above
70 80 33 10 1 5 1
35.0 40.0 16.5 5.0 .5 2.5 .5
35.0 75.0 91.5 96.5 97.0 99.5 100.0
Educational Background Primary education Secondary education Tertiary education Vocational institution
7 27
3.5 13.5
3.5 17.0
154 10
77.0 5.0
94.0 99.0
Other
2
1.0
100.0
186
93.0
93.0
2 7
1.0 3.5
94.0 97.5
Part time worker
5
2.5
100.0
Never heard of them
5
2.5
2.5
I am aware but I have never used them Use it only sometimes Use it on a regular basis
86
43.0
45.5
61
30.5
76.0
48
24.0
100.0
92
46.0
46.0
35
17.5
63.5
18
9.0
72.5
32
16.0
88.5
15 8
7.5 4.0
96.0 100.0
71 52 31 8 8 30
35.5 26.0 15.5 4.0 4.0 15.0
35.5 61.5 77.0 81.0 85.0 100.0
61 12
30.5 6.0
30.5 36.5
66 20 41
33.0 10.0 20.5
69.5 79.5 100.0
102 83 13 2
51.0 41.5 6.5 1.0
51.0 92.5 99.0 100.0
103 69
51.5 34.5
51.5 86.0
25
12.5
98.5
3
1.5
100.0
Employment Status Employed for wages Self Employed Out of work and seeking employment
Most recent use of credit cards Never/Almost never 6 months to 1 year ago 1 month to 6 months ago Under one month ago Last week Yesterday Current debt level None P1000-P3000 P3000-P5999 P6000-P8999 P9000-P11999 P12000 and above Likelihood of future usage Very unlikely Somewhat unlikely Not sure Somewhat likely Very likely Number of credit cards owned None 1 credit card 2 credit cards 3 credit cards Frequency of credit card use Never At least once a month A few times a month A few times a week
Out of the 200 respondents from the survey the data was screened according to gender to acquire the results of the respondents’ opinions in relation to this attribute. The majority of the respondents were female, making up a total of 117 and in percentage figures 59% of the survey was answered by females while the
remaining 41% of the sample study was male. The dominating age group from the survey was the range between 26-30 years. This was a total of 59 of all the respondents or 29% of the study. The bulk of respondents in the survey were classified “single”.
0.5 and higher are considered adequate for measurement as shown in previous studies (Pedhazur, Pedhazur Schmelkin, 1991);(Caplan, Naidu, & Tripathi, et al. 1984); (Ellis, et al. 1988);(Nunnally, et al. 1978).
The lower most represented members of the survey were the widowed. Seven income groups were identified and the actual figures were presented in the figure. As illustrated, the majority of respondents had an income level ranging between P5000-P9999. This consisted of 80 out of the 200 respondents or 40% representation. The findings indicated that most of the respondents had a tertiary education qualification. This was a sum of 154 respondents that formed the larger fraction of the respondents of 77%. The findings indicated that the highest number of respondents was under “employed for wages”. This group summed to 186 out of the 200 respondents or a massive 93% of the total findings. In addition to commonly used demographic characteristics, narrower profiling in relation to credit card familiarity provided additional findings for the survey. A majority of the respondents, making a total of 86 out of 200 had awareness of credit cards but had never used them. This group presented a 43% majority.Moreover, From the findings, 92 respondents had never or almost never used a credit card; this amounted to 46% of the results. The highest frequency in debt level from the survey was found to be null with 71 respondents out of the 200. This was a 35% absence in debt among the respondents. The results of the survey showed that 66 out of the 200 respondents were not sure of their likelihood to use a credit card in the future. These respondents made up 33.5% of the survey. The majority did not own any credit card and this group summed to 102 respondents or 51% absence of credit card ownership. The survey data results indicated that 103 out 200 respondents never used a credit card and this marked a more than half the sample with 51% valid percent. b.
Means and Reliability of Variables
Table 4.2 shows the mean values. Standard deviation and the reliabity of each variables. Financial Confidence had the highest mean (FC=3.743) and followed by Financial Literacy (FL=3.244), Financial Well-being (FW= 2.663) and Attitude towards credit (ATC=2.474). Customer Spending variable had the lowest mean (CS=2.003). The majority of the variables had a reliability coefficient greater than 0.6 which is considered good. For financial literacy and financial confidence, the reliability coefficients were greater than 0.60 one having 0.773 and the other 0.754 reliability respectively. These figures were considered good in terms of data analysis. Only financial wellbeing and debt (dependent variable) had a lower coefficient of 0.570 and 0.546 respectively. Given that most of the reliability coefficients of the variables were greater than 0.50, the internal consistency reliability for the study could be considered acceptable. Reliability of
ID
Variables
Mea n
FW
Financial Wellbeing Attitude towards Credit Financial Literacy Financial Confiden ce Customer Spending Interest Rate Credit Card Debt
2.66 3
AT C FL FC
CS IR CC D
Std. Deviatio n 0.7622
Conbach ’s Alpha
2.47 4
0.6375
0.661
3.24 4
0.7995
0.773
0.570
0.6655
3.74 3 2.00 3 3.19 6 3.47 8
0.754
0.6771
0.657
0.6820
0.607
0.8413
0.546
TABLE 4.2: MEAN AND RELIABILITY OF VARIABLES
c.
Correlation Analysis (Matrix)
Table 4.3 showed that there was a significant positive correlation for debt to financial wellbeing, attitude towards credit, financial literacy, financial confidence and interest rate. There was however no significant correlation between debt and consumer spending at the given significance level. TABLE 4.3: CORRELATION MATRIX FW FW ATC
ATC
FL
FC
CS
IR
Debt
1 0.093
1
FL
-0.164*
-0.010
1
FC
-0.152*
-0.032
0.605**
1
CS
0.323**
0.213**
-0.076
-0.061
1
IR
0.136
0.170*
0.266**
0.311**
0.191**
1
Debt
0.198**
0.187**
0.230**
0.177*
0.128
0.493**
1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
d.
Regression Analysis Table 4.4 showed the correlation of the identified explanatory variables with the dependent variable as 0.289 or 28.9% once the intercorrelations of the explanatory variables were accounted for. A better measure of the correlation was shown with the adjusted R value (Adjusted R Square) indicated as 0.267. This measured the explained variance or change in the dependent variable due to influence or change in the independent variables. Adjusted R Square value indicated that 26.7% of the changes that occurred in debt level were explained by the changes in the independent variables. TABLE 4.4: MULTIPLE REGRESSION ANALYSIS Model
R
R Square
Adjusted R Square
1
0.538
0.289
0.267
Std. Error of the Estimate 0.72010
a. Predictors: (Constant), Interest Rate, Financial Wellbeing, Attitude towards Credit, Financial Literacy, Consumer Spending, Financial Confidence
Table 4.5 showed the output for Multiple Regression Coefficient. Basing on Beta coefficient, the highest value of the variables that contributed to a larger value change in Debt level was the Interest rate with a beta coefficient of 0.422. On the other extreme, the predictor variable which had a lower contribution to value change was consumer spending with a beta of 0.016. In order for the hypotheses statements to be supported, the pvalue (Sig.) had to be less than the 0.05 level of significance (Dallal, 2003). According to the findings, the only independent variables with a p-level less than the 0.05 significance level were financial wellbeing, financial literacy, and interest rate with p-values of 0.017, 0.043, and .000 respectively. The rest of the independent variables fell above the 0.05 significance level therefore only the aforementioned being Financial Wellbeing, Financial Literacy and Interest Rate where the only supported among the six hypothesis statements formulated in the previous section of the study. The p-value for Attitude towards Credit was 0.099 which was greater than the significance level used of 0.05. For Financial Confidence, the p-value was recorded at 0.769 while that of Consumer Spending was 0.812. The overall assessment of the regression model may be done by checking multicollinearity using Variance Inflation Factor (VIF) and Tolerance (Al-Hammad, 2001). Stevens et. al (2002) suggested that the cutoff point for VIF should be a value of 10 where a value beyond that was considered to reveal multicollinearity. On the other hand, according to O’Brien
et. al (2007) a tolerance less than 0.20 or 0.10 indicates multicollinearity problem. In the data findings, the problem of multicollinearity did not exists as all the tolerance and variance inflation factor numbers fell within the appropriate ranges. There was sufficient measure that there were no misleading p-values (Motulsky, 2002) as indicated by the collinearity statistics. TABLE 4.5: MULTIPLE REGRESSION COEFFICIENT
Model 1 (Constant)
Unstandardized Coefficients Std. B Error 0.617 0.429
Financial 0.175 Wellbeing Attitude 0.137 Towards Credit Financial 0.166 Literacy Financial -0.029 Confidence Consumer -0.020 Spending Interest 0.521 Rate a. Dependent Variable: Debt
Standard ized Coefficie nts Beta
t 1.439
Sig. 0.152
Collinearity Statistics Tolera nce VIF
0.072
0.159
2.415
0.017
0.854
1.171
0.083
0.104
1.658
0.099
0.933
1.072
0.081
0.158
2.041
0.043
0.618
1.618
0.099
-0.023
-0.294
0.769
0.600
1.667
0.082
-0.016
-0.239
0.812
0.843
1.187
0.083
0.422
6.270
0.000
0.811
1.232
Table 4.6 below illustrated the substantiated hypothesis statements and those that were not substantiated. Financial Wellbeing with a p-value of 0.017, followed by Financial Literacy with a significance value of 0.043, and Interest Rate with a p-value of 0.00 made up the variables indicated to cause significant change on the dependent variable of study, Debt. On the other hand, Attitude Towards Credit, Financial Confidence, and Consumer Spending were not significant variables in the study with p-values that exceeded the 0.05 significant levels with p-values of 0.099, 0.769, and 0.812 respectively. TABLE 4.6: SUMMARY OF HYPOTESES TEST
Hypotheses
pDecision value 0.017 Supported
H1 Low financial wellbeing will positively influence credit card debt level. 0.099 Not H2 High positive attitude towards Supported credit will positively influence credit card debt level. 0.043 Supported H3 Low financial
literacy will positively influence credit card debt level. 0.769 Not H4 High financial confidence will Supported positively influence credit card debt level. 0.812 Not H5 High consumer spending will Supported positively influence credit card debt level. 0.000 Supported H6 High interest rate will positively influence credit card debt level. v.
DISCUSSION AND CONCLUSION
The objective of this study was to characterize the usage patterns of credit card owners among working Batswana and the results indicated a minimal usage pattern of credit cards. The findings revealed the minimal ownership between one and two cards with only a few users possessing more than two. The study indicated that financial wellbeing, interest rate, and financial literacy had a significant influence of the variations in household debt. On the other hand, attitude towards credit, financial confidence, and consumer spending did not make a significant influence on the changes in household debt levels. These findings provide room for further analysis of more possible determinants of household debt. Lastly, the study was meant to provide a platform for introducing policies to improve the household debt burden faced by many individuals. Based on the findings, the government may consider modification in the regulatory framework towards financial institutions in order to safeguard the interest of households from the claws of high profit oriented lending institutions that disregard the financial stability of consumers. The government may establish a ceiling on the interest rates charged on credit card debt to avoid the cumulative feature of interest rate charges that have been practiced in most developed nations. Furthermore, policies orientated towards improving financial knowledge and understanding among credit card users and the rest of the productive members of society may be introduced. All these efforts would ultimately pave the way towards achieving the nations Vision 2016 as the wellbeing of society members would be better positioned to focus efforts on achieving this long term goal. During the study a few limitations were encountered above most being the limited time measurement for data collection. The process required
sufficient time to identify potential respondents and distribute optimum questionnaires to people but due to limited time period of distribution, distributing to many respondents was difficult and there was little time to explain difficult questions or get feedback from respondents during collection. There was also a scope limitation of the study due to focus on a few variables of influence including financial wellbeing, attitude towards credit, financial literacy, financial confidence, consumer spending and interest. The study could have also focused on more influencing factors beyond the abovementioned. In addition, the scope restricted the in-depth analysis of relationships among the identified variables, which could be helpful in research. Another limitation was the low response rate. There was a low response rate which amounted to only 200 valid respondents out of an expected 305. This limitation restricted the findings to be sufficient enough to generalize to the rest of the population. The last limitation of the study was the location. The study was conducted in Botswana which presented difficulties during data collection and distribution. The different areas where data was collected were far apart and this made it difficult to collect the data. Nevertheless, the acknowledged limitations give room for further research that may be advanced for the benefit of basic research related to the topic. In order to improve the quality of future research, development of a longer time measurement for data collection should be considered. A proper time frame of about two months may be used to ensure that there is sufficient time to distribute questionnaires as well as receive them. With a longer time frame, there can be clarification and feedback between the researcher and the selected respondents which will be useful in providing reliable and sufficient data. In the future, researchers may also extend the scope of the study beyond the one covered within this research. There are more influential factors that affect household debt which may include important factors including institutional information sharing or government regulations. Further future researchers may expand the scope of study to include relationships that exist among variables of interest which were not mentioned in the scope of this research. Future research should further expand the pool of the sample respondents in order to obtain sufficient data for analysis. In addition, increasing the number of respondents would be a contingency for unfilled or invalid questionnaires obtained during collection and screening. The larger the distributed questionnaires, the better the response rate which is vital for data analysis. In the future, researchers should also consider the location of their studies. In order to improve the data collection process, the location of study needs to have sufficient individuals to pick for sampling and using different sampling styles like random sampling instead of convenience sampling would allow for more accurate results.
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