Abstract

Background. Financial crises become more probable and severe when debt levels exceed specific thresholds. This finding strongly indicates that debt can become excessive in some circumstances. All the above are from previous studies that mentioned the statistics of debts, financial crises, and defaults in countries without mentioning the reasons that led to the debts. Objective. The aim of this study is to identify the causes that led to the accumulation of debts by individuals. Methods and Materials. A cross-sectional survey was administered to the common populace in KSA, utilizing Google shapes to gather information. The target group comprised 143 males (34.5%) and 272 females (65.5%) responders. Statistical analysis methods were based on factor analysis and regression analysis of fractional factorials. Results. The results of the analysis were summarized by the factors for the first stage: the reasons that led to the debt crisis for individuals were S1, S2, and S5–S13. The results of the second method, regression analysis, show that the reasons that led to the debt crisis for individuals are S4 and S11. Conclusion. Referring to the results of the analysis of the two methods, it was found that there is a common factor between them, S11, and thus, S11 (poor management and inefficiency) becomes the actual cause that led to the debt crisis for individuals.

1. Introduction and Previous Studies

Debt can be considered a double-edged sword. It enhances welfare when used sensibly and in moderation. However, when taken irresponsibly and in excess, the consequences can be disastrous. Overborrowing leads to bankruptcy and financial catastrophes for both individuals and businesses. Individuals can spend even though they do not have any present income if they can borrow and save money. Businesses can use debt to invest when their sales prevent them from doing so. Fiscal authorities can also play a role in macroeconomic stabilization when they are able to borrow. However, borrowing has a history of weaknesses. Financial crises become more probable and severe when debt levels exceed specific thresholds. This finding strongly indicates that debt can become excessive in some circumstances.

The National Commercial Bank had the largest volume of nonperforming loans among Saudi banks listed on the stock exchange, as seen in Table 1. Its nonperforming loans totaled 4.67 billion riyals at the end of September 2017, despite the Commercial Bank’s total loans totaling 256.9 billion riyals. The data also show that Banque Saudi Fransi ranks second in terms of nonperforming loans for the same period, with a total of 128.9 billion riyals. However, among Saudi banks, Bank Albilad has the lowest bad debt balance. At the end of September, it had 476.3 million riyals in nonperforming loans, out of a total of 41.8 billion riyals [1]. The indicators are compared to those of 2019 to provide a clearer view of the impact of the pandemic on businesses. The company’s profitability declined by 1%, 2%, and 0.28% by the end of the second half of 2020, as evidenced by decreases in profitability metrics, such as the rate of return on assets, rate of return on equity, and net profit margin. The results also show a 16% drop in the liquidity index in 2020 compared with 2019. In 2019, the average revenue was 1.488 million dollars; however, in 2020, it fell by 77 348 million dollars. Finally, between the two years, the leverage ratio is only 4% [2].

By evaluating data from a selection of Saudi banks, this study aims to determine the amount of nonperforming loans as well as the economic and financial factors that influence them. Many previous studies [3] show that nonperforming loans are a serious problem that banks face in their daily operations, as they result in the freezing of a significant portion of the banks’ funds due to borrowers’ inability to repay the loan and its interest, exposing the bank granting these loans to potential losses.

Nonperforming loans are debts for which the available information leads to the conclusion that their collect ability has deteriorated and that determining the percentage or value of what can be collected from them has become problematic [4].

The households’ financial position revealed that their average gross monthly per capita income was 1,320 SAR ($352). This was determined using the household’s total annual gross income. All money obtained from employment, the government, charity organizations, friends or family members, “good people” (a local word referring to private individuals who make anonymous donations to the impoverished) and in-kind donations are included in this category. It is worth noting that because Saudi Arabia has no income tax, gross income can be regarded as income after taxes and subsidies. When all revenue and in-kind gifts received from social assistance payments are removed, the gross monthly per capita income falls to 1,108 SAR ($299). Furthermore, on average, social assistance payments accounted for 23% of household income, or 212 SAR ($56) per person per month. The citizens’ account programs, traditional social security, and charitable organizations all make payments in this category. Female-headed households were more reliant on social welfare, with social welfare accounting for 48% of their income. Furthermore, when income increased, the percentage of the overall income from social welfare decreased. However, it appears that even middle-class Saudi families receive social welfare payments [5].

All the above are from previous studies that mentioned the statistics of debts, financial crises, and defaults in countries without mentioning the reasons that led to the debts. The aim of this study is to identify the causes that led to the accumulation of debts on individuals. The paper’s main contribution is to extract statistical results from the first and second designs, and if the results of the factors are similar, we are taken into account for the research.

The remainder of this paper is organized as follows: In the second section, the materials and methods used are presented. In the third section, the results and discussion are presented along with the application of the two methods used. Finally, conclusions are presented in the last section.

2. Materials and Methods

2.1. The Questionnaire
(i)S1: Instability and declining per capita income.(ii)S2: loan or sale.(iii)S3: Low cash income.(iv)S4: Excessive population growth has contributed to slowing economic performance and economic growth.(v)S5: The increasing resort to debt.(vi)S6: Bad debt is the debt that the bank estimates based on the customer’s financial position to guarantee debt and possibility of repayment.(vii)S7: Interim planned bad debts are due to the expected gap between use and resources.(viii)S8: Nonperforming debts are random, and they happen accidently when the project is surprised by accidents that are difficult to predict or control.(ix)S9: Clients stumbled over the novelty of their experiences.(x)S10: Client’s stumble upon engaging in activities they are not aware of.(xi)S11: Poor management and inefficiency.(xii)S12: The client expanded borrowing, especially from nonbank sources such as suppliers.(xiii)S13: The client expanded forward selling operations on a large scale without conscious study.(xiv)Y: The response code is the individual’s monthly income.
2.2. Data Analysis and Study Test

Table 2 displays demographic data such as area, age, gender, university specialization, employment status, and specific field of work. All of the participants spoke Arabic fluently. SPSS version 25.0 was used to analyze the data. Quantitative analysis used inference statistics to calculate the frequency and percentage of demographic data.

The target group comprised 143 males (34.5%) and 272 female (65.5%) responders.

2.3. Factor Analysis

The major goal of factor analysis is to describe some of the more fundamental but less-obvious latent variables concealed in a set of measured variables. A few factors are used to describe a large number of linked indicators or factors, and each class variable can become a factor of numerous factors with less data, reflecting the majority of the original information. We suppose that these variables have been standardized (mean = 0, standard deviation = 1) according to factor requirements, with n primitive variables, represented as x1, x2, …, xn. Assume that p variables can be represented by a mix of x1, x2, and xp factors [6]. Factor analysis is a mathematical model that is defined as follows:

More details can be found in [6].

2.4. Regression Analysis

Multiple linear regression analysis is a statistical analysis method based on the least-squares principle. Under statistical assumptions, this is the best linear unbiased estimate. In the study of linear relationships, a multiple linear regression model represents the effect of two or more independent variables on a dependent variable. The selection of independent variables should be considered while creating a multiple linear regression model to ensure the regression model’s outstanding interpretation ability and prediction effect. The explanatory variable (dependent variable) is Y, the explanatory variable (independent variable) is X, the random error is, and the regression parameter is [6]. The mathematical model can be expressed as follows:

More details can be found in [6].

3. Results and Discussion

3.1. Factor Analysis

Table 2 shows the demographic information. Among the 415 samples examined, n = 143 (34.5%) were male and n = 272 (65.5%) were female; the bulk of the participants’ (n = 173, 41.7%) ages ranged from 22 to 32, while the percentage of those aged 52 and over was n = 20. (4.5%, respectively). Government employees made up a sizable proportion (45.8%), while private sector employees made up a small proportion (14.7%), and I did not work (39.5%). In Table 3, 48 of the 78 correlations in the matrix are greater than 0.30. This criterion for data appropriateness for the factor analysis were met. In Table 4, the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy is described as magnificent in the 0.90 s, meritorious in the 0.80 s, middling in the 0.70 s, mediocre in the 0.60 s, wretched in the 0.50 s, and undesirable below 0.50. For this set of variables, the KMO measure of sampling adequacy was 0.895, which is over the permitted standard.

In Table 5, the measurements of sampling adequacy for the individual variables on the diagonal of the matrix are contained in the Anti-image Correlation Matrix. The anti-image correlation matrix shows that each variable’s sampling adequacy is greater than 0.50. These variables are S1 (0.814), S2 (0.920), S3 (0.826), S4 (0.913), S5 (0.933), S6 (0.901), S7 (0.907), S8 (0.910), S9 (0.891), S10 (0.863), S12 (0.902), S13 (0.901). In Table 6, we use the latent root criterion to count how many eigenvalues are greater than 1.0. Because there are two components or factors in this table, this criterion supports the presence of two components or factors. The number of components needed to explain 51% or more of the variance in the original set of variables is counted. Two components, we reach the minimum of 51 percent in this study. In Table 7, we look at the loading pattern for a simple structure, which means that each variable has a significant loading on only one element. Each variable has one significant loading on a component in this component matrix. If one or more variables did not have a significant loading on a factor, we would re-run the factor analysis, removing those variables one by one, until we had a solution in which all of the variables in the analysis were loaded on at least one factor. Each of the original variables has a significant loading on only one factor in this component matrix. If a variable has a significant loading on more than one variable, we call it “complex,” which means it has a link with two or more of the derived components. For dealing with complex variables, there are a range of prescriptions. The straightforward solution is to ignore the variable’s complexity and treat it as though it belonged to the factor with the highest loading. Another simple way to deal with complexity is to leave the complicated variable out of the factor analysis. Other times, I’ve seen authors choose to include it as a variable in many factors or assign it to a component at random for conceptual reasons. We have seen again in Table 7, component 1 has 11 variables that are greater than 0.5, which are S1, S2, and S5–S13, whereas component 2 has two variables, which are S1 and S3.

3.2. Regression Analysis of Fractional Factorial with Highest Resolution

A ready-made design is chosen from a published scientific paper in this section (see [7]). This design was searched for in all the data in the questionnaire, taking into account the diversity of the main factors. To obtain a complete design, the design was analyzed to search for statistical metadata using an SPSS program.

3.2.1. Application One of Fractional Factorial

Referring to Table 8, it is analyzed by the statistical program (SPSS), where the following results appear in Table 9, which are the arithmetic mean with a value of 7562.5000 and the standard deviation with a value of 4746.70939. Looking at Table 10 through the descriptive analysis of these data, it was found that the reason that led to the debt crisis for individuals was the fourth reason (S4), only because its statistical value was higher than 0.05. The statistical equation used was Y = 7562.500 − 3687.500 S4 + ɛ. As a result, excessive population growth has contributed to slowing economic performance, and lower economic growth (S4) is the actual cause of individual debt accumulation.

3.2.2. Application 2 of Fractional Factorial

Referring to Table 11, it is analyzed by the statistical program (SPSS), where the following results appear in Table 12, which are the arithmetic mean with a value of 4162.5000 and the standard deviation with a value of 5227.38326. Looking at Table 13, through the descriptive analysis of these data, we did not find the reasons that led to the debt crisis for individuals, which is a statistical value higher than 0.05.

3.2.3. Application 3 of Fractional Factorial

Referring to Table 14, it is analyzed by the statistical program (SPSS), where the following results appear in Table 15, which are the arithmetic mean with a value of 11750.0000 and a standard deviation of 10819.95511. Looking at Table 16, through the descriptive analysis of these data, we did not find the reasons that led to the debt crisis for individuals, which is a statistical value higher than 0.05.

3.2.4. Application 4 of Fractional Factorial

Referring to Table 17, it is analyzed by the statistical program (SPSS), where the following results appear in Table 18, which are the arithmetic mean with a value of 12500.0000 and a standard deviation of 22437.21399. Looking at Table 19, through the descriptive analysis of these data, we did not find the reasons that led to the debt crisis for individuals, which is a statistical value higher than 0.05.

3.2.5. Application 5 of Fractional Factorial

Referring to Table 20, it is analyzed by the statistical program (SPSS), where the following results appear in Table 21, which are the arithmetic mean with a value of 6307.0000 and a standard deviation of 6068.65888. Looking at Table 22 through the descriptive analysis of these data, it was found that the reasons that led to the debt crisis for individuals are the eleven reasons (S11) only because their statistical value is higher than 0.05. The statistical equation was Y = 6307.0000–6068.65888 S11 + ɛ. As a result, poor management and inefficiency (S11) are the true causes of debt accumulation on individuals.

4. Conclusion

Financial crises become more probable and severe when debt levels exceed specific thresholds. This finding strongly indicates that debt can become excessive in some circumstances. The goal of this research was to determine what causes people to become indebted in the first place. In this research, two methods were used to analyze the resolution, which were the method of factor analysis and the method of regression analysis for a ready-made design of a scientific paper. The results of the analysis were summarized by the factors for the first stage: the reasons that led to the debt crisis for individuals were S1, S2, and S5–S13. The results of the second method, regression analysis, show that the reasons that led to the debt crisis for individuals are S4 and S11. Referring to the results of the analysis of the two methods, it was found that there is a common factor between them, S11, and thus, S11 (poor management and inefficiency) becomes the actual cause that led to the debt crisis for individuals. Whenever mentioned, governments should consider this reason and search for actual solutions to limit the spread of the debt phenomenon to individuals.

Data Availability

The data supporting the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

The authors express their gratitude to the Deanship of Research at the University of Ha’il, Saudi Arabia, for funding this project (project no. GR-22 001).