Analysis of Factors that Encourage Customers Towards the Process of Purchase of Goods on Credit

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5343 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 Analysis of Factors that Encourage Customers Towards the Process of Purchase of Goods on Credit Abdurrahman1, Haerwati2, Winda Nur Cahyo3 1,2,3 Department of Industrial Engineering, Faculty of Industrial Technology, Islamic University of Indonesia ABSTRACT: This study aims to analyze the factors that influence customers in purchasing goods on credit among the community of Belinyu, Bangka Belitung. The growing trend of credit-based purchases among residents—most of whom work as fishermen and tin miners—serves as the key background of this research. A quantitative approach was employed using Structural Equation Modeling (SEM) with AMOS software. The variables analyzed include economic, psychological, social, and marketing factors, with the perception of credit convenience as a mediating variable and credit purchasing decisions as the dependent variable. The results indicate that psychological, social, and marketing factors significantly influence both the perception of credit convenience and the decision to purchase on credit. Conversely, economic factors do not show a significant effect. The perception of credit convenience also plays a mediating role in the relationship between the influencing factors and purchasing decisions. These findings offer practical implications for businesses to design more effective credit marketing strategies tailored to consumer characteristics in the region. KEYWORDS: Credit Purchase Decision, Economic Factors, Marketing Factors, Perceived Credit Convenience, Psychological Factors, Social Factors, Belinyu Bangka Belitung. I. INTRODUCTION In recent years, the method of purchasing goods on credit has been increasingly used by the public, both in urban and rural areas. Credit facilities provide an opportunity for consumers to obtain goods that are difficult to reach in cash due to limited direct funds. This phenomenon reflects a change in consumer behavior that increasingly prioritizes financial flexibility and ease of transactions. In Indonesia, especially in semi-urban areas such as Belinyu, Bangka Belitung, the pattern of purchasing on credit is a prominent alternative, especially for people with irregular incomes such as fishermen and mine workers. This phenomenon is also driven by the emergence of schemes Buy Now Pay Later (BNPL) and similar fintech services that are increasingly popular in Indonesia and globally. (Khaliq, 2025; Kumar et al., 2024; Rahmawati, 2025; Setiawan & al., 2025). However, this increase also brings risks such as late payments and personal financial management issues (Hayashi & Routh, 2025; Singh, 2025). Although the practice of purchasing on credit continues to increase, scientific studies that discuss the factors driving consumers' decisions to use credit are still limited. Most previous research has focused on urban communities or on formal financial systems, so there is still a gap in understanding how economic, psychological, social, and marketing factors influence credit purchasing decisions in suburban areas (Athar, 2020; Kusmalinda, 2025; Sarwoto et al., 2024). In addition, the role of the perception of credit facility as a mediating variable has not been studied in depth, especially in the context of consumer behavior in Indonesia. This knowledge gap emphasizes the importance of studying the dimensions of consumer behavior in credit purchases, especially in areas where access to formal financial services is still limited. Understanding the reasons why consumers in Belinyu choose credit purchases not only has scientific value but also practical value. From a scientific perspective, this study enriches the literature in the field of consumer behavior and marketing; Meanwhile, from a practical perspective, the results can be used as a basis for designing marketing strategies and credit schemes that are more in line with local characteristics. This study aims to analyze the influence of economic, psychological, social, and marketing factors on the purchase decision of goods on credit, by including the perception of ease of credit as a mediating variable. The model is designed to provide a comprehensive understanding of how internal and external factors interact with each other in the consumer decision-making process. This article makes a real contribution to the development of science, especially in expanding the understanding of credit purchasing behavior in semi-urban areas of Indonesia that has not received much attention in scientific studies. In addition, the findings of this International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5344 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 study are expected to provide practical input for financing institutions and business actors in developing more effective and targeted marketing and credit service strategies. II. LITERATURE REVIEW Understanding credit purchasing decisions in the context of semi-urban societies requires a multidimensional approach that includes behavioral economics theory, consumer psychology, and marketing theory. The grand theory underlying this study is Theory of Planned Behavior (Ajzen, 1991), which explains that a person's decisions are influenced by attitudes, subjective norms, and perceptions of control over behavior. Recent studies show that TPB remains relevant in the context of fintech and BNPL adoption, including perceptions of convenience and usefulness (Ahmad, 2025; Fatmawati & Suwardi, 2024). In addition, technology adoption theories such as TAM and UTAUT are also used in the study of consumer behavior in the financial sector (Aftab, 2025; Al Mamun, 2025; Yamuna & al., 2025). This theory is relevant in explaining how the perception of ease of credit can mediate the influence of various factors on purchasing decisions. In addition, middle range theory such as consumer decision-making process (Kotler & Keller, 2016) describes the consumer's thought process before buying, which is influenced by internal and external factors such as economics, psychology, social, and marketing. In this context, the purchase decision is defined as the process of consumer selection and action in obtaining goods or services based on rational and emotional considerations (Athar, 2020; Kusmalinda, 2025). Various previous studies have explored the factors that influence credit purchasing decisions. Research by Wulandari (2020) also emphasizes the importance of promotion and after-sales service in influencing credit electronic purchasing decisions among the millennial generation. Furthermore, in a qualitative study conducted by Rahmadani (2021), it was found that the social influence of peers and the surrounding environment affects consumer preferences for credit purchases in suburban areas. Research by Yusuf and Fitriani (2022) revealed that despite the perception of high credit risk, trust in financing institutions can increase the tendency to buy on credit. These results show a diversity of factors that play a role in the credit decision-making process, but have not integrated the perception of credit ease as an explicit mediating variable. The theoretical and empirical gap in previous studies lies in the lack of optimal integration between consumer behavior theory and structural models that combine the mediating role of credit ease perception. Various previous studies have explored the factors that influence credit purchasing decisions. Recent research in Indonesia highlights the role of trust, financial literacy, and social capital in the adoption of digital financial services (Budiyanto et al., 2025; Setiawan & al., 2025; Thomas & al., 2024). Psychological factors such as hedonism and consumptive behavior have also been shown to be influential in the decision to use PayLater (Juviyanty & al., 2024; Suherman, 2025). Meanwhile, international studies reveal that BNPL can increase consumer spending but also pose the risk of overspending (Abed & Alkadi, 2024; Smith, 2024). Many previous studies have been partial, highlighting only one or two factors without considering the complex relationships between variables. In addition, most of the research was conducted in urban areas with relatively economically stable populations, so it did not represent the conditions of semi-urban communities with fluctuating incomes such as in Belinyu. Furthermore, there have not been many studies that use the Structural Equation Modeling (SEM) approach to test the model of the relationship between variables in the context of purchasing credit from local communities. Based on the literature review, it can be concluded that this article makes a scientific contribution by offering a more comprehensive approach in understanding credit purchasing decisions. By including the perception of ease of credit as a mediating variable in the SEM model, this study complements the shortcomings in the previous literature that tend to be fragmentary. The study also expands the empirical context by focusing on semi-urban communities in Indonesia, which have received less attention in consumer behavior research. The findings of this article are expected to enrich academic discourse as well as provide a practical foundation for business actors and financial institutions in designing more contextual and effective marketing strategies. III. METHODS This study uses a quantitative approach with a survey method because the main purpose of the study is to test the relationship between variables through numerical measurement and statistical analysis. The quantitative approach is considered appropriate to obtain an objective picture of the factors that influence the decision to purchase goods on credit in society, as well as to test structural models based on the theoretical framework that has been developed. The research was carried out in Belinyu District, Bangka Regency, Bangka Belitung Islands Province. This area was chosen because it reflects the socio-economic characteristics of semi-urban communities that make a lot of use of credit purchases in their consumptive International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5345 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 activities. Data collection was carried out from February to April 2024 through the distribution of questionnaires directly to respondents who met the criteria. The population in this study is the entire Belinyu community who have purchased goods on credit. The sample was selected using the purposive sampling technique with the main criteria being at least 17 years old and having experience making purchases on credit in the past year. The number of samples used was 200 respondents. The determination of the number is based on contemporary guidelines which state that a sample size of 200 respondents is considered sufficient for a simple SEM (Piriyakul, 2021), and pay attention to a ratio of at least 5:1 to 10:1 between the number of samples and parameters, or up to 20:1 if the data is not normally distributed (Lim, 2024). which suggests a minimum of 5–10 times the number of indicators in the Structural Equation Modeling (SEM) model, and considers the number of indicators as many as 18 items in this study. Sample size was determined with reference to SEM guidelines (Hair et al., 2010) as well as recent research applying SEM to financial behavior studies. Validity and reliability are measured using CFA, CR, and AVE, as recommended in the latest literature (Alamsyah, 2025). The data collection instrument is in the form of a closed questionnaire with a 5-point Likert scale. This questionnaire includes six main variables, namely economic, psychological, social, marketing, perception of ease of credit, and credit purchase decisions, each measured by three indicators. The validity of the construct was tested through Confirmatory Factor Analysis (CFA) analysis with a minimum loading factor value of 0.6. The reliability of the instrument was assessed using Construct Reliability (CR) and Average Variance Extracted (AVE) values, all of which met the cut-off criteria (> 0.7 for CR and > 0.5 for AVE). Data was collected by distributing questionnaires directly to respondents after going through a selection process based on inclusion criteria. Data analysis was carried out using the Structural Equation Modeling (SEM) method using AMOS software version 24. The analysis includes testing measurement models to confirm the validity of indicators, as well as testing structural models to evaluate causal relationships between variables, including direct and indirect effects through mediated variables of credit facility perception. The model evaluation was carried out based on a number of goodness-of-fit indices such as RMSEA, CFI, TLI, and Chi-square/df. 3.1. Data Analysis Data collection in this study is by giving a questionnaire to the respondents, after the data is collected and then processed through validity and reliability tests which will then be processed in the AMOS 22 software. 3.2. Respond The characteristics analyzed included gender, age group, and type of occupation. Data was obtained from 70 respondents through a questionnaire distributed online. 3.3. Quality of research data To ensure the validity and reliability of the research findings, an analysis of the quality of the data was carried out through the Structural Equation Modeling (SEM) approach. The SEM method allows researchers to test relationships between latent constructs simultaneously, including direct and indirect linkages through mediation variables. This approach is also very appropriate in the context of social research that involves the structure of complex relationships between variables. IV. RESULTS Data collection in this study is by giving a questionnaire to the respondents, after the data is collected and then processed through validity and reliability tests which will then be processed in the AMOS 22 software. 1. Respponden The characteristics analyzed included gender, age group, and type of occupation. Data was obtained from 70 respondents through a questionnaire distributed online. 2. Quality of research data To ensure the validity and reliability of the research findings, an analysis of the quality of the data was carried out through the Structural Equation Modeling (SEM) approach. The SEM method allows researchers to test relationships between latent constructs simultaneously, including direct and indirect linkages through mediation variables. This approach is also very appropriate in the context of social research that involves the structure of complex relationships between variables. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5346 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 a. Analisis Asumsi Structural Equation Modelling (SEM) The structural model used in this study is built based on the theoretical framework and hypotheses that have been determined. The free variables consisted of economic factors (X1), psychological factors (X2), social factors (X3), and marketing factors (X4). All four are assumed to affect the perception of credit facility (M) as a mediating variable, and directly or indirectly affect credit purchasing decisions (Y) as an endogenous variable. The structure of the tested model shows that economic variables (X1) influence the perception of credit facility (M) and also have a direct path to credit purchase decisions (Y). The same applies to psychological (X2), social (X3), and marketing (X4) variables that are assumed to have an influence both directly on purchasing decisions and indirectly through the perception of ease of credit. Thus, the perception of ease of credit plays a role as the main mediator that bridges the influence of driving factors on credit purchasing decisions. Figure 1. Path Diagram The model quality test is carried out through several stages, including the preparation of path diagrams and structural model estimation. Each latent variable was measured by three observable indicators obtained from the questionnaire. The model was then evaluated using goodness-of-fit criteria such as RMSEA, CFI, TLI, and Chi-square/df. The results of the evaluation showed that the model had a good match to the empirical data, with the fit values of the model that met the feasibility criteria of the SEM analysis. The findings of this model show that the developed model is not only statistically appropriate, but also capable of comprehensively explaining the credit purchase decision-making process. The perception of ease of credit has proven to be a significant connecting variable in explaining how economic and marketing factors specifically impact consumer decisions. This approach makes a theoretical contribution to strengthening the understanding of credit-based purchasing behavior, as well as opening up opportunities for the development of consumer behavior models in the context of semi-urban societies. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5347 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 b. Normality Test The normality test in Structural Equation Modeling (SEM) analysis aims to assess whether the data on variable indicators are distributed normally, both at the univariate and multivariate levels. Normality is an important assumption in SEM because it affects parameter estimation and significance tests. The normality assessment in this study refers to the critical ratio (CR) value of skewness and kurtosis as suggested by Byrne (2010), with the normal distribution criterion if the CR is in the range of ±2.58 at a significance level of 0.01. The results of the Assessment of Normality analysis from AMOS show that most of the indicators in the model have skewness and kurtosis values that are within that tolerance range. This means that univariately, the majority of the data meets the assumption of normality. However, there is one indicator, namely X2#3, which shows a violation of the assumption of univariate normality, with a CR skewness value of -3.926. The unfairness in this indicator shows the potential for data distribution bias, although overall the data can still be categorized as normal univariately because the dominance of other indicators remains within reasonable limits. Table 1. Normality Test Results Meanwhile, the results of the multivariate normality test produced a kurtosis value of 56.381 with a CR of 5.893. This value exceeds the threshold of ±5, suggesting that the data are multivariate and do not fully meet the assumption of normality. However, violations of the assumption of multivariate normality in the context of SEM with large sample numbers (n > 200) are still tolerable, given the robustness of the maximum likelihood estimation method used (Weston & Gore, 2006). Therefore, the fixed model can be further analyzed assuming a near-normal distribution of the data. c. Uji Outlier Outlier detection is an important step in ensuring the quality and validity of the model in Structural Equation Modeling (SEM) analysis. The presence of outliers—i.e. data that significantly deviate from the general distribution pattern—can lead to parameter estimation bias and degrade the overall accuracy of the model. Therefore, it is important to identify and address potential data as an outlier before proceeding to the structural analysis stage. In this study, outlier detection was carried out using the Mahalanobis Distance (D²), which is a technique for measuring the multivariate distance between each respondent to the data distribution center. Each Mahalanobis value D² is compared to the critical value of the Chi-Square distribution (χ²) at a significance level of 0.001 and a degree of freedom equal to the number of indicators in International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5348 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 the model, which is 27. Respondents with a Mahalanobis D² value above this limit were categorized as significant multivariate outliers (Ketchen, 2013; Kline, 2001). Based on the results of data processing with AMOS, the highest Mahalanobis D² value was recorded at 38.705, which came from respondent number 3. This value is still below the critical value χ² of 54.572 for df = 27 at α = 0.001. Thus, none of the respondents were identified as multivariate outliers. This indicates that all the data collected is within the normal range of a statistically acceptable distribution. The absence of outliers in the data strengthens the validity of the SEM model analysis to be performed. All respondent data can be used without the need for further transformation, deletion, or adjustment. This ensures that the results of the resulting structural analysis reflect the patterns of relationships between variables in a representative manner and can be interpreted scientifically with a high degree of reliability. Table 2. Outlier Test Results d. Validity Test The validity of the construct in the Structural Equation Modeling (SEM) analysis was measured using Standardized Factor Loading (SFL) from the Confirmatory Factor Analysis (CFA) analysis. An indicator is declared valid if it has a loading factor value International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5349 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 of ≥ 0.50 and ideally ≥ 0.70 (Hair et al., 2019). This value reflects the strength of the relationship between the observable indicator and the latent construct being measured. Indicators that do not meet these minimum limits are generally eliminated from the model to maintain measurement integrity. The results of the CFA show that all indicators in this study have adequate loading factor values, which are in the range of 0.629 to 0.964. This shows that each indicator has a significant contribution in representing its construct statistically. No indicators need to be excluded from the model, as they all meet the established validity criteria. Table 3. Validity Test Results For example, the indicator in the Economic Factor construct has a loading factor between 0.708 and 0.868, indicating that all items are able to explain the economic construct well. The same thing can also be seen in other constructs. Psychological, Social, and Marketing Factors show the consistency of strong indicator contributions, including the indicator from the Perception of Credit Facility which has a very high value (0.812–0.964), which indicates a very strong and stable measurement of the construct. Overall, these findings indicate that all indicators in this model are statistically valid and feasible for use in subsequent structural analyses. The strength of the relationship between the indicators and the constructs each provides empirical support for the reliability of the measuring instruments used in this study, while strengthening the basis of the inferential analysis to be carried out. e. Reliability Test The reliability of the construct in the Structural Equation Modeling (SEM) model is an important indicator to ensure the consistency and stability of measurements between indicators in one latent variable. In this study, reliability was tested using two commonly used approaches, namely Construct Reliability (CR) and Average Variance Extracted (AVE), as recommended by Hair et al. (2019). Konstruk Indikator Loading Factor Keterangan Ekonomi X1#1 0,708 Valid X1#2 0,868 Valid X1#3 0,842 Valid X1#4 0,783 Valid Psikologis X2#1 0,629 Valid X2#2 0,784 Valid X2#4 0,773 Valid Sosial X3#1 0,769 Valid X3#2 0,717 Valid X3#3 0,763 Valid X3#4 0,890 Valid Pemasaran X4#1 0,920 Valid X4#2 0,931 Valid X4#3 0,835 Valid X4#4 0,866 Valid Persepsi Kemudahan Kredit (M) M1#1 0,895 Valid M1#2 0,964 Valid M1#3 0,941 Valid M1#4 0,897 Valid M1#5 0,812 Valid Keputusan Pembelian Kredit (Y) Y1#1 0,777 Valid Y1#2 0,780 Valid Y1#3 0,839 Valid Y1#4 0,863 Valid International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5350 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 CR measures the internal consistency of indicators in measuring latent constructs, while AVE measures how much variance of indicators can be explained by those constructs. A construct is declared reliable if the CR value is ≥ 0.70 and the AVE value is ≥ 0.50. A high CR value reflects a strong degree of consistency between indicators, while a high AVE indicates that most of the indicator's variance is successfully explained by the construct it represents. The calculation results show that all constructs in the model have CR and AVE values that exceed the minimum limit. Economic Factors (CR = 0.878; AVE = 0.644) and Psychological Factors (CR = 0.822; AVE = 0.544) shows good reliability. Meanwhile, the constructs of Social, Marketing, Perception of Credit Ease of Credit, and Credit Purchase Decision even showed very high reliability with a CR value above 0.90 and AVE above 0.70. This reflects that the indicators used in this study are not only consistent, but also able to explain the variability of the construct very well. Table 4. Reliability Test Results Thus, all constructs analyzed in this study have met the reliability requirements of the instrument. These results also support the convergent validity of the construct, which has previously been proven through high loading factor values. Therefore, this model is considered to have a strong measurement foundation and is ready to be used in structural analysis to test the hypothesis of relationships between variables. f. Uji Goodness of Fit Evaluation Goodness of Fit (GOF) is an important stage in the analysis Structural Equation Modeling (SEM) to assess the extent to which the theoretical model constructed can adequately represent the empirical data. The model is said to have a good fit when the relationship between the latent variable and its indicators can be explained statistically and theoretically, as explained by Hair et al (Ketchen, 2013). and is reinforced by studies on the effect of model size on the fit-index (CFI, TLI, RMSEA) which shows that even if the theoretical indicators are adequate, models with many indicators or latent variables can affect statistical fit performance (Shi et al., 2019), GOF testing aims to measure the global suitability of the model through various fit measures that include statistical aspects, absolute index suitability, comparative indices, and model complexity-based indices. Konstruk CR AVE Keterangan Ekonomi (X1) 0,878 0,644 Reliabel Psikologis (X2) 0,822 0,544 Reliabel Sosial (X3) 0,915 0,722 Sangat Reliabel Pemasaran (X4) 0,943 0,823 Sangat Reliabel Persepsi Kemudahan Kredit (M) 0,956 0,790 Sangat Reliabel Keputusan Pembelian Kredit (Y) 0,935 0,779 Sangat Reliabel International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5351 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 Figure 2 Confirmatory analysis results In this study, the results of GOF testing based on AMOS output showed that most of the model fit sizes have not reached the recommended criteria. The resulting Chi-Square (CMIN) value of 471.019 with a p-value of 0.000 shows a statistical inconsistency in the model. This indicates that there is a significant difference between the observed and estimated covariance matrices, so the initial model cannot be said to be a complete fit. Tabel 5 Uji Goodness of Fit No. Ukuran Kelayakan Model Cut-off Value Hasil AMOS Keterangan 1 Chi-Square (CMIN) Kecil dan tidak signifikan 471,019 Tidak Memenuhi 2 Probability (P) ≥ 0,05 0,000 Tidak Memenuhi 3 GFI (Goodness of Fit Index) ≥ 0,90 0,650 Tidak Memenuhi 4 AGFI (Adjusted GFI) ≥ 0,90 0,565 Tidak Memenuhi 5 RMSEA ≤ 0,08 0,128 Tidak Memenuhi 6 TLI (Tucker Lewis Index) ≥ 0,90 0,828 Marginal 7 NFI (Normed Fit Index) ≥ 0,90 0,740 Tidak Memenuhi 8 PCFI ≥ 0,90 (lebih tinggi lebih baik) 0,742 Marginal 9 PNFI ≥ 0,90 (lebih tinggi lebih baik) 0,646 Tidak Memenuhi International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-43, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 5358 *Corresponding Author: Abdurrahman Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 5343-5358 Pendidikan Indonesia), 10(2), 506. https://doi.org/10.29210/020243881 21. Setiawan, B., & al., et. (2025). 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Analysis of Factors that Encourage Customers Towards the Process of Purchase of Goods on Credit. International Journal of Current Science Research and Review, 8(10), pp. 5343-5358. DOI: https://doi.org/10.47191/ijcsrr/V8-i10-43