Microsoft Word - ETASR_V13_N4_pp11177-11181 Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11177-11181 11177 www.etasr.com Magashi et al.: Indicative Factors for SACCOs Failure in Tanzania Indicative Factors for SACCOs Failure in Tanzania Cosmas Magashi Department of Information Technology, System Development, and Management (ITSDM), Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania magashi80@yahoo.com (corresponding author) Anael Sam Department of Information Technology, System Development, and Management (ITSDM), Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania anael.sam@nm-aist.ac.tz John Agbinya School of Information Technology and Engineering, Melbourne Institute of Technology (MIT), Australia jagbinya@mit.edu.au Jimmy Mbelwa Department of Computer Science and Engineering, University of Dar es Salaam (UDSM), Tanzania jimmymbelwa@gmail.com Received: 23 April 2023 | Accepted: 1 May 2023 Licensed under a CC-BY 4.0 license | Copyright (c) by the authors | DOI: https://doi.org/10.48084/etasr.5989 ABSTRACT SACCOs are viewed as a feasible opportunity toward financial inclusion in an economy where most of the citizens are poor, as they are very essential for the socio-economic development of members, the community, and the world at large. However, SACCOs sometimes do not realize the expected socio- economic potential, especially when they fail. This study aimed to comprehensively assess financial and non-financial factors, at institutional and personal levels, that contribute to the failure of SACCOS in Tanzania. The data were collected using a questionnaire on 5,000 members of SACCOs, obtained using stratified random sampling. Data collected were analyzed using descriptive statistics and binary logistic regression. The findings showed that both financial and non-financial factors, at personal and institutional levels, had a statistically significant and positive relationship with the failure of SACCOs. Therefore, the performance of SACCOs and other Microfinance Financial Institutions (MFIs) should be addressed from a comprehensive view of both financial and non-financial factors, at personal or institutional levels. In other words, the failure of MFIs should be addressed from a holistic point of view. Keywords-SACCOs failure; financial factors; non-financial factors I. INTRODUCTION The business ecosystems are continually evolving in the current era of science and technology [1], and Savings and Credit Cooperative Organisations (SACCOs) form one of these ecosystems. SACCOs are defined as voluntary associations where members are encouraged to make regular savings and subsequently obtain credit for use in different activities [2]. SACCOs are viewed as a feasible opportunity for financial inclusion in an economy where the majority of citizens are poor [3-5] and are considered very essential for the socio-economic development of members, the community, and the world at large [6-8]. SACCOs have attracted the interest of several studies for their contributions to the socio-economic development of their members and the community at large. SACCOs have been an engine for mobilizing human, financial, and capital resources for national development [6]. SACCOs help their members accumulate capital and loans for social needs, such as building houses, buying clothes, paying school fees, organizing weddings, and other social activities [5]. They enable easy access to financial services, encourage savings, create employment opportunities, and directly support community development efforts, such as helping the community access social services, stimulating growth, etc. [7]. SACCOs make finance more accessible to underprivileged members, limit the incidence of financial exclusion, stimulate a thrift culture, aware ordinary people how Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11177-11181 11178 www.etasr.com Magashi et al.: Indicative Factors for SACCOs Failure in Tanzania to create assets, and generally improve the economic performance of developing countries [9-10]. Regardless of such socioeconomic development and contributions, some unexpected issues of SACCOs' failures are noted. Some SACCOs fail from time to time due to not being operationally and financially stable [2, 5, 11]. Some studies revealed financial and non-financial factors for the failure of SACCOs [12-15], but they were not comprehensive, as they addressed limited factors and focused mainly on financial factors. In addition, these studies used only descriptive statistics to analyze these few factors that contribute to SACCO failure, instead of advanced statistical methods, such as logistic regression, which could show more logical relationships. Furthermore, several studies concentrated on the failure of MFIs and not specifically on SACCOs. The failure was addressed indirectly as poor loan repayment or loan default and not directly as a failure. The failure of SACCOs is more than poor loan repayment or loan default. Therefore, this study aimed to identify comprehensive statistical relationships between financial and non-financial factors that contribute to the failure of SACCOs in Tanzania. Various studies investigated the failure of SACCOs after their formation. For example, out of 103 SACCOs in Tanzania, 61% and 51% failed operationally financially, respectively [2]. The number of SACCOs in Tanzania decreased from 4,177 at the end of December 2018 to 3,714 at the end of December 2019 [7]. Several SACCOs failed to provide adequate socio- economic benefits to members as expected. The failure of SACCOs is reflected directly or indirectly in terms of loan repayment or loan default. As loans are the key products of SACCOs, failure of loan repayment causes them to fail [19]. Poor loan repayment or loan default is the decline and failure of some microfinance institutions, including SACCOs [10]. The failure of SACCOs is caused by both internal and external factors, such as financial constraints, weak leadership, unreliable loan interest rates, and poor record-keeping systems [5]. In [11], it was shown that poor monitoring of loan use, poor intrapreneurship and environmental business opportunities, unfavorable loan repayment conditions, and poor strategies for choosing borrower groups were important factors for the failure of SACCOs. In [12], six factors out of ten were found to influence the failure of SACCOs, namely: poor educational level, lending methods, the proximity of borrower's residence to the institutions, family size, income from loan- financed activities, and training. In [13], clients' age, interest rates charged, and loss of collateral were noted as the principal factors leading to the failure of SACCOs. In [14], it was shown that factors such as training, education level, and annual income could cause the failure of SACCOs. The Institutional Theory stresses the evaluation of MFIs, such as SACCOs, indicating that many SACCOs do not experience a proper circle of money market, mass distribution of credit, and uplifting microfinance systems as best practices for improving efficiency due to poor management systems, finance and accountability, marketing, and service delivery. Additionally, Working Capital Management Theories (WCMT) indicate that the failure of SACCOs depends on existing or anticipated internal and external constraints, which vary from operational, managerial, or resource constraints. The Information Asymmetry Theory (IAT) portrays that the lack of proper information may be one of the non-financial factors that cause the failure of SACCOs in Tanzania. Lenders fail to adequately differentiate the information between one borrower and another [16-18]. Misaligned information between lenders and borrowers may lead to losses when the money issued is delivered to the wrong borrowers. Furthermore, prospective SACCO borrowers may lack or have limited information, making them not borrow as expected [13]. This study attempted to distinguish the factors mentioned above into predictor and outcome variables. These factors were categorized into financial and non-financial factors and further divided into personal (SACCOs clients) and institutional (SACCOs) factors. The personal non-financial factors were clients' age, education level, family size, proximity of borrowers' residence to the institution, gender, type of business, and entrepreneurship/business environment. The institutional non-financial factors were the supervisory environment, record keeping, infrastructure, supportive regulations, human resources, choosing borrowers, training, and lending method. Personal financial factors were loan repayment, collateral loss, annual income, loan utilization, high transaction cost, client credit experience, knowledge of financial management, and loan management strategies. Financial institutional factors were loan interest, loan amount, loan category, lending methodology, criteria used in granting micro credits, unfavorable loan repayment conditions, income from loan- financed activities, and interest rates charged. II. METHODOLOGY This study used a quantitative approach, following the nature of the specific objective which is characterized by a causal-effect relationship, attempting to explain the relationship between variables. This approach uses statistical data as a tool to save time and resources by focusing on numbers and figures in data collection and analysis [20]. This study applied the explanatory cross-sectional survey research design, due to various reasons following the nature of its objective. Each SACCO was treated as a unit of analysis covering a large geographical area of Tanzania, to provide fast, well-organized, and precise means of analyzing failure information. The study was dominated by "what" questions, which prompt the explanatory cross-sectional survey research design [21-22]. The study was carried out in Tanzania, based on five regions, namely Dar es Salaam, Arusha, Mbeya, Mwanza, and Dodoma, which have high concentrations of SACCOs and can represent the country as a whole. These regions have both successful and failed SACCOs, and also have SACCOs with the alert of the possibility of being closed by TCDC. The population of this study included members, staff, and management of SACCOs, and TCDC management. The members were considered because they were the owners of the SACCOs; hence, they were very well informed of their performance. The SACCOs staff and management and the TCDC management are the key informants on any performance, success or failure, regarding SACCOS. First, the sample was chosen from the Dar es Salaam, Arusha, Mbeya, Mwanza, and Dodoma regions and the sample Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11177-11181 11179 www.etasr.com Magashi et al.: Indicative Factors for SACCOs Failure in Tanzania population of SACCOS members was obtained. The population of SACCOS members obtained from the given regions was 5,000, i.e. 1011 in Dar es Salaam, 1000 in Arusha, 1030 in Mbeya, 999 in Mwanza, and 960 in Dodoma. The formula in [23] was used to calculate the sample size for this study as indicated below: � = �$��� �$ � �� �$��� (1) where n is the sample size for a finite population, N is the size of the population that is the number of SACCOS members (5000), p is the population reliability (or frequency estimated for a sample of size n) taken as 0.5 for all developing countries population, p+q=1, e is the margin of error considered 3%, and Z $ is the normal reduced variable at 0.05 level of significance, which was chosen as 1.96. According to the above formula, the sample size for this study was: � = �.���$× �.�×�.��×���� �.���$× ���� �� �.���$×�.�×�.� (2) � ≈ 880 The primary data of this study were collected mainly using a questionnaire and 880 questionnaires were distributed to the members of SACCOs. However, only 500 questionnaires were received and found to be complete and useful for data analysis. The response rate was 57% and could not affect the assumption (sample size) of the binary logistic regression model for data analysis. The questionnaire was able to produce information from the respondents at a low cost with a large and geographically spread population. The questionnaire provided the respondents with a suitable time to give well-thought responses and allowed not easily approachable respondents to be reached. It was a structured questionnaire, prepared in advance with fixed, concrete, and predetermined questions. The questions had the same wording and order for all respondents, with the same stated responses, and had closed answers. The respondents completed the questionnaire on their own. Furthermore, the secondary data of this study were collected using a documentary review of published and non-published reports on the performance of SACCOs in Tanzania and other developing countries, such as Kenya, Ethiopia, Nigeria, and others. Secondary data provided the basis for collecting primary data and finally making comparisons. To ensure the quality of the findings, the questionnaire was first pre-tested and piloted in a small intended sample to examine its reliability and variability. The comments given during the pre-testing and pilot study were scrutinized and incorporated accordingly. Cronbach's alpha coefficient was used to investigate whether the study scales were free from random error, of which a minimum level of 0.7 was used [24]. The use of multiple data collection methods, i.e. questionnaire and documentary reviews, also ensured validity and reliability. The use of various respondents, i.e., SACCOS members, staff, management, and TCDC management, likely ensured triangulation, validity, and reliability of the findings. This study adapted scales from previous empirical studies to ensure empirical evidence and confidence of definitions, measurements, and operationalization of variables and scales as a way to ensure validity and reliability. The collected data were analyzed using binary logistic regression. The model was used to test and predict a dichotomous dependent variable from several categorical or continuous independent variables. Specifically, the dependent variable is SACCO's sustainability, where 0 indicates "Fail" and 1 indicates "Successful". The model allowed for the establishment of the predictive ability of sets of independent variables. Data analysis was performed using R Statistics version 4.0.5. III. FINDINGS Table I briefly describes the personal information of the respondents, to ensure that homoscedasticity, independence of residuals/relations, normality, sample size, outliers, and multicollinearity were met before running the model. TABLE I. PARTICIPANTS' INFORMATION Personal Information Scale Frequency % Gender Male 300 60 Female 200 40 Total 500 100 Age 21-24 years 95 19 25-29 years 150 30 21-24 years 95 19 25-29 years 150 30 30-34 years 185 37 35-39 years 50 10 40 years and above 20 4 Total 350 100 Family status Single 295 59 Married 150 30 Divorced 30 6 Widow 25 5 Total 500 100 Region Arusha 100 20 Dar es Salaam 102 20 Mbeya 103 21 Mwanza 99 20 Dodoma 96 19 Total 500 100 Educational level Formal Education 95 19 Primary Education 150 30 Secondary Education 185 37 Undergraduate Education 50 10 Postgraduate Education 20 4 Total 500 100 This study had one main question: "What are the factors that predict the failure of SACCOS in Tanzania?", or alternatively: "How well does a set of factors predict and explain the failure of SACCOS in Tanzania?". The question was answered using a binary logistic regression model. Although the model produced a lot of information, this section just highlights the results that are straightforward in explaining the influence of financial and non-financial factors on SACCOS failure in Tanzania. Regarding the overall model fit, the omnibus test of model coefficients was used to indicate in general how well the model performed, frequently referred to as the goodness of fit. The results showed that this value was 0.000 (p<.0005). The overall model fit was also tested using chi-square, showing a value of 87.02 with 4 degrees of freedom (df). Additionally, the results of the Hosmer-Lemeshow test Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11177-11181 11180 www.etasr.com Magashi et al.: Indicative Factors for SACCOs Failure in Tanzania portray the reliability of the model fit. The chi-square value for the Hosmer-Lemeshow test was 17.019 with a significance level of 0.309, and as this value is larger than 0.05 it indicates support for the model. It should be noted that the Hosmer- Lemeshow goodness of fit is indicated by a significance value greater than 0.05, while the omnibus test of model coefficients is indicated by a significance value less than 0.05 [25-26]. The usefulness of the model was also established. This information provides the variation capacity in the outcome variable explained by the used model, i.e. the usefulness of the model. Table II shows these results, along with the Cox and Snell R squared and the Nagelkerke R squared values. However, the pseudo-R square statistics were used rather than the true R square values as provided in the multiple linear regression results. The results indicated two values of 0.371 and 0.463, meaning that between 37% and 47.3% of the variability is explained by this set of variables. TABLE II. THE OVERALL MODEL FIT Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 87.02 4 0.000 Block 87.02 4 0.000 Model 87.02 4 0.000 Hosmer and Lemeshow Test Step Chi-square df Sig. 1 17.019 8 0.309 Model Summary Step 2-log likelihood Cox and Snell R 2 Nagelkerke R 2 1 352.376 a 0.371 0.463 The contribution of financial and non-financial factors to the failure of SACCOs was similarly established. Table III shows the predictive ability of each predictor variable using the Wald test. The results indicated that all predictor variables were statistically significant. All factors influenced the failure of SACCOs: poor loan repayment or excessive loan defaults. Checking the contribution capacity of each factor, it is noted that clients' and institutional financial factors had major contributions to the failure of SACCOs compared to non- financial factors. The direction of all predictor variables was positive, showing that all financial and non-financial factors positively impact the failure of SACCOs. TABLE III. INFLUENCE OF FINANCIAL AND NON- FINANCIAL FACTORS ON SACCOs' FAILURE B S. E Wald Df Sig. Step 1a Clients' F.N. fs 1.987 .325 37.311 1 0.023 Inst. F.N fs 3.634 .336 37.311 1 0.012 Clients' F. fs. 5.012 .365 39.671 1 0.000 Inst. F fs. 4.339 .315 38.331 1 0.000 Constant 2.954 2.434 2.825 1 0.006 a. Variable (s) entered on step 1: Personal F.N. fs, Inst. F.N fs, Personal F. fs., Inst. F fs. Table IV displays the predictive ability of each variable on SACCOs' failure, showing that all predictor variables had a statistically significant and positive relationship (p ≤ 0.05) with the failure of SACCOs. Generally, both financial and non- financial factors at personal or institutional levels lead to the failure of SACCOs. These results are consistent with previous studies [10-13] that did not exhaust such a large set of predictor factors. These studies noted the inconsistency of some factors, but this study did not. These factors apply not only to SACCO's failure but also to other sectors. For example, time, price, costs, designs, labor issues, scarce preparation, and resource shortage have been found to cause failure in construction projects [26]. These factors are similar to the ones highlighted in SACCO failure, except for the language used. SMEs are similarly affected by financial resources, technical upgrading, marketing, training and development of human resources, lack of strategic planning, and lack of advanced industrial digital technologies [1]. Total Productive Maintenance (TPM) in different companies is affected by strategic, managerial, departmental, financial, and social constraints, and workers' insufficient training and lack of commitment [28]. TABLE IV. INFLUENCE OF FACTORS ON SACCOs FAILURE B S. E Wal d df Sig. Credit experience 1.907 0.341 4.356 1 0.000 Loan amount 1.523 0.333 7.311 1 0.000 Annual income 1.978 0.329 6.673 1 0.000 Loan Mgt/Utzn. 0.769 0.386 7.733 1 0.032 Loan interest 3.328 0.288 8.125 1 0.000 Age 0.328 0.088 0.738 1 0.024 Gender 0.717 0.191 6.531 1 0.043 Family size 1.591 0.298 7.037 1 0.012 Residence distance 1.175 0.397 6.319 1 0.002 Business type 1.175 0.395 7.017 1 0.004 Business env/ment 1.444 0.360 6.913 1 0.001 Record keeping 1.733 0.638 8.072 1 0.000 Infrastructure 1.733 0.392 4.754 1 0.030 Regulations 1.034 0.371 7.031 1 0.065 HRs 1.461 0.325 7.171 1 0.001 Loan category 1.361 0.325 6.732 1 0.000 Collateral 1.284 0.219 5.621 1 0.000 Training 1.339 0.219 7.079 1 0.024 Lending method 0.915 0.162 5.127 1 0.007 Constant 5.123 4.931 7.923 1 0.002 Examining the classification of cases in Table V shows that the overall percentage of correctly classified cases was approximately 77%, meaning that there was a higher percentage of respondents regarding a particular financial and non-financial factor leading to the failure of SACCOs. Table VI highlights the results of a set of predictor variables to improve the accuracy of the predictions, i.e., testing the set model of predictor variables, showing that these factors contributed to the SACCOs' failure by 82%. TABLE V. CLASSIFICATION TABLE a,b Observed Predicted Prob failure recode 01 % Correct No Yes Step 0 prob failure recode No 0 117 100.0 01 Yes 0 383 0.0 Overall percentage 76.6 TABLE VI. CLASSIFICATION TABLEa Observed Predicted Prob failure recode 01 % Correct No Yes Step 1 prob failure recode No 89 294 17.8 01 Yes 32 85 82.2 Overall percentage 100.0 Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11177-11181 11181 www.etasr.com Magashi et al.: Indicative Factors for SACCOs Failure in Tanzania IV. CONCLUSION AND RECOMMENDATIONS The failure of SACCOs is caused by several financial and non-financial factors at personal or institutional levels. 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