Operational Risk Management And Performance Of Cooperative Microfinance In Thailand

Haris Annannab; Arpah Abu  Bakar; Shazida Jan Bt.  Mohd Khan

doi:10.36923/ijsser.v4i1.148

Open Access Original Research

Haris Annannab ⁽¹⁾ , Arpah Abu Bakar ⁽²⁾ , Shazida Jan Bt. Mohd Khan ⁽³⁾

(1) School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia , Thailand

(2) School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia , Malaysia

(3) School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia , Malaysia

https://doi.org/10.36923/ijsser.v4i1.148

Issue
Vol. 4 No. 1 (2022)

Submitted
February 3, 2022

Published
March 31, 2022

Keywords:

Operational Risk Management, Cooperative Microfinance, Specialization, Thailand

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Abstract

Purpose of the study: This study aims to examine the operational risk management (ORM) impact on cooperative microfinance performance by identifying specialization as a moderator with respect to the relationship between ORM and cooperative microfinance performance.

Methodology: The survey questionnaires were distributed to 455 respondents, all of whom were senior officers of saving and credit cooperative microfinance service providers (MSPs) in Thailand, using a quantitative technique. The data analysis was performed employing SmartPLS software version 3.0 and the Structural Equation Modeling (SEM) method.

Main Findings: The result indicates that cooperative microfinance performance is significantly affected. Here, specialization moderates the relationship between ORM and cooperative microfinance performance.

Research limitations/implications: The survey used a cross-sectional design by only examining credit and saving cooperative MSPs. It employed a quantitative method and relied on a single mode of data collection. The implication of this study will be important for policymakers like the Bank of Thailand and the Ministry of Finance in Thailand when it comes to developing microfinance policies and programs.

Novelty/Originality of this study: This study is unique in that it examines the impact of ORM on cooperative microfinance performance and recognizes specialization as a moderator.

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References

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Authors

Haris Annannab

School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

https://orcid.org/0000-0002-1244-4210

haris.a@pkru.ac.th (Primary Contact)

Arpah Abu Bakar

School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Shazida Jan Bt. Mohd Khan

School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

https://orcid.org/0000-0002-0463-2051

Author Biographies

Haris Annannab, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Haris Annannab: He is a PhD candidate at University Utara Malaysia. He received a bachelor’s degree in Islamic Economic and Banking from Yarmouk University, Jordan and a master’s degree in Islamic Banking and Finance from International Islamic University Malaysia. His current field placement is in Economics, Banking and Entrepreneurship. He has been teaching at Phuket Rajabhat University, Thailand, since 2016.

Arpah Abu Bakar, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Arpah Abu Bakar: Dr. Arpah has more than 20 years of experience in risk management and insurance education. She is an Associate Professor at Universiti Utara Malaysia (UUM) College of Business and a research fellow at the Institute of Risk Management, UUM. She served UUM in various leadership capacities, including Head of Banking & Risk Management Department, Deputy Dean of School of Economics, Finance and Banking, AACSB Accreditation Unit. Currently, she is the Director of the Centre of International Affairs and Cooperation (CIAC). She contributes to the University-wide initiative in bridging the gap between academia and industry-community. Dr Arpah has been an active member of the Malaysian Association of Risk and Insurance Management (MARIM) since 2005. She has served as the Honorary Secretary for many years, headed the conference and forum organizing committees, and led the development of the risk management professional certification program. Her main research interests include risk management, health insurance, and risk financing. She has been involved in disaster risk reduction and climate change adaptation research focusing on risk financing for several years.

Shazida Jan Bt. Mohd Khan, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Shazida Jan Bt. Mohd Khan: Shazida Jan Mohd Khan graduated with PhD in Economics from the University of Wales, Bangor, in 2011. Her Thesis focuses on Banks’ Efficiency, Competition, and Concentration in Southeast Asia. Shazida Jan Mohd Khan’s broad area of interest in Southeast Asia economics development, concentrating on banking and finance studies, focusing on the financial crisis, structure, efficiency, and performance of banking markets covering conventional and Islamic banking. Her recent work also involves studies on Sustainable Finance, Education Financing, and Micro Financing (SME). She published in various reputable journals, including Journal of Social Science, Research Journal of Applied Sciences, Investment Management and Financial Innovations, America Journal of Economics and Malaysian Management Journal.

Annannab, H., Bakar, A. A. ., & Mohd Khan, S. J. B. . (2022). Operational Risk Management And Performance Of Cooperative Microfinance In Thailand. Innovation Journal of Social Sciences and Economic Review, 4(1), 17–24. https://doi.org/10.36923/ijsser.v4i1.148

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1. INTRODUCTION

Microfinance loans are a vehicle to start a company for low-income individuals as well as to expand their working capital for more income. Housewives, for example, are skilled in weaving and making delicious desserts but lack the resources to buy wood, instruments, equipment, and raw materials, restricting their earning ability. A microfinance loan is like a small finance loan that goes into the system to help them continue to run. Furthermore, microfinance loans are a better option for low-income groups who want to fund their companies. Finally, microfinance that works effectively will minimize the issue of declining informal debt.

Disadvantaged and low-income communities can now apply for loans without accessing commercial banks via microfinance (Ledgerwood, Earne, & Nelson, 2013). It also has a powerful social emphasis. However, offering loans to poor and low-income groups may be expensive for the company, interfering with the microfinance company’s financial results (Cull, Demirgu Ç‐Kunt, & Morduch, 2007).

The last decade has continuously underlined microfinance’s operational risk management (ORM). However, finance’s operational risk problems remain relatively unprecedented (Ebenezer, Islam, Yusoff, & Shamsuddin, 2018). One of the most prominent risk categories for banks and financial institutions is operational risk. Because of its impact on the broader economy as well as the financial sector, it is an essential subject for regulation and research (Bodur, 2012). Weak organizational risk management is particularly problematic in microfinance institutions (MFIs) due to the failure of possible economic and social impacts. Moreover, it operates in a setting where it is the only source of banking services and credit extension for disadvantaged or oppressed communities (Armendariz & Labie, 2011). As a result, these institutions are responsible for countless social and economic effects in their societies, including enhanced access to capital, the increased economic well-being of women, and poverty reduction (Amersdorffer, Buchenrieder, Bokusheva, & Wolz, 2015). At the same time, failure by MFIs, mostly triggered by operational risk deficiencies such as bad lending practices, may have significant negative impacts on their communities as they withdraw financial assistance (Zuru, Hashim, & Arshad, 2017). Thus, the effectiveness (or failure) of MFIs’ ORM may have a direct economic and social effect on their populations.

Many studies have centered on ORM in the banking industry (Malaysia, Taiwan, and Spain) over the years (Jaafar, Syamsuddin, & Sarkawi, 2017; Yang, Hsu, Sarker, & Lee, 2017). Moreover, MSPs’ ORM in Zimbabwe, Tanzania, and Kenya has been investigated by (Njuguna, Gakure, Waititu, & Katuse, 2017; Siminyu, Clive, & Musiega, 2016). However, it creates a gap in the research of this study regarding the ORM impact on MSPs’ results, particularly for emerging countries, given that the institutions and external factors (i.e., political, cultural, and economic environments) differ. Therefore, this research aims to examine the ORM impact on cooperative microfinance performance in Thailand, which includes the specialization role as a moderator in the relationship between ORM and cooperative microfinance performance.

2. LITERATURE REVIEW

2.1. Moreover, Cooperative Microfinance in Thailand

In Thailand, operational risk has cost many organizations, businesses, individuals, and financial institutions their money, negatively impacting Thailand’s economy. In 2017, two microfinance service providers’ in Thailand collapsed, causing fear among the public that caused many clients to lose faith in the sector. This, in turn, affects their success and Thailand’s economic growth. Credit and saving cooperative microfinance service providers have the most money in Thailand. Therefore, its collapse or inability to perform better would have a negative effect on credit and saving cooperative MSPs.

In 2017, Thailand was close to 6,597 cooperative microfinance providers, down from 6,741 in 2016. The decrease was primarily due to closures related to weak risk management that affected 117 of the closed suppliers. In 2017, Thailand’s cooperative MSPs had a total cumulative total revenue of approximately 1.7 trillion THB, including revenues, investments, and credits of over 12 million THB, with reported capital of 523 billion THB. The cooperative MSPs’ total benefit recorded more than 30 billion THB recently. Moreover, cooperative MSPs lent at least 14% to rural households in 2007. In 2017, 1,400 credit and saving cooperative MSPs facilitated household as well as small and microentrepreneurs’ financial services (Financial Analysis Unit, Center of Information and Communication Technology, 2019).

2.2. Development of Hypotheses

2.2.1. Operational Risk Management (Orm) And Performance Of Cooperative Microfinance

There is a link between cooperative microfinance, operational risk management, and performance, as stated by several researchers. Cooperative microfinance organizations in Kenya, for example, should guarantee that solid operational risk management (ORM) is adopted and executed by overcoming a lack of understanding among implementing managers through training and risk management advocacy among their employees (Siminyu et al., 2016). Moreover, Njuguna et al. (2017) revealed that sound operative risk management policies and procedures may have influenced cooperative microfinance’s progress. Additionally, adherence to operating procedures in financial operations has significantly affected cooperative microfinance’s growth. In addition, duties segregation, assurance, approval, and authorization of internal audit and transactions functions have major consequences on financial performance (Ngari, 2017). In determining the financial performance of cooperative microfinance, Ngari (2017) found that internal processes are critical.

According to Harelimana (2017), the technological danger of IT affects the financial profitability and sustainability, productivity, and financial efficiency of cooperative microfinance. Nevertheless, Rozzani, Mohamed, and Syed Yusuf (2017) show that customers are satisfied with loan disbursement through a mobile solution. However, problems with mobile banking pose a major challenge to its effectiveness during the payment process.

Apart from that, Siminyu et al. (2016) investigated the impact of operational risk on Kakamega County savings and credit unions’ financial performance. A semi-structured questionnaire was employed in data collection from 56 participants in this study. The analysis found that the financial structures and financial results of Savings and Credit Cooperative Societies (SACCOs) were significantly linearly positive in Kakamega County. Therefore, the authors assumed that SACCOs and other financial institutions would concentrate their operational risks on financial systems to mitigate them. From here, we obtain the first hypothesis given as follows:

H1: ORM is a significant impact on the cooperative microfinance performance

2.2.2. Specialization as a Moderator

Specialization applies to people and groups based on their restricted activities (Adeyoyin, Agbeze-Unazi, Oyewunmi, Adegun, & Ayodele, 2015). Note that this specialization allows employees to leave roles that they are not as qualified to carry out, leaving such positions to others who are more suited. Specialization, also known as labor division, occurs when workers understand how to execute specific tasks well instead of concentrating on several different tasks. Specialization is one of the main aspects of the new capitalist economy and gives workers and organizations various advantages (Hamel, 2008). One of the essential aspects of specialization is its ability to improve the efficiency and output of workers. While efficiency appears to benefit employers, specialization may also benefit employees because it can increase job prospects. Specialized workers are also more attractive than people with only general skills. Many positions also require employees to be regarded with particular expertise and skills.

Hamel (2008) noted that although specialization can be useful, employees often have certain general skills. As per Inegbedion, Inegbedion, Peter, and Harry (2020), everyone can be specialists in what they do. Here, specialization includes giving individual employees tasks to eliminate responsibility for other employment and, in particular, minimize the worker’s capacity for one job. Moreover, Inegbedion et al. (2020) noted that specialization changed people’s social roles. From the beginning to the end, people were interested in the whole process in the past, and they were pleased with being useful to others. With specialization, people seldom reach the end consumers of the goods they manufacture and simply sell their work at a price like a commodity. They are similar to others in that their happiness declines while an ethic of employment, “just doing my job,” lowers the consistency of the job’s output. Therefore, we established the following hypothesis:

H2: Specialization moderate s the relationship between ORM and cooperative microfinance performance

Following the literature review concerning the ORM impact on cooperative microfinance performance, specialization moderates the relationship between ORM and cooperative microfinance performance. Figure 1 below illustrates the causal relations between these constructs.

Figure 1. Conceptual Framework

3. METHODOLOGY

The population of the present study comprises the senior officers in the credit and saving cooperative MSPs in Thailand, of which there exists around 1,400 of them. For example, Davies and Haubenstock (2002) stated that senior management’s support and commitment to good operational risk management (ORM) were significant and well-deserved. Here, the crucial argument is that organizations should distribute resources accordingly. The survey questionnaires were distributed to 455 respondents, who were senior officers of credit and saving cooperative MSPs in Thailand. There are seven respondents’ categories comprising gender, education, specialization, position, experience, duration, and ownership.

Apart from that, the survey results demonstrate 62.6% female respondents and 37.4% male respondents, while 69.2% of the respondents have bachelor’s degrees. Furthermore, 53% of respondents specialized in business and administration, while 38.5% were on the board of directors. Finally, regarding experience, 45.9% have experience of more than ten years and above, with the duration of working between 6 to 10 years (55.2%).

This study focuses on a high-quality target group with relevant expertise and knowledge. Therefore, the purposive sampling method is the most appropriate technique as senior officers provide data about their perceptions or belief regarding microfinance performance in their respective MSPs. Thus, we determine that the individual senior officers are the analysis unit.

This study applies partial least squares structural equation modeling (PLS-SEM). Several variables influenced the decision to choose the PLS-SEM system. Here, the dependent variables, also known as endogenous latent constructs, are often utilized to enhance the specified variance. PLS-SEM is known as a latent variable modeling approach that recognizes measurement mistakes directly by combining several dependent constructs (Hair, Hult, Ringle, & Sarstedt, 2017). The PLS-SEM algorithm allows each indicator to modify how much it adds value to the composite score of the latent variable to offer lower weightings to indicators with weaker correlations with respect to related indicators and the latent construct (Chin, Marcolin, & Newsted, 2003). Note that PLS-SEM possesses a good capacity in modeling latent constructs in non-normal conditions under stringent minimum requirements relying on residual distribution and sample size (Chin et al., 2003). In addition, Hair et al. (2017) reported that PLS-SEM is appropriate for defining composite and dynamic correlations in a structural model.

The constructs were derived from published research, as noted in the literature review. A total of 30 components made up the instrument (see Table 1). Moreover, responses were scored on a five-point Likert scale, in which 1 indicates strong disagreement while 6 indicates strong agreement concerning the ORM. In contrast, a scale ranging from 1 = “Not at all” to 6 = “To a very considerable amount” was employed for cooperative microfinance performance.

Table 1.Items’ Measures and SourcesSource: Added by the author.
Variables	Items	No. of Items	Sources
Operational Risk Management
Internal Process Risk	My organization adopts a centralized management systemThe staff in my organization is given tasks according to their post There are corrective measures in the event of any unexpected management procedures and processes in my organization The procedures and processes are reviewed regularly by external consultants in my organization There are adequate procedures and policies for managing operational risks in my organization The financial department adheres to the written procedures and policies in managing the operational risks in my organization. The internal control systems in my organization are effective at detecting fraud or other significant operational risks	7	Siminyu et al. (2016)
People Risk	My staff is given appropriate training for their post in my organization employ staff with high competencies to work in my organization. There are a sufficient number of staff in my organization. The compensation system in my organization is sufficient. There is effective communication among the staff in my organization. My staff is able to communicate effectively when engaging with a client. My organization provides continuous training on fraud prevention. There is a low staff turnover rate in my organization	8	Alaoui and Tkiouat (2017)
Technology Risk	The organization support training for IT adoption procedure to avoid errors in an internal system. My organization manage information technology, hardware, networks, software, efficiently There is a sufficient backup system in my organization. There is a sufficient data storage facility in my organization. The technology and system in my organization operate with protection from the security threat. My organization has a backup plan if there is a deficiency in the internal communication system. My organization has control and examination of the security system	7	Ibrahimovic and Franke (2017)
Performance of Cooperative Microfinance
Financial Performance	In my organization, the number of clients has increased. There is an increase in the number of female clients in my organization. There is an increase in average savings deposits size in my organization There is a decreasing trend in the value of clients’ outstanding loans in my organization There is an increment in the average loan size in my organization. There is an increment in the number of clients’ retention rate in my organization. In my organization, the percentage of rural clients has increased	7	Mahazril ‘Aini, Hafizah, and Zuraini (2012)
Social Performance	There is an increase in profit in my organization. My organization’s sales have increased. My organization’s return on assets (ROA) has increased. My organization’s return on equity (ROE) has increased. My organization’s return on sales (ROS) has improved. My organization capital has increased My organization has increased the number of loan disbursements	7	Hashim, Adeyemi, and Alhabshi (2018)

The researcher evaluated the measurement model in Figure 2 for convergent validity by employing the approach provided by Hair, Hult, Ringle, and Sarstedt(2014). Moreover, Hair et al. (2014) suggested factor loading analysis, average variance extracted (AVE) analysis, and composite reliability (CR) analysis in testing convergent validity, with suggested cut-off parameters of 0.5, 0.5 as well as 0.7, correspondingly. As per Hair et al. (2014), in exploratory research, the suggested cut-off threshold for factor loading analysis is 0.5. Here, Cronbach’s alpha and outer loadings determine the internal accuracy.

Figure 2. Measurement Model

In the construction phase, conventional threshold settings of the value 0.7 for each Cronbach’s alpha and CR tested the reliability (Hair et al., 2014). Table 2 outlines the CR estimation, Cronbach’s alpha, as well as the AVE measured. Here, Cronbach alpha and CR above 0.7 for both variables indicate high levels of reliability. Furthermore, the average extracted variance values tested were significantly above the 0.50 threshold. All of the trials show that the scales have convergent validity. According to this analysis, the outer loads for all indicators of the constructs exceed the recommended value of over 0.5.

Moreover, the researchers utilized the Fornell-Larcker criterion to measure discriminant validity (Hair et al., 2014). According to the statistics, all of the indicators’ outer loadings on each connected variable were larger than their cross-loads (see Table 3). Therefore, the cross-loading criteria, AVE values, and construct correlations demonstrated that all variables have discriminant validity. Moreover, all AVEs were greater than the squared inter-construct correlations depending on the Fornell-Larcker criterion, indicating that all variables possess appropriate discriminant validity.

Table 2.Validity of the data
Variables	Code	Factors Loading	Cronbach's Alpha	Composite Reliability	AVE
Operational Risk Management	Internal Process Risk	0.923	0.888	0.930	0.815
People Risk	0.925
Technology Risk	0.859
Performance of Cooperative Microfinance	Financial Performance	0.940	0.820	0.916	0.845
Social Performance	0.898

Table 3.Discriminant Validity
	Operational Risk Management	Performance of Cooperative Microfinance
Operational Risk Management	0.903
Performance of Cooperative Microfinance	0.552	0.919

4. RESULTS AND DATA ANALYSIS

4.1. Assessment of the Structural Model

Five tests were employed to test the structural models of VIF, f2, R2, Q2, and relationship significance. Here, collinearity was a problem since the VIF values for all latent variables in each group were less than 5. As a consequence, no collinearity issues exist in this research.

We tested the structural model to determine the model’s prediction capabilities and the correlations between the variables. Here, SmartPLS 3.0 software’s bootstrap approach employing 455 examples and 5000 re-samples investigated the importance of the t-values corresponding with each direction. As per Hair et al. (2014), 1.65 (significant level= 10 %), 1.96 (significant level= 5 %), as well as 2.57 (significant level= 1 %) are the most common employed critical values for two-tailed studies. In this research, the hypotheses were approved by relying on a 5% significant level (1.96) (Hair et al., 2014). In addition, Table 4 outlines the statistical significance of the correlation between ORM and efficiency. Here, hypothesis testing and the significance of the path coefficients of hypothesis 1 (H1) reveal indicators β = 0.522, t = 15.193, and p = 0.00.

Table 4.Hypothesis Testing
Beta	SD	T Value	f ²	R ²	Q ²	P Values	Result
ORM Performance of Cooperative Microfinance	0.552	0.036	15.193	0.439	0.305	0.251	0.00	Supported

R2 values of the samples assessed the construct’s predictive capability on cooperative microfinance performance. Here, the R2 coefficient was 0.305. It also used the blindfolding process to generate Q2 values for cooperative microfinance performance. Moreover, Q2 values greater than 0 were employed to examine the predictive significance of the samples’ models (Hair et al., 2017). Finally, we also assessed the f2 impact sizes of one predictor variable to determine how they affected cooperative microfinance performance. Table 4 reveals that ORM (f2 = 0.439) significantly impacted cooperative microfinance performance. Individually, f2 values of 0.02, 0.15, and 0.35 resemble small, medium, and large effects, as supported by Hair et al. (2017).

4.2. Moderation Analysis

The primary goal of this section is to experimentally examine the H2 research hypothesis, which concerns the investigation of the moderating effect of specialization on ORM and cooperative microfinance performance (Figure 2). Here, a moderator in a sample can change the intensity and frequency of a correlation between two constructs (Hair et al., 2017).

Figure 3.Moderation Analysis

A moderating effect of specialization was applied to the model as supported by Hayes, Montoya, and Rockwood (2017), Hair et al. (2017), and Preacher and Hayes (2008). Note that the bootstrapping method comprises 496 bootstrap cases, 5000 bootstrap samples employing the option of no sign adjustments, and the mean replacement for missing values to conduct the moderator’s significance test. This results in a t-value of 3.400 (<1.96), having p = 0.001 (<0.05) for the direction connected to the moderator specialization. Thus, we can infer that the moderating effect is statistically important, suggesting that H2 was supported.

5. DISCUSSION

The study’s goal was to determine how ORM affected the performance of Thailand’s cooperative MSPs. The ORM has a considerable influence on the cooperative microfinance performance in Thailand, according to Hypothesis H1. With indicators β= 0.522, t = 15.193 and p = 0.00, this research’s results revealed a significant correlation between performance and ORM. As a result, our findings support the empirical relationship between performance and ORM.

These findings align with studies of Siminyu et al. (2016) and Ngari (2017), who illustrated a relationship between performance and ORM. Furthermore, several research outcomes also indicate a significant positive relationship between the firm performance of banks and ORM, such as Dinger and Hagen (2009) and Kauppi, Longoni, Caniato, and Kuula(2016). Here, the authors mentioned that favorable risk management could improve operational performance. Furthermore, according to Siminyu et al. (2016), the ORM among managers may increase awareness and help overcome problems through education and promotion of cooperative microfinance efficiency. In addition, ORM policies and procedures may affect the growth of cooperative microfinance (Njuguna et al., 2017).

The second objective of this research assesses specialization as a moderator in the relationship between cooperative microfinance performance and ORM. Hypothesis H1 suggested that specialization moderates the relationship between ORM and cooperative microfinance performance.

Hence, we demonstrated that specialization moderates the correlation between cooperative microfinance performance and ORM in this research, with indicators of β = 0.155, t = 3.400, and p = 0.001, supporting hypothesis H2. With specialization, people seldom reach the end consumers of the goods they manufacture and sell their work at a price like a commodity. As a result, they are similar to others, in which their happiness declines, and an ethic of employment of “just doing my job” lowers the consistency of the job’s output. Moreover, Hamel (2008) noted that although specialization can be useful, employees often have certain general skills. According to Inegbedion et al. (2020), every person can be a specialist in what they do. The specialization includes giving individual employees tasks to eliminate responsibility for other employment and, in particular, minimize the worker’s capacity for one job.

6. CONCLUSION

Ultimately, the data revealed that ORM influences cooperative microfinance performance, with specialization moderating the association between ORM and cooperative microfinance performance. The higher the worker’s degree of specialization in the job, the greater the level of performance displayed towards the organization. The outcomes of this research suggest that good ORM contributes to cooperative microfinance’s high performance. Meanwhile, worker specialization is a booster factor in improving performance based on specialization as an asset of knowledge and expertise that can contribute to managing the organization. Implications of this study are also discussed as a reminder and awareness to organizational management, emphasizing the commitment to design a holistic ORM practice that will enhance their performance.

7. LIMITATION S AND FURTHER RECOMMENDATIONS

The limitation of this research is that, firstly, this study examined only credit and saving cooperative microfinance service providers. The researcher did not include other sectors such as insurance, small-scale enterprises, manufacturing industries, healthcare, Fintech, and education. Future studies should consider looking into various industries to get more in-depth results. Second, the current study used a quantitative method and a single data gathering technique. The sole instrument utilized to collect data in this study was a questionnaire. Respondents may not always be willing to provide complete answers to inquiries. As a result, the replies might not correctly and consistently assess the research variables. Future studies will be of interest to combine qualitative and quantitative methods and perform in-depth research on the performance of credit and saving cooperative microfinance service providers (MSPs) in Thailand.

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Operational Risk Management And Performance Of Cooperative Microfinance In Thailand

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Haris Annannab, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Arpah Abu Bakar, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Shazida Jan Bt. Mohd Khan, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

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Haris Annannab, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Arpah Abu Bakar, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

Shazida Jan Bt. Mohd Khan, School of Economics, Finance & Banking, Universiti Utara Malaysia, Sintok, Malaysia

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