Knowledge Management, Responsibility and Organizational Factors that Affect
Microfinance Support to Phuket, Thailand Entrepreneurs
The financial cost of entrepreneurial borrowing in Thailand, in particular
Phuket, an island province with a large tourism and marine industry, is difficult
and expensive. With the bank of Thailands
recent rule changes, licensed yearly bank loans of greater than 15% are now
legal but the inability to get loans from legitimate sources still drives the
small borrowers into the arms of loan sharks who charge as much as 20% a month.
Still, these same licensed financial institutions continue to offer almost no
lending services to very small business operators, particularly street vendors
and wet market traders, because borrowers such as these lack financial and accounting
track records and are high-risk. In Bangladesh, however, the Grameen Bank microfinance
system implemented a revolutionary tool to eradicate poverty of the rural people
and overcome the many obstacles to lending, becoming the largest microfinance
bank in Bangladesh and probably the biggest microcredit organization in the
world. It provides loans to asset-less and landless poor people whom no commercial
bank will give a loan. This study is, therefore, focused on the mechanisms that
affect the 37% of all loans made within Thailand, specifically those made to
SMEs and various forms of entrepreneurs. The researchers, therefore, undertook
this study focusing on influences of knowledge management, organizational factors
and client responsibility that affect microfinance support. Phuket province
was selected, as it has a significant tourism and marine industry largely made
up of smaller, entrepreneurial enterprises.
Received: January 19, 2014;
Accepted: May 02, 2014;
Published: June 23, 2014
In the competitive business world of today inwhich flexibility, speed and adaptability
are essential for survival and progress, small and medium sized enterprises
(SMEs) play an extremely important role in any countrys economic development.
And as a part of this economic development, SMEs in Thailand have contributed
greatly to the economic vitality in many ways with SMEs share of GDP reaching
39%. If farm income and agricultural processing are also included, this figure
rises to 50% (OSMEP, 2003). Additionally, as a percentage
of exports, the SME share of manufactured goods reached 38.2% of the total value
of Thailands exports. When employment is taken into consideration, Thai
SMEs employ about 69% of the nations workers (OSMEP,
2003). Thus, SMEs are vitally important to the Thai economy.
The term SME encompasses a broad spectrum of definitions. The definition
varies from country to country. Generally, these guidelines are based upon either
head count or sales or assets. For example Egypt defines SMEs as having more
than 5 and fewer than 50 employees (Pandya, 2012), whereas
Vietnam considers SMEs to have between 10-300 employees (MoEA,
2006). Thailand on the other hand, defines Small to Medium sized Enterprises
(SMEs) as those employing less than 200 employees, having investment capital
of less than 100 million Baht and fixed assets of less than 100 million Baht
However, the Inter-American Development Bank defines SMEs as having a maximum
of 100 employees and less than $3 million in revenue. In Europe, they are defined
as having manpower fewer than 250 employees and United States define them with
employees less than 500 (Natarajan and Wyrick, 2011).
As general guidelines, the World Bank defines SMEs as those enterprises with
a maximum of 300 employees, $15 million in annual revenue and $15 million in
Research from Kachembere (2011) noted that SMEs are
playing pivotal role in promoting grassroots economic growth and equitable sustainable
development. It is not only the fact that high rates of economic growth contribute
to economic and social development and poverty reduction. However, more it depends
on the quality of growth. Quality of growth includes the composition of growth,
its spread and distribution and most importantly the degree of sustainability.
Hence, it becomes important to understand various factors responsible for quality
growth through the route of SMEs.
The contribution of small and medium sized enterprises (SMEs) to the Thai economy
in terms of business numbers, employment, income and economic growth increased
rapidly from 1994-2008. Their total number increased from 438,805 in 1994 to
2,827,633 in 2008. By 2008, they represented over 99% of all business establishments
in the country and were particularly dense in the trade and repairs, services
and manufacturing sector. On average, they employed more than 7 million workers
annually over the period 1994-2008, equivalent to more than 73% of total employment
in the private sector 2 and contributed 37.9% of total GDP by 2008 (OSMEP,
SMEs are now generally recognized as being the most significant enterprises
in accelerating Thai economic growth and development (Dhanani
and Scholtes, 2002; Wiboonchutikula, 2002; Ha,
2006). SMEs also play important roles and functions in assisting large enterprises,
particularly in the context of regional production networks (Regnier,
2000; Brimble et al., 2002; Mephokee,
2003; OSMEP, 2007; OSMEP, 2008),
by being key sources of goods, services, information and knowledge (Regnier,
2000). SMEs also contribute to regional development, poverty alleviation
and economic empowerment for minorities and women (Harvie,
2008). SMEs are, therefore, the backbone of the Thai economy, contributing
greatly to the social and economic development of the country (Brimble
et al., 2002; Huang, 2003; Sahakijpicharn,
The cost of success, however does not come cheap as recent news from the Thai
Finance Ministry, indicates that the ministry will issue licenses to non-financial
institutions that will allow them to make microloans with annual interest rates
of more than 15%. The aim is to combat loan sharks and only microfinance loans
ranging from B30,000-B100,000 (about $1,000-$3,300 USD) will qualify, because
the government wants to offer individuals a working chance (Phuket
News, 2013). Earlier in 2012, the director-general of Thailands Fiscal
Policy Office stated that "The United States has allowed underground lenders
to register their businesses and Thailand might apply this model to solve problems
related to our off-system money market" (The Nation, 2012).
The bank of Thailands deputy governor for financial institutions stability
stated that he supported the plan as well (The Nation, 2012).
In one of the poorest countries of the world however, another method was created to bring micro financing to the poorest of the poor. This method and the legacy of Professor Muhammad Yunus are today the topics of countless articles, studies and research.
The beginning of Grameen Bank and Dr. Yunusbegan, the early 1970s when Professor
Muhammad Yunus envisioned a means of alleviating poverty by circumventing, the
major impediment to lending to the poorest in society the need for collateral.
He tested this instinct in an experiment in 1976 when he lent about $27-42 women
about 62% each in an ordinary Bangladeshi village. To his pleasant surprise,
all the borrowers repaid the loans, in the process convincing him that this
success could be replicated across Bangladesh. Just 30 years later, Grameen
Bank has more than 3.2 million borrowers (95% of whom are women), 1,178 branches,
services in 41,000 villages and assets of more than $3 billion (Mainsah
et al., 2004).
In keeping with Yunuss dislike of handouts, since its founding Grameen
Banks objective has been to promote financial independence among the poor.
Yunus encourages all borrowers to become savers so that their local capital
can be converted into new loans; since 1995, Grameen has funded 90% of its loans
with interest income and deposits, aligning the interests of its borrowers and
depositor shareholders. Grameen is proud of this record, as it represents a
reversal of conventional financial institutions traditional conversion
of deposits from rural areas into loans for the urban, educated elite. Grameen
distinguishes itself from such institutions by converting deposits made in villages
into loans for women in villages (Yunus and Jolis, 1998).
A new study by the World Bank (Khandker and Samad, 2014)
is distinctive because of its size (it covers more than 3,000 households in
87 villages) and longevity, interviews took place over 20 years. Bangladesh
has well over 500 microfinance providers and the survey found that almost a
third of rural households are members of more than one. Critics of microfinance
argue that borrowing from multiple sources leads to over-indebtedness, trapping
people in poverty.
The study found no evidence of that, however. Rather, borrowing, whether from one institution or several, increases personal expenditure, household assets, the labour supply and childrens education. Moreover, loans do benefit women more than men as they are designed to.
A 10% increase in mens borrowing raises household spending by 0.04% and the male labour supply by 0.18% (though the figures are modest, they are significant). Borrowing by women pushes up household spending by one and a half times as much and the female labour supply by nearly three times as much (because even a tiny loan frees women to work who might otherwise be trapped in household chores). It also raises school enrolment rates by 8% points. And multiple credit institutions encourage households to diversify their income-earning activities. According to the study, the Bangladesh government should look at the study and stop interfering with Grameens efforts to cut poverty.
For Thailand with more than 2 million small and medium-sized enterprises (SMEs) representing 99% of the total, microcredit is one tool in helping domestic micro-enterprises. In addition, Phuket as the largest island province in Thailand whose economy is centered around marine and ocean tourism which has become a large and growing source for potential business, employment and foreign tourism. The population has increased steadily with a higher standard of living and consumption rate compared to other regions. Therefore, it is necessary to have SMEs to meet the needs of consumers in Phuket.
In 2011, a Phuket province census found that 1 in 7 households had an entrepreneurial business and the business operator suffered from lack of loans. The researchers objectives were to study factors affecting the financing of entrepreneurial companies in Phuket.
The purpose of the study was to assist in developing systems for entrepreneurial and small business funding which helps with the creation and survival of small enterprises.
To measure the success of a business, two groups of factors need to be considered
in the analysis. These include both quantitative targets and qualitative goals
Quantitative targets: Include return on investment, stock market prices,
return on sales, cash flow, dividends, payout and financial ratios. Many other
scholars and institutions have undertaken research in these areas such as the
(NESDB, 2002), who noted that the ratio of export profits
of production and global or regional market share (Pongprasertchai,
2007; Shelton et al., 2005; Meengern,
2008; Rujithamrongkul, 2005) is a measure of the
success of the operation.
Qualitative goals: Include technology leadership, controlling costs,
managing finances, leadership reputation and image, focusing on market share,
social goals, being a leader in quality and service and focusing on the customer.
As a result, customers have been satisfied (Pongprasertchai,
2007; Polsaram, 1998).
Knowledge management: Knowledge can be characterized as information
in context, together with an understanding of how to use it. Examples would
include knowledge about drainage in astreet, derived from looking at a schematic
and understanding how the placement of houses may or may not affect drainage
(Mayo, 2001; Stewart, 1997).
Intellectual capital is to be defined as the non-financial and non-physical
resources used by and within a company, it is knowledge which can be converted
into profits (Sullivan, 1999). Intellectual management
deals with the interactions between all resources, tangible and intangibel to
create maximum value (Sullivan, 1999).
Knowledge management is a method to assess intellectual capital. This is a
task that needs to be analyzed as the potential in terms of the dissemination
of knowledge to the user is great. To protect intellectual capital, users must
seek opportunities to make better decisions about products and services through
an increase in intellectual capital which adds value and flexibility (Chongvisal,
Trainer (2002) concerned, The Simpler Way, it
was discussed how life and communities could be simplified with simpler lifestyles
in small, highly self-sufficient local economies where individuals were more
cooperative in creating a new economy, producing less than the present economy.
These individuals should also exhibit different values, especially cooperation
not competition and frugality and self-sufficiency not acquisitiveness and consuming.
Additionally, Trainer (2002) argues that there must
be a new approach to poverty alleviation and the old neo-classical
approach doesnt work.
Knowledge, of how consumers perceive alternative banks on important attributes,
provides a foundation for understanding market structure. Market structure analysis
can assist the bank in identifying potential opportunities in differentiation
and in assessing the viability of low cost as a competitive advantage giving
best value to customers (Young, 1999) as
well as, a way to learn in different ways (Loucks et
al., 2010). An example might be that lenders are required to provide
training to the borrower, helping them know about the loan type so that it is
Responsibility: In modern business operations, entrepreneurs should
not only aim for profit or return on investment alone. They need to take responsibility
consisting of responsibility to employees, customers and creditors which is
considered good investment and corporate social responsibility (Wongprasert,
2009). Social responsibility is a principle which takes under consideration
four aspects; economic, legal, ethical and public charity (Saengsurathum,
2007). Therefore, the responsibility for the provision of loans or microcredit
from Grameen Bank is that regardless of the subjects collateral, the bank
will make a loan to a lender group (Iqbal, 2002) which
is a different policy from banks. This is to add a shared responsibility to
pay the debt or increase the ability to pay back debts to financial institutions
which is a fundamental characteristic of the operation of Grameen Bank which
gives social development opportunities to the disadvantaged. If any members
of the group are weak and need help, a supportive social network acts as a safety
net which provides assistance or social responsibility (Valente,
Organizational factors: SWOT analysis is an acronym for strengths, weaknesses,
opportunities and strengths (Fig. 1) which is a powerful strategic
management and planning tool. The technique is credited to Albert Humphrey who
led a convention at the Stanford Research Institute in the 1960s and 1970s using
data from Fortune 500 companies (Humphrey, 2005):
||Helps a firm to identify its core competencies
||Helps a firm to focus on the future given its past and present condition
||Enables a firm to make a U-turn of its weaknesses
||Helps a firm to build its strengths
||Points to the opportunities that a firm can maximize to reap maximum gains
||Is a source of strategic planning as well as marketing
||Helps the firm to redefine and set its overall objectives
Organizations can choose to act or not to act (Dye, 2012),
so it can be seen that the Grameen Bank microfinance policies are different
In the 1980s, Dr. Salehuddin Ahmed, of the Bangladesh Rural Advancement Committee
(BRAC), declared that small is beautiful but big is necessary.
He was referring to the beauty of the smallest of enterprises as an essential
means for many of the worlds poor, particularly women, to make a living.
He said if you walk with your hand in someone elses pocket, you
must go where they go (Coyle et al., 2006).
Without determining the type of borrower certain types of financing consider
the credit group (Iqbal, 2002) which provides the consideration
for the loan which is a different manner from the general case (Kajimo-Shakantu
and Evans, 2007) such as the history of savings of the loan which is the
consideration or conditions that are oriented to the women involved (Iqbal,
2002) and so on.
From the above conceptual review and development, the researchers have developed
the following five hypotheses for the present study (Fig. 2):
||Knowledge management influences responsibly
||Knowledge management influences microfinance
||Responsibility influences microfinance
||Organizational factors influencesknowledge management
||Organizational factors influencesmicrofinance
|| SWOT analysis
|| Results model
Data collection: The sample group for this study included 119 bank executives from both private and public banks from the island province of Phuket, Thailand using both quantitative and qualitative research methods.
Quantitative research: A sample is a set drawn from the population (Keller,
2009). As the non-probability sampling is applied, there is no specific
method in determining sample size. But, it is not practical to collect data
from the entire target population, so the researcher uses a sample instead (Field,
2005). A minimum sample size of 100-200 is often recommended (Comrey,
1973, 1978; Gorsuch, 1983;
Guildford, 1954; Hair et al.,
1979; Lindeman et al., 1980; Loo,
1983). The recommendation for a minimum sample size of 100-200 observations
is probably based on the argument that a correlation coefficient becomes an
adequate estimator of the population correlation coefficient when sample sizes
reach this level (Guadagnoli and Velicer, 1988).
As this study was to employ factor analysis and multiple regressions, the sample
size was based on obtaining the minimum requirement for those techniques. As
general rule, for factor analysis, the minimum is to have 5 times as many observations
as there are variables to be analyzed (Hair et al.,
1998). Although a minimum ratio is 5:1 for multiple regression, the desired
level is between 15-20 observations for each independent variable, while 200
is considered optimal (Hair et al., 1998). The
final sample size obtained was comprised of 119 respondents.
Qualitative research: Qualitative study was conducted by collecting information from the management of individual banks to verify the models derived from quantitative study. With a sample of 10 individuals selected for sampling by use of non-probability sampling techniques while using random sampling (purposive sampling).
Questionnaire design: For this study, the measurement instrument or
questionnaires utilized was prepared from the literature. This questionnaire
was used to investigate how and which variables affected micro financing of
Phuket entrepreneurs. The 7-Point Likert Scale (Likert,
1932) was used for a post-study survey. The draft questionnaire was created
with items which were later checked for their content validity by five experts
in their respective fields based on the Item-Objective Congruence (IOC) index.
The items with IOC index higher than 0.5 were acceptable. In order to test the
proper reliability of the questionnaire, the questionnaire was piloted with
119 banking sector executives and calculated for proper reliability value by
determining the internal consistency measured by coefficient alpha (a-coefficient)
of Akron BAC (Cronbach) to calculate the average value of the correlation coefficient.
Dependent variable microfinance: (Micro_finance) analysis used a
measurement instrument or questionnaires utilizing a 7-Point Likert Scale (Likert,
1932) and have been constructed with the scales developed enabling measurement
of corporate image, profit and income revenue (Office of Small and Medium Enterprises
Promotion) (OSMEP, 2008; NESDB, 2002;
Pongprasertchai, 2007; Shelton
et al., 2005; Meengern, 2008; Rujithamrongkul,
2005; Pongprasertchai, 2007; Polsaram,
Knowledge management: Knowledge analysis used a measurement instrument
or questionnaires utilizing a 7-Point Likert Scale (Likert,
1932) and have been constructed with four aspects (Table 1)
including Learning Methods (type), Learning Method (learn) and Experiences (experience)
(Chongvisal, 2010; Trainer, 2002;
Young, 1999; Loucks et al.,
Responsibility (responsibility) analysis used a measurement instrument or questionnaires
utilizing a 7-Point Likert Scale (Likert, 1932) and
have been constructed with four aspects (Table 1) including
Debt Repayment Responsibility (respond_debt) and Social Development Responsibility
(respond_social) (Wongprasert, 2009; Saengsurathum,
2007; Iqbal, 2002; Valente, 2011).
Organizational factors (org. factor) a nalysis used a measurement instrument
or questionnaires utilizing a 7-Point Likert Scale (Likert,
1932) and have been
constructed with four aspects (Table 1) including Strategies
(strategy), Policies (policy) and Credit Terms (condition) (Dye,
2012; Ahmed, 2009; Iqbal, 2002;
Kajimo-Shakantu and Evans, 2007).
|| Statistic values presenting convergent validity of reflective
scales of latent variables
ANALYSIS AND RESULTS
Partial Least Squares has been applied for analysis of quantitative data by
the researcher. It is data analysis for Confirmatory Factor Analysis (CFA) relating
to the determination of manifest variable and latent variable and testing of
research hypothesis exhibiting in structural model analyzed by using the applications
of PLS-Graph (Chin, 2001):
||According to the analysis result of scale validity and reliability,
scale investigation has been conducted using internal consistency measurement
coefficient alpha (α-coefficient) of Akron BAC (Cronbach) to calculate
the average value of the correlation coefficient was found that alpha coefficients
ranged from 0.888-0.933 which is considered to have high reliability
||In case of measure variables with reflective analysis, convergent validity
has been conducted. Loading is used as consideration criteria and must be
positive quantity and indicator loading has been more than 0.707 and all
values have been statistically significant (|t|≥1.96) representing convergent
validity of scales (Lauro and Vinzi, 2004; Henseler
et al., 2009; Piriyakul, 2010) and analysis
results as shown in Table 1
Knowledge management (knowledge) factors underlying the external variables influence on Learning methods (learn), Learning category (type) and Experience (experience), with loading values of 0.9195, 9001 and 0.8039, respectively. There was a significant level of 95% confidence (t-stat>1.96) which considers such factors highly reliable. These factors affect microfinance.
Responsibility (responsibility) factors underlying the external variables influence on the responsibility for Social Development (respond_social) and Debt Repayment Responsibility (respond_debt) and with loading values of 0.9524 and 0.9123, respectively. There was a significant level of 95% confidence (t-stat>1.96) which considers such factors highly reliable. These factors affect microfinance.
Organizational factors (org. factor) factors underlying the external variables influence on Policies (policy), Credit Terms (condition) and Strategies (strategy), with loading values of 0.9270, 09162 and 0.9091, respectively. There was a significant level of 95% confidence (t-stat>1.96) which considers such factors highly reliable. These factors affect microfinance.
Microfinance (micro-finance) factors underlying the external variables influence on Profit (profit), Income (revenue) and corporate image (image), with loading values of 0.9132, 0.8774 and 0.8613, respectively. There was a significant level of 95% confidence (t-stat>1.96) which considers such factors highly reliable affecting lending to entrepreneurs:
||The reflective model in Table 1 shows the
discriminant validity of the internal latent variables and the correlation
of variables. It also depicts the scale reliability which has been analyzed
from Composite Reliability (CR) as well as the Average Variance Extracted
(AVE) and R2. The CR value should not go below 0.60 and the AVE values should
also drop below 0.50 and R2 values should not be under 0.20 (Lauro
and Vinzi, 2004; Henseler et al., 2009;
Boontawan and Montree, 2010)
||Table 2 shows that the measurement of the study is a
measurement of discriminant validity, indicating a high reliability
The samples were analyzed to assure the research hypotheses criteria of the
following 5 assumptions (Table 3). Furthermore, the structural
analysis model framework was used to study the t-test coefficients and their
relationship of each path of the t-test hypotheses with significance greater
than 1.96. This explains the results obtained from analysis as shown in Table
1, 2 and 3.
An analysis of knowledge management, responsibility and organizational factors that affect microfinance support to Phuket, Thailand entrepreneurs found that of the 5 assumptions, only hypothesis 2 (H2) was not supported. Knowledge management, therefore, has no direct and positive effect on microfinance which means at the statistically level of 0.01, it was insignificant. But the factors that influence indirectly through responsible factors responsible are shown in Fig. 2 and 3.
|| Results of Confirmatory Factor Analysis (CFA) for measurement
|Statistical significance level is at 0.01 and diagonal figures
|| Research hypotheses test results
|| Final model analysis of factors that affect microfinance
||Influencing factor affecting the credit support to small entrepreneurs
and the influence of the Hosmer-Lemeshow Goodness of Fit (GoF) test
The hosmer-lemeshow test (Table 4) is a statistical test
for goodness of fit or logistic regression models. It is used frequently in
risk prediction models. The test assesses whether or not the observed event
rates match expected event rates in subgroups of the model population. The Hosmer-lemeshow
test specifically identifies subgroups as the deciles of fitted risk values.
Models for which expected and observed event rates in subgroups are similar
are called well calibrated. Model Validation or PLS fit indexis a measure of
the following GoF equation as follows: (Piriyakul, 2010):
Goodness of Fit .
It can be concluded that the accuracy of the overall structural equation model and measurement equation is greater than 96%.
RESULTS AND DISCUSSION
The findings and influencing factors from the Knowledge Management, Responsibility and Organizational Factors that affect microfinance support to Phuket, Thailand entrepreneurs revealed as follows:
Organizational factors: These will consist of policies, strategies and
credit terms which contribute to the granting of loans (microfinance) to small
business entrepreneurs. This is consistent with the research of (Dye,
2012) which stated that organizations can choose to act or not to act, so
it can be seen that the Grameen Bank microfinance policies are different from
Other case studies show that, if savings systems are flexible and suitable
to their needs, women are capable of saving and repaying housing loans. The
results also suggest that the accumulated group savings and the savings schemes
themselves act as good collateral. However, despite of showing interest in involvement
in the low-income sector, banks do not have a financially viable and workable
business model to exploit this potential market (Kajimo-Shakantu
and Evans, 2007).
Iqbal (2002) additionally examined the role of micro-credit
on the liquidity constraints faced by poor landless and near-landless households
in rural Bangladesh. The objective of the study was to examine whether collateral
free interest bearing micro loans from the Grameen Bank could help households
overcome such constraints and utilize their labor resources to rise above poverty.
The findings indicated that the application of small amounts of credit offers
scope to reduce poverty among the poorest households in Bangladesh, namely by
having a strong significant effect on employment and income levels. Most interestingly,
the results also demonstrate that the unexpected or surprise
credit, that households receive, have a positive impact on both household labor
use and household income.
Responsibility factors: These will consist of the development of social
responsibility and commitment to the community which is a factor in affecting
the granting loans to small entrepreneurs. This is consistent with other study
that entrepreneurs need to be responsible in many areas such as with their customers,
employees, investors, creditors and society (Wongprasert,
2009) which noted that a measure of the success of the organization is not
only the earnings but it must be responsive to the expectations of society or
in other words, have social responsibility. It is a factor that can determine
the survival and advancement of the business to sustainable growth in the future.
Institutions which provide loans to entrepreneurs and small businesses to demonstrate
their social responsibility aspects provide opportunities for access to capital
(Valente, 2011) due to the current era of globalization.
Access to credit can enable the landless poor to acquire resources and utilize
them towards an income-earning endeavor as they see fit (Iqbal,
2002). Hence, credit seems to be the key. Perotti (1996)
found a positive and significant relationship between credit availability and
the rate of economic growth. Chuta and Carl (1984) have
also reported that credit and capital are the greatest assistance needs and
perceived bottlenecks to rural entrepreneurs.
Knowledge management: A variable influencing entrepreneurial retail
lending including learning methods, learning categories and experience. This
is consistent with the study of Trainer (2002) which
noted that economic development must be implemented to suit each community and
each culture and will require the development of knowledge in various aspects
in order to increase the competitiveness of the organization. (Tangkoonsombati,
2006) also analyzed the market (Young, 1999) education
about the allocation of funds knowledge about equivalent credits is important.
Microfinance: Microfinance in recent years (which in its wider dimension
microcredit known as microfinance), has become a much favoured intervention
for poverty alleviation in the developing countries and least development countries.
Therefore, it is not surprising that microfinance has emerged as one of the
significant approaches to poverty alleviation (Ahmed, 2003).
The importance of earnings, revenues and brand image is also consistent with
the study of the Office of Small and Medium Enterprises Promotion (OSMEP,
2008) which stated that the goal of the business in addition to quantity
goals should also include the number of results in terms of profit and revenue
(Shelton et al., 2005; Rujithamrongkul,
2005; Meengern, 2008). Organizations also need qualitative
goals which include leadership, reputation and public image including the credit
institution helping the individual to find organizations where they can find
This study indicated that the microfinance will create profit and revenue for
the bank as well as helping the image of the bank's corporate social responsibility
(Valente, 2011). The greatest influencing variables (both
direct and indirect) on microfinance are organizational factors because organizational
factors influence knowledge management and responsibility. Organizational factors
elements include organizational policy, the lending conditions, corporate policies
and strategies, all contributing to the directionof the organization itself.
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