Influencing Factors of Micro Finance Institute on Innovative Entrepreneurship Growth: In Case of West Guji Zone Bule Hora Woreda

Gada Gizachew Wakjira

Department of Marketing Management, Bule Hora University, 144 Suro Road, Ethiopia

Corresponding Author’s Email: gada.gizachew@bhu.edu.et

Abstract

The study aims at the Micro Financial Institute on Innovative Entrepreneurship growth: The case of Bule Hora Woreda Ethiopia, data analysis using (SPSS version 23) to be used during the study time. The research philosophy of research Paradigms has designed Quantitative and Deductive research approach, the data analyses with Exploratory Factor Analysis (EFA), to analysis Correlation Matrix of Significant P-value tested, KMO, Communalities, Average variance Extracted, Rotated Component Matrix, Scree plot component number, Component rotated space and to test Convergent construct validity and Discriminant Reliability to test Cronbach alpha result of Exploratory Factor Analyze to measure statistical methods to use, date techniques and to measure used probability sampling technique that used the Stradd from Members of Micro Finance Service, Construction member, Trade and urban Agriculture in West Guji zone Bule Hora woreda and additionally, from Woredas Micro Finance Service Officer, Budget planer was targeted population of this study and in quantitative research design that measure based on the literature theory to used, the probability sampling techniques that analyzed the procedure that the researcher would adopt. Totally from unknown population 384 total respondents distributed the questionnaire.

Keywords: Micro Finance Institute; Innovative Entrepreneurship Growth; Entrepreneurship Awareness; Financial Factors; Locational Influential Factors; Transformational Entrepreneur

Introduction

The Small Business Entrepreneurship maintains a skill development programme in specific sectors, such as the micro financial institution, for long-term support and the marketing network of entrepreneurs and businesses. Gregory et al. (2018). Initially, microfinance institutions were introduced worldwide by Mohammed Yunus in Jobra’s village, Bangladesh, in 1976. It is currently an effective instrument for poverty reduction. The contribution of microfinance to poverty reduction got more attention in 2005 after the announcement by the UN. Many microfinance institutions have developed international microcredit, attracting poorer communities to develop new strategies to realize their vision. Also, most developing countries have been using microfinance as the best strategy to eradicate poverty, and so several microfinance institutions emerged in Africa to fulfil the entrepreneurs' profits (Chomen, 2021).

The impact of business innovation capability, entrepreneurial competencies, and quality management on the performance of Malaysian SME's and the growth of innovative entrepreneurship has aided in the business institute, according to Ali and Iskandar (2016). The successful implementation of entrepreneurship could use customer services, which include some lightning components that help new technological and innovative entrepreneurship achieve measurable and sustainable improvement. Control and operation of dint satisfaction, and it leveraged highly experienced consultants for production and product developments. It provides sales force consulting services using best practises to help local entrepreneurs improve customer experiences and get a competitive edge in innovative new markets and marketing system technology (Buccieri, Javalgi & Cavusgil, 2020).

Microfinance institutions were established in Ethiopia in 1995 to alleviate poverty, and the country's modern finance services have grown significantly since then. Presently, numerous microfinance institutions are operating throughout the country. In recent times, the government of Ethiopia has developed various developmental strategies, such as a poverty reduction strategy. This is aimed at enhancing and supporting growth among those who regard microfinance as the best reference in achieving the intended development's objective and commuting or minimising the risky trend in poverty problem meeting the millennium development goals. Almost all Ethiopian microfinance institutions provide coins and saving services (Chomen, 2021).

Poverty, unemployment, unfair economic distribution, and food insecurity are the main challenges and fundamental issues of economic development in Ethiopia. To address the challenges of unemployment, economic development, and fairness in the country, the federal and regional governments of Ethiopia have implemented development programmes such as the micro- and small-enterprise programme to increase income, assets, and employment opportunities (Hagos et al. 2017).

Therefore, this research is aimed to investigate factors influencing microfinance institutions, awareness of entrepreneurs, financial factors, locational factors, and transformational entrepreneurs on the growth of innovative entrepreneurship with respect to certain indicators to fill those gaps and build innovative entrepreneurship in Oromia Regional State, west Guji Zone, Bule Hora Woreda, Ethiopia.

Investigation Objectives

• To identify the factors that contribute to microfinance institutions among innovative entrepreneurs.

• To examine the relationship between the Micro Finance Institute and innovative entrepreneurial growth.

• To investigate the relative influence of the Micro Finance Institute among innovative entrepreneurs.

Micro Financial Institution

Like a bank, a microfinance institution is a provider of credit, but the size of the loans is smaller than those granted by traditional banks, and small loans are known as microcredit. The clients of an MFI are often microentrepreneurs in need of economic support to launch their businesses, and the microfinance institution’s exposure to environmental and social risks is typically low (Chirwa, 2008). Because social development is part of their mandate, microfinance institutions are concerned with the environmental and social risks of their transactions and are taking steps to manage these risks to reduce negative impacts in their communities and Microfinance generally refers to the provision of basic financial services such as loans, savings accounts, and insurances for low-income but economically active people. In most instances, the term "microfinance" refers to the provision of small loans and microcredits for microentrepreneurs (Tumbas, Schmiedel & Vom Brocke, 2015).

Factor influencing Micro Finance Institute on Innovative Entrepreneurship Entrepreneurship Awareness

It was frequently carried out in rural areas at first to reach a larger population. The Entrepreneurship Development Program, normally after awareness programmes or at periodic intervals, has trained thousands of entrepreneurs. A one-of-a-kind training model for capacity building and preparing for new business ventures includes empowerment sessions. Business exposure visits and interactions with successful entrepreneurs' government officials and support agencies provide networking, escort service to dints, and programme setup for participants. Additionally, we also conduct skill development programmes in specific sectors for the sustainable support of the marketing network of entrepreneurs (Putro et al. 2022).

Financial Factors

Financial factors consist of financial policies, financial positions. It is an important internal factor that has a substantial impact on cooperative business functioning and the capital structure of Micro Financial Institute. It facilitates the requirement to start and operate the members, which is important, and to use a series of net incomes to gain a better look at a business line and compare gross profit to net sales. It is possible to determine whether the member's profit margin

has increased in comparison to similar businesses. The interests of members and the net sales to working capital, are due to increased sales volume at higher prices and fixed assets. This suggested that the company invest money. Additionally, the operating environment and corporate culture of the business depended on overseas clients or suppliers to design financial policies for cooperative achievement (Piwowar-Sulej, 2021).

Locational Factors

Variation in the size, scope, and buoyancy of demand in local markets is likely to affect innovative entrepreneurship. Growth opportunities on the supply side in terms of variation in cost and availability of labor, premises, and services are also essential. Nonetheless, owner- managed businesses are frequently adaptable, employing various strategies to deal with the local availability of variables and simply growing. Orientation does not guarantee growth for businesses set up to exploit identified market opportunities, which would be expected to have a stronger growth orientation than those set up to launch alternative opportunities. It is important to identify the factors most relevant to the business and then exploit them to expand and grow the enterprise (Nazir & Roomi, 2020).

Transformational Entrepreneur

To achieve progress in society and business practices, more entrepreneurship is needed to encourage action and enhance social capital. Innovative entrepreneurial growth offers a way of integrating sustainability practises while focusing on sustainable future needs and trends. It uses novel business practises to reduce poverty and increase equality in the marketplace and shows how it transforms society through creative solutions. This enables change for a better understanding of emerging and to contribute to the extension of existing, which is dependent on understanding how to get action from digital platforms and, in particular, in a specific scenario for the adoption of digital platforms by increasing and changing customers and entrepreneurs. (Qureshi, 2020).

Investigation Gap

Usually, following the revision of diverse publications, it has been noted that diverse investigations have been completed on the topic of influencing factors at the Micro Financial Institute (Lubbadeh, 2020). There are a lot of problems in the many research journals and investigation credentials (Lesener, 2019). These problems challenge the way microfinancial institutions work and make it hard to find out what's going on in the West Guji zone. Bule Hora Wereda wants to help close the gap between microfinance institutions and the growth of new businesses with new ideas. The association between investigation and the Micro Financial Institute predicator is based on the worth count parameters of techniques in a logical vacuum like investigation approach, design, information interpretation, and investigation (Strah, Rupp & Morris, 2022; Pereira et al. 2021).

The sampling technique was facing a challenge to give a good reason for sample size assortment due to a deficit of information interpretation and investigation (Guthier, Dormann & Voelkle, 2020; Halcomb et al. 2009).

Deficit of Information Source and Collection Techniques, deficiency of data analysis and interpretation ability, and finally, defecating factors that influence influencing four enablers and the exploratory factor (CEF) predictor investigation do not fit. As a result of its investigation, the Micro Financial Institute has proposed solutions to these gaps. As a result, research will have solved the issue of the Micro Financial Institute's rise to fill these gaps (Wakaba, 2014).

Conceptual Structure

Figure 1: Conceptual Structure

Source: AMOS Exploratory Factor Analysis (EFA) Output (2022)

On the foundation of reviewed literature, the researchers framed with below four alternative hypotheses (refer to figure 1).

Hypotheses

H1a: There is a statistically significant association between entrepreneurial awareness and innovative entrepreneurial growth.

H2a: There is a statistically significant association between financial factors and innovative entrepreneurial growth.

H3a: There is a statistically significant association between locational factors and innovative entrepreneurial growth.

H4a: There is a statistically significant link between transformational entrepreneurship and the growth of innovative entrepreneurship.

Investigation Technology and Design

Based on the research proposal, the most commonly used quantitative research approach is to be used for cases of statistical conclusion to collect actionable insights of essential and numerical importance. They provide a better perspective for making and drawing from complex numerical data and analyzing it to prove exploratory factor analysis (EFA). The correlation matrix of the significant P-value tested, KMO, communalities, extracted average variance, rotated component matrix, scree plot component number, component rotated space, and test convergent construct validity and discriminant reliability were investigated. To test Cronbach's alpha of multivariate EFA statistical methods that attempt to identify the smallest number of hypothetical constructs that can parsimoniously explain the co-variation observed among a set of measured individuals, this is directly manifested in the scores attained by those individuals on the measured variables Brown (2015).

Results & Discussion

Target Population and Sampling Techniques

Data has been collected from members of Micro Finance Service, Construction, Trade, and Urban Agriculture in West Guji Zone Bule Hora Wereda, respectively, for an unknown population. The study is primarily focused on the Oromia regional state, specifically the West Guji Zone, Bule Hora Woreda. Thus, the unknown population has to be assorted from different categories within each stratum. The Woredas Micro Finance Institute Officer and Budget Planner targeted populations for this study as well. In literature, probability sampling is a simplified method where equal opportunity is given to individuals from the population to be chosen members of the Micro Finance Service, those in construction, those in trade, and those in urban agriculture (Ives et al. 2007).

Sample Size

Suppose we want to calculate the sample size of a large population whose degree of variability is not known. Assuming a maximum variability, N-total population number of 50% (p = 0.5), and a 95% confidence level with 0.5% precision, the required sample size will be at an infinite or "N" unknown (Kothari, 2004) formula to develop and calculate a representative developed sample for proportions (Cochrane, 1963).

Where no is the sample size, z is the selected critical value of the desired confidence level, p is the estimated proportion of an attribute that is present in the population, q = 1− p, and e is the desired level of precision.

1. p = 0.5 and hence q =1-0.5 = 0.5; e = 0.05; z =1.96

Table 1: Overall Results of Cronbach's Alpha Reliability Test

Reliability Statistics

Source: SPSS Output (2022)

Construct Variable of Cronbach's Alpha Reliability Test

Table 2: Construct Variable of Cronbach's Alpha Reliability Test

Source: SPSS Output (2022)

The results show that scale meets the reliability requirement. The Cronbach’s Alpha test produced microfinance institutions and Innovative Entrepreneurship (INE), total measurement, and construct reliability and validity values that were higher than 0.70. According to this forecast, entrepreneurial awareness (0.805), financial factors (0.747), locational factors (0.817), innovative entrepreneurship (0.923), and the overall Cronbach alpha reliability statistic result must all be 0.863. The factors were all reflective due to the fact that their indicators were highly correlated and largely interchangeable (Jarvis, Mackenzie & Podsakoff, 2004) (refer to tables 1 and 2).

Exploratory Predicator Investigation

Hypotheses

Figure 2: Hypotheses

Source: AMOS Exploratory Factor Analysis (EFA) Output (2022)

Table 3: Correlation Matrix of Predicator Investigation

Source: SPSS Output (2022)

The correlation matrix table that displays the correlation coefficients for different variables and depicts correlation between all the possible pairs of values is a powerful tool to summarize large data sets and identify all patterns in the data. It can be seen that all the variables are positively correlated, necessitating the significance arrow where the diagonal number is more than 0.3 for the variables after delayed correlation. The values of Entrepreneurial Awareness (EA) (0.552), Financial Factor (FF) (0.459), Locational Factors (LF) (0.554), and Transformational Entrepreneurship (TE) (0.680) in the field of correlation coefficients are justified for the use of exploratory factor analysis hypotheses tests as acceptable and significant correlation matrices in this study (refer to figure 2 & table 3).

KMO and Bartlett's Test

Table 4: KMO and Bartlett's Test

Source: SPSS Exploratory Factor Analysis (EFA) Output (2022)

Based on this information, the test of Bartlett is significant at 0.876, which is associated with a Chi-Square degree of freedom of 2405.54 and 105 probabilities less than 0.05. The P-value for the Bartlett test for information is 0.000, indicating that the maximum significance fits well with the predictor investigation (table 4).

Communalities

Table 5: Communalities Calculate of Variance

Source: SPSS Exploratory Factor Analysis (EFA Output (2022)

The communality was observed in the community information investigation as the squared correlation with its own ordinary proportion, which is the proportion of variance explained by the ordinary predictors. In another sense, the communality is the square of predicators, whereas greater communality than 0.50 explains the maximum measuring predicator to which the related indicators are all fitted, but communalities of information were calculated for TE 4 indicators, which had the highest predicator loading of 0.734 with each predicator too, as stated in the square of predicator loading (please see table 5 above).

Rotated Component Matrixa

Table 6: Rotated Component Matrixa

Source: SPSS Exploratory Factor Analysis (EFA) Output (2022)

The rotated component matrix, referred to as a loading, is the key output of principal component analysis. It contains estimates of both exogenous and indigenous variables separated into five components. To calculate the loading factor based on the output value, multiply the value of all variables with significant separation by 0.60. The Cronbach alpha value is 0.70%, the AVE% results are also ≥ 0.50%, and the major rotation oblique is generally best predicted. When all prior information on his own components is considered, the predicator may be correlated, all enablers are equally loaded, and five components of the matrix are highly loaded (refer to table 6).

Total Variance Explained

Table 7: Total Variance Explained

Extraction Method: Principal Component Analysis

Source: SPSS Output (2022)

In Total Variance Explained, those 5 factors that were found in exploratory factor analysis now have eigenvalues. Based on these assumptions and the factor analyses that produced eigenvalues, the results shown are more than one. Just above that, which reflects 61.699 percent of its total variance, are about 1.126 of its eigenvalues. Eigenvalues have possible rules that may be used for choosing the number of factors based on the eigenvalue rule of greater than 1.0, which seems to work the best (see table 7 above).

Scree Plot

Figure 3: Scree Plot Component Number

Source: SPSS Exploratory Factor Analysis (EFA) Output (2022)

The current scree plot output result, which corresponds to Eigenvalues for eighteen separate measurement constructs, indicates that only four measurement constructs with values greater than 1.0 must be returned. Three factors must be returned: an elbow toward a less step observation scree plot and a curve of declined value greater than 1.0 eigenvalues of doubt. Based on this reason, the scree plot separation has to be accepted (refer to figure 3).

Component Plot in Rotated Space

Figure 4: Component Plot in Rotated Space

Source: SPSS Exploratory Factor Analysis (EFA) Output (2022)

Oblique rotation direct relation direct rotation in the axis such that the vertices can have a 90- degree angle. This allows predictors to be correlated, and one can specify the parameter delta to control the extent to which predictors can be zero or negative. With the number yielding a nearly orthogonal solution five times, a majority is orthogonal and rotated on its axis based on this reason. The exploratory predictor analyses in the components of the plot in rotated space are very well done, and the result will be supported (refer to figure 4).

Construct Validity and Reliability investigation

Table 8: Reliability and Validity of the Measurement Variables

Source: SPSS Exploratory Factor Analysis (EFA) Output (2022)

To construct validity and discriminant reliability, we extracted common factors whose factor loading varied from 0.615 to 0.831. These values are significantly higher than the critical value of 0.70, which is determined by Cronbach's alpha and ranges from 0.724 to 0.832. It shows a good level of instrument reliability. KMO indices are varying from 0.5 to 0.821 to 0.877, which are either equal or AVE%. A value greater than 0.50 indicates sufficient and adequate sampling of all communities. SPSS version 22 was used to analyze the results of exploratory factor analysis and a further reduced set of variables in the proposed model. It is used to construct validity, and discriminant reliability investigations fit this model, which is highly accepted (refer to Table 8).

Conclusion

A correlation matrix table that displays the correlation coefficients for different variables depicts correlation between all the possible pairs of values. It is an effective tool for aggregating large data sets and identifying all data patterns. All the variables are positively correlated, necessitating the significance arrow 1 diagonal number being more than 0.3. The variables after delayed correlation The values of entrepreneurial awareness (0.552), financial factor (0.459), locational factors (0.554), and entrepreneurship (0.680) in the field of correlation coefficients are justified for the use of exploratory factor analysis hypotheses tests as acceptable and significant correlation matrices in this study.

The communality was observed in the community information investigation as the squared correlation with its own ordinary proportion, which is the proportion of variance explained by the ordinary predictors. In another sense, communality is the square of predictors, whereas greater communality than 0.50 explains the maximum measuring predictor to which the related indicator is fitted. But communalities of information were calculated for TE 4 indicators, which had the highest predicator loading of 0.734 with each predicator too, as stated in the square of predicator loading.

The rotated component matrix, referred to as a loading, is the key output of principal component analysis. It contains estimates of both exogenous and indigenous variables separated into five components. To calculate the loading factor based on the output value, multiply the values of all variables with significant separation by 0.60. The Cronbach alpha value is 0.70%, the AVE% results are also ≥ 0.50%, and the major rotation oblique is generally best predicted when all prior information on his own components indicates that the predictor may be correlated, all enablers are equally loaded, and five components of the matrix are highly loaded.

The five factors discovered in exploratory factor analysis now have more than one eigenvalue in Total Variance Explained. Based on these assumptions, the factor analyses that were produced had eigenvalues just above that, which reflected 61.699 percent of its total variance. with approximately 1.126 of its eigenvalues and eigenvalues possible rules for determining the number of factors. Based on the eigenvalues rule of greater than 1.0, it seems to work the best.

The current scree plot output result, which corresponds to Eigenvalues for eighteen separate measurement constructs, indicates that only four measurement constructs with values greater than 1.0 must be returned. Three factors must be returned: an elbow toward a less step observation scree plot and a curve of declining values greater than 1.0 eigenvalues. Based on this reason, the scree plot separation has to be accepted.

Oblique rotation direct relation direct rotation in the axis such that the vertices can have a 90- degree angle. This allows predictors to be correlated, and one can specify the parameter delta to control the extent to which predictors can be zero or negative. With the number yielding a nearly orthogonal solution five times, a majority is orthogonal when rotated on its axis. Based on this reason, the exploratory predictor analyses in the components of the plot in rotated space are very well done, and the result will be supported.

The extracted common factors have factor loadings ranging from 0.615 to 0.831, indicating that they are well above the critical value of 0.70 for construct validity and discriminant reliability. Cronbach's alpha, which ranges from 0.724 to 0.83, is used to assess construct reliability. It shows a good level of instrument reliability. KMO indices are varying from 0.5 to 0.821 to 0.877, which are either equal or AVE%. A value greater than 0.50 indicates sufficient and adequate sampling of all communities. SPSS version 22 was used based on the results of exploratory factor analysis and the further reduced set of variables in the proposed model. Construct validity and discriminant reliability studies used to fit this model and were widely accepted.

Conflict of Interest

The author declares that he has no conflict of interests.

Acknowledgement

The author is thankful to the institutional authority for completion of the work.

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