Keywords

Entrepreneurship, Women performance, Entrepreneurial competency, Entrepreneur, MSEs.

Introduction

Micro and small business entrepreneurship is becoming more important in the 21st century. Numerous studies demonstrate that the success of micro and small enterprises (hence referred to as MSEs) is a main contributor to a nation's growth and generates employment, wealth, and innovation (Mozumdar, Van Der Velde, & Omta, 2020). Female entrepreneurship is an expanding phenomenon on an international scale that has attracted much study interest (Henry , Foss, & Ahl, 2016). Women's entrepreneurship is one of the most pressing challenges in developing countries like Bangladesh (Gehrels & Beqo, 2014). Redefining conventional ideas about men can open up a typical opportunity for women. For example, while the self-employment rate is only 16% for women and 50% for men, the net recruitment rate for men and women is 93% and 96%, respectively. Women earn almost half as much as men ($830 vs. $1,633) (Bangladesh Bureau of Statistics, Statistical Division, 2009). A nation cannot be said to have made effective progress without the active participation of its female population (Cardella, Hernández- Sánchez, & Sánchez-García, 2020). Women entrepreneurs are women who launch, organize, and manage a business. A woman entrepreneur is also someone who has started or inherited a business and is willing to take on financial, managerial and social risks and commitments and to participate in day-to-day managerial responsibilities (United Nations Development Programme, 2004). The hidden commercial ability of women has gradually changed as people's insight of the place of business in society has grown. A significant factor in women's presence in business is their skills, flexibility and business acumen (Rao, 2012). Despite the multiple barriers, it should be recognized that a new, motivated, female business elite has emerged to deal with the downsides posed by these male-dominated, cutthroat, and complicated civilizations. The substantive actions and engagement of governmental and non-governmental financial institutions have progressed to this level (Ramaswamy, 2015). In reality, these types of resource organizations inspired and supported rural women and enabled them to become business owners and entrepreneurs in a variety of fields including farming (crops, livestock, and fisheries), small businesses, tailoring, crafts, etc. (Rahman, 2011). The current study mainly focuses on fostering entrepreneurial growth in a developing country considering the above background. Improving entrepreneurial growth on economic and social levels means strengthening entrepreneurial strength. Furthermore, there is a clear connection between this and strengthening a nation's progress.

Motivation of the Study

Only a few researches have been done to enhance women's entrepreneurial traits for the performance of the sector. Additionally, the relevance of women in organizational growth has not been covered by the majority of earlier study. The goal of the study is to close this gap by identifying a number of variables that affect how well women entrepreneurs succeed, which will aid in the growth of micro and small enterprises in Bangladesh. Through entrepreneurship, the effects of the identified factors were tempered and women's performance enhanced.

Justification of the Research

The major goal of this study is to increase women's entrepreneurial capacity for the development of micro and small industries. Although there aren't many studies on start-up growth, most researchers have concentrated on a few practical indicators. The rise of women's entrepreneurship in developing nations is the sole subject of this study, in contrast to other studies that concentrate on the development of economic growth or the identification of structural obstacles. This work will make a lot of contributions in a lot of different domains. Theoretically, given that this study directly impacted many women entrepreneurs, it would be simple to take their existing situation and provide them new insights. From a practical perspective, it is essential to appreciate the female entrepreneurial vitality that drives corporate growth. In addition, this activity provides information and experience to common people who want to start a business but are unaware of how to do it. Last but not least, these components serve as helpful benchmarks for boosting women's entrepreneurial skills.

Context of Bangladesh

Women entrepreneurs in the most underserved areas of society have received special attention recently in Bangladesh and other developing nations worldwide. Everyone in the discussion agrees that gender discrimination cannot allow society to lose half of its human resources. Gender differences are the main cause of the 63% difference in the likelihood between men and women of receiving outside venture investment. (Guzman, 2019). Governmental awareness has resulted in the creation of national policies that support the success of women in all fields, particularly economic activity with a focus on entrepreneurship. Out of 58 economies, Bangladesh ranks among the least competitive countries. This makes it one of the most difficult areas for female entrepreneurs. Card M. (2020). With scores ranging from 40 to 50, Tunisia, Saudi Arabia, and Turkey also scored badly. However, Bangladesh, Algeria, and Egypt all received scores between 30 and 40 points, which is exceedingly low. Just ahead of Algeria, Bangladesh was ranked 57th in the section that assesses the progression of women across all economies. The Bangladesh Bank, the nation's central bank, has suggested numerous new bank rules in an effort to provide loans to small and medium- sized firms (SMEs) that employ women priority, with lower interest rates and more favourable conditions. NGOs are also putting together the required plans to offer suitable business education in both urban and rural locations (Atandi, 2021). The BBS study indicates that women's access to credit, land, and other sorts of property (other than land) has continuously increased, with scores of 0.80, 0.30, and 0.50, respectively, indicating that women may have more access to credit than other types of property ownership (Bangladesh Bureau of Statistics, Statistical Division, 2009). Due to the small number of women who are actively engaged in significant economic activities, the population's great potential is not being fully used. For instance, just 16% of women out of the overall 66% of self-employed people are self-employed, according to entrepreneurship status (USAID, 2011). Today, women make up a substantially larger portion of the nation's economic and social growth (Ahmed, 2014). Rural communities have independent business owners in both the agricultural and non-agricultural industries. This list includes tasks like farming, raising livestock, raising poultry and fish, planting trees, making tools and other crafts, processing food, producing garments, milling rice, and other similar pursuits. It was 9% in 2011 and has grown to 18% from 9% by 2013. (Weisul, 2015). However, managing these businesses is challenging due to a number of issues that hinder the growth and development of women's entrepreneurship (Chowdhury, 2006; Ahmed, 2014).

Literature Review

This section uses the works of several academics, researchers, and practitioners to provide an overview of the empirical literature on the variables impacting the success of women entrepreneurs. In this context, several empirical studies on the variables impacting the success of female entrepreneurs were conducted utilizing various techniques and case studies from across the world. According to the findings of several researches on the variables impacting the success of women entrepreneurs, however, other factors have also been discovered to have varied effects on performance. A focused and systematic review of the literature on relatively recent empirical studies found the most commonly used factors as performance predictors. In this regard, the author reviewed studies by (Kanapathipillai & Azam, 2019; Khan, Salamzadeh, Ali, Shah, & Hussain, 2021; Hossain, Naser, Zaman, & Nuseibeh, 2009; Hasan & Almubarak, 2016; Alene, 2020; Schneider, 2017; Zizile, 2018; Ristovska & Blazeska, 2020; Baharudin, Rusok, Sapiai, Ghazali, & Salleh, 2021; Meresa, 2020)m. As a consequence, six independent variables— entrepreneurial orientation, access to capital, enabling environment, entrepreneurial drive, technological adoption, and government support—were taken out of the investigations. Therefore, in order to provide a coherent conceptual framework, the performance of women entrepreneurs as well as entrepreneurial competence as a mediating variable is explored below (Figure 1).

Entrepreneurial Orientation and EC

An organization's level of entrepreneurship is represented by its entrepreneurial orientation, which pertains to the procedures, routines, and decision-making processes that result in new entries (Lumpkin & Dess, 1996). Since the majority of the micro- enterprises have less than 16 employees, it is expected that women entrepreneurs will make up a sizable portion of the workforce in most instances. In light of this, one way to define entrepreneurial orientation is the range of actualized entrepreneurial practices, activities, and decision-making among women entrepreneurs. (Wang, 2008). In order to assess entrepreneurial orientation, he employed a variety of criteria, including market responsiveness, risk-taking, competitive aggressiveness, and determined innovations. Entrepreneurial orientations in our nation needed business success, navigating uncertainty, examining opportunities, innovation, and creating distinctions. Recent studies indicate that women may significantly contribute to business (Cardella G. M.-S.-G., 2013) and economic development (Brush, 2017; Hechavarria, 2019).

Access to Finance and EC

The availability of finance ensures the profitability of companies as they inject working capital. High working capital permits drive huge business and enable the Use of advanced technology that increases productivity levels and quality (Danga, Chongela, & Kaudunde, 2019; Kamunge & Tirimba, 2014; Tekele, 2019). Furthermore, entrepreneurs who suffer from capital constraints in their initial business investments have lower profits and their survival rate is lower than those who had adequate capital (Wangari, 2017). Therefore, women entrepreneurs perform better in their companies when they are financially accessible (George, 2018). Certainly, female entrepreneurs face more financial problems than male entrepreneurs. So it creates obstacles to growth. If the government and NGOs take the necessary steps to resolve financial insecurity, it would help build strength. Only fund support has a positive impact on the performance of women entrepreneurs in Bangladesh SME companies (Towhidur, Alam, & Kar, 2013). To address the problem of access to finance, many governments around the world have implemented financial sector reforms with the approval of the financial sector.

Favourable Environment and EC

The entrepreneurial environment refers to the factors that entrepreneurs must face when doing business in order to realize entrepreneurial ideals. Environmental factors influencing individual intentions, entrepreneurial behaviour, and innovation cover the context of the art version relevant to the industry (Autio, Kenney, Mustar, Siegen, & Wright, 2014). Environmental support can make an important contribution to facilitating the activities of entrepreneurs (Carayannis, D, & M, 2003; Luthje & Franke, 2003; Pittaway & Cope, 2007). Savitha, et al., (2014) found that financial support and institutional support positively influenced the entrepreneurial behaviour of women entrepreneurs in India. Reisi, et al., (2016) said that environmental and social factors play a role in the progress of agricultural development. A comfortable environment is the most important factor for women entrepreneurs. When the rural area welcomes women to be in business or to run a business, then a comfortable infrastructure is ensured and the supply support ensures the strength of women entrepreneurs. The environment influences the entrepreneur's intentions, while parental involvement, further development and the shaping of the business idea have a greater influence (Fournier & Lee, 2009).

Entrepreneurial Motivation and EC

Entrepreneurial motivation is the driving force that drives women to become entrepreneurs. It can be said that motivation drives people to undertake certain activities in which they are autonomous and willing to work towards achieving the goal. Entrepreneurial motivation can be divided into short-term and long-term motivation. The motivation to be an entrepreneur means the woman's intention and desire to be an entrepreneur. The level of motivation directly affects the entrepreneur's behaviour and performance, and then success or failure in running a business. At the same time, entrepreneurship education and learning provides entrepreneurs with the information, knowledge and also various other resources they need, thereby creating a solid environment for development and entrepreneurship as well, reducing environmental insecurity and creating a good atmosphere for development is created as ascension. The development of women's entrepreneurship in our country is that they do not receive sufficient guidelines either from the government or from NGOs. In fact, they are transformative in nature, using failure as a tool and stepping stone to success (Nyang’au, 2014).

Adoption of Technology and EC

Digital technologies are key drivers of small business performance (Benitez-Amado & Liorens-Montes, 2010). With the advent of ever more powerful collaboration and productivity tools, the adoption of ICT is crucial for competitiveness, growth and survival (Alegre, Berbegal-Mirabent, & Ribeiro-Soriano, 2010; Manley, Hair, Williams, & McDowell, 2013; Orser-Martin, Barbara and Riding, Allan and Li, & Yanhong, 2010; Benitez-Amado & Liorens-Montes, 2010). Governments have instituted policies and programs to encourage technology adoption among entrepreneurs. Scholars argue that most technology and innovation interventions are gender blind (Walker, 1999; Henry Poggesi, 2017)) and therefore emphasize the activities of men and often unconsciously exclude women entrepreneurs (Foss, 2019; Rowe, 2016). A loss of entrepreneurial opportunity follows. Finally, female entrepreneurship is almost non-existent in digital and tech ventures; This denies women access to one of the fastest growing markets in the world (OECD/The European Commission, 2019).

Government Support and EC

External support can affect an individual's ability to exercise entrepreneurial behaviour. The government plays an important role in supporting economic development initiatives to strengthen the local economy. Government support such as the provision of aid, funds, training projects and tax breaks can play a crucial role in the success of women entrepreneurship organisations. Salah & Kaplan, (2018) found, government support for women entrepreneurs is beneficial. However, the government's tough policies on investment and micro and small businesses are having a negative impact on women entrepreneurs. Furthermore, previous studies (Haxhiu, 2015; Zeb, Jan, Ihsan, & Shah, 2019) have found that legal and administrative factors have the greatest impact on women entrepreneurs' performance. This means that this is due to the lack of government support, access to policy makers, bureaucracy and the general legal and administrative factors. Developed countries where female entrepreneurs have different supportive rules and regulations for starting and running their own business (Hasan & Almubarak, 2016).

Entrepreneurial Competencies and Firms’ Success

Entrepreneurial competencies are related to the entrepreneur's ability to be innovative and creative, to recognize opportunities and to identify strengths and weaknesses. Ahmad N. H., Ramayah, Wilson, & Kummerow, (2010) define entrepreneurial competencies as the overall ability of an entrepreneur to successfully perform his or her role. (Mitchelmore & Rowley, (2013) propose that entrepreneurial competencies are measured by individual- level competencies, which can be grouped into four main categories, namely personal and relational, business and managerial, entrepreneurial and interpersonal skills. Personal and relational skills relate to the ability to negotiate with others, maintain a good personal network of work contacts, and build long-term trusting and loyal relationships with others (Mitchelmore & Rowley, 2013). Entrepreneurship skills are related to the entrepreneur's ability to be innovative and creative and to be able to identify opportunities, strengths and weaknesses. Entrepreneurial skills contribute to business success in SMEs, and the link between entrepreneurial skills and business success was clearer in dynamic and hostile environments than in favourable and stable ones (Ahmad, Ramayah, Wilson, & Kummerow, 2010).

Methodology

Research design, Data, and Sample Size Determination

In the southern region of Bangladesh, Gopalganj and Khulna are the study's locations. The data is gathered from company owners in Gopalganj and Khulna who are registered with the Bangladesh Small and Cottage Industries Corporation. Only trustworthy firms are targeted. The target market consists of women who run micro and small enterprises, which are, in the context of Bangladesh, defined as establishments with more than 15 but less than 30 workers for micro establishments and more than 30 but fewer than 120 employees for small establishments (Industrial Policy, 2016). Using standardized questionnaires, we gathered data from female business owners between December 2021 and June 2022. 2000 women-owned small and micro companies are registered, run, and managed by female directors in these two locations. For the selection of the sample we used the model calculation table from Krejcie & Morgan (1970) since we took all the company details from BSCIC and SME Foundation. We selected the sample using a simple random sampling method, since small companies often use this method (Healey, 2014). Every sample or firm has the same probability when using simple random sampling techniques (Kumar, 2008). We emailed and mailed 323 questionnaires to select the appropriate samples for the Krejcie and Morgan table. Much of the information is collected through entrepreneurs' private communications. A total of 282 questionnaires were sent to us after distribution. Out of this total, 32 of the questionnaires contained some missing values and were therefore not included in our final sample. Thus, 154 responses from Khulna and 96 from Gopalganj were combined for this study.

Instrument Determination

Closed questions were used to collect the data. The performance of women entrepreneurs was assessed using a Likert scale to collect data on their entrepreneurial motivation, entrepreneurial orientation, use of technology, access to capital, enabling environment, government support and entrepreneurial skills. Various statements indicating knowledge, feelings, and practice components were sorted into five groups using the Likert scale: strongly agree, agree, neither agree, nor disagree, and strongly disagree. All variables were rated on ratio scales. In the same way, the dependent variable for improving women's performance was assessed using a five-category Likert scale including Strongly Agree, Disagree, Strongly Disagree, and Strongly Disagree.

Respondents in the sample received 322 questionnaires to fill out during the data collection process. 78% of people responded (all respondents returned the completed questionnaire). The results of the survey (Table 1) show that the majority of entrepreneurs were in the 2024, 2529 and 30-34 age groups, with an age range between 15 and 44. More than two-thirds of the women who own their businesses are it married. More than the interviewees had a high school diploma and a high school diploma, the rest of the respondents had a high school diploma and a master's degree, depending on the level of education of the respondents. In addition, more than three-quarters of respondents said they conduct their business both online and offline. More than 80% of survey respondents are engaged.

Table 1 Demographic Description
		Frequency	Percent	Valid Percent	Cumulative Percent
Age	15-19	3	1.2	1.2	1.2
	20-24	68	27.2	27.2	28.4
	25-29	55	22.0	22.0	50.4
	30-34	76	30.4	30.4	80.8
	35-39	26	10.4	10.4	91.2
	40-44	22	8.8	8.8	100.0
Marital Status	Married	171	68.4	68.4	68.4
	Single	79	31.6	31.6	100.0
Education level	S, S, C or less	64	25.6	25.6	25.6
	Intermediate	85	34.0	34.0	59.6
	Graduated	61	24.4	24.4	84.0
	Masters	40	16.0	16.0	100.0
Conduct Business	Physical	77	30.8	30.8	30.8
	Online	91	36.4	36.4	67.2
	Both online and physical	82	32.8	32.8	100.0
Business Type	cottage	107	42.8	42.8	42.8
	Micro	98	39.2	39.2	82.0
	Small	45	18.0	18.0	100.0

Test of Reliability, Validity, and Identification of Factors

Reliability assessment provides consistency in measuring variables. The most popular psychometric measure for evaluating scales and survey instruments is the reliability of internal consistency (Zhang, Waszink, & Wijngaard, 2000). To determine reliability based on internal consistency, one can use the simple formula of Cronbach's alpha (Kim & Cha, 2002). Table 2 gives the alpha value for the entrepreneurship coefficient of 0.891, which is a reliable indicator of entrepreneurship. Cronbach's alpha value has been used more than 60 times as a measure of a reliable instrument (Gerber & Malhotra , 2008). The reliability of a study is determined by its methodology and design, which must be precise and accurate (Cooper & Schindler, 2008). If the results are consistent with a replica performed under the same circumstances, the research can be trusted (Newman, 2006). In addition, the consistency of the intra-item scale was assessed using the Cronbach alpha coefficient. The coefficient, which ranges from 0 (very low) to 1 (very high), indicates how consistently the items are with each other on a scale measuring latent variables. The overall scope of the survey was thus reliable, with a Cronbach's alpha score of 0.906 showing that the survey questions matched, according to (Gerber & Malhotra , 2008), who found a Cronbach's alpha of 0.60 or greater to be acceptable. This shows that all queries on the independent and dependent variables are standardized and can refer to the general guidelines for Cronbach's alpha coefficient (Nunnally & Bernstein, Psychological theory , 1994). The six items assessing entrepreneurial propensity was 0.873, which is the highest value. Six measures were used to measure entrepreneurial competence, and Cronbach's second-highest alpha score was 0.860. However, the lowest value was 0.822, which was used to calculate the importance of entrepreneurial motivation (5 items). The Cronbach values for the other dimensions are: state support (0.822 for 5 items), favourable environment (0.838 for 5 items), and empowerment of women (0.859 for 6 items), entrepreneurial motivation (0.822 for 5 items) and access to finance (0.853 for 6 items) (0.825 for 5 items). According to Kaiser-Meyer-Olkin (KMO), the sample size of the study is sufficient. The KMO value is greater than 0.60 and the significance value of Bartlett's sphericity test is sufficient for the study to proceed to factor analysis, which should be emphasized. The study KMO score was 0.850.

Table 2 Overall Results Of The Eight-Factor Study
Factors with Items Loaded in Each Factor	Factor Loading	Eigen-value of Rotated Factors	Cronbach ’s alpha
Entrepreneurial Orientation		19.424	0.873
EO1- works toward success.	.839
EO2- helps to deal with uncertainty	.792
EO3- explores potential opportunities.	.727
EO4- emphasis on more research & development.	.832
EO5- make a recognize differences.	.619
EO6-operate business smoothly.	.701
Entrepreneurial Competencies		11.722	0.860
EC1- seek out new challenges	.815
EC2- attending entrepreneurial events.	.780
EC3-            good         networking            will          enhance entrepreneurial strength.	.827
EC4- Desire for self-employment of women	.820
EC5-Flexibility required.	.493
EC6-more           research        and      development         are required.	.535
Access to Finance		9.485	0.853
AF1- financial support is required.	.790
AF2-short term can motivate	.835
AF3-Government low-interest rate is necessary.	.830
AF4-NGO’s loan is necessary	.622
AF5-Effective work needs financial assistance	.706
AF6-Family finance helps the entrepreneurs	.562
Favorable Environment		5.593	0.838
FE1-convenient environment is necessary.	.670
FE2-healthy environment creates a role model	.814
FE3-friendly              environment           ensures          high motivation.	.736
FE4-family supports help to create a new environment.	.705
FE5-adequate              training           and         knowledge required.	.819
Entrepreneurial Motivation		4.122	0.822
EM1- uniqueness has more possibility to get success	.784
EM2- strong motivation or resolving the	.729
problems.
EM3- Gender inequality directly discourages women entrepreneur’s inspiration	.781
EM4-Higher level of education motivates entrepreneurs.	.546
EM5- marital status affect women’s entrepreneurial strength	.634
Adoption of Technology		3.803	0.823
AOT1- new technology helps women’s entrepreneurial success	.804
AOT2- new technology means a new possibility	.736
AOT3- Technology adaptation reduces resource waste	.778
AOT4- new technology helps to produce more	.531
AOT5- New technology adaptation increases	.789
Government Support		3.179	0.825
GS1- government regulation is less convenient	.841
GS2- Social class has a huge impact	.778
GS3- political stability has on great effect	.798
GS4- unemployment rate will be decreased	.815
GS5- female person engaging in business means more development in a country	.447
Enhancing Entrepreneurial Performance		5.936	0.859
EWP1- women’s entrepreneurial sector will establish national growth.	.569
EWP2- economic growth establishments.	.667
EWP3- Creating more women entrepreneurs is the creation of more employment.	.714
EWP4-          helps       to     raise      per-person        capital income.	.781
EWP5- ensures the proper use of human resources.	.687
EWP6- strength works with innovation that affects economic growth.	.702
The eigenvalue of rotated factors		63.264
Total (55 items)			0.906
Rotation method: Varimax KMO- 0.850 Bartlett’s Test of Sphericity sig. .000

Content Validity Test

The extent to which the content of a particular sample is appropriate, or whether the instrument truly serves as a comprehensive representation of the subject, is referred to as content validity. Its definition is both critical and arbitrary (Nunnally, Psychometric Theory. 2nd Edition., 1978). The questionnaire, which was created on the basis of a thorough literature review and the opinions of professionals in the field of MSD, shows content validity.

Factor Analysis

The scale of measurement for the same construct is called construct validity. To identify the underlying components and examine correlations between significant interval-scaled items related to intention to identify the elements of success of MSEs, an exploratory factor analysis was performed. After Varimax rotation with Kaiser normalization, the primary axis was captured. Varimax rotation provides improved interpretability. The Kaiser-Mayer-Olkin (KMO) measure of sample adequacy was calculated primarily to assess the appropriateness of using factor analysis. After that, they kept the component with an eigenvalue greater than 1.0 and eliminated the factor with an eigenvalue less than
1.0. Eight factors were identified whose eigenvalues were greater than 1.0. The total eigenvalue of the eight factors was 63.264%. The overall results of the eight-factor study are presented in Table 2.

Multiple Regression Model Analysis

The study involved using multiple regression analysis to identify a line of best fit (Creswell, 2008) for more than one independent variable in predicting or explaining a dependent variable. This analysis is necessary in order to attempt to answer the research question of the study. Multiple regression analysis was used to show the impact of all independent variables in promoting women entrepreneurship. Multiple regression is a statistical technique that examines the combined influence of multiple variables to predict or explain a dependent variable. The researcher used SPSS version 24 to evaluate the calculation. In particular, linear multiple regression analysis with a stepwise approach was used because it uses the best predictors in estimating the regression model. The following sections report an analysis of the multiple regression results.

Multiple regression analysis showed that entrepreneurial competencies related to government support, en

Table 3 Model Summary
Model	R	R Square	Adjusted R Square	Std. The error in the Estimate	Change Statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.650a	0.422	0.405	0.83331	0.422	25.256	7	242	0.000
a. Predictors: (Constant), Government Support, Favourable Environment, Entrepreneurial Competency, Adoption of Technology, Entrepreneurial Orientation, Access to Finance, Entrepreneur Motivation.
b. Dependent Variable: Enhancing Women’s Performance.

abling environment, technology adoption, entrepreneurial orientation, access to finance, and entrepreneurial motivation combined to explain the improved performance of women for micro and small business development with r= seem to be. 650, r2 = 0.442 and adjusted r2 = 0.405 (Table-3). The regression model fits the data with an F-test = 25.25, which was significant at the p<0.001 level. Table 4 contains the beta weights (slopes) of each variable for constant business performance. The table includes both unstandardized and standardized coefficients along with the t-score and significance level. Combining the independent variables can predict reasons for the improvement in women's performance for micro and small business success.

Table 4 Anovaa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	122.766	7	17.538	25.256	.000b
	Residual	168.048	242	.694
	Total	290.814	249

1. Dependent Variable: Enhancing Women’s Performance.
2. Predictors: (Constant), Government Support, Favourable Environment, Entrepreneurial Competency, Adoption of Technology, Entrepreneurial Orientation, Access to Finance, Entrepreneur Motivation.

Linearity Test

Here, to test linearity, the study had performed multiple regressions and scanned the significance values to identify the variables for which the majority of values are less than
0.05. The correlation matrix table helped identify those variables that were later excluded. To observe the linearity problem, the study then checked whether or not there was a correlation between the variables greater than 0.80. The result showed that the highest correlation coefficient was 0.232. So there is no linearity problem for any article.

Two methods were used to determine the presence of multicollinearity between the independent variables, and these methods calculated tolerance test and variance inflation (VIF) factors (Kleninbaum, Kupper, & Muller, 1988). Tables 5,6 of these study shows that multicollinearity was not an issue and the VIF range is acceptable (VIF range 1.142 to 1.51) as it is well below 10. In addition, the tolerance level of the data was (< or equal to 0.01).

Table 5 Coefficientsa
Model	Collinearity Statistics
	Tolerance	VIF
Entrepreneurial Orientation	.777	1.286
Entrepreneurial Competency	.689	1.451
Access to Finance	.733	1.363
Favorable Environment	.761	1.314
Entrepreneurial Motivation	.692	1.445
Adoption of Technology	.786	1.272
Government Support	.876	1.142
a. Dependent Variable: Enhancing Women’s Performance

Discussion on Performance Determinants

Determining the factors that can significantly influence the performance of women entrepreneurs in MSE requires the use of econometric analysis. In this context, the study therefore used binary logistic regression analysis (logit model) to identify factors that significantly influence the performance of women entrepreneurs. Consequently, it has been proposed to use entrepreneurial profit as a means of measuring the performance of women entrepreneurs in MSEs. Based on the analysis, Table 7 contains the binary logistic regression results of each variable, while Table 6 also contains the correlation matrix of each dummy variable, indicating that there is no multicollinearity problem between the independent variables. To this end, the table has shown that there is not a strong relationship between each independent variable. The result of the regression output presented in Table 7 showed that entrepreneurial orientation is statistically significant and positively related to female entrepreneurship performance. This implies that companies owned and managed by women entrepreneurs with different entrepreneurial orientations empower women to run the business smoothly and are able to acquire a broad knowledge of entrepreneurship. The result of the regression showed that the mediating variable entrepreneurial competence has a positive and significant influence on the performance of women entrepreneurs (Table 7). This is also confirmed by the correlation matrix, which shows that there is a positive association between entrepreneurial competence and female entrepreneurship performance (Table 6). This implies that entrepreneurship skills help entrepreneurs figure out how to boost business. This is to mean that different types of knowledge and skills give entrepreneurs the confidence to go ahead and do anything. The study confirmed that an enabling environment is also statistically significant and positively associated with female entrepreneurship performance (Table 7). This showed that women entrepreneurs perform better in their company when they find a conducive environment to develop the relevant skills and knowledge needed to increase company performance. This means that women entrepreneurs in SMEs supported by the government improve and strengthen business growth, new business ideas and customer relationships. Results from the logit model indicated that land ownership was statistically significant and positively associated with female entrepreneurship performance (Table 7). This implies that women entrepreneurs thrive in micro and small businesses. Entrepreneurial motivation also correlates with the promotion of women entrepreneurs for the success of micro and small enterprises. The correlation matrix (Tables 6 and 7) confirms that entrepreneurial motivation has a statistically significant impact on the promotion of women entrepreneurs in micro and small enterprises.

Table 6 Pearson Correlations
	EO	EC	AF	EWP	FE	EM	AOT	GS
EO	1
EC	.183**	1
AF	.193**	.021	1
EWP	.413**	.360**	.171**	1
FE	.406**	-.049	.278**	.346**	1
EM	.223**	.525**	.063	.509**	.110	1
AOT	.132*	.047	.442**	.032	.124	.081	1
GS	.107	.209**	.218**	.212**	-.026	.194**	.222**	1
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed).

Table 7 Result Of The Hypothesis
	Hypothesized Paths	Standardized Coefficients Beta		t		sig		Result
	(Constant)			- 1.040		0.299
H1	Entrepreneurial Orientation-Entrepreneurial Competency- Enhancing Women’s Performance	0.208		3.757		0.000		Supported
H2	Entrepreneurial Competency- Enhancing Women’s Performance	0.128		2.180		0.030		Supported
H3	Access to Finance- Entrepreneurial Competency- Enhancing Women’s Performance	0.068		1.199		0.232		Not Supported
H4	Favorable Environment- Entrepreneurial Competency- Enhancing Women’s Performance	0.227		4.046		0.000		Supported
H5	Entrepreneurial Motivation- Entrepreneurial Competency- Enhancing Women’s Performance	.354		6.021		.000		Supported
H6	Adoption of Technology- Entrepreneurial Competency- Enhancing Women’s Performance	-.113		- 2.056		.041		Supported
H7	Government Support- Entrepreneurial Competency- Enhancing Women’s Performance	.111		2.120		.035		Supported
Dependent Variable – Enhancing Women’s Performance

The adoption of technology correlates with the advancement of women entrepreneurs. Evidence of the regression result revealed that technology adoption has a statistically significant effect on women entrepreneurs' performance and is positively associated with performance, as indicated by the correlation matrix (Tables 6 and 7). Government support has been included in the model and also correlates with support for women entrepreneurs for micro and small business success. In this regard, evidence of the regression result showed that government support has a statistically significant impact on female entrepreneurship performance and is positively associated with performance as indicated by the correlation matrix (Tables 6 and 7). This implies that women companies that have access to government support packages such as policy and legal infrastructure development, technology, incentives and the provision of social recognition performed better than their counterparts. In this regard, the current study is consistent with the research findings of (Giday, 2017; Kamunge & Tirimba, 2014; Zeb, Jan, Ihsan, & Shah, 2019). Access to finance, on the other hand, is not statistically significant and was not positively associated with women entrepreneurship performance, as shown by the logit model and correlation matrix (Tables 6 and 7). This implies that women entrepreneurs believe that other competences such as guidance, motivation, adoption of technology, enabling environment and government support are available in a given area and access to finance is not important for the business. The government is currently taking the necessary steps to secure funding. Accordingly, the current study concurs with the research findings of (Leszczy?ski, 2016) who found that successful women entrepreneurs rely primarily on their resources (financial capital) to fund their operational activities and the short- to medium-term growth needs of their firms . On the other hand, the result of this study contradicts that of (Wangari, 2017) who reported that women entrepreneurs who suffer from capital constraints in their initial business investments make lower profits and their survival rate is lower than those who had sufficient capital.

Conclusion

Considering that entrepreneurship is an important driver of economic growth and development (Mandawa, 2016; Mozumdar, Van Der Velde, & Omta, 2020; Wangari, 2017), understanding which determining variables the performance of women entrepreneurs enhance the success of micro and small businesses. In this context, this study provides new empirical evidence on determinants affecting women entrepreneurs' performance based on the data collected from 250 women entrepreneurs in the cities of Khulna and Gopalganj, Bangladesh, using regression analysis. Consequently, the result of the regression output provided statistically significant evidence of six out of seven variables for improving the performance of women entrepreneurs in MSE at a significance level of 5%. Therefore, on the one hand entrepreneurial orientation, favorable framework conditions, entrepreneurial motivation, introduction of technology and state support were important. However, access to finance turns out to be an insignificant variable. Accordingly, the result of the regression showed that companies owned and managed by women entrepreneurs with an entrepreneurial spirit help women to smoothly launch the business, since entrepreneurial spirit increases the skill level of entrepreneurs. In addition, a favorable environment and government support ensure an appropriate business environment for the women to find the right market for specific products. These facilities empower women, enabling micro and small businesses to produce more products for business success and increase national income. In addition, entrepreneurial motivation and the adoption of technology help women entrepreneurs to create new things to meet market demand, and new technologies help to increase productivity and ensure the perfection of the product, as well as increase the level of efficiency for the development of the enterprise increase micro and small businesses. Entrepreneurial orientations, enabling environment, entrepreneurial motivation, adoption of technology, access to finance and government support increase entrepreneurial skills of women entrepreneurs for micro and small business success in Bangladesh. Entrepreneurship competency acts as a mediating variable, maintaining a significant relationship between independent variables (entrepreneurial orientation, access to finance, entrepreneurial motivation, technology adoption and government support) and dependent variables (improving women's performance) for micro and small business success upright. The results of this study therefore suggest that women entrepreneurs need proper guidance and motivation and embrace new technologies and various support programs to improve women's performance in micro and small businesses in Gopalganj and Khulna City in Bangladesh.

Limitations and Further Implications of the Study

Women entrepreneurs are becoming more common in the business world in underdeveloped countries like Bangladesh. However, a variety of circumstances affect the prosperity of micro and small businesses owned by women. These aspects of entrepreneurship are insufficient in Bangladesh. Certain studies (Hossain, Naser, Zaman, & Nuseibeh, 2009; Islam & Ahmed, 2017; Baharudin, Rusok, Sapiai, Ghazali, & Salleh, 2021; Hasan & Almubarak, 2016; Kanapathipillai & Azam, 2019; Kamunge & Tirimba, 2014) say Tell me to look at the variables that affect the performance of micro and small businesses. There is a paucity of literature in Bangladesh that discusses how to improve women's performance for micro and small business growth. Another disadvantage is that only 250 samples were used in the study. This number may not reflect all SME entrepreneurs in Bangladesh. Nonetheless, the results of the present study can be used as a starting point for future research and as policy recommendations for policy makers in developing countries in general and in the field of study in particular to improve the performance of women entrepreneurs for the growth of micro and small businesses.

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Received: 28-Nov-2022, Manuscript No. AMSJ-22-12925; Editor assigned: 06-Dec-2022, PreQC No. AMSJ-22-12925(PQ); Reviewed: 21-Dec-2022, QC No. AMSJ-22-12925; Revised: 24-Jan-2023, Manuscript No. AMSJ-22-12940(R); Published: 07-Feb-2023