Factors Affecting Women Entrepreneurs? Success: A Study of Small- And Medium-Sized Enterprises in an Emerging Market.

References

[1] 1

[2] Need for achievement

[3] “to achieve in relation to a set “standard, and to strive to succeed. In other words, need for achievement is a Behaviour directed towards competition with a standard of excellence.”

[4] McClelland,1961

[5] 2

[6] Risk-Taking

[7] "It involves pursuing strategic initiatives with a high degree of uncertainty, balancing potential gains against the possibility of substantial losses."

[8] Sitkin, S. B., & Pablo, A. L.,1992

[9] 3

[10] Self-Confidence

[11] “Self-confidence is an individual's belief in their own abilities, qualities, and judgment, fostering a sense of trust in their capacity to succeed."

[12] Bandura,1997

[13] 4

[14] Economic factors

[15] An organization or investment's quality is determined by several key elements, such as internal funding and external economic conditions.

[16] CITATION Mul10 \l 1033 (Mulugeta, 2010)

[17] 5

[18] Socio-Cultural factors

[19] “These elements comprise a mix of social and cultural elements that influence the success of female entrepreneurs.”

[20] CITATION Mul10 \l 1033 (Mulugeta, 2010)

[21] Conceptual model

[22] IIV. Hypotheses Development:

[23] 1. Need for Achievement and women entrepreneurs’ success

[24] McClelland (1961) & CITATION Way07 \l 1033 (wayne & Philip, 2007) claimed that a high need for achievement, which is represented by a drive to perform well in order to feel accomplished, makes a person more likely to look for an entrepreneurial position because the entrepreneur feels that this type of work offers greater achievement satisfaction than other varieties of jobs. An aspect of entrepreneurs' personalities that can have a significant impact on the launch, growth, and success of small businesses is their drive for successCITATION Jas18 \l 16393 (Jasna & Bostjan, 2018). Based on previous research, we suggest that the success of female entrepreneurs with strong goal and a goal for success can significantly and favorably affect their company's success.

[25] H1: The Need for achievement has a substantial positive impact on women entrepreneurs' success.

[26] 2. Risk-taking and women entrepreneurs’ success

[27] Risk taking is a critical component that can build or fail a firm in the fast-paced world of entrepreneurship. Entrepreneurs that are successful know how important it is to take calculated risks and make decisions that advance their businesses CITATION Ste25 \l 1033 (Stephen , 2025). CITATION KHa23 \l 1033 (K, HL, & varghese, 2023)find that a woman's attitude toward risk significantly affects her success as an entrepreneur. Women who take calculated risks are more likely to succeed in their businesses. CITATION Ala19 \l 1033 (Alaa, Colins, Choudhary, Hassanein, & Hany, 2019) examined that how women take more risks, which has a big impact on business performance and profitability, particularly in developing economies.

[28] H2: Risk-taking has a substantial positive impact on women entrepreneurs’ success.

[29] 3. Self-confidence and women entrepreneurs’ success

[30] Both the prospect of becoming an entrepreneur and the success of an entrepreneurial endeavor may be enhanced by high levels of self-confidence CITATION Ter23 \l 1033 (Terhi & Jutta, 2023). CITATION Wil07 \l 1033 (Wilson, Kickul, & Marlino, 2007) discovered that, especially for women, entrepreneurship education considerably raised their entrepreneurial skills level in their study, As a result, encouraging self-confidence in others may encourage entrepreneurship and entrepreneurial performance in society. However, the amount of self-confidence also influences entrepreneurial aim, and it will be hard to compete in the volatile markets without these elements CITATION sal17 \l 1033 (salime , Massimiliano, Andrea , & Dianne, 2017).

[31] H3: Self-confidence has a substantial positive impact on women entrepreneurs’ success.

[32] 4. Economic factors and women entrepreneurs’ success

[33] CITATION Muh12 \l 1033 (Muhammad A. S., 2012) thought that outside variables including the environment, governmental regulations, and political difficulties in emerging economies had a big impact on the success of female entrepreneurs. Past study refers that in addition to creating money and boosting the national economy, female entrepreneurs may also be able to provide much-needed job opportunities CITATION Ama211 \l 16393 (Dhaliwal & Sahay, 2021). CITATION Sam23 \l 16393 (Samuel, 2023)find that enhancements to social and economic growth to support women entrepreneurs' effective operations in MSEs.

[34] H4: Economic factors has a substantial positive impact on women entrepreneurs’ success.

[35] 5 Socio-cultural factors and women entrepreneurs’ success

[36] In accordance to a socio-cultural perspective, social influence has a significant effect on female entrepreneurs CITATION DrP17 \l 16393 (Dr. Pradeepika, 2017). Women are often the only ones responsible for taking care of their families and raising children, which prevents them from engaging in commercial endeavors CITATION pat11 \l 16393 (patricia H., Domingo, & David, 2011). But still pas study refers that socio-cultural have a major and favorable impact on Indian women entrepreneurs' successCITATION Nay25 \l 16393 (Nayak, Joshi, & Pushpraja, 2025). Socio-cultural contexts in developing nations do not support women's involvement in entrepreneurship.

[37] H5: Socio-cultural has a substantial positive impact on women entrepreneurs’ success.

[38] V. RESEARCH METHODOLOGY

[39] 1. Objectives of the study:

[40] To identify the internal factors including the need for achievements, risk-taking, and self-confidence and external factors including economic factors and socio-cultural factors influencing The achievement of women entrepreneurs in SMEs within emerging markets.

[41] To identified different factors and the success of women-led SMEs.

[42] 2. Procedure and participants:

[43] The study uses a mixed-method approach, integrating quantitative data analysis with qualitative interviews with female entrepreneurs in a number of emerging market economies. The interactions between important variables are examined using structural equation modelling (SEM), which offers a thorough grasp of the elements affecting the success of women-led SMEs.

[44] 3. The women entrepreneurs’ success:

[45] The women entrepreneurs’ success is dependent variable, which is quantified using a four-item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[46] 4. Need for achievement:

[47] The need for achievement is an independent main variable that relates to an employee's endeavor toward achieving the primary goal. In this study need for achievement quantified using a four-item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[48] 5. Risk taking:

[49] Next, risk taking is an independent main variable; it refers that pick to create a new route by grabbing an external chance. In this study risk taking quantified using a three- item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[50] 6. Self-confidence:

[51] Self-confidence is a third independent main variable; it refers motivate and inspire employees to attain their goals. In this study self-confidence quantified using a four-item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[52] 7. Economic factors:

[53] Economic factor is a fourth independent main variable; it refers to the various economic elements that affect the firm and economic wellness. In this study economic factors quantified using a four-item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[54] 8. Socio-cultural factors:

[55] Socio-cultural factor is a fifth independent main variable; it refers that the impact of varied external cultural contexts on personnel characteristics can cause instability inside firms and the broader economy. In this study socio-culture factors quantified using a three-item questionnaire adapted from prior research used by CITATION Riz21 \l 16393 (Khan, Yashar, Shah, & Mazhar, 2021).

[56] VI. DEMOGRAPHIC PROFILE OF RESPONDENTS

[57] Table No.1 Age of the respondents

[58] Age

[59] Frequency

[60] percent

[61] vaild percent

[62] cumulative percent

[63] 18 - 30

[64] 30

[65] 25

[66] 25

[67] 25

[68] 31 - 40

[69] 27

[70] 22.5

[71] 22.5

[72] 47.5

[73] Above 40

[74] 63

[75] 52.5

[76] 52.5

[77] 100

[78] Total

[79] 120

[80] 100

[81] 100

[82] Interpretation: This table shows the age distribution of 120 female respondents, divided into three groups: 18-30, 31-40, and Over 40. The "Above 40" age group makes up the majority of respondents, accounting for 52.5% of the entire sample (63 people). This indicates that a major portion of the female respondents are older adults, possibly with more life experience. The "18-30" age group accounted for 25% of responses, or 30 people, indicating a significant representation of young women in the sample. The "31-40" age group, which includes 27 people, represents 22.5% of all respondents and has a slightly lower proportion of women in their prime career-building years. According to the cumulative percentage column, about half (47.5%) of female respondents are 40 years old or younger, while the other half (52.5%) are over 40. Overall, the findings show a slightly older population among female responders, with a distinct concentration in the "Above 40" age category.

[83] Table No.2 Marital status of the respondents

[84] Marital status

[85] Frequency

[86] Percent

[87] Valid percent

[88] Cumulative percent

[89] Married

[90] 90

[91] 75

[92] 75

[93] 75

[94] Single

[95] 30

[96] 25

[97] 25

[98] 100

[99] Total

[100] 120

[101] 100

[102] 100

[103] 5040929852200

[104] Interpretation: The table depicts the marital status of the female respondents, which clearly indicates the vast majority are married. 90 women are married, accounting for 75% of the 120 respondents. This is shown in the two "Percent" and "Valid Percent" columns, indicating that 75% of total respondents and 75% of valid responses (excluding any possible missing data) are married. In contrast, 30 women, or 25% of the total and genuine respondents, are single. The "Cumulative Percent" column indicates that 75% of respondents are married, which, when combined with the 25% who are single, equals 100% of the sample. This data shows a high proportion of married women in the questioned population, implying that marriage is a common and possibly expected life event for women in this demographic. The significant disparity between married and single numbers suggests a possible concentration on or depiction of a group in which marriage is the major determinant

[105] Table No.3 Work status of the respondents

[106] Work status

[107] Frequency

[108] Percent

[109] Valid percent

[110] Cumulative percent

[111] Part-time

[112] 30

[113] 25

[114] 25

[115] 25

[116] Full time

[117] 90

[118] 75

[119] 75

[120] 100

[121] Total

[122] 120

[123] 100

[124] 100

[125] Interpretation: The table shows the distribution of job status among female respondents, demonstrating a definite preference for full-time employment. 90 women, or 75% of the sample, work full-time. This is evident in both the "Percent" and "Valid Percent" columns, which show that 75% of total respondents and 75% of valid responses work full-time. In comparison, 30 women, or 25% of total and valid responders, work part-time. The "Cumulative Percent" column indicates that 25% of respondents work part-time, which, when coupled with the 75% who work full-time, equals 100% of the sample. This data shows a high proportion of full-time employment among the polled women, implying that full-time work is both available and preferred in this demographic. The considerable disparity between full-time and part-time percentages suggests a demographic in which full-time work is the predominant determinant, possibly reflecting a focus on career growth or financial security among these women.

[126] Table No.4 Education level of the respondents

[127] Education level

[128] Frequency

[129] Percent

[130] Valid percent

[131] Cumulative percent

[132] HSC

[133] 78

[134] 65

[135] 65

[136] 65

[137] Bachelor

[138] 33

[139] 27.5

[140] 27.5

[141] 92.5

[142] Master

[143] 9

[144] 7.5

[145] 7.5

[146] 100

[147] PHD

[148] 0

[149] 0

[150] 0

[151] Total

[152] 120

[153] 100

[154] 100

[155] Interpretation: The table shows the female respondents' educational attainment levels, with a notable emphasis at the Higher Secondary Certificate (HSC) level. Out of 120 respondents, 78 women finished their HSC, accounting for 65% of the entire sample. This is evident in both of the "Percent" and "Valid Percent" columns, which show that 65% of total respondents and 65% of valid responses have an HSC qualification. At higher educational levels, the quantity of respondents reduces substantially. 33 women, or 27.5% of all responders, have a bachelor's degree. The proportion decreases further for those with a Master's degree, with only 9 women, or 7.5% of respondents, holding this level. Notably, none of the responders have a PhD. The table shows the female respondents' educational attainment levels, with a notable emphasis at the Higher Secondary Certificate (HSC) level. Out of 120 respondents, 78 women finished their HSC, accounting for 65% of the entire sample. This is evident in both of the "Percent" and "Valid Percent" columns, which show that 65% of total respondents and 65% of valid responses have an HSC qualification. At higher educational levels, the quantity of respondents reduces substantially. 33 women, or 27.5% of all responders, have a bachelor's degree. The proportion decreases further for those with a Master's degree, with only 9 women, or 7.5% of respondents, holding this level. Notably, none of the responders have a PhD.

[156] Table No.5 Business size of the respondents

[157] Business size

[158] Frequency

[159] Percent

[160] Valid percent

[161] Cumulative percent

[162] Less than 10 employees

[163] 99

[164] 82.5

[165] 82.5

[166] 82.5

[167] 11 - 20 employees

[168] 12

[169] 10

[170] 10

[171] 92.5

[172] 21 - 50 employees

[173] 9

[174] 7.5

[175] 7.5

[176] 100

[177] 51 - 100 employees

[178] 0

[179] 0

[180] 0

[181] 100

[182] More than 100 employee

[183] 0

[184] 0

[185] 0

[186] 100

[187] Total

[188] 120

[189] 100

[190] 100

[191] Interpretation: The table demonstrates a high prevalence of women-owned enterprises in every small size group, notably those with fewer than ten employees. Out of the 120 respondents, 99 women own firms in this size range, accounting for 82.5% of the sample. This is shown in the two "Percent" and "Valid Percent" columns, demonstrating that 82.5% of women run enterprises with fewer than ten employees. As firm size increases, the number of women-owned businesses declines significantly. 12 women, or 10% of all responses, own firms with 11 to 20 employees, while 9 women, or 7.5%, own enterprises with 21 to 50 employees. For example, there are no respondents who own enterprises with 51-100 employees or more. According to the "Cumulative Percent" column, 82.5% of respondents own firms with fewer than ten employees, 92.5% own businesses with more than twenty employees, and fully own businesses with more than fifty employees. This data reveals a distinct prevalence of very tiny businesses among female respondents, implying that entrepreneurship in this group is predominantly concentrated on micro-enterprises. The considerable decline in holding a company as the size area increases suggests a potential trend in which female entrepreneurs in our sample operate smaller, more manageable businesses.

[192] VII. RELIABILITY OF THE DATA

[193] The variables were assessed using a five-point Likert scale, with one representing strong disagreement and five representing strong agreement. Cronbach's Alpha for all variable above 0.6, indicating the reliability of the data.

[194] Table No.6

[195] Reliability reporting

[196] Cronbach's Alpha

[197] N of Items

[198] Remarks

[199] .678

[200] 22

[201] Reliable

[202] VIII. RESULT

[203] 1. Discriminant Validity

[204] Table No.7 Fornell & Larcker criterion (square root of AVE compared to construct correlations)

[205] Constructs

[206] WES

[207] SCF

[208] NFA

[209] EF

[210] SOC

[211] RTF

[212] women entrepreneur’s success

[213] 0.69

[214] self-confidence

[215] 0.65

[216] 0.70

[217] Need for achievement

[218] 0.62

[219] 0.67

[220] 0.72

[221] Economic factor

[222] 0.65

[223] 0.65

[224] 0.64

[225] 0.73

[226] Socio-cultural factor

[227] 0.66

[228] 0.68

[229] 0.63

[230] 0.77

[231] 0.71

[232] Risk-taking

[233] 0.70

[234] 0.73

[235] 0.62

[236] 0.74

[237] 0.65

[238] 0.67

[239] Source: SPSS Amos Output

[240] The Average Variance Extracted (AVE) values for each construct are shown on the diagonal in the table that is supplied. According to the Fornell-Larcker criterion (1981), each construct's AVE must be higher than the squared correlation between it and every other construct in the model in order to be considered discriminantly valid.

[241] 2. Convergent Validity

[242] Table No.8 HTMT Analysis

[243] Constructs

[244] WES

[245] SCF

[246] NFA

[247] EF

[248] SOC

[249] RTF

[250] women entrepreneur’s success

[251] self-confidence

[252] 0.796

[253] Need for achievement

[254] 0.590

[255] 1.110

[256] Economic factor

[257] 0.701

[258] 0.756

[259] 0.811

[260] Socio-cultural factor

[261] 0.562

[262] 0.065

[263] 0.986

[264] 0.05

[265] Risk-taking

[266] 0.462

[267] 0.730

[268] 0.880

[269] 0.567

[270] 0.09

[271] -

[272] Source: SPSS Amos Output

[273] This study also used the Heterotrait-Monotrait (HTMT) criterion, a more modern method of evaluating discriminant validity. In order to demonstrate discriminant validity, HTMT values should ideally be less than 0.85, as advised by Hair et al. (2017). This criterion is satisfied, indicating sufficient discriminant validity among the constructs being studied, according to the values shown in Table.

[274] 3. Model Fitness Test

[275] Table No.9 Goodness of fit measures

[276] Goodness of fit measures

[277] Df/χ2

[278] GFI

[279] NFI

[280] CFI

[281] TLI

[282] RMSEA

[283] Structural Model

[284] 5.969

[285] 0.612

[286] 0.304

[287] 0.220

[288] 0.253

[289] 0.20

[290] Source: SPSS Amos Output

[291] Note: Df/χ2=Relative chi-square; GFI=Goodness of Fit Index; NFI=Normed Fixed Index; CFI= Comparative Fit Index; TLI= Tucker-Lewis Index; RMSEA=Root Mean Squared Error of Approximation

[292] 4. Structural Equation Modelling:

[293] 5. SEM Assessment

[294] Table No.10 SEM Result

[295] Hypothesis

[296] Regression Path

[297] Estime

[298] S.E

[299] C.R

[300] P-Value

[301] Decision

[302] H1

[303] 3537939251500SCF WES

[304] 0.071

[305] 0.034

[306] 2.099

[307] 0.36

[308] Not Supported

[309] H2

[310] 3315687810500NFA WES

[311] 0.289

[312] 0.084

[313] 3.453

[314] ***

[315] Supported

[316] H3

[317] 3315689046300EF WES

[318] 0.025

[319] 0.024

[320] 1.068

[321] 0.285

[322] Not Supported

[323] H4

[324] 3257069632400SOC WES

[325] -0.049

[326] 0.024

[327] -2.029

[328] 0.042

[329] Supported

[330] H5

[331] 3186238719100RTF WES

[332] 0.083

[333] 0.031

[334] 2.678

[335] 0.007

[336] Supported

[337] Source: SPSS Amos Output

[338] IX. DISCUSSION

[339] The study found that women entrepreneurs' performance is positively influenced by internal variables such as self-confidence, risk-taking, and a need for achievement, as well as external elements including economic and socio-cultural factors. The findings are supported by the previous studies made by CITATION Riz21 \l 1033 (Khan, Yashar, Shah, & Mazhar, 2021). CITATION Dhr24 \l 1033 (Dhruba & kumar, 2024) suggests that self-confidence and external factors contribute to the success of female entrepreneurs by improving their internal competitiveness. This research confirms the critical role of internal factors, in particular the need for achievement and self-confidence, in guiding women entrepreneurs' success, ultimately enhancing their risk tendencies; a strong need for achievement encourages entrepreneurs to take calculated risks and invest in development and creativity, while self-confidence encourages them to consider risks as achievable, thereby improving their risk-bearing capacity, However external socio-cultural and economic factors influence these personality traits and enterprises experiences. CITATION Dhr24 \l 1033 (Dhruba & kumar, 2024) said that Social encouragement, determined by socio-cultural factors, significantly impact entrepreneurial success: favorable incentives fuel ambition, while negative ones hinder growth.

[340] X. CONCLUSION

[341] The study's findings show that personal characteristics, such as Need for achievement, Risk-taking and Self-confidence are important predictors of entrepreneurial success. However, these intrinsic powers are insufficient by themselves. The study also emphasizes the strong favorable impact of external factors such as economic factors and socio-cultural factors. The study give useful information for policymakers, development agencies, and financial institutions who strive to help women entrepreneurs in emerging

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@article{1711402,
      author = {Jaimika Dighe, Dr. Amin Vohra, Prof. Dr. Ajay Trivedi},
      title = {Factors Affecting Women Entrepreneurs? Success: A Study of Small- And Medium-Sized Enterprises in an Emerging Market.},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {1091-1103},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711402.pdf},
      abstract = {Purpose ? The purpose of this study is to investigate the important determinants impacting the success of women entrepreneurs in small and medium-sized enterprises (SMEs) in emerging economies. This study provides a better understanding of the particular obstacles and possibilities that women entrepreneurs encounter in these situations by identifying both internal and external factors that influence their entrepreneurial path.

Design/methodology/approach ? The research adopts a mixed-method approach, combining qualitative interviews with women entrepreneurs across several emerging market economies and quantitative data analysis. Structural Equation Modeling (SEM) is used to analyze the relationships between key variables, providing An in-depth knowledge of the factors influencing the success of women-led SMEs. The study analyzes data from a diverse range of industries to identify common patterns and variations based on geographical and cultural contexts.

Findings ? The study finds that access to finance, social networks, education, and mentorship are significant determinants of success for women entrepreneurs. Additionally, societal perceptions, gender biases, and policy frameworks in emerging markets play a crucial role in shaping the opportunities available to women-led SMEs. Women entrepreneurs in these markets also report navigating unique personal and professional challenges, balancing familial responsibilities with business growth.

Practical implications ? The findings provide valuable insights for policymakers, development agencies, and financial institutions aiming to support women entrepreneurs in emerging markets. By addressing the gender-specific barriers and enhancing access to resources, targeted interventions can help women entrepreneurs maximize their business potential and contribute to the economic development of emerging markets.

Originality/value ? This study contributes to the growing body of literature on women entrepreneurship in emerging markets by focusing on SMEs, an area often under-researched. It highlights the importance of contextual factors, offering practical recommendations for stakeholders to create a more a supportive atmosphere for the success of women businesses.},
      keywords = {Women Entrepreneurs, Emerging Markets, Small And Medium-Sized Enterprises, Success Factors, Gender Equality, Business Growth, Financial Access, Structural Equation Modeling.},
      month = {October},
  }