Women entrepreneurs in developing countries face significant constraints while striving to enhance their businesses in highly competitive environments. This study aims to enrich the understanding of women’s entrepreneurship in Pakistan by identifying and addressing the barriers they face. It highlights the need to eradicate these barriers to boost business performance.
Targeting women entrepreneurs in small and medium enterprises (SMEs) in Pakistan, the study used a multi-criteria decision-making model based on the analytical hierarchy process. Through a literature review and consultations with 127 experts, 10 main barrier categories and 50 sub-categories were identified and ranked. Key barriers include lack of entrepreneurial skills, macro business environment and social networking. A sensitivity analysis further evaluated the stability of these rankings.
The findings suggest that women entrepreneurs should persist despite these challenges. However, identifying barriers alone is insufficient; strategic interventions are essential for significant business growth. This research supports the support program for women entrepreneurs, governmental officials and practitioners in empowering women entrepreneurs.
By tackling critical barriers, this study supports national economic growth by strengthening women’s soft skills and promoting the development of SMEs.
1. Introduction
Female entrepreneurs have made significant contributions to both national and global economies (Jiang et al., 2024), and women’s entrepreneurship is on the rise (Chatterjee et al., 2022). According to the Global Entrepreneurship Monitor (2024) (GEM, 2023 / 24), women’s startup activity rates have increased from an average of 6.1% in the 2001–2005 period to 10.4% in the 2021–2023 period across 30 GEM-participating countries. Recent studies also highlight a growing number of women entrepreneurs in the developing world (Ogundana et al., 2021; Simba et al., 2023; Ssekiziyivu et al., 2025).
The rise in women’s entrepreneurship is closely linked to socio-cultural changes (Wiig et al., 2024). According to Chen and Chen (2024), the surge in female entrepreneurship in rural China resulted from profound social transformations and cultural shifts. Furthermore, there has been a significant change in social perceptions of women entrepreneurs (Jiang et al., 2024). More women have access to education, and many are participating in online business ventures. For instance, in countries like Pakistan, where traditional norms previously limited women to domestic roles, societal attitudes are beginning to shift. Evidence from the Global Entrepreneurship Monitor (GEM) reports indicates that these changes have led to a 10% increase in women’s total early-stage entrepreneurial activity over the past two decades.
Recent studies have reinforced and expanded upon these findings, noting that despite increasing opportunities for women to become entrepreneurs, they continue to face a wide array of challenges, particularly in developing countries (Jiang et al., 2024; Moral et al., 2024; Wiig et al., 2024). Female entrepreneurs in the Global South often confront more acute constraints compared to their counterparts in developed economies due to context-specific issues such as limited access to training, informal business environments and patriarchal gender norms (Aravamudhan et al., 2024; Chhabra et al., 2023; Simba et al., 2023). Financial constraints remain a core issue (Dutta and Mallick, 2023), while gender roles, male-dominated networks and societal expectations for women to prioritize family responsibilities further inhibit entrepreneurial growth (Wiig et al., 2024). Moral et al. (2024), in their study on women entrepreneurs in India, highlighted specific barriers such as rising costs of raw materials, lack of entrepreneurial and managerial skills and limited market access, particularly among marginalized women. Taken together, these studies reveal a systemic interplay of financial, social and institutional challenges that continue to disadvantage women entrepreneurs, especially in low- and middle-income economies.
Building upon these foundational insights, it becomes evident that a focused investigation into the barriers faced by female entrepreneurs in Pakistan is both timely and necessary. Pakistan represents a unique socio-cultural and economic landscape where patriarchal norms, limited institutional responsiveness and deep-seated structural inequities converge to form a particularly restrictive environment for women entrepreneurs (Ali and Qureshi, 2022; Rahman et al., 2023). While global scholarship often aggregates data from diverse developing contexts or centers on Western economies, it seldom captures the granular realities of entrepreneurship in South Asian settings, particularly in underexplored economies such as Pakistan.
This study makes three contributions to understanding the barriers faced by women entrepreneurs. First, the research provides a comprehensive identification and ranking of these barriers using the analytical hierarchy process (AHP) method, which prioritizes and addresses the sensitivity of each barrier, offering a structured way to understand the specific challenges women entrepreneurs face in this sector (Akram et al., 2022). Given that more than 90% of startups in Pakistan fail each year, with only about five out of 300 startups operating successfully, this contribution is crucial for improving the success rate of women-led ventures (Rizvi et al., 2023). Second, the study differentiates its findings from those conducted in Western contexts, where businesses benefit from stronger educational, environmental and institutional support, and where entrepreneurship theories are more mature (Schlaegel and Koenig, 2014; Wu et al., 2019). This context-specific research provides insights into the unique socio-economic and cultural barriers that Pakistani women face, which are often not addressed in studies conducted in more developed settings. Finally, this emphasis on the local context is essential for developing targeted policies and interventions that can effectively support women entrepreneurs.
2. Literature review
2.1 Women entrepreneurship
Women’s entrepreneurship encompasses the initiation, development and management of business ventures by women. It has been increasingly acknowledged as a multidimensional mechanism for socio-economic advancement, serving as a viable pathway out of poverty (Chatterjee et al., 2022), enhancing community development (Agarwal et al., 2021) and contributing to national modernization processes (Chatterjee et al., 2022). Empirical studies have established that women-led enterprises significantly contribute to macroeconomic growth and development (Jiang et al., 2024). Women’s entrepreneurship reduces both social and economic pressure on the state (Ng et al., 2022). In addition to stimulating innovation, women’s entrepreneurship is positively associated with employment generation and poverty reduction, particularly in low- and middle-income countries (Kitole and Genda, 2024; Wiig et al., 2024).
Women increasingly engage in informal entrepreneurial activities as a means of balancing domestic responsibilities with income-generating pursuits (Martins and Perez, 2025). Such entrepreneurial engagement enables them to manage household duties while contributing economically through home-based work (Oladipo et al., 2023). Notably, women’s entrepreneurship is experiencing substantial growth in rural areas (Aggarwal and Johal, 2021). For instance, Chen and Barcus (2024) highlighted the transformative role of women entrepreneurs in rural China, where they have made significant contributions in areas such as resource integration, employment generation, livelihood enhancement and community development. This entrepreneurial success, in turn, reinforces and amplifies their ongoing engagement in entrepreneurial ventures (Jiang et al., 2024).
The scope and nature of women’s participation in entrepreneurial activities differ substantially across geographic regions and socio-economic settings. Empirical evidence suggests that women operating within pro-entrepreneurial policy environments and supportive cultural contexts exhibit higher rates of entrepreneurial success (Liñán et al., 2022; Liñán et al., 2024). Access to financial capital remains a fundamental determinant of business sustainability and growth, with government subsidies serving as a key enabler in many developing economies (Chhabra et al., 2023; Ogundana et al., 2021). Furthermore, the formation and utilization of entrepreneurial networks significantly enhance women’s ability to mobilize critical resources and navigate institutional challenges (Ribeiro et al., 2021). Strategic decision-making is equally vital, as the adoption of contextually responsive strategies directly influences business performance (Anggadwita and Indarti, 2023). As articulated by Ojong et al. (2021), it is the interplay among contextual conditions, resource accessibility and strategic orientation that ultimately shapes the trajectories and outcomes of women’s entrepreneurial ventures.
2.2 Barriers to women’s entrepreneurship
Women entrepreneurs encounter a broad spectrum of challenges arising from economic, social, familial, market and regulatory environments (Aravamudhan et al., 2024). In the context of Uganda, Ssekiziyivu et al. (2025) identified several critical barriers, including intense market competition, liquidity and financial constraints, gender-based discouragement and discrimination, limited family support, harassment, time limitations due to domestic responsibilities, inadequate access to entrepreneurial training and poor business locations. Similarly, a study by James and Onoshakpor (2025) on Nigerian women entrepreneurs highlighted institutional voids, high inflation rates, difficulties in maintaining work–life balance and restricted access to viable business opportunities as key impediments to their entrepreneurial success.
Cultural barriers continue to hinder women’s participation in entrepreneurial activities (Wiig et al., 2024). Empirical evidence indicates that women are consistently more likely than men to perceive both themselves and the broader business environment as unfavorable, irrespective of their entrepreneurial intentions or motivations (Jiang et al., 2024). In response to these challenges, women often seek to establish their own enterprising communities to provide mutual support and empowerment (Agarwal et al., 2021). The active involvement of women in entrepreneurial and productive endeavors is inherently positive and plays a critical role in advancing their socio-economic empowerment (Ng et al., 2022). However, there are social and cultural challenges to be overcome to transform attitudes towards gender roles in entrepreneurship (Nguyen et al., 2024).
Women entrepreneurs frequently encounter substantial financial constraints in initiating, sustaining and expanding their business ventures. Evidence suggests that their access to financial capital remains significantly limited in comparison to their male counterparts (Jiang et al., 2024). In response to these challenges, women often employ a variety of adaptive strategies that draw upon both community-based mechanisms and personal networks. For example, Simba et al. (2023) emphasized the importance of community financing schemes in supporting women’s entrepreneurial trajectories. Similarly, the strategic use of social capital, particularly familial support, has been shown to play a critical role in overcoming financial and structural impediments (Chhabra et al., 2023). In the Chinese context, Jiang et al. (2024) demonstrated that female entrepreneurs frequently rely on guanxi (interpersonal networks), especially family connections, to navigate entrepreneurial hurdles. Additionally, digital affordances such as virtual networking, online learning, opportunity creation and business scaling have proven instrumental in circumventing cultural barriers to women’s entrepreneurial engagement (Wiig et al., 2024). However, despite these adaptive mechanisms, women continue to face persistent obstacles, including a lack of cooperation from male family members, limited bargaining power, low self-esteem and various psychological barriers (Aravamudhan et al., 2024).
3. Research methodology
The current study introduced a novel multi-criteria decision-making (MCDM) model based on the AHP method, consisting of four major stages: classification, weight estimation, prioritization and validation. The classification stage involved organizing the barriers into major and sub-barrier categories, which were derived from a synthesis of the literature and expert input. Drawing on previous empirical studies and relevant literature (Roomi and Parrott, 2008), a structured questionnaire was developed to gather insights from experts, policymakers and entrepreneurial ecosystem stakeholders across Pakistan.
In the subsequent stage, the AHP, a widely recognized method for addressing complex decision-making problems involving both qualitative and quantitative criteria, was employed to evaluate and rank the identified barriers. This technique can support to solve complex decision-making problems and facilitate to decompose a decision problem into its constituent parts in a hierarchical form. Originally developed by Saaty (1980), the AHP enables decision makers to perform structured pairwise comparisons and derive relative weights across competing factors (Saaty, 2008). Its hierarchical framework allows for systematic decomposition of problems, facilitating clarity in the assignment of priorities. This makes AHP particularly suitable for the study’s aim: to determine the relative significance of various entrepreneurial barriers hindering female participation in the small- and medium-sized enterprise (SME) sector in Pakistan and to understand their interrelationships in a structured manner (Ishizaka and Labib, 2011). The AHP approach used in this study to prioritize the identified barriers and the detailed steps of the proposed solution methodology are shown in Figure 1.
The flowchart begins with literature review and expert input, leading to establishing a decision group of entrepreneurship experts and consultants. The group identifies barriers faced by women entrepreneurs, assesses subjectivity and vagueness, and structures a decision hierarchy. If the hierarchy is not approved, the process loops back for revision. If approved, the process moves to phase two, analytical hierarchy process. This involves defining a scale of relative importance, constructing a pairwise comparison matrix using expert input, and testing for consistency. If the consistency ratio exceeds 0.10, the process loops back for adjustment. If it is within 0.10, the pairwise comparison matrix is finalised, priority weights of barriers are computed, and sensitivity analysis is performed, leading to policy implications and conclusions.Proposed AHP model to prioritize the barriers faced by women entrepreneurs
Source: Authors’ own work
The flowchart begins with literature review and expert input, leading to establishing a decision group of entrepreneurship experts and consultants. The group identifies barriers faced by women entrepreneurs, assesses subjectivity and vagueness, and structures a decision hierarchy. If the hierarchy is not approved, the process loops back for revision. If approved, the process moves to phase two, analytical hierarchy process. This involves defining a scale of relative importance, constructing a pairwise comparison matrix using expert input, and testing for consistency. If the consistency ratio exceeds 0.10, the process loops back for adjustment. If it is within 0.10, the pairwise comparison matrix is finalised, priority weights of barriers are computed, and sensitivity analysis is performed, leading to policy implications and conclusions.Proposed AHP model to prioritize the barriers faced by women entrepreneurs
Source: Authors’ own work
3.1 Overview of the analytical hierarchy process
The AHP is a widely recognized MCDM technique used in complex decision-making environments where multiple, and often conflicting, criteria must be evaluated. Originally developed by Saaty (1980, 1986a;1986b), the AHP enables decision-makers to structure a problem hierarchically into goal, criteria and alternatives, allowing for systematic analysis through pairwise comparisons and priority weight estimations derived from eigenvector calculations. The strength of the AHP lies in its mathematical rigor and flexibility to incorporate both qualitative and quantitative inputs, making it suitable for hierarchical problems faced by organizations in uncertain environments (Douligeris and Pereira, 1994). The AHP has become considered well-known approach in entrepreneurship research for recognizing and prioritizing elements that impact entrepreneurial choices, including startup challenges, strategic priorities and policy development (Ghimire and Kim, 2018).
This study used the AHP to compute the weights of the identified criteria. The AHP methodology involves a systematic and structured approach, comprising three fundamental procedural stages, as outlined by Deviren et al. (2009):
the identification of key barriers and the development of a hierarchical prioritization model;
the design and distribution of a structured questionnaire for expert data collection; and
the calculation of normalized weight vectors for each major and sub-barrier category.
Expert input was gathered from individuals with substantial entrepreneurial experience through a carefully crafted questionnaire.
This study used a convenience sampling strategy to select participants for the analytic hierarchy process (AHP) evaluation. While convenience sampling does not yield a statistically representative sample, it was selected due to the highly specialized nature of the research topic and the limited accessibility of individuals with relevant expertise. In the context of MCDM studies, especially those using AHP, the priority is often to obtain informed, high-quality judgments rather than a large, random sample. The selected participants were subject-matter experts with deep, contextual knowledge, making them uniquely qualified to provide reliable pairwise comparisons for barrier prioritization.
To ensure the validity and relevance of the data collected, experts were selected based on clearly defined eligibility criteria, consistent with best practices in expert judgment research (Okoli and Pawlowski, 2004; Rowe and Wright, 2011). Each participant possessed a minimum of five years of professional experience directly related to the domain under investigation, thereby meeting the threshold for domain-specific expertise recommended in prior methodological literature (Hsu and Sandford, 2007). Demonstrated expertise was evidenced through academic publications, industry reports, consultancy engagements or leadership roles in relevant sectors, ensuring a combination of theoretical and practical knowledge. Furthermore, all selected experts confirmed their availability and willingness to participate in both the initial pairwise comparison process and the subsequent validation stage, a procedural step emphasized in multi-criteria decision-making studies to enhance the robustness of results (Saaty, 2008).
The collected data were subsequently analyzed using the AHP technique. To ensure the reliability of the pairwise comparison matrices, a consistency ratio (CR) test was conducted. The CR is determined through the following computational steps:
compute the values of the relative weights and λmax for each matrix of order n; and
compute the consistency index (CI) for each matrix of order n using the formula:
The CR is calculated by dividing the CI by a random index (RI). After obtaining the CI and RI values, calculate the CR value using the below formula:
The calculated CR value is then compared to a threshold, typically 0.1 (or 10%). Suppose CR ≤ 0.1, the comparisons are considered acceptably consistent and if CR > 0.1, the comparisons are considered inconsistent. Therefore the decision-maker should review and potentially revise their judgements while making the comparisons.
3.2 Rationale for using multi-criteria decision-making methods
MCDM methods are widely employed to address complex decision contexts characterized by multiple, and often conflicting, criteria, particularly under conditions of uncertainty (Zavadskas et al., 2014; Velasquez and Hester, 2013). These approaches provide a structured analytical framework for evaluating alternatives and identifying compromise solutions that balance diverse objectives. By systematically analyzing criteria and their relative weights, MCDM methods facilitate more informed and effective decision-making compared to purely intuitive or subjective approaches (Belton and Stewart, 2002). Importantly, many MCDM techniques are capable of incorporating uncertainty in both data and stakeholder preferences, thereby enhancing the robustness of decisions in dynamic and complex environments (Govindan and Jepsen, 2016).
MCDM methods for weight computation and alternative ranking can generally be classified into two categories, namely, subjective weighting methods, which rely on expert judgements, and objective weighting methods, which derive weights from data characteristics (Zavadskas et al., 2014). Prominent subjective methods include the AHP, Analytic Network Process, Best–Worst Method and approaches based on Fuzzy Set Theory (Saaty, 2008; Rezaei, 2015). Common objective methods include the Entropy Method, CRITIC and MEREC (Diakoulaki et al., 1995; Ghorabaee et al., 2021). For prioritization tasks, widely used techniques include TOPSIS, VIKOR and PROMETHEE (Behzadian et al., 2012; Opricovic and Tzeng, 2004). Collectively, these methods provide decision-makers with a rigorous, transparent and replicable toolkit for navigating multidimensional decision problems.
The applicability of MCDM methods spans multiple domains, including business management, engineering, health care, energy planning, supplier selection and sustainability assessment (Velasquez and Hester, 2013). Among these, the AHP has gained significant traction due to its structured pairwise comparison process, ability to integrate expert judgment, and capacity to test for consistency (Saaty, 2008). The method has been successfully applied in SME supplier selection (Manik, 2023), sustainable procurement (Waris and Hameed, 2023), transportation planning (Kriswardhana et al., 2025) and public sector technology adoption (Al Riyami and Ashrafi, 2016). In addition, the AHP has been used in performance appraisal (Islam and Rasad, 2006; Taylor et al., 1998), supply chain prioritization (Singh, 2013) and the identification of barriers to women’s entrepreneurial intentions (Patra and Lenka, 2022). Compared to alternative MCDM methods such as fuzzy techniques, the AHP often engages a larger pool of experts, which can enhance the reliability and generalizability of results (Nazam et al., 2015). Furthermore, its transparent structure facilitates objective, consistent and reproducible rankings across varied contexts, including entrepreneurial ecosystems.
4. Application of solution methodology
4.1 Research context and problem scenario
In line with trends observed across many developing nations, Pakistan exhibits considerable potential for the growth of SMEs. Recognizing this, policymakers have increasingly emphasized the importance of promoting female entrepreneurship as a means of fostering inclusive economic development and enhancing women’s self-efficacy. However, despite the anticipated opportunities, women entrepreneurs in Pakistan continue to face persistent barriers that hinder their participation and growth in the entrepreneurial ecosystem.
The present study focused on female entrepreneurs operating SMEs in Pakistan’s largest province, examining the specific obstacles they encounter within an increasingly competitive global market. The challenges commonly faced by women entrepreneurs such as limited access to finance and societal biases in different regions and provinces across the country. In response to these challenges, small and medium enterprise development authority (SMEDA) seeks to identify and eliminate structural and institutional barriers that limit women’s entrepreneurial engagement. This research is therefore timely and relevant, aiming to contribute to both scholarly understanding and practical policy interventions. The need for such insights is further supported by recent findings that underscore the significance of enabling conditions for women-led SMEs in emerging economies (Gopi and Subramoniam, 2024).
As in other developing countries, Pakistan offers business opportunities for women entrepreneurs, supported by policies aimed at promoting women’s entrepreneurship (Hussain et al., 2019). These efforts help eliminate barriers typically faced during the entrepreneurial process, especially amid rising global market competition (Gopi and Subramoniam, 2024). In this context, the present study focuses on women entrepreneurs operating SMEs in Pakistan’s largest province. The Punjab province was selected for the collection of data as it has a diverse range of SMEs and demographics of respondents.
4.2 Questionnaire development
In this stage, questionnaire was designed to facilitate data collection process which comprise on two main phases. The data collection for this study was carried out through a structured, two-phase survey process designed to identify and prioritize barriers faced by women entrepreneurs. In Phase 1, an exploratory survey was administered to capture a broad set of commonly encountered challenges. Phase 2 then refined this focus by identifying the most critical barriers taken from the responses in the initial phase. The study specifically targeted women entrepreneurs operating at various levels of management within SMEs across the Punjab province of Pakistan. Prior to administering the survey, participants were provided with comprehensive documentation outlining the research objectives and objective of the study. The profile of the respondents from the initial survey, including business types, ownership status, number of employees and work experience, is summarized in Table 1.
Profile of the women interviewees and their enterprises
| Description | Total | % |
|---|---|---|
| Business type | ||
| Graphic designer | 23 | 18.11 |
| Software developer | 15 | 11.82 |
| Online tuition services | 09 | 7.086 |
| Beauty parlor | 21 | 16.53 |
| Educational consultant | 22 | 17.33 |
| Freelancer | 20 | 15.74 |
| Clinical psychologist | 17 | 13.38 |
| Total | 127 | 100 |
| Nature of ownership | ||
| Sole proprietorship | 79 | 62.20 |
| Partnership or joint venture | 48 | 37.79 |
| Total | 127 | 100 |
| Work experience (years) | ||
| <05 | 47 | 37.00 |
| 6–10 | 29 | 22.83 |
| 11–15 | 27 | 21.25 |
| >15 | 24 | 18.89 |
| Total | 127 | 100 |
| Number of employees | ||
| <10 | 39 | 30.70 |
| 11–30 | 31 | 24.40 |
| 31–50 | 38 | 29.92 |
| >50 | 19 | 14.96 |
| Total | 127 | 100 |
| Description | Total | % |
|---|---|---|
| Business type | ||
| Graphic designer | 23 | 18.11 |
| Software developer | 15 | 11.82 |
| Online tuition services | 09 | 7.086 |
| Beauty parlor | 21 | 16.53 |
| Educational consultant | 22 | 17.33 |
| Freelancer | 20 | 15.74 |
| Clinical psychologist | 17 | 13.38 |
| Total | 127 | 100 |
| Nature of ownership | ||
| Sole proprietorship | 79 | 62.20 |
| Partnership or joint venture | 48 | 37.79 |
| Total | 127 | 100 |
| Work experience (years) | ||
| <05 | 47 | 37.00 |
| 6–10 | 29 | 22.83 |
| 11–15 | 27 | 21.25 |
| >15 | 24 | 18.89 |
| Total | 127 | 100 |
| Number of employees | ||
| <10 | 39 | 30.70 |
| 11–30 | 31 | 24.40 |
| 31–50 | 38 | 29.92 |
| >50 | 19 | 14.96 |
| Total | 127 | 100 |
4.3 Two-phase process for prioritization of barriers
4.3.1 Phase 1: initial survey to identify the common barriers.
To identify the most prevalent barriers encountered by women entrepreneurs, an initial survey was conducted based on 86 barriers identified through an extensive review of existing literature. Respondents were asked to indicate whether each barrier was relevant to their entrepreneurial experience by selecting either “Yes” or “No.” The questionnaires were distributed via surface mail to 249 SMEs across the Punjab province in August 2023, with follow-up communications conducted periodically to encourage timely and adequate responses. The data collection phase was completed within a two-month period.
Of the 249 surveys distributed, 127 valid responses were received, yielding a usable response rate of 51%. The remaining responses included 53 incomplete submissions, 47 refusals to participate and 22 blank returns. According to Malhotra and Grover (1998), a response rate exceeding 20% is considered acceptable and indicative of a reasonably successful survey effort. The response rate achieved in this study suggests a substantial level of awareness among women entrepreneurs in Pakistani SMEs regarding the challenges they face.
4.3.2 Phase 2: identification of essential barriers.
In the second phase, the study used the AHP to identify and prioritize the most critical barriers to female entrepreneurship in SMEs. Building on the initial survey conducted in Phase 1, 10 major barrier categories and 50 corresponding sub-barriers were extracted and elevated for further analysis based on their perceived significance.
To structure the decision-making process, the problem was formulated into a four-tier hierarchical framework, as illustrated in Figure 1. The hierarchical levels were defined as follows:
Level 1: The overarching objective of the study;
Level 2: Major categories of barriers;
Level 3: Sub-barrier dimensions under each category; and
Level 4: Relative prioritization of individual barriers.
Using this hierarchical framework, the AHP methodology was applied to perform pairwise comparisons across two levels: among the major barrier categories and among the associated sub-barrier items within each category. Participants comprising stakeholders and experts within the SME sector were asked to evaluate the relative importance of each barrier using Saaty’s nine-point scale.
The data collected were used to construct pairwise comparison matrices, enabling the computation of normalized weight vectors for each barrier. To enhance the reliability of the results, a refined nine-point scale was adopted to assess the relative importance of the categories and sub-barriers (as shown in Table 2). The random consistency index, used to validate the consistency of the judgments, is presented in Table 3. The final computed AHP weights, representing the relative importance of the major barrier categories, are detailed in Figure 2. Finally, the prioritized values of barriers were tested through sensitivity analysis by observing the fluctuation among results and suggest remedial measures in case of high variations.
Scale for relative importance used in the pairwise comparison matrix
| Linguistic variables | Equally important | Weekly important | Strongly important | Very strongly important | Extremely more important | Reciprocal values |
|---|---|---|---|---|---|---|
| Intensity ofimportance | 1 | 3 | 5 | 7 | 9 | 2, 4, 6, 8 |
| Linguistic variables | Equally important | Weekly important | Strongly important | Very strongly important | Extremely more important | Reciprocal values |
|---|---|---|---|---|---|---|
| Intensity ofimportance | 1 | 3 | 5 | 7 | 9 | 2, 4, 6, 8 |
The random consistency index
| Size (n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| RI | 0 | 0 | 0.52 | 0.89 | 1.11 | 1.25 | 1.35 | 1.40 | 1.45 | 1.49 |
| Size (n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0.52 | 0.89 | 1.11 | 1.25 | 1.35 | 1.40 | 1.45 | 1.49 |
The radar chart displays evaluation scores of barriers to women entrepreneurs. Radial axes represent barrier categories coded as E S, M B, S N B, F B, K B, S A, R A, F F, and ATC, each with numbered subcategories. Multiple coloured lines radiate outward, representing expert scoring weights ranging from 0.2070 to 0.0207. Higher scores, such as E S 1 to E S 6, extend further outward, while others cluster closer to the centre, such as MBEB categories. The overlapping lines show variation among categories but a general trend of higher weighting for entrepreneurial skills barriers compared with management, finance, and support barriers.Results of sensitivity analysis for entrepreneurial skills (ES)
Source: Authors’ own work
The radar chart displays evaluation scores of barriers to women entrepreneurs. Radial axes represent barrier categories coded as E S, M B, S N B, F B, K B, S A, R A, F F, and ATC, each with numbered subcategories. Multiple coloured lines radiate outward, representing expert scoring weights ranging from 0.2070 to 0.0207. Higher scores, such as E S 1 to E S 6, extend further outward, while others cluster closer to the centre, such as MBEB categories. The overlapping lines show variation among categories but a general trend of higher weighting for entrepreneurial skills barriers compared with management, finance, and support barriers.Results of sensitivity analysis for entrepreneurial skills (ES)
Source: Authors’ own work
5. Results and discussions
5.1 Main barrier categories and ranking order
The study identifies ten primary categories of barriers, which are ranked in descending order based on their corresponding weight values, as presented in Table 4. The most significant barrier is entrepreneurial skills (ES) with a weight value of 0.2020, followed by macro business environment barriers (MBEB) (0.1730), social networking barriers (SNB) (0.1278), market barriers (MB) (0.1246) and financial barriers (FB) (0.0883). These are succeeded by knowledge barriers (KB) (0.0835), stress barriers (SB) (0.0643), risk avoidance (RA) (0.0511), fear of failure (FF) (0.0444), and, finally, attitude towards change (ATC) (0.0359). These rankings are consistent with insights found in the existing literature, which highlights the centrality of entrepreneurial skills and environmental constraints as key inhibitors to entrepreneurial activity, particularly in developing country contexts (Amankwah-Amoah et al., 2018; Kirkley, 2017) and are systematically detailed in Table 4.
AHP weights for the major barrier categories
| Barrier category | Weight value | Judgment procedure |
|---|---|---|
| ES | 0.2070 | λ Max = 11.3856 |
| MBEB | 0.1730 | λ Min = 10.1086 |
| SNB | 0.1278 | Order of Matrix = 10 |
| MB | 0.1246 | RI = 1.49, |
| FB | 0.0883 | CI = (λmax-n)/(n - 1) |
| KB | 0.0835 | CI = 0.1061 |
| SB | 0.0643 | CR = CI/RI |
| RA | 0.0511 | CR = 0.09694 < 0.1 |
| FF | 0.0444 | CR< 0.1, Matrix is Consistent |
| ATC | 0.0359 |
| Barrier category | Weight value | Judgment procedure |
|---|---|---|
| 0.2070 | λ Max = 11.3856 | |
| 0.1730 | λ Min = 10.1086 | |
| 0.1278 | Order of Matrix = 10 | |
| 0.1246 | RI = 1.49, | |
| 0.0883 | ||
| 0.0835 | CI = 0.1061 | |
| 0.0643 | ||
| 0.0511 | CR = 0.09694 < 0.1 | |
| 0.0444 | CR< 0.1, Matrix is Consistent | |
| 0.0359 |
Based on the above rankings, it can be observed that among these barriers, entrepreneurial skills emerge as the most critical, significantly affecting the feasibility of women entrepreneurs’ businesses. Following closely, macro business environment barriers exhibit substantial weight, highlighting their impact on entrepreneurial endeavors. Conversely, attitude towards change is identified with the lowest weight in this study, indicating its lesser significance compared to other barriers. This ranking underscores the pivotal role of entrepreneurial skills and macro business environment factors in shaping the entrepreneurial landscape for women. It aligns with previous research findings and provides a structured framework for understanding and addressing barriers that hinder women’s entrepreneurial success.
5.2 Sub-barrier categories and ranking order
Table 5 presents the relative weights and global weights of all barrier and sub-barrier categories, which collectively determine the overall ranking based on the AHP methodology. The global weights are calculated by multiplying the relative weight values of major barrier categories with those of each corresponding sub-barrier category. These weights provide a comprehensive assessment of the hierarchy of barriers faced by women entrepreneurs, offering insights into their relative importance within the overall framework.
Relative weights and global weights of all barrier categories along ranking
| Major criterion | Major barrier weight | Sub-barrier | CR | Relative weights using AHP | Global weight using AHP | Ranking |
|---|---|---|---|---|---|---|
| ES | 0.2070 | Lack of recognition of opportunity (ES1) | 0.0640 | 0.3497 | 0.0724 | 2 |
| Lack of problem-solving skills (ES2) | 0.2410 | 0.0499 | 3 | |||
| Lack of creativity (ES3) | 0.1360 | 0.0281 | 11 | |||
| Lack of flexibility (ES4) | 0.1326 | 0.0275 | 12 | |||
| Lack of leadership and communication skills (ES5) | 0.0857 | 0.0177 | 22 | |||
| Lack of development of new products and services (ES6) | 0.0550 | 0.0114 | 35 | |||
| MBEB | 0.1730 | Current economic situation (MBEB1) | 0.0945 | 0.1688 | 0.0292 | 9 |
| Political instability(MBEB2) | 0.1664 | 0.0288 | 10 | |||
| Economic instability (MBEB3) | 0.1312 | 0.0227 | 16 | |||
| Lack of control on corruption (MBEB4) | 0.1140 | 0.0197 | 19 | |||
| Heavy taxes (MBEB5) | 0.0886 | 0.0153 | 24 | |||
| Lack of government support and assistance (MBEB6) | 0.0707 | 0.0122 | 31 | |||
| Inability to forecast demand (MBEB7) | 0.0657 | 0.0114 | 36 | |||
| Lack of legal aid/counseling (MBEB8) | 0.0529 | 0.0091 | 39 | |||
| Lack of support from people around me (MBEB9) | 0.0451 | 0.0078 | 41 | |||
| Lack of research and development (MBEB10) | 0.0416 | 0.0072 | 43 | |||
| Uncertainty in market (MBEB11) | 0.0291 | 0.0050 | 46 | |||
| Lack of proper infrastructure (MBEB12) | 0.0259 | 0.0045 | 50 | |||
| SNB | 0.1278 | Lack of social networking makes it difficult to start new business (SNB1) | 0.0901 | 0.3626 | 0.0463 | 4 |
| Good social network increases the probability of success (SNB2) | 0.2534 | 0.0324 | 7 | |||
| Having a social network is important to start a company (SNB3) | 0.1767 | 0.0226 | 17 | |||
| When I need help, I usually rely on my existing social network (SNB4) | 0.1137 | 0.0145 | 26 | |||
| A powerful social network is definitely important to a company (SNB5) | 0.0936 | 0.0120 | 32 | |||
| MB | 0.1246 | New companies will face stiff competition from large companies (MB1) | 0.0327 | 0.6378 | 0.0795 | 1 |
| New companies will face difficulties in gaining acceptance in the market (MB2) | 0.2577 | 0.0321 | 8 | |||
| Good business contacts will facilitate the acquisition of new business project(MB3) | 0.1045 | 0.0130 | 30 | |||
| FB | 0.0883 | It is hard to find capital providers to start-up companies (FB1) | 0.0743 | 0.4085 | 0.0361 | 6 |
| Lack of financial resources is a barrier for a new business startup (FB2) | 0.2187 | 0.0193 | 20 | |||
| There are not sufficient subsidies available for new companies (FB3) | 0.1953 | 0.0172 | 23 | |||
| Financial institutions impose a high interest rates on loan for new projects (FB4) | 0.1156 | 0.0102 | 38 | |||
| Banks do not readily give credit to start-up companies (FB5) | 0.0619 | 0.0055 | 45 | |||
| KB | 0.0835 | It is difficult to establish a new company without business experience (KB1) | 0.0899 | 0.4506 | 0.0376 | 5 |
| Marketing knowledge is necessary for a successful new company (KB2) | 0.2602 | 0.0217 | 18 | |||
| It is difficult to establish a new company without business knowledge (KB3) | 0.1835 | 0.0153 | 25 | |||
| It is difficult to have entrepreneurial competence (KB4) | 0.1057 | 0.0088 | 40 | |||
| SB | 0.0643 | The thought of stress leads to restlessness (SB1) | 0.0878 | 0.4139 | 0.0266 | 13 |
| I can work for long hours (SB2) | 0.2216 | 0.0143 | 27 | |||
| Hard work negatively affects life (SB3) | 0.1825 | 0.0117 | 34 | |||
| Hard work is not good for health (SB4) | 0.1080 | 0.0069 | 44 | |||
| Difficulties in managing hard work (SB5) | 0.0740 | 0.0048 | 49 | |||
| RA | 0.0511 | Prefer job security than risky business (RA1) | 0.0974 | 0.4466 | 0.0228 | 15 |
| People who take risks are more likely to succeed (RA2) | 0.2572 | 0.0131 | 29 | |||
| People who can assume risks are more likely to succeed (RA3) | 0.2018 | 0.0103 | 37 | |||
| I am fine with the risk of Irregular income (RA4) | 0.0944 | 0.0048 | 48 | |||
| FF | 0.0444 | Fear of failure is a barrier to starting-up a new business (FF1) | 0.0057 | 0.5399 | 0.0240 | 14 |
| Embarrassment from failing in business ventures is a barrier to starting-up a new business (FF2) | 0.2971 | 0.0132 | 28 | |||
| Fear of failure is one reason for the limited number of business start-ups (FF3) | 0.1630 | 0.0072 | 42 | |||
| ATC | 0.0359 | Constant change to remain motivated (ATC1) | 0.0384 | 0.5283 | 0.0190 | 21 |
| Feel boring in stable environment (ATC2) | 0.3328 | 0.0120 | 33 | |||
| Prefer govt. job rather than private business (ATC3) | 0.1388 | 0.0050 | 47 |
| Major criterion | Major barrier weight | Sub-barrier | Relative weights using | Global weight using | Ranking | |
|---|---|---|---|---|---|---|
| 0.2070 | Lack of recognition of opportunity (ES1) | 0.0640 | 0.3497 | 0.0724 | 2 | |
| Lack of problem-solving skills (ES2) | 0.2410 | 0.0499 | 3 | |||
| Lack of creativity (ES3) | 0.1360 | 0.0281 | 11 | |||
| Lack of flexibility (ES4) | 0.1326 | 0.0275 | 12 | |||
| Lack of leadership and communication skills (ES5) | 0.0857 | 0.0177 | 22 | |||
| Lack of development of new products and services (ES6) | 0.0550 | 0.0114 | 35 | |||
| 0.1730 | Current economic situation (MBEB1) | 0.0945 | 0.1688 | 0.0292 | 9 | |
| Political instability(MBEB2) | 0.1664 | 0.0288 | 10 | |||
| Economic instability (MBEB3) | 0.1312 | 0.0227 | 16 | |||
| Lack of control on corruption (MBEB4) | 0.1140 | 0.0197 | 19 | |||
| Heavy taxes (MBEB5) | 0.0886 | 0.0153 | 24 | |||
| Lack of government support and assistance (MBEB6) | 0.0707 | 0.0122 | 31 | |||
| Inability to forecast demand (MBEB7) | 0.0657 | 0.0114 | 36 | |||
| Lack of legal aid/counseling (MBEB8) | 0.0529 | 0.0091 | 39 | |||
| Lack of support from people around me (MBEB9) | 0.0451 | 0.0078 | 41 | |||
| Lack of research and development (MBEB10) | 0.0416 | 0.0072 | 43 | |||
| Uncertainty in market (MBEB11) | 0.0291 | 0.0050 | 46 | |||
| Lack of proper infrastructure (MBEB12) | 0.0259 | 0.0045 | 50 | |||
| 0.1278 | Lack of social networking makes it difficult to start new business (SNB1) | 0.0901 | 0.3626 | 0.0463 | 4 | |
| Good social network increases the probability of success (SNB2) | 0.2534 | 0.0324 | 7 | |||
| Having a social network is important to start a company (SNB3) | 0.1767 | 0.0226 | 17 | |||
| When I need help, I usually rely on my existing social network (SNB4) | 0.1137 | 0.0145 | 26 | |||
| A powerful social network is definitely important to a company (SNB5) | 0.0936 | 0.0120 | 32 | |||
| 0.1246 | New companies will face stiff competition from large companies (MB1) | 0.0327 | 0.6378 | 0.0795 | 1 | |
| New companies will face difficulties in gaining acceptance in the market (MB2) | 0.2577 | 0.0321 | 8 | |||
| Good business contacts will facilitate the acquisition of new business project(MB3) | 0.1045 | 0.0130 | 30 | |||
| 0.0883 | It is hard to find capital providers to start-up companies (FB1) | 0.0743 | 0.4085 | 0.0361 | 6 | |
| Lack of financial resources is a barrier for a new business startup (FB2) | 0.2187 | 0.0193 | 20 | |||
| There are not sufficient subsidies available for new companies (FB3) | 0.1953 | 0.0172 | 23 | |||
| Financial institutions impose a high interest rates on loan for new projects (FB4) | 0.1156 | 0.0102 | 38 | |||
| Banks do not readily give credit to start-up companies (FB5) | 0.0619 | 0.0055 | 45 | |||
| 0.0835 | It is difficult to establish a new company without business experience (KB1) | 0.0899 | 0.4506 | 0.0376 | 5 | |
| Marketing knowledge is necessary for a successful new company (KB2) | 0.2602 | 0.0217 | 18 | |||
| It is difficult to establish a new company without business knowledge (KB3) | 0.1835 | 0.0153 | 25 | |||
| It is difficult to have entrepreneurial competence (KB4) | 0.1057 | 0.0088 | 40 | |||
| 0.0643 | The thought of stress leads to restlessness (SB1) | 0.0878 | 0.4139 | 0.0266 | 13 | |
| I can work for long hours (SB2) | 0.2216 | 0.0143 | 27 | |||
| Hard work negatively affects life (SB3) | 0.1825 | 0.0117 | 34 | |||
| Hard work is not good for health (SB4) | 0.1080 | 0.0069 | 44 | |||
| Difficulties in managing hard work (SB5) | 0.0740 | 0.0048 | 49 | |||
| 0.0511 | Prefer job security than risky business (RA1) | 0.0974 | 0.4466 | 0.0228 | 15 | |
| People who take risks are more likely to succeed (RA2) | 0.2572 | 0.0131 | 29 | |||
| People who can assume risks are more likely to succeed (RA3) | 0.2018 | 0.0103 | 37 | |||
| I am fine with the risk of Irregular income (RA4) | 0.0944 | 0.0048 | 48 | |||
| 0.0444 | Fear of failure is a barrier to starting-up a new business (FF1) | 0.0057 | 0.5399 | 0.0240 | 14 | |
| Embarrassment from failing in business ventures is a barrier to starting-up a new business (FF2) | 0.2971 | 0.0132 | 28 | |||
| Fear of failure is one reason for the limited number of business start-ups (FF3) | 0.1630 | 0.0072 | 42 | |||
| 0.0359 | Constant change to remain motivated (ATC1) | 0.0384 | 0.5283 | 0.0190 | 21 | |
| Feel boring in stable environment (ATC2) | 0.3328 | 0.0120 | 33 | |||
| Prefer govt. job rather than private business (ATC3) | 0.1388 | 0.0050 | 47 |
The following subsections provide detailed interpretations and implications of the ranked sub-barriers, shedding light on their significance and impact on women’s entrepreneurial endeavors. This structured approach enables a nuanced understanding of the diverse challenges women entrepreneurs encounter and underscores the strategic priorities for intervention and support.
5.2.1 Entrepreneurial skills.
The ES barrier category reveals crucial insights into the core competencies that women entrepreneurs require to succeed in competitive and evolving business environments. This category encompasses six sub-barriers, each representing a skill area that influences entrepreneurial effectiveness. The most significant barrier identified is the lack of opportunity recognition (ES1). This finding is consistent with earlier scholarship that emphasized opportunity recognition as a cornerstone of entrepreneurial behavior (Roomi, 2013), and it continues to be supported by recent studies (Al-Mamun et al., 2022). Recognizing and acting upon entrepreneurial opportunities is especially critical in dynamic and resource-constrained settings, such as those faced by many women-led SMEs in developing countries.
Closely following is the lack of problem-solving skills (ES2), ranked third overall. This barrier highlights a vital entrepreneurial capability linked to effective decision-making, risk management and resilience (Fatima et al., 2024a, 2024b). Research has shown that women entrepreneurs with stronger problem-solving capacities are more likely to demonstrate entrepreneurial self-efficacy and proactive business conduct (Barba-Sánchez et al., 2022). Without this competency, navigating daily operational challenges becomes increasingly difficult, thereby limiting business sustainability and growth.
The third highest-ranked sub-barrier in this category is insufficient creativity (ES3), which stands at 11th overall. Creativity plays a pivotal role in driving innovation, enabling differentiation, and responding to market volatility (Naqvi et al., 2023). Earlier research cautioned that a lack of creative capability can hinder product and service innovation, restrict strategic agility and ultimately reduce the long-term viability of entrepreneurial ventures (Solesvik et al., 2014).
The remaining sub-barriers in this category, although ranked lower, still reflect essential gaps in entrepreneurial skillsets. Lack of flexibility (ES4) ranked 12th overall, points to challenges in adapting swiftly to shifting market conditions, a critical trait for resilience in uncertain economic climates. The absence of leadership and communication skills (ES5), placed at 22nd, underscores the need for structured capacity-building in these areas. Effective communication and leadership are instrumental in managing teams, fostering internal collaboration and guiding business vision (Roomi, 2013; Solesvik et al., 2014). Finally, the lowest ranked but still relevant sub-barrier, lack of capabilities in developing new products and services (ES6), at 35th overall, reflects gaps in research and development (R&D) capacity and technical expertise. This highlights a need for strategic interventions focused on strengthening innovation and product development pipelines.
Taken together, the findings emphasize that addressing entrepreneurial skill gaps, particularly in opportunity recognition, problem-solving, creativity and leadership, through tailored training programs and policy support is critical for enabling women entrepreneurs in Pakistan. While efforts to promote female entrepreneurship have gained traction globally since the mid-2000s, this study reinforces the importance of localized and targeted strategies to overcome persistent capability-related barriers.
5.2.2 Macro business environment barriers.
The findings from the Macro Business Environment Barriers (MBEB) category reveal that macro-level conditions pose substantial impediments to women’s entrepreneurial engagement in Pakistan. In total, 12 subcategories were identified within this domain, with political instability (MBEB2) and economic uncertainty (MBEB3) emerging as the most critical constraints, as evidenced by low scores for newly introduced tax policies and unstable governance structures. These conditions discourage entrepreneurship, prompting university graduates to prioritize employment over entrepreneurial ventures, consistent with recent evidence on the influence of macroeconomic policy volatility on entrepreneurial intentions (Ahmad et al., 2023; Khattak and Javed, 2022).
Corruption remains a deeply rooted institutional issue, with 75% of respondents identifying it as a critical deterrent to entrepreneurial ambition. This aligns with the findings of Khan et al (2025), who emphasized that corruption erodes trust and increases the costs of doing business. Moreover, excessive taxation and the lack of government support further discourage female entrepreneurship, underscoring the pivotal role of regulatory and institutional backing in nurturing entrepreneurial ecosystems (Iqbal, Rasheed, and Gul, 2021).
Other systemic challenges identified include difficulty in demand forecasting, limited legal aid and weak community support. These findings reflect institutional voids in advisory infrastructure and social capital, both crucial for fostering confidence among women entrepreneurs. Similar patterns have been observed in developing ecosystems where the absence of R&D support and community endorsement weakens innovation potential (see e.g. Cooke and Wills, 1999). Although infrastructure was rated comparatively less important, it remains foundational to sustaining enterprise growth and competitiveness (Gamidullaeva et al., 2020). Overall, the results highlight the need for integrated macroeconomic reforms and institutional strengthening to support women-led entrepreneurship in Pakistan.
5.2.3 Social networking barriers.
The SNB category, consisting of five sub-barriers, emerged as a critical determinant influencing the entrepreneurial journey of women in Pakistan. The findings highlight that the absence of robust social networks significantly impedes women’s ability to initiate and grow their ventures. Specifically, the inability to build or access social capital (SNB1) is ranked among the top barriers, underscoring the difficulties women face in leveraging personal and professional connections to facilitate business development. This result resonates with recent literature suggesting that limited access to entrepreneurial networks curtails information flow, opportunity recognition and legitimacy factors crucial for venture success (Hanson and Blake, 2009; Khan et al., 2025).
Furthermore, the inability to maintain strategic relationships (SNB2) and inability to rely on established networks during early-stage development (SNB4) further exacerbate challenges for women-led SMEs. In patriarchal societies such as Pakistan, where male dominance in the business landscape limits women’s access to informal mentorship and investor circles, social isolation can reduce access to critical resources and collaborative opportunities (Zahra, 2022). Strong networking has been shown to mitigate business risks and promote resilience by enabling resource mobilization and reducing information asymmetry (Ahmad et al., 2022). Additionally, dependence on word-of-mouth and trust-based marketing (SNB5) in Pakistan’s entrepreneurial ecosystem elevates the importance of these networks, placing women at a disadvantage when they are excluded from such social circles. The findings of this study affirm that social networking barriers are not only prevalent but also systemic, shaped by deep-rooted socio-cultural norms. Therefore, targeted policy interventions that facilitate networking platforms, mentorship programs and inclusive business forums could significantly enhance women’s entrepreneurial participation and success in Pakistan.
5.2.4 Market barriers.
This category comprises only three sub-barriers under consideration. The highest-ranked sub-barrier within this category is the stiff competition from large companies (MB1). Women entrepreneurs perceive this as a significant deterrent, fearing that they may be unable to execute strategies that provide a sustainable competitive advantage (Roomi, 2013). This concern is supported by previous studies indicating a negative and significant impact of market barriers on entrepreneurial activities (Acs and Audretsch, 1988).
The other major sub-barrier is the difficulty new companies face in gaining market acceptance (MB2). This is particularly challenging for women entrepreneurs, as giant competitors often dominate the market, making it hard for new entrants to establish themselves (Carter et al., 2003). The importance of good business contacts in facilitating the acquisition of new business projects (MB3) also presents a significant barrier. Established firms with extensive networks can penetrate the market more easily, whereas women entrepreneurs often struggle due to limited connections (Brush et al., 2009). These market barriers underscore the need for targeted support and strategic initiatives to help women entrepreneurs overcome competitive pressures and establish a foothold in the market.
5.2.5 Financial barriers.
The FB category consists of five sub-barriers that collectively reflect the systemic financial constraints experienced by female entrepreneurs in Pakistan’s SME sector. The analysis reveals that the most critical obstacle is the difficulty in finding capital providers for startup ventures (FB1). This challenge echoes the findings of Zamberi Ahmad (2012), who identified access to financial support as the most pressing issue for women entrepreneurs in Saudi Arabia, with over 90% of respondents highlighting its severity. This issue is further compounded by the second-highest-ranked sub-barrier in this category, lack of initial financial resources for launching new ventures (FB2). Similar trends have been reported in Malaysia, where financial constraints emerged as a predominant concern among SMEs and nascent entrepreneurs (Saleh and Ndubisi, 2006).
Despite the presence of institutions designed to support entrepreneurship, insufficient subsidies for new businesses (FB3) and high interest rates on microloans (FB4) remain persistent obstacles. According to Khan et al. (2021), although SME banks offer financial instruments tailored to women-led enterprises, high borrowing costs act as a deterrent, particularly in the absence of robust financial literacy or collateral. The lowest-ranked but still significant barrier banks’ reluctance to provide loans without guarantors or substantial collateral (FB5) was also reported in Roomi (2013) study, underscoring institutional risk aversion toward new and informal businesses. More recent research continues to affirm that structural biases in lending institutions, coupled with a lack of targeted financing models, limit women’s access to essential capital (Singh and Dash, 2021).
These findings collectively suggest an urgent need for gender-responsive financial systems, inclusive credit policies and subsidized startup funding programs. Addressing these constraints could significantly enhance women’s entrepreneurial participation, especially in developing economies where informal financing remains the default option for many aspiring entrepreneurs.
5.2.6 Knowledge barriers.
The findings related to knowledge barriers reveal that a lack of prior business experience (KB1) is the most critical sub-barrier for women entrepreneurs among Pakistan’s SMEs. Even when financial resources are accessible, the absence of hands-on entrepreneurial experience significantly hampers business initiation and sustainability (Roomi, 2013). This aligns with current literature that emphasizes that business experience contributes to strategic decision-making, opportunity exploitation, and risk mitigation capabilities essential in volatile emerging markets (Al-Mamun et al., 2022). The second-ranked sub-barrier, insufficient marketing knowledge (KB2), further limits women’s entrepreneurial success. Without strategic insight into market segmentation, promotion and positioning, new ventures struggle to establish competitive footholds (Ahmad et al., 2023; Shane, 2003). Closely related is a lack of general business knowledge (KB3), which restricts an entrepreneur’s ability to develop viable models, manage operations and sustain long-term growth (Cooper et al., 1994). Interestingly, entrepreneurial competence (KB4), while ranked the least significant sub-barrier in this category, remains a relevant constraint. Recent research confirms that even when other support structures are present, women entrepreneurs lacking an entrepreneurial mindset and soft skills are less likely to scale their businesses (Man et al., 2002; Ogundana et al., 2024). These results collectively indicate that, while technical education is valuable, experience-driven learning and context-specific business training are vital to overcoming knowledge-related constraints. Therefore, investing in capacity-building interventions such as mentorship, experiential workshops and modular training tailored could be pivotal in enhancing the entrepreneurial effectiveness of women in Pakistan (Germann et al., 2024).
5.2.7 Stress barriers.
The findings within the SB category reveal that psychological and physical strain significantly hinder the entrepreneurial pursuits of women in Pakistan’s. The most prominent sub-barrier, stress leading to restlessness (SB1), underscores the emotional toll borne by women who simultaneously manage household responsibilities and entrepreneurial roles (Khan et al., 2021). This dual burden intensifies mental fatigue and contributes to entrepreneurial burnout, particularly in socio-cultural contexts where gendered expectations are rigid. The second-ranked sub-barrier, inability to work long hours (SB2), further reflects the compounding effect of role overload. As Salahuddin et al. (2022) observed, the extended working hours required to sustain a business are particularly challenging for women with caregiving duties, exacerbating exhaustion and limiting productivity. Hard work negatively affecting personal life (SB3) also ranks highly, highlighting how entrepreneurship can disrupt familial balance in cultures characterized by high uncertainty avoidance, such as Pakistan, where stress is culturally perceived as a threat to well-being (Hussain et al., 2019). While health-related concerns about hard work (SB4) and difficulty managing hard work (SB5) are ranked lower, they remain non-trivial, as chronic overwork can deteriorate both physical health and business continuity (Noor and Isa, 2020; Rizvi et al., 2023). These findings suggest an urgent need for targeted stress-management interventions and holistic support systems, including mental health services, time-management training, and flexible work environments, to foster sustainable entrepreneurship among women. As recent research has emphasized, improving women’s well-being is not only a matter of equity but also a critical enabler of long-term business viability and ecosystem resilience (Germann et al., 2024; Ogundana et al., 2024).
5.2.8 Risk avoidance.
Within the RA category, a strong preference for job security over entrepreneurial risk (RA1) remains the most significant deterrent for women entrepreneurs in Pakistan SMEs. This aligns with long-standing observations that women, particularly in emerging economies, often opt for stable salaried employment due to its predictability and financial reliability, as opposed to the uncertain outcomes associated with entrepreneurial ventures (GEM, 2012). This tendency is further reinforced by cultural expectations and limited social safety nets, which make risk-taking a less viable option for many women. The belief that risk-takers are more likely to succeed was ranked next, suggesting a cognitive acknowledgement of the value of risk in entrepreneurship, yet one that may not translate into action. As recent studies show, women in high uncertainty avoidance societies like Pakistan tend to internalize risk as a threat rather than an opportunity, thereby suppressing entrepreneurial intentions (Akhtar et al., 2023; Habiba et al., 2022). Interestingly, the perception that entrepreneurs are comfortable with the risk of irregular income (RA4) was ranked least significant. This could reflect the reality that many educated women in Pakistan already have access to relatively stable job markets, particularly in education, health care and administration, making the volatility of entrepreneurial income less attractive (Salahuddin et al., 2022; Shakeel et al., 2020). These findings imply that addressing risk avoidance requires more than motivational messaging; it calls for creating policy frameworks, such as startup subsidies, business failure safety nets, and gender-sensitive incubation programs, that reduce the real and perceived risks of entrepreneurship. By lowering the stakes and normalizing risk-taking, such interventions could empower more women to transition from job seekers to opportunity-driven entrepreneurs.
5.2.9 Fear of failure.
The findings on fear of failure reveal its substantial influence in deterring women from entrepreneurial pursuits in Pakistan. The most significant sub-barrier, general fear of failure (FF1), emerges as a primary psychological obstacle, echoing global evidence that fear of personal or financial loss inhibits business creation, particularly among women in conservative or risk-averse cultures (Noor and Isa, 2020). This barrier undermines self-confidence and entrepreneurial intent, especially where support systems for failure recovery are weak. The second-ranked sub-barrier, fear of embarrassment due to business failure (FF2), highlights the socio-cultural dimension of risk aversion. In high uncertainty avoidance societies like Pakistan, entrepreneurial failure is often socially stigmatized, resulting in reputational damage, particularly for women, whose competence may be judged more harshly (Akhtar et al., 2023). This fear is intensified by societal norms that identify failure as a personal inadequacy rather than as a learning opportunity. Although fear of failure contributing to fewer business startups (FF3) ranks lowest among the sub-barriers, it still reflects a lingering reluctance to initiate ventures due to anticipated setbacks, including financial insolvency and reputational harm (Khan et al., 2021). Recent studies have also shown that, in similar emerging contexts, this psychological barrier continues to suppress female entrepreneurial intention unless addressed through ecosystem-level interventions (Ahmed et al., 2025). To mitigate these challenges, policymakers and incubators should consider implementing resilience-building programs, success-story campaigns and culturally contextualized mentorship to normalize failure and foster a growth mindset among aspiring women entrepreneurs.
5.2.10 Attitude toward change.
The analysis of the ATC category highlights key psychological and motivational barriers that affect women’s adaptability in Pakistan’s entrepreneurial ecosystem. The most significant sub-barrier, embracing constant change to maintain motivation (AC1), suggests that many women entrepreneurs struggle with the dynamic and often unpredictable nature of business environments. As recent studies show, the inability to adjust to shifting market demands, technological disruptions, or evolving customer preferences can hinder long-term growth and sustainability, particularly in developing economies where institutional support is limited (Habiba et al., 2022; Mashapure et al., 2022; Raimi et al., 2023; Rizvi et al., 2023). The second sub-barrier, feeling bored in a stable environment (AC2), reveals that some women entrepreneurs may be more motivated by variety and challenge than by routine work. However, this can be a double-edged sword; while routine may discourage innovation, excessive change without proper coping strategies may also lead to burnout or decision fatigue (Kuratko and Hodgetts, 2004). The third sub-barrier, preference for government jobs over private business (AC3), although ranked least important, still reflects a notable cultural trend. In high uncertainty avoidance societies such as Pakistan, the perceived job security, prestige and benefits of government employment often deter women from taking entrepreneurial risks (Al Halbusi et al., 2024). These findings point to the importance of promoting entrepreneurial adaptability and psychological readiness among women through targeted interventions such as resilience training, entrepreneurial mindset development and exposure to flexible business models. By building confidence in change management and reducing the perceived risks of private enterprise, these strategies can help women entrepreneurs better navigate uncertainty and unlock innovative potential.
While each barrier has been analyzed independently, the findings highlight that these challenges are interdependent and collectively influence the entrepreneurial landscape for women in Pakistan. For instance, the lack of entrepreneurial skills constrains opportunity recognition and business management, which also reduces women’s ability to develop and leverage professional networks. Weak networking further limits access to markets and finance, compounding challenges of market acceptance (Brush et al., 2009). Similarly, macro-level factors such as political instability and weak institutional support exacerbate financial barriers by discouraging credit providers from investing in women-led SMEs (Khan et al., 2025). Social and cultural restrictions also intersect with financial and knowledge barriers, where women lacking strong networks or market exposure face higher risks of exclusion from formal credit channels (Simba et al., 2023).
These interdependencies suggest that addressing one barrier can generate positive spillover effects across others. For example, strengthening entrepreneurial skills through targeted training not only enhances women’s managerial capacity but also builds confidence to engage in professional networks, thereby improving both market acceptance and access to financial opportunities (Naqvi et al., 2023; Al-Mamun et al., 2022). Similarly, expanding inclusive networking platforms and mentorship opportunities can simultaneously mitigate market-related constraints and reduce psychological barriers such as fear of failure (Ahmad et al., 2022; Zahra, 2022). Viewing these barriers as interconnected within an entrepreneurial ecosystem underscores the need for integrated interventions that target multiple challenges simultaneously rather than in isolation (Autio et al., 2018; Stam, 2015).
6. Sensitivity analysis
A sensitivity analysis was conducted to evaluate the robustness of the barrier rankings in the study, focusing specifically on the impact of entrepreneurial skills-related barriers on other barrier categories. Results presented in Table 6 reveal that the ES category carries substantial weight, exerting significant influence over the rankings of other barrier categories (Noor and Isa, 2020; Shakeel et al., 2020). This finding underscores the sensitivity of barrier rankings to even minor adjustments in relative weights, emphasizing the need to test ranking stability under varying barrier weights.
Barriers when increasing entrepreneurial skills (ES) values (0.2070 * 0.9….0.2070 * 0.1)
| Barrier categories | Normal weight | Increment changes | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| ES | 0.2070 | 0.1863 | 0.1656 | 0.1449 | 0.1242 | 0.1035 | 0.0828 | 0.0621 | 0.0414 | 0.0207 |
| MBEB | 0.1730 | 0.1766 | 0.1803 | 0.1840 | 0.1878 | 0.1917 | 0.1956 | 0.1997 | 0.2038 | 0.2080 |
| SNB | 0.1278 | 0.1305 | 0.1332 | 0.1359 | 0.1387 | 0.1416 | 0.1445 | 0.1475 | 0.1506 | 0.1537 |
| MB | 0.1246 | 0.1272 | 0.1298 | 0.1325 | 0.1352 | 0.1380 | 0.1409 | 0.1438 | 0.1468 | 0.1498 |
| FB | 0.0883 | 0.0901 | 0.0920 | 0.0939 | 0.0958 | 0.0978 | 0.0998 | 0.1019 | 0.1040 | 0.1062 |
| KB | 0.0835 | 0.0853 | 0.0870 | 0.0888 | 0.0907 | 0.0925 | 0.0944 | 0.0964 | 0.0984 | 0.1004 |
| SB | 0.0643 | 0.0657 | 0.0670 | 0.0684 | 0.0698 | 0.0713 | 0.0727 | 0.0742 | 0.0758 | 0.0773 |
| RA | 0.0511 | 0.0521 | 0.0532 | 0.0543 | 0.0555 | 0.0566 | 0.0578 | 0.0590 | 0.0602 | 0.0614 |
| FF | 0.0444 | 0.0454 | 0.0463 | 0.0472 | 0.0482 | 0.0492 | 0.0502 | 0.0513 | 0.0523 | 0.0534 |
| ATC | 0.0359 | 0.0367 | 0.0374 | 0.0382 | 0.0390 | 0.0398 | 0.0406 | 0.0415 | 0.0423 | 0.0432 |
| Barrier categories | Normal weight | Increment changes | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2070 | 0.1863 | 0.1656 | 0.1449 | 0.1242 | 0.1035 | 0.0828 | 0.0621 | 0.0414 | 0.0207 | |
| 0.1730 | 0.1766 | 0.1803 | 0.1840 | 0.1878 | 0.1917 | 0.1956 | 0.1997 | 0.2038 | 0.2080 | |
| 0.1278 | 0.1305 | 0.1332 | 0.1359 | 0.1387 | 0.1416 | 0.1445 | 0.1475 | 0.1506 | 0.1537 | |
| 0.1246 | 0.1272 | 0.1298 | 0.1325 | 0.1352 | 0.1380 | 0.1409 | 0.1438 | 0.1468 | 0.1498 | |
| 0.0883 | 0.0901 | 0.0920 | 0.0939 | 0.0958 | 0.0978 | 0.0998 | 0.1019 | 0.1040 | 0.1062 | |
| 0.0835 | 0.0853 | 0.0870 | 0.0888 | 0.0907 | 0.0925 | 0.0944 | 0.0964 | 0.0984 | 0.1004 | |
| 0.0643 | 0.0657 | 0.0670 | 0.0684 | 0.0698 | 0.0713 | 0.0727 | 0.0742 | 0.0758 | 0.0773 | |
| 0.0511 | 0.0521 | 0.0532 | 0.0543 | 0.0555 | 0.0566 | 0.0578 | 0.0590 | 0.0602 | 0.0614 | |
| 0.0444 | 0.0454 | 0.0463 | 0.0472 | 0.0482 | 0.0492 | 0.0502 | 0.0513 | 0.0523 | 0.0534 | |
| 0.0359 | 0.0367 | 0.0374 | 0.0382 | 0.0390 | 0.0398 | 0.0406 | 0.0415 | 0.0423 | 0.0432 |
To validate these findings, sensitivity analysis was performed by varying the weight of the ES category from 0.9 to 0.1 in increments of 0.1. This variation notably affected the rankings of other barrier categories, with the MBEB category exhibiting the most pronounced changes. Detailed changes in barrier category weights can be found in Table 7. For instance, when the weight of the ES category was set at 0.9, barrier MB1 ranked first, whereas barrier MBEB12 ranked last. However, as the weight decreased to 0.1, the rank of MB1 dropped significantly from first to 45th place, illustrating the sensitivity of barrier rankings to alterations in weights across categories.
Sensitivity analysis of sub-barriers with entrepreneurial skills barriers changes from (0.2070 * 0.9…0.2070 * 0.1)
| Barrier categories | ES= 0.2070 Normal | ES = 0.1863 | ES= 0.1656 | ES= 0.1449 | ES= 0.1242 | ES= 0.1035 | ES= 0.0828 | ES= 0.0621 | ES= 0.0414 | ES= 0.0207 |
|---|---|---|---|---|---|---|---|---|---|---|
| ES1 | 2 | 2 | 2 | 2 | 3 | 5 | 10 | 18 | 26 | 39 |
| ES2 | 3 | 4 | 4 | 6 | 10 | 15 | 19 | 27 | 36 | 46 |
| ES3 | 11 | 12 | 17 | 20 | 21 | 28 | 35 | 38 | 45 | 47 |
| ES4 | 12 | 13 | 18 | 21 | 24 | 29 | 36 | 41 | 47 | 48 |
| ES5 | 22 | 23 | 27 | 34 | 37 | 39 | 43 | 48 | 49 | 49 |
| ES6 | 35 | 38 | 40 | 41 | 44 | 45 | 50 | 50 | 50 | 50 |
| MBEB1 | 9 | 9 | 9 | 9 | 8 | 8 | 7 | 7 | 7 | 7 |
| MBEB2 | 10 | 10 | 10 | 10 | 9 | 9 | 8 | 8 | 8 | 8 |
| MBEB3 | 16 | 16 | 14 | 14 | 14 | 13 | 13 | 12 | 12 | 12 |
| MBEB4 | 19 | 19 | 19 | 17 | 17 | 17 | 16 | 15 | 15 | 15 |
| MBEB5 | 24 | 24 | 23 | 23 | 22 | 21 | 21 | 20 | 19 | 19 |
| MBEB6 | 31 | 31 | 31 | 30 | 30 | 30 | 28 | 28 | 27 | 26 |
| MBEB7 | 36 | 35 | 35 | 35 | 34 | 34 | 32 | 32 | 31 | 30 |
| MBEB8 | 39 | 39 | 38 | 38 | 38 | 37 | 37 | 35 | 34 | 33 |
| MBEB9 | 41 | 41 | 41 | 40 | 40 | 40 | 39 | 37 | 37 | 35 |
| MBEB10 | 43 | 43 | 43 | 43 | 42 | 42 | 41 | 40 | 39 | 37 |
| MBEB11 | 46 | 46 | 46 | 46 | 46 | 46 | 45 | 44 | 42 | 41 |
| MBEB12 | 50 | 50 | 50 | 50 | 50 | 50 | 49 | 49 | 48 | 45 |
| SNB1 | 4 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 |
| SNB2 | 7 | 7 | 7 | 7 | 6 | 6 | 5 | 5 | 5 | 5 |
| SNB3 | 17 | 17 | 15 | 15 | 15 | 14 | 14 | 13 | 13 | 13 |
| SNB4 | 26 | 26 | 25 | 25 | 25 | 23 | 23 | 22 | 21 | 21 |
| SNB5 | 32 | 32 | 32 | 31 | 31 | 31 | 29 | 29 | 28 | 27 |
| MB1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| MB2 | 8 | 8 | 8 | 8 | 7 | 7 | 6 | 6 | 6 | 6 |
| MB3 | 30 | 30 | 30 | 29 | 29 | 27 | 27 | 26 | 25 | 25 |
| FB1 | 6 | 6 | 6 | 5 | 5 | 4 | 4 | 4 | 4 | 4 |
| FB2 | 20 | 20 | 20 | 18 | 18 | 18 | 17 | 16 | 16 | 16 |
| FB3 | 23 | 22 | 22 | 22 | 20 | 20 | 20 | 19 | 18 | 18 |
| FB4 | 38 | 37 | 37 | 37 | 36 | 36 | 34 | 34 | 33 | 32 |
| FB5 | 45 | 45 | 45 | 45 | 45 | 44 | 44 | 43 | 41 | 40 |
| KB1 | 5 | 5 | 5 | 4 | 4 | 3 | 3 | 3 | 3 | 3 |
| KB2 | 18 | 18 | 16 | 16 | 16 | 16 | 15 | 14 | 14 | 14 |
| KB3 | 25 | 25 | 24 | 24 | 23 | 22 | 22 | 21 | 20 | 20 |
| KB4 | 40 | 40 | 39 | 39 | 39 | 38 | 38 | 36 | 35 | 34 |
| SA1 | 13 | 11 | 11 | 11 | 11 | 10 | 9 | 9 | 9 | 9 |
| SA2 | 27 | 27 | 26 | 26 | 26 | 24 | 24 | 23 | 22 | 22 |
| SA3 | 34 | 34 | 34 | 33 | 33 | 33 | 31 | 31 | 30 | 29 |
| SA4 | 44 | 44 | 44 | 44 | 43 | 43 | 42 | 42 | 40 | 38 |
| SA5 | 49 | 49 | 49 | 49 | 49 | 49 | 48 | 47 | 46 | 44 |
| RA1 | 15 | 15 | 13 | 13 | 13 | 12 | 12 | 11 | 11 | 11 |
| RA2 | 29 | 29 | 29 | 28 | 28 | 26 | 26 | 25 | 24 | 24 |
| RA3 | 37 | 36 | 36 | 36 | 35 | 35 | 33 | 33 | 32 | 31 |
| RA4 | 48 | 48 | 48 | 48 | 48 | 48 | 47 | 46 | 44 | 43 |
| FF1 | 14 | 14 | 12 | 12 | 12 | 11 | 11 | 10 | 10 | 10 |
| FF2 | 28 | 28 | 28 | 27 | 27 | 25 | 25 | 24 | 23 | 23 |
| FF3 | 42 | 42 | 42 | 42 | 41 | 41 | 40 | 39 | 38 | 36 |
| ATC1 | 21 | 21 | 21 | 19 | 19 | 19 | 18 | 17 | 17 | 17 |
| ATC2 | 33 | 33 | 33 | 32 | 32 | 32 | 30 | 30 | 29 | 28 |
| ATC3 | 47 | 47 | 47 | 47 | 47 | 47 | 46 | 45 | 43 | 42 |
| Barrier categories | ES= 0.2070 Normal | ES = 0.1863 | ES= 0.1656 | ES= 0.1449 | ES= 0.1242 | ES= 0.1035 | ES= 0.0828 | ES= 0.0621 | ES= 0.0414 | ES= 0.0207 |
|---|---|---|---|---|---|---|---|---|---|---|
| ES1 | 2 | 2 | 2 | 2 | 3 | 5 | 10 | 18 | 26 | 39 |
| ES2 | 3 | 4 | 4 | 6 | 10 | 15 | 19 | 27 | 36 | 46 |
| ES3 | 11 | 12 | 17 | 20 | 21 | 28 | 35 | 38 | 45 | 47 |
| ES4 | 12 | 13 | 18 | 21 | 24 | 29 | 36 | 41 | 47 | 48 |
| ES5 | 22 | 23 | 27 | 34 | 37 | 39 | 43 | 48 | 49 | 49 |
| ES6 | 35 | 38 | 40 | 41 | 44 | 45 | 50 | 50 | 50 | 50 |
| MBEB1 | 9 | 9 | 9 | 9 | 8 | 8 | 7 | 7 | 7 | 7 |
| MBEB2 | 10 | 10 | 10 | 10 | 9 | 9 | 8 | 8 | 8 | 8 |
| MBEB3 | 16 | 16 | 14 | 14 | 14 | 13 | 13 | 12 | 12 | 12 |
| MBEB4 | 19 | 19 | 19 | 17 | 17 | 17 | 16 | 15 | 15 | 15 |
| MBEB5 | 24 | 24 | 23 | 23 | 22 | 21 | 21 | 20 | 19 | 19 |
| MBEB6 | 31 | 31 | 31 | 30 | 30 | 30 | 28 | 28 | 27 | 26 |
| MBEB7 | 36 | 35 | 35 | 35 | 34 | 34 | 32 | 32 | 31 | 30 |
| MBEB8 | 39 | 39 | 38 | 38 | 38 | 37 | 37 | 35 | 34 | 33 |
| MBEB9 | 41 | 41 | 41 | 40 | 40 | 40 | 39 | 37 | 37 | 35 |
| MBEB10 | 43 | 43 | 43 | 43 | 42 | 42 | 41 | 40 | 39 | 37 |
| MBEB11 | 46 | 46 | 46 | 46 | 46 | 46 | 45 | 44 | 42 | 41 |
| MBEB12 | 50 | 50 | 50 | 50 | 50 | 50 | 49 | 49 | 48 | 45 |
| SNB1 | 4 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 |
| SNB2 | 7 | 7 | 7 | 7 | 6 | 6 | 5 | 5 | 5 | 5 |
| SNB3 | 17 | 17 | 15 | 15 | 15 | 14 | 14 | 13 | 13 | 13 |
| SNB4 | 26 | 26 | 25 | 25 | 25 | 23 | 23 | 22 | 21 | 21 |
| SNB5 | 32 | 32 | 32 | 31 | 31 | 31 | 29 | 29 | 28 | 27 |
| MB1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| MB2 | 8 | 8 | 8 | 8 | 7 | 7 | 6 | 6 | 6 | 6 |
| MB3 | 30 | 30 | 30 | 29 | 29 | 27 | 27 | 26 | 25 | 25 |
| FB1 | 6 | 6 | 6 | 5 | 5 | 4 | 4 | 4 | 4 | 4 |
| FB2 | 20 | 20 | 20 | 18 | 18 | 18 | 17 | 16 | 16 | 16 |
| FB3 | 23 | 22 | 22 | 22 | 20 | 20 | 20 | 19 | 18 | 18 |
| FB4 | 38 | 37 | 37 | 37 | 36 | 36 | 34 | 34 | 33 | 32 |
| FB5 | 45 | 45 | 45 | 45 | 45 | 44 | 44 | 43 | 41 | 40 |
| KB1 | 5 | 5 | 5 | 4 | 4 | 3 | 3 | 3 | 3 | 3 |
| KB2 | 18 | 18 | 16 | 16 | 16 | 16 | 15 | 14 | 14 | 14 |
| KB3 | 25 | 25 | 24 | 24 | 23 | 22 | 22 | 21 | 20 | 20 |
| KB4 | 40 | 40 | 39 | 39 | 39 | 38 | 38 | 36 | 35 | 34 |
| SA1 | 13 | 11 | 11 | 11 | 11 | 10 | 9 | 9 | 9 | 9 |
| SA2 | 27 | 27 | 26 | 26 | 26 | 24 | 24 | 23 | 22 | 22 |
| SA3 | 34 | 34 | 34 | 33 | 33 | 33 | 31 | 31 | 30 | 29 |
| SA4 | 44 | 44 | 44 | 44 | 43 | 43 | 42 | 42 | 40 | 38 |
| SA5 | 49 | 49 | 49 | 49 | 49 | 49 | 48 | 47 | 46 | 44 |
| RA1 | 15 | 15 | 13 | 13 | 13 | 12 | 12 | 11 | 11 | 11 |
| RA2 | 29 | 29 | 29 | 28 | 28 | 26 | 26 | 25 | 24 | 24 |
| RA3 | 37 | 36 | 36 | 36 | 35 | 35 | 33 | 33 | 32 | 31 |
| RA4 | 48 | 48 | 48 | 48 | 48 | 48 | 47 | 46 | 44 | 43 |
| FF1 | 14 | 14 | 12 | 12 | 12 | 11 | 11 | 10 | 10 | 10 |
| FF2 | 28 | 28 | 28 | 27 | 27 | 25 | 25 | 24 | 23 | 23 |
| FF3 | 42 | 42 | 42 | 42 | 41 | 41 | 40 | 39 | 38 | 36 |
| ATC1 | 21 | 21 | 21 | 19 | 19 | 19 | 18 | 17 | 17 | 17 |
| ATC2 | 33 | 33 | 33 | 32 | 32 | 32 | 30 | 30 | 29 | 28 |
| ATC3 | 47 | 47 | 47 | 47 | 47 | 47 | 46 | 45 | 43 | 42 |
Figure 2 provides a visual representation of the incremental changes in rank orders resulting from variations in the weight of the ES category barrier. This sensitivity analysis underscores the potential for addressing barriers within the ES category to alleviate challenges across other barrier categories faced by women entrepreneurs. Effectively mitigating barriers related to entrepreneurial skills could lead to more manageable subsequent barriers.
In conclusion, the study highlights the sensitivity of barrier rankings in entrepreneurship to changes in barrier weights. Addressing the identified barriers, particularly those associated with entrepreneurial skills, is essential for enhancing the sustainability and competitiveness of women entrepreneurs in the marketplace (Nazam et al., 2015).
7. Policy implications of the research
The study’s findings underscore the barriers faced by women entrepreneurs in developing countries such as Pakistan. Despite governmental efforts to promote women’s entrepreneurship, there is a clear need for increased private sector support for these initiatives. This suggests that both the government and private sector should offer practical training opportunities to aspiring women entrepreneurs, including specialized entrepreneurship courses at the universities to enhance their market knowledge and business acumen.
The study identified a lack of entrepreneurial skills as the foremost barrier faced by Pakistani women entrepreneurs. To address this, the government of Pakistan should establish dedicated research and training institutes across the country, focusing on relevant business domains. Additionally, there is a critical need for government support in R&D for SMEs, along with the development of a comprehensive market information system. Allocating a separate budget specifically for women entrepreneurs would further support these initiatives.
Policymakers should design entrepreneurial policies aimed at imparting effective entrepreneurial skills among women. Encouraging women to participate more actively in entrepreneurship requires targeted efforts to dismantle the identified barriers. Moreover, establishing solution-oriented business clinics could provide practical assistance in areas such as information technology, business education, supply chain management, marketing, finance and general management. Charging nominal consultancy fees from women entrepreneurs for these services would ensure sustained engagement and access to up-to-date business insights.
Finally, both government and private agencies should organize seminars and workshops focused on empowering women. These initiatives can help women entrepreneurs develop resilience to risk, manage stress, embrace change and overcome the fear of failure. By implementing these policy recommendations, Pakistan can foster a more conducive environment for women entrepreneurs, enabling them to thrive in the competitive business landscape.
8. Conclusion
Women entrepreneurs play a pivotal role in driving economic development. Therefore, identifying and prioritizing barriers faced by Pakistani women entrepreneurs holds significant implications for both theory and practice. This study employed the AHP method to mitigate human subjectivity in assessing and ranking these barriers. The primary objective of this study was to identify and rank various barriers encountered by women entrepreneurs in Pakistan’s emerging economy. The barriers were categorized as entrepreneurial skills, macro business environment barriers, market barriers, social networking barriers, attitude toward change, risk avoidance, stress barriers, knowledge barriers, financial barriers and fear of failure. Notably, entrepreneurial skills emerged as the most critical barrier hindering viable business ventures for Pakistani women entrepreneurs.
This study further demonstrates that the barriers faced by women entrepreneurs in Pakistan are not discrete but rather form a mutually reinforcing system. Entrepreneurial skills, social networking, financial access and institutional support are deeply intertwined, with weaknesses in one area often amplifying constraints in others. Therefore, policy and programmatic interventions should adopt an ecosystem-based perspective (Stam, 2015), designing holistic support mechanisms that simultaneously address skills development, network building, financial inclusion and regulatory reforms. Such integrated approaches are more likely to generate sustainable improvements in women’s entrepreneurial participation and success than interventions targeting isolated barriers.
8.1 Limitations of the study
This study focused on the practical application of the AHP approach for identifying and ranking of barriers for women entrepreneurs in Pakistan. The identification and ranking of the barriers were based on experts’ opinion and all pairwise calculations were also done by the experts. The viewpoint and subjectivity may vary from person to person due to the uncertain environment. The study was conducted in only one country and cannot be generalized to the broader scope; however, it may be compared with similar countries of the Asia Pacific region to check the authenticity of the results.
The use of a convenience sampling approach introduces potential selection bias, as participants were drawn from a network of accessible experts rather than a randomized population. This limits the generalizability of findings beyond the specific sample studied. While the methodological focus of AHP prioritization emphasizes expert judgement over statistical representation, the results should be interpreted with caution when applied to broader contexts. Future studies could address this limitation by incorporating a more diverse panel of experts drawn from multiple regions, sectors and organizational levels to enhance external validity.
8.2 Future research avenues
While this study provides valuable insights into the barriers faced by women entrepreneurs in Pakistan’s tourism sector, several avenues for future research remain open. First, the current analysis is based on a specific geographic and sectoral focus; hence, future studies may explore whether similar barriers persist in other regions and industries within Pakistan or in other developing economies. A comparative cross-sectoral or cross-country analysis could enrich the understanding of context-specific and universal challenges.
Second, although the AHP method effectively facilitated the ranking of barriers, integrating it with other MCDM methods – such as DEMATEL or TOPSIS – could provide a more nuanced view of causal relationships among these barriers. Further methodological triangulation could also enhance the robustness of the findings.
Third, this study adopts a static perspective by capturing the perceptions of entrepreneurs at a single point in time. A longitudinal design would be beneficial to examine how the perceived severity of these barriers evolves over time, especially in response to policy interventions or changes in the macroeconomic environment.
Fourth, while the study identifies entrepreneurial skills as the most critical barrier, deeper qualitative investigations could be conducted to unpack which specific skillsets are lacking and how these deficiencies intersect with other challenges like gender bias, limited mentorship or institutional voids. Such inquiries could inform the design of more tailored capacity-building programs.
Finally, the role of digital technologies and platforms in overcoming traditional entrepreneurial barriers merits further investigation. Given the increasing digitalization of business activities, especially post-COVID-19, it would be valuable to explore how access to digital tools and e-commerce platforms may alleviate some of the structural and operational constraints faced by women-led tourism ventures.

