In today's world digitalization has dramatically transformed various aspects of life, with cutting-edge and innovative technologies significantly impacting every facet of human existence. With the emergence of the internet and advancement in Information & Communication Technology (ICTs) has profoundly transformed the business globally. Therefore, it is unsurprising that the spread of digital technologies has established a favourable environment for entrepreneurship, greatly encouraging entrepreneurship to utilise these technologies in launching new ventures and managing their activities (Namabisan and Baron, 2021).

Entrepreneurship is undergoing a profound transformation due to major shifts in the marketplace and rapid advancement. Thus, e-entrepreneurship represents a modern and advanced approach to entrepreneurship that utilizes innovative internet technologies as the cornerstone for launching and developing new business ventures. E-entrepreneurship represents a distinct branch of entrepreneurship that emphasizes the utilization of digital technologies and online platforms to create and manage business ventures, and this area has emerged as a focal point of scholarly and practical interest in recent years (Farooq et al., 2018; Lai and To, 2020). E-entrepreneurship refers to the conduct and management of business activities through digital technologies and internet-based platforms. It relies significantly on tools such as email, social media, online forums, e-commerce websites, and other digital applications to initiate, operate, and expand entrepreneurial ventures (Farooq et al., 2018; Yong et al., 2015).

E-entrepreneurship presents a promising opportunity for women, offering significant advantages on two fronts. First, at a personal level, it aligns with women’s daily responsibilities and caregiving roles integrating seamlessly into their lifestyle. Second, from a business perspective, e-entrepreneurship provides several key advantages: it simplifies market research (Hair et al., 2012), reduces operational costs (Nambisan, 2017), and leverages social media for global market reach, which is instrumental in fostering customer relationships (Hair et al., 2012; Nambisan, 2017). Despite the rapid growth of female entrepreneurs globally, with women now representing a significant and expanding segment of the entrepreneurial landscape, there remains a notable lack of comprehensive studies focusing on this group (Brush & Cooper, 2012; Rosca et al., 2020). Additionally, significant gender disparities persist in the use of technologies (Pappas et al., 2018), highlighting the ongoing underrepresentation of women in this sector, and there is a need for further investigation (Pappas et al., 2018; Rajahonka & Villman, 2019).

Recognizing the significant economic benefits of female entrepreneurship, governments, policymakers, academic institutions, and business leaders are actively collaborating to motivate and support women in starting and growing their businesses (Islam et al., 2018). The study found that demographic factors significantly influence the motivational drivers of women entrepreneurs, with trained and experienced women being more responsive to such factors(Shunmugasundaram, 2023). Integrating e-entrepreneurship education into a formal curriculum has the potential to motivate and equip women with the skills and confidence needed to start their businesses. E-entrepreneurial activity drives economic expansion, putting increasing pressure on academic institutions to cultivate an entrepreneurial mindset among students. Entrepreneurship education plays a vital role in shaping students' career aspirations by encouraging them to consider self-employment and entrepreneurial ventures as viable career paths (Zhang et al., 2014).  Grasping the concept of intention is vital for comprehending the mindset of the younger generation, particularly students especially regarding their career choices, and also entrepreneurship education significantly influences the development of entrepreneurial intention (Hassan et al., 2021). Entrepreneurial intentions are shaped by a range of psychological traits and tendencies as well as by an individual’s skill, abilities, and social environment (Zhao et al., 2005). E-entrepreneurial intention refers to the desire to launch a new venture via online platforms, the ambition to own and operate a digital business, or the aspiration to achieve self-employment through internet-based means (Zhao et al. 2010). The present research explores how e-entrepreneurial attitude and digital entrepreneurial alertness impact the intention to engage in e-entrepreneurship among female university students in India, while also assessing the moderating effect of e-entrepreneurial self-efficacy on these relationships. The structure of the paper is as follows: Section 2 provides the theoretical foundation, reviews relevant literature, and develops the research hypotheses. Section 3 describes the data collection procedures and the methodological framework used to achieve the study's objectives. Section 4 introduces the empirical model and offers a detailed discussion of the results. The final sections of the paper, namely Sections 5, 6, and 7, present the key findings and conclusions, examine the practical and theoretical implications, acknowledge the limitations of the study, and outline recommendations for future research.

2.1 Theoretical Background:

A research study focusing on "behavior and behavioral intention" was developed using the Theory of Planned Behavior (TPB) proposed by (Ajzen, 1991). The study aimed to explore how behavioral changes are shaped by various elements, including social norms, personal attitudes, and behavioral viewpoints and also these factors can directly impact an individual's behavior, particularly when the individual has adequate control over their actions under appropriate circumstances (Ajzen, 1971, 1991). This theory involves five key components: attitude, subjective norms, perceived control over behavior, intention to act, and the actual behavior itself (Ajzen, 1991). Attitude represents an individual’s evaluative judgment, either favorable or unfavorable, towards engaging in a specific behavior. Subjective norms involve the beliefs about whether important others approve or disapprove of the behavior. In the context of behavioral studies, perceived behavioral control denotes the degree to which an individual feels confident in their ability to execute a particular action, taking into account both internal and external factors that may facilitate or hinder the behavior. Finally, behavioral intention refers to the motivational aspects that drive an individual towards a specific behavior, where stronger intentions make it more likely that the behavior will be carried out. Initially, the Theory of Planned Behavior (TPB) emphasizes the importance of individuals' conscious intentions in guiding their behavior and actions. However, this focus on deliberate intentions is not fully addressed within the framework of the Theory of Reasoned Action (TRA), another prominent behavioral theory in social psychology (Ajzen and Fishbein, 1975). As an extension of the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB) (Madden et al.,1992), has been employed to gain insights into particular intentions and behaviors. It specifically examines how attitudes towards certain behaviors and subjective norms influence these intentions. The Theory of Planned Behavior, as articulated by Ajzen (2013), has become one of the most extensively applied models for examining entrepreneurial intentions in prior scholarly studies. In recent years, the Theory of Planned Behavior (TPB) has increasingly been utilized to forecast entrepreneurial intentions across diverse groups, including university students (Mensah I. K. et al., 2021), vocational college students (Niu X. et al., 2022), and academic researchers (Feola R. et al., 2019). Moreover, researchers have sought to combine the Theory of Planned Behavior (TPB) with other theoretical models, including self-efficacy theory (Vafaei-Zadeh A. et al., 2023), self-determination theory (Al-Jubari I. 2019), and social cognitive theory (Chu C. C. et al., 2020) to create new frameworks that offer a more comprehensive understanding of factors which affecting entrepreneurial intentions (EI). Social cognitive theory was developed by (Bandura, 1986) and it posits that personal, behavioral, and environmental factors are interdependent and exert mutual influence on one another. The theory emphasizes two central elements: self-efficacy and expected outcomes. This theory is utilized in examining both behavioral (Boudreaux et al., 2019) and entrepreneurial intentions in various studies (Boutaky & Sahib Eddine, 2022).

2.2 Digital entrepreneurial alertness and E-entrepreneurial intentions:

Kirzner (1979) The notion of entrepreneurial alertness was initially proposed to explain an individual’s unique ability to recognize and act upon business opportunities that are often overlooked by others. Entrepreneurial alertness forms a crucial aspect of the entrepreneurial mindset (Cui & Bell, 2022). Entrepreneurial alertness, a key component of the entrepreneurial mindset, refers to a cognitive process where individuals actively seek out and identify signals, recognize patterns and linkages, and critically evaluate information about potential opportunities (Cui & Bell, 2022; Tang et al., 2012). On the other hand, Entrepreneurial intention is a crucial element of entrepreneurial behavior, as it involves careful deliberation and decision-making and this intention is closely linked to an individual's behavior or personality, particularly when they are determining the type of career they wish to follow in the future (Kolvereid and Isaksen, 2006). The entrepreneurial journey starts with identifying opportunities, where perceptive individuals possess the cognitive skills needed to spot these opportunities (Saadat et al., 2021; Hassan et al., 2020). An individual with elevated entrepreneurial alertness is more likely to exhibit stronger entrepreneurial intentions, motivating them to transform a business idea into a viable venture (Awwad & Al-Aseer, 2021). Thompson (2009) highlights a strong link between entrepreneurial intention and alertness. It has been experimentally validated that entrepreneurial alertness strengthens the connection with entrepreneurial intention, as both factors contribute to improving an individual's decision-making regarding future career choices, and this enhancement in alertness and intention aids in refining the decision-making process for one's professional trajectory (McMullen and Shepherd, 2006). An individual's heightened sensitivity to recognizing and interpreting business opportunities plays a crucial role in the emergence of entrepreneurial intentions (Hu et al., 2018).  Hence, Previous research indicates a correlation between entrepreneurial alertness and the entrepreneurial intention.

H1_: Digital entrepreneurial alertness positively influences e-entrepreneurial intention.

2.3 E-entrepreneurial attitude and E-entrepreneurial intentions:

According to the Theory of Planned Behavior (TPB), an individual’s intention to perform a particular behavior is primarily influenced by three core components: their personal attitude towards the behavior, the perceived social pressure to engage in the behavior (subjective norms), and their perceived ability to control or perform the behavior (perceived behavioral control). In context of entrepreneurial studies, attitude frequently plays a significant role in understanding how these intentions are formed and influenced (Wardana et al., 2020). Attitude refers to a person’s personal view or evaluation of themselves, others, objects, events, and phenomena (Liu et al., 2019). It encompasses a positive or negative judgment about behaviors and their potential outcomes (Abdelfattah et al., 2022). From a different viewpoint, it can be argued that entrepreneurial attitudes play a crucial role in shaping one's adaptability, capabilities, and proactive actions throughout the business process (Mitchell et al., 2002). The capacity to adjust to various situations highlights why attitude is a crucial predictor of entrepreneurial aspirations, which can be viewed through the lens of an individual's motivation to start a new business (Farooq et al., 2018b; Pirzada et al., 2017). Attitudes have a straightforward and positive influence on the development of entrepreneurial tendencies (Arniati, Puspita, Amin, & Pirzada, 2019). Numerous empirical studies have consistently demonstrated a strong and positive connection between an individual's attitude and their intention to pursue entrepreneurial ventures. For instance, studies conducted in Hong Kong indicate that students with favorable perceptions of entrepreneurship are more likely to exhibit strong intentions to pursue entrepreneurial ventures (Law and Breznik, 2017). People are likely to start a business only when they have a positive alignment between their attitude towards entrepreneurship and their intentions (Gieure et al., 2020). Drawing from the insights discussed above, this research advances the following hypothesis.

H2: Attitude towards e-entrepreneurship positively affects the E-entrepreneurial intentions

2.4 Moderating Role of Entrepreneurial Self-efficacy:

Self-efficacy, a core concept within Social Cognitive Theory, refers to an individual’s belief in their ability to plan and carry out the behaviors necessary to accomplish particular objectives (Bandura, 1997). Self-efficacy denotes an individual's belief in their capability to carry out a specific task effectively, especially when they possess the necessary competencies (Liu et al., 2019). Self-efficacy has garnered significant attention in research focused on entrepreneurial intentions (Pihie & Bagheri, 2013). Enhanced entrepreneurial self-efficacy strengthens an individual's capacity to navigate challenges and embrace risks, ultimately leading to success in the entrepreneurial journey (Elnadi and Gheith, 2021). Batool et al. (2015) contend that self-efficacy is just as crucial in the context of e-entrepreneurship. Entrepreneurial self-efficacy is widely recognized as a key determinant of both the intention to initiate a business and the subsequent entrepreneurial actions. It is posited that self-efficacy influences entrepreneurial intention not only directly but also indirectly by shaping an individual's decision-making process regarding entrepreneurial pursuits (Lee et al., 2011; Krueger et al., 2000). A substantial body of research utilizing the theory of planned behavior has consistently demonstrated that the three key attitudinal factors—namely, attitude towards entrepreneurship, subjective norms, and perceived behavioral control—are crucial in shaping the intention to pursue entrepreneurship (Kolvereid, 1996a; Krueger et al., 2000). Self-efficacy, however, can influence the strength of relationship between one's attitude towards entrepreneurship and their intentions to pursue entrepreneurial activities. Numerous studies have demonstrated a strong correlation between the entrepreneurial attitudes and intention to engage in entrepreneurial activities (Kolvereid, 1996b; Autio et al., 2001; Liñán & Chen, 2009). An individual's disposition towards a specific behavior is often influenced by their perceptions of the likely consequences associated with performing that behavior (Bandura, 1977). Alternatively, when e-entrepreneurial self-efficacy (ESE) is examined as a moderating variable between entrepreneurial alertness and entrepreneurial intention (EI), it significantly enhances the overall entrepreneurial ecosystem by strengthening the relationship between opportunity recognition and the intention to pursue entrepreneurship. Entrepreneurial self-efficacy strengthens the connection between entrepreneurial alertness and entrepreneurial intentions, serving as a catalyst that increases the confidence needed to identify and capitalize on opportunities (Otache et al., 2024). Individuals who possess high levels of self-efficacy tend to remain dedicated to their entrepreneurial pursuits and demonstrate greater resilience in the face of challenges. This persistence can enhance the influence of their digital entrepreneurial alertness on the formation of entrepreneurial intentions. Research indicates that a strong sense of entrepreneurial self-efficacy (ESE) is positively associated with heightened entrepreneurial intentions. This correlation is particularly evident when individuals are adept at recognizing and seizing business opportunities (Amani et al., 2024). Therefore, e-entrepreneurial self-efficacy may serve as a moderating variable in the relationship between attitude towards e-entrepreneurship, digital entrepreneurial alertness, and e-entrepreneurial intention. Accordingly, the following hypotheses were formulated to examine the moderating effects.

H3: The relationship between digital entrepreneurial alertness and e-entrepreneurial intention is moderated by e-entrepreneurial self-efficacy.

H4: The relationship between attitude towards e-entrepreneurship and e-entrepreneurial intention is moderated by e-entrepreneurial self-efficacy.

Figure I: The Research Framework of the Study (Source: Author’s Own Compilation)

3.1 Research Design and Sample

The existing body of entrepreneurial literature primarily emphasizes male entrepreneurs, often overlooking the experiences and contributions of women in this domain (Turker and Selcuk, 2009). Israr and Saleem (2018) suggest that students possess the greatest potential for entrepreneurship compared to other age groups seeking to launch businesses. This study considered female students aged 18 years and above from different universities in India. Henderson and Robertson (2000) emphasised that the future lies in the innovative and creative capacities of the younger generation. This study adopted a cross-sectional design, as outlined by Dana and Dana (2005). According to Mukyala et al. (2017), this research design focuses on analyzing data gathered from a population or a representative subset at a specific point in time, providing a snapshot of current conditions or phenomena. This approach allows for the analysis of multiple variables and their interactions within a defined group or sample, helping to determine the frequency or occurrence of a specific condition, behavior, or trait within the larger population.

3.2 Data Collection:

The study employed a self-designed questionnaire that was distributed through a non-probability convenience sampling method. This technique has also been used in previous similar studies (Anwar et al., 2020; Sharma, N. et al., 2023). The study draws on primary data collected from a total of 354 female students enrolled in undergraduate and postgraduate programs across diverse academic disciplines such as Commerce, Engineering, Business, Economics, and others, representing various universities across India. A structured questionnaire, consisting of two main sections, was used as the research tool. The first section, "Section A," focuses on gathering demographic information about the respondents, while "Section B" is aimed at assessing key variables such as e-entrepreneurial intention, e-entrepreneurial self-efficacy, and digital entrepreneurial alertness.

3.3 Variable Measurement:

To evaluate e-entrepreneurial intention, digital entrepreneurial alertness, e-entrepreneurial attitude, and e-entrepreneurial self-efficacy, we employed measurement scales that were previously established and validated in past studies. A five-point Likert scale was utilized in the questionnaire, where participants rated their agreement from '1 – strongly disagree' to '5 – strongly agree, E-entrepreneurial intentions are measured by adopting the six items from (Solesvik, 2013; Jeong and Choi, 2017), digital entrepreneurial alertness was assessed through seven-item measures developed by (Tang et al., 2012), and the e-entrepreneurial attitude was assessed using a set of four items originally suggested by another source (Miralles et al., 2016). Finally, e-entrepreneurial self-efficacy was assessed using a set of five items adapted from existing scales (Pham, M. et al., 2023). Study employed modified version of previously validated measurement scales, which had been used in various research settings and with different sample groups. To confirm their suitability for this research, the reliability and validity of these adapted scales were rigorously tested.

4. DATA ANALYSIS:

The research employed Partial Least Squares (PLS) method to evaluate hypothesized model. Study's model was assessed with the aid of Smart-PLS version 4.1.0.6 software (Ringle et al., 2015). Following the recommendations of (Hair et al., 2017), a two-step analytical approach was applied in this evaluation. Analysis begins by assessing measurement model to establish its reliability and validity, which followed by the evaluation of structural model to examine the proposed relationships between variables.

4.1 Measurement Model:

The initial phase of the analysis focuses on assessing the measurement model to ensure the constructs and their corresponding items are both valid and reliable. A composite construct analysis was employed to determine both the validity and reliability of data (Hair et al., 2020). Study assessed reliability through two main indicators: the consistency of the construct measures and the internal coherence while Validity was evaluated using both convergent and discriminant validity as key indicators d (Klarner et al., 2013).

Table I: Item loading, Cronbach's Alpha, Construct Reliability, and Convergent validity

Notes: CA = Cronbach's Alpha, CR = Composite Reliability, AVE = Average Variance Extracted.

Table II: Discriminant validity via the HTMT criterion

Table I presents the results of the item consistency analysis, revealing no major concerns. Each construct demonstrated indicator loadings above 0.6, indicating reliable measurement, in line with the consistency standards recommended by Hair et al. (2016). To assess the reliability of the constructs, we employed Cronbach's alpha (CA) and composite reliability (CR), and both measures exceeded recommended threshold of 0.70, which indicates strong internal consistency (Hair et al., 2017). Average variance extracted (AVE) for each construct exceeded 0.5, which establishes that convergent validity was achieved (Hair et al., 2017). There were no concerns identified regarding discriminant validity. In Table II we can see the Discriminant validity via the HTMT criterion. Heterotrait-Monotrait (HTMT) ratio of correlations is based on the multitrait-multimethod matrix, representing a reliable process (Henseler et al., 2016). Following guidelines established by Hair et al. (2019) and Henseler et al. (2014), the HTMT values shown in Table 2 are all below the accepted thresholds of 0.85 or 0.90, suggesting that discriminant validity is adequately established with no significant issues.

4.2 Structural Model:

The structural model was evaluated through several procedures. Initially, the model's fit was determined by examining Standard Root Mean Square Residual (SRMR). Henseler et al. (2016) suggest that an SRMR value under 0.10 indicates a well-fitting model. In our analysis, the SRMR value was 0.039, which is below this threshold, confirming that the structural model aligns well with the gathered data. Secondly, multicollinearity was assessed using Variance Inflation Factors (VIF), with all values falling below the acceptable limit of 3.3 (Hair et al., 2017). This confirms that, multicollinearity is not a concern in analysis. Thirdly, coefficient of determination (R²) was applied to evaluate the predictive accuracy of model. As presented in Table III, the R² values exceed the 0.26 threshold (Cohen, 2013) which is 0.764, demonstrating that the model's predictive capability is strong and dependable. The Stone-Geisser (Q²) test was conducted, providing further validation and reinforcement of the previously obtained results and, the Q² values of the variables were calculated using the blindfolding technique, and all obtained values were positive, indicating that the model possesses strong predictive relevance.

Table III: R², Q², and VIF (Collinearity test)

Figure II: Structural Model

Table IV: Hypothesis Testing

Notes *** P<0.001, ** P<0.01, * P<0.05

Hypotheses were evaluated through generating t-values and significance levels through a bootstrapping procedure, utilizing 5,000 subsamples for statistical testing (Hair et al., 2011; Henseler et al., 2009). Results indicate that path coefficients play a critical role, confirming each of proposed hypotheses as outlined in Table IV. Additionally, study assessed effect size (f²) to determine the extent to which an exogenous construct significantly influences an endogenous variable. This evaluation follows Cohen's (1992) guidelines, where f² value = 0.35 indicates strong effect, 0.15 represents moderate effect, and 0.02 suggests small effect. As illustrated in Table 4, majority of independent variables demonstrate a significant impact on dependent variables.

Table 4 presents findings related to hypotheses H1 through H4. Hypothesis testing shows primary direct effect (H1): Digital entrepreneurial alertness is positively influencing the e-entrepreneurial intention (β = 0.413, t = 12.368, and P < 0.000). Likewise, the second immediate outcome (H2) Attitude towards e-entrepreneurship, significantly affects the E-entrepreneurial intentions (β = 0.433, t = 13.132, and P < 0.000). The moderation analysis was crucial in evaluating whether e-entrepreneurial self-efficacy affected the strength of the relationship between the independent variables—digital entrepreneurial alertness and e-entrepreneurial attitude—and the dependent variable, e-entrepreneurial intention. The moderation analysis results revealed a notable and positive interaction effect, consistent with established benchmarks, hence (H3) e-entrepreneurial self-efficacy positively moderated relationship between digital entrepreneurial alertness and e-entrepreneurial intention (β = 0.091, t = 2.784, and P < 0.005). Similarly, in (H4) e-entrepreneurial self-efficacy positively moderated relationship between e-entrepreneurial attitude and e-entrepreneurial intention (β = 0.062, t = 2.077, and P < 0.038).

The growth of digital entrepreneurship globally calls for a heightened focus on factors that influence e-entrepreneurial intentions. In this study, we focused on e-entrepreneurial intentions of female students and also various variables which influence their e-entrepreneurial intentions. This study focused on exploring the influence of Digital Entrepreneurial Alertness (DEA) and E-entrepreneurial Attitude (EEA) on the e-entrepreneurial intentions (EEI) of female university students. Additionally, it examined how E-entrepreneurial Self-efficacy (ESE) moderates the relationship between these factors. This study initially explored the connection between Digital Entrepreneurial Alertness and e-entrepreneurial intention. Findings underscored that Digital Entrepreneurial Alertness plays pivotal role in shaping e-entrepreneurial intention which are supported by various studies (McMullen and Shepherd, 2006; Mir et al., 2022). Result is consistent with Saptono et al. (2020), who emphasized significance of fostering an entrepreneurial mindset, comparable to a heightened sense of awareness, in equipping students for entrepreneurship. Secondly in this study we explored the connection between e-entrepreneurial attitude and e-entrepreneurial intention where finding highlighted that e-entrepreneurial attitude positively influenced the e-entrepreneurial intention (Singh et al., 2022; Neacșu, et al., 2015). So this finding explain that a person's optimistic perception of the business they plan to launch significantly influences and enhances their motivation to pursue it (Liñán et al., 2013). In study we have consider E-entrepreneurial Self-efficacy as a moderating variable which moderate the relationship of Digital Entrepreneurial Alertness and e-entrepreneurial intention and also relationship of e-entrepreneurial attitude and e-entrepreneurial intention. Identified self-efficacy as significant factor influencing intentions (Badura, 1977). The relationship between supportive factors and the intention to become an entrepreneur may be shaped by an individual's belief in their own entrepreneurial capabilities (Boyd and Vozikis, 1994). In finding we can see that E-entrepreneurial Self-efficacy affect relationship of Digital Entrepreneurial Alertness and e-entrepreneurial intention and also relationship of e-entrepreneurial attitude and e-entrepreneurial intention. A person with strong self-efficacy in a specific area trusts their ability to gather necessary resources and influence the course of events in their life e (Yang, 2016).

The study conducted on female students in Indian higher education institutions confirms that all proposed factors have a significant impact on their intentions to engage in e-entrepreneurship. If we talk about the practical implications of the study, so to foster e-entrepreneurship among Indian women, it is essential to cultivate a proactive digital mindset, heightened entrepreneurial awareness in the digital space, and a strong sense of self-confidence in managing online ventures. Furthermore, it is essential for policymakers to focus on providing educational resources and structural assistance to foster the development of future women entrepreneurs. This research aims to provide fresh insights into the factors influencing women's e-entrepreneurship from various angles. This framework can assist the government in formulating effective policies aimed at fostering women's e-entrepreneurship in India. Greater emphasis is needed on understanding how education influences women's engagement in e-entrepreneurship. The insights gained can guide educational institutions to move beyond merely raising awareness. They can focus on identifying and supporting interested women students through dedicated initiatives, such as launching targeted programs and setting up incubation centers (Sharma, 2023; Anwar et al., 2020; Almobaireek and Manolova, 2013).

6. LIMITATIONS & RECOMMENDATIONS:

In the discussion of the limitations of the study, one significant methodological drawback of this research lies in its cross-sectional design. To gain deeper and more meaningful insights, future research should adopt a longitudinal approach. One significant drawback of the study was the use of convenience sampling. Since only a small number of higher education institutions were included, the findings have restricted applicability and cannot be broadly generalized. The study's scope is restricted as it focuses solely on young female participants, without examining variations across caste or socioeconomic class, factors that can significantly influence outcomes in developing nations like India. Future research could explore additional factors that influence entrepreneurial intentions, such as entrepreneurial resilience, entrepreneurial education, academic and extracurricular achievements, perceived structural and relational support, and perceived potential for e-entrepreneurship.

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