Examining Antecedents of Entrepreneurial Propensity in India (2024) PDF

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The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2045-2101.htm Journal of Examining the antecedents of Entrepreneurship and Public Policy entrepreneurial propensity: a study among university students in India Gohar Abass Khan and Irfan Bashir Received 25 April 2023 Revised 9 October 2023 Department of Management Studies, Islamic University of Science and Technology, 21 January 2024 Kashmir, India 18 March 2024 10 May 2024 Mohammed Alshiha Accepted 13 May 2024 Saudi Electronic University, Riyadh, Saudi Arabia, and Ahmed Abdulaziz Alshiha King Saud University, Riyadh, Saudi Arabia Abstract Purpose – The primary objective of this paper is to determine the factors that affect the entrepreneurship propensity of students undergoing compulsory entrepreneurship education courses at various universities. Design/methodology/approach – A research instrument was developed and implemented on a sample of 380 students who were offered compulsory entrepreneurship education courses at six major universities in the Jammu and Kashmir region of India. The study employed multiple cross-sectional designs with a simple random sampling technique to gather data. The collected data was subjected to descriptive statistics and structural equation modeling using SMART-PLS (Version 4). Findings – The findings reveal that conceptualization, opportunity identification and implementation are the three antecedents of entrepreneurship propensity. The results indicate that the conceptualization factor is one of the most important predictors of entrepreneurship propensity, followed by opportunity identification, whereas implementation through education has the weakest influence on students’ entrepreneurship propensity. Practical implications – This research provides important insights to universities for designing and developing entrepreneurship courses that can foster the start-up culture. The results will be helpful for policymakers to devise various programs to boost entrepreneurship. Originality/value – The study integrated the theories of planned behavior and human capital to evaluate the effectiveness of entrepreneurship courses at the university level. The three factors, namely, conceptual factors, actualization factors and implementation factors of entrepreneurship propensity are under-researched. Keywords Implementation, Conceptualization, Opportunity identification, Entrepreneurial propensity, Entrepreneurship education programs Paper type Research paper 1. Introduction Entrepreneurs, recognized as the catalysts for economic growth and contributors to social development, production, and innovation, play a crucial role in various aspects of societal advancement (Koe et al., 2012; Apostu et al., 2022). The generation of ideas for novel business ventures or market entry is a key facet of entrepreneurial activity, as highlighted by research (Marchisio et al., 2010; Saeedikiya et al., 2023). Within the academic realm, entrepreneurial behavior is perceived as the predominant force driving entrepreneurial discovery across economic and human spheres (Analoui et al., 2009). Notably, human capital emerges as a pivotal success factor in entrepreneurship development, a consensus echoed by Jotab
a et al. (2022). Economists worldwide concur that investments in human capital are instrumental in Journal of Entrepreneurship and Public Policy supporting the growth of emerging entrepreneurs (Davidsson and Honig, 2003; Lehmann © Emerald Publishing Limited 2045-2101 et al., 2019). Human capital is a critical component of the entrepreneurial process, DOI 10.1108/JEPP-04-2023-0036 JEPP encompassing facets such as knowledge, skills, attitude, competency, and individual abilities process (Hindle et al., 2009). Entrepreneurial education emerges as a pivotal mechanism for cultivating human capital, potentially leading to an inclination towards entrepreneurship (Arshed et al., 2021). Entrepreneurial education, recognized for its efficacy in building knowledge and skill acquisition (Mehdi and Singh, 2023) is anticipated to play a key role in fostering entrepreneurial tendencies. The increasing recognition of the potential economic and social benefits associated with entrepreneurship has led numerous nations to invest significantly in university-level entrepreneurship programs. These initiatives aim to heighten awareness of unexplored business opportunities, nurture entrepreneurial readiness, and foster creativity and innovation among students and faculty through entrepreneurship education programs on business creation (Ajzen, 1991). Universities and higher education institutions are recognized as pivotal environments for nurturing entrepreneurship, playing a crucial role in the overall entrepreneurial ecosystem (Nastiti et al., 2010; Secundo et al., 2015). Despite substantial investments in the implementation of such programs, there exists a pressing need to gain a deeper understanding effectiveness of these initiatives in inculcating entrepreneurial propensity among students (Al-Mamary and Alraja, 2022). The domain of scholarly inquiry concerning the precursors of entrepreneurial intention has attained a considerable level of maturity, exhibiting a broad scope encompassing diverse contextual frameworks. It has garnered progressive interest from both academic scholars and industry practitioners. Recently, one stream of research has emerged about the digital competencies on entrepreneurship intention (Abaddi, 2023); Sammarro (2022), Ballano et al., (2022), Lv et al., (2021). Another stream has focused on examining the role of skills in Entrepreneurial Propensity (Vega-G
omez et al., 2020; Shahzad et al., 2021). Another stream has investigated the impact of personality factors (e.g. innovativeness, risk-taking, alertness, extroversion, proactiveness, self-confidence, etc.) on Entrepreneurial Propensity (Weligodapola et al., 2023; Sun et al., 2020; Araujo et al., 2023; Kottika et al., 2020). Yet some other studies have stressed on role of social and psychological factors on Entrepreneurial Propensity (Kurjono et al., 2020; Bazkiaei et al., 2021; Rezaei et al., 2020; Azim and Islam, 2022). However, the research on the role of entrepreneurial education in inculcating Entrepreneurial Propensity is fragmented and inconclusive. Some researchers studied the impact of entrepreneurship education programs on Entrepreneurial Propensity among different target students groups such as graduates (Shah et al., 2020; Paray and Kumar, 2020), post-graduates (Hassan et al., 2020; Poupalan et al., 2023) and doctoral students (Sherkat and Chenari, 2022; Barba-S
anchez et al., 2022). Although entrepreneurship-based courses are offered either as elective or compulsory courses in universities and colleges to promote entrepreneurship among students, however, little research is undertaken to determine the effectiveness of these courses. Also, there is limited focus on studies investigating the role of entrepreneurship education in building human capital in the form of conceptualization, opportunity identification, and implementation, especially in the context of developing countries such as India. Consequently, this study aims to answer the following research questions: Q1. How does the cultivation of human capital, encompassing knowledge, skills, attitude, competency, and individual abilities, through entrepreneurship education courses influence the Entrepreneurial propensity of students? Q2. How do university-level compulsory entrepreneurship courses contribute to heightening awareness of unexplored business opportunities, nurturing entrepreneurial readiness, and fostering creativity and innovation among students? Q3. In what ways does the interplay between Human Capital Theory and the Theory of Planned Behavior shape the intentions of students enrolled in entrepreneurship  courses, and how does this understanding contribute to the broader comprehension Journal of of the entrepreneurial ecosystem within universities and higher education Entrepreneurship institutions? and Public Policy Thus, this study seeks to integrate the Human Capital Theory and the Theory of Planned Behavior to comprehensively understand students’ intentions towards entrepreneurship, particularly those enrolled in compulsory entrepreneurship courses. By examining how cultivating human capital through education influences cognitive abilities and entrepreneurial inclination, and how the Theory of Planned Behavior elucidates the intention-behavior relationship, this research aims to provide a nuanced understanding of the factors shaping students’ Entrepreneurial propensity. The remainder of the article follows this structure: the subsequent section introduces the study’s theoretical framework, followed by a review of the literature and the development of hypotheses. The subsequent section justifies the adopted methodology, detailing the tools and techniques employed for analysis. Following this, the results are presented. Lastly, a thorough discussion of the findings and a note on the study’s significant implications and limitations are provided. 2. The theoretical background of the study 2.1 Human capital theory The term human capital was originally developed to study the importance of education in promoting entrepreneurship (Becker, 2008; Unger et al., 2011). According to the concept of human capital individuals should invest in their education, growth, and productivity to improve their human capital (Woodhall, 1987; Atalay, 2015). The theory describes the importance of skills and knowledge for successful entrepreneurship development (Ucbasaran et al., 2009). The human capital theory has been widely applied to study entrepreneurship behavior (Unger et al., 2011) and the theory is viewed as important for the further development of skills and knowledge of humans (Mellander and Florida, 2021). The theory provides strong support for understanding how a person’s education, training, skills, and knowledge affect their entrepreneurial behavior (Volery et al., 2013; Kuzminov et al., 2019). The crucial function of entrepreneurship training in fostering entrepreneurial potential is well-established in the literature (Olugbola, 2017). Research has found that investments in human capital are beneficial for budding successful entrepreneurs and boost business growth 2003 Hickie (2011), Lehmann et al., (2019). The connection between human capital theory and entrepreneurial behavior is evident in how human capital shapes an individual’s entrepreneurial inclination and success (Marvel, 2013; Piva and Rossi-Lamastra, 2018). Hence, the theory has broad application and has gained empirical support to predict entrepreneurial behavior (Becker, 2008; Tan, 2014; Debarliev et al., 2022). 2.2 Theory of planned behavior (TPB) The theory of planned behavior (TPB) propounded by Ajzen (1991) offers a more holistic view of predicting and determining individuals’ behavior. The Theory of Planned Behavior (TPB) is a psychological theory often used to explain human behavior by examining the influence of individual attitudes (Ajzen, 2020), which includes beliefs about the possible outcomes or consequences of behavior include, and the overall positive or positive effect takes into account negative evaluation of these results (Rivis and Sheeran, 2003). Stronger intentions to perform the behavior are associated with a more positive attitude towards it, subjective norms are a person’s perception of social pressure or influence to engage in a particular behavior (Liao et al., 2010), and perceived behavioral control is a person’s confidence in their ability to perform the behavior successfully to carry out behavior (George, 2004). According JEPP to TPB, behavioral intention is the main factor that influences a person’s willingness to engage in a particular behavior (Park and Ha, 2014). The interaction of attitudes, subjective norms, perceived behavioral control and intentions are key elements of the TPB (Dinc et al., 2016) and have an impact on an individual’s potential for entrepreneurship (Kashif et al., 2018).TPB recognizes that there may be external influences and limitations that prevent a person from carrying out their intention (Farrukh et al., 2018). TPB can provide insight into why some individuals are more likely to be entrepreneurs than other (Al-Mamary and Alraja, 2022). According to the TPB individuals’ intention is the best surrogate of his/her actual behavior (Stone et al., 2010; Kim et al., 2016). A framework for understanding the psychological aspects that affect a person’s decision to engage in entrepreneurial activity is provided by the theory of planned behavior (Han et al., 2010; Ajzen, 2011). Because entrepreneurial activity is the product of purpose, it is crucial to recognize the characteristics that predict entrepreneurial intent (Koe et al., 2012). This intention depends on factors affecting the entrepreneurial propensity to start a business (Gorji and Rahimian, 2012). Therefore, it is important to consider the factors driving Entrepreneurial propensity and the difficulties associated with entrepreneurial growth (Kadir et al., 2011). These factors are shaped through entrepreneurial programs (Sitaridis and Kitsios, 2017; Farrukh et al., 2019). Studies such as (Fayolle et al., 2006; Flores Asenjo and Palao Barber
a, 2014), which focus on assessing the impact of entrepreneurship education programs on entrepreneurial intent, take a more creative approach. 2.3 Integration of TPB and HCT The amalgamation of TPB and HCT establishes a robust framework for comprehending individuals’ decisions to engage in entrepreneurial pursuits (Kaur and Chawla, 2023). Ajzen (1991) contends that behavioral intention serves as the most accurate predictor of actual behavior, aligning with TPB’s assertion that gauging intention is the optimal approach to forecast behavior. Conversely, HCT emphasizes the significance of attributes like knowledge and skills in shaping entrepreneurial inclinations (Cseh et al., 2023). Consequently, the integration of these two theories forms a solid theoretical foundation for assessing the efficacy of entrepreneurship courses in fostering entrepreneurial tendencies. Previous studies have successfully integrated TPB and HCT to predict behavior in general and entrepreneurship in particular. For instance, Zhuang et al. (2022) empirically explored the effects of variables of HCT on TPB. Similarly, Novotn
y et al. (2020) linked the key parameters of HCT into the framework of TPB. These theories have been integrated to examine migration behavior and leadership behavior by Sultana and Rahman (2020) and Mahusain and Raman Nair (2022), respectively. In the same line mini scholars have integrated HCT and TPB to study entrepreneurship behavior (Munir et al., 2019; Leroy et al., 2015). Again, this backdrop we integrate HCT and TPB to propose a unified framework to explore how an individual’s human capital, acquired through entrepreneurial education, impacts their inclination to partake in entrepreneurial ventures. Essentially, one’s attitude and intention toward entrepreneurial activities are more likely to be enhanced if they have gained pertinent skills and knowledge (human capital) related to entrepreneurship (Zhuang et al., 2022). 3. Literature review and hypotheses development 3.1 Entrepreneurial propensity Entrepreneurial inclination denotes individuals’ likelihood or propensity to participate in entrepreneurial activities (Siyanbola et al., 2012; Ramoglou and Tsang, 2016). Specifically, it refers to the intention to pursue self-employment. Extensive literature explores entrepreneurial propensity from various perspectives, including the impact of entrepreneurial education programs (Passaro et al., 2017; Boso et al., 2019; Breznitz and Journal of Zhang, 2022; Pierrakis and Owen, 2022). This area of academic study has experienced rapid Entrepreneurship growth and is considered a key predictor of entrepreneurial opportunities (Kirkwood, 2009). and Public Policy The correlation between entrepreneurial propensity and entrepreneurial education programs stands as a central focus in the entrepreneurship research (Raposo et al., 2008; S
anchez-Robles et al., 2023). Br€annback and Carsrud (2018) assert that entrepreneurial propensity is rooted in the willingness of young individuals to engage in entrepreneurial activities, their capacity to capitalize on environmental opportunities, and the motivation driving their entrepreneurial readiness (Ruiz et al., 2016). Understanding how education and training can impact individuals’ inclination and capability to participate in entrepreneurship is essential for cultivating an entrepreneurial culture (Do Paço et al., 2015; Ferreira et al., 2017; Esfandiar et al., 2019). 3.2 Conceptualization In the realm of entrepreneurship, conceptualization refers to the capacity to generate and cultivate innovative and imaginative ideas for creative products, services, or business processes (Davidsson, 2015; Pryor et al., 2016). Entrepreneurship education emerges as an effective means of sparking students’ interest in the entrepreneurship (Boldureanu et al., 2020), playing a substantial role in influencing the conceptualization (Agboola, 2021). The impact of entrepreneurship education on the development of entrepreneurial skills and the ability to conceptualize business ideas is noteworthy (Henry, 2020). Dutta et al. (2011), posit a constructive relationship between entrepreneurship education and an individual’s inclination for future entrepreneurship. Conceptualization holds paramount importance in shaping an individual’s entrepreneurial propensity, as it shapes their attitudes and intentions (Howard, 2023). Entrepreneurship intentions represent a significant outcome of the entrepreneurship education (Bae et al., 2014). Fayolle and Gailly (2015), found that entrepreneurship education programs influence the propensity for optimism in entrepreneurial conceptualization. While research often indicates a positive correlation between entrepreneurship education, the development of entrepreneurial skills, and attitudes (Fayolle and Gailly, 2015; Mei et al., 2020), it’s noteworthy that not everyone who undergoes entrepreneurial education ventures into starting their own business (Neck and Greene, 2011; Fejes et al., 2019). Some studies even suggest that the impact of entrepreneurship education on the inclination to engage in an enterprise may be absent or negative in certain cases (Oosterbeek et al., 2010a; Nabi et al., 2017; Voda and Florea, 2019). H1. Conceptualization has a significant positive effect on entrepreneurial propensity. 3.3 Opportunity identification The capability to discern opportunities stands as a fundamental skill for entrepreneurs (Tang, 2010; Saadat et al., 2022). Opportunities, defined as favorable circumstances or market gaps (Mary George et al., 2016), involve identifying unmet needs or gaps in the market and devising creative approaches to address them (Edquist and Zabala- Iturriagagoitia, 2012). The process of identifying opportunities is widely acknowledged as a crucial step in entrepreneurship (Li~ n
an et al., 2011; Bennett and Chatterji, 2023) with entrepreneurship itself being inseparable from the act of identifying opportunities (Short et al., 2010). Regarding the impact of educational programs on entrepreneurship propensity and opportunity recognition, entrepreneurship research has produced varied outcomes (Olugbola, 2017). Entrepreneurship education has, in some instances, facilitated the identification of more opportunities by imparting the necessary information and skills for undertaking entrepreneurial ventures (Mu~ noz et al., 2011; Uddin et al., 2022). Educational JEPP programs have the potential to augment an individual’s capacity to recognize and assess opportunities for initiating a new business (Karimi et al., 2016). However, contrasting findings exist in other research, suggesting that educational programs may not consistently yield the anticipated positive effects on entrepreneurship and opportunity n
an et al., 2011; Ruiz-Palomino and Mart
ınez-Ca~ recognition (Singh et al., 2008; Li~ nas, 2021; Hosseininia et al., 2023). H2. Opportunity identification has a significant positive effect on entrepreneurial propensity. 3.4 Implementation Implementation in entrepreneurship fosters the mindset and skills necessary to realize business aspirations with greater confidence and competence (Arruti and Pa~ nos-Castro, 2020; Bauman and Lucy, 2021). The effective execution of entrepreneurship education can significantly influence the entrepreneurial propensity (Iwu et al., 2021), yielding evident benefits such as the promotion of new business start-ups (Maritz et al., 2022). According to Lee and Tsang (2001), the correlation between the educational level of entrepreneurs and the rate of implementation can contribute to the diversification of implementation to varying degrees. Entrepreneurship education plays a crucial role in the entrepreneurial process, involving the transformation of an initial idea or concept into a tangible business. Robinson and Sexton (1994), identified a correlation between educational level and business success, emphasizing the importance of education in entrepreneurship. However, it is important to note that not all entrepreneurship education programs necessarily lead to effective implementation (Mawonedzo et al., 2020). The positive impact of entrepreneurship education on how entrepreneurs implement their ideas may not be universal (Turner and Gianiodis, 2018). Parker and Van Praag (2006) also observed that education has both direct and indirect effects on organizational implementation and success (Liao et al., 2022). The correlation between elevated levels of implementation skills and active participation in entrepreneurial ventures has garnered considerable attention in entrepreneurial studies. Scholars such as Abaddi (2023), Haddoud et al. (2020), etc. have provided validation for the notion that a robust capacity for implementation is closely tied to a heightened likelihood of engaging in entrepreneurial behaviors. Implementation skills, which encompass the adept execution of plans and the transformation of conceptual ideas into tangible actions, play a crucial role in the entrepreneurial journey. Individuals endowed with strong implementation skills exhibit a notable ability to navigate the complexities of business development, from the initial conceptualization of innovative ideas to the establishment and growth of successful ventures. This capacity for effective execution is not only instrumental in resource utilization and risk management but also contributes to adaptability, problem-solving, and the overall resilience required in the dynamic landscape of entrepreneurship. Thus, entrepreneurs with superior implementation skills are more likely to achieve positive business outcomes, demonstrating that the ability to turn ideas into practical, operational realities is a key determinant of Entrepreneurial Propensity and success. H3. Implementation has a significant positive effect on entrepreneurial propensity. Therefore, in line with the literature, our model posits that Conceptualization, Opportunity Identification, and Implementation factors constitute the three components of entrepreneurial propensity, as illustrated in Figure 1. The objective of this study is to examine the direct impact of these independent factors on the entrepreneurial propensity of the surveyed students.  Human Capital Journal of TPB Entrepreneurship Theory and Public Policy ConceptualizaƟon H1 EducaƟon Entrepreneurial Opportunity IdenƟficaƟon H2 Programs Propensity H3 ImplementaƟon Figure 1. Proposed model Source(s): Authors own work 4. Methodology 4.1 Measurement and instrument A research instrument about four constructs was adopted from previous studies with slight modifications to fit into the content. The first section contains six questions related to the demographic details of respondents measured on a nominal scale. In the second section, a total of 20 items, as shown in Table, adapted from previous studies were measured on a five- point Likert scale ranging from to no extent (1) to a little extent (2), to some extent (3), to a moderate extent (4), and a great extent (5). Participants were asked to rate the extent to which they agreed or disagreed with certain statements. The questionnaire was evaluated by three independent experts and ten respondents to ensure face validity. The questionnaire contained a cover letter explaining the purpose and details of the research. 4.2 Data collection Data were prospectively collected from the students of management, commerce, economics, science and engineering streams of six major universities (Mata Vishnu Devi University, Jammu University, Islamic University of Science and Technology, University of Kashmir, Central University of Kashmir, and Sheri Kashmir University of Agricultural Science and Technology) that were offering entrepreneurship development as a compulsory course in their curriculum. A multiple cross-sectional design was adopted in which data was collected in two phases but only once from an element. In the first phase, 300 questionnaires (100 in each university) were distributed among the students of the three universities (viz. Islamic University of Science and Technology, University of Kashmir, and Central University of Kashmir) out of six selected universities from May 2023 to July 2023 of which 220 usable responses were recorded resulting in 73.33% response rate. The same process was repeated in the second phase from October 2023 to December 2023 in which 300 questionnaires (100 in each university) were distributed to the remaining three universities (viz. Mata Vishnu Devi University, Jammu University and Sheri Kashmir University of Agricultural Science and Technology) of which only 160 usable responses were received, reporting a 53.33% response rate. Thus, in total 600 questionnaires were distrusted which resulted in 380 usable responses, representing a 63.33% response rate. 5. Data analysis and results The study employs descriptive analysis (percentage and frequency) to explain the respondents’ demographic characteristics. The structure equation modeling using JEPP SmartPLS 4 was employed to test the proposed relationships between the independent variables (conceptualization, opportunity identification and implementation) and dependent variable (entrepreneurial propensity). Non-response bias was assessed by comparing the responses obtained in the first phase with those gathered in the second phase using an independent t-test. The results indicated an absence of non-response bias, as evidenced by the t-value. Harman’s single factor was used to test Common Method Bias (CMB) and the results indicated that the first factor accounted for only 29.45% of the variance, thus, confirming that CMB is not a significant issue in the study. In addition, we tested the possibility of collinearity by Variance Inflation Factor (VIF). The values of the VIF were found to be less than 5 for study items (as shown in Table 1), which indicates that there is no issue of collinearity. 5.1 Respondent Profile The demographic characteristics outlined in Table 2 for the sample of 380 respondents provide a comprehensive overview of the population under study. To draw similarities and compare this sample with the entire population, it is essential to consider whether the sample is representative and captures the diversity present in the broader demographic. The distribution across various universities indicates a reasonably proportional representation, with percentages ranging from 14.21% to 20.26%, suggesting a fair inclusion of students from different academic institutions. Similarly, the gender distribution in the sample, with 49.20% male and 50.80% female respondents, reflects a nearly equal representation of both genders. The age distribution, with 31.80% below 20 years, 41.60% aged 20–25 years, and 26.60% aged 26–30 years, broadly mirrors the expected age distribution in a university context. Undergraduates dominate educational program enrollment at 62.63%, but the inclusion of 37.37% of postgraduate students maintains a diverse representation. S.No Code Items Source 1 CF1 Idea development Hu et al. (2018) 2 CF2 Lifestyle of an entrepreneur Marcketti (2006) 3 CF3 Family of entrepreneur Marques et al. (2018) 4 CF4 Self Confidence Martin et al. (2013) 5 CF5 Writing a business plan Burke et al. (2010) 6 OI1 Problem recognition and opportunity development Anwar et al. (2022) 7 OI2 Business planning Burke et al. (2010) 8 OI3 Market research and validation Alqahtani et al. (2022) 9 OI4 Access and procurement of funds Padiaychee (2016) 10 OI5 Entrepreneurial curriculum and content Boldureanu et al. (2020) 11 IF1 Legal and intellectual property Acs et al. (2008), Graham and Sichelman (2010) 12 IF2 Team building and management Brinckmann and Hoegl (2011) 13 IF3 Role model Abbasianchavari and Moritz (2021) 14 IF4 Network development and ties Chen et al. (2015) 15 IF5 Social responsibilities of an entrepreneur Sahban et al. (2016) 16 EP1 If a lucrative business opportunity arises, I won’t mind Singh Sandhu et al. (2011) quitting my education 18 EP2 Entrepreneurship is seriously viewed as a highly Ooi and Ahmad (2012) desired job choice 19 EP3 I have big dreams to start my own company when I Ooi and Ahmad (2012) finish my education Table 1. 20 EP4 I am always inclined toward entrepreneurship Sandhu et al. (2011) Measurement scale Source(s): Authors own work  Journal of Profile (n 5 380) Frequency PercentEntrepreneurship University Mata Vishnu Devi University 64 16.84% and Public Policy Jammu University 65 17.11% Islamic University of Science and Technology 77 20.26% University of Kashmir 62 16.32% Central University of Kashmir 54 14.21% Sheri Kashmir University of Agricultural Science and 58 15.26% Technology Gender Male 187 49.20% Female 193 50.80% Age Age below 20 years 121 31.80% Age 20–25 years 158 41.60% Age 26–30 years 101 26.60% Educational Program Graduation 238 62.63% enrolled in Post-graduation 142 37.37% Residence Rural 90 23.68% Urban 88 23.16% Semi-urban 102 26.84% Stream Science 104 27.37% Table 2. Management/Commerce/Economics 160 42.11% Demographic Engineering 116 30.53% characteristics of Source(s): Authors own work sample (380 N) Residence types in the sample exhibit a balanced distribution across rural (23.68%), urban (23.16%), and semi-urban (26.84%) areas, suggesting a comprehensive geographical representation. Lastly, the distribution across academic streams, with 27.37% in Science, 42.11% in Management/Commerce and Economics, and 30.53% in Engineering, reflects a diverse academic background within the sample. To ascertain the degree of similarity to the entire population, additional information about the demographic composition of the broader population is necessary. If the sample closely mirrors the demographic proportions of the entire population, it can be considered representative. Conversely, if certain demographics are underrepresented or overrepresented, caution should be exercised in generalizing findings. Overall, a balanced representation across universities, genders, age groups, educational programs, residence types, and academic streams in the sample suggests a potential likeness to the broader population, but a thorough understanding of the demographics of the entire population is essential for a robust comparison. 5.2 PLS-SEM A two-step Structural Equation Modeling (SEM) was conducted using SMART PLS (Version 4). PLS-SEM was considered an appropriate analytical technique for two main reasons. First, to predict how conceptualization, opportunity identification and implementation lead to entrepreneurial propensity. Second, PLS-SEM is deemed appropriate for exploratory theoretical construction and doesn’t necessitate data normality. (1) Outer model assessment In the first phase of the PLS-SEM model fit, the reliability and validity of the scale were assessed. The Standardized Root Mean Square Residual value (SRMR) was less than 95% quantile, thus, confirming good model fit. Further, reliability and validity were assessed by measuring Cronbach’s alpha, Rho-A, composite reliability, and average variance extracted, As shown in Table 3 all values met the established criteria (Hair et al., 2017). JEPP VIF Cronbach’s Rho_ Composite (outer Research construct Variables alpha A reliability AVE values) Conceptualization 0.886 0.817 0.915 0.684 Idea development CF1 1.767 Lifestyle of an entrepreneur CF2 3.416 Family of entrepreneur CF3 2.715 Career adaptability and decision CF4 2.272 making Writing a business plan CF5 1.855 Entrepreneurial 0.9 0.916 0.923 0.668 propensity If a lucrative business EP1 2.622 opportunity arises, I won’t mind quitting my education Entrepreneurship is seriously EP2 2.696 viewed as a highly desired job choice I have big dreams to start my EP3 2.059 own company when I finish my education I am always inclined toward EP4 2.333 entrepreneurship Opportunity identification 0.829 0.851 0.877 0.59 Problem recognition and OI1 1.919 opportunity development Business planning OI2 1.625 Market research and validation OI3 2.025 Access and procurement of funds OI4 1.466 Business models OI5 2.281 Implementation 0.725 0.751 0.829 0.551 Legal and intellectual property IF1 1.566 Team building and management IF2 1.249 Table 3. Process from idea to realization of IF3 1.269 Measurement model a startup and VIF for Network development and ties IF4 1.655 multicollinearity Source(s): Authors own work Discriminant validity was tested by employing the Fornell-Larcker criterion (root square of the AVE criterion), and the heterotrait-monotrait ratio (Hair et al., 2011). As shown in Table 4 the diagonal values are larger than the inter-variable correlation, thus, supporting discriminant validity (Hair et al., 2017). According to Leguina (2015), the HTMT values must be below 0.90. As shown in Table 4 the HTMT values are below the reference value. Thus, the results reveal that the validity and reliability of the measurement instrument are adequate. (2) Inner model assessment After confirming the outer model, a bootstrapping method was performed in Smart PLS and model fit was assessed. To ensure a good model fit the SRMR value must be less than 0.08 and the NFI value should be greater than 0.90 (Henseler et al., 2009). The SRMR was 0.037 and the NFI value was 0.932, which is above the recommended threshold and confirms a good fit. The coefficient of determination of the students’ entrepreneurial propensity is moderate (R-square 5 0.561); which indicates that this endogenous latent construct is explained by up to 56.1%. The path analysis results shown in Table 5 indicate that Conceptualization has a Journal of significant positive (β 5 0.645, t-value 5 11.07, p 5 0.001) influence on entrepreneurial Entrepreneurship propensity, thus, confirming H1. The results also show that Opportunity Identification has a and Public Policy significant positive (β 5 0.325; t-value 5 8.828; p 5 0.005) effect on entrepreneurial propensity. Therefore, hypothesis 2 is also supported. Finally, results indicate that the Implementation factor exerts a significant positive influence on entrepreneurial propensity (β 5 0.280, t-value 5 5.295, p 5 0.003), therefore, hypothesis 3 is also confirmed. 6. Discussion The primary objective of this study was to explore the effectiveness of entrepreneurial education courses in fostering the propensity towards entrepreneurship in the light of human capital theory and the Theory of Planned Behavior. The findings confirm that entrepreneurial propensity is a consequence of acquired human capital, facilitated by entrepreneurial education programs (Krieger et al., 2022). Emphasizing the significance of entrepreneurship, especially in developing countries (Sergi et al., 2019)., universities are recognized as pivotal institutions for disseminating knowledge and playing a central role in shaping skills and attitudes conducive to entrepreneurial propensity among students (Rocha et al., 2022; Sansone et al., 2021). The study highlights the positive impact of entrepreneurial education on building human capital and Entrepreneurial Propensity among students. Notably, the explanatory variables, specifically entrepreneurship education programs, are given particular attention in the validated model, which posits three antecedents (Conceptualization, Opportunity Identification, and Implementation) of entrepreneurial propensity. The model explains a substantial 56.1% of the variance in entrepreneurial propensity. The results reveal that amongst the three factors, conceptualization exerts the greatest impact on entrepreneurial propensity, indicating its crucial role in supporting entrepreneurial inclination (Kuvshinikov and Kuvshinikov, 2023). The study connects entrepreneurial conceptualization with the Theory of Planned Behavior, emphasizing the role of cognitions on intentions and supporting the utility of TPB in understanding and predicting entrepreneurial propensity (Alam et al., 2019; Zaremohzzabieh et al., 2019). The research investigates the process of conceptualizing entrepreneurship, describing it as the creation of concepts and mental models related to starting and operating a business, contrary to claims Fornell–Larcker Criterion HTMT Results 1 2 3 4 1 2 3 4 Conceptualization 0.920 Opportunity Identification 0.620 0.910 0.705 Table 4. Entrepreneurial Propensity 0.410 0.247 0.930 0.730 0.870 Fornell–Larcker Implementation 0.370 0.224 0.386 0.970 0.670 0.780 0.760 criterion, and HTMT Source(s): Authors own work findings Hypothesis path β-value t-value p-value Decision H1 Conceptualization > Entrepreneurial Propensity 0.645 11.075 0.001 Accepted H2 Opportunity Identification > Entrepreneurial Propensity 0.325 8.828 0.005 Accepted Table 5. H3 Implementation > Entrepreneurial Propensity 0.280 5.295 0.003 Accepted Result of hypothesis Source(s): Authors own work testing JEPP that the intention to start a business does not benefit from entrepreneurship education (Oosterbeek et al., 2010b). This result aligns with Fayolle and Gailly (2015) conclusion that educational programs positively influence entrepreneurial conception, advocating for universities to focus on conceptualization to enhance the entrepreneurial culture among students (Cruz-Sandoval et al., 2022). Furthermore, the study informs entrepreneurship education decisions by underscoring the importance of human capital outcomes, emphasizing the role of universities in promoting entrepreneurship among students (Aboobaker and Renjini, 2020). Thus, it implies that universities act as active contributors to the entrepreneurship ecosystem, enhancing entrepreneurial prospects (Ferreira et al., 2023). Further, results highlight the substantial influence of Opportunity Identification on entrepreneurial propensity through entrepreneurship education. Thus, it implies that entrepreneurship education can enhance students’ entrepreneurial competence, fostering the identification of new and improved problem-solving approaches (Adeel et al., 2023; Martin et al., 2013). However, the study notes a comparatively weaker impact of Implementation factors on the propensity to start a business, suggesting that intrinsic and extrinsic factors may influence the implementation of ideas and opportunities beyond the scope of entrepreneurship education (Alferaih, 2022). In conclusion, this study provides valuable insights into the complex interplay of human capital, entrepreneurial education, and factors influencing entrepreneurial propensity, offering implications for educational institutions and policymakers in fostering an entrepreneurial mindset among students. 7. Conclusion In conclusion, this research has proposed a unified model by combining Human Capital Theory (HCT) and the Theory of Planned Behavior (TPB) to evaluate the entrepreneurial propensity of students in Jammu and Kashmir through entrepreneurial education. The core constructs of the model, including conceptualization, identification, and implementation of opportunities, were identified as key predictors of entrepreneurial propensity. The research model significantly explained 51.1% of the variance (adjusted R2) in entrepreneurial propensity, suggesting its robustness as an ideal framework for analyzing factors contributing to entrepreneurial inclination. However, the study prompts further investigation on two important fronts. Firstly, the limited sample size, confined to five institutions in a specific region, calls for broader research encompassing diverse universities and colleges across the country. Conducting comparable studies in other Indian universities and contrasting results with those from different Asian countries could enhance the scope and depth of understanding. Secondly, recognizing the potential influence of various environmental factors and personality traits on students’ entrepreneurial aspirations, future research should employ more sophisticated Entrepreneurial Propensity (EI) models, considering a broader range of variables to capture additional nuances. Additionally, while this study focused on a single dependent variable, recognizing the complexity of entrepreneurial propensity, future research may benefit from incorporating multiple dependent variables, making Structural Equation Modeling (SEM) a more suitable analytical technique in such cases. These avenues for further exploration underscore the dynamic and multifaceted nature of factors shaping students’ entrepreneurial inclinations, providing directions for more nuanced and comprehensive future investigations. 8. Implications The implications drawn from this study underscore the need for educators in entrepreneurship programs to streamline the adoption of effective teaching approaches that specifically enhance students’ conceptualization skills. By incorporating tailored educational activities and teaching methods, entrepreneurship education can play a pivotal Journal of role in significantly influencing students’ ability to generate innovative ideas. This helps in Entrepreneurship fostering an intention to pursue business careers. The research suggests that universities, and Public Policy which have recognized the transformative impact of entrepreneurship education programs can actively shape and stimulate students’ capacity to identify entrepreneurial opportunities. Participation in such programs equips students not only to pinpoint problems but also to formulate comprehensive business plans. Notably, the study findings affirm that entrepreneurship education programs, as evidenced by higher scores, do not hinder the implementation phase. Consequently, it is recommended that when these programs involve individuals already motivated to become entrepreneurs with a positive attitude towards entrepreneurial tendencies, the focus should shift towards educating and training for the establishment of start-ups rather than providing general entrepreneurship awareness training (Li~n
an and Chen, 2009). The research outcomes have significant implications for both policymakers and educators, offering insights to design effective entrepreneurship education programs tailored to individuals’ needs, helping them overcome the challenges of implementing ideas and successfully launching businesses. Looking ahead, the challenge lies in identifying the specific types of entrepreneurship education programs that exert the most significant impact on Entrepreneurial Propensity (EI). This insight can contribute to developing a comprehensive academic curriculum across various degrees, ensuring the inclusion of cross-curricular entrepreneurial skills, regardless of the subject studied. In essence, this study highlights that entrepreneurship education programs can profoundly influence the development and implementation of new business ideas. Educators and policymakers can enhance program effectiveness and contribute to fostering successful and innovative entrepreneurs by implementing strategic interventions, including customized curriculum design, experiential learning, and real-world problem-solving approaches. 9. Limitations and future research directions While this study provides valuable insights into the factors influencing entrepreneurship propensity among students undergoing compulsory entrepreneurship education, certain limitations must be considered. The regional specificity, and the focus exclusively on six major universities in the Jammu and Kashmir region of India, raise questions about the generalizability of the findings to a broader context. Variations in cultural, economic, and social factors across regions might impact entrepreneurship propensity differently. Moreover, the sample size of 380 students, though considered sufficient for this study, may limit the comprehensive understanding of the diverse student population in universities. The cross-sectional design offers a snapshot of entrepreneurship propensity factors at a specific point in time, which prompts the need for future research to adopt a longitudinal approach for a more nuanced understanding of developmental trajectories. Additionally, the reliance on self-reported data introduces the potential for response bias and inaccuracies, as students may provide socially desirable responses or may not accurately recall their experiences related to entrepreneurship education. Future studies should explore these aspects using qualitative research methods, such as interviews or focus group discussions, to gain deeper insights into students’ subjective experiences. Moving forward, researchers should conduct comparative analyses across different regions, educational systems, and cultural contexts to enhance the generalizability of the findings. Exploring variations in entrepreneurship propensity factors among diverse student populations can contribute to a more comprehensive understanding. A more in-depth examination of the specific aspects of the implementation phase, identified in this study as having the weakest influence, is crucial. Future research should delve into these factors to inform targeted interventions aimed at improving the effectiveness of implementation- JEPP focused components in entrepreneurship courses. Additionally, adopting a longitudinal approach would allow researchers to trace the development of entrepreneurship propensity over an extended period, providing insights into the sustainability of the effects of entrepreneurship education. Lastly, expanding the research scope to include universities from various geographic locations and cultural backgrounds could contribute to a globally relevant understanding of entrepreneurship education. This approach would inform international policymakers and educators about the factors influencing entrepreneurship propensity in different contexts, fostering a more comprehensive and inclusive approach to entrepreneurship education. Furthermore, future research should focus on examining the influence and role of leveraging artificial intelligence (AI) to foster digital entrepreneurial propensity as indicated by Abaddi (2023). The integration of artificial intelligence and advanced language models like GPT could provide innovative ways to enhance entrepreneurship education, incorporating digital strategies and preparing students for the evolving landscape of entrepreneurship-digital or otherwise. 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