Role Models and their Influence on Young Entrepreneurs: Case studies from India

In an unpredictable industrial and recessionary environment, entrepreneurship is one alternative that is anticipated to propel economic growth. Through knowledge transfer, employment creation, offering creative and varied goods and services, and heightened market competitiveness, entrepreneurship promotes economic growth (Fayolle, 2007). Creating jobs, stimulating the economy, and fostering sustainable community development may all be facilitated by educating, guiding, and training the entrepreneurs of tomorrow. Young people are still interested in starting their own businesses as a profession, but they believe they need more training and experience to be ready for the demands of this field. Numerous entrepreneurial exposures, such as prior entrepreneurial events, entrepreneurial education, and entrepreneurial role models (ERM), have been identified in several studies that have been done to model the growth of entrepreneurial intention (EI) (Martins & Perez, 2020; Zapkau et al., 2017). The absorptive capacity increases with education level, hence the role of ERM becomes more crucial (Bosma et al., 2012). The research scope states that this study is being carried out at institutions that have made EE a required undergraduate programme, so encouraging the next generation of entrepreneurs. The findings of this study should provide a thorough understanding of the critical role that EE and ERM play in forming and nurturing entrepreneurs.

Organisational EO is a strategic pillar that guides businesses to act in an entrepreneurial manner. Numerous studies have demonstrated that this approach is what separates stagnant businesses from those that are expanding or evolving. Innovativeness, proactivity, and risk-taking are three crucial EO pillars that have been the subject of continuous research (Dai et al., 2009). Researchers have begun to focus on EO as a driving force for individuals as well as organisations (Bolton & Lane, 2012; Kraus et al., 2019). Several studies have examined the influence of critical individual characteristics and competencies on postentrepreneurial activity (Al Issa, 2020; Kumar et al., 2020). Studies that promote the emergence of IEO through pre-entrepreneurial exposure, however, have not gotten much attention. People are better able to understand the value of planning and the attention to detail needed when identifying possible business prospects thanks to the existence of ERM. To encourage projects and The willingness to take calculated risks exhibited by ERM may alter a person's perception of the danger. Prior to launching a firm, role models' assistance and attention also aid in assessing and reducing potential hazards.

One strategy pillar that directs companies to behave entrepreneurially is organisational EO. Several studies have shown that this strategy is what distinguishes growing or changing companies from those that remain stationary. Three essential EO pillars that have been the focus of ongoing study are innovativeness, proactivity, and risk-taking (Dai et al., 2009). Scholars are now concentrating on EO as a motivator for people and organisations (Bolton & Lane, 2012; Kraus et al., 2019). The impact of crucial personal traits and abilities on post-entrepreneurial activity has been the subject of several research (Al Issa, 2020; Kumar et al., 2020). However, there hasn't been much focus on studies that support the establishment of IEO through pre-entrepreneurial experience. The existence of ERM has improved people's understanding of the importance of planning and the level of information required when assessing potential business chances. Active role models might act as real-life examples to promote initiatives and speed up the development of similar attitudes and abilities. The readiness to accept measured risks displayed by ERM has the potential to change an individual's perspective of risk. The attention and support of role models help identify and minimise possible risks before a business is launched.

Because survey research aims to gather people's views, perceptions, and preferences, it is used to investigate the link between variables in the study model (Galvío et al., 2020). Because of government laws restricting travel during the research, data is gathered through the distribution of online surveys. The topics are Indonesian students enrolled in private university business and management programmes studying EE. Subjects for EI study include those who plan to start their own business and who choose to pursue a job in the professional sector following graduation (Ozaralli & Rivenburgh, 2016). Purposive and random sampling are used to choose the subjects (Hair et al., 2017). PLS-SEM is used to maximise the variance of the dependent variables in order to evaluate the research model (Hair et al., 2017). Tests are also conducted to ascertain the appropriateness of the variables in the model and how they relate to one another. The

Survey research is used to examine the relationship between variables in the study model since its goal is to collect people's opinions, perceptions, and preferences (Galvío et al., 2020). Since travel during research is restricted by government rules, data collection is done through the dissemination of online questionnaires. The subjects are Indonesian students studying EE in business and management degrees at private universities. EI research subjects include individuals who want to launch their own company and those who decide to seek employment in the business world after graduation (Ozaralli & Rivenburgh, 2016). The respondents are selected via random and purposeful selection (Hair et al., 2017). To assess the research model, PLS-SEM is utilised to maximise the variance of the dependent variables (Hair et al., 2017). Additionally, tests are run to see whether the variables in the model are acceptable and how they connect to one another. The computing and analysis processes are carried out using the SmartPLS tool.

The correlation coefficient between the variable and factor is known as factor loading. Factor loading displays the variance on that specific factor that may be attributed to that variable. Generally speaking, a factor loading of 0.7 or above in the SEM technique indicates that the factor is able to sufficiently extract variation from the variable. By calculating the loading factor connected to each indicator's latent construct on the same variable, this dependability is examined. As a result, 0.7 is the maximum number that the loading factor's permitted threshold value should be (Hair et al., 2017). Table 2 demonstrates that each indicator's loading factor is higher than the threshold value. According to Hair et al. (2017), the latent variable must account for at least half of the variance indicator, with a threshold AVE value of 0.5. Every variable's AVE value exceeds the threshold, as Table 2 demonstrates. Scale item internal consistency is measured by composite reliability (CR). The CR value is used to determine the latent construct's internal consistency. When the CR value is more than 0.70, Hair et al. (2017) state that internal consistency is taken into consideration in the criterion. The total CR value is higher than the threshold, as Table 2 demonstrates. Both discriminant validity and convergent validity are regarded as subclasses or subcategories of concept validity. It is necessary to demonstrate that measurements that shouldn't be connected in order to prove discriminant validity. The model's discriminant validity establishes whether or not there is a strong correlation between the constructs. Table 3 illustrates that the square root of the AVE must be

Note: ***p < 0.001; **p < 0.01; *p < 0.05.

For every endogenous latent variable, the relationship's coefficient of determination of R2 (explained variance) is checked using the structural model measurement, as stated by Hair et al. (2011). The analysis of how variations in one variable may be accounted for by variations in another variable is done using the coefficient of determination, or R2. According to Figure 2, the route model's EE innovativeness has the greatest coefficient value at 0.477, whereas the lowest and highest coefficient determination values are, respectively, 0.256 and 0.494. To determine the degree of significance of the association between variables, the bootstrapping approach is employed. To obtain a more precise p-value, 5000 iterations are performed (Hair et al., 2017). The likelihood of getting outcomes from a statistical hypothesis test that are at least as severe as the actual results, given that the null hypothesis is true, is known as the p-value in statistics. Hair et al. (2017) state that the Q2 value is bigger than the threshold of 0 and takes the form of cross-validated redundancy for each of the corresponding endogenous variables between 0.176 to 0.381. This number shows that the construct in each individual model has the capacity to predict significance.

components—risk-taking, innovation, and proactivity—increases the desire for an entrepreneurial career, as demonstrated by the empirical data presented in this study. Eight of the eleven offered theories are accepted, while three are not. Regarding H8 acceptance, EE in pupils has a direct bearing on raising EI. The empirical findings of Galvão et al. (2020) and Vuong et al. (2020) are consistent with the study's outcomes. Students' interest in doing business is increased by the EE programme in higher education, which demonstrates to them that business prospects and potential rewards are there to be taken advantage of once they are able to utilise business innovations. Moreover, only H 9 is recognised based on the IEO examination findings of EI; H10 and H11 are refused. According to the study paradigm, the sole factor that affects EI is an individual's innovativeness. The results of this investigation are consistent with those of Bell (2019) and Syed et al. (2020), who found no relationship between individual risk-taking and proactive behaviour and IE. These also support the findings of studies by Ferreira et al. (2017) and Bandera et al. (2018), which found no relationship between an individual's risk tolerance and proactive behaviour and emotional intelligence (EI).

Programmes like EE and ERM should encourage development entrepreneurship, particularly in raising EI. Researchers haven't paid much attention to how EE and ERM combine to raise EI, though. Thus, this study contends that innovativeness, proactivity, and risk-taking should be encouraged at the individual level through pre-entrepreneurial exposure in order to develop entrepreneurial talents and competences. Owing to the significant time and resource commitment needed to develop EI, consistent attention to developing individual inventiveness is needed. Furthermore, by using a social learning theory method to examine the link between ERM and IEO, this research adds significant theoretical implications, particularly in addressing research gaps. This study model may serve as a guide for future investigations, particularly those that look closely at the conceptual connections among ERM, EE, IEO, and EI.

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