Influence of Financial Literacy and Educational Skills on Entrepreneurial Intent: Empirical Evidence from Young Entrepreneurs of Pakistan

Abstract

This paper attempts to study the impact of Financial Literacy on Youth Entrepreneurial Intent in Pakistan. A closed-ended self-administered structured questionnaire covering financial literacy, computer knowledge, financial attitude, and financial knowledge with Entrepreneurial Intent was collected from young entrepreneurs. The research tried to investigate the education level with computer skill to inspect the effect of financial literateness on young generation Entrepreneurial Intent in the context of Pakistan. The research model was tested using PLS-SEM and authenticating a measurement model through the advanced methodology and their association with Entrepreneurial Intent. Results revealed that financial literacy and its two parts (financial attitude and financial knowledge) have a positive impact on Entrepreneurial Intent. The size of the joint impact of financial literacy and its components on Entrepreneurial Intent was assessed to be adequate. Entrepreneurial Intent is essential for creating new firms to maintain economic development. Furthermore, it is determined in this research that if youth has better financial knowledge and financial attitude, the probability of Entrepreneurial Intent increases. This suggests that if the youth in Pakistan desire to attain a higher limit of Entrepreneurial Intent, they must implement financial literacy models for enhancing and promoting their current Entrepreneurial Intent.

1. Introduction

Akanbi (2016) stated that in recent years, entrepreneurship has gained much attention from academicians, economists, policymakers, investors, and governments. An individual’s pre-business stages are from the age group of 15-19 years with very limited experience, skills, and maturity level (Hongdiyanto et al., 2020). The potential entrepreneurs are people in the middle 20s with some experience, knowledge, and wealth. Khuong and An (2016) discussed that entrepreneurship activities and startups can be found in every corner of the world now. The business world has highly developed, and this has been considered as one of the major driving forces of the global economy.

Rugimbana and Oseifuah (2010) clarified that entrepreneurs are involved in the process of resource utilization, decision making, and other activities on daily basis, regardless of what age group they fall in. Entrepreneurs must be financially sound and literate, to function properly and effectively. Research studies show that poor financial knowledge and improper management limits their activities. Therefore, the entrepreneurial process needs them to be capable to fill the gaps and deficiencies in the market. According to Garba et al. (2014), entrepreneurship is just not a field in education but a necessity for the development of an economy. Existing meanings of entrepreneurship or descriptions of entrepreneurship study emphasize development (Gartner, 1988; Shane & Venkataraman, 2000).

Heuer and Liñán (2013) discussed that explaining an individual’s behavior is a very difficult task, and the importance of one’s cognition has also been recognized. It also helps to deliver an insight into the subject of entrepreneurship as well as provides a ground to prove positive results and strengthens a country’s economy. Ahmad and Seymour (2008) in their paper, stated that in modern times, policymakers are more interested to facilitate and encourage the growth of new enterprises and boost the economy through the growth of entrepreneurship because it is regarded as a driving force. Since business creation is not just about generating money and creating employment opportunities rather, it is about value creation, which eventually creates a good image of the company as well as about the country. Furthermore, they revealed that different countries focus on entrepreneurship according to their requirements. Some use it for creating employment opportunities in the country, especially when its youths are jobless, while others use it for solving environmental problems occurring in the country, making contributions to social inclusion.

Entrepreneurial activities in any form or any investment to the economy, motivate its people to come up and play their part in economic development. Remeikiene et al. (2013) aimed at the establishment of the impact of entrepreneurial education promoting entrepreneurship among young people. The results of the research confirmed that the main factors of entrepreneurial intention are personality traits (self-efficacy, risk-taking, need for achievement, proactiveness, attitude towards entrepreneurship, behavioral control, and internal locus of control), and they can be developed during the study process (Braunstein & Welch, 2002; Perry et al., 2008). Krueger et al. (2000) came up with a thought-provoking point in their paper that Intent always feels interesting to those who always are in search of new venture formations. Entrepreneurship is a way of thinking, a way of thinking that emphasizes opportunities over threats. The opportunity identification process is intentional, and, therefore, entrepreneurial intentions clearly merit our attention. Equally important, they offer a means to better explain and predict entrepreneurship. They may respond to the conditions around us, such as an intriguing market niche, by starting a new venture 

According to Hilgert et al. (2003), most professionals come to an agreement that self-beneficial financial performance seems directly associated with financial knowledge. For instance, Lusardi and Mitchell (2011) studied financial learning among the young generation of the United States of America and concluded that less than 33% of youth have a simple understanding of different financial terms. It is, thus, necessary to know whether financial understanding (one of the parts of financial literacy) has any link with Entrepreneurial Intent among youth. The motivation behind this study to study the impact of financial literacy on youth Entrepreneurial Intent in Pakistan. The core purpose of this research was to inspect the effect of financial literateness and its components on young generation Entrepreneurial Intent in the context of Pakistan

2. Literature Review

The theory of planned behavior is the general guidelines that explain the procedures of starting a new firm whereby it views behavioral aim as a direct cause of planned behavior (Ajzen, 1991; Ajzen & Fishbein, 1977). It relates mainly when the behavior is infrequent, difficult to detect, or includes changeable time intervals. According to Krueger Jr et al. (2000), Intentions are the single best predictor of any planned behavior, including entrepreneurship. Understanding the antecedents of intentions increases our understanding of the intended behavior. However, research addressing causes for students’ Entrepreneurial Intention is very limited (Lüthje & Franke, 2003; Hoda et al., 2020; Wang & Wong, 2004). Douglas and Fitzsimmons (2005) stated that an individual’s intention to behave entrepreneurially will have attitudinal and self-efficacy antecedents. Nascent entrepreneurs also have the choice to behave entrepreneurially in their own new business or to behave entrepreneurially in an existing business. When faced with choices, intentions are driven by attitudes towards elements of the outcomes associated with the choice items. They confirmed that an individual’s attitudes to autonomy, ownership, and risk defined the Entrepreneurship Intent.

Akanbi (2016) further elaborated that Entrepreneurship is not just about finding and filling gaps in the market, rather it is a process of discovering the true entrepreneurial potential, evaluating the risks, and taking chances in creating future products. This discovery starts with the identification and recognition of the opportunities, where individuals translate Entrepreneurial Intentions into the creation of new enterprises. Entrepreneurship has become a need of time not only for survival but also for sustainability in such dynamic age as it brings competitiveness in economic activities globally and creates employment opportunities (Ismail et al., 2015; Polin et al., 2016).

Remund (2010) explained financial literacy as “Financial literacy is the level to which one knows important business and financial terms and has the capability and sureness to accomplish personal capitals over suitable, short- range decision-making and thorough, long-term financial scheduling, although aware of life actions and varying economic circumstances.” Financial Literacy is not just convenience or knowledge that everyone has, but an indispensable survival tool that individuals must have to survive in today‘s modern society (Jacob et al., 2000; Nguyen & Nguyen, 2020). According to Messy and Monticone (2016) regardless of its significance, several types of research in the world draw attention to the issue that financial illiteracy is still affecting a major part of the world’s people, thus this problem should be solved as a priority basis. Persons with more financial literacy, tend to make rare errors in financial choices and thus are in good financial positions (Meier & Sprenger, 2013).

Gerardi et al. (2010) investigated whether a particular aspect of borrowers’ financial literacy -their numerical ability - may have played a role. They measured several aspects of financial literacy and cognitive ability in a survey of subprime mortgage borrowers who took out mortgages in 2006 or 2007 and match these measures to objective data on mortgage characteristics and repayment performance. They found a large and statistically significant negative correlation between numerical ability and various measures of delinquency and default. Foreclosure starts are approximately two-thirds lower in the group with the highest measured level of numerical ability compared with the group with the lowest measured level. The result is robust to controlling for a broad set of sociodemographic variables and not driven by other aspects of cognitive ability or the characteristics of the mortgage contracts. Their results raised the possibility that limitations in certain aspects of financial literacy played an important role in the subprime mortgage crisis. Financial literacy has two parts as recommended by Huston (2010): Knowledge, that is the person’s familiarity and understanding of finance or financial learning, and its usage, i.e. the use of such information and knowledge in one’s financial management.

Von Gaudecker (2015) examined how financial literacy and advice of experts affect the decisions of households regarding portfolio diversification. Findings showed after running OLS regression that the households get better investment outcomes who uptake advice from experts than those who believe in their own decision making. Lusardi and Mitchell (2011) argued that while it is valuable to measure the financial literacy of individuals, in reality, it is difficult to discern the method of how people use financial knowledge and take judgments established on this information. The reason is that financial literacy covers several ideas, which include financial know-how and information, financial abilities, and financial competency, and it is difficult to get all this data in a realistic span of the period to research.

Another significant outcome is that students who take their under graduation level studies at colleges offering entrepreneurship training (in specific districts) probably grow a positive approach towards following an entrepreneurial profession (Walter & Dohse, 2009). Schooling and knowledge play an important role in detecting entrepreneurial chances (Davidsson & Honig, 2003; Shane & Venkataraman, 2000; Ucbasaran et al., 2008) and in magnificently taking advantage of them (Gimeno et al., 1997; Lofstrom et al., 2014; Robinson & Sexton, 1994).

As shown by the quick development in the number of entrepreneurship courses and programs at universities (Katz, 2007; Klandt, 2004; Vesper & Gartner, 1997), many strategy creators appear to share the trust that universities transfer vital entrepreneurial knowledge and thus investments in training and education deals may eventually result in further (fruitful) entrepreneurship. Researchers have intensively discussed whether entrepreneurship can be officially imparted and educated (Aronsson, 2004; Gendron, 2004; Gorman et al., 1997). Numerous analysts adopt that university education can handover at least some entrepreneurial knowledge (Henry et al., 2005).

2.1. Theoretical Framework and Hypotheses Development

2.1.1. Relationship of Computer Literacy and Entrepreneurial Intent

In recent decades, the research field of entrepreneurship phenomenon has significantly increased in both quantity and sophistication. Bui et al. (2020) evaluated the factors that affect the entrepreneurial intention of IT students in Vietnam. Results highlighted five independent variables affecting the dependent variable, the entrepreneurial intention, in descending order as following: entrepreneurial educational environment, personal characteristics, perception of feasibility, entrepreneurial supports, and financial accessibility. Besides, this research has proved that the variable attitudes towards entrepreneurship partially mediated among the interrelationship of the aforementioned variables. The use of information knowledge has a positive effect on financial development (Kopnina & Meijers. 2014; Yoon et al., 2018). Though less care has been given to the effect of ICT use on entrepreneurship, numerous studies have indirectly explored the ICT use by entrepreneurs and the relationship between public ICT plans and entrepreneurship (Hashim et al., 2011; Kemal, 2019; Ndubisi & Kahraman, 2005). The increasing need for automation is due to globalization. The more computer literate can help in searching new business ideas which will help in making his/her aim towards new startups. This contention can be affirmed by the structure of the paper of Rugimbana and Oseifuah (2010) which revealed that financial literacy among youth entrepreneurs appears to be above average and contributes meaningfully to their entrepreneurship skills. Education and training with an emphasis on financial literacy and entrepreneurial skills can have significant implications for the development and growth of youth entrepreneurs

2.1.2. Relationship of Financial Attitude and Entrepreneurial Intent

Veciana et al. (2005) concluded that there is a general agreement that attitudes towards the entrepreneur, entrepreneurial activity, and its social functions are determinant factors for university students to decide an entrepreneurial career. Their empirical study aimed at evaluating and comparing the attitudes of university students toward entrepreneurship and enterprise formation in Catalonia and Puerto Rico. Results revealed a positive entrepreneur’s image. The samples had a favorable perception of the desirability of new venture creation. So, based on these findings of the research, the third hypothesis has been proposed.

2.1.3. Relationship of Financial Knowledge and Entrepreneurial Intent

In a fast-moving environment, entities require to frequently detect new chances outside of current capabilities for their survival and success (Doz et al., 1989; McGrath et al., 1996). The findings of opportunities has been acknowledged as one of the most significant capabilities of prosperous entrepreneurs (Ardichvili et al., 2003) and therefore has become a vital component of the intellectual study of entrepreneurship and (Gaglio & Katz, 2001); Alvarez and Busenitz (2001) developed and applied the ideas of the resource-based theory to entrepreneurship. They extended the concepts of resources to include the various cognitive abilities possessed by the individual entrepreneur to create and combine new heterogeneous resources. The first goal of this analysis was to describe activities and skills that can be seen as resources. These include opportunity-seeking behavior, combining and organizing resources, assembling the resources into a firm, and creating heterogeneous outputs through the firm that are superior to the market. Entrepreneurial cognition facilitates competitive advantage through opportunity discovery and development of the firm through its early stages. There has been extensive attention on why, when, and how some persons are intelligent to identify opportunities, though others are not able to identify (Shane & Venkataraman, 2000).

Realizing entrepreneurial opportunities needs that people not only hold some type of previous information and knowledge, but they should also have the intellectual capabilities and skills that permit them to value and use that information (Corbett, 2007). The nature and origins of new ideas are examined, focusing particularly on how existing knowledge shapes those ideas and on cognitive processes by which people access and use their knowledge. The origin of an idea can be determined through a variety of processes accessing knowledge, such as conceptual combination, analogy, and problem formulation. This can also be applied to entrepreneurship. Careful application of a number of various basic processes can put knowledge to effective use and improve entrepreneurial creativity (Ward, 2004).

As per the arguments of Gaglio and Katz (2001), understanding the opportunity identification process is one of the main demands within the field of entrepreneurship. Scholars have asserted that opportunity identification may be linked to, amongst other causes, entrepreneurial attentiveness (Kirzner, 2009), previous information and knowledge (Shane & Venkataraman, 2000), social links (Singh et al., 1999), entrepreneurial thought (Baron, 1998), and possible financial returns (Schumpeter, 1942). While every area of these studies has made a role in our understanding of chance detection, previous information and knowledge and possible financial return have been essential to numerous current researches. For instance, social linkages are mainly essential for gaining access to and decreasing the costs of assets essential for entrepreneurial action (Cromie, 1994; Lin et al., 1981; Portes, 1998); they are a foundation of knowledge about new opportunities (enhance knowledge) and make the entrepreneurial act more financially pleasing (Birley, 1985; Burt, 1997; Johannisson & Huse, 2000).

Previous information and knowledge is an individual’s distinguishing knowledge about a specific topic and offers him or her the ability to identify certain opportunities (Shane & Venkataraman, 2000; Venkataraman, 2002). Financial literacy and financial education are phenomena that have been overly explored in the developed world, on the other end are scarcely examined in developing economies. Much need to be done in terms of research on the subject matter, there is a need to restructure the institutional frameworks to enhance initiatives and policies for upselling financial literacy educational outreach in developing countries. (Mabula & Ping, 2019).

2.1.4. Financial Literacy and Entrepreneurship

In general, it is recognized that most of the customers do not have the necessary financial knowledge essential for making vital economic judgments and choices in their greatest interest (Perry et al., 2008). Consumer financial literacy has become a growing concern to educators, community groups, businesses, government agencies, and policymakers. Correspondingly, there has been an increase in the number and types of financial education programs available to households. Many of these programs focus on providing information to consumers and operate under the implicit assumption that increases in information and knowledge will lead to changes in financial- management practices and behaviors. (Hilgert et al., 2003). It was discovered that there was a great relationship between financial literacy and successful entrepreneurship. Above all, with their small sample, they found that the financial literacy of the entrepreneurs was above average (Mabula & Ping, 2019). 

Financial literacy is explained by Noctor et al. (1992) as: “The capability to create knowledgeable decisions & choices about the use and management of capital”. The Financial Literacy and Education Commission (FLEC) of the US defines financial literacy as “the capability to take well- versed decisions and to take active arrangements concerning the present and upcoming usage and management of capital” (Maes & Basu, 2005). As per the definition of OECD, financial literateness is: “‘A combination of awareness, knowledge, skill, attitude, and behavior necessary to make sound financial decisions and ultimately achieve individual financial wellbeing”. Mandell and Klein (2009) examined the differential impact of high school students of a personal financial management course. The findings indicated that those who took the course were no more financially literate than those who had not. Besides, those who took the course did not evaluate themselves to be more savings-oriented and did not appear to have better financial behavior than those who had not taken the course. The study raises serious questions about the longer-term effectiveness of high school financial literacy courses. The above explanations proposed that monetary and financial information or abilities and fluctuations in financial performance are increased from financial literacy (Hilgert et al., 2003).

Financial knowledge has an impact on persons, families, financial organizations, and the wider economy as it is an essential part of our day-to-day lives and its absence has been mentioned by several analysts as a central cause for reducing saving amounts (Hilgert et al., 2003), rising customer obligation (Stango & Zinman, 2007), insufficient preparation for superannuation (Lusardi & Mitchell, 2007), the foundation for separation, psychological disease & the range of further hopeless practices (Kinnunen & Pulkkinen, 1998). Much research, which has been done mostly in advanced nations, has revealed that financial literacy is a vital element of comprehensive financial choice creating and may create crucial consequences for economic conduct. i.e. debt- taking problem is mostly with low financial literate people (Lusardi et al., 2011). Their participation in the share market is also very low (Van Rooij et al., 2007), and fewer chances to take low fee benefits of investment products (Hastings & Tejeda-Ashton, 2008). With no financial literacy, there is less chance that they gather capital and manage capital efficiently (Stango & Zinman, 2009) and may not make a strategy for superannuation (Lusardi, 2008).

H₁ : There is a relationship between educational attainment level and entrepreneurial intent.

H₂: There is a relationship between the level of computer literacy and entrepreneurial intent.

H₃: There is an association between financial attitude and entrepreneurial intent.

H_4: There is an association between financial knowledge and entrepreneurial intent.

3. Method

3.1. Measurement scale

The aim of the Theoretical model as adapted by

Rugimbana and Oseifuah (2010), is to examine and to have deep knowledge of financial literacy and youth Entrepreneurial Intent. The nature of this research was quantitative. According to Saunders and Lewis (2012), logical research is causal research that is used to test the relationship between factors. A single data collection technique was used followed by a parallel quantitative analysis process (Tabachnick et al., 2007). A closed-ended self-administered questionnaire (self-administered as the respondents fill it in themselves, without an interviewer) as a research tool was used. The questionnaire consists of questions to be answered by participants, with closely defined alternatives (Sekaran & Bougie, 2010). The aim was to get legal, consistent, neutral data from a representative sample of participants (McColl et al., 2001). Organized survey forms were used for data collection. The questionnaire was adapted from the study of Rugimbana and Oseifuah (2010).

The population in this study is youth; hence, the data was collected from youth from the age of 18 to 35. G Power, a software used to calculate power for a wide variety of statistical tests, was used to determine the sample size. The minimum sample size was 148 as determined from G power. A convenience sampling method (Non-Probability) was used. Saunders and Lewis (2012) trust data collection from population members who are appropriately accessible to contribute to the study.

3.2. Analytical tool

The data is analyzed through a smart-PLS and estimated by Partial Least Square (PLS). PLS was used to check the impact of independent variables (more than one) on the dependent variable (Hair et al., 2006; Rehman et al., 2020). Hair et al. (2017) defined Structural Equation Modeling (SEM) as “a multivariate statistical analysis technique that is used to analyze structural relationships”.

4. Data Analysis

4.1. Descriptive

The questionnaire consists of questions on Qualification, Computer literacy, Age, Occupation, and Gender. A major portion of the respondents had a bachelor’s degree (48.3%), 32.5% had a Master’s/MS degree and 19.2% had Intermediate or less education. Overall, respondents had graduation or higher qualification i.e. 80.8%. Of the computer Knowledge, 68.4% of respondents had good computer literacy and 31.6% had no or little computer literacy. 224 males and 99 females were among the respondents. 222 students, 86 employed, and 15 unemployed respondents filled the questionnaires with 71.5% between the age of 18-25 and 28.5% between the age of 26-35. Data is presented in Table 1.

Table 1: Descriptive Statistics

4.2. Measurement Model Analysis (Stage-1)

The relationship between dependent and independent variables is shown by the measurement model (Hair et al., 2017). In this research, the data that was gathered through the survey was broken down using SEM. Generally operated software for Partial Least Square-SEM (Smart PLS form 3.02.7) was being used for the analysis of quantitative data. In this study, the measurement model is analyzed into 2 diverse phases.

In the first phase, the dimensions of Entrepreneurial Intent and Financial Literacy are measured for reliability and validity using the criteria suggested by Hair et al. (2017). In the second phase, the bootstrapping technique is used to assess the structural model to get the ‘t-statistics’ and ‘p’ values. The t-statistic is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. The T-statistic is used in a T-test to determine if we should support or reject the null hypothesis. The p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis. Hence, the assessment of the measurement model was very important, which gives a comprehensive summary of the constructs.

4.2.1. Reliability

Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. It is considered to be a measure of scale reliability. As shown in Table 2, Cronbach’s coefficient Alpha values demonstrate good consistency. Hence, is reliable. The lowest Cronbach’s coefficient Alpha value is 0.730 while the highest value is 0.901 indicating that the questionnaires have reliable & constant outcomes. Cronbach’s coefficient Alpha accepts that all the indicators are similarly consistent. Composite reliability or construct reliability is a measure of internal consistency in scale items, similar to Cronbach’s alpha. Table 2 shows that the composite reliability (CR) values are within the acceptable range of 0.863 to 0.935. Thus, it is observed from the results that the instrument used in this research is reliable.

4.2.2. Convergent Validity

Convergent validity refers to how closely the new scale is related to other variables and other measures of the same construct. While the construct must correlate with related variables, it should not correlate with dissimilar, unrelated ones. Therefore, the variance or convergence should be high for the indicators of a construct. The average variance extracted (AVE) is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error. To establish convergent validity, the outer/ factor loadings of the indicator, composite reliability (CR), and the average variance extracted (AVE) have to be considered. For the outer loadings of a construct to be high, it should be above 0.708 (ideally), which represents that the factor extracts sufficient variance from that variable (Hair et al., 2017). As shown in Table 2, the constructs have factor loadings greater than the threshold value of 0.708. This shows that the indicators for the constructs in this research had much in common.

As shown in Table 2, the AVE values for the construct are more than 0.5 indicating that the construct explains the greater part of the difference of its indicators. Hence, from the outcomes of factor loadings and the AVE, it is resolved that there are no convergent validity issues for the constructs (Table 2).

4.2.3. Discriminant Validity

Table 3 offered the outcomes for the Fornell and Larcker (1981) criterion. Discriminant Validity determines whether the constructs in the model are highly correlated among them or not. It compares the square root of AVE of a particular construct with the correlation between that construct with other constructs. The value of the square root of AVE should be higher than the correlation. The outcomes show that the square root of the AVE of the constructs is higher than the association (correlation) between the constructs. From these outcomes, it can be recognized that the discriminant validity problem does not exist in current research.

Table 2: Reliability and Convergent Validity 

Note: CA= Cronbach’s Alpha, CR= Composite Reliability, AVE= Average Variance Extracted. The two exogenous constructs education level and computer knowledge are not reported due to a single item.

4.2.4. HTMT Criteria for Discriminant Validity Assessment

Henseler et al. (2015) have given one more technique for discriminant validity to assess discriminant validity: Heterotrait –Monotrait Ratio of correlations. According to this technique, the value of HTMT < 0.9 shows that the two constructs are differently distinct from each other. Table 3 presents the values, which are less than the threshold of 0.9; hence, the discriminant validity has been established between two constructs

4.3. Assessment of the Structural Model (Stage-2)

The relationships between the examined constructs are displayed by the structural model. Figure 1 depicts the structural model highlighting the association between financial literacy, computer knowledge, and education level, and Entrepreneurial Intent. While Figure 1 also shows the structural model showing the relationships between the dimensions of financial literacy and Entrepreneurial Intent. Moreover, the model was evaluated to decide if the independent variables had been an effect on the dependent variable. In the current research, financial literacy and its two parts (financial attitude and financial knowledge) were assumed to have a positive impact on Entrepreneurial Intent. Figure 1 shows a beta value of 0.179 for financial attitude and 0.122 for financial knowledge, 0.119 for computer knowledge, and 0.559 for education level Table 5.

Table 3: Discriminant Validity (Fornell and Larcker (1981) Criterion & HTMT-Ratio) Table 4: Collinearity Diagnostic

Note: Table represents VIF (Variance Inflation Factor) results, recommended threshold < 03 (Joe F Hair Jr et al., 2020).

4.3.1. Collinearity Assessment

Furthermore, it is suggested that the collinearity measurement (Variance Inflation Factor) must be examined for the construct (Hair et al., 2020). Multicollinearity is a problem because it undermines the statistical significance of an independent variable. It reduces the precision of the estimate coefficients, which weakens the statistical power of the model. Multicollinearity was measured by variance inflation factors (VIF) and tolerance. VIF and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. The recommended VIF is 5, and the values of VIF that exceed 5 are regarded as indicating multicollinearity (Hair et al., 2006). The results of the VIF are exhibited in Table 4, and it shows the most extreme VIF value for the manifest variable was 1.095, which was well under the suggested value, hence it indicates that there are no multicollinearity issues (Figure 1).

Figure 1: Structural Model

4.3.2. Path Coefficient

The path coefficient tells the significance of hypothesized relations among the constructs. In this model, four latent variables are tested for path coefficient, hypothesized in development section 2.2. The results for the path coefficient for each hypothesis are shown in Table 5. It is found that Financial attitude (β 0.179; p< 0.001), financial knowledge (β 0.122; p< 0.005), computer knowledge (β 0.119; p< 0.000), and education level (β 0.559; p< 0.000) all had a positive significant effect on Entrepreneurial Intent (P-value is below the 0.05). Thus, it is confirmed that all the hypotheses are supported, as shown in Table 5.

Table 5: Structural model Summary 

Note: The β= path coefficients of partial least square results. STDEV= standard error for specific coefficients. T= t-statistics and decision rules are for alternative hypotheses developed in chapter two.

Table 6: f -Square (Effect Size), R-Square, & Predictive relevance

4.3.3. Assessment of R²

Another step of assessment in the structural model analysis is R², it is also called the coefficient of determination (Hair et al., 2020). The coefficient of determination is the proportion of the variance in the dependent variable that is predictable from the independent variable(s) (analyze how differences in one variable can be explained by a difference in a second variable) From Table 6, we see that 44% of the change in Entrepreneurial Intent (R² 0.44) was explained by financial literacy and its dimensions. Therefore, we can determine that the strength of the relationship between variables is significant.

4.3.4. Assessment of Effect Size ( f ²)

Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. The effect size (f²) of the coefficient of determination (R²) was vital since it regulates the strength of the change described by the independent variables. The f ² (effect size) of 0.02 is considered as minor, 0.15 as mediocre & 0.35 as high (Cohen et al., 2013). Therefore, as per Cohen’s criteria, the impact of financial attitude (f ² 0.054) on Entrepreneurial Intent was mediocre, while the impact of financial knowledge (f ² 0.025) on Entrepreneurial Intent was minor. The highest effect size was from education level on Entrepreneurial Intent (f ² 0.521) as shown in Table 6.

4.3.5. Assessment of the Predictive Relevance (Q²)

The final stage of structural model analysis is predictive relevance. It is donated by Q². The blindfolding technique has been used to analyze the predictive relevance of the independent variables over the dependent variable (Hair et al., 2017, 2020). It is recommended by Hair et al. (2020) that the value of Q² (Stone Geisser) should be observed after the R² assessment as a measurement of predictive accuracy. In the structural model, a Q² value larger than zero for a certain construct indicates the PLS path model has predictive relevance for that construct. Table 6 shows 0.287 for Q² which is greater than zero. This value is significant, and it shows that financial attitude, financial knowledge, computer knowledge, and education level predicts Entrepreneurial Intent.

5. Discussion and Conclusion

The core purpose of this research was to inspect the effect of financial literateness and its components on young generation Entrepreneurial Intent in the context of Pakistan. Generally, the research added and contributed to current works by suggesting & authenticating a measurement model and the association with Entrepreneurial Intent. The hypothetically resulting research model was verified using PLS-SEM.

Moreover, the research has given the experimental confirmation that financial literacy has a positive impact on Entrepreneurial Intent. Likewise, the research provides truthful and accurate evidence that two dimensions (financial attitude and financial knowledge) of financial literacy has a major positive effect on Entrepreneurial Intent. The size of the joint impact of financial literacy and its components on Entrepreneurial Intent was detected to be adequate (Khuong & An, 2016; Mabula & Ping, 2019; Rugimbana & Oseifuah, 2010).

Entrepreneurial Intent, particularly in developing countries like Pakistan, is very essential for creating new firms to maintain economic development. The main objective of the research was to find the influence of financial learning and its dimensions on Entrepreneurial Intent to discover their fundamental interrelationships. As per the earlier studies and the experimental proof of the current research, this has been confirmed that financial literacy can provide a lot of benefits to a country like Pakistan for the i) Creation of new firms and ii) it positively affect Entrepreneurial Intent (McMillan & Woodruff, 2002; Tanveer et al., 2011).

Furthermore, it is determined in this research that if youth has better financial knowledge and financial attitude, the probability of Entrepreneurial Intent may increase. This will increase the number of new firms and help in the reduction of unemployment which is one of the major issues in Pakistan (Abid et al., 2015). Youth will become job creators instead of job seekers. In the situation of Pakistan, we determine that financial literacy models influence Entrepreneurial Intent positively (Mubarka et al., 2012). However, to date, there is no agreement presently on a single financial literacy model that is accepted internationally. Hence, it is arguable. Finally, the results of this study add to the understanding of in what way societies can make use of financial literacy in enhancing the probabilities of creating new firms by increasing Entrepreneurial Intent among youth (Faizunnisa & Ikram, 2004; Kasim et al., 2014; Lerner, et al., 2009).