Assessment of the Traits which transform the Orientation of students and faculty members that impact their Entrepreneurial intentions

Pius Kwame Agyekum¹*, K. M. Sharath Kumar², Stephen Asunka³

¹Ph.D. Scholar, M.S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka.

²Director – Research, M.S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka.

³Head-ITSS, Ghana Technology University College, Tesano, Accra.

*Corresponding Author E-mail: pagyekum@gtuc.edu.gh, agyekumpius@yahoo.co.uk

ABSTRACT:

This research article looks at the personality traits that impart Entrepreneurial Intentions (EI) among students and faculty members in selected Universities of Ghana. The study broadly considers how personality traits and behavior, personality model, parent background and demographic factors impact EI through knowledge and skill acquisition in a University environment. Four major hypothesis listed below were considered for the analysis:

1. H₀: Personality traits impact students and faculty members with EI through knowledge and skills acquisition

2. H₀: Big Five Personality Model impacts EI

3. H₀: Education and occupational background of parents impact EI of students and faculty

4. H₀: Personality traits, personality model and parent background impact EI and subsequently moderated by demographic background (Ethnicity, Age, Sex) of students and faculty members.

A total of 800 students were strategically drawn from seven private and public Universities in Ghana to participate in the research. But 584 questionnaires constituting 71 % response rate were eventually received and used for the analysis. Online or electronic method of data collection was initially employed but had to be changed after two weeks for the traditional face-to-face interviews with questionnaires for faster and better results. A mixture of purposive, accidental, convenience and quota sampling techniques were employed in the data collection. SPSS and PLS-SEM 3 software were used to analyze the data. The Cronbach Alpha value of 0.896 was obtained on 12-demographic data items to ensure validity and internal consistency of the data. The Cronbach Alpha value signified that the data was highly consistent, reliable, thus, can be validated. Results confirmed personality trait/behaviour impact positively on EI of students and faculty members. The Big Five personality model involving Extraversion, Openness, Neuroticism, Conscientiousness, Agreeableness relatively impact EI of students and faculty members. Further, the parent background negatively impacted on EI which is contrary to what most literature confirm. A unit change in parent background can reduce impact on EI by 0.03 units. On the other hand, Need for Achievement (NA), Locus of Control (LC), Subjective Norm (SN), Risk Taking (RT) positively impacted EI. Attitude (ATT) of students and faculty members showed negative relationship (-0.08) among the personality traits with a non-significant p-value of 0.790, disapproving what some literature and theories affirm. Lastly, the demographic background of students and faculty members with knowledge and skills acquisition impacted EI positively in parallel.

KEYWORDS: Big Five Personality Model, Demographic Background, Locus of Control.

1. INTRODUCTION:

Entrepreneurship creates wealth and recognition for startup innovators (Mattews and Moser, 1995). Policy Makers, Government and Scholars in recent times have realized the important role of entrepreneurial activities for promoting economic and social development in order to curb the unemployment phenomena confronting all Countries across the world. Governments are supporting entrepreneurship activities among University graduates to create their own businesses. The promotion of entrepreneurship among University graduates and the employment drive by entrepreneurship have led to studies in Entrepreneurial Intentions (EI). In recent times, factors that affect EI among University students and faculty members are also gaining significant research directions. The study analyzed personality traits and other factors that impact EI of students and faculty members across public and private Universities in Ghana. Ajzen (1987, page 95) argued that personal inclinations to entrepreneurship predict entrepreneurial behaviors. To better explain EI, many authors have proposed models which include Entrepreneurial Event Model by Shapero (1982), Theory of Planned Behavior by Ajzen (1991) and Contextual Model of EI by Elfving, Brannback and Carsrud (2009). These models advance arguments to modify and critique one another to find lasting solutions to the emerging problems within the models. Tabachnick and Fidell (2001) sighted two problems emerging out of EI and its influencing factors as partial, non-comprehensive and non-systematic. Moreover, the linear relationships between these variables are difficult to present, thereby, leading to biased results. To overcome these challenges, a systematic study on intentions is attempted by including multi-dimensional factors such as the individual, family and social environment. The authors incorporated these factors to determine their impact on EI among students and faculty members in the Ghanaian Universities.

1.1 Problem Identification:

EI is derived from entrepreneurship and intention. Entrepreneurship is the process by which individuals pursue opportunities without regard to resources that can be currently controlled. Intention is a “person motivation” to make an effort to act upon a conscious plan or decision (Chen, Greene and Crick, 1998). Existing literature argued that there is no clear definition and a reliable instrument to measure intentions. What researchers use is entrepreneurial inclination (Bird, 1988), which is the beginning of a new venture creation in a process (Lee and Wong, 2004; Krueger et al., 2000). EI describes individual’s judgment of having a own venture (Crant, 1996) and deemed as a personal decision (Krueger et al., 1994). When more individual intentions are formed, aspiring students can eventually translate into more entrepreneurial activities (Migliore, 2011). This has caused Government, Policy Makers and University Management to promote entrepreneurship and entrepreneurial studies to increase job establishments in order to overcome the menace of unemployment among the teeming youths. Therefore, studies into personality traits and behaviors that impact EI have become important to Educators, Researchers, Government and University Management. Moreover, harnessing personality traits of students and faculty members at the University level is a critical success factor. As a baseline, Graduates from the University must be trained to create jobs but not to search for jobs after graduation. As it happens in Srilankan and African Countries, fresh University graduates prefer to search for salaried employment rather than exploring opportunities for entrepreneurial career (Ranwala et al., 2016). Studies reveal that the local Universities in Srilanka produces less than 5% of entrepreneurs (Perera, 2012).

1.2 Measurement of EI:

The measurement of EI is carried out by Single Variable method such as individual expectation, plan and behavioral anticipations (Perera et al., 2012). This lacks validity and reliability in the present context. This warrants for multi-variable method which assess the various dimensions; reduce errors and enhance validity and reliability. (Chen, Greene and Crick, 1998; VanGelderen et al., 2008). To overcome these challenges, the present study used various dimensions such as Personality Traits, Big 5 Personality Model, Parental Background, Socio-Economic Background (Age, Gender, and Ethnicity) even though other scholars have added Psychological make-up to determine EI. Literature argued that males inert EI more than their Female counterparts (Matthew and Moser, 1995). Females have low EI with limited capabilities to start their own businesses leading to low-self efficiency. Further, females have stereotypic images, for instance, their responsibility of supporting and raising the family and children affects their EI (Lee et al., 2011).

1.3 Personality Traits:

These are the attributes and characteristics of individuals that make him or her unique among colleagues and other people with respect to entrepreneurship.

1.3.1 Need for Achievement (NA):

Shaver (1995) defined Need for Achievement (NA) as strong achievement orientation. Personality traits were perceived as an important factor in analyzing EI (Gartner, 1985).

1.3.2 Locus of Control:

Locus of Control (LC) is defined as the perception of the easiness or difficulty in the fulfilment of the behavior of interest (Bandura, 1997). It is similarly perceived as self-efficacy and feasibility (Shapero and Sokole, 1982). The three connote the sense of capacity regarding the fulfilment of a firm creation behavior (Ajzen, 1997). It is not only about the feeling but also the behavior controllability which describes the extent to which performing or not depends upon the person. LC, self-efficacy and feasibility are adopted as a model to study the intention to start a venture: (Krueger, 1993; Kolvereid, 1996; Fayolle and Gailly, 2004). Researchers have verified the impact of self-efficacy or LC on EI (Chen et al., 1998; Krueger and Brazeal, 1994). It was argued that self-efficacy or LC not only influence individual’s intentions but ultimately lead to venture creation in the long run.

1.3.3 Risk Taking Propensity:

Risk Taking Propensity (RT) is defined as the willingness to take risk. The endurance and intelligence is considered as a yard stick for measuring an individual’s EI.

1.3.4 Attitude:

Attitude (ATT) towards a behavior is described as the degree to which the individual holds a positive or negative personal valuation about being an entrepreneur. Ajzen and Fishbein (1977) and Ajzen (1991) argued that ATT significantly influence intentions (Kolvereid, 1996). Further, ATT includes both affective and evaluative consideration along with strong individual control.

1.3.5 Subjective Norm:

Subjective Norm (SN) refers to the perception that reference people can approve the decision to become an entrepreneur or not. SN measures the perceived social pressure to carryout entrepreneurial behavior or not. The perceived expectation of the level of people who are key to the individual in question include friends, relatives, parents, colleagues’ who matters. Krueger Jr. and Dickson (1993) also added SN as an important factor influencing individual’s EI.

1.4 Big Five Personality Model:

Big Five Personality Model conducts research on entrepreneurial inclinations which is endorsed by various scholars like Brandsttatter (2013); Luthje and Franke (2003); Zhao and Seibert (2006). The Personality model impact intentions (Zhao et al., 2005) and confirmed by Luthje and Franke (2003).

1.5 Knowledge and Skills Acquisition:

Individual Competence which include Knowledge and Skills is also identified as an important factor in the early stages of starting a business. Mc Clelland (1961) posited that adult EI have their basis from the experiences students go through during their younger years. Competencies in recognizing opportunities, relationship, conceptual, organizing, strategic and commitment are cited as factors which determine EI (Man, 2000). Krueger (1993) argued that prior entrepreneurial experience influence future intentions but Davidson (1995) reported that it may slightly or not influence at all. Experience and Education are widely highlighted due to the increased knowledge and skills that can positively influence intentions and attitude. Therefore, entrepreneurial knowledge has distinct and significant effect on intentions.

1.6 Family Background:

While some scholars have opined that family background factors, particularly parents, have significant impact on an individual’s EI. Conversely, Krueger and Dickson (1993) hold a contrary view, signifying Entrepreneurs’ children do not proportionally become Entrepreneurs.

1.7 Demographic Factors:

Demographic or situational factors such as Age, Gender, and Ethnicity have an influence on intentions (Lee and Wong, 2004). Gender differences exist as far as EI is concerned (Zhao and Seibert, 2006). However, the differences help to ascertain the moderating effects on intentions. Zhao, Siebert and Hills (2005) opined that gender makes difference in EI. On the other hand, Kolvereid (1996) found that gender makes no difference. Fayolle (2006) opined that male entrepreneurs dominate female counterparts because of some stereotypes for women. Some scholars believe that similarities with gender is much more pronounced than the differences. The study has carefully used Theory of Planned Behavior described by Ajzen (1991) and used by Kolvereid (1996) and Fayolle and Gailly (2004) in the conceptualization phase of the study.

2. Conceptual Framework:

The conceptual framework of the research is demonstrated in figure 1. This describes what a student go through from the time of admission to the time of his or her four-year or two-year University education is completed. Students enter University with their own personality traits and behavior, Big 5 personality model, occupational and educational background of their parents which make each one unique. The students go through the University system as a mill to acquire intellectual knowledge and skills, enhancing their EI for job creation to become entrepreneurs during or after college. Scholars have argued that Age, Ethnicity and Sex of individual students or faculty members differently moderates on EIs.

2.1 Research Hypothesis:

Hypothesis describes individual conjectures based on literature. Theory assesses multiple hypotheses that are logically linked together and tested empirically. Scholars have propounded various EI theories that are empirically tested, among which, the critical contribution includes Ajzen’s Theory of Planned Behavior (1987). Based on the conceptual framework and research objective, the study tested four main hypothesis under personality traits and behavior, Big 5 personality model, occupational and educational background of students and faculty members. The moderated effect of demographic background of students on EI were also assessed.

The hypotheses include:

1. H₀₁: Personality traits impact students EI through knowledge and skills acquisition

H_1a: Attitude of students and faculty impact EI

H_1b: Need for Achievement by students and faculty impact EI

H_1c: LC of students and faculty impact EI

H_1d: SN of society impact EI of students and faculty

H_1e: Risk taking propensity of students and faculty impact EI

2. H₀₂: Big 5 personality model impacts EI

H_2a: Extraversion model impact EI

H_2b: Agreeableness model impacts EI

H_2c: Neuroticism impacts EI

H_2d: Openness to Experience impacts EI

H_2e: Conscientiousness impacts EI

3. H₀₃: Parents educational and occupational backgrounds impact EI of students and faculty

4. H₀₄: Relationships between Personality Traits, Personality Model, Parent Background and EI through knowledge and skills acquisition are moderated by demographic background (Ethnicity, Sex, Age) of students and faculty members

3. RESEARCH METHODOLOGY:

Online/electronic survey method was initially employed using the Ramboll-Survey XACT platform. Being an employee at Ghana Technology University College, the scholar had access to emails of students and faculty members. The survey was electronically sent to students and faculty members whose emails were available to participate. The response rate within a period of two weeks was so low that the research scholar decided to discontinue with the online method and switched to the widely known manual method (face-to-face interviews and focus group discussions with questionnaire) for data collection in Africa and for that matter Ghana. These have proven to be faster and have better results (Eithier et al., 2016). The Class Representatives, Teaching Assistants and Lecturers were approached to allow students to fill the questionnaires before the class begins or immediately after the class. The filled out questionnaires were handed over to the Class Representatives and Teaching Assistants as soon as students finished filling them. This process was repeated across all the seven Universities in Ghana. The Research Assistants involved followed up and collected all the filled questionnaires sent out to the seven selected Universities across Ghana . In summary, questionnaires were designed and used as a tool to collect the needed information. Respondents answered Likert Scale questions that best described them as unique individuals. Out of a total of eight hundred (800) questionnaires distributed among students and faculty members in the selected Universities, five hundred and eighty four (584) were returned but five hundred and sixty eight (568) could be used for the analysis and sixteen (16) were rejected because of either half-filled or not filled at all. Hence, the successful response rate for the survey questionnaires was 71%. A mixture of sampling techniques being purposive, accidental, convenience and quota sampling were employed. Furthermore, SPSS and PLS-SEM 3 software were used to analyze the data and interpret Results and Discussions.

3.2 Validation of Study:

To ensure data quality, reliability and validity test, Cronbach Alpha values from SPSS were used. Composite reliability was determined to ensure internal consistency and reliability of all the constructs in the model designed with PLS-SEM 3 software. The Cronbach Alpha assumes that all indicators in a model are equally reliable while PLS-SEM 3 prioritizes the indicators according to their individual reliability. Generally, a composite reliability value of 0.60 to 0.90 is regarded as satisfactory. The structural model estimates were established after examining the reliability and the validity of constructs. Assessment were made to verify the structural model’s ability to predict. A composite reliability value below 0.60 indicates a lack of internal consistency and reliability. However, Cronbach Alpha value of 0.896 was obtained from the demographic data signifying that the data was highly consistent, reliable and validated. The Likert Scale questionnaires that were administered on a 5-point scale has a composite Cronbach Alpha value of 0.836 signifying a highly consistent, reliable data that can also be validated and generalized.

4. Analyses of the Demographic data:

The detailed analysis of demographic data on the respondents are described in the below sections:

4.1.1 Participating Universities:

The respondents were drawn from two public Universities, (Kwame Nkrumah University of Science and Technology-KNUST and University of Ghana, Legon), three private Universities (Ghana Technology University College-GTUC, University of Professional Studies-UPSA and RADFORD University) and two technical Universities in Accra and Kumasi (Accra Technical University-Accra TU and Kumasi Technical University-Kumasi TU). The rationale for selection of Universities was to ensure that students and faculty members are drawn from the buckets of public, private and technical Universities in Ghana. The distribution of respondents comprise of 151 representing 26.6% from KNUST; 16.5% (91) from Legon; 16.7% (94) from UPSA; 13.9% (79) from GTUC; 9.7% (55) from RADFORD; 11.4% (65) from Accra TU and 5.8% (33) from Kumasi TU.

4.1.2 Academic Status:

Altogether, 94.7% (538) of the total respondents were students pursuing various academic degrees. In that, 4.6% (26) are University Lecturers and 0.7% (4) were teaching and pursuing further studies at the same time.

4.1.3 Level of Education:

Majority of respondents were Level 400 students (216) which constituted 38%, followed by 99 in Degree Level 100 (17.4%); 79 in Level 300 (13.9%); 48 in level 100 Diploma (8.5%); 47 in level 200 degree (8.3%); 36 in Level 200 Diploma (6.3%); 26 Masters Students (4.6%) and 17 Ph.D. students (3%).

4.1.4 Academic Programmes:

275 respondents being the majority indicated pursuing Business Management (48.4%) and 107 pursuing Engineering (18.8%). The remaining 95 were pursuing Social Sciences (5.8%) and Architecture/Planning/Bio-Technology (3.0%). The rest were pursuing Information Technology (2.5%), Physical Sciences (2.3%), Medical Sciences (1.4%), Computer Science (0.9%) and all others constituted (16.9%).

4.1.5 Age Bracket:

The respondents were mostly youthful as the majority (510) representing 89.8% were in the 20-29 years age bracket; 7.4% (42) in 30-39 age bracket; 1.2% (7) within 40-49 age bracket and 1.6% (9) in the 50-59 age bracket.

4.1.6 Marital Status:

As many as 91.9% participants (522) indicated not married (thus Single), with 6.3% married (36), 0.5% divorced (3) and 1.2% widowed (7).

4.1.7 Dependents:

When asked whether they had dependents or children, 93.0% indicated none; 2.6% responded having one child; 2.1% indicated two children. Those who had either three or four children were 1.2% and 0.5% respectively. A negligible percentage (0.2%) of the respondents indicated having either five, six or seven children.

4.1.8 Ethnicity:

In terms of ethnic background of the participants, 52.5% (298) indicated Akan; 19.9% (110) belong to Ewe; 15.5% (88) relate to Ga Dangbe; 1.8% (10) belong to Dagombas; 1.6% (9) represent Dagombas/Walis; 1.2% (7) indicate Gurune/ Frafra; 1.8% (10) indicated Nzima and others 6.3%.

4.1.9 Educational and Occupational Background of Families Pertaining to Respondents:

Majority of the survey participants (75.1%) indicated that their parents were Tertiary/College Degree holders (37.0%); Masters’ Degree holders (12.7%), Secondary School leavers (25.4%) and Ph.D. holders (6.3%). A few had no formal education (9.8%) and Primary education (6.9%). The parents were professionals with own Business (33.2%); Salaried Workers (28.9%); Skilled Workers (8.3%) and Medical Doctors (1.1%). The others were Agriculturists (4.0%); Teachers (7.7%); Executive Clerks (1.7%) and Retirees (7.9%).

4.2 Analysis of Research results using Structural Equation Models:

The Statistical Product and Service Solution (SPSS) software, AMOS version 23 and Partial Least Squares-Structural Equation Model version 3 (PLS-SEM 3) software were used to analyze the 5-point Likert Scale questionnaire responses. The data was processed by SPSS and the analysis was conducted by applying descriptive, regression and inferential statistics such as mean, standard deviation, cross tabulations, chi-square and hypothesis testing. The PLS-SEM 3 software was used to design the measurement models signifying their constructs and relationships. The PLS path modeling used the PLS algorithm to maximum iterations of 300 and stop criterion at 7. Bootstrapping, a non-parametric procedure, that allows testing of statistical significance of the various PLS-SEM 3 results, path coefficients, Cronbach Alpha, R² values, tests of significance (p-values), t-statistics at confidence level of p<0.05; two tailed were determined. Direct and indirect effects of variables, composite reliabilities, construct validity and reliability were also calculated at subsamples of 1000 to ensure stability of results and significance level of 0.05 (p<0.05).

Five main reasons accounted for the choice of PLS-SEM 3 approach to estimate the relationship in the structural equation models are:

1. PLS-SEM 3 has the ability to predict and explain target constructs in situation where theory is not well developed. It becomes the preferred method for theory development and explanation of variance (prediction of constructs)

2. Efficiently works in situations where small samples and complex models are employed and makes no assumptions on data used

3. Handles formative and reflective measurement models as well as single item-constructs which were applied in this research

4. Applicability in variety of research situations

5. Statistical power and ability to render specific relationship when it is significant in the population

The following inferences can be deduced from the reflective measurement of the above structural equation model. The personality traits and behaviour impacts significantly on EI. A unit change in personality trait can cause a corresponding change of 0.7 units in EI. The p-value of 0.000 and bootstrapping at 0.05% confidence level indicated that personality traits and behaviour significantly impacts EI. The Big 5 personality model relatively impacts EI. A unit change in Big 5 personality model can cause a corresponding change of 0.2 units in EI. The p-value of 0.000 implies that its impact on EI is significant. The null hypothesis (H₀) is also accepted. Similarly, knowledge and skill acquisition positively impact on EI of students and faculty members. A unit change in knowledge and skill acquisition can cause a corresponding change of 0.13 units in EI. The p-value after bootstrapping at 0.05% confidence level was 0.000 indicating positive relationship and significant predictor. Consequently, the socio-economic background of students and faculty members impacted the EI positively. A unit change in socioeconomic background can cause a corresponding change in EI by 0.04 units. The p-values after bootstrapping at 0.05% confidence level was 0.000 indicating significant impact and predictor. The parent background of students showed a negative impact. A unit change in parent background can reduce impact on EI by 0.03 units. The p-value of 0.097 which is greater than p< 0.05 signifies a weak relationship. The null hypothesis (H₀) is therefore rejected. Thus, higher the educational level and occupation of Parents, greater the impact on students and EI, and vice versa. On the contrary, Alexandrina et al., (2019) affirmed that students with parents without self -employed experience are more likely to start own venture than the rest that have parents with self-employed experiences. The demographic background of students and faculty members also impact on EI. All the components of personality traits significantly impact EI. The NA, LC, SN, Risk taking were all found to be positively impacting students’ EI, which is similar to the results of the EI of the survey on foreign students in Turkey carried out by Usman and Yennita (2019) and Khadeeja et al.. (2017). Moreover, all the p-values of 0.000 called for the acceptance of the null hypothesis and rejection of the alternative hypothesis. The ATT construct of personality trait was, however, found to have negative relationship (-0.008) with EI with a non-significant p-value of 0.790. A negative attitude can reduce EI among students by 0.08 units. In summary, all the components of Big 5 personality model significantly impacted EI. The most significant predictor of EI was Extraversion, followed by conscientiousness, and Agreeableness. The coefficient of determination (R²) value of 99% infer that all the variance in EI can be explained by the exogenous constructs (personality traits, Big 5 personality model, parents background, knowledge and skills acquisition, and demographic characteristics of students and faculty members). Table 1 shows the p-values and decision criteria of various constructs that impact on EI.

4.3 Big 5 Personality model Behaviours Among Students and faculty members in Ghanaian Universities:

Based on the survey, the participants selected on their Big 5 personality traits inferring that the majority (87.5%) strongly agreed as being Conscientious persons; followed by 86.8% as Agreeable persons; 77.85% as Openness to Experience persons; 69.7% as Extraversion persons and 30.6% as Neuroticism persons.

Table 1. Impact of Personality Traits, Knowledge and Skill Acquisition, Big 5 Personality Model, Demographic and Parent Backgrounds on EI

Impact of Personality Traits/Big5 Personality Model, Parent Background, Knowledge and Skills on EI of Students and Faculty Members	Sample	Mean of the Sample	Standard Deviation	t-Statistics	Signific-ant Values	Decision
ATT -> Personality Traits and Behavior	-0.008	-0.004	0.030	0.266	0.790	Reject H₀
Agreeableness -> Big 5 Personality Model	0.134	0.135	0.032	4.157	0.000	Accept H₀
Big 5 Personality Model -> EI	0.160	0.156	0.029	5.577	0.000	Accept H₀
Conscientiousness -> Big 5 Personality Model	0.176	0.175	0.026	6.647	0.000	Accept H₀
Extraversion -> Big 5 Personality Model	0.372	0.372	0.017	21.538	0.000	Accept H₀
Knowledge and Skill Acquisition impact EI	0.136	0.135	0.008	18.137	0.000	Accept H₀
LC -> Personality Traits and Behavior	0.358	0.359	0.030	11.825	0.000	Accept H₀
NA -> Personality Traits and Behavior	0.261	0.258	0.051	5.080	0.000	Accept H₀
Neuroticism -> Big 5 Personality Model	0.246	0.248	0.013	18.374	0.000	Accept H₀
Openness to Experience -> Big 5 Personality Model	0.127	0.125	0.026	4.797	0.000	Accept H₀
Parent Background Impact EI	-0.031	-0.032	0.018	1.661	0.097	Reject H₀
Personality Traits and Behavior Impact Entrepreneurial Intentions	0.703	0.710	0.038	18.733	0.000	Accept H₀
RT -> Personality Traits and Behavior	0.119	0.120	0.044	2.711	0.007	Accept H₀
SN -> Personality Traits and Behavior	0.275	0.272	0.033	8.334	0.000	Accept H₀
Socioeconomic Background of Impact EI	0.045	0.044	0.010	4.275	0.000	Accept H₀

Figure 3. Big 5 Personality Traits among Students and Faculty Members

In summary, these category of persons and factors considered have implications on their EI.

5. CONCLUSIONS:

EI can be useful to Government, Policy and Decision Makers, University Management, Practitioners who are constantly seeking to equip graduates for creating jobs but not to seek for jobs after University education. The study revealed that personality traits and behaviour, Big 5 personality model, knowledge and skills acquisition, demographic background of students and faculty members positively impact on EI. In particular, personality traits, however, is the dominant predictor of EI followed by Knowledge and skills acquisition and Big five personality model. The coefficient of determination (R²) value of 99.0 suggest that 99% of all the variance in EI can be explained by personality traits, Big 5 personality model, parents background, knowledge and skills acquisition, along with demographic characteristics of students/faculty members. The parent background of students showed a negative relationship. Hence, there is a weak relationship between parent background and student EI. Thus, a student may not necessarily follow their parent’s profession as reported in several literature. The higher the educational level and occupation of parents, the greater the impact on students and EI and vice versa. Among all the components of personality traits, LC, SN and NA significantly impacted EI respectively. These components are dominant exogenous constructs to predict EI. The ATT construct of personality trait has a negative relationship with EI. A negative attitude of a Student towards entrepreneurship can significantly impact on his or her EI. Any attempt by University Authorities, Parents, Governments and Development Practioners to encourage and harness students and faculty members on EI can boost morale and produce job creators to boost future economies of developing Countries. Over 70% of all the students and faculty members responded having high aptitude in skills such as recognition of Entrepreneurial Opportunity (71.3%), Creativity (79%), Problem Solving (80%), Leadership and Communication Skills (70%). All the components of Big 5 personality model significantly impacted constructs on EI. The most significant predictors of Big 5 personality model on EI were Conscientiousness followed by Agreeableness and Extraversion respectively. Almost 88% of the total respondents (568) strongly agreed as being Conscientious persons. This study has contributed to the existing literature by analyzing EI through a multi-dimensional approach.

REFERENCES:

1. Ajzen, I. and Fishbein, M., (1997) Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84(05), pp. 888-918.

2. Ajzen, I., (1987) Attitudes, Traits and Actions: Dispositional Prediction of Behavior in Personality and Social Psychology. Advances in Experimental Social Psychological, 20(01), pp. 01-63.

3. Ajzen, I., (1991) The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(02), pp. 179-211.

4. Alexandrina, M. P, Onise A. Tudor. E and Rodica M. Zaharia (2018). What Determinants Inﬂuence Students to Start Their Own Business? Empirical Evidence from United Arab Emirates Universities. Sustainability 2019, 11, 92; doi: 10.3390/su11010092 www.mdpi.com/journal/sustainability

5. Bandura, A. (1997), Self –Efficacy: The Exercise of Control, W.H. Freeman.

6. Bird, B. J., (1988) Implementing Entrepreneurial Ideas: The Case for Intention. Academy of Management Review, 13(01) pp. 442-453.

7. Brandstatter, H., (2003) Personality Aspects of Entrepreneurship: A Look at Five Meta-Analyses. Personality and Individual Differences.

8. Crant (1996) The Proactive Personality Scale as a Predictor of Entrepreneurial Intentions’. J. of small Business Management 34(03), pp. 42-49.

9. Davidson (1915b) Determinants of Entrepreneurial Intentions. Presented at the Rent IX Workshop, Piecenza, Italy.

10. Elfving, J., Brannback, M., and Carsrud, A., (2009) Toward a Contextual Model of Entrepreneurial Intentions. In. Carsrud A. L, and Brannback M., (Eds). Understanding the Entrepreneurial Mind, Opening the Black Box. New York: Springer, pp. 23-33.

11. Ethier, R.G, Poe G.L., Schulze W.D. and Clark J. (2000). A Comparison of Hypothetical Phone and Mail Contingent Valuation Responses for Green-Pricing Electricity Programmes. Land Econ., 76(01), pp. 54-67.

12. Fayolle, A., Gailly, B., and Lassas-Clerc, N., (2006) Assessing the Impact of Entrepreneurship Education Programmes: A New Methodology. J. of European Industrial Training, 30(09), pp. NA.

13. Khadeeja, A.S.T., Sayang, N.R, and Idris, M.N.., (2017). The factors affecting the entrepreneurial intentions of University students in Malaysia. International Journal of Business and Technopreneurship Volume 7, No. 2, June 2017 [189-202]

14. Kolvereid, L. (1996) Organizational Employment versus Self-Employment: Reasons for Career Choice Intentions’. Entrepreneurship: Theory and Practice, 21(3) 23-31.

15. Krueger, N. F. Jr, and Dickson, P., (1993) Perceived Self-Efficacy and Perceptions of Opportunity and Threat. Psychological Reports, 73(03), pp. 1235-1240.

16. Krueger, N. F, Jr, and Brazeal, D.V., (1994) Entrepreneurship Potential and Potential Entrepreneurs. Entrepreneurship Theory and Practice, 18(03), pp. 91-104.

17. Lee, L, Wong, P. K, Foo, M. D., and Leung, A., (2011) Entrepreneurial Intentions: The Influence of Organizational and Individual Factors. J. of Business Venturing, 26(01), pp. 124-136.

18. Lee, S. H., and Wong, P. K., (2004) An Exploratory Study of Technopreneurial Intentions: A Career Anchor Perspective. J. of Business Venturing, 19(01), PP. NA

19. Luthje, C., and Franke, N., (2003) The “Making” of an Entrepreneur: Testing a Model of the “Making” of an Entrepreneur: of R&D Management.

20. Man, T. W. Y., (2000) Entrepreneurial Competence of SME Owner/Managers in the Hong Kong Services Sector, a Qualitative Analysis. J. of Enterprising Culture, 08(03), pp. 235-254.

21. Mattews, C., and Moser, S., (1995) Family Background and Gender: Implications for Interest in Small Firm Ownership. Entrepreneurship and Regional Development, 07(01), pp. 365-377.

22. McClelland, D. C., (1961) The Achieving Society. New York

23. Migliore, A. (2011) Relationship between Big Five Personality Traits and Hofstede’s Cultural Dimensions, a Sample from India and USA. Cross Cultural Management: An International Journal, 18(01), pp. NA.

24. Perera, Q., (2012) Local Universities Producing Less than 5% of SL’s Entrepreneurs. Retrieved December 25, 2012, from Sunday Times: http:/www.sundaytimes.ik/120506/BusinessTimes/ bt16.html.

25. Ranwala R.S. and Dissanayake D.M.N.S. (2016) Social and Basic Science Research Reviews. Volume 4 ISSN: 2313-6758

26. Shapero, A., and Sokole, L., (1982) The Social Dimensions of Entrepreneurship. In C.A. Kent, D.L. Sexton, and K.H. Vesper (Eds.). Encyclopedia of Entrepreneurship. Englewood Cliffs, NJ: Prentice-Hall, pp.72-90.

27. Shaver, K. G., (1995) The Entrepreneurial Personality Myth. Business and Economic Review, 41(03), pp.20-23.

28. Tabachnick, B. G., and Fidell, L. S., (2001) Using Multivariate Statistics. Boston: Allyn and Bacon.

29. Usman and Yennita (2019). Understanding the entrepreneurial intention among international students in Turkey. Journal of Global Entrepreneurship Research. https://doi.org/10.1186/ s40497-018-0136-0

30. Van Gelderen, M., Brand, M., Van Praag, M., Bodewes, W., Poutsma, E., and Van Gils, A., (2008) Explaining Entrepreneurial Intentions by Means of the Theory of Planned Behavior. Career Development International, 13(06), pp. 538-559.

31. Zhao, H. S., Seibert, S. E., and Hills, G. E., (2005) The Mediating Role of Self-Efficacy in the Development of Entrepreneurial Intentions. J. of Applied Psychology, 90(01), pp. 189-202.

32. Zhao, H., and Seibert, S. E., (2006) The Big Five Personality Dimensions and Entrepreneurial Status: A Meta-Analytical Review. J. of Applied Psychology, pp. 256-289.

Symbol

Description

ATT

SPSS

PLS-SEM 3

H₀

H_1,2,3

Need for Achievement

Locus of Control

Risk Taking

Attitude

Subjective Norm

Statistical Packages for Social Sciences

Partial Least Square for Structural Equation Model version 3

Null Hypothesis

Alternative hypotheses 1,2,3