Open Access

Impact of family capital & social capital on youth entrepreneurship – a study of Uttarakhand state, India

Journal of Global Entrepreneurship Research20144:14

https://doi.org/10.1186/s40497-014-0014-3

Received: 2 June 2014

Accepted: 11 August 2014

Published: 17 November 2014

The Erratum to this article has been published in Journal of Global Entrepreneurship Research 2015 5:10

Abstract

The research paper intends to interpret how the three forms of family capital viz. family’s financial capital, family’s man power capital and family’s human capital influences the career choice intention of students of HEI’s of Uttarakhand, India. Additionally the study also evaluates the impact of student’s individual social capital on his career intent. This is a quantitative study conducted at Uttarakhand state of India on a large sample of students studying in various professional courses of Uttarakhand. The research validates a positive relationship between the family’s financial capital and higher education intention of students. The study found no influence of family’s financial capital (measured as father’s annual income), manpower capital (measured as family size) and human capital (measured as father’s occupation) on career intentions of students. The study confirmed that there is a significant impact of students’ social capital network span on his career intentions, especially in taking up entrepreneurship as a career choice.

Jel Codes: D20; G34; L10

Keywords

EntrepreneurshipFactors influencing career choicesFamily impact on careerYouth entrepreneurship in UttarakhandFamily and career intentionsFamily capital & youth entrepreneurshipSocial capital & youth entrepreneurship

Background

The Forbes list of billionaires featured 55 Indians in 2013 and the net worth of top ten billionaires was estimated at $102.1 billion. In contrast, every second malnourished child and every third poor person in the world is also an Indian. This picture of sharp contrast clearly highlights that India has sharp rise in inequalities over years. Uttarakhand is the 27th state of India. It is located at the foothills of the Himalayan mountain ranges; it is largely a hill state, having international boundaries with China in the north and Nepal in the east. Uttarakhand has a unique culture of its own. It has a multiethnic population, mostly dependent on agriculture, tourism and handicrafts. People are spread across two recognized geo-cultural regions: the Garhwal, which corresponds roughly to the northwestern half of the state and the Kumaun, which spans the southeast. Uttarakhand houses seven different ethnic groups, having as many as seven different cultures. More than four-fifths of Uttarakhand’s residents are Hindu. Smaller communities of Muslims, Sikhs, Christians, Buddhists, and Jains make up most of the remainder of Uttarakhand’s people. Struggling with the problems of rising rate of unemployment and immigration, the state has introduced host of financial incentives to promote and develop entrepreneurship in the state. But the lukewarm response, especially from the youth in the state, has predicted that local people of the state are not business oriented people, they are more inclined towards jobs.

According to Lin ([2000]), differences in distribution of various types of capital across different groups in society lead to inequalities in career aspirations and economic achievement. Resource-based theory of entrepreneurship envisages that people’s likelihood of becoming an entrepreneur is influenced by their access to valuable and unique resources (Alvarez & Busenitz, [2001]; Barney, [2001]). Resources refer to financial capital (Schweinbacher, [2007]), family capital (Parcel & Menaghan, [1993]) as well as human and social forms of capital (Chiles et al.[2007]; Davidsson & Honig, [2003]). We have hereby emphasized on social forms of capital including family capital. The notion of social capital has been around for decades. It is with the early works of Jacobs ([1961]), Bourdieu ([1983]), Coleman ([1988]) and Putnam ([1993]; [2000]) that it gradually received recognition. According to Putnam ([2000]), social capital refers to connections among individuals – social networks and the norms of reciprocity and trustworthiness that arise from them. He further explains that social capital is closely related to civic virtue, the difference being that social capital calls attention to the fact that civic virtue is most powerful when embedded in a dense network of reciprocal social relations. In other words, social capital enables people to collaborate, socialize, establish communities and live together by adhering to moral obligations, norms and social values. As such, Social Capital Theory refers to the ability of actors to extract benefits from their social structures, networks, and memberships (Lin et al.[1981]; Portes, [1998]). Broadly the relevant literature has accepted that social capital enhances the likelihood of several individual outcomes (Burt, [1997]; Nahapiet & Ghoshal, [1998]). Entrepreneurs require information, capital, skills, and labor to start business activities. Although they try to manage many of these resources on their own, the shortfalls are fulfilled by accessing their contacts (Aldrich & Zimmer, [1986]; Aldrich et al.[1991]; Cooper et al.[1995]; Hansen, [1995]). When the entrepreneurs’ social contacts contribute to their entrepreneurial goals, these social contacts are their social capital (Burt, [1992]). Social capital helps in entrepreneurship development (Bhagvatula et al.[2010]; Birley, [1986]) but its relevance for youth career intent needs further exploration.

Family capital is considered to be a special type of social capital that exists in family relations (Parcel & Menaghan, [1993]). Economic organization is largely structured around the family, with the family’s survival essential to the long-term functioning of society (Coale, [1973]). Chrisman et al. ([2002]) asserted that family represents a critical and often used resource for startups. It is well-documented in the entrepreneurship literature that entrepreneurs tend to rely heavily on their family capital to derive various benefits (Zimmer & Aldrich, [1987]). Several studies indicate that family plays an important role in the mobilization of financial resources during the initial stage (Aldrich & Waldinger, [1990]; Steier & Greenwood, [2000]) and during survival times (Holtz-Eakin et al.[1994]). According to Smallbone and Welter ([2001]), in most developing countries, people are dependent on their income to handle financial constraints in starting their business. A vast majority of entrepreneurs in these countries use their own resources to finance their business (Acs et al.[1999]). Since students of HEI’s in developing countries are either earning part time to generate some money to support themselves or entirely depend upon their families for their education and other needed support, the role of families become even more important in career intent especially in taking up entrepreneurship as a career option. The career choice preference of an individual is at its highest point at student life and as such the influence of others, especially family & society, can result in determining entrepreneurial intention (Gelderen et al.[2008]; Leffel & Darling, [2009]). Family is seen to provide support for entrepreneurial start up in many ways. It has been found that the family plays an important role in the transmission of values such as independence, ambition, career orientation and actual career choice (Grimstad & Way, [1993]). It helps in the provision of unique skills & pool of information (Greve & Salaff, [2003]), human resources (Dyer & Handler, [1994]; Aldrich & Langton, [1997]) and physical resources (Birley, [1986]) in the form of space and premises for enterprise development. Several research scholars (Wilson et al.[2007]; Mueller, [2006]; Matthews & Moser, [1996]) have argued that family role models influence the preferences for self-employment. Cetindamar et al. ([2012]) states that family capital can be especially beneficial in facilitating individual’s entrepreneurial entry in developing countries. Another type of capital which is seen to play a significant role in entrepreneurship development is human capital. Human capital refers to economically salient personal resources (skills, specific knowledge associated with particular jobs, general education) of the sort that might for example be considered by prospective employers as justifying offers of employment (Becker, [1964]; Coleman, [1988]). Scholars (Hitt et al.[2001]) argue that human capital is the most critical resource that economic actors possess. When new economic opportunities exist, individuals with better human capital should have a higher likelihood of identifying and exploiting them. (Cetindamar et al.[2012]).

Demographic structures of developed countries (Bianchi, [2014]; Aksoy et al.[2012]) as well as developing countries like India (James, [2011]) are changing continuously and changing fast. It is seen that demographic variables have a significant impact not only on the economic variables like GDP, investments and inflation (Aksoy et al.[2012]) but also on changes in the family (Bianchi, [2014]). Demographic trends reveal an increasingly diverse and complex family life and a more ambiguous and fluid set of categories traditionally used to define the family (Cherlin, [2010]). Changing trends in family composition and family members’ role and relationships are witnessed in developed economies like USA (Walsch, [2012]; Aldrich & Cliff, [2003]) as well as in developing economies like Pakistan (Rehman & Roomi, [2012]). Continuous changes in demographic structures and families could challenge the outcome of the earlier studies done on the role played by the demographic and family variables on entrepreneurial outcomes. Cramton ([1993]) has provided strong evidence that organizational foundings may represent responses to changing family relationships rather than outcomes of the rational assessments of discovered economic opportunities. This calls for a continuous study of effect of demographic and more specifically family variables on entrepreneurial outcomes. Furthermore, inspite of several studies on impact of social variables and demographic variables on entrepreneurial outcomes, the role of the family context for entrepreneurial orientation is not yet well understood (Cruz & Nordqvist, [2012]). Similarly, Thornton et al. ([2011]) posit that the influence of social and cultural factors on enterprise development remains understudied.

Based on the above discussion, we have undertaken three forms of family capital viz. father’s occupation as a measure of family’s human capital, father’s income as a measure of family’s financial capital, family size as a measure of family’s man power capital and social network span of student as a measure of his individual social capital resource and evaluated their impact on students’ career intent, more specifically entrepreneurial intent. The outcomes of the research would help the state government understand the impact of social & familial factors on youth entrepreneurship which can be used to formulate appropriate policies for youth entrepreneurship development in Uttarakhand. The research would also help educational institutions to do appropriate modifications in the curriculum to develop youth entrepreneurship.

Review of literature and hypothesis formulation

The youths’ access to financial resources help ease the transition into self-employment (Henley, [2005]; Delmar & Davidson, [2000]) and that is one of the main reasons why the children born in rich family find it easy to venture on their own. The children may have less pressure to make a living by finding a stable job and tend to be more risk-taking. These factors indirectly impact entrepreneurial intention through the perception of desirability and feasibility (Wang et al.[2011]). According to Dunn & Holtz-Eakin ([2000]) high income households are not only able to better provide the necessary financial resources to entrepreneurial firm growth but are also likely to see more entrepreneurial growth opportunities. Raijman ([2001]) posited that financial resources in the family have direct bearing on entrepreneurial intentions. A lot of studies have shown that family income influences the career development of youth (Alibaygi & Pouya, [2011]; Mortimer, [1992]) and self-employment of youth (Hundley [2006]; Henley [2005]). The financial status of the family has been observed to have an impact on the child’s choice of entrepreneurship (Hsu et al.[2007]). Study by Millman et al. ([2010]) also confirmed that household incomes are positively related to their entrepreneurial Intentions. A recent study done by Nandamuri and Gowthami ([2013]) tested 11 competencies related to entrepreneurial orientation of management students and found that the household income significantly influences nine out of eleven competencies. Accordingly we propose the first hypothesis.

H1: Annual income of the family has an influence on the career intentions of students’ of HEI’s

According to Schulenberg et al. ([1984]), family size appears to influence adolescent career aspirations because parents with large families tend to have less money to aid the older children in attending college, while younger children may receive more financial assistance since the financial strain is less once the older children leave home. A recent study by Cetindamar et al. in [2012] at Turkey found that family size was positively associated with the likelihood of engaging in entrepreneurship only when family size is more than seven people. People who had a family size of seven or more were 1.768 times more likely to engage in entrepreneurship compared with people who had fewer than three people in their family. In contrast, another study conducted by Pushpalatha ([2013]) in India found that majority of the women in Andhra Pradesh, India who turned into entrepreneurs had an average family size of 2–3 members. She interpreted that small family size have lessened their family responsibilities which motivated them to enter into entrepreneurship. Weber ([1978]) posited that cooperation from within a family stems not just from pure self-interest, but from a greater moral order in which the accumulation of obligations among members builds a kind of social cohesion that may be described as ‘household communism’. According to Cetindamar et al. ([2012]), family members can be trusted in under-the-counter transactions aimed at evading taxes and other government regulations that are common in new and small businesses in many parts of the world, such as in developing countries. While larger families could contribute to greater motivation, larger social network and better support. Smaller families may provide more time and fewer obligations that would enable them to engage in diverse activities required in new business settings and taking more risks. A recent report by Global Entrepreneurship Monitor (Kelly et al.[2012]) found that average household size for male and female entrepreneurs ranged from three people in Europe and the U.S. to five people in Sub-Saharan Africa and MENA/Mid-Asia. Finland, Austria, Denmark and Germany showed the smallest household sizes for both female entrepreneurs and non-entrepreneurs/business owners: less than three people per household on average. In Pakistan, Angola and Palestine, on the other hand, households for both groups averaged over six individuals; accordingly we propose our next hypothesis.

H2: Size of the family has an influence on the career intentions of students’ of HEI’s

Most of the authors (Colombier & Masclet, [2008]; Sørensen, [2007]; Carr & Sequeira, [2007]; Drennan et al.[2005]; Arum & Mueller, [2004]; McElwee & Al-Riyami, [2003]; Krueger, [1993a]; Krueger [1993b]; Scott & Twomey, [1988]) in the past have advocated that the children who grew up with entrepreneur parents had a greater propensity to choose an entrepreneurial career. According to Wang et al. ([2011]), due to the example of the self-employed parents, children with family business background have a higher probability to become entrepreneurs. Moreover, self-employed parents can provide more financial and (or) social support to their children to start their own business. According to Corak and Piraino ([2011]) & Dunn and Holtz-Eakin ([2000]) second generation entrepreneurs are two to three times more likely to work in the same occupation as their fathers. A study by Olomi and Sinyamwale ([2009]) found that the students who had families which engaged in business had been better socialized to the entrepreneurial career and were likely to engage in starting their own businesses. Studies conducted in different countries like US (Crant, [1996]); Singapore (Phan et al.[2002]); UK (Altinay et al.[2012]) and Malaysia (Tong et al.[2011]) gave similar results and found that entrepreneurial families or parents positively influence entrepreneurial career intentions in their children. Lindquist et al. ([2012]) found that having an entrepreneur for a parent increases the probability that own-birth children become entrepreneurs by 60%. Davidsson showed that the average of 40% of small business owner managers in Sweden have had a self employed parent(s) (Davidsson, [1995]). The impact of family business background has been proven in several other studies as well (White et al.[2007]; Hout & Rosen, [2000]). On the basis of the above discussion, the following next hypothesis is proposed.

H3: Father’s occupation has an influence on the career intentions of students’ of HEI’s

Social capital refers to the relationships, either formal or informal, generated by individuals in their interaction with other individuals trying to obtain an expected reward in the market, a capital captured in the form of social relationships (Lin, [2003]). Social capital comprises of a person’s social connection in family, professional and social networks, friends, entrepreneurial role models and other known supporting networks comprising of investors, potential customers, bankers etc. Social capital has been linked to a variety of positive social outcomes, such as better public health, lower crime rates, and more efficient financial markets (Adler & Kwon, [2002]). One of the latest research on social capital showed that individuals in communities with high levels of social trust are more likely to be self-employed compared to individuals in communities with lower levels of social trust. Additionally, membership in organizations connected to the larger community is associated with higher levels of self-employment, but membership in isolated organizations that lack connections to the larger community is associated with lower levels of self-employment (Kwon et al.[2013]). According to Davidsson and Honig ([2003]) bridging and bonding social capital, consisting of both strong and weak ties, was a robust predictor regarding who became a nascent entrepreneur as well as for advancing through the start-up process. A recent study by Kreiser et al. ([2013]) found that an increase in network tie strength is negatively associated with founding activities whereas an increase in the number of ties is positively associated with founding activities. Many other authors (Tararko & Schmidt, [2013]; Bauernschuster et al.[2010], Linan, [2007]) have also emphasized upon the positive role of social capital in facilitating entrepreneurship. Social capital helps in opportunity identification (Bhagvatula et al.[2010]), establishment of business (Birley, [1986]) as well as in firm performance (Stam et al.[2014]). Another recent study by Light and Dana ([2013]) suggests that social capital promotes entrepreneurship only when supportive cultural capital is in place. Similarly Malecki ([2009]) has also argued that people living in different regions have different levels of trust and interaction among themselves so regional outcomes with regard to social entrepreneurship will vary. As such we formulate our next hypothesis.

H4: The social capital network span of student has an influence on the career intentions of students’ of HEI’s

Research Methodology

Quantitative research was used to conduct this study. The quantitative approach has helped us to prevent bias in gathering and presenting research data and the discussion and experimentation involved in the process are more objective. A self-administered questionnaire was developed & used as the main data-gathering instrument for this study. Questions regarding the career intentions after completion of degree, family size, father’s occupation and father’s income were asked directly and multiple choices were given to them to choose from. In order to determine the extent of social capital of students, 24 statements derived from ‘Sociological Capital Assessment’ developed by Roberts ([2010]) were used. The answers were recorded on Likert’s 5 point scale. Since the study tends to evaluate the preferred career choices of students of professional courses the target respondents were the final year students of higher educational institutions of Uttarakhand, studying in B.Tech., MBA, PGDM, BHMCT, B.Pharm. and MCA courses. Students were typically in the age group of 21–25 years. Gender ratio of the respondents was 25% female students and 75% male students. The sampling method used in this research is proportionate stratified sampling. Students from each course were picked up based on their prevalence in the universe. This type of sampling has helped us in properly representing each stratum so the sample size drawn from the stratum is proportionate to the stratum’s share of the total population. The respondents were the students of Uttarakhand state only. The whole universe of the target respondents was nearly 20,300. For a size of population which falls in the range of 20,000, the sample size for a 95% confidence level with a reliability of ±3% the sample size suggested is 530 (Zikmund, [2010]). Accordingly, the sample size taken for this study is 530.

Data Analysis and Interpretation

On the basis of the data collected, the hypotheses have been tested using various statistical tools.

H1: Annual income of the family has an influence on the career intentions of students’ of HEI’s

Based on the average annual income of their family, the students were divided into three different income categories (Table 1).

To test the above hypothesis we have cross tabulated the data and used Chi-square test Table 2.

Cross tabulation displays the number of cases in each category defined by two grouping variables i.e. ‘Average annual income’ and ‘Career choice preference after completion of degree’. Total out of 530 respondents, 355 respondents are from ‘Low’ income group, 125 respondents are from ‘Middle’ income group and 50 respondents are from ‘High’ income group category. The data indicates that irrespective of the income group the intention to become an entrepreneur remains to be low among all three income groups however the desire to seek a job is significantly reduced in high income group students in comparison to low and middle income group students and desire to go for higher education significantly increases in comparison to low and middle income group students.

Table 3 contains the output of the Chi-square test. A low significance value of 0.012 and 0.031 of Pearson Chi-square test and likelihood ratio typically below 0.05 indicates that there may be a relationship between the two variables. Since the calculated value of Chi-square (~16.287) is greater than the tabulated value (~12.592), it is evident that variables ‘Income category’ and ‘Career intention after completion of degree’ are dependent. Since opting for higher studies is not actually a career, it is a postponement of the career choice, we have discounted the two options of ‘Go for higher studies’ and ‘Not yet decided’ and reapplied Chi-square test on the variables in order to get a precise picture of the relationship between income category & students’ career intentions of starting a new business or seeking a job Table 4.
Table 1

‘Income category’ vis-à-vis ‘Average annual income’

Average annual income

Income category

Below Rs.3.00 Lakh PA

Low

Rs.3.00 – Rs.5.99 Lakh PA

Middle

Rs.6.00 Lakh and above

High

Table 2

Cross tabulation: ‘Income category’ and ‘Career choice preference after completion of degree’

Career choice preference after completion of degree

Income category

Total

Low

Middle

High

 

Start a new business

21

6

2

29

Seek a suitable job

269

94

27

390

Go for higher studies

46

16

16

78

Not yet decided

19

9

5

33

Total

355

125

50

530

Table 3

Chi-square Test: ‘Income category’ and ‘Career choice preference after completion of degree (including option of ‘Going for higher studies and ‘Not yet decided’

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

16.287a

6

.012

Likelihood ratio

13.878

6

.031

Linear-by-linear association

7.956

1

.005

N of valid cases

530

  

a2 cells (16.7%) have expected count less than 5. The minimum expected count is 2.74.

Table 4

Chi-square Test: ‘Income category’ and ‘Career choice preference after completion of degree’ (excluding option of ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

.178a

2

.915

Likelihood ratio

.183

2

.913

Linear-by-linear association

.087

1

.768

N of valid cases

419

  

a1 cells (16.7%) have expected count less than 5. The minimum expected count is 2.01.

Table 5

Cross tabulation: ‘Family size’ and ‘Career choice preference after completion of degree’

Career choice preference after completion of degree

Total family members

Total

Less than or = 4 members

5-6 members

7-8 members

9 and above members

 

Start a new business

11

11

5

2

29

Seek a suitable job

191

160

25

14

390

Go for higher studies

32

29

12

5

78

Not yet decided

15

14

3

1

33

Total

249

214

45

22

530

Since the calculated value of Chi-square (~.178) is greater than the tabulated value (~5.99), it becomes clear that ‘Annual income’ of the family does not actually influence the career intentions of starting an enterprise or seeking a job but the test applied earlier at Table 3 confirms that it does have an influence on the intention to pursue higher studies.

H2: Size of the family has an influence on the career intentions of students’ of HEI’s

Based on the family size of student, four categories were defined. The first category was “Less than or equal to 4 members”, second category was “5 to 6 members”, third category was “7 to 8 members” and the fourth category was “9 members and above”. The career choice preferences of students were bifurcated based on the above defined four categories Table 5.

Cross tabulation displays the number of cases in each category defined by two grouping variables i.e. ‘Family size’ and ‘Career choice preference after completion of degree’. Out of a total of 530 respondents 249 respondents fall in the category of ‘Less than or = 4 members’, 214 respondents fall in the ‘5-6 members’ category, 45 respondents fall in the category of ‘7-8 members’ and only 22 respondents fall in the ‘9 members and above’ category. To test the above hypothesis, researcher has applied Chi-square test.

Table 6 contains the output of the Chi-square test. A significance value of 0.181 and 0.262 of Pearson Chi-square indicates non-dependence of ‘Family size’ on ‘Career intention of student after completion of degree.’ Since the tabulated value of Chi-square (~16.919) is greater than the calculated value (12.621), it is evident that variables ‘Family size’ and ‘Career intention after completion of degree’ are independent. We again applied Chi-square on the variables after discounting the two options of ‘Go for higher studies’ and ‘Not yet decided’, in order to get a precise picture of relationship between students’ family size and their career intentions of starting a new business or seeking a job Table 7.
Table 6

Chi-square Test: ‘Family size and ‘Career intention after completion of degree’ (including option of ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

12.621a

9

.181

Likelihood ratio

11.206

9

.262

Linear-by-linear association

.334

1

.563

N of valid cases

530

  

a5 cells (31.3%) have expected count less than 5. The minimum expected count is 1.20.

Table 7

Chi-square Test: ‘Family size and ‘Career intention after completion of degree’ (excluding option of ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

5.942a

3

.114

Likelihood ratio

4.690

3

.196

Linear-by-linear association

3.674

1

.055

N of valid cases

419

  

a2 cells (25.0%) have expected count less than 5. The minimum expected count is 1.11.

Table 8

Cross tabulation: ‘Father’s occupation’ and ‘Career choice preference after completion of degree’

Career choice preference after completion of degree

Father’s occupation

Top mgt. executives/ Large business owners

Reputed salaried employees

Professionals

Small bus. owners

Farmers

Others

Total

 

Start a new business

0

8

1

9

9

2

29

Seek a suitable job

0

196

24

86

61

23

390

Go for higher studies

0

35

9

14

15

5

78

Not yet decided

0

17

1

4

7

4

33

Total

0

256

35

113

92

34

530

Again the calculated value of Chi-square (~5.942) is greater than the tabulated value (~7.82). This confirms that we can reject the hypothesis at 5% level of significance. Hence we can say that manpower capital of the family, measured as the family size, does not influence the career intentions of students.

H3: Father’s occupation has an influence on the career intentions of students’ of HEI’s

Father’s occupation was categorized into six different categories and arranged in a hierarchical structure based on both income and status. In the first and lowest in the hierarchy was the category ‘Others’ which included those fathers who are retired or are home makers or have expired. In the second category we included farmers & skilled persons like electricians, mechanics etc. or low paid employees, in the third category we included small business owners, in the fourth category we included self practicing professionals like doctors, lawyers, chartered accountants etc., in the fifth category we included employees working in responsible positions in government departments or private organizations, finally in the sixth category & highest in the hierarchy we included top management executives or mid sized and large business owners. Students were asked a direct question about their father’s occupation and six multiple choices, as discussed above, were given to the students. The data was cross tabulated on the basis of students’ career choice preference and their father’s occupation (Refer to table 8). To test the above hypothesis we have applied Chi-square test.

Cross tabulation displays the number of cases in each category defined by two grouping variables i.e. ‘Prior experience in business’ and ‘Career choice preference after completion of degree’. Total out of 530 respondents 256 respondents said that their father fall in the category of ‘Reputed salaried employees’, 113 told that their father is a small business owner, 92 said that their father is a farmer, 35 students told that their father is a professional and 34 students placed their father’s occupation in ‘others’ category which meant that their father was either retired or not working or had expired. The above data clearly reveals that over 76% of the students whose father’s occupation status was ‘Reputed salaried employees’ want to take up a job while only 7.9% of the students whose father’s occupation status was ‘small business owners’ want to become entrepreneurs.

Table 9 contains the output of the Chi-square test. A high significance value of 0.174 & .202 and significance value above 0.05 indicates that there is no relationship between the two variables. The tabulated value of Chi-square (~21.026) is found to be greater than the calculated value (~16.399), it therefore becomes evident that variables ‘Fathers’ occupation’ and ‘Intention after completion of degree’ are independent. We again applied Chi-square on the variables after discounting the two options of ‘Go for higher studies’ and ‘Not yet decided’, in order to get a precise picture of relationship between fathers’ occupation and students’ career intentions Table 10.
Table 9

Chi-square Test: ‘Father’s occupation’ and ‘Career intention after completion of degree’ (including the options ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

16.399a

12

.174

Likelihood ratio

15.764

12

.202

Linear-by-linear association

.328

1

.567

N of valid cases

530

  

a4 cells (20.0%) have expected count less than 5. The minimum expected count is 1.86.

Table 10

Chi-square Test: ‘Fathers’ occupation’ and ‘Career intention after completion of degree’(excluding the options ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

8.015a

4

.091

Likelihood ratio

7.750

4

.101

Linear-by-linear association

2.395

1

.122

N of valid cases

419

  

a3 cells (30.0%) have expected count less than 5. The minimum expected count is 1.73.

Table 11

Social capital network span vis-à-vis score of the candidate

Score of the candidate

Social capital network

24 – 54

Small

55 – 90

Moderate

91 – 120

Large

Table 12

Cross tabulation: ‘Social capital network’ span and ‘Career intention after completion of degree’

Career choice preference after completion of degree

Social capital network

Total

Large

Moderate

Small

 

Start a new business

21

8

0

29

Seek a suitable job

157

189

44

390

Go for higher studies

40

33

5

78

Not yet decided

17

11

5

33

Total

235

241

54

530

Again the calculated value of Chi-square (~8.015) is greater than the tabulated value (~9.49). This confirms that we can reject the hypothesis at 5% level of significance. This justifies that there is no relationship between father’s occupation and the career intentions of students after completion of their course or in other words between human capital of the family and career intent.

H4: The social capital network span of student has an influence on the career intentions of students’ of HEI’s

We used 24 statements taken from Roberts ([2010]) ‘Sociological Capital Assessment’ to identify the extent of social capital. Score stood 5 points for strongly agree and 1 point for strongly disagree. Accordingly, three categories were developed between the score of 24 and 120 as given below in Table 11.

In order to test this hypothesis we have applied Chi-square test Table 12.

Cross tabulation displays the number of cases in each category defined by two grouping variables i.e. ‘Social capital network’ and ‘Career choice preference after completion of degree’. Total out of 530 respondents 235 respondents have ‘Large’ level of social capital network, 241 respondents have ‘Moderate’ level of social capital network and 54 respondents have ‘Small’ level of social capital network span. The above data reveals that as the level of social capital network span increase there is a corresponding increase in the entrepreneurial intentions of students and decrease in the ratio of job seeking preferences of students.

Table 13 contains the output of the Chi-square test. A low significance value of 0.009 & .003, typically below .05 indicates that there is a relationship between the two variables. Further, since the calculated value of Chi-square (17.203) is greater than the tabulated value (12.592), it becomes evident that the variables ‘Social capital network’ and ‘Career intention after completion of degree’ are dependent. We again applied Chi-square on the variables after discounting the two options of ‘Go for higher studies’ and ‘Not yet decided’, in order to get a precise picture of relationship between students’ career intentions and their social capital network span Table 14.
Table 13

Chi-square Test: ‘Social capital network span’ and ‘Career choice preference after completion of degree’ (including the options ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

17.203a

6

.009

Likelihood ratio

19.791

6

.003

Linear-by-linear association

.018

1

.894

N of valid cases

530

  

a2 cells (16.7%) have expected count less than 5. The minimum expected count is 2.95.

Table 14

Chi-square Test: ‘Social capital network span’ and ‘Career choice preference after completion of degree’ (excluding the options ‘Going for higher studies and ‘Not yet decided’)

 

Value

df

Asymp. sig. (2-sided)

Pearson Chi-square

13.496a

2

.001

Likelihood ratio

15.092

2

.001

Linear-by-linear association

12.802

1

.000

N of valid cases

419

  

a1 cells (16.7%) have expected count less than 5. The minimum expected count is 2.28.

The calculated value of Chi-square (~13.496) is again found to be greater than the tabulated value (~9.21) at 1% level of significance thus we accept the hypothesis. This justifies that there is a significant influence of social capital network span on the career intentions of students.

Result and discussion

The findings of the study reveal that in Uttarakhand state there is an effect of income of family on the students’ intention of going for higher studies after completion of degree. In comparison to 13% of the respondents from the ‘Low’ & ‘Middle’ income group who are interested in higher studies after completion of their degree there are 32% respondents from ‘High’ income group who are interested to continue with higher studies. The analysis revealed that family income has an influence on the students’ choice of going for higher studies but has no influence on the actual career choice of starting a business or seeking a job. The result compliments Torche and Spilerman ([2010]) who posited that wealthier households are less restricted and can make long-term investments, such as education for children. The finding also supports Gary Becker’s household production theory and the human capital theory which directly links household resources and investments to the educational attainments of children (Becker, [1964]). A considerable shift in job intentions of students is seen while moving from ‘Low’ and ‘Middle’ income group to ‘High’ income group families. While 77% of the respondents from the ‘Low’ income group and 75% of the respondents from the ‘Middle’ income group want to take up job after passing their degree, a comparatively low 54% of the respondents from the ‘High’ income group are eager to take up jobs. The results compliment the earlier studies by Kothari ([2013]), who ascertained that students of ‘High’ income group families are less inclined towards taking up jobs but also disagrees with him & Hsu et al. ([2007]) on the aspect that students of ‘High’ income group families are likely to take up entrepreneurship as a career. Our study observes that interest in starting up a new venture is seen to be considerably low (less than 6%) among all income groups and no significant variation was seen in all three income groups. This shows that merely belonging to a high income group family does not guarantee entrepreneurship or even transfer of entrepreneurship from father to children supporting the studies by Thrikawala ([2011]), Mueller ([2006]) and Kim et al. ([2003]) who found that there was no significant relationship between family income and overall entrepreneurial intention among the students. Sørensen ([2007]) also stated that parental wealth does not explain the transfer of entrepreneurship. Studies by Fairlie and Robb ([2007]) & Aldrich et al. ([1998]) found that a very low number of business owners borrow capital from their family. Again father’s occupation of the respondents was also seen to have no influence on the career intention of students. Over 76% of the students whose father’s occupation status was ‘salaried employee’ wanted to take up a job while only 7.9% of the students whose father’s occupation status was ‘small business owners’ wanted to become entrepreneurs. Although this finding is in conflict with most of the earlier studies (Lindquist et al.[2012]; Plant & Ren, [2010]; Carr & Sequeira, [2007]; Mueller, [2006]; Mc Elwee & Al-Riyami, [2003]) advocating that children who have entrepreneur parents have a greater propensity to choose an entrepreneurial career, our finding is in agreement with two recent studies done in two different universities of Pakistan first by Mubarka et al. ([2012]), who explored the impact of father’s occupation on the entrepreneurial inclination of the students and found no significant impact. The second study was by Ali et al. ([2010]), who found that father’s income and profession of father does not affect entrepreneurial attributes significantly. The possible explanation to this effect could be the poor socio-economic climate and unfeasible business environment. Ali et al. ([2010]) has talked about poor socio-economic climate and poor business environment in Pakistan and the same is indicated to be true for Uttarakhand (Sharma & Madan, [2014]). Keeping the existing theories of effect of parental income and occupation into account and the present study, we may extend the existing theory that the demographic factors like parental income & occupation has an effect on the entrepreneurial inclination, when the business environment is conducive. A recent study by Cetindamar et al. ([2012]) gives further support to the argument. The study found that income has a positive influence on entry into entrepreneurship. The study was conducted in Turkey, which is another developing country like India but it has witnessed tremendous economic growth and development. Turkey is known to be the 17th most industrialized nation in the world and has a worldwide ranking of 92 on GDP (Cetindamar et al.[2012]) indicating that Turkey has a healthy economic and business environment. Another aspect that needs to be noted in this context is the recent study by Zellweger et al. ([2011]) who proved that transitive likelihood of career intent depends on degree of entrepreneurial self-efficacy and the independence motive. They found that high levels of internal locus of control lead to a preference of employment, which challenges traditional entrepreneurship research and suggests that the feasibility of an entrepreneurial career path does not automatically make it desirable. Their findings suggest that students with family business background are pessimistic about being in control in an entrepreneurial career. No influence of family size is seen on the career intention of students. The career choice intention of becoming an entrepreneur remained to be low among all family sizes. This finding is in disagreement with the study by Cetindamar et al. ([2012]) but we do agree with them that the result may not be considered to be conclusive in nature and it is an area which needs further indepth research. Our study confirms that social capital of student has a significant influence on the career intentions of students. As the level of social capital network span increase there is a gradual increase in the entrepreneurial intention of students and a corresponding decrease in the ratio of job seeking preferences of students. The results of Chi-square test have confirmed that this shift is not by chance but there is a relationship between the variables. The finding is in agreement with most of the similar studies done earlier (Tararko, [2013]; Leiffel et al.[2009]; Linan, [2007]) which have stressed that social capital can facilitate the implementation of entrepreneurial intentions. Taking the results into consideration, the study digests that state government need to work upon improving the business environment in the state. The government and educational institutions may include community building activities in enterprise training programs, such activities have been found to be highly effective in micro enterprise training programs being conducted at USA. Two such programs (ex. Women’s Initiative in San Francisco and Working Capital in Boston) build community primarily by creating inter-program & intra-program networks, this builds social capital which in turn supports entrepreneurship development. These programs have been found to be successful in building social capital and subsequently entrepreneurship development at USA (Servon, [1998]). With the continuous changes being witnessed in the demographic structures and families some non-obvious results challenging the traditional results may be expected in future studies done to find the impact of familial and social variables on entrepreneurial outcomes. The author therefore encourages future research to be carried out in similar areas and in different regions.

Conclusion and Limitations

The study confirmed that financial capital of the family (measured as father’s occupation) has no influence on the actual career choice of starting a business or seeking a job, although it does influences the students’ intention of going for higher studies. Students who fell in the high income group showed higher intention of studying further in comparison to students from the low and middle income groups. The study also revealed that there is no impact of human capital (measured as father’s occupation) and man power capital (measured as family size) of the family on the career intentions of students of students of HEI’s of Uttarakhand. The study observed a significant impact of social capital of the students on their career intent. Students who had large social capital network span showed higher intentions of taking up entrepreneurship as a career. Like all studies, this study too has certain limitations which could be improved in future studies. We have used only one dimension to measure the human capital of family i.e. father’s occupation, where as we could have taken into account the occupation of mother and other family members. In addition to father’s occupation, we could have also included the parents’ educational level in order to draw more accurate results. For example Nettles & Millett ([1999]) used parents’ occupation & education to evaluate human capital. Similarly we have calculated only father’s annual income in order to calculate the family’s financial capital, other assets and property of the family were not taken into account. Additionally, including the income of all the family members might have given more precise results. Another area which could be further explored and bears a scholarly value is the impact of social capital of the family of student on his career choice intent.

Notes

Abbreviations

ACCION: 

Americans for Community Co-operation in Other Nations

ASSEFA: 

Association of Sarva Seva Farms

BC: 

Backward Class

BDB: 

Bank Dagan Bali

BRI: 

Bank Rakyat Indonesia

CDS: 

Community Development Society

CTM: 

Chinnathippasamudram

DESMAK: 

Women taken decision in household matters

FTYPE: 

Type of family

HEDCN: 

Husband education

HHINCOME: 

Household Income

HHASSET: 

Household asset

HINCOME: 

Husband income

LOANAMT: 

Loan amount

MCP: 

Micro Credit Programme

MFI: 

Microfinance Institution

mn: 

Million

MSTATUS: 

Marital Status

MYRADA: 

Mysore Resettlement and Development Authority

NABARD: 

National Bank for Agriculture and Rural Development

NGO: 

Non-Governmental Organization

OC: 

Other Class

PRADAN: 

Professional Assistance for Development Action

PREM: 

People’s Rural Education Movement

RBI: 

Reserve Bank of India

RNF: 

Rural Non-Farm

ROSCAs: 

Rotating Savings and Credit Associations

RRB: 

Regional Rural Bank

SC: 

Scheduled Caste

SC/STs: 

Scheduled Caste/ Scheduled Tribes

SEWA: 

Self-Employed Women’s Association

SHG: 

Self-Help Group

SHG-BLM: 

Self-Help Group-Bank Linkage Model

SHG-BLP: 

Self-Help Group- Bank Linkage Programme

SHGMAGE: 

Self-Help Group members age

SHGMEDCN: 

Self-Help Group members education

SHGMINCM: 

Self-Help Group members yearly income

USA: 

United State of America

ST: 

Scheduled Tribe

U: 

Error term

Declarations

Acknowledgement

The author would like to thank all the students who have sincerely and enthusiastically participated in this research activity. The author would also like to extend his gratitude to the Directors & the Heads of various institutions of Uttarakhand who have cooperated in this research by allowing me to interact with their students even during the busy time of university examination. Special thanks are also conveyed to the Director & the Management of Quantum School of Business who have provided all support in carrying out this research.

Authors’ Affiliations

(1)
Quantum School of Business

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© Sharma; licensee Springer. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

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