Determinants of Entrepreneurial Intention among University

Students: A Comparative Study between IPB University (Indonesia)

and WULSS-SGGW (Poland)

Maya Rizki Sari

, Jono M. Munandar

, Eko Ruddy Cahyadi

, Michał Borowy

, Ishbir Mujahid Adha

Dept. Management, Bogor Agricultural University, Bogor, Indonesia

Szkoła Główna Gospodarstwa Wiejskiego W Warszawie, Warsaw,Poland

Faculty of Economics and Business, Universitas Sumatera Utara, Medan, Indonesia

ishbir.ma@gmail.com

Keywords: Entrepreneur Intention, Theory of Plan Behaviour, Attitude, Subjective Norms, Perceive Behavioural Control

Abstract: Entrepreneurship is a trend, this entrepreneurial growth brings tremendous economic improvement to a

country, so that more and more a country has entrepreneurship, the economy will increase. Type of country

according to base sector in Poland is agrarian country, same with Indonesia. The low number of entrepreneurs

is indicated as a gap between human resources and education problems in farmer’s level. Colleges play an

important role in creating young agricultural entrepreneurs which is a crucial issue in this country. Based on

the various descriptions above, it is necessary to study the level of interest in entrepreneurship among students.

In addition, it is necessary to conduct a study of what factors influence entrepreneurial interest in students.

1 INTRODUCTION

Entrepreneurship is an important issue in the world

economy. The economic progress or deterioration of

a nation is largely determined by the existence and

role of entrepreneurial groups. There is no nation in

the world that can become a developed country

without being supported by a number of youth and

communities who are self-employed. The high level

of scientific research, its relation to student education

and the diversity and attractiveness of our teaching

determine the position of universities in the country

and throughout the world. The campus is forward-

thinking where high-quality education meets the

world class of research and innovation, but the output

is for the global labor market as a worker. In fact, the

opportunity to open a business is equally great. This

is certainly interesting to discuss, with the Theory of

plan behavior researchers try to analyze the factors

that influence Entrepreneurial Intention on Students

at IPB University and WULS-SGGW.

The entry of Poland to become a member of the

European Union in 2004 has also increasingly

encouraged significant progress in all sectors. The

annexation process promotes the process of structural

reform and opens up possibilities for new

development with funding from the European Union.

Same with the country of Indonesia, agrarian country.

Given that entrepreneurship has become an

international issue regarding the development of

quality and increasing the number of entrepreneurs in

their respective countries because entrepreneurship

has an important role for the advancement of a

country, entrepreneurial spirit needs to be grown in

Indonesian students as prospective university

graduates and young people who will help continue

the course of the Indonesian economy, so that it

becomes a superior human resource.

As an agrarian country with tremendous natural

resources, agricultural sector of Indonesia is a very

potential business area to be developed by youths.

Developing agricultural entrepreneurship is needed to

drive human resource productivity of the sector (Lee

and Wong, 2004). Business actors in agricultural

commodity are only about 44.20 million people (0.17

per cent) of the Indonesian population. The low

number of entrepreneurs is indicated as a gap between

human resources and education problems in famer’s

level. Colleges play an important role in creating

young agricultural entrepreneurs which is a crucial

issue in this country. The colleges are responsible on

increasing growth of graduated scholar to be

360

Sari, M., Munandar, J., Cahyadi, E., Borowy, M. and Adha, I.

Determinants of Entrepreneurial Intention among University Students: A Comparative Study between IPB University (Indonesia) and WULSS-SGGW (Poland).

DOI: 10.5220/0009204603600368

In Proceedings of the 2nd Economics and Business International Conference (EBIC 2019) - Economics and Business in Industrial Revolution 4.0, pages 360-368

ISBN: 978-989-758-498-5

unemployment about 14.5 per cent in period 2012-

2015. There is low intention of those scholars for

being entrepreneurs in agriculture due to financial

factors which needs huge financial capital (Badan

Pusat Statistik, 2019).

2 LITERATURE REVIEW

2.1 Entrepreneurial Intention

Entrepreneurial intention can be interpreted as the

first step in the process of establishing a business that

is generally long-term (Boateng and Bampoe, 2014;

Kruger, 1993). Entrepreneurial intention reflects a

person's commitment to start a new business and is a

central issue that needs to be considered in

understanding the entrepreneurial process of

establishing a new business. The intention of

entrepreneurship has recently begun to get attention

for research because it is believed that an intention

related to behavior can prove to be a reflection of real

behavior (Tjahjono and Ardi, 2008). In the theory of

planned behavior it is believed that factors such as

attitudes, subjective norms will shape one's intentions

and then directly affect behavior.

The intention of entrepreneurship is a

determination to do entrepreneurship with certain

goals that are owned by individuals. The intention of

entrepreneurship is a representation of actions

planned to carry out entrepreneurship. Besides the

intention of entrepreneurship can be interpreted as a

possibility or intention of someone to create

something new by using available and needed

resources by looking at the opportunities that exist

and without ignoring the risks that will be faced in the

future. Besides the intention of entrepreneurship can

be interpreted as a possibility or intention of someone

to create something new by using available and

needed resources by looking at the opportunities that

exist and without ignoring the risks that will be faced

in the future, To measure students' intentions for

entrepreneu rship there are several indicators (Engle

et al., 2010) which were then used as indicators of

entrepreneurial intention in this study, namely: Happy

entrepreneurship, Readiness for entrepreneurship, A

mature consideration for entrepreneurship, Decide to

become an entrepreneur.

2.2 Theory of Planned Behaviour

Theory of Planned Behavior (TPB) which is the

development or refinement of Reason Action Theory

by Fishbein and Ajzen in 1975. The theory explain

the notion of intention as a dimension of individual

subjective probability in relation to self and behavior.

This theory explains the intentions (intentions) of

individuals to carry out certain actions or actions

(Ajzen, 1991). Intention is considered to be able to

see motivational factors that influence behavior.

Intention (intention) is an indication of how much

effort an individual will make to do something. Then

the intention is someone's estimate of how likely he

is to carry out a certain action.

Theory of planned of behavior states that

intention (intention) is a function of three basic

determinants, namely:

1. Attitude behaves (attitude toward the

behavior).

2. Subjective norms.

3. Perceived behavioral control.

Being a basic theory in this study is because the

determination of three independent variables in this

study is a factor found in the theory of planned

behavior, namely the need for achievement,

subjective norms, and self-efficacy. This theory was

chosen as the basic theory of research because

according to Ajzen (1991) planned behavior theory is

suitable to explain any behavior that requires

planning, such as entrepreneurship. Entrepreneurship

is clearly categorized into planned behavior because

individuals form expectations and assess behaviors

that are carried out on the results obtained afterwards.

Figure 1: Model of Intention in Theory of Planned

Behaviour

The model used in this study is based on previous

research (Linan and Chen, 2009). This study aims to

test and find out how much intention students have to

be able to work independently (entrepreneurship) and

what factors influence it. The model that will be used

as the basis for carrying out this research is

'Entrepreneurial Intention Model' which is an

integration model, Theory of Planned Behavior.

Determinants of Entrepreneurial Intention among University Students: A Comparative Study between IPB University (Indonesia) and

WULSS-SGGW (Poland)

361

2.3 Attitude toward Behaviour

Attitude is a function based on behavioral beliefs,

namely a person's belief in positive and / or negative

consequences that someone will get when doing a

behavior (salient outcome beliefs). Attitudes toward

behavior (attitude toward the behavior) are defined as

the level of positive or negative assessment of an

individual towards a behavior. Attitude toward the

behavior is determined by a combination between

individual values about the positive and / or negative

consequences of the behavior raised (behavioral

beliefs) with a person's subjective value of the

consequences of such behavior (outcome evaluation)

(Ajzen, 2006).

Based on the TPB theory, personal attitudes

toward a behavior are derived from beliefs about the

consequences caused by these behaviors, which are

termed behavioral beliefs. Furthermore, based on

TPB, someone who believes that displaying certain

behaviors will lead to positive results will have a

favorable attitude towards the behavior, while people

who believe that displaying certain behaviors will

lead to negative results, then he will have an

unfavorable attitude.

2.4 Subjective Norms

Subjective norms are the beliefs of individuals to

comply with or fulfill suggestions or input from

surrounding people to participate in entrepreneurship

activities. Subjective norms are functions based on

beliefs or beliefs called normative beliefs, namely

beliefs about agreement and / or disagreement that

come from references of other people or groups that

are considered important and influential for

individuals, such as family, friends, and people which

is considered important.

Subjective norms are a measure of social pressure

to determine whether entrepreneurial behavior needs

to be done or not. The social pressure refers to the

perception of certain groups (reference people) who

approve or not a person's decision for

entrepreneurship and usually individuals try to adhere

to the perceptions of the group. Subjective norms

relate to perceptions where a group of people exerts a

large influence on people's behavior, studying where

social networks influence individual behavior.

2.5 Perceived Behavioral Control

Perception of self-control is defined as a function

based on control beliefs, namely the belief of

someone about the presence or absence of supporting

or inhibiting factors to be able to emerge behavior.

Belief can be obtained from the individual's previous

experience of a behavior, information that an

individual has about a behavior obtained by making

observations on knowledge possessed by themselves

and other people known to individuals, and also by

various other factors that can increase or decrease

individual feelings about levels difficulty in carrying

out a behavior. The more individuals feel a lot of

supporting factors and a few inhibiting factors to be

able to do a behavior, the greater the control they feel

for the behavior and vice versa, the fewer individuals

feel the supporting factors and many inhibiting

factors to perform a behavior, then the individual will

tend to perceive themselves difficult to do this

behavior. There are two factors for determining

perceived behavioral control, namely belief and

perceived power control.

3 RESEARCH METHOD

3.1 Population and Samples

The population in the study was undergraduate and

postgraduate students of IPB University and WULS-

SGGW. In this study the sampling technique using

convience sampling. This is sampling based on the

availability of elements and the ease of getting them.

Samples are taken / selected because the samples are

in the right place and time. The sample will be

examined with the following conditions: a) Student

active in Bogor Agricultural University or Warsaw

University of Life Science b) Level of education are

bachelor and master.

This study uses surveys and questionnaires as a

data collection tool. Data collection is done by

distributing questionnaires to students in IPB

University and WULS-SGGW with survey online.

In this research the samples used were 150 from

each university.

Sampling Procedures

1. Questionnaire Online

2. Giving questionnaires to respondents with

Likert scale 1-6.

3. Using social media to share or find direct

participant to fill the questionnaire online

In this study, the data needed are primary data.

The acquisition of primary data is done through the

distribution of questionnaires, interviews to

observations to get an overview of intention

entrepreneurs.

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3.2 Data Analysis Technique

To analysis data, researcher using Structural Equation

Modelling (SEM) with software Smart PLS. Because

of its ability to process data both for formative and

reflective SEM models. Formative SEM models have

characteristics including latent or construct variabels

to indicator variables. The reflective SEM model is a

SEM model which the construct variable is a

reflection of the indicator variable, so that the arrow

leads from the indicator variable to the latent variable.

Statistically, the consequence is that there will be no

error in the indicator variable.

Structural Equation Model (SEM) is part of a

statistical model that can explain the relationships

between variables (Ghozali, 2006). In general, a SEM

model can be divided into two. The first is the

measurement model and the second is the structural

model. Measurement model is part of the SEM model

that describes the relationship between latent

variables and indicators. Structural model is a model

that describes the relationship between latent

variables with one another or between exogenous

latent variables with endogenous latent variables.

Because there are two parts in the SEM model, there

are also two errors in SEM, namely an error in a

measurement model and in a structural model.

Partial Least Square (PLS) is a technique of

making models that are currently popular in

management research and entrepreneurship (Hair et

al., 2014). Procedure Structural equation models try

to explain the structure or pattern between a

collection of latent constructs measured by several or

one indicator. The PLS approach is used to see the

direct influence between individual internal and

external variables in assuming entrepreneurial

intentions. Based on the results of the PLS analysis

various indicators that are really strong in describing

each of the latent variables will be obtained. PLS is a

powerful analytical method because it can be applied

to all types of data scales (distribution free) where it

does not assume data with a certain distribution so

that data can be either nominal, category, ordinal,

interval or ratio. In addition to being able to be used

as a confirmation theory, PLS can also be used to

build relationships that have no theoretical basis.

The data were analyzed using Smart-PLS (Ringle

et al., 2020). In order to evaluate the importance of

each aspect of perceived service quality, a second-

order model was proposed in this study. The proposed

model summarized in Figure 1. The model was

evaluated for its validity and reliability based on

partial-least-squared criterion.

4 RESULT AND DISCUSSION

4.1 Validity and Reliability Model

The PLS model evaluation require validity and

reliability test for the given model. The indicator for

each construct should be able to explain its variables

well according to the its average variance extracted

(AVE) and composite reliability. The AVE value for

each construct should be more than 0.5 to achieve the

validity model. The composite reliability value for

each construct should be more than 0.7 to achieve the

reliability model.

The results of SmartPLS calculations that have

met the validity and reliability requirements are

shown in Figure 2 and Figure 3 below.

Figure 2: Structural and measurement models for IPB University before removed

Determinants of Entrepreneurial Intention among University Students: A Comparative Study between IPB University (Indonesia) and

WULSS-SGGW (Poland)

363

Figure 3: Structural and measurement models for WULS-SGGW before removed

There are 2 types of validity in PLS SEM, namely

discriminant validity and convergent validity.

1. Discriminant validity

Valid indicators also need to be tested for

discriminant validity by cross loading or the average

value of the extracted variant (Average Variance

Extracted/AVE). Discriminant validity is an

additional concept which means that two

conceptually different concepts must demonstrate

adequate differentiation. The point is that the set of

indicators combined is not expected to be

unidimensional. Criteria indicators that can represent

a latent if it has a AVE value above 0.5. Table 1 and

2 gives AVE values above 0.5 for almost all

constructs contained in the research model.

Based on Table 1 and Table 2 it can be seen that

all constructs have a value of AVE> 0.5, this shows

that all constructs are valid.

2. Convergent validity

Convergent validity if a set of indocators can

represent a latent (valid) and the underlying latent can

be seen using factor loading values. An indicator is

declared valid if it has a loading factor> 0.5 or> 0.7

according to the researchers' determination of the

intended construct. Indicators that have a loading

factor <0.5 are removed, The following are valid

indicators:

Table 3. Loading Factor Value (IPB University)

No Variable Statement Loading Factor Result

1 Attitude (X.1)

Compabilit

(X.1.3) 0.922 Vali

Full of Challen

e 1 (X.1.4) 0.95 Vali

Full of Challen

e 2 (X.1.5) 0.603 Vali

Subjective

Norms (X.2)

Superior’s Influence 2 (X.2.2) 0.894 Vali

Superior’s Influence 3 (X.2.3) 0.91 Vali

Pee

Influence 1 (X.2.4) 0.857 Vali

Pee

Influence 2 (X.2.5) 0.637 Vali

Pee

Influence 3 (X.2.6) 0.573 Vali

Perceive

Behavior (X.3)

Adaptabilit

1 (X.3.1) 0.85 Vali

Adaptabilit

2 (X.3.2) 0.588 Vali

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No Variable Statement Loading Factor Result

Confidence 1 (X.3.3) 0.591 Vali

Confidence 2 (X.3.4) 0.829 Vali

Self Efficac

1 (X.3.5) 0.883 Vali

Self Efficac

2 (X.3.6) 0.608 Vali

Entrepreneur

Intention (Y)

Prefe

eentrepreneu

(Y.1) 0.977 Vali

Improve social status (Y.2) 0.977 Vali

Table 4: Loading Factor Value (WULS-SGGW)

No Variable Statement

Factor

Result

Attitude

towards (X.1)

Locus of Control 1 (X.1.1) 0.772 Vali

Locus of Control 2 (X.1.2) 0.684 Vali

Perceived Usufulness

(X.1.3)

0.828 Valid

Compabilit

(X.1.4) 0.797 Vali

Full of Challen

e 2 (X.1.6) 0.76 Vali

Subjective

Norms (X.2)

Superior’s Influence 1

(X.2.1)

0.895 Valid

Superior’s Influence 2

(X.2.2)

0.901 Valid

Superior’s Influence 3

(X.2.3)

0.624 Valid

Pee

Influence 1 (X.2.4) 0.564 Vali

Pee

Influence 2 (X.2.5) 0.838 Vali

Perceived

Behavior (X.3)

Adaptabilit

1 (X.3.1) 0.819 Vali

Adaptabilit

2 (X.3.2) 0.909 Vali

Confidence 1 (X.3.3) 0.908 Vali

Confidence 2 (X.3.4) 0.538 Vali

Self Efficac

1 (X.3.5) 0.633 Vali

Self Efficac

2 (X.3.6) 0.616 Vali

Entrepreneur

Intemtion (Y)

Prefer to be entrepreneur

(Y.1)

0.563 Valid

Improve social status (Y.2) 0.887 Vali

Profitable (Y.3) 0.939 Vali

There are 2 tests to measure the consistency of

reliability. If the composite reliability value is ≥ 0.7,

it is reliable. So if <0.7 then it is not reliable. From

table 4, it shows that the composite reliability value

for almost all constructs is ≥ 0.7, only the

characteristics of fishermen and education are <0.7.

This shows that almost all constructs in the estimated

model meet the composite reliability criteria.

Reliability testing can also be strengthened with

Cronbach’s Alpha. Cronbach’s Alpha is a measure of

reliability that has values ranging from zero to one

(Hair et al., 2010). The recommended value in the

Cronbach's Alpha test is> 0.6 (reliable), and in Table

4 shows all constructs have a Cronbach’s Alpha

value> 0.6, so it can be said that all constructs are

reliable.

4.2 Structural Model Testing (Inner

Model)

After the estimated model meets the Outer Model

criteria, the structural model (Inner model) is then

tested.

Creteria R

is endogenous latent variable:

value of 0.67 is categorized as substantial,

value of 0.33 is categorized as moderate,

value of 0.19 is categorized as weak

value> 0.7 is categorized as strong

Determinants of Entrepreneurial Intention among University Students: A Comparative Study between IPB University (Indonesia) and

WULSS-SGGW (Poland)

365

Table 5. Output R-square (IPB University)

R Square R Square Adjusted

Y 0.849 0.846

Based on Table 5 we can draw conclusions, the

influence of variables X.1, X.2, and X.3 on the Y

variable gives an R-sq value of 0.849 which can be

interpreted that the variability of the construct Y can

be explained by variables X.1, X. 2, and X.3 is 84.9%

while the remaining 16.1% is explained by other

variables not in the research model.

Table 6. Output R-square (WULS-SGGW)

R Square R Square Adjusted

Y 0.648 0.64

Based on Table 6 we can draw conclusions, the

influence of variables X.1, X.2, and X.3 on the Y

variable gives an R-sq value of 0.648 which can be

interpreted that the variability of the construct Y can

be explained by variables X.1, X. 2, and X.3 is 64.8%

while the remaining 35.2% is explained by other

variables not in the research model.

4.3 Hypothesis Testing

The hypothesis testing criteria in this study are of

significance level (α) of 5% and are determined by the

following criteria:

1. If t arithmetic> t table (1.96) then the hypothesis

is accepted.

2. If t arithmetic <t table (1.96) then the hypothesis

is rejected.

3. Hypothesis testing can also be determined by the

P-value with criteria as follows:

4. If the P-value <0.05, the hypothesis is accepted.

5. If P-value> 0.05, the hypothesis is rejected

Testing the complete hypothesis can be explained as

follows:

Table 7. The effect of each exogenous variable on endogenous variables (IPB university)

Original Sample

(

)

Sample Mean

(

)

Standard Deviation

(

STDEV

)

TStatistics

O/STDEV

Values

X.1 ->

Y 0.405 0.384 0.105 3.854 0.00

X.2 ->

Y 0.269 0.278 0.116 2.325 0.02

X.3 ->

Y 0.303 0.315 0.055 5.555 0.00

Based on the picture above we can draw

conclusions, the influence of Attitudes towards

Entrepreneurs Intention gives an original sample

value of 0.405 which can be interpreted about the

magnitude of the Entrepreneurial Intention that can be

emulated by Attitudes towards 40.5% while 59.5% is

needed by other variables in the research model. The

attitude towards positive toward the Intention of

Entrepreneurs is getting bigger The attitude towards

the Intention of Entrepreneur will be higher The t-

statistic value of Attitude toward is 3,854 and the t-

table value with a significance level of 5% = 1.96,

then the t-statistic value is greater than t-table.

Important attitude towards having significance to

entrepreneur's intentions.

Subjective Norms of Entrepreneurial Intentions

provide an original sample value of 0.269 which can

be interpreted as a large Entrepreneurial Intention that

can be understood by Subjective Norms is 26.9%

while 73.1% can be influenced by other variables that

are not in the research model. Subjective Norms have

a positive impact on Entrepreneurial Intention so that

the greater the Subjective Norms, the Entrepreneurial

Intention will be higher. The t-statistic value of

Subjective Norms is 2.325 and the value of t-table

with a significance level of 5% = 1.96, then the t-

statistic value is greater than t-table. Adding

Subjective Norms to Entrepreneur Intention.

The effect of Perceived Behavior on Entrepreneur

Intention gives the original sample value of 0.303

which can be interpreted as the amount of

Entrepreneurial Intention that can be understood by

Perceived Behavior is 30.3% while 69.7% is

discussed by other variables that are not present in the

research model. Perceived Behavior has a positive

impact on Entrepreneur's Intention so that the greater

the perceived Behavior the Entrepreneur's Intention

will be higher. Perceived Behavior t-statistic value is

5.555 and t-table value with a significance level of

5% = 1.96, then the t-statistic value is greater than t-

table. Perceived behavior has significance to the

Entrepreneurs Intention.

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Table 8. The effect of each exogenous variable on

endogenous variables (WULS-SGGW)

Original

Sample (O)

Sample

Mean (M)

Standard Deviation

(STDEV)

t-Statistics

(|O/STDEV|)

Sig

X.1 -

> Y 0.256 0.246 0.143 1.784

0.0

X.2 -

> Y 0.152 0.152 0.111 1.371

0.1

X.3 -

> Y 0.434 0.451 0.185 2.348

0.0

Based on the picture above we can draw a

conclusion, the effect of Attitude towards

Entrepreneur Intention gives an original sample value

of 0.256 which can be interpreted that the influence

of Entrepreneur Intention that can be explained by

Attitude towards is 25.6% while the remaining 74.4%

is explained by other variables that do not exist in the

research model. Attitude towards a positive effect on

Entrepreneur Intention so that the greater the Attitude

toward the Entrepreneur Intention will be higher. The

t-statistic value of Attitude towards is 1,784 and the t-

table value with a significance level of 5% = 1.96,

then the t-statistic value is smaller than t-table. This

means that Attitude towards does not have a

significant effect on Entrepreneur Intention.

The influence of Subjective Norms on

Entrepreneur Intention gives an original sample value

of 0.152 which can be interpreted that the influence

of Entrepreneur Intention that can be explained by

Subjective Norms is 15.2% while the remaining

84.8% is explained by other variables that are not in

the research model. Subjective Norms have a positive

effect on Entrepreneur Intention so that the greater the

Subjective Norms, the higher the Entrepreneur

Intention. The t-statistic value of Subjective Norms is

1,372 and the value of t-table with a significance level

of 5% = 1.96, then the t-statistic value is greater than

t-table. This means that Subjective Norms do not have

a significant effect on Entrepreneur Intention.

The effect of Perceived Behavior on Entrepreneur

Intention gives an original sample value of 0.434

which can be interpreted that the influence of

Entrepreneur Intention that can be explained by

Perceived Behavior is 43.4% while the remaining

56.6% is explained by other variables that are not in

the research model. Perceived Behavior has a positive

effect on Entrepreneur Intention so that the greater the

Perceived Behavior, the higher the Entrepreneur

Intention. Perceived Behavior t-statistic value is

2.348 and t-table value with a significance level of

5% = 1.96, then the t-statistic value is greater than t-

table. This means that Perceived Behavior has a

significant effect on Entrepreneur Intention.

5 CONCLUSION

Each country has a different dominant entrepreneurial

intention factor. This is influenced by factors

mentioned in the study and also there are several other

factors which are not mentioned. In this case, a

functional campus is needed to maximize students

who have business entrepreneurial intentions. So that,

the intention can later be realized well, not only in the

form of intention but also in real action which in the

end will become a momentum to increase the number

of entrepreneurs in the country.

ACKNOWLEDGMENT

Authors gratefully acknowledge that the presented

research is supported by IPB University and Warsaw

University of Life Science-SGGW.

REFERENCES

Badan Pusat Statistik (BPS), Proyeksi pertumbuhan

penduduk Indonesia tahun 2013-2016.www.bps.go.id.

per 06, Februari, 2019

Boateng, G.O., Boateng, A.A. and Bampoe, H.S. (2014),

“Barriers to youthful entrepreneurship in rural areas of

Ghana”, Global journal of business research, Vol. 8 No.

3, pp. 110-119.

Lee, S.H. & Wong, P.K. 2004. An Exploratory Study of

Technopreneurial Intentions: A Career Anchor

Perspective. Journal of Business Venturing, 19(1): 7-

28.

Krueger, N. 1993. The Impact of Prior Entrepre-neurial

Exposure on Perceptions of New Venture Feasibility

and Desirability. Entrepre-neurial Theory Practice,

18(1): 5–21.

Tjahjono, H.K. & Ardi, H. 2008. Kajian Niat Mahasiswa

Manajemen Universitas Muham-madiyah Yogyakarta

untuk Menjadi Wirausaha. Utilitas Jurnal Manajemen

dan Bisnis, 16(1): 46-63.

Engle, R.L., Dimitriadi, N., Gavidia, J.V., Schlaegel, C.,

Delanoe, S., Alvarado, I., He, X., Buame, S. and Wolff,

B. (2010) ‘Entrepreneurial intent: a twelve country

evaluation of Ajzen’s model of planned behavior’,

International Journal of Entrepreneurial Behavior and

Research, Vol. 16, No. 1, pp.35–57.

Ajzen, I. 1991. "The theory of planned behavior."

Organizational Behavior and Human Decision

Processes, Vol. 50, No. 2 h. 179–211.

Linan F, Chen YW. 2009. Development and cross-cultural

application of a specific instrument to measure

entrepreneurial intentions. ET&P.33(3):593-

617.doi:10.1111/j.1540-6520.2009.00318.

Determinants of Entrepreneurial Intention among University Students: A Comparative Study between IPB University (Indonesia) and

WULSS-SGGW (Poland)

367

Ajzen, I. 2006. "Nature and Operation of Attitudes". Annual

Review of Psychology, Vol. 52, No.1, h. 27–58

Krueger NF, Reilly MD, Carsrud AL. 2000. Competing

models of entrepreneurial intentions. Journal of

Business Venturing 15:411-432

Ghozali I. 2006. Structural Equation Modeling: Alternative

Methods with Partial Least Square. Semarang (ID):

Undip Publishing Agency.

C.M. Ringle, S. Wende, and J.M. Becker, 2020, SmartPLS

3, Boenningstedt: SmartPLS, http:www.smartpls.com

J.F. Hair Jr., G. Hult, C.M. Ringle, and M.Sarstedt, A

Primer on Partial Least Squares Structural Equation

Modeling (PLS-SEM), United States of America:

SAGE Publications, Inc., 2014

EBIC 2019 - Economics and Business International Conference 2019

368