IDENTIFICATION OF THE INFLUENCE OF ACADEMIC’S PERSONAL CHARACTERISTICS AND ACADEMIC’S PERCEIVED SUPPORT ON UNIVERSITY STUDENTS’ INTENTION TO BECOME TECHNOPRENEUR

Entrepreneurshipis one of the driving factors ofthe country’s economy. More entrepreneurs meanmore potential for new business areas and thus more employment to grow. The development of industrial revolution 4.0 encourages entrepreneurs to be technological entrepreneurs to face increasing competition. It is important for the university, an educational institution, to produce graduates who can compete in the era of Industry4.0 and provide solutions to state problems such as poverty and unemployment. Hence, measuring students’ intention to become a technopreneur is necessary to see whether the university can prepare its students to become technopreneur who will be useful for the country and society. This study aims to measure the influence of factors in academic’s personal characteristics and academic’s perceived support on the university students’ intention to become technopreneur.

Increasing the number of entrepreneurs is the focus of many countries in the world. The reason is that entrepreneurs can boost the economy of a country. In 2018, the number of entrepreneurs in Indonesia reached 3.1% of the total population. A survey from the Global Entrepreneurship Monitor (GEM) in 2013 showed that the desire for entrepreneurship in Indonesia was the second highest in ASEAN (Putra, 2018). Growth in entrepreneurship increased from year to year; the number of entrepreneurs increased from 1.64% in 2014 to 3.1% at the end of 2018, and the increase was predicted to continue in the following years (Liputan 6, 2018). This shows that there is a great interest among the Indonesian people in entrepreneurship. Many systems are made to beonline and integrated in today's era of Indus-try4.0, which arecausing entrepreneurs to turn to technology to face a competitive environment.Based on the data reported by the Ministry of Cooperatives and Small and Medium Enterprises (Kemenkop UKM), around 3.79 millions micro, small and medium enterprises (MSMEs) have used online platforms to market their products (Yuliani, 2017).
The industrial revolution 4.0 is a system that integrates the online world with industrial production. The revolution can increase production efficiency due to the use of digital technology and automation, as well as changes in the composition of employment. Thus, technopreneurship is the future trend for the world in this digital business era. However, developing competitive entrepreneurs is a big challenge, according to a survey from the Global Entrepreneurship Monitor (GEM) (Koe, et al., 2018).
Developing young human resources to be innovative and entrepreneurial is very important in facing the challenges posed by the Industry 4.0 (Koe, et al., 2018). The academic environment contributes indirectly to technology transfer activities by providing highly educated and qualified human resources for the industry. Universities and other higher education institutions are important sources of new scientific knowledge, both technical and entrepreneurship related knowledge. Thus, the development of technological entrepreneurship, or what is often called technopreneurship, is closely related to the world of education, especially the students (Prodan, 2007). According to Dwi Larso, Deputy Chairperson of the ITB Alumni Association, when more graduates are ready to become technopreneurs, many industrial problems will be solved because technopreneurscan generate a lot of value for thecountry (Tifany, 2017). Thus, the education world must be able to create graduates who can compete in the era of Industry 4.0 (Rahman, 2018). Universities are now being pressuredto produce more graduates who can become technopreneurs (Tifany, 2018).

LITERATURE REVIEW Technopreneurship
Technopreneursip is a combination of words derived from the "technology" and "entrepreneurship" (Mopangga, 2015). Technopreneurship is a strong willingness to master technology which has synergy in its process and a thorough understanding of the concept of entrepreneurship. Technopreneurship combines theconcepts of entrepreneurship and technology.

Model of Academic'sIntention to Become Technological Entrepreneurs
The model that forms the basis of this study refers to the model from research conducted by Prodan in Slovenia about Technopreneurship (Prodan, 2007). This research model in technopreneurship consists of 7 main stakeholders, namely: technopreneur, university, company, capitalization, market/consumer, government, and consultant.

Student Intention to Become a Technopreneur
Entrepreneurial intention determines the interest or desire of a person to be an entrepreneur. As quoted by Trihudiyatmanto, the entrepreneurial intention is defined as a process of finding information that can be used to achieve the goal of forming a business. Someone to start a business will have better readiness and progress in running a business compared to someone without the intention to start a business (Trihudiyatmanto, 2017).

Academic's Personal Characteristics
According to his research, Sunaga argues that personal characteristic and educational effectiveness are interrelated with each other (Sunaga Yusuke, et al., 2017). Karimi argues that according to many studies, there are many factors related to an individual's intention to start a business, including individual personalities and environmental contexts (Karimi, et al., 2015). Academic's personal characteristicshave some indicators that is entrepreneurial self-efficacy, motivational factors, and personal networks (Prodan, 2007).

Academic/Academic-Entrepreneur's Perceived Support
Academic support is important to arouse the desire for entrepreneurship through self-efficacy, this desire arises when students are facilitated to get more experience in conducting entrepreneurial practices and not only be supported through entrepreneurial education (Ikhtiagung and Soedihono, 2018).The university provides a role in increasing entrepreneurial interest in the students. Academic's perceived support has some indicators, that is perceived role models and Perceived Support from Academic Institution (Prodan, 2007).

Frame of Thoughts
Hypothesis H1: Academic's Personal Characteristic (X1) has a positive effect on the intentions of Telkom University students to become technopreneur (Y). H0: Academic's Personal Characteristic (X1) does not have a positive effect on the intentions of Telkom University students to become technopreneur (Y). H2: Academic's Perceived Support (X2) has a positive effect on the intentions of Telkom University students to become technopreneur (Y). H0: Academic's Perceived Support (X2) does not have a positive effect on the intentions of Telkom University students to become technopreneur (Y).

Simultaneous Hypothesis
H1: Academic's Personal Characteristic (X1) and Academic's Perceived Support (X2) simultaneously have a significant effect on the intentions of Telkom University students to become technopreneur (Y). H0: Academic's Personal Characteristic (X1) and Academic's perceived support (X2) simultaneously do not have a significant effect on the intentions of Telkom University students to become technopreneur (Y).

Method of Successive Interval
One of the requirements for parametric analysis is that the data should at least be interval data. Therefore ordinal data obtained from the results of the questionnaire must first be transformed into interval data. To solve this problem, the measurement scale needs to be transformed into an interval scale through the "Method of Successive Interval" (Triono, 2014), with the following steps:  First, note each item in the respondent's answer from the distributed questionnaire.  On each item, determine how many people get a score of 1, 2, 3, 4, and 5, which is referred to as frequencies.
The above frame of thoughts explains that Academic's Personal Characteristics variable(with indicators such as entrepreneurial self-efficacy, motivational factors, and personal networks) andAcademic's perceived support (with indicators such as perceived role models and perceived support from the academic institution) are related to students' intention to become technopreneur. This frame of thoughts is taken from the research of Prodan (2007) entitled Technological Entrepreneurship: Technology Transfer from Academia to New Firms. In Prodan'sresearch, it is known that his frame of thoughts model has an X3 variable, namely the academic's working environment, but the author does not use that variable because the object examined in the academic's working variable is not the samewith the object of this research. The research object in the academic's working environment refers to higher academic levels.  Each frequency is then divided by the number of respondents, and the result is called proportion.  Determine the value of cumulative proportion by adding the proportions in sequence per column score.  Use the normal distribution table to calculate the Z value for each cumulative proportion obtained.  Determine the high density value for each Z value obtained (using the High Density table).  Determine the scale value using the formula: Determine the transformation value using the formula:

Research variables
Indrawati (2015),defines variables as everything that can affect the value, and those values can vary and change. In this study, the variables used are independent and dependent variables. Independent variables are variables that are directly used to answer research problems (Indrawati, 2015: 73). Dependent variables are variables that are influenced by other variables (Indrawati, 2015). In this research, the independent variables used are: X1: Academic's Personal Characteristic X2: Academic's Perceived Support The dependent variable used in this research is: Y : Students' Intention to Become Technopreneur These variables are the basis for the preparation of questionnaire questions used in this study. The following are the definitions and operational variables used in this research.

Stagesof Research
The author took several steps to complete the research. The sequence of the research process is described as follows.

Figure 1 Stages of Research
Source: Author's Research, 2018

Population and sample size
A population is a group of people, events, objects that attract researchers to research on them (Indrawati, 2015). Whereas, according to Sujarweni (2015). The population is the total number of objects or subjects that have certain characteristics and qualities, which are set to be researched and to conclude from by the researcher. The population in this research is the diplomaand Strata 1student of Telkom University in 2018/2019 academic year. The total number of the population is: JUNE 2019

Mediany Kriseka Putri, Grisna Anggadwita, Mukti Bawono Wicaksono
Samples are members of the selected population to be included in the study, either to be observed, treated, or asked for opinions on the problem being studied. The sampling technique used in this study is non-probability sampling. Non-probability sampling is a sampling technique that does not allow members in the population to have an equal opportunity to be chosen, or it cannot be determined whether members have equal opportunityto be chosen or not. The type of sampling used is quota sampling. Quota sampling is the type of convenience sampling from a limited population with a predetermined number of samples. Convenience sampling is done by conveniently choosing some members to be sampled to provide the information needed for the research (Indrawati, 2015) Sampling is used because the population in this study is too large, and it is not possible to research all members of the population. The number of required samples is determined using the Slovin formula. The population is 22,895 persons, and the desired level of confidence is 95% (5% error rate). Slovin formula, according to Roscoe (1975) in Sujarweni (2015), is as follows: Where: n = Sample size N = Population e = Margin of error. Thus, the calculation of the number of samples to represent the population in this research is: So, the researchers determined the sample size to be 394 Diploma 1 Telkom University students in 2018/2019 academic year. From the total sample size, the composition of the sample size for each faculty was determined by Where: n i = Sample size of each faculty N i = Population of each faculty n = Sample size N = Population

Data Sources and Data Collection
The data source, based on how it is obtained, is considered as primary data, namely data that is directly obtained from the source. Primary data collection is done by using a tool in the form of a questionnaire. The questionnaire was sent to respondents via the internet using Google Form.

Descriptive Analysis
Recapitulation of Respondents' Responses Regarding Academic's Personal Characteristic (X1) Variable. The following are the results of the respondents' responses to the Academic's Personal Characteristic (X1) variable:

Table 3 Recapitulation of Respondents' Responses Regarding Academic's Personal Characteristic (X1) Variable
Based on the data in Table 3, the overall percentage of r espondents' responses to the Academic's Personal Characteristic (X1) variable is 68.42%, which falls in the "Good" category. The interpretation criteria for the score on the Academic's Personal Characteristic (X1) variable can be seen in Figure 2 as follows.

Source: Author's Research, 2019
Recapitulation of Respondents' Responses Regarding Academic's Perceived Support (X 2 ) Variable. The following are the results of the respondents' responses to the Academic's Perceived Support (X 2 ) variable: Based on the data in Table 4, the overall percentage of r espondents' responses to the Academic's Perceived Support (X2) variable is 63.88%, which falls in the "Enough" category. The interpretation criteria for the score on the Academic's Perceived Support (X2) variable can be seen in Figure 3 as follows.  From Table 5, the average responses of respondents regarding the Students' Intention to Become a Technopreneur (Y) variable can be known. The highest percentage for the Students' Intention to Become a Technopreneur (Y) variable occurs in question number 48 at table 5 (I am interested in building my own business) with a percentage of 68.83% in the "Good" category. While the lowest percentage in the Students' Intention to Become a Technopreneur (Y) variable occurs in question number 48 at table 5 (I have a high level of certainty that I will have my own company) with a percentage of 60.81% in the "Enough" category.
Overall the percentage of respondents' responses regarding the Students' Intention to Become a Technopreneur (Y) variable, which is equal to 65.60%,falls in the "Enough" category. The score interpretation criteria for Students' Intention to Become a Technopreneur (Y) variable can be seen in figure 4 below.

Figure 4 Continuum Line for Students' Intention to Become a Technopreneur (Y) variable
Source: Author's Research, 2019 Normal P-P Plot of Regression Standardized Residual Dependent Variable: Niat Mahasiswa untuk Menjadi Technopreneur (Y)

Data Normality Test
Based on Table 6, it can be known that the value of Kolmogorov-SmirnovZ is 1,339 with a probability value of 0.055. It can be concluded that the unstandardized residual data has normal distribution because the probability value of the estimation result is greater than the real level of significance set (0.055> 0.05). From Figure 5 above, we can see that the points spread around the line, and they follow the diagonal line. This shows that the model in this research is normally distributed.

Multicollinearity Test
Source: Author's Research, 2019 Table 6 Probability Value Test Using One-Sample Kolmogorov-Smirnov Test

Figure 6 Heteroscedasticity Test Scatterplot
Source: Author's Research, 2019 Based on the scatterplot in Figure 6, it can be observed that the distribution of residual data does not form certain patterns, the points scatter above and below number 0 on the Y axis, so it can be concluded that this regression model has no symptoms of heteroscedasticity. Or in other words, the regression model has fulfilled the assumption of homoskedasticity. Based on the data in Table 8, the probability value of each independent variable can be known: Academic's Personal Characteristic (X1) = 0.290 and Academic's Perceived Support (X2) = 0.254. It can be concluded that the data has fulfilled the assumption of homoskedasticity because the probability value of the estimation result for each independent variable is greater than the real level of significance set (0.05).

Multiple Linear Regression Analysis
Multiple

Correlation Coefficient & Determination
With the help of the SPSS program, the output for the correlation coefficient estimation results is as follows.
From the output in Table 9, the intercept and the regression coefficient values are known so the multiple linear regression equation can be formed as follows: Y = 0.241 + 0.141 X1 -0.258 X2 + ε a = 0.241 means that if the Academic's Personal Characteristic (X1) and Academic's Perceived Support (X2) variables are zero (0), then the Students' Intention to Become a Technopreneur (Y) will be 0.241 units, thus it can be seen that the regression lines cut the Y axis at 0.241 point.b1 = 0.141 means that if the Academic's Personal Characteristic (X1) increases by one unit while the other variables are constant, then the Students' Intention to Become a Technopreneur (Y) will increase by 0.141 units.b2 = -0.258 means that if Academic's Perceived Support (X2) increases by one unit while the other variables are constant, then the Students' In-Source: Author's Research, 2019 Based on Table 11, the correlation coefficient of 0.706 indicates a strong relationship between the independent variables and the dependent variables.
After the correlation coefficient value is known, the determination coefficient can be calculated using the following formula. KD = R 2 × 100% = (0.706) 2 × 100% = 49.9% Thus, KD value of 49.9% is obtained which shows that the Academic's Personal Characteristic (X1) and Academic's Perceived Support (X2) variables simultaneously contribute 49.9% in influencing Students' Intention to Become a Technopreneur (Y). While the remaining 50.1% is influenced by other factors not observed in this study.

Simultaneous Hypothesis Test Hypothesis test:
There is no significant influence from Academic's Personal Characteristic (X1) and Academic's Perceived Support (X2) simultaneously on Students' Intention to Become a Technopreneur (Y); Academic's Personal Characteristic (X1) and Academic's Perceived Support (X2) simultaneously on Students' Intention to Become a Technopreneur (Y)

Statistical test:
Test Fat the significance level () = 5%. Test criteria: With the help of the SPSS program, F count value is obtained as follows: From the analysis in Table 10, it can be knownthat the value of the correlation coefficient (R) is equal to 0.706. This value is then interpreted based on the following criteria.  Based on the data in Table 12, F count value is obtained to be 194.542. This value is then compared with theFvalue in Fdistribution table. Forá = 5%, df 1 = k = 2, and df 2 = n-k-1 = 394-2-1 = 391, F table is obtained to be 3.019.

Figure 7 Simultaneous Hypotheses Test Curve
Source: Author's Research, 2019 Based on Figure 7, F count is known to be larger than the value ofF table (194.542>3.019), soH 0 is rejected, and H 1 is accepted, which means that there is significant influence fromAcademic's Personal Characteristic (X 1 ) dan Academic's Perceived Support (X 2 ) simultaneously on Students' Intention to Becomea Technopreneur (Y).

Partial Hypothesis Test (t-Test) Hypothesis test:
Academic's Per sonal Characteristic (X1) does not have a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y); Characteristic (X1) has a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y); Academic's Perceived Support (X 2 ) does not have a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y); Support (X 2 ) has a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y); Statistical test: t-Test at significance level () = 5%. Criteria used are as follows: Reject H 0 and accept H 1 ift -count e" t -table ; or Accept H 0 and reject H 1 if t count < t -table .
With the help of the SPSS program, t -count value is obtained as follows: Based on the data in Table 13,t -count value for Academic's Personal Characteristic (X 1 ) variable is obtained to be 19.445 and Academic's Perceived Support (X 2 ) to be -5.503. The t -count values are then compared with thetvalue in the t distribution table. For  = 5% and df = n-k-1 = 394-2-1 = 391, t -table value is obtained fromtdistribution table for one-sided test to be 1.649.
To make it easier to understand the test criteria, thet -count valueand thet -table value are mapped into the two-sided test curve as follows.  Based on the aforementioned test criteria, it can be observed that the t -count value for X 1 variable is within theH 0 rejection area (19.445 > 1.649). This shows that H 0 is rejected and H 1 is accepted, which means that Academic's Personal Characteristic (X 1 ) has a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y).

Identification of The Influence of Academic's Personal Characteristics and...
Based on the aforementioned test criteria, it can be observed that the t -count value for X 2 is within the H 0 rejection area (-5.503 < 1.649). This shows that H 0 is accepted and H 1 is rejected, which means that Academic's Perceived Support (X 2 ) does not have a significant and positive effect partially on Students' Intention to Become a Technopreneur (Y);

DISCUSSIONS
Based on the descriptive analysis in this research, it can be observed that the average percentage score for the variable is at 66.15%.It can be observed that the two variables are in the "Enough" category on the continuum line and each variable has a score above 52% with different decimal values; the academic's personal characteristic variable has the higher value with a percentage value of 68.42%, which can be interpreted that the personality and character of an academic individual is more dominant in growing students' intention to become a technopreneur compared to academic's support, which can be in the form of technology entrepreneurship education and other supports in the form of facilities or programs such as start-up incubation programs and copyright commercialization facilities provided by Telkom University. Telkom University students tend to be more willing to become a technopreneur when they have the willingness within themselves. Based on the measurement results, the motivation factor in academic's personal characteristic is the most influential thing to foster students' intention to become a technopreneur with a percentage value of 81.20%. Strong motivation is likely to give birth to real action. While academic's perceived support has a lower rating with a percentage value of 63.88%, this number is not low because it still falls in the"Enough" category, and it can be interpreted that academic's support also influences students to grow their intentions as well as to develop their intereststo become a technopreneur.
This research also determines the determination coefficient value be 0.49. This shows that the effect of Academic'sPersonal Characteristic (X1) and Academic's Perceived Support (X2) on Students' Intention to Become a Technopreneur (Y) in the regression model is at 49.9%, while the remain-ing 50.1% is influenced by other variables that are not studied in this research. It can be interpreted that there is a correlation of 49.9% between academic's personal characteristic and academic's perceived support with students' intention to become a technopreneur. It can hence be concluded that simultaneously, the academic's personal characteristic and academic's perceived support variables have an effect of 49.9% on students' intention to become a technopreneur.
This research also shows that the Academic's Personal Characteristic (X1) variable has an accepted H 1 value, which means that the academic's personal characteristic variable has a positive effect partially on the Intentions of Telkom University Students to Become a Technopreneur (Y). This is evidenced by the fulfillment of the statistical test requirement, namely to reject H 0 and accept H 1 if tcount  t -table , which can be determined through t he tcount test using SPSS program.It is determined that the t -count value is 19,445, so Academic's Personal Characteristic (X1) meets the H 1 test required to be accepted (19,445> 1,649). The accepted H 1 value in this research means that the indicators of entrepreneurial self-efficacy, motivational factors, and personal networks influence students' intention to become a technopreneur. In the thesis research that was done by Sunu regarding the intention of students to become a technopreneur studied using path analysis method with the object of research to be Bandung Technological Institution students majoring in informatics engineering, it was shownthat partially the influence values for entrepreneurial selfefficacy and personal networkswere significant, but the influence value for motivational factorswas insignificant to students' intention to become a technopreneur (Triono, 2014). It can be concluded that there are similarities in the research variables, but in this research, the three variables (entrepreneurial self-efficacy, motivational factors, and personal networks) are grouped into a single category (academic's personal characteristic) to facilitate and simplify the research (Prodan, 2007). Partially, the Academic'sPersonal Characteristic (X1) variable has an effect of 47.5% on students' intention to become a technopreneur, while the Academic's Perceived Support (X2) is categorized from perceived role models and perceived support from academic institutions (Prodan, 2007). In the dissertation research by Prodan using the structural equation model method with the object of research to be students at 3 universities including the University of Ljubljana in Slovenia, Eindhoven University of Technology in the Netherlands, and the University of Cambridge in the UK,it was shown that the perceived role model variable had a significant effect on students'intention to become a technopreneur while perceived support from academics institution variables did not have a positive influence on students' intention to become a technopreneur (Prodan, 2007).In this study, the variable academic perceived support has H1 value rejected according to the statistical test requirements, namely H1 is rejected if tcount <t table. From the measurement results, it was found the results (-5.503 <1,649) interpreted that in this study the variable academic 'perceived support (X2) did not significantly positive influence students' intention to become technopreneurs. In this study, the academic's perceived support variable has an influence of 2.4% on students' intention to become a technopreneur.

CONCLUSION
It can be concluded that the Academic's Personal Characteristic of Telkom University students are within the "Good" category. Academic's perceived support of Telkom University students is within the "Enough" category. The intention to become a technopreneur for Telkom University students is within the "Enough" category.Based on the explanation in the previous chapter, it can be concluded that the academic's personal characteristic has a positive and significant influence partially on the intention to become a technopreneur for Telkom University students with the value of 47.5% while academic perceived support does not have a positive and significant influence on the intention to become a technopreneur for Telkom University students with a value of 2.4%.Based on the explanation in the previous chapter, it can be concluded that personal academic characteristics and academic perceived support have a significant influence si-multaneously on the intention of Telkom University students to become technopreneurs with a high value of 49.9% while the remaining 50.1% is influenced by other factors which are not observed in this study.