대학과 대학원 학생의 창업역량과 창업장애물의 인식은 멘토링의 발전에 중요성을 가정하고 있다. 본 연구는 대부분의 사업을 시작하는 창업의도에 대해 기여하는 요인을 확인하는 것을 목표로 하고 있다. 창업능력과 창업장애물이 창업경험에 미치는 영향력을 검증하고자 하며, 다음으로 창업경험이 창업의도에 미치는 영향력을 검증하고자 하며, 마지막으로 창업경험과 창업의도간의 관계에 창업멘토링의 조절효과를 검증하고자 하였다.
우리는 139개의 샘플을 사용하였으며, 연구 결과는 창업역량은 창업경험의 유의한 영향력을 미치는 것으로 나타났으며, 창업장애물은 창업경험에 미치는 영향력이 유의하지 않게 나타났으며, 창업경험은 창업의도 유의한 영향력을 미치는 것으로 나타났으며, 마지막으로 창업경험과 창업의도 간의 관계에 대한 창업멘토링 조절효과는 유의하게 나타났다.
향후에는 창업멘토링의 영향력을 세분화할 필요성이 제기되며, 본 연구에서는 창업멘토링의 측정문항을 외국 문헌의 자료를 인용하여 사용하였기에 우리나라 실정에 약간은 맞지 않다는 한계점이 있다.
According to previous research entrepreneurial capacity in the field of competitive advantage, Hult(2002) has found that entrepreneurship coupled with sustainable competitive advantage and entrepreneurial usefulness sustain the creativeness of new firm within the existent firm and the renovation of continual firm. Lyon, Lumpkin and Dess(2000), Lumpkin and Dess(2001), Hult, Hurley and Knight(2004), Li, Huang and Tsai(2008) have found that entrepreneurial orientation is positively related to business performance.
A possible solution for SMMEs and entrepreneurs is the introduction of mentoring. Mentoring entrepreneurs is the function of nurturing and supporting entrepreneurs by providing them with professional skills development and moral support in an attempt to positively impact on the business’s sustainability. The study is the first of its kind in that it relates specifically to entrepreneurship mentors. This study empirically endeavours to determine the basic profile of entrepreneurship mentors in Korea and their skills sets. The study aims to represent the importance of the skills as represented in the model and to understand the entrepreneurship mentoring environment in Korea.
An entrepreneurial orientation refers to the methods, practices, and decision-making activities that cause new entry which can be performed by joining new or founded markets with new or surviving goods or services(Miller, 1983; Lumpkin and Dess, 1996). According to an entrepreneurial orientation, the most important purpose of firm is finding opportunities in competition with other entrepreneurial firm. Entrepreneurial skill is an intelligent merchant skill has rapid decision to undergo risk-taking. While Newbert, Gopalakrishnan and Kirchoff(2008) defined that as dynamic capability that the firm improves based on their past experience, collected tacit knowledge and learning by doing.
Entrepreneurial capacity is applied from entrepreneurial orientation, which linked with capability. It is learn and a new knowledge assists enterprise more create and finish than mean performance. This research gives dynamic capabilities that include specific organizational process such as strategic decision-making and the likelihood that creates value of firms through the improvement of resources. Therefore entrepreneurial capacity is purposed to indicate the degree of a firm to hold the competency to meet new integrations of resources and capabilities. There are two groups of researchers setting the dimension of entrepreneurial orientation. The first group has three dimensions: innovativeness, risk-taking, and proactiveness(Covin and Slevin, 1989; Zahra and Covin, 1995; Weerawardena and O’Cass, 2004; Luo, Zhou and Liu, 2005; Wiklund and Shepherd, 2005; Keh, Nguyen and Ng, 2007; Green, Covin and Slevin, 2008; Jantunen, Nummela, Puumalainen and Saarenketo, 2008; Runyan, Droge and Swinney, 2008), and another has five dimensions: innovativeness, risk-taking, proactiveness, competitive aggressiveness and autonomy(Lumpkin and dess, 1996; Lee and Peterson, 2000; Li, Huang and Tsai, 2008). The concept of entrepreneurial capacity in this study is adopted those of Covin and Slevin’s entrepreneurship orientation.
The perception of lack of financial support does not affect the probability of being in any of the stages ofthe entrepreneurial process. It does not seem to discourage respondents in setting up a business and becoming entrepreneur. The same holds true for the lack of sufficient information. Also, the fact of perceiving an unfavorable economic climate does not play a role in switching through the whole entrepreneurial system, although in the last two binary regressions concerning levels of high involvement, this variable does have a significant effect. The fact that a respondent perceives it to be difficult to start a business due to complex administrative procedures has a negative impact on the probability of being in the more ‘active’ levels of entrepreneurship. Furthermore, if one is more risk tolerant, one is more likely to move to a higher engagement level in the entrepreneurial system than staying in the present engagement level.
Prior business ownership experience impacts on an experienced habitual entrepreneur’s mindset as well as his or her knowledge base to identify and exploit business opportunities. Comparing novice and habitual entrepreneurs may offer some important insight into the heterogeneity of entrepreneurial behavior that may contribute to a more informed evaluation process by private equity firms.
Mentors must use their education, their skills as mentors and their experience as skilled businesspeople/entrepreneurs to assist mentees to achieve certain goals(Bell, 1996). The mentor’s skills and experience are fundamental to the mentor-mentee relationship. As in the case of entrepreneurship mentors, these skills not only include the above, but also the entrepreneurial and business skills of the mentor. The following will detail these skills in terms of the multiplicative entrepreneurial performance model(Antonites and Van Vuuren(2001)
The Entrepreneurship Performance Model is based on the Motivational and Expectation theory of Vroom in De V Maasdorp and Van Vuuren in Marx et al.(1998). The Vroom theory of motivation and expectation assists in the development of the above training model and the basis of Vroom’s theory relates to personal achievement.
The concept of what motivates the entrepreneur has been extensively researched. Timmons(1999) discuss the theory of motivation based on the research of McClelland and Atkinson who believe there are three needs that motivate individuals. Firstly, the need for achievement, secondly, the need for power and thirdly, the need for affiliation. Longenecker, Moore and Petty(2003) accentuate the fact that motivation for the entrepreneur is based on the potential rewards. These rewards can be broken into three categories; namely, profit, independence and personal fulfillment.
Business skills are those skills required by both mentors and entrepreneurs in order to equip them to start and manage a business.
“Entrepreneurial intention” is one’s willingness in undertaking entrepreneurial activity, or in other words become self employed. The opposition of self-employment is becoming a waged or salaried individual(Tkachev and Kolvereid, 1999). From this perspective, measuring entrepreneurial intentions may be regarded as measuring latent entrepreneurship(Verheul, Thurik and Grilo, 2006).
The data of this study were gathered via survey(using 7-point Likert sacles) in 2012. A sample of 139 university/graduate school students in Korea.
SEM with AMOS 18.0 tested the fit of the measurement, structural, and moderation models using maximum likelihood(ML) estimation. ML was chosen based on the normal distribution of the data, sample size, and measurement using interval-level scales(Schermelleh-Engel, Moosbrugger, and Muller 2003). The standard two-step process was used, where CFA were conducted before testing the structural and moderation models (Anderson and Gerbing 1988).
Model fit was assessed using several methods. We assessed the χ² statistic, which evaluates the difference between the specified model’s convariance structure and the observed convariance structure(Bollen, 1989). We reviewed the standardized residual matrices to identify large residuals(positive of negative) that contributed most to poor fit. Modification indices based on Lagrangian multiplier(LM) tests were used to identify parameters not specified, which if specified would contribute to better model fit. However, modifications contrary to theory or logic were not made.
Several other statistics were used to assess fit. These included root mean square error of approximation(RMSEA), comparative fit index(CFI), and adjusted goodness of fit(AGFI). These indices adjust for model complexity (Kline, 1998; Bollen, 1990), as the χ² statistic is sensitive to model complexity. We used the following cutoff criteria:
(1) for “acceptable” model fit: RMSEA< 0.08; AGFI > 0.90; CFI > 0.90; and (2) for “good” model fit: RMSEA < 0.06; AGFI > 0.90; CFI > 0.95. These criteria are generally accepted(Hu and Bentler, 1999; Kline, 1998; Bollen, 1989; Bagozzi and Yi, 1988; Bagozzi and Yi, 1990).
We first evaluated EC, EO, EE and EI separately, and then evaluated them together. Results of the CFA were χ² = 128.582, df = 80, p = .000; RMSEA = 0.066; AGFI = 0.844; CFI = 0.948. All parameter estimates were significant at the p < .05 level, indicating convergent validity. The composite reliability for EC, EO, EE, EI was 0.73, 0.800, 0.849, 0.874(see Table 1).