• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.7-16
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• 1985

To grasp canonical correlations, their related backgrounds in various growth factors of stem, the characteristics of stem by synthetical dispersion analysis, principal component analysis and canonical correlation analysis as optimum method were applied to Larix leptolepis. The results are as follows; 1) There were high or low correlation among all factors (height ($x_1$), clear height ($x_2$), form height ($x_3$), breast height diameter (D. B. H.: $x_4$), mid diameter ($x_5$), crown diameter ($x_6$) and stem volume ($x_7$)) except normal form factor ($x_8$). Especially stem volume showed high correlation with the D.B.H., height, mid diameter (cf. table 1). 3) (1) Canonical correlation coefficients and canonical variate between stem volume and composite variate of various height growth factors ($x_1$, $x_2$ and $x_3$) are ${\gamma}_{u1,v1}=0.82980^{**}$, $\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3$. (2) Those of stem volume and composite variate of various diameter growth factors ($x_4$, $x_5$ and $x_6$) are ${\gamma}_{u1,v1}=0.98198^{**}$, $\{{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6$. (3) And canonical correlation between stem volume and composite variate of six factors including various heights and diameters are ${\gamma}_{u1,v1}=0.98700^{**}$, $\{^u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6$. All the cases showed the high canonical correlation. Height in the case of (1), D.B.H. in that of (2), and the D.B.H, and height in that of (3) respectively make an absolute contribution to the canonical correlation. Synthetical characteristics of each qualitative growth are largely affected by each factor. Especially in the case of (3) the influence by the D.B.H. is the most significant in the above six factors (cf. table 2). 3) Canonical correlation coefficient and canonical variate between composite variate of various height growth factors and that of the various diameter factors are ${\gamma}_{u1,v1}=0.78556^{**}$, $\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076x_5+0.05285x_6$. As shown in the above facts, only height and D.B.H. affected considerably to the canonical correlation. Thus, it was revealed that the synthetical characteristics of height growth was determined by height and those of the growth in thickness by D.B.H., respectively (cf. table 2). 4) Synthetical characteristics (1st-3rd principal component) derived from eight growth factors of stem, on the basis of 85% accumulated proportion aimed, are as follows; Ist principal component ($z_1$): $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$, 2nd principal component ($z_2$): $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$, 3rd principal component ($z_3$): $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$. The first principal component ($z_1$) as a "size factor" showed the high information absorption power with 63.26% (proportion), and its principal component score is determined by stem volume, D.B.H., mid diameter and height, which have considerably high factor loading. The second principal component ($z_2$) is the "shape factor" which indicates cubic similarity of the stem and its score is formed under the absolute influence of normal form factor. The third principal component ($z_3$) is the "shape factor" which shows the degree of thickness and length of stem. These three principal components have the satisfactory information absorption power with 88.36% of the accumulated percentage. variance (cf. table 3). 5) Thus the principal component and canonical correlation analyses could be applied to the field of forest measurement, judgement of site qualities, management diagnoses for the forest management and the forest products industries, and the other fields which require the assessment of synthetical characteristics.

• Korean small business review
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• v.31 no.2
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• pp.103-120
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• 2009

As business incubation centers (BICs) have been operating for more than 10 years in Korea, many early stage startups tend to use the services provided by the incubating centers. BICs in Korea have accumulated the knowledge and experience in the past ten years and their services have been considerably improved. The business incubating service has three facets : (1) business infrastructure service, (2) direct service, and (3) indirect service. The mission of BICs is to provide the early stage entrepreneurs with the incubating service in a limited period time to help them grow strong enough to survive the fierce competition after graduating from the incubation. However, the incubating services sometimes fail to foster the independence of new startup companies, and raise the dependence of many companies on BICs. Thus, the dependence on BICs is a very important factor to understand the survival of the incubated startup companies after graduation from BICs. The purpose of this study is to identify the main factors that influence the firm's dependence on BICs and to characterize the relationships among the identified factors. The business incubating service is a core construct of this study. It includes various activities and resources, such as offering the physical facilities, legal service, and connecting them with outside organizations. These services are extensive and take various forms. They are provided by BICs directly or indirectly. Past studies have identified various incubating services and classify them in different ways. Based on the past studies, we classify the business incubating service into three categories as mentioned above : (1) business infrastructure support, (2) direct support, and (3) networking support. The business infrastructure support is to provide the essential resources to start the business, such as physical facilities. The direct support is to offer the business resources available in the BICs, such as human, technical, and administrational resources. Finally, the indirect service was to support the resource in the outside of business incubation center. Dependence is generally defined as the degree to which a client firm needs the resources provided by the service provider in order to achieve its goals. Dependence is generated when a firm recognizes the benefits of interacting with its counterpart. Hence, the more positive outcomes a firm derives from its relationship with the partner, the more dependent on the partner the firm must inevitably become. In business incubating, as a resident firm is incubated in longer period, we can predict that her dependence on BICs would be stronger. In order to foster the independence of the incubated firms, BICs have to be able to manipulate the provision of their services to control the firms' dependence on BICs. Based on the above discussion, the research model for relationships between dependence and its affecting factors was developed. We surveyed the companies residing in BICs to test our research model. The instrument of our study was modified, in part, on the basis of previous relevant studies. For the purposes of testing reliability and validity, preliminary testing was conducted with firms that were residing in BICs and incubated by the BICs in the region of Gwangju and Jeonnam. The questionnaire was modified in accordance with the pre-test feedback. We mailed to all of the firms that had been incubated by the BICs with the help of business incubating managers of each BIC. The survey was conducted over a three week period. Gifts (of approximately ₩10,000 value) were offered to all actively participating respondents. The incubating period was reported by the business incubating managers, and it was transformed using natural logarithms. A total of 180 firms participated in the survey. However, we excluded 4 cases due to a lack of consistency using reversed items in the answers of the companies, and 176 cases were used for the analysis. We acknowledge that 176 samples may not be sufficient to conduct regression analyses with 5 research variables in our study. Each variable was measured through multiple items. We conducted an exploratory factor analysis to assess their unidimensionality. In an effort to test the construct validity of the instruments, a principal component factor analysis was conducted with Varimax rotation. The items correspond well to each singular factor, demonstrating a high degree of convergent validity. As the factor loadings for a variable (or factor) are higher than the factor loadings for the other variables, the instrument's discriminant validity is shown to be clear. Each factor was extracted as expected, which explained 70.97, 66.321, and 52.97 percent, respectively, of the total variance each with eigen values greater than 1.000. The internal consistency reliability of the variables was evaluated by computing Cronbach's alphas. The Cronbach's alpha values of the variables, which ranged from 0.717 to 0.950, were all securely over 0.700, which is satisfactory. The reliability and validity of the research variables are all, therefore, considered acceptable. The effects of dependence were assessed using a regression analysis. The Pearson correlations were calculated for the variables, measured by interval or ratio scales. Potential multicollinearity among the antecedents was evaluated prior to the multiple regression analysis, as some of the variables were significantly correlated with others (e.g., direct service and indirect service). Although several variables show the evidence of significant correlations, their tolerance values range between 0.334 and 0.613, thereby demonstrating that multicollinearity is not a likely threat to the parameter estimates. Checking some basic assumptions for the regression analyses, we decided to conduct multiple regression analyses and moderated regression analyses to test the given hypotheses. The results of the regression analyses indicate that the regression model is significant at p < 0.001 (F = 44.260), and that the predictors of the research model explain 42.6 percent of the total variance. Hypotheses 1, 2, and 3 address the relationships between the dependence of the incubated firms and the business incubating services. Business infrastructure service, direct service, and indirect service are all significantly related with dependence (β = 0.300, p < 0.001; β = 0.230, p < 0.001; β = 0.226, p < 0.001), thus supporting Hypotheses 1, 2, and 3. When the incubating period is the moderator and dependence is the dependent variable, the addition of the interaction terms with the antecedents to the regression equation yielded a significant increase in R2 (F change = 2.789, p < 0.05). In particular, direct service and indirect service exert different effects on dependence. Hence, the results support Hypotheses 5 and 6. This study provides several strategies and specific calls to action for BICs, based on our empirical findings. Business infrastructure service has more effect on the firm's dependence than the other two services. The introduction of an additional high charge rate for a graduated but allowed to stay in the BIC is a basic and legitimate condition for the BIC to control the firm's dependence. We detected the differential effects of direct and indirect services on the firm's dependence. The firms with long incubating period are more sensitive to indirect service positively, and more sensitive to direct service negatively, when assessing their levels of dependence. This implies that BICs must develop a strategy on the basis of a firm's incubating period. Last but not least, it would be valuable to discover other important variables that influence the firm's dependence in the future studies. Moreover, future studies to explain the independence of startup companies in BICs would also be valuable.