| Literature DB >> 19321013 |
Kyung-Nam Kang1, Tae-Kyu Ryu, Yoon-Sik Lee.
Abstract
BACKGROUND: Concerns have recently been raised about the negative effects of patents on innovation. In this study, the effects of patents on innovations in the Korean biotech SMEs (small and medium-sized entrepreneurs) were examined using survey data and statistical analysis.Entities:
Mesh:
Year: 2009 PMID: 19321013 PMCID: PMC2682799 DOI: 10.1186/1472-6750-9-25
Source DB: PubMed Journal: BMC Biotechnol ISSN: 1472-6750 Impact factor: 2.563
Variables (X) considered in t-test analysis
| Variable | Score | Definition | |
| Main effect of patents | +1 | (positive terms) | Fostering information sharing, improving productivity |
| --1 | (negative terms) | Increase in costs, restrictions on access to information, stifling effect on subsequent innovation | |
| Evaluating effects of patents on innovation | --2 ~ +2 | --2 = highly negative; --1 = negative; | |
Variables (X) considered in OLR analysis
| Classification | Variable | Definition |
| Independent variable | Attitude toward patents | --2 = highly negative; --1 = negative; |
| Dependent variable | Degree of difficulty in acquiring research tools | 1 = very easy; 2 = easy; 3 = neither easy nor difficult; 4 = difficult; 5 = very difficult |
| Control variable | Size | Log number of employees in 2007 |
| Age | Number of months until 2007 | |
Variables (X) considered in Poisson regression
| Classification | Variable | Definition |
| Independent variable | Innovation performance | Total number of patents |
| Dependent variable | Degree of difficulty in acquiring research tools | 1 = very easy; 2 = easy; |
| Control variable | Size | Log number of employees in 2007 |
| Age | Number of months until 2007 | |
Figure 1Methods for acquiring patented research tools.
Figure 2Causes of difficulties in using patented research tools.
Where do concerns occur? t-test analysis
| Business field | N | Mean | Std. deviation | t | Df | Sig. (2-tailed) | |
| Main Effect*** | Biomedical | 39 | --0.026 | 1.013 | --2.975 | 67.652 | 0.004 |
| Others | 70 | 0.543 | 0.846 | ||||
| Evaluating effects** | Biomedical | 39 | --0.282 | 0.972 | --2.483 | 107.000 | 0.015 |
| Others | 70 | 0.143 | 0.785 | ||||
*** denotes statistical significance at the < 0.01 level.
** denotes statistical significance at the < 0.05 level.
Are "restricted access problems" serious in the biotechnology industry? OLR results (1)
| Evaluating effects | Coeff. | Std. err. | P > z |
| Difficulty | 0.121 | 0.189 | 0.521 |
| Size | 0.188 | 0.475 | 0.693 |
| Age** | 0.014 | 0.006 | 0.031 |
| Number of obs. | 76 | ||
| Log likelihood | --90.164 | ||
| LR chi2(3) | 6.75 | ||
| Prob > chi2 | 0.080 | ||
** denotes statistical significance at the < 0.05 level.
Are "restricted access problems" serious in the biomedical sector? OLR results (2)
| Evaluating effects | Coeff. | Std. err. | P > z |
| Difficulty* | 0.511 | 0.302 | 0.091 |
| Size | --0.151 | 0.646 | 0.815 |
| Age** | 0.021 | 0.010 | 0.038 |
| Number of obs. | 32 | ||
| Log likelihood | --39.123 | ||
| LR chi2(3) | 7.32 | ||
| Prob > chi2 | 0.063 | ||
** denotes statistical significance at the < 0.05 level.
* denotes statistical significance at the < 0.10 level.
Descriptive statistics and correlation (n = 76)
| Mean | S. D. | Difficulty | Size | Age | |
| Difficulty | 2.566 | 1.159 | 1.000 | ||
| Size | 1.276 | 0.473 | 0.030 | 1.000 | |
| Age | 95.658 | 38.037 | --0.091 | 0.357 | 1.000 |
Restricted access problems and innovation performance in the biotechnology industry: Poisson regression results of variables versus innovation performance
| Innovation performance | Coeff. | Std. err. | P > z |
| Constant*** | 1.377 | 0.266 | 0.000 |
| Difficulty** | 0.073 | 0.034 | 0.033 |
| Size* | 0.290 | 0.171 | 0.090 |
| Age | 0.003 | 0.002 | 0.242 |
| Sigma | 0.826 | 0.048 | 0.000 |
| Number of obs. | 76 | ||
| Log likelihood | --856.413 | ||
| Chi squared | 1121.803 | ||
| Prob > chi2 | 0.000 | ||
Poisson model with normal heterogeneity
*** denotes the correlation coefficient observed at significance level < 0.01.
** denotes the correlation coefficient observed at significance level < 0.05.
* denotes the correlation coefficient observed at significance level < 0.10.
Restricted access problems and innovation performance in the biomedical sector: Poisson regression results of variables versus innovation performance
| Innovation performance | Coeff. | Std. err. | P > z |
| Constant** | 1.289 | 0.612 | 0.035 |
| Difficulty | 0.116 | 0.091 | 0.200 |
| Size | 0.156 | 0.352 | 0.657 |
| Age | 0.004 | 0.006 | 0.512 |
| Sigma | 0.862 | 0.094 | 0.000 |
| Number of obs. | 32 | ||
| Log likelihood | --128.999 | ||
| Chi squared | 678.769 | ||
| Prob > chi2 | 0.000 | ||
Poisson model with normal heterogeneity
** denotes the correlation coefficient observed at significance level < 0.05.
Comparing respondents and nonrespondents
| Main Characteristics | Respondents | Nonrespondents | Sig. |
| Age | 7.9 yr | 7.3 yr | n.s. |
| Size (number of employees) | 30.7 | 23.8 | n.s. |
| Number of patents | 9.0 | 8.4 | n.s. |
| Business fields | |||
| Biomedical field | 35.8% | 28.6% | n.s. |
| Biochemical field | 13.8% | 15.7% | n.s. |
| Bio-food field | 33.0% | 23.5% | n.s. |
Note: Respondents, n = 109; nonrespondents, n = 217; data for 2006