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RESULTS

The results of the tests of the hypotheses are presented in Tables 2–4. In each table, control variables are included in the first model, and independent variables for testing each hypothesis are added one at a time in subsequent models.

TABLE 2.
Results Of Regression Analysis. Number Of Patents As The Dependent

Variable. Unstandardized Regression Coefficients Reported; Standard Errors Shown In Parentheses. MODEL

	1	2	3
Intercept	4.54#	2.83	-0.08
	(2.5)	(2.84)	(4.6)
R&D age			0.04
			(0.05)
N R&D		1.89*	1.91#
		(0.81)	(1.02)
Ln rd	3.50*	2.16	2.59
	(1.46)	(1.65)	(1.97)
R2	0.087	0.163	0.163
Adj. R2	0.072	0.131	0.104

# p<0.1, * p<0.05, ** p<0.01 (two–tailed tests)

In Table 2, the control variable, R&D expenditure of the company is included in Model 1. Models 2 and 3 test for the effects of the number of R&D collaborative partners, and the partner age, respectively. As shown in Table 2, the number of technological collaborative partners (N R&D) is positively related to innovation, giving support for Hypothesis 2a. Also the increase in overall R2 is significant. New biotechnology firms with more collaborative partners are likely to produce more patents. However, we fail to find a significant relationship between partner age (R&D age) and innovative output in this sample, although the coefficient is in the right direction.

Table 3 looks at the effects on radical innovation. In contrast with the previous hypotheses, we proposed a negative relationship between collaborative characteristics and radical innovation, and this prediction is actually born out. As shown in Table 3, the number of R&D collaborative partners (N R&D) and the citations the patents of the start–up receive are negatively related (models 2 and 3). Hypothesis 2b is thus strongly supported.

There is also some support for hypothesis 1b, which proposed a negative relationship between partner age and radical innovation. Although the coefficient is in the right direction, and it increases the overall explanatory power, this relationship fails to reach significance when we control for the age of the firm (Founding) and R&D expenditures (Ln rd; see model 3). A possible explanation of this result is the missing values that limited the sample size of the R&D age variable.

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