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1、本科毕业论文（设计）外 文 翻 译原文：Innovation and firm performanceFactors influencing the decision to innovateSeveral studies empirically test the propensity of firms to innovate. Felder, et al. (1996) used the Mannheim Innovation Panel to test the relation between R&D and other innovation expenditures. The data s
2、et contains a small firm subset containing firms with 5 up till 49 workers. The participation decision to innovate raises strongly with size. However, once innovating, the amounts invested as percentage of total sales is larger with small firms than with large firms. This is confirmed by Vossen & No
3、oteboom (1996). This effect is most pronounced for the total innovation expenditures. The relationship between firm size and R&D seems U-shaped. Vossen & Nooteboom conclude that small firms participate less in R&D, but at a greater intensity and with a greater productivity once they participate (Vos
4、sen & Nooteboom, 1996, 167). Also Kleinknecht (2000) and Kleinknecht and Mohnen (2002) found that the propensity to innovate is positively related with size although the relationship may not be linear and that among the innovators, smaller firms tend to have higher shares in sales of innovative prod
5、ucts.Lf et al. (2001) used OECD and CIS data in their empirical work. Using a Cobb-Douglas production function they try to explain variation in productivity growth between the Nordic countries, using standard inputs (labour and capital) and the innovation investment variable, which substitutes the R
6、&D variable. Using a Crpon, Duguet and Mairesse model (1998) they estimate the following four equations:(1) firms prosperity to innovate/decision to innovate;(2) innovation inputs (innovation investment per worker);(3) innovation output (log of innovation sales per worker); and(4) productivity (sale
7、s per employee).Throughput is not formally included in this set of equations. A two-step investment model is applied: First, the decision to engage in research must be taken (eq. 1);Next, conditional on engaging in research, the amount of investment must be decided upon (eq. 2). The decision to inno
8、vate is modelled as a Probit 0,1 model. To explain the propensity to innovate Lf et al. (2001) use the following variables: firm size (employees), export intensity, prior patent applications, % non-R&D engineers, % administrators and several control variables. Firm size and patent applications are s
9、ignificant in all three countries, export intensity in two countries, the other variables in only one country. The control variables were not significant.Technological opportunities, factor intensity and sector characteristics also influence the innovation decision (Lf et al., 2001). In a sector wit
10、h high technological potentials, firms are more inclined to innovate. If they do not innovate, they may lose their market position. To estimate the effects of these variables, dummies were included in the regression, however, the results are not reported.To summarize, the decision to innovate is an
11、important decision for companies. Once a company decides to be active in innovation, the company has to dedicate resources to the innovation process. The decision is influenced by the size of the firm, the export intensity, prior R&D and characteristics of the employees (level of education). Also pr
12、ocess characteristics such as the mission of the firm influence the innovation decision.Innovative intensityConditional on engaging in innovations, the innovation intensity must be assessed. It concentrates on understanding the determinants that influence the level of resources dedicated to the inno
13、vation process. These resources are typical financial or human.The literature provides us with several indicators of the innovation intensity. Traditionally and still the most popular input indicator is the expenditures on R&D (Klomp and Van Leeuwen, 1999, Lf et al., 2001). The expenditures are ofte
14、n divided by total sales to come to the R&D intensity of a company. The R&D indicator is still further developed as an indicator. The main advantage of this indicator is that it is relatively easy to measure and collect. The extensive use of this indicator also improves the comparability of the diff
15、erent studies. However, several weaknesses can be mentioned (see Kleinknecht, 2000, for an extensive review). First, R&D expenditures are merely an input to the innovation process, but it states nothing on the results, or the efficiency. Second, R&D related inputs make for a minority of innovation e
16、xpenditures, varying from 25-50 percent. Third, R&D data tend to underestimate innovations in services. Finally, R&D questionnaires underestimate the small scale and often informal R&D activities in smaller companies. Complex questioning may result in such underestimation.Several new definitions and improved R&D expenditures have been proposed. First, the 1992 Oslo-manual of the OECD posits a new definition of expenditures related to (technologi
