Abstract
This paper investigates whether firms’ innovation strategies affect their patenting behavior, as measured by both the probability of having a patent portfolio and the number of active patents held. Three main dimensions of an innovation strategy are taken into account: the relative importance of basic research, applied research and development work in total R&D activities, the product or process orientation of innovation efforts, and the extent to which firms enter into collaborative R&D with other institutions. The major findings can be summarized as follows: (1) taking into account the various dimensions of an innovation strategy turns out to approximate the patenting behavior of firms better than the traditional Schumpeterian hypotheses related to firm size and market power; (2) there is a positive relationship between the patent portfolio of firms and an outward-oriented innovation strategy characterized by R&D partnerships with external organizations - scientific institutions and competitors in particular; (3) process-oriented innovators patent less than product-oriented innovators; (4) a stronger focus on basic and applied research is associated with a more active patenting behavior; (5) firms that perceive high barriers to innovation (internal, risk-related or external barriers) have smaller patent portfolios; (6) the perceived limitations of the patent system do not significantly influence the patenting behavior, suggesting that firms patent for other strategic reasons than merely protecting innovation rents.
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Notes
Patent-based indicators are only one measure of innovation output. They are imperfect for three main reasons (Griliches, 1990). First, not all innovations are patentable, since the three conditions of non-obviousness, inventive step and industrial application must be satisfied in order to get a patent application granted. Second, the propensity to patent ‘patentable’ inventions varies considerably across firms, time, and industry [see for instance Scherer (1983), Hall et al. (1986) and Arora (1997)]. Third, in some sectors, patent protection is relatively inefficient and secrecy is favored as a mechanism to secure the rents due to an invention. The importance of the various protection mechanisms varies across industries and patents are important for only a few of them, mainly chemicals and pharmaceuticals [Mansfield (1986), Levin et al. (1987)].
Since the distribution of the patent variable is skewed towards low values, a negative binomial model is more appropriate than a Poisson model. It allows the conditional mean and variance of the dependent variable to differ.
The OECD classifies manufacturing sectors into four categories: high-tech (HT), medium-high-tech (MH), medium-low-tech (ML), and low-tech (LT). HT=aeronautic construction, desks and computing machines, pharmaceuticals products, radio, TV and telecommunication machines; MH=professional equipment, motorcar vehicles, electric machines, chemical industries, other transport equipment, non-electric machines; ML=rubber and plastic materials, naval construction, other industrial sectors, non-iron metals, non-metallic mineral products, metallic works, petroleum and coal, steel industry; LT=paper, printing and editing, textile industry, clothing and leather, food, drinks and tobacco, wood and furniture. A category was added for all service companies: commerce, hotels and restaurants, transports, posts and telecommunications, insurances, financial services, real estate activities, computer activities.
In the remainder of this section, large firms refer to firms with 500 employees or more, medium firms have between 200 and 499 employees, and small firms do not exceed 199 employees.
A detailed description of the factor analyses may be found in Peeters and van Pottelsberghe de la Potterie (2003b).
Appendix 3 provides a synthetic table with the definition of all variables introduced in the regressions.
The estimated parameters of the logit model are not readily interpretable in terms of variation in the probability of observing a patent portfolio. However, a simple transformation enables to interpret them as variations in the odds ratios: \((e^{{\widehat{\beta }}} - 1) = \Delta {\left( {\frac{{P{\left( {Y = 1} \right)}}}{{P{\left( {Y = 0} \right)}}}} \right)}\). The value of coefficients estimated through the negative binomial and tobit models cannot be interpreted directly either, as they depend on the value of the explanatory variables. The advantage of the tobit model is that it formally differentiates firms that have a patent portfolio from those that do not. However, following Hausman et al. (1984) the negative binomial estimates will be used to interpret the results. As a count model, it explicitly takes into account the non-negativity and discreteness of the data. Moreover it enables to deal with distributions that are skewed towards low values, which is the case in this study where a relatively high proportion of firms have no or only a small number of patents. The discussion in the remainder of this section will rely on average elasticities computed for a hypothetic firm characterized by all explanatory variables equal to their average value: \(\lambda *\widehat{\beta }\) with \(\lambda = e^{{\widehat{\beta }^{\prime } \overline{X} }}\). This hypothetic firm is referred to as the ‘average firm’.
Sector dummies would have been another way to control for sector effects. However, this would have multiplied the number of dummies in the model and eventually lead to a lack of degrees of freedom given the small size of the sample. Moreover, the problem of assigning a particular sector of activity to multi-business firms would have been accentuated.
It can actually be shown that the largest patent portfolios are those of firms pursuing an innovation strategy exclusively targeted at new product development, followed by firms pursuing a mixed strategy aiming at both product and process innovations. The patent portfolio of exclusive process innovators is not significantly different from firms that do not seek high innovation goals of any kind.
The effect of firms’ total R&D intensity (percentage of sales allocated to R&D) has been tested as well. This variable proved significant only in the count model at the 15% level and not in the binary model. In other words, the firms’ R&D intensity has some determining influence for the number of patents firms hold but not for the probability of patenting. Moreover, the introduction of this variable induced a sharp decrease in the number of observations due to a low response rate (firms were more willing to provide information on the composition of their R&D activities than their total budget for R&D). Therefore the R&D intensity was not included in the final regressions.
The drawback of working with factorial axes as explanatory variables is that it is difficult to interpret what a one point increase in a firm’s coordinates on an axis represents in reality.
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The authors thank the participants to the 2004 International Schumpeter Society Conference, the 2004 Association d’Econometrie Appliquee Conference on Innovation and Intellectual Property, and the 2005 DRUID Summer Conference, and an anonymous reviewer for their insightful comments. This research was partly performed when Bruno van Pottelsberghe was Visiting Professor at the Institute of Innovation Research (IIR), Hitotsubashi University. Carine Peeters greatly acknowledges a post-doctoral fellowship from the Belgian American Educational Foundation (BAEF).
Appendices
Appendix 1: Composition of the sample of 148 surveyed firms
Appendix 2: Construction of the factorial axes
The following three factor analyses relate to the type of institutions with which firms collaborate, the perceived barriers to innovation, and the perceived barriers to patenting. The asterisks mark the survey items that contribute the most to the interpretation of the factorial axes, and the percentage of total variance explained by the factorial axes used in empirical study.
Research partnerships
Partnerships | Coordinates | Contributions | Cosinus squared | |||
|---|---|---|---|---|---|---|
Factor 1 | Factor 2 | Factor 1 | Factor 2 | Factor 1 | Factor 2 | |
Competitors: Yes | 1.18 | 0.84* | 8.90 | 12.00* | 0.28 | 0.14* |
No | −0.24 | −0.17 | 1.80 | 2.40 | 0.28 | 0.14 |
Vertical: Yes | 0.39 | −0.28 | 4.10 | 5.30 | 0.34 | 0.17 |
No | −0.87 | 0.61* | 9.00 | 11.80* | 0.34 | 0.17* |
Research instit: Yes | 0.78* | 0.25 | 11.50* | 3.20 | 0.59* | 0.06 |
No | −0.76* | −0.25 | 11.20* | 3.10 | 0.59* | 0.06 |
Universities: Yes | 0.71* | 0.22 | 10.90* | 2.70 | 0.65* | 0.06 |
No | −0.92* | −0.28 | 14.20* | 3.50 | 0.65* | 0.06 |
Inside group: Yes | 0.42 | 0.07 | 4.10 | 0.30 | 0.26 | 0.01 |
No | −0.62 | −0.10 | 6.00 | 0.40 | 0.26 | 0.01 |
Consultants: Yes | 0.55 | −1.00* | 3.90 | 34.00* | 0.15 | 0.50* |
No | −0.28 | 0.50* | 2.00 | 17.00* | 0.15 | 0.50* |
Other firms: Yes | 0.67 | −0.24 | 7.30 | 2.40 | 0.33 | 0.04 |
No | −0.49 | 0.17 | 5.30 | 1.80 | 0.33 | 0 |
Factors | Eigenvalues | Percentages | Cumulated percentages |
|---|---|---|---|
1 | 0.37 | 37.14 | 37.14* |
2 | 0.14 | 14.01 | 51.15* |
3 | 0.14 | 13.82 | 64.97 |
4 | 0.12 | 11.82 | 76.79 |
5 | 0.10 | 10.11 | 86.89 |
6 | 0.09 | 9.30 | 96.19 |
7 | 0.04 | 3.81 | 100.00 |
Barriers to the use of patents
Patents barriers | Coordinates on the axes | ||
|---|---|---|---|
Factor 1 | Factor 2 | Factor 3 | |
Cost of fees | 0.86* | 0.02 | 0.23 |
Protection cost | 0.86* | 0.06 | 0.23 |
Efficiency lack | 0.86* | 0.09 | −0.08 |
Secrecy better | 0.84* | 0.27 | 0.13 |
Market lead better | 0.84* | 0.00 | −0.25 |
Short PLC | 0.72* | −0.34 | −0.51 |
Disclosure risk | 0.84* | 0.18 | 0.07 |
Risk of copy | 0.91* | 0.10 | −0.06 |
Lack of information | 0.46 | −0.80 | 0.28 |
Factors | Eigenvalues | Percentages | Cumulated percentages |
|---|---|---|---|
1 | 5.87 | 65.26 | 65.26* |
2 | 0.89 | 9.88 | 75.15 |
3 | 0.71 | 7.87 | 83.02 |
4 | 0.54 | 6.01 | 89.03 |
5 | 0.35 | 3.94 | 92.97 |
6 | 0.26 | 2.91 | 95.88 |
7 | 0.20 | 2.23 | 98.12 |
8 | 0.13 | 1.50 | 99.61 |
9 | 0.03 | 0.39 | 100.00 |
Barriers to innovation
Innovation barriers | Coordinates on the axes | ||
|---|---|---|---|
Factor 1 | Factor 2 | Factor 3 | |
Economic risk | 0.28 | −0.80* | 0.00 |
High costs | 0.24 | −0.83* | 0.14 |
Lack of financing | 0.32 | −0.58* | 0.27 |
Internal rigidities | 0.68* | 0.19 | 0.02 |
Customers rigidities | 0.48 | 0.03 | −0.60* |
Resistance to change | 0.64* | 0.17 | −0.20 |
Lack of competencies | 0.70* | 0.03 | 0.24 |
Customers reaction lack | 0.52 | −0.02 | −0.55* |
Public regulations | 0.26 | −0.33 | −0.44* |
Time constraints | 0.70* | −0.02 | 0.18 |
Lack of communication | 0.70* | 0.30 | 0.18 |
Lack of leadership | 0.70* | 0.30 | 0.36 |
Factors | Eigenvalues | Percentages | Cumulated percentages |
|---|---|---|---|
1 | 3.76 | 31.33 | 31.33* |
2 | 2.02 | 16.86 | 48.19* |
3 | 1.24 | 10.33 | 58.52* |
4 | 1.00 | 8.32 | 66.84 |
5 | 0.89 | 7.39 | 74.23 |
6 | 0.61 | 5.11 | 79.34 |
7 | 0.54 | 4.53 | 83.87 |
8 | 0.54 | 4.47 | 88.35 |
9 | 0.42 | 3.49 | 91.83 |
10 | 0.41 | 3.44 | 95.27 |
11 | 0.31 | 2.58 | 97.85 |
12 | 0.26 | 2.15 | 100.00 |
Appendix 3: Definition of variables
Name | Construction/interpretation | Type |
Dependent variables: | ||
-Existence of a patent portfolio | The firm has at least one patent (yes/no) | 0/1 |
-Size of the patent portfolio | Number of patents in the firm’s patent portfolio | # |
Control variables: | ||
Firms’ characteristics: | ||
-Size | Number of employees in the firm in 2000 | # |
-Age | Number of years since the creation of the company | # |
-Foreign subsidiary | The firm belongs to a foreign group (yes/no) | 0/1 |
-Degree of internationalization | Number of countries in which the firm has customer contacts | # |
Sector characteristics: | ||
-Concentration | (Sales of the four largest firms of the sector/total sales of the sector)*100 | % |
-High-tech | The firm is active in a high-tech or medium-high-tech sector (yes/no) | 0/1 |
-Service | The firm is active in a service sector (yes/no) | 0/1 |
Innovation strategy variables: | ||
-New products development | 4 or 5 on a 5-point scale for the importance of new products development | 0 / 1 |
-New processes development | 4 or 5 on a 5-point scale for the importance of new processes development | 0 / 1 |
-Percent (%) basic and applied in R&D | Percent (%) of the total R&D budget allocated to basic and applied research | % |
Collaboration partners: | Coordinates on factorial axes | |
-Universities & research institutes | Scientific institutions as partners for R&D collaborations | |
-Competitors><consultants & vertical partners | R&D partnerships with competitors, as opposed to consultants, suppliers and customers | |
Barriers perception variables: | ||
-Barriers to patenting | Barriers perceived by firms to the use of the patent system | |
Barriers to innovation: | ||
-Internal to the firm | Internal organizational barriers perceived by firms | |
-Risks-and costs-related | Risks- and costs-related barriers perceived by firms | |
-External to the firm | External barriers from customers and regulations perceived by firms | |
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Peeters, C., van Pottelsberghe de la Potterie, B. Innovation strategy and the patenting behavior of firms. J Evol Econ 16, 109–135 (2006). https://doi.org/10.1007/s00191-005-0010-4
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DOI: https://doi.org/10.1007/s00191-005-0010-4