Drivers of Innovation Capacity and Consequences for Open Innovation
Abstract
:1. Introduction
2. Theoretical Framework
2.1. Patents, Trademarks, Innovation Capacity, and their Determinants
2.2. Research Hypotheses
3. Method
3.1. Objective, Data Collection, and Analysis
3.2. Variables and Statistical Techniques
4. Results
4.1. Macro Analysis
4.1.1. Influential Variables in the Number of Patent Applications
4.1.2. Validation of the Model Assumptions for Patents/Inventions
4.1.3. Influential Variables in the Number of Trademark Registrations and Other Distinguishing Trade Signs
4.1.4. Validation of the Model Assumptions for Trademark
4.2. Micro Analysis
Influence of Tax Incentives on the Creation of Value through Intangible Assets
5. Discussion
5.1. General Discussion Concerning the Drivers of Innovation Capacity
5.2. Drivers of Innovation Capacity and the Possibility of Fostering Open Innovation
6. Concluding Remarks
6.1. Main Findings and Implications for Open Innovation
6.2. Limitations and Avenues for Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Level of Analysis | Dimensions | Variables | Authors | Hypothesis | Expected Relationships |
---|---|---|---|---|---|
Macro | Patent Applications (Y1) Trademark Registrations (Y2) | ||||
Workforce * | MSE’s human resources (X1); Human resources of large enterprises (X2). | [44,49,53,63] | H1: Human resources of the corporate segment influence the number of requests for deposits of inventions/patents and trademark registrations. | + | |
Public R&D expenditures | Public R&D investments (X3). | [43,47,57,58,63,64] | H2: Public investments in R&D influence the number of requests for deposits of inventions/patents and trademark registrations. | + | |
Scientífic Production | Scientific Production in Exact Sciences (X4); Scientific Production of Engineering (X5); Medical Scientific Production (X6); Scientific production in Agrarian (X7); Scientific Production in Social Sciences (X8); Scientific Production in Humanities (X9). | [42,44,63,64,65,66] | H3: Scientific production influences the number of requests for deposits of inventions/patents and trademark registrations. | + | |
Researchers dedicated to full-time R&D ** | Companies Researchers (X10); State Researchers (X11); University Researchers (X12); Non-profit Researchers (X13). | [42,67,68,69] | H4: Researchers dedicated to full-time R&D influence the number of requests for deposits of inventions/patents and trademark registrations. | + | |
Micro | Intangible Asset Value Indicator (Y3) | ||||
Tax incentives for R&D | Sifide-2013 (X14); Sifide-2014 (X15); Sifide-2015 (X16); Sifide-2016 (X17); | [59,60,61,62] | H5: Tax incentives for corporate R&D influence the creation of value through intangible assets. | + |
Model | B | SE B | β | t-Value |
---|---|---|---|---|
(Y1) Number of patent applications | Intercept: 0.8197 | 0.481 | −1.154 | −1.703 |
Non-profit Researchers (X13): 8.545 × 10−4 | 1.356 × 10−4 | 0.769 * | 6.303 | |
Companies Researchers (X10): 5.919 × 10−5 | 2.942 × 10−5 | 0.246 ** | 2.012 |
Models | Adjusted R2 | Akaike (AICc) | ANOVA |
---|---|---|---|
Model with the inclusion of the variable researchers of Companies dedicated to R&D | 64.39% | 86.50 | p-value ≤ 0.05 |
Model without the inclusion of the variable researchers of Companies dedicated to R&D | 59.66% | 87.87 | - |
Model | B | SE B | β | t-Value |
---|---|---|---|---|
(Y2) Number of trademark registrations | Intercept: 0.327 | 0.117 | 0.327 | 2.794 |
Non-profit Researchers (X13): 1.066 × 10−4 | 3.297 × 10−5 | 0.502 * | 3.233 | |
Companies Researchers(X10): 2.024 × 10−5 | 7.154 × 10−6 | 0.439 * | 2.829 |
Model | B | SE B | t-Value |
---|---|---|---|
Intangible Asset Value Indicator (Y3) | Intercept: 9.425 × 102 | 1.874 × 102 | 5.030 |
Sifide-2013 (X14): 1.047 × 10−4 * | 4.333 × 10−4 | 0.242 | |
Sifide-2014 (X15): 8.037 × 10−4 * | 6.219 × 10−4 | 1.292 | |
Sifide-2015 (X16): −9.171 × 10−5 * | 2.206 × 10−4 | −0.416 | |
Sifide-2016 (X17): −3.914 × 10−4 * | 7.587 × 10−4 | −0.516 |
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Walter, C.E.; Ferreira Polónia, D.; Au-Yong-Oliveira, M.; Miranda Veloso, C.; Santos Leite, R.Â.; Aragão, I. Drivers of Innovation Capacity and Consequences for Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 140. https://doi.org/10.3390/joitmc7020140
Walter CE, Ferreira Polónia D, Au-Yong-Oliveira M, Miranda Veloso C, Santos Leite RÂ, Aragão I. Drivers of Innovation Capacity and Consequences for Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(2):140. https://doi.org/10.3390/joitmc7020140
Chicago/Turabian StyleWalter, Cicero Eduardo, Daniel Ferreira Polónia, Manuel Au-Yong-Oliveira, Cláudia Miranda Veloso, Rafael Ângelo Santos Leite, and Iracema Aragão. 2021. "Drivers of Innovation Capacity and Consequences for Open Innovation" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 2: 140. https://doi.org/10.3390/joitmc7020140