Abstract
Innovation is related to economic cycles. Often seen as a procyclical phenomenon, many innovation actors try and succeed in maintaining (and even increasing) their innovation efforts to gain competitive advantage during the crises. In this chapter, departing from the recent developments in regional studies, which understand resilience as an evolutionary capacity of socio-economic systems, we suggest the notion of ‘resilience of innovation’ as the capacity of an innovation process to maintain its function at different levels of operation. Drawing upon the results from a survey on knowledge provision and needs of maritime cluster innovative actors in the European Atlantic Area, our analysis focuses on the evolution of innovation and knowledge services. We provide parametric and non-parametric evidence of the differences in the provision and utilisation of these services and provide econometric evidence of the main factors that influence the resilience of innovation at the organizational level.
Keywords
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- 1.
The survey was part of the European project KIMERAA (available at www.kimeraa.eu) aimed at developing economic niches of excellence through the creation of strong linkages between firms and science organizations within the marine sciences and maritime activities.
- 2.
The descriptive statistics report “Knowledge needs and innovation in the maritime economy” with interim data collection is available in the project website.
- 3.
We used for this section the IBM SPSS Statistics 21.
- 4.
These two variables do not follow a normal distribution. The graphical intuition provided but the Q-Q plots and histograms is confirmed by the Kolmogorov-Smirnov test (1.869 and 3.759 compared to n > 40 and Sig 1% = 0.25205) (see histograms in Appendix).
- 5.
Looking for the homogeneity of variances, Levene test does not reject its null hypotheses of groups having homogeneous variances for the variable “utilisation”. In this case ANOVA is valid (results in Appendix). But for “provision”, the test rejects this H0 meaning that we need to use a non-parametric technique. We used Kruskal-Wallis that reinforced the findings (table test is also presented in Appendix).
- 6.
“SIZE_BIG” is a binary variable that assumes the value 1 if the organization has 250 or more workers. “UNIV_PROS” assumes 1 if organization is a university or other PRO. “MARKET_VARIATION” is a dummy that assumes value 1 if organizations believe that their market experienced an increased or at least an equal demand of innovation and knowledge-based services during the last 3 years.
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Acknowledgements
This chapter was stimulated by KIMERAA—Knowledge transfer to Improve Marine Economy in Regions from the Atlantic Area, project developed between 2011 and 2014, co-financed by the European cooperation program INTERREG Atlantic Area through the ERDF—European Regional Development Fund. The help of project partners in the data collection is gratefully acknowledged. Carla Nogueira benefits from the financial support from FCT—Portuguese Science and Technology Foundation (SFRH/BD/117398/2016). Hugo Pinto also acknowledges the financial support from FCT (SFRH/BPD/84038/2012).
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Appendix
Appendix
1.1 Histogram of Variety of Uses and Provisions
1.2 Anova
Sum of squares | df | Mean square | F | Sig. | ||
---|---|---|---|---|---|---|
Variety of knowledge exchange activities or services used in the last 3 years | Between groups | 423.221 | 5 | 84.644 | 12.165 | .000 |
Within groups | 667.955 | 96 | 6.958 | |||
Total | 1091.176 | 101 | ||||
Variety of knowledge exchange activities or services provided in the last 3 years | Between groups | 181.297 | 5 | 36.259 | 7.019 | .000 |
Within groups | 495.958 | 96 | 5.166 | |||
Total | 677.255 | 101 |
1.3 Kruskal-Wallis Test
Size of organization | N | Mean rank | |
---|---|---|---|
Variety of knowledge exchange activities or services used in the last 3 years | 0 | 33 | 29.26 |
Less than 10 employees | 28 | 48.00 | |
10–50 employees | 13 | 70.08 | |
50–250 employees | 9 | 84.61 | |
250–500 employees | 11 | 62.09 | |
> 500 employees | 8 | 73.50 | |
Total | 102 | ||
Variety of knowledge exchange activities or services provided in the last 3 years | 0 | 33 | 37.80 |
Less than 10 employees | 28 | 47.66 | |
10–50 employees | 13 | 57.15 | |
50–250 employees | 9 | 79.44 | |
250–500 employees | 11 | 59.23 | |
> 500 employees | 8 | 70.19 | |
Total | 102 |
Kruskal Wallis test | Variety of knowledge exchange activities or services used in the last 3 years | Variety of knowledge exchange activities or services provided in the last 3 years |
---|---|---|
Chi-Square | 43.218 | 27.451 |
Df | 5 | 5 |
Asymp. Sig. | 0.000 | 0.000 |
1.4 Mann–Whitney Test
Universities and PROs | N | Mean rank | Sum of ranks | |
---|---|---|---|---|
Variety of knowledge exchange activities or services used in the last 3 years | Other | 86 | 47.62 | 4095.50 |
University or PRO | 16 | 72.34 | 1157.50 | |
Total | 102 | |||
Variety of knowledge exchange activities or services provided in the last 3 years | Other | 86 | 46.28 | 3980.00 |
University or PRO | 16 | 79.56 | 1273.00 | |
Total | 102 |
Variety of knowledge exchange activities or services used in the last 3 years | Variety of knowledge exchange activities or services provided in the last 3 years | |
---|---|---|
Mann-Whitney U | 354.500 | 239.000 |
Wilcoxon W | 4095.500 | 3980.000 |
Z | −3.140 | −4.842 |
Asymp. Sig. (2-tailed) | 0.002 | 0.000 |
1.5 Tests for Independence
Association of between “Does your organization provide or administer any knowledge exchange services or schemes?: * Variety of knowledge exchange activities or services used in the last 3 years”.
Chi-square tests | |||
---|---|---|---|
Value | df | Asymp. Sig. (2-sided) | |
Pearson Chi-square | 103.831a | 33 | 0.000 |
Likelihood ratio | 112.200 | 33 | 0.000 |
Linear-by-linear association | 5.112 | 1 | 0.024 |
N of valid cases | 102 |
Symmetric measures | |||
---|---|---|---|
Value | Approx. Sig. | ||
Nominal by nominal | Contingency coefficient | 0.710 | 0.000 |
N of valid cases | 102 |
Association between “Does your organization provide or administer any knowledge exchange services or schemes? * Variety of knowledge exchange activities or services provided in the last 3 years”.
Chi-square tests | |||
---|---|---|---|
Value | df | Asymp. Sig. (2-sided) | |
Pearson Chi-square | 97.740a | 33 | 0.000 |
Likelihood ratio | 123.252 | 33 | 0.000 |
Linear-by-linear association | 1.394 | 1 | 0.238 |
N of valid cases | 102 |
Symmetric measures | |||
---|---|---|---|
Value | Approx. Sig. | ||
Nominal by nominal | Contingency coefficient | 0.700 | 0.000 |
N of valid cases | 102 |
1.6 Predictive Capacity of Probit Model
Model in E-Views: resilience c client_firm clients_export size_big univ_pros kmanag eval use provision market_variation.
1.6.1 Global Model
Mean dependent var | 0.401961 | S.D. dependent var | 0.492715 | |
S.E. of regression | 0.309832 | Akaike info criterion | 0.711688 | |
Sum squared resid | 8.831618 | Schwarz criterion | 0.969038 | |
Log likelihood | −26.29607 | Hannan-Quinn criter. | 0.815898 | |
Restr. log likelihood | −68.72747 | Avg. log likelihood | −0.257805 | |
LR statistic (9 df) | 84.86279 | McFadden R-squared | 0.617386 | |
Probability(LR stat) | 1.74E-14 | |||
Obs with Dep = 0 | 61 | Total obs | 102 | |
Obs with Dep = 1 | 41 |
Global model prediction evaluation (success cutoff C = 0.5) | ||||||
---|---|---|---|---|---|---|
Estimated equation | Constant probability | |||||
Dep = 0 | Dep = 1 | Total | Dep = 0 | Dep = 1 | Total | |
P(Dep = 1) < =C | 55 | 7 | 62 | 61 | 41 | 102 |
P(Dep = 1) > C | 6 | 34 | 40 | 0 | 0 | 0 |
Total | 61 | 41 | 102 | 61 | 41 | 102 |
Correct | 55 | 34 | 89 | 61 | 0 | 61 |
% Correct | 90.16 | 82.93 | 87.25 | 100.00 | 0.00 | 59.80 |
% Incorrect | 9.84 | 17.07 | 12.75 | 0.00 | 100.00 | 40.20 |
Total Gaina | −9.84 | 82.93 | 27.45 | |||
Percent Gainb | NA | 82.93 | 68.29 | |||
Estimated equation | Constant probability | |||||
Dep = 0 | Dep = 1 | Total | Dep = 0 | Dep = 1 | Total | |
E(# of Dep = 0) | 52.69 | 8.58 | 61.27 | 36.48 | 24.52 | 61.00 |
E(# of Dep = 1) | 8.31 | 32.42 | 40.73 | 24.52 | 16.48 | 41.00 |
Total | 61.00 | 41.00 | 102.00 | 61.00 | 41.00 | 102.00 |
Correct | 52.69 | 32.42 | 85.11 | 36.48 | 16.48 | 52.96 |
% Correct | 86.38 | 79.07 | 83.44 | 59.80 | 40.20 | 51.92 |
% Incorrect | 13.62 | 20.93 | 16.56 | 40.20 | 59.80 | 48.08 |
Total Gaina | 26.57 | 38.87 | 31.52 | |||
Percent Gainb | 66.11 | 65.00 | 65.56 |
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Pinto, H., Uyarra, E., Bleda, M., Nogueira, C., Almeida, H. (2018). Economic Crisis, Turbulence and the Resilience of Innovation: Insights from the Atlantic Maritime Cluster. In: Pinto, H., Noronha, T., Vaz, E. (eds) Resilience and Regional Dynamics. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-95135-5_4
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