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Determinants of Innovation

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This hinders the effectiveness of innovation policies that are based on a generic understanding of the innovation process and are not sufficiently nuanced for the digital age. Treating the phenomenon of innovation as one-dimensional does not adequately capture the richness of the construct of organizational innovation.

Introduction

  • Background and motivation
  • Research design
  • Thesis organization
  • Summary and conclusions

An important caveat about CIS data (but not WIPO) is that they reflect perceptions of innovation. The main novelty of the thesis is the introduction of several levels of abstraction in the investigation of the multifaceted phenomenon of innovation.

Figure 1. Global Innovation Index, Germany.
Figure 1. Global Innovation Index, Germany.

Obstacles to innovation

  • A historical perspective
  • The business perspective
  • The research perspective
  • An emerging taxonomy of the obstacles to innovation

Concerns about the representativeness of top management opinion emerged in the early 1980s [50]. Most importantly, the current classification scheme fits very well and acts as a "noise reduction" filter in the processing of barriers to innovation presented in the CIS (Table 2).

Table 2. Distribution of the obstacles to innovation in CIS releases.
Table 2. Distribution of the obstacles to innovation in CIS releases.

Data

Eurostat data

Regarding the levels of importance, CIS firms were asked to rate their perceived degree of importance of each of the factors in Table 8 that inhibit their innovation activity (or lack thereof). To facilitate the analyzes in this thesis, a new way of agglomerating raw data is presented in the form of a set of systematic steps that guarantee the reproducibility of the results.

Table 8. List of CIS data parameters and components in CIS 2020.
Table 8. List of CIS data parameters and components in CIS 2020.

WIPO data

Number of patent applications of the most innovative firm in the group (as measured by its patent applications);. Number of patent applications in the industrial sector with the largest number of cluster patents filed;. As can be observed, among the top 10 clusters based on the PCT entries [37], Japan and the USA report the leading positions with each country reporting three top clusters.

While one of the strengths of CIS data is its reproducibility and broad methodological consistency, WIPO's cluster reports provide self-contained, independent datasets.

Table 10. Cluster ranking based on total 2011-2015 PCT filings * .
Table 10. Cluster ranking based on total 2011-2015 PCT filings * .

Methodology

Probit regression

The full list of the independent variables (regressors) is summarized in Table 12 with the associated comments and coded levels developed from the processed and calibrated CIS data. In fact, the coefficient of the interaction term should not be used at all to draw conclusions in categorical models such as Probit [82]. Purely for exploratory purposes, the use of the interaction term is shown in Appendix A (Table A4).

The effect of the interaction term 𝛽𝑖12 in (6), however, is felt through the adjustment it imposes on the values ​​of the coefficients 𝛽𝑖0, 𝛽𝑖1 and 𝛽𝑖2 compared to those in the non-interactive model in (5).

Marginal effects

Marginal effects reflect how the value of dependent variable changes with a unit change in one of the regressor variables. Thus, the predicted importance of an obstacle for a specific category of the companies (specified size class and operating sector) can be obtained, which better corresponds to the purpose of this thesis. On the other hand, keeping the regressor values ​​at manually selected representative values ​​deprives the results of accuracy and representativeness.

The analysis in this thesis therefore proceeds based on the Probit model in (6) and the report of the average adjusted forecasts, to estimate the probability 𝑃(𝑖) of the event.

The variables

Therefore, the countries with the richest and most comprehensive coverage of the above business categories and the greatest availability of data were selected to present different socio-economic, cultural and innovation levels. The statistics include the number of companies and the ratio of innovators across each of the variables. The results typically include the coefficients of predictive margins (and their statistical significance) in tabular form.

Predictive margins: Innovative companies rate every obstacle as very important across all size classes and sectors in Germany in CIS 2016.

Table 14. Control variables.
Table 14. Control variables.

Firm layer

Germany analysis and results

Figure 5, adapted from [28], shows the predictive margins for each of the eight barriers in three class sizes separately for innovative and non-innovative firms. The total curve shows how the perceptions of the entire sample stack up against the perceptions of innovators and non-innovators. While the first part of this analysis explores the relationship between firm size and innovation, this chapter continues by analyzing the importance of innovation barriers in firms in different sectors of operation.

Similarly, Figure 6 adapted from [28], shows the predictive margins (and their statistical significance) in both operating sectors from innovative and non-innovative companies separately with the total curve reflecting the opinions of the entire sample.

Table 15. Descriptive statistics of the Germany sample from CIS 2016.
Table 15. Descriptive statistics of the Germany sample from CIS 2016.

Beyond the benchmarking example

In contrast to the first variable, the differentiation of the perceptions of the companies within their sector does not show a significant impact on the resulting predicted importance of the innovation barriers. In summary, the results so far in this chapter show that the relative importance of barriers to innovation varies depending on firm-level characteristics, such as size and the sector in which they operate. Often, companies entering the innovation arena for the first time are unaware of the relative importance of the obstacles they face and tend to imitate practices of innovative companies without paying attention to their relative size.

A better understanding of the relative importance of barriers to innovation is also essential to promote policy interventions aimed at removing them.

Figure 7. Predictive margins: Innovative firms assessing lack of internal finance as highly  important across size classes in different countries
Figure 7. Predictive margins: Innovative firms assessing lack of internal finance as highly important across size classes in different countries

Operational layer

Operational environment across East-West axis

Only 38% of the enterprises surveyed in North Macedonia and 24% in Poland qualified as INNO under this criterion. The cumulative graph in Figure 9 (a) summarizes the predicted importance of the innovation barriers as perceived by the European "EAST" (Poland and North Macedonia) and "WEST" (Germany and Portugal), clearly demonstrating that although " WEST". Distribution in Figure 9 (b) shows that there are very few discernible differences regarding the ranking of the obstacles across the four countries.

Regarding firms in different size classes or sectors, Figure 10 (a) and (b) demonstrate somewhat deeper differences in the relative importance of innovation barriers.

Figure 9. Ranking of obstacles across countries.
Figure 9. Ranking of obstacles across countries.

Operational environment across the “Global North-South” axis

Analysis in this chapter reveals that one of the key determinants of the gender gap lies in access to the digital resources necessary for innovation and the skills to exploit these resources. Male employees contributed 64% of the responses (108 out of 169) and female employees contributed 36% (61 out of 108), very close to the gender ratio in the corresponding departments. For the other two sectors, there was an apparent parity in the gender distribution of the response rate.

As innovation activities are at the heart of the digital transformation, greater inclusion of women in the digital economy can have tangible economic and social value.

Table 16. Gender distribution of the surveyed sample.
Table 16. Gender distribution of the surveyed sample.

Process layer

Knowledge acquisition and management processes

COMPT and FAIRS form the second group of interest, while all other sources received 5% of the vote or less. The fundamental conclusion from this part of the analysis is that innovative firms in Germany obtain their knowledge for innovation internally or from companies within the enterprise group (37%) and externally from their clients or clients from the private sector (21%). Secondary sources are competitors or other companies from the same sector (9%) and conferences, trade fairs or exhibitions (8%).

This classification of knowledge sources is quite robust with respect to company size and sectors.

Table 19 of dependent variables.
Table 19 of dependent variables.

Protecting innovation output processes

For manufacturing firms in the INNO category, the probability of involvement in IPR-related activities increases significantly with firm size for all forms of IPR recorded. All intellectual property rights Patent Trademark Utility model Industrial design Trade secret Copyright Forms of intellectual property rights in innovative companies in different size classes. All intellectual property rights Patent Trademark Utility model Industrial design Trade secret Copyright Forms of intellectual property rights in non-innovative companies in different size classes.

Figure 16 and Figure 17 thus succinctly illustrate the differences in the preferred modalities of IPR-related activities for INNO and NON-INNO companies, regardless of their business size.

Figure 16. Forms of IPRs reported in innovative firms across size classes.
Figure 16. Forms of IPRs reported in innovative firms across size classes.

Diversity processes in innovation hotspots

The primary objective of this analysis is to measure the effect of gender diversity, as measured by the share of women investors (WI), on the innovation productivity of a given innovation hotspot, as measured by its total number of patent applications (PCTF), while controlling for the possible moderating effect of the total population of a hotspot's area (POP) [111]. Descriptive statistics of the variables, as well as the Shapiro-Wilk test are presented in Table C5 in Appendix C. The correlation matrix in Table 23 shows that there is a statistically significant correlation between the population in a hotspot area and the total patent productivity of the hotspot, a somewhat intuitive outcome.

In fact, lack of statistical significance does not necessarily imply lack of effect on a question.

Table 22. Top innovation hotspots in the USA * .  Rank  Innovation hotspot  Total
Table 22. Top innovation hotspots in the USA * . Rank Innovation hotspot Total

Policy layer

Policy interventions aiming innovation hotspots

A small extract of the data is shown in Table 10 and the full list of clusters is shown in Table C6 in Appendix C. Similarly, the greater the contribution of PRO to the creative activity of a group, the more small is the total productivity of the group. . The analysis of the main manufacturing groups in this study shows that a higher degree of hierarchy appears to be a distinct advantage in terms of innovation.

The analysis of the best industry clusters in this chapter shows that cluster diversity tends to be a hindrance and specialization an advantage in terms of innovation performance.

Table 24. Correlation matrix of the normalized variables.
Table 24. Correlation matrix of the normalized variables.

Policy interventions outputs and results

For example, financial, market and knowledge barriers were of great importance for 86%, 78% and 38% of the non-innovative companies in CIS 2014 respectively. To increase the granularity of these results, the company layer characteristics (firm) size and operating sector) were introduced into the analysis. Thus, Figure 19 shows the predicted importance of the barriers for small, medium and large enterprises in CIS 2014, CIS 2016 and CIS 2018. Similarly, Figure 20 shows the predicted importance of the barriers of PROD and SERV enterprises in CIS 2014, CIS2 016 and CIS 2018.

The determinants of innovation in the digital age include very broad interventions (such as initiatives to develop a highly skilled workforce or to increase gender diversity in R&D) – in stark contrast to the narrow, targeted or sector-specific policies of the old.

Table 27. Demographics of CIS 2014, CIS 2016, CIS 2018 and CIS 2020.
Table 27. Demographics of CIS 2014, CIS 2016, CIS 2018 and CIS 2020.

Conclusions and recommendations

Contributions

  • Firm layer
  • Operational layer
  • Process layer
  • Policy layer

For Kazakhstan, a country in the Global South, a gender-balanced scientific workforce has proven to be an important enabler for innovation. Policy recommendation: Initiatives to promote greater inclusion of women in the digital economy can have tangible economic and social benefits. A gender-balanced scientific workforce has also been shown to be an important factor for innovation in industrial clusters.

Policy recommendation: Initiatives to encourage greater inclusion of women inventors in the industrial clusters and innovation hotspots by providing funding for women-led initiatives and start-ups.

Limitations and future work

DE 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative enterprises (NON-INNO). IT 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative firms (NON-INNO). PL 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative enterprises (NON-INNO).

PT 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative companies (NON-INNO). EE 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative companies (NON-INNO). RO 2016 LFIN_IN LFIN_EXT H_COST L_SUBS U_DMND H_COMP L_EMPL L_PRTN Non-innovative companies (NON-INNO).

Table A1. Descriptive statistics of the sample DE.
Table A1. Descriptive statistics of the sample DE.

Сурет

Table 2. Distribution of the obstacles to innovation in CIS releases.
Table 6. Identified clusters of the obstacles to innovation in research literature.
Table 8. List of CIS data parameters and components in CIS 2020.
Table 9. Excerpt of the raw CIS data: Innovative firms in Germany in CIS 2016.
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