Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis
Abstract
:1. Introduction
2. Literature Review
2.1. Approaches to Assessing the Level of Innovation
2.2. Pandemic and Innovation
2.3. The Impact of Innovation on Socioeconomic Indicators
3. Data and Methodology
- The Anglo-Saxon model: Ireland, United Kingdom, Canada, United States of America, Australia, and New Zealand;
- The Rhenish (German) model: Belgium, Germany, the Kingdom of the Netherlands, and Switzerland;
- The Scandinavian (Swedish) model: Denmark, Finland, Iceland, Norway, and Sweden;
- The Japanese model: Indonesia, Japan, and Malaysia;
- The Chinese model: China and Vietnam.
- The values of the Global Innovation Index (GII);
- The values of such GII pillars as institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs.
- The dynamics of the GII pillars during the pandemic period versus the pre-pandemic one;
- The dynamics of the GII pillars during the post-pandemic period versus the pandemic one;
- The differences in the GII pillar ranking for the countries from various socioeconomic models;
- The differences in GII pillar rankings for countries from the same socioeconomic models.
- GDP per capita;
- Unemployment rate;
- Inflation rate.
4. Results
4.1. Innovation Performance of the Countries from Various Socioeconomic Models
- Business sophistication and creative outputs: statistically significant differences were found in all paired cases, except one—between the Rhenish (German) and the Scandinavian (Swedish) models;
- Knowledge and technology outputs: differences between the models were not identified in one case—between the Anglo-Saxon and the Chinese models;
- Human capital and research: no differences were identified in two tests—between the Rhenish (German) and Scandinavian (Swedish) models and between the Japanese and Chinese models;
- Institutions: statistically significant differences were not detected in three paired cases—between the Anglo-Saxon and Rhenish (German) models, between the Anglo-Saxon and Scandinavian models, and between the Rhenish (German) and Scandinavian (Swedish) models.
- Infrastructure: no differences were found between the Anglo-Saxon and Rhenish (German) models, the Anglo-Saxon and Scandinavian (Swedish) models, the Rhenish (German) and Scandinavian (Swedish) models, or the Japanese and Chinese models;
- Market sophistication: the t-test showed differences only between the Anglo-Saxon and Japanese models and between the Anglo-Saxon and Chinese models.
4.2. The Impact of Innovativeness on the Economic Performance Indicators of the Countries
4.2.1. The Impact of 2019 Innovation Indicators on the Economic Performance Indicators for 2020
4.2.2. The Impact of 2020 Innovation Indicators on the Economic Performance Indicators for 2020
4.2.3. The Impact of 2019 Innovation Indicators on the Economic Performance Indicators for 2021
4.2.4. The Impact of 2021 Innovation Indicators on the Economic Performance Indicators for 2021
- The GII human capital and research and infrastructure innovation pillars had the greatest effects on the GDP per capita level and the inflation rate, while the institutions, business sophistication, and creative outputs pillars only influenced the GDP per capita level. The knowledge and technology outputs pillar sporadically influenced the GDP per capita level.
- The 2019 GII pillars had the most lasting effects on the economic performance indicators for 2019, 2020, and 2021 (e.g., the GDP per capita level in 2021). The innovation potential inherent to the pre-pandemic year 2019 influenced the economic development of the countries during the crisis years, but this potential had been exhausted by 2022.
- There was no statistically significant impact of the innovation indicators for 2019–2022 on the economic performance in 2022.
- There was no statistically significant impact of innovation indicators on the unemployment rate regardless of the analysis horizon.
- There was no revealed effect of the GII market sophistication pillar on the economic performance of the countries.
5. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Global Innovation Index Pillar Indicators (2019–2022)
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 88.8 | 88.7 | 88.3 | 77.2 |
Belgium | 82.0 | 81.2 | 80.8 | 71.5 |
Canada | 92.3 | 90.2 | 90.1 | 80.4 |
China | 64.1 | 64.6 | 64.4 | 64.8 |
Denmark | 91.7 | 88.3 | 88.8 | 82.8 |
Finland | 93.6 | 93.5 | 93.3 | 82.5 |
Germany | 86.4 | 84.6 | 84.3 | 76.5 |
Iceland | 86.8 | 86.6 | 86.8 | 80.4 |
Indonesia | 53.2 | 51.0 | 51.2 | 55.1 |
Ireland | 85.5 | 85.3 | 84.3 | 79.2 |
Japan | 89.9 | 89.3 | 88.8 | 75.8 |
Malaysia | 71.6 | 72.5 | 72.3 | 68.8 |
Netherlands (Kingdom of the) | 90.9 | 89.7 | 88.9 | 86.9 |
New Zealand | 92.1 | 90.9 | 90.7 | 83.3 |
Norway | 93.9 | 92.5 | 92.6 | 87.1 |
Sweden | 90.1 | 88.7 | 88.8 | 76.5 |
Switzerland | 89.1 | 88.0 | 87.3 | 89.2 |
United Kingdom | 87.1 | 86.1 | 86.6 | 74.5 |
United States of America | 89.7 | 88.9 | 87.6 | 80.9 |
Vietnam | 58.6 | 58.5 | 58.8 | 60.6 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 57.7 | 59.0 | 57.4 | 61.7 |
Belgium | 55.0 | 57.8 | 59.7 | 56.1 |
Canada | 50.9 | 51.8 | 52.4 | 57.7 |
China | 47.6 | 49.4 | 50.6 | 53.1 |
Denmark | 63.1 | 62.9 | 62.3 | 59.4 |
Finland | 63.4 | 61.5 | 62.4 | 60.6 |
Germany | 63.2 | 61.1 | 62.7 | 64.1 |
Iceland | 45.4 | 46.1 | 49.7 | 46.4 |
Indonesia | 21.3 | 21.0 | 22.4 | 22.4 |
Ireland | 48.4 | 48.5 | 48.5 | 48.9 |
Japan | 49.1 | 47.3 | 50.8 | 52.7 |
Malaysia | 44.2 | 46.0 | 40.6 | 41.0 |
Netherlands (Kingdom of the) | 52.4 | 55.3 | 55.9 | 57.4 |
New Zealand | 52.6 | 54.4 | 54.2 | 54.9 |
Norway | 53.9 | 55.1 | 56.8 | 53.6 |
Sweden | 62.1 | 62.4 | 64.1 | 62.6 |
Switzerland | 61.9 | 60.7 | 60.7 | 62.4 |
United Kingdom | 59.3 | 58 | 58.2 | 61.5 |
United States of America | 55.7 | 56.3 | 58.1 | 59.9 |
Vietnam | 31.1 | 26 | 28.1 | 27.2 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 60.9 | 55.8 | 55.7 | 58.8 |
Belgium | 57.2 | 52.2 | 52 | 53.7 |
Canada | 58.5 | 53.3 | 53.7 | 57 |
China | 58.7 | 52.1 | 54.6 | 57.5 |
Denmark | 65.8 | 61.5 | 60.8 | 64.3 |
Finland | 62.1 | 59.9 | 59.5 | 65.9 |
Germany | 62 | 58 | 55.6 | 57.7 |
Iceland | 59.2 | 52.8 | 54.5 | 57.8 |
Indonesia | 44.2 | 37.7 | 41.4 | 43.4 |
Ireland | 66.3 | 59.2 | 62.1 | 60.1 |
Japan | 64 | 60 | 59.8 | 61.3 |
Malaysia | 51.8 | 46.4 | 46.7 | 48.6 |
Netherlands (Kingdom of the) | 61.8 | 57.4 | 57.7 | 60.1 |
New Zealand | 60.9 | 57.7 | 55.5 | 57.9 |
Norway | 69.9 | 64.6 | 64.8 | 66.5 |
Sweden | 69.1 | 64.6 | 62.6 | 67 |
Switzerland | 68.2 | 62 | 62.7 | 65.7 |
United Kingdom | 64.4 | 60.3 | 59.7 | 62.9 |
United States of America | 59.2 | 54.7 | 55.3 | 58.7 |
Vietnam | 42 | 38.4 | 38.2 | 42.5 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 68.3 | 67.1 | 66.4 | 50.1 |
Belgium | 55.3 | 54.5 | 54.1 | 38.2 |
Canada | 80.4 | 78.5 | 84.7 | 65 |
China | 58.6 | 58.5 | 61.5 | 56 |
Denmark | 66.9 | 66.3 | 68 | 53.1 |
Finland | 57.3 | 53.1 | 58.7 | 51.7 |
Germany | 58.6 | 56.1 | 57.8 | 53.7 |
Iceland | 56 | 49.8 | 56.8 | 40 |
Indonesia | 48.8 | 48.1 | 48.5 | 41.7 |
Ireland | 54.6 | 52.5 | 49.7 | 35.8 |
Japan | 65.8 | 64.3 | 62.1 | 59 |
Malaysia | 57.8 | 58.3 | 55.3 | 45.3 |
Netherlands (Kingdom of the) | 58.2 | 56.5 | 55.2 | 50.7 |
New Zealand | 68.5 | 63.9 | 63 | 45.7 |
Norway | 58.6 | 56.1 | 57.6 | 44.6 |
Sweden | 62.1 | 62.3 | 64.6 | 55.6 |
Switzerland | 68.4 | 72.3 | 71.5 | 59.8 |
United Kingdom | 76 | 74.4 | 78.1 | 67.6 |
United States of America | 87 | 81.4 | 81.5 | 80.8 |
Vietnam | 57 | 53 | 57.2 | 38.4 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 46.1 | 43.6 | 43 | 48.6 |
Belgium | 54.1 | 52.5 | 51.7 | 56.7 |
Canada | 49.9 | 50.5 | 50.1 | 52.3 |
China | 55.4 | 52.9 | 54.3 | 55.9 |
Denmark | 59.1 | 54.8 | 55.2 | 54.3 |
Finland | 63.9 | 59.9 | 61 | 61.6 |
Germany | 56.1 | 53.7 | 54.5 | 52.7 |
Iceland | 48 | 51.1 | 50.4 | 54.8 |
Indonesia | 25.7 | 17.8 | 17.5 | 22.1 |
Ireland | 55.8 | 53.1 | 51.5 | 55.1 |
Japan | 56.5 | 57.1 | 57.3 | 58.1 |
Malaysia | 39.3 | 38 | 34.1 | 36.3 |
Netherlands (Kingdom of the) | 63.7 | 63.4 | 61 | 56.8 |
New Zealand | 41.4 | 37.9 | 37.7 | 43.8 |
Norway | 50.2 | 45.1 | 45.7 | 52 |
Sweden | 68.8 | 68 | 68.1 | 69.8 |
Switzerland | 67.5 | 64.1 | 62.6 | 60.6 |
United Kingdom | 54.3 | 51 | 49.7 | 51.7 |
United States of America | 62.7 | 62.8 | 63 | 64.5 |
Vietnam | 30 | 34.5 | 30.8 | 31.6 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 31.6 | 30.4 | 29.1 | 32.2 |
Belgium | 40.8 | 42.3 | 42.3 | 44.4 |
Canada | 41.3 | 39.1 | 38.3 | 39.3 |
China | 57.2 | 55.1 | 58.5 | 56.8 |
Denmark | 46.4 | 48.3 | 47.6 | 51.9 |
Finland | 55.1 | 55.1 | 56.5 | 59.6 |
Germany | 52.7 | 51.7 | 53.3 | 54.8 |
Iceland | 37.6 | 33 | 37 | 39.7 |
Indonesia | 17.6 | 17.9 | 18.3 | 18.9 |
Ireland | 56.9 | 55.1 | 47.6 | 47 |
Japan | 50.8 | 46.4 | 48.3 | 52.6 |
Malaysia | 32.1 | 31.3 | 33.4 | 31.5 |
Netherlands (Kingdom of the) | 61.8 | 54.5 | 54.8 | 57.9 |
New Zealand | 29.8 | 31.2 | 29.7 | 36 |
Norway | 33.7 | 33.1 | 35.4 | 39.3 |
Sweden | 61.8 | 59.8 | 60.3 | 62.9 |
Switzerland | 70.3 | 65.5 | 63.9 | 67.1 |
United Kingdom | 56.6 | 54.4 | 52.3 | 55.7 |
United States of America | 59.7 | 56.8 | 59.2 | 60.8 |
Vietnam | 35.6 | 31.7 | 29.4 | 26 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 41.1 | 37.3 | 39.6 | 37.8 |
Belgium | 38.5 | 35 | 35.1 | 32.6 |
Canada | 41.4 | 40.2 | 41.9 | 38.7 |
China | 48.3 | 47 | 46.5 | 49.3 |
Denmark | 48.6 | 48.3 | 47.7 | 46.3 |
Finland | 48.1 | 41.8 | 42.9 | 39 |
Germany | 49.6 | 49.1 | 50 | 52.3 |
Iceland | 50.4 | 49.3 | 50.7 | 46.4 |
Indonesia | 24 | 17.8 | 17.5 | 18.6 |
Ireland | 43.3 | 37.6 | 36.7 | 35.5 |
Japan | 37.9 | 37.2 | 42.1 | 38.9 |
Malaysia | 32.8 | 33.9 | 34.5 | 27.4 |
Netherlands (Kingdom of the) | 53.2 | 51.7 | 52.2 | 49.4 |
New Zealand | 42.2 | 34.9 | 39.8 | 38.4 |
Norway | 43.2 | 38.7 | 39.3 | 34.6 |
Sweden | 51.9 | 51.7 | 52.9 | 50.7 |
Switzerland | 56.6 | 60 | 60.2 | 56.3 |
United Kingdom | 52.2 | 52.7 | 54 | 55.9 |
United States of America | 45.5 | 47.7 | 47.8 | 48.4 |
Vietnam | 32.3 | 32.7 | 33.4 | 30.8 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 54,941.43 | 51,720.37 | 60,443.11 | 60,798.00 |
Belgium | 46,638.68 | 45,517.79 | 51,247.01 | 53,156.00 |
Canada | 46,328.67 | 43,258.26 | 51,987.94 | 44,910.44 |
China | 10,143.84 | 10,408.67 | 12,556.33 | 11,560.00 |
Denmark | 59,592.98 | 60,915.42 | 68,007.76 | 60,113.00 |
Finland | 48,629.86 | 49,170.75 | 53,654.75 | 47,088.00 |
Germany | 46,793.69 | 46,772.83 | 51,203.55 | 43,032.00 |
Iceland | 68,853.72 | 59,200.18 | 68,727.64 | 55,887.00 |
Indonesia | 12,484 | 12,235 | 13,159 | 14,687 |
Ireland | 80,927.07 | 85,420.19 | 100,172.08 | 988,562.00 |
Japan | 40,458.00 | 39,918.17 | 39,312.66 | 36,032.00 |
Malaysia | 11,132.02 | 10,160.78 | 11,109.26 | 11,372.00 |
Netherlands (Kingdom of the) | 52,476.27 | 52,162.57 | 57,767.88 | 49,980.00 |
New Zealand | 42,865.23 | 41,596.51 | 48,781.03 | 42,272.00 |
Norway | 75,719.75 | 67,328.68 | 89,154.28 | 79,639.00 |
Sweden | 51,939.43 | 52,837.90 | 61,028.74 | 55,482.00 |
Switzerland | 84,121.93 | 85,656.32 | 91,999.60 | 88,464.00 |
United Kingdom | 42,747.08 | 40,318.56 | 46,510.28 | 47,232.00 |
United States of America | 65,120.39 | 63,530.63 | 70,248.63 | 62,867.00 |
Vietnam | 3491.09 | 3586.35 | 3756.49 | 3655.00 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 1.6 | 0.9 | 2.8 | 6.6 |
Belgium | 1.2 | 0.4 | 3.2 | 10.3 |
Canada | 1.9 | 0.7 | 3.4 | 6.8 |
China | 2.9 | 2.5 | 0.9 | 1.9 |
Denmark | 0.7 | 0.3 | 1.9 | 8.5 |
Finland | 1.1 | 0.4 | 2.1 | 7.2 |
Germany | 1.4 | 0.4 | 3.2 | 8.7 |
Iceland | 3 | 2.8 | 4.5 | 8.3 |
Indonesia | 2.8 | 2 | 1.6 | 4.2 |
Ireland | 0.9 | −0.5 | 2.4 | 8.1 |
Japan | 0.5 | 0 | −0.2 | 2.5 |
Malaysia | 0.7 | −1.1 | 2.5 | 3.4 |
Netherlands (Kingdom of the) | 2.7 | 1.1 | 2.8 | 11.6 |
New Zealand | 1.6 | 1.7 | 3.9 | 7.2 |
Norway | 2.2 | 1.3 | 3.5 | 5.8 |
Sweden | 1.7 | 0.7 | 2.7 | 8.1 |
Switzerland | 0.4 | −0.7 | 0.6 | 2.8 |
United Kingdom | 1.8 | 0.9 | 2.6 | 9.1 |
United States of America | 1.8 | 1.3 | 4.7 | 8 |
Vietnam | 2.8 | 3.2 | 1.8 | 3.2 |
Country | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|
Australia | 5.2 | 6.5 | 5.1 | 3.7 |
Belgium | 5.4 | 5.6 | 6.3 | 5.5 |
Canada | 5.7 | 9.7 | 7.5 | 5.3 |
China | 3.6 | 4.2 | 4 | 4.2 |
Denmark | 5 | 5.6 | 5.1 | 4.5 |
Finland | 6.7 | 7.8 | 7.6 | 6.8 |
Germany | 3 | 3.6 | 3.6 | 3.1 |
Iceland | 3.9 | 6.4 | 6 | 3.8 |
Indonesia | 5.2 | 7.1 | 6.5 | 5.9 |
Ireland | 5 | 5.9 | 6.3 | 4.5 |
Japan | 2.4 | 2.8 | 2.8 | 2.6 |
Malaysia | 3.3 | 4.5 | 4.7 | 3.8 |
Netherlands (Kingdom of the) | 4.4 | 4.9 | 4.2 | 3.5 |
New Zealand | 4.1 | 4.6 | 3.8 | 3.3 |
Norway | 3.7 | 4.6 | 4.4 | 3.3 |
Sweden | 7 | 8.5 | 8.8 | 7.5 |
Switzerland | 2.3 | 3.2 | 3 | 2.2 |
United Kingdom | 3.8 | 4.6 | 4.5 | 3.7 |
United States of America | 3.7 | 8.1 | 5.4 | 3.5 |
Vietnam | 2.2 | 2.5 | 3.2 | 2.3 |
Appendix B. Paired-Samples t-Test of the GII Pillar Values for Various Socioeconomic Models (2019–2022)
Paired Sample Name * | Average Group 1 | Average Group 2 | t-Value | p | S Group 1 | S Group 2 | F Crit |
A-S, Ins. (av) vs. Rhen, Ins. (av) | 86.19500 | 84.83500 | 0.5075 | 0.629893 | 4.662628 | 2.642303 | 3.11384 |
A-S, Ins. (av) vs. Sc, Ins.(av) | 86.19500 | 88.26500 | −0.6520 | 0.53530 | 4.662628 | 4.309567 | 1.17056 |
A-S, Ins. (av) vs. Jap, Ins. (av) | 86.19500 | 69.96000 | 6.2527 | 0.000776 | 4.662628 | 2.286278 | 4.15914 |
A-S, Ins. (av) vs. Chin, Ins.(av) | 86.19500 | 61.80000 | 10.3757 | 0.000047 | 4.662628 | 0.609645 | 58.49354 |
Rhen, Ins. (av) vs. Sc, Ins.(av) | 84.83500 | 88.26500 | −1.3570 | 0.223595 | 2.642303 | 4.309567 | 2.66012 |
Rhen, Ins. (av) vs. Jap, Ins. (av) | 84.83500 | 69.96000 | 8.5143 | 0.000144 | 2.642303 | 2.286278 | 1.33569 |
Rhen, Ins. (av) vs. Chin, Ins.(av) | 84.83500 | 61.80000 | 16.9892 | 0.000003 | 2.642303 | 0.609645 | 18.78502 |
Sc, Ins.(av) vs. Jap, Ins. (av) | 88.26500 | 69.96000 | 7.5044 | 0.000290 | 4.309567 | 2.286278 | 3.55311 |
Sc, Ins.(av) vs. Chin, Ins.(av) | 88.26500 | 61.80000 | 12.1609 | 0.000019 | 4.309567 | 0.609645 | 49.97049 |
Jap, Ins. (av) vs. Chin, Ins.(av) | 69.96000 | 61.80000 | 6.8972 | 0.000459 | 2.286278 | 0.609645 | 14.06386 |
A-S, HC (av) vs. Rhen, HC (av) | 55.25000 | 59.15250 | −4.5281 | 0.003983 | 1.484790 | 0.875457 | 2.87647 |
A-S, HC (av) vs. Sc, HC (av) | 55.25000 | 57.69000 | −2.6891 | 0.036096 | 1.484790 | 1.043392 | 2.02505 |
A-S, HC (av) vs. Jap, HC (av) | 55.25000 | 38.23250 | 22.3733 | 0.000001 | 1.484790 | 0.330996 | 20.12261 |
A-S, HC (av) vs. Chin, HC (av) | 55.25000 | 39.13750 | 17.8335 | 0.000002 | 1.484790 | 1.029866 | 2.07859 |
Rhen, HC (av) vs. Sc, HC (av) | 59.15250 | 57.69000 | 2.1475 | 0.075361 | 0.875457 | 1.043392 | 1.42045 |
Rhen, HC (av) vs. Jap, HC (av) | 59.15250 | 38.23250 | 44.7037 | 0.000000 | 0.875457 | 0.330996 | 6.99559 |
Rhen, HC (av) vs. Chin, HC (av) | 59.15250 | 39.13750 | 29.6149 | 0.000000 | 0.875457 | 1.029866 | 1.38386 |
Sc, HC (av) vs. Jap, HC (av) | 57.69000 | 38.23250 | 35.5507 | 0.000000 | 1.043392 | 0.330996 | 9.93687 |
Sc, HC (av) vs. Chin, HC (av) | 57.69000 | 39.13750 | 25.3096 | 0.000000 | 1.043392 | 1.029866 | 1.02644 |
Jap, HC (av) vs. Chin, HC (av) | 38.23250 | 39.13750 | −1.6732 | 0.145312 | 0.330996 | 1.029866 | 9.68092 |
A-S, Infr. (av) vs. Rhen, Infr. (av) | 58.98750 | 59.00000 | −0.00793 | 0.993933 | 2.025066 | 2.417988 | 1.425706 |
A-S, Infr. (av) vs. Sc, Infr.(av) | 58.98750 | 62.66000 | −2.30756 | 0.060470 | 2.025066 | 2.455742 | 1.470574 |
A-S, Infr. (av) vs. Jap, Infr. (av) | 58.98750 | 50.44000 | 5.57596 | 0.001412 | 2.025066 | 2.301840 | 1.292028 |
A-S, Infr. (av) vs. Chin, Infr.(av) | 58.98750 | 48.00000 | 6.73395 | 0.000522 | 2.025066 | 2.558971 | 1.596807 |
Rhen, Infr. (av) vs. Sc, Infr.(av) | 59.00000 | 62.66000 | −2.12399 | 0.077854 | 2.417988 | 2.455742 | 1.031471 |
Rhen, Infr. (av) vs. Jap, Infr. (av) | 59.00000 | 50.44000 | 5.12815 | 0.002161 | 2.417988 | 2.301840 | 1.103464 |
Rhen, Infr. (av) vs. Chin, Infr.(av) | 59.00000 | 48.00000 | 6.24884 | 0.000779 | 2.417988 | 2.558971 | 1.120011 |
Sc, Infr.(av) vs. Jap, Infr. (av) | 62.66000 | 50.44000 | 7.26111 | 0.000347 | 2.455742 | 2.301840 | 1.138191 |
Sc, Infr.(av) vs. Chin, Infr.(av) | 62.66000 | 48.00000 | 8.26687 | 0.000170 | 2.455742 | 2.558971 | 1.085839 |
Jap, Infr. (av) vs. Chin, Infr.(av) | 50.44000 | 48.00000 | 1.41781 | 0.206027 | 2.301840 | 2.558971 | 1.235892 |
A-S, MS. (av) vs. Rhen, MS. (av) | 67.49250 | 57.55750 | 2.41844 | 0.051973 | 6.778655 | 4.642509 | 2.131972 |
A-S, MS. (av) vs. Sc, MS.(av) | 67.49250 | 56.96000 | 2.40911 | 0.052637 | 6.778655 | 5.523163 | 1.506299 |
A-S, MS. (av) vs. Jap, MS. (av) | 67.49250 | 54.58500 | 3.26946 | 0.017046 | 6.778655 | 4.048905 | 2.802927 |
A-S, MS. (av) vs. Chin, MS.(av) | 67.49250 | 55.02500 | 2.87279 | 0.028324 | 6.778655 | 5.421024 | 1.563595 |
Rhen, MS. (av) vs. Sc, MS.(av) | 57.55750 | 56.96000 | 0.16562 | 0.873893 | 4.642509 | 5.523163 | 1.415371 |
Rhen, MS. (av) vs. Jap, MS. (av) | 57.55750 | 54.58500 | 0.96509 | 0.371772 | 4.642509 | 4.048905 | 1.314711 |
Rhen, MS. (av) vs. Chin, MS.(av) | 57.55750 | 55.02500 | 0.70966 | 0.504548 | 4.642509 | 5.421024 | 1.363506 |
Sc, MS.(av) vs. Jap, MS. (av) | 56.96000 | 54.58500 | 0.69360 | 0.513872 | 5.523163 | 4.048905 | 1.860804 |
Sc, MS.(av) vs. Chin, MS.(av) | 56.96000 | 55.02500 | 0.50006 | 0.634839 | 5.523163 | 5.421024 | 1.038038 |
Jap, MS. (av) vs. Chin, MS.(av) | 54.58500 | 55.02500 | −0.13006 | 0.900771 | 4.048905 | 5.421024 | 1.792617 |
A-S, BS. (av) vs. Rhen, BS. (av) | 50.84000 | 58.23250 | −6.5253 | 0.000618 | 1.624582 | 1.579417 | 1.058010 |
A-S, BS. (av) vs. Sc, BS.(av) | 50.84000 | 57.09000 | −5.8991 | 0.001054 | 1.624582 | 1.360441 | 1.426014 |
A-S, BS. (av) vs. Jap, BS. (av) | 50.84000 | 38.31500 | 10.3756 | 0.000047 | 1.624582 | 1.785954 | 1.208530 |
A-S, BS. (av) vs. Chin, BS.(av) | 50.84000 | 43.17500 | 8.7826 | 0.000121 | 1.624582 | 0.638357 | 6.476728 |
Rhen, BS. (av) vs. Sc, BS.(av) | 58.23250 | 57.09000 | 1.0962 | 0.315045 | 1.579417 | 1.360441 | 1.347827 |
Rhen, BS. (av) vs. Jap, BS. (av) | 58.23250 | 38.31500 | 16.7082 | 0.000003 | 1.579417 | 1.785954 | 1.278636 |
Rhen, BS. (av) vs. Chin, BS.(av) | 58.23250 | 43.17500 | 17.6779 | 0.000002 | 1.579417 | 0.638357 | 6.121616 |
Sc, BS.(av) vs. Jap, BS. (av) | 57.09000 | 38.31500 | 16.7254 | 0.000003 | 1.360441 | 1.785954 | 1.723381 |
Sc, BS.(av) vs. Chin, BS.(av) | 57.09000 | 43.17500 | 18.5192 | 0.000002 | 1.360441 | 0.638357 | 4.541840 |
Jap, BS. (av) vs. Chin, BS.(av) | 38.31500 | 43.17500 | −5.1249 | 0.002168 | 1.785954 | 0.638357 | 7.827321 |
A-S, Know. (av) vs. Rhen, Know. (av) | 44.58750 | 54.88250 | −9.8449 | 0.000063 | 1.396266 | 1.557099 | 1.243645 |
A-S, Know. (av) vs. Sc, Know.(av) | 44.58750 | 47.70500 | −2.4881 | 0.047282 | 1.396266 | 2.080857 | 2.220999 |
A-S, Know. (av) vs. Jap, Know. (av) | 44.58750 | 33.25750 | 13.0914 | 0.000012 | 1.396266 | 1.022982 | 1.862947 |
A-S, Know. (av) vs. Chin, Know.(av) | 44.58750 | 43.78750 | 0.6434 | 0.543738 | 1.396266 | 2.057658 | 2.171753 |
Rhen, Know. (av) vs. Sc, Know.(av) | 54.88250 | 47.70500 | 5.5234 | 0.001482 | 1.557099 | 2.080857 | 1.785879 |
Rhen, Know. (av) vs. Jap, Know. (av) | 54.88250 | 33.25750 | 23.2143 | 0.000000 | 1.557099 | 1.022982 | 2.316844 |
Rhen, Know. (av) vs. Chin, Know. (av) | 54.88250 | 43.78750 | 8.5994 | 0.000136 | 1.557099 | 2.057658 | 1.746280 |
Sc, Know. (av) vs. Jap, Know. (av) | 47.70500 | 33.25750 | 12.4616 | 0.000016 | 2.080857 | 1.022982 | 4.137603 |
Sc, Know. (av) vs. Chin, Know. (av) | 47.70500 | 43.78750 | 2.6773 | 0.036666 | 2.080857 | 2.057658 | 1.022676 |
Jap, KNOW. (av) vs. Chin, Know. (av) | 33.25750 | 43.78750 | −9.1648 | 0.000095 | 1.022982 | 2.057658 | 4.045860 |
A-S, CO. (av) vs. Rhen, CO. (av) | 42.94000 | 48.86500 | −8.5547 | 0.000140 | 1.099909 | 0.842002 | 1.7064 |
A-S, CO. (av) vs. Sc, CO.(av) | 42.94000 | 46.12500 | −2.6941 | 0.035859 | 1.099909 | 2.093060 | 3.6212 |
A-S, CO. (av) vs. Jap, CO. (av) | 42.94000 | 30.21750 | 13.4052 | 0.000011 | 1.099909 | 1.546984 | 1.9781 |
A-S, CO. (av) vs. Chin, CO.(av) | 42.94000 | 40.03750 | 5.1982 | 0.002018 | 1.099909 | 0.193111 | 32.4416 |
Rhen, CO. (av) vs. Sc, CO.(av) | 48.86500 | 46.12500 | 2.4290 | 0.051232 | 0.842002 | 2.093060 | 6.1793 |
Rhen, CO. (av) vs. Jap, CO. (av) | 48.86500 | 30.21750 | 21.1749 | 0.000001 | 0.842002 | 1.546984 | 3.3756 |
Rhen, CO. (av) vs. Chin, CO.(av) | 48.86500 | 40.03750 | 20.4373 | 0.000001 | 0.842002 | 0.193111 | 19.0114 |
Sc, CO.(av) vs. Jap, CO. (av) | 46.12500 | 30.21750 | 12.2238 | 0.000018 | 2.093060 | 1.546984 | 1.8306 |
Sc, CO.(av) vs. Chin, CO.(av) | 46.12500 | 40.03750 | 5.7922 | 0.001159 | 2.093060 | 0.193111 | 117.4766 |
Jap, CO. (av) vs. Chin, CO.(av) | 30.21750 | 40.03750 | −12.5979 | 0.000015 | 1.546984 | 0.193111 | 64.1741 |
Appendix C. The Impact of Innovativeness on Socioeconomic Indicators of the Countries
Regression Model | Correlation |
---|---|
GDPPC_2020 = −76.396 + 1.4604 × Institutions_2019 | 0.766914 * p = 0.00008 R2 = 0.588 t = 5.0701 F = 25.70591 |
GDPPC_2020 = −22.2496 + 1.3163 × Human capital and research_2019 | 0.619737 * p = 0. 003563 R2 = 0. 384074 t = 3.35026 F = 11.22427 |
GDPPC_2020 = −102.7365 + 2.4676 × Infrastructure_2019 | 0.788879 * p = 0.000036 R2 = 0.62233 t = 5.44616 F = 29.66065 |
GDPPC_2020 = 6.7962 + 0.62157 × Market sophistication_2019 | 0.257437 |
GDPPC_2020 = −22.4939 + 1.30815 × Business sophistication_2019 | 0.659651 * p = 0. 001554 R2 =0.43514 t = 3.72375 F = 13.866 |
GDPPC_2020 = 8.70526 + 0.804.4 × Knowledge and technology outputs_2019 | 0.484597 * p = 0.030357 R2= 0.2348338 t = 2.350383 F = 5.524302 |
GDPPC_2020 = −30.32549 + 1.7345 × Creative outputs_2019 | 0.614040 * p = 0. 00398 R2 = 0. 37705 t = 3.30069 F = 10.89455 |
Regression Model | Correlation |
---|---|
Unemployment_2020 = 2.2359 + 0.03934 × Institutions_2019 | 0.240321 |
Unemployment_2020 = 4.5249 + 0.01946 × Human capital and research_2019 | 0.106571 |
Unemployment_2020 = 4.5364 + 0.01656 × Infrastructure_2019 | 0.061583 |
Unemployment_2020 = 1.8666 + 0.05804 × Market sophistication_2019 | 0.279637 |
Unemployment_2020 = 4.1312 + 0.02678 × Business sophistication_2019 | 0.157094 |
Unemployment_2020 = 5.8429 − 0.0066 × Knowledge and technology outputs_2019 | −0.046442 |
Unemployment_2020 = 5.4098 + 0.00284 × Creative outputs_2019 | 0.011704 |
Regression Model | Correlation |
---|---|
Inflation_2020 = 4.1980 − 0.0391 × Institutions_2019 | −0.417990 |
Inflation_2020 = 3.5484 − 0.0507 × Human capital and research_2019 | −0.485629 * p = 0.029954 R2 = 0. 23583 t = 3.1125 F = 5.555142 |
Inflation_2020 = 5.1957 − 0.0710 × Infrastructure_2019 | −0.461407 * p = 0.040579 R2 = 0.212897 t = 2.65952 F = 4.868662 |
Inflation_2020 = 1.8098 − 0.0142 × Market sophistication_2019 | −0.119219 |
Inflation_2020 = 3.1708 − 0.0430 × Business sophistication_2019 | −0.441215 |
Inflation_2020 = 2.2082 − 0.0278 × Knowledge and technology outputs_2019 | −0.340909 |
Inflation_2020 = 1.8435 − 0.0211 × Creative outputs_2019 | −0.151727 |
Regression Model | Correlation |
---|---|
GDPPC_2021 = −89.3626 + 1.692 × Institutions_2019 | 0.761359 * p = 0.000096 R2 = 0.58 t = 4.98229 F = 24.82322 |
GDPPC_2021 = −23.952 + 1.4734 × Human capital and research_2019 | 0.594438 * p = 0.005708 R2 = 0.353356 t = 3.136247 F = 9.836047 |
GDPPC_2021 = −121.88 + 2.8921 × Infrastructure_2019 | 0.792250 * p = 0.000031 R2 = 0.62766 t = 5.5084 F = 30.34299 |
GDPPC_2021 = 10.530 + 0.66463 × Market sophistication_2019 | 0.235873 |
GDPPC_2021 = −21.4784 + 1.4119 × Business sophistication_2019 | 0.610080 * p = 0. 004285 R2 =0.3721 t = 3.26672 F = 10.671 |
GDPPC_2021 = 14.881 + 0.81043 × Knowledge and technology outputs_2019 | 0.418353 |
GDPPC_2021 = −32.5997 + 1.9326 × Creative outputs_2019 | 0.586271 * p = 0. 006593 R2 = 0.3437 t = 3.07035 F = 9.427046 |
Regression Model | Correlation |
---|---|
Unemployment_2021 = 3.6558 + 0.01770 × Institutions_2019 | 0.129264 |
Unemployment_2021 = 4.5623 + 0.01113 × Human capital and research_2019 | 0.072876 |
Unemployment_2021 = 4.3539 + 0.01303 × Infrastructure_2019 | 0.057962 |
Unemployment_2021 = 5.6816 − 0.0086 × Market sophistication_2019 | −0.049361 |
Unemployment_2021 = 4.0345 + 0.02109 × Business sophistication_2019 | 0.147902 |
Unemployment_2021 = 5.3557 − 0.0046 × Knowledge and technology outputs_2019 | −0.038895 |
Unemployment_2021 = 5.3222 − 0.0041 × Creative outputs_2019 | −0.020367 |
Regression Model | Correlation |
---|---|
Inflation_2021 = −0.3562 + 0.03459 × Institutions_2019 | 0.336925 |
Inflation_2021 = 1.3131 + 0.02368 × Human capital and research_2019 | 0.036266 |
Inflation_2021 = 2.1761 + 0.00612 × Infrastructure_2019 | 0.036266 |
Inflation_2021 = 0.43167 + 0.03343 × Market sophistication_2019 | 0.256828 |
Inflation_2021 = 2.7733 − 0.0044 × Business sophistication_2019 | −0.040726 |
Inflation_2021 = 3.4385 − 0.0192 × Knowledge and technology outputs_2019 | −0.214843 |
Inflation_2021 = 1.7625 + 0.01776 × Creative outputs_2019 | 0.116622 |
Regression Model | Correlation |
---|---|
GDPPC_2022 = −75.1416 + 2.0029 × Institutions_2019 | 0.113836 |
GDPPC_2022 = 97.250 − 0.08496 × Human capital and research_2019 | −0.004329 |
GDPPC_2022 = −380.5354 + 7.849 × Infrastructure_2019 | 0.271576 |
GDPPC_2022 = 338.2836 − 3.883 × Market sophistication_2019 | −0.174056 |
GDPPC_2022 = −35.6919 + 2.4517 × Business sophistication_2019 | 0.133805 |
GDPPC_2022 = −60.9899 + 3.3103 × Knowledge and technology outputs_2019 | 0.215831 |
GDPPC_2022 = 43.303 + 1.1244 × Creative outputs_2019 | 0.043083 |
Regression Model | Correlation |
---|---|
Unemployment_2022 = 4.0667 + 0.00099 × Institutions_2019 | 0.008423 |
Unemployment_2022 = 3.6346 + 0.00993 × Human capital and research_2019 | 0.075452 |
Unemployment_2022 = 3.7363 + 0.00686 × Infrastructure_2019 | 0.035399 |
Unemployment_2022 = 6.0544 − 0.0301 × Market sophistication_2019 | −0.201440 |
Unemployment_2022 = 3.1432 + 0.01920 × Business sophistication_2019 | 0.156342 |
Unemployment_2022 = 4.2604 − 0.0024 × Knowledge and technology outputs_2019 | −0.023099 |
Unemployment_2022 = 4.5474 − 0.0090 × Creative outputs_2019 | −0.051551 |
Regression Model | Correlation |
---|---|
Inflation_2022 = −3.511 + 0.12074 × Institutions_2019 | 0.527280 |
Inflation_2022 = 0.76464 + 0.11269 × Human capital and research_2019 | 0.441218 |
Inflation_2022 = −0.6359 + 0.12023 × Infrastructure_2019 | 0.319628 |
Inflation_2022 = 4.6787 + 0.03063 × Market sophistication_2019 | 0.105506 |
Inflation_2022 = 2.0772 + 0.08656 × Business sophistication_2019 | 0.362972 |
Inflation_2022 = 4.9008 + 0.03689 × Knowledge and technology outputs_2019 | 0.184804 |
Inflation_2022 = 0.55329 + 0.13759 × Creative outputs_2019 | 0.405083 |
Regression Model | Correlation |
---|---|
GDPPC_2020 = −75.9112 + 1.4706 × Institutions_2020 | 0.75832 * p = 0.000107 R2 = 0.575 t = 4.93543 F = 24.35848 |
GDPPC _2020 = −20.161 + 1.2732 × Human capital and research_2020 | 0.62220 * p = 0.003396 R2 = 0.387133 t = 3.37197 F = 11.37017 |
GDPPC_2020 = −81.555 + 2.3027 × Infrastructure_2020 | 0.75049 * p = 0.000138 R2 = 0.56323 t = 4.81783 F = 23.21147 |
GDPPC_2020 = 0.0217 + 0.00047 × Market sophistication_2020 | 0.26240 |
GDPPC_2020 = −13.1003 + 1.1699 × Business sophistication_2020 | 0.60831 * p = 0.004429 R2 = 0.37004 t = 3.251651 F = 10.573 |
GDPPC_2020 = 6.4485 + 0.88803 × Knowledge and technology outputs_2020 | 0.49829 * p = 0.025346 R2 = 0.248292 t = 2.438336 F = 5.9455 |
GDPPC_2020 = −7.136 + 1.2603 × Creative outputs_2020 | 0.52605 * p = 0. 017197 R2 = 0. 2767 t = 2.624295 F = 6.886923 |
Regression Model | Correlation |
---|---|
Unemployment_2020 = 2.4222 + 0.03752 × Institutions_2020 | 0.225101 |
Unemployment_2020 = 4.0933 + 0.02771 × Human capital and research_2020 | 0.157527 |
Unemployment_2020 = 4.7716 + 0.01377 × Infrastructure_2020 | 0.157527 |
Unemployment_2020 = 2.6705 + 0.04669 × Market sophistication_2020 | 0.225804 |
Unemployment_2020 = 4.4266 + 0.02191 × Business sophistication_2020 | 0.132539 |
Unemployment_2020 = 5.6072 − 0.0016 × Knowledge and technology outputs_2020 | −0.010563 |
Unemployment_2020 = 6.0552 − 0.0123 × Creative outputs_2020 | −0.059815 |
Regression Model | Correlation |
---|---|
Inflation_2020 = 4.2569 − 0.0403 × Institutions_2020 | −0.422405 |
Inflation_2020 = 3.4709 − 0.0491 × Human capital and research_2020 | −0.488127 * p = 0. 028996 R2 = 0. 23827 t = 3.1535 F = 5.630357 |
Inflation_2020 = 4.6832 − 0.0680 × Infrastructure_2020 | −0.450520 * p = 0.046213 R2 = 0.20297 t = 2.6382 F = 4.583800 |
Inflation_2020 = 2.7396 − 0.0297 × Market sophistication_2020 | −0.251398 |
Inflation_2020 = 2.6417 − 0.0341 × Business sophistication_2020 | −0.360865 |
Inflation_2020 = 2.4421 − 0.0342 × Knowledge and technology outputs_2020 | −0.177414 |
Inflation_2020 = 1.7977 − 0.0209 × Creative outputs_2020 | −0.419522 |
Regression Model | Correlation |
---|---|
GDPPC_2021 = −88.5497 + 1.7053 × Institutions_2021 | 0.745526 * p = 0.000161 R2 = 0.556 t = 4.74586 F = 22.52316 |
GDPPC_2021 = −27.7316 + 1.5209 × Human capital and research_2021 | 0.630992 * p = 0.002852 R2 = 0.398151 t = 3.45078 F = 11.90786 |
GDPPC_2021 = −118.8231 + 3.0796 × Infrastructure_2021 | 0.795981 * p = 0.000027 R2 = 0.633585 t = 5.57895 F = 31.12467 |
GDPPC_2021 = 20.536 + 0.51115 × Market sophistication_2021 | 0.190204 |
GDPPC_2021 = −8.113 + 1.2141 × Business sophistication_2021 | 0.561982 * p = 0. 009912 R2 = 0.31582 t = 2.882534 F = 8.3090 |
GDPPC_2021 = 17.306 + 0.7872 × Knowledge and technology outputs_2021 | 0.377676 |
GDPPC_2021 = −4.695 + 1.3237 × Creative outputs_2021 | 0.470165 * p = 0. 036445 R2 = 0. 2211 t = 2.260131 F = 5.108191 |
Regression Model | Correlation |
---|---|
Unemployment_2021 = 3.6823 + 0.01762 × Institutions_2021 | 0.125033 |
Unemployment_2021 = 4.2177 + 0.01747 × Human capital and research_2021 | 0.117685 |
Unemployment_2021 = 4.1576 + 0.01766 × Infrastructure_2021 | 0.074075 |
Unemployment_2021 = 4.8047 + 0.00535 × Market sophistication_2021 | 0.032343 |
Unemployment_2021 = 4.2030 + 0.01875 × Business sophistication_2021 | 0.140923 |
Unemployment_2021 = 5.1094 + 0.00068 × Knowledge and technology outputs_2021 | 0.005321 |
Unemployment_2021 = 6.2903 − 0.0266 × Creative outputs_2021 | −0.153387 |
Regression Model | Correlation |
---|---|
Inflation_2021 = −0.4027 + 0.03563 × Institutions_2021 | 0.337138 |
Inflation_2021 = 1.2527 + 0.02448 × Human capital and research_2021 | 0.219873 |
Inflation_2021 = 2.4961 + 0.00088 × Infrastructure_2021 | 0.004916 |
Inflation_2021 = 1.2416 + 0.02082 × Market sophistication_2021 | 0.167663 |
Inflation_2021 = 2.5172 + 0.00056 × Business sophistication_2021 | 0.005583 |
Inflation_2021 = 3.3103 − 0.0171 × Knowledge and technology outputs_2021 | −0.177567 |
Inflation_2021 = 2.2321 + 0.00724 × Creative outputs_2021 | 0.055634 |
Regression Model | Correlation |
---|---|
GDPPC_2022 = −187.2007 + 3.6511 × Institutions_2022 | 0.152886 |
GDPPC_2022 = 109.5689 − 0.3146 × Human capital and research_2022 | −0.016883 |
GDPPC_2022 = −152.4557 + 4.2024 × Infrastructure_2022 | 0.137834 |
GDPPC_2022 = 384.0864 − 5.64 × Market sophistication_2022 | −0.299954 |
GDPPC_2022 = −36.2047 + 2.4833 × Business sophistication_2022 | 0.131546 |
GDPPC_2022 = 51.538 + 0.88401 × Knowledge and technology outputs_2022 | 0.055794 |
GDPPC _2022 = 169.1489 − 1.845 × Creative outputs_2022 | −0.086798 |
Regression Model | Correlation |
---|---|
Unemployment_2022 = 6.0976 − 0.0254 × Institutions_2022 | −0.158599 |
Unemployment_2022 = 3.7897 + 0.00678 × Human capital and research_2022 | 0.054239 |
Unemployment_2022 = 2.7397 + 0.02416 × Infrastructure_2022 | 0.118207 |
Unemployment_2022 = 4.6645 − 0.0100 × Market sophistication_2022 | −0.079043 |
Unemployment_2022 = 2.9742 + 0.02263 × Business sophistication_2022 | 0.178787 |
Unemployment_2022 = 3.8660 + 0.00608 × Knowledge and technology outputs_2022 | 0.057221 |
Unemployment_2022 = 5.2495 − 0.0266 × Creative outputs_2022 | −0.186548 |
Regression Model | Correlation |
---|---|
Inflation_2022 = −3.473 + 0.13153 × Institutions_2022 | 0.423184 |
Inflation_2022 = 0.82766 + 0.10883 × Human capital and research_2022 | 0.448750 |
Inflation_2022 = −0.4028 + 0.12023 × Infrastructure_2022 | 0.302995 |
Inflation_2022 = 6.4429 + 0.00333 × Market sophistication_2022 | 0.013619 |
Inflation_2022 = 1.9222 + 0.09031 × Business sophistication_2022 | 0.367568 |
Inflation_2022 = 4.2550 + 0.05051 × Knowledge and technology outputs_2022 | 0.244965 |
Inflation_2022 = 3.2658 + 0.08097 × Creative outputs_2022 | 0.292710 |
References
- Adhikari, Tamanna, and Karl Whelan. 2023. Did raising doing business scores boost GDP? Journal of Comparative Economics 51: 1011–30. [Google Scholar] [CrossRef]
- Ahmad, Najid, Shuyun Li, Mouna Hdia, Jaroslav Bélas, and Wan Mohd Hirwani Wan Hussain. 2023. Assessing the COVID-19 pandemic impact on tourism arrivals: The role of innovation to reshape the future work for sustainable development. Journal of Innovation & Knowledge 8: 100344. [Google Scholar] [CrossRef]
- Akindinova, Natalia V., Eduard F. Baranov, Vladimir A. Bessonov, Nikolay V. Kondrashov, Alexey O. Kuznetsov, Valery V. Mironov, Svetlana G. Misikhina, Sergey G. Pukhov, Sergey V. Smirnov, Alena A. Chepel, and et al. 2021. Macroeconomic Effects of the COVID-19 Pandemic and Prospects for Economic Recovery. Paper presented at the XXII April International Scientific Conference on Development Issues of Economy and Society, Moscow, Russia, April 13–30; Edited by Natalia V. Akindinova. Moscow: Higher School of Economics Publishing House. Available online: https://conf.hse.ru/mirror/pubs/share/460914594.pdf (accessed on 25 May 2024).
- Amit, Raphael, and Christoph Zott. 2012. Creating Value through Business Model Innovation. MIT Sloan Management Review 53: 41–49. Available online: https://www.researchgate.net/publication/279555624_Creating_Value_Through_Business_Model_Innovation (accessed on 25 May 2024).
- Beynon, James Malcolm, Paul Jones, and David Pickernell. 2023. Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach. Journal of Business Research 159: 113703. [Google Scholar] [CrossRef]
- Bodlaj, Mateja, and Barbara Čater. 2019. The impact of environmental turbulence on the perceived importance of innovation and innovativeness in SMEs. Journal of Small Business Management 57: 417–35. [Google Scholar] [CrossRef]
- Borisova, Ekaterina Yu. 2010. Estimating the Innovations Impact on a Country’s Economic Welfare. Applied Econometrics 18: 79–89. Available online: https://elibrary.ru/item.asp?id=14805507 (accessed on 25 May 2024).
- Chundakkadan, Radeef, and Subash Sasidharan. 2023. The role of government support on E-commerce and firm innovation during pandemic crisis. Economic Analysis and Policy 78: 904–13. [Google Scholar] [CrossRef]
- Colla-De-Robertis, Esteban, and Rafael Garduno Rivera. 2021. The effect of a free trade agreement with the United States on member countries’ per capita GDP: A synthetic control analysis. Regional Science Policy & Practice 13: 1129–46. [Google Scholar] [CrossRef]
- Dempere, Juan, Muhammad Qamar, Hesham Allam, and Sabir Malik. 2023. The impact of innovation on economic growth, foreign direct investment, and self-employment: A global perspective. Economies 11: 182. [Google Scholar] [CrossRef]
- Deng, Yaotian, Lingqian Zhang, Jian Zhang, and Umar Farooq. 2023. Does green innovation promote trade credit activities? New empirical evidence from BRICS. Borsa Istanbul Review 23: 1322–32. [Google Scholar] [CrossRef]
- Edquist, Charles. 2014. Striving towards a Holistic Innovation Policy in European Countries—But linearity Still Prevails! STI Policy Review 5: 1–19. Available online: https://www.researchgate.net/publication/269630493_Striving_Towards_a_Holistic_Innovation_Policy_in_European_Countries_-But_Linearity_Still_Prevails (accessed on 25 May 2024).
- Federal State Statistics Service. 2023. News of Rosstat. Available online: https://eng.rosstat.gov.ru/ (accessed on 25 May 2024).
- Gillanders, Robert, and Karl Whelan. 2014. Open For Business? Institutions, Business Environment and Economic Development. Kyklos 67: 535–58. [Google Scholar] [CrossRef]
- Guliyev, Hasraddin, Natiq Huseynov, and Nasimi Nuriyev. 2023. The relationship between artificial intelligence, big data, and unemployment in G7 countries: New insights from dynamic panel data model. World Development Sustainability 3: 100107. [Google Scholar] [CrossRef]
- Hakimi, Abdelaziz, Helmi Hamdi, and Roula Inglesi-Lotz. 2024. To innovate or to import innovation? Evidence from African countries. Regional Science Policy & Practice 16: 12594. [Google Scholar] [CrossRef]
- Harada, Tsutomu. 2015. Structural change and economic growth with relation-specific investment. Structural Change and Economic Dynamics 32: 1–10. [Google Scholar] [CrossRef]
- Hilmawan, Rian, Yesi Aprianti, Rizky Yudaruddin, Ratih Fenty Anggraini Bintoro, Suharsono, Yuli Fitrianto, and Noor Wahyuningsih. 2023. Public sector innovation in local government and its impact on development outcomes: Empirical evidence in Indonesia. Heliyon 9: e22833. [Google Scholar] [CrossRef]
- Ifa, Adel, and Imène Guetat. 2018. Does public expenditure on education promote Tunisian and Moroccan GDP per capita? ARDL approach. The Journal of Finance and Data Science 4: 234–46. [Google Scholar] [CrossRef]
- Irshad, Rimsha, Mehr-un-Nisa, and Naghmana Ghafoor. 2022. Infrastructure and economic growth: Evidence from lower middle-income countries. Journal of the Knowledge Economy 14: 161–79. [Google Scholar] [CrossRef]
- Keizer, Jimme A., Lieuwe Dijkstra, and Johannes I. M. Halman. 2002. Explaining innovative efforts of SMEs: An exploratory survey among SMEs in the mechanical and electrical engineering sector in the Netherlands. Technovations 22: 1–13. [Google Scholar] [CrossRef]
- Khalil, Muhamad Luqman, Norzalita Abd Aziz, Fei Long, and Huan Zhang. 2023. What factors affect firm performance in the hotel industry post-Covid-19 pandemic? Examining the impacts of big data analytics capability, organizational agility and innovation. Journal of Open Innovation: Technology, Market, and Complexity 9: 100081. [Google Scholar] [CrossRef]
- Khan, Mohd Naved, Ahmad Ali Jan, Mohammad Asif, Fong-Woon Lai, Muhammad Kashif Shad, and Saima Shadab. 2023. Do domestic innovations promote trade openness? Empirical evidence from emerging economies. Heliyon 9: e22848. [Google Scholar] [CrossRef] [PubMed]
- Law, Siong Hook, Tamat Sarmidi, and Lim Thye Goh. 2020. Impact of innovation on economic growth: Evidence from Malaysia. Malaysian Journal of Economic Studies 57: 113–32. [Google Scholar] [CrossRef]
- Li, Biao, Saeed Mousa, Johanna Rosali Reyes Reinoso, Haitham M. Alzoubi, Anis Ali, and Anh Duong Hoang. 2023. The role of technology innovation, customer retention and business continuity on firm performance after post-pandemic era in China’s SMEs. Economic Analysis and Policy 78: 1209–20. [Google Scholar] [CrossRef]
- Lu, Zhou, Yajie Huang, Peiliang Du, Fang Li, and Zhenhui Li. 2023. Pandemics uncertainty and informational globalization in CEE countries: The role of innovation diffusion. Heliyon 9: e21489. [Google Scholar] [CrossRef] [PubMed]
- Ma, Xiaoyu, Yizhi Hao, Xiao Li, Jun Liu, and Jiasen Qi. 2023. Evaluating global intelligence innovation: An index based on machine learning methods. Technological Forecasting and Social Change 194: 122736. [Google Scholar] [CrossRef]
- Manohar, Sridhar, Justin Paul, Carolyn Strong, and Amit Mittal. 2023. INNOSERV: Generalized scale for perceived service innovation. Journal of Business Research 160: 113723. [Google Scholar] [CrossRef]
- Medeiros, Maurício Z., Priscila F. Soares, Beatriz C. Fialho, Leandro Gauss, Fábio S. Piran, and Daniel P. Lacerda. 2022. Vaccine innovation model: A technology transfer perspective in pandemic contexts. Vaccine 40: 4748–63. [Google Scholar] [CrossRef]
- Nahar, Sharmin. 2024. Modeling the effects of artificial intelligence (AI)-based innovation on sustainable development goals (SDGs): Applying a system dynamics perspective in a cross-country setting. Technological Forecasting and Social Change 201: 123203. [Google Scholar] [CrossRef]
- Nikonova, Yana I. 2016. Exploring the Relationship between Innovation and Economic Growth of National Economies. Concept 15: 2001–5. Available online: https://e-koncept.ru/2016/96319.htm (accessed on 25 May 2024).
- Parvez, M. Omar, Ali Öztüren, Cihan Cobanoglu, Huseyin Arasli, and Kayode K. Eluwole. 2022. Employees’ perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic. International Journal of Hospitality Management 107: 103336. [Google Scholar] [CrossRef]
- Rafiee, Elham, Zahra Dehghan Shabani, and Ebrahim Hadian. 2024. Driving factors on unemployment in Iran’s provinces: Do temporal and spatial dynamics matter? Regional Science Policy & Practice 16: 12715. [Google Scholar] [CrossRef]
- Rathi, Sawan, Adrija Majumdar, and Chirantan Chatterjee. 2024. Did the COVID-19 pandemic propel usage of AI in pharmaceutical innovation? New evidence from patenting data. Technological Forecasting and Social Change 198: 122940. [Google Scholar] [CrossRef]
- Rubio-Andrés, Mercedes, Maria del Mar Ramos-González, Miguel Ángel Sastre-Castillo, and Santiago Gutiérrez-Broncano. 2023. Stakeholder pressure and innovation capacity of SMEs in the COVID-19 pandemic: Mediating and multigroup analysis. Technological Forecasting and Social Change 190: 122432. [Google Scholar] [CrossRef]
- Sargento, Ana, and Ana Sofia Lopes. 2024. Territorial resilience on the aftermaths of COVID-19 crisis—An exploratory analysis on the role of innovation. Regional Science Policy & Practice 16: 12697. [Google Scholar] [CrossRef]
- Serpukhovitin, Dmitry A. 2020. Identification of prospective direction for improving the national innovation system of Russia, based on the analysis of the global innovation index and GDP of the countries of the world. Bulletin of the Institute of Economics of the Russian Academy of Sciences 5: 175–97. [Google Scholar] [CrossRef]
- Sharma, Gagan Deep, Sascha Kraus, Mrinalini Srivastava, Ritika Chopra, and Andreas Kallmuenzer. 2022. The changing role of innovation for crisis management in times of COVID-19: An integrative literature review. Journal of Innovation & Knowledge 7: 100281. [Google Scholar] [CrossRef]
- Singh, Rashmeet, Deepak Chandrashekar, Bala Subrahmanya Mungila Hillemane, Arun Sukumar, and Vahid Jafari-Sadeghi. 2022. Network cooperation and economic performance of SMEs: Direct and mediating impacts of innovation and internationalization. Journal of Business Research 148: 116–30. [Google Scholar] [CrossRef]
- Sipior, Janice C. 2020. Considerations for development and use of AI in response to COVID-19. International Journal of Information Management 55: 102170. [Google Scholar] [CrossRef]
- Strielkowski, Wadim, Svetlana Kalyugina, Victor Fursov, and Oxana Mukhoryanova. 2023. Improving the system of indicators for assessing the effectiveness of modern regional innovation systems. Economies 11: 228. [Google Scholar] [CrossRef]
- Tereshkina, Natalia E. 2021. The pandemic impact on the development of innovations in the world. Russian Journal of Innovation Economics 11: 1289–300. [Google Scholar] [CrossRef]
- Vasin, Sergey M. 2022. Comparative analysis of socioeconomic models in COVID-19 pandemic. Economies 10: 278. [Google Scholar] [CrossRef]
- Vasin, Sergey M., and Leyla A. Gamidullaeva. 2015. Development a basic model of the innovation system. Review of European Studies 7: 175–83. [Google Scholar] [CrossRef]
- Vlasova, Valeriya V., Leonid M. Gokhberg, Galina A. Gracheva, Kirill A. Ditkovsky, Irina A. Kuznetsova, Svetlana V. Martynova, Tatyana V. Ratay, Larisa A. Rosovetskaya, Ekaterina A. Streltsova, and Svetlana Yu. Fridlyanova. 2023. Indicators of Innovation Activity 2023: Statistical Data Book. Edited by Nikita Yu. Anisimov, Leonid M. Gokhberg, Yaroslav I. Kuzminov and Maxim A. Kolesnikov. Moscow: National Research University Higher School of Economics. Available online: https://publications.hse.ru/en/books/820489012 (accessed on 25 May 2024).
- Wang, Lipeng, Mengyu Zhang, and Thanos Verousis. 2021. The road to economic recovery: Pandemics and innovation. International Review of Financial Analysis 75: 101729. [Google Scholar] [CrossRef]
- World Intellectual Property Organization. 2019. Global Innovation Index 2019: Creating Healthy Lives—The Future of Medical Innovation, 12th ed. Edited by Soumitra Dutta, Bruno Lanvin and Sacha Wunsch-Vincent. Ithaca: Cornell University, Fontainebleau: INSEAD. Geneva: WIPO. Available online: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2019.pdf (accessed on 11 May 2024).
- World Intellectual Property Organization. 2020. Global Innovation Index 2020: Who Will Finance Innovation? 13th ed. Edited by Soumitra Dutta, Bruno Lanvin and Sacha Wunsch-Vincent. Ithaca: Cornell University, Fontainebleau: INSEAD. Geneva: WIPO. Available online: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2020.pdf (accessed on 25 May 2024).
- World Intellectual Property Organization. 2021. Global Innovation Index 2021: Tracking Innovation through the COVID-19 Crisis, 14th ed. Edited by Soumitra Dutta, Bruno Lanvin, Lorena Rivera León and Sacha Wunsch-Vincent. Geneva: WIPO. Available online: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2021.pdf (accessed on 25 May 2024).
- World Intellectual Property Organization. 2022. Global Innovation Index 2022: What is the Future of Innovation-Driven Growth? 15th ed. Edited by Soumitra Dutta, Bruno Lanvin, Lorena Rivera León and Sacha Wunsch-Vincent. Geneva: WIPO. Available online: https://www.wipo.int/edocs/pubdocs/en/wipo-pub-2000-2022-en-main-report-global-innovation-index-2022-15th-edition.pdf (accessed on 25 May 2024).
- World Intellectual Property Organization. 2023. Global Innovation Index 2023. About the Global Innovation Index. Available online: https://www.wipo.int/gii-ranking/en/about (accessed on 25 May 2024).
- Zofio, Jose Luis, Juan Aparicio, Javier Barbero, and Jon Mikel Zabala-Iturriagagoitia. 2023. The influence of bottlenecks on innovation systems performance: Put the slowest climber first. Technological Forecasting and Social Change 193: 122607. [Google Scholar] [CrossRef]
Socioeconomic Model | |||||
---|---|---|---|---|---|
GII Pillar | The Anglo-Saxon Model | The Scandinavian (Swedish) Model | The Rhenish (German) Model | The Japanese Model | The Chinese Model |
Institutions | Minimal decline in 2020, recovery in 2021, drastic decline in 2022 | Minimal decline in 2020, recovery in 2021, drastic decline in 2022 | Minimal decline in 2020, neither dynamic in 2021, decline in 2022 | Minimal decline in 2020, neither dynamic in 2021, decline in 2022 | Minimal increase in 2020, neither dynamic in 2021, increase in 2022 |
Human capital and research | Minimal increase in 2020, neither dynamic in 2021, sharp increase in 2022 | Neither dynamic in 2020, minimal increase in 2021, decline to 2020 level in 2022 | Increase in 2020 and 2021, minimal increase in 2022 | On average—neither dynamic in 2020 and 2021, minimal increase in 2022 | Minimal decline in 2020, recovery in 2021, minimal increase in 2022 |
Infrastructure | Drastic decline in 2020, decline in 2021, increase in 2022 | Drastic decline in 2020, neither dynamic in 2021, sharp increase in 2022 | Drastic decline in 2020, minimal decline in 2021, increase in 2022 | Decline in 2020, increase in 2021 and 2022 | Decline in 2020, neither dynamic in 2021, recovery in 2022 |
Market sophistication | Minimal decline in 2020, recovery in 2021, drastic decline in 2022 | Decline in 2020, increase in 2021, drastic decline in 2022 | On average—neither dynamic in 2020 and 2021, drastic decline in 2022 | Minimal decline in 2020 and 2021, decline in 2022 | Minimal decline in 2020, increase in 2021, drastic decline in 2022 |
Business sophistication | Minimal decline in 2020 and 2021, recovery in 2022 | Minimal decline in 2020, neither dynamic in 2021, recovery in 2022 | Decline in 2020, minimal decline in 2021 and 2022 | Minimal decline in 2020 and 2021, increase in 2022 | Minimal increase in 2020, minimal decline in 2021, minimal increase in 2022 |
Knowledge and technology outputs | Minimal decline in 2020, decline in 2021, increase in 2022 | Minimal decline in 2020, minimal increase in 2021, increase in 2022 | Decline in 2020, minimal increase in 2021, increase in 2022 | Minimal decline in 2020, minimal increase in 2021 and 2022 | Minimal decline in 2020, minimal increase in 2021, minimal decline in 2022 |
Creative outputs | Decline in 2020, increase in 2021, minimal decline in 2022 | Decline in 2020, increase in 2021, decline in 2022 | Minimal decline in 2020, recovery in 2021, decline in 2022 | Decline in 2020, increase in 2021, decline in 2022 | Neither noticeable dynamic during the overall 3-year period |
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Vasin, S.M.; Timokhina, D.M. Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis. Economies 2024, 12, 190. https://doi.org/10.3390/economies12080190
Vasin SM, Timokhina DM. Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis. Economies. 2024; 12(8):190. https://doi.org/10.3390/economies12080190
Chicago/Turabian StyleVasin, Sergey Mikhailovich, and Daria Mikhailovna Timokhina. 2024. "Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis" Economies 12, no. 8: 190. https://doi.org/10.3390/economies12080190