|
on Economics of Strategic Management |
| By: | Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo |
| Abstract: | In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. |
| JEL: | O30 O31 O32 O33 O34 |
| Date: | 2022–08–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114121&r= |
| By: | Cinnirella, Francesco (University of Bergamo); Hornung, Eric (University of Cologne); Koschnick, Julius (London School of Economics) |
| Abstract: | Economic societies emerged during the late eighteenth-century. We argue that these institutions reduced the costs of accessing useful knowledge by adopting, producing, and diffusing new ideas. Combining location information for the universe of 3,300 members across active economic societies in Germany with those of patent holders and World’s Fair exhibitors, we show that regions with more members were more innovative in the late nineteenth-century. This long-lasting effect of societies arguably arose through agglomeration economies and localized knowledge spillovers. To support this claim, we provide evidence suggesting an immediate increase in manufacturing, an earlier establishment of vocational schools, and a higher density of highly skilled mechanical workers by mid-nineteenth century in regions with more members. We also show that regions with members from the same society had higher similarity in patenting, suggesting that social networks facilitated spatial knowledge diffusion and, to some extent, shaped the geography of innovation |
| Keywords: | Economic Societies ; Useful Knowledge ; Knowledge Diffusion ; Innovation ; Social Networks JEL Classification: N33 ; O33 ; O31 ; O43 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:cge:wacage:632&r= |
| By: | Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio |
| Abstract: | In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation |
| JEL: | O30 O31 O32 O33 O34 |
| Date: | 2022–08–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114215&r= |
| By: | Costantiello, Alberto; Laureti, Lucio; Leogrande, Angelo |
| Abstract: | The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%. |
| Keywords: | Innovation, and Invention: Processes and Incentives; Management of Technological Innovation and R&D; Diffusion Processes; Open Innovation. |
| JEL: | O30 O31 O32 O33 O34 |
| Date: | 2022–08–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114053&r= |
| By: | SPEROTTO, Fernanda Q.; TARTARUGA, Iván G. P. |
| Abstract: | This study aimed to provide an overview of eco-innovations in the Brazilian industry. To address this issue, we analyzed specific data of eco-innovative companies. In addition, we applied the cluster heatmap technique, which allowed us to analyze the different drivers and impacts of eco-innovations in different sectors. According to the results, companies that stated that innovation made it possible to reduce their environmental impact represent a third of all innovators. Moreover, they are companies that have shown greater effort to innovate and greater susceptibility to the benefits and obstacles of innovation. Furthermore, the eco-innovation strategy is mainly driven by market factors, such as reputation and codes of good practice. The impacts are mainly associated with the use of more widespread and less complex technologies, such as recycling. In addition to these results, the study considers some alternatives to guide the innovation policy, especially related to eco-innovations in semi-peripherical countries. |
| Keywords: | sustainability; green technologies; environmental innovation; industry; Brazil |
| JEL: | L6 O31 Q55 Q56 |
| Date: | 2021–07–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109149&r= |
| By: | Anaïs Garin (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Mathias Béjean (IRG - Institut de Recherche en Gestion - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - Université Gustave Eiffel); Stefan Meisiek (The University of Sydney Business School) |
| Abstract: | Innovation ecosystems have been increasingly studied in the past few years. Previous research focused on the orchestration of innovation ecosystems and the diverse activities needed to maintain an ecosystem over time. However, few scholars studied the capabilities required to carry out orchestration activities. Drawing on the dynamic capabilities framework, we seek to understand how dynamic capabilities support orchestration activities in innovation ecosystems. Our single case study of an innovation ecosystem in the Medtech industry reveals that orchestration activities can be shared among several ecosystem actors and can be associated with sensing, seizing, and reconfiguring dynamic capabilities. By doing so, we contribute to the literature on innovation ecosystem orchestration. Our findings also point out the importance of historical and subjective time when studying dynamic phenomena. This complements recent research that views collective memory and history as valuable dynamic capabilities. We conclude by suggesting a rethinking of the dynamic capabilities framework to embrace the dynamic and heterogeneous nature of innovation ecosystems. |
| Date: | 2022–07–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03709784&r= |