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1 – 3 of 3Gustavo Hermínio Salati Marcondes de Moraes, Bruno Fischer, Sergio Salles-Filho, Dirk Meissner and Marina Dabic
Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence…
Abstract
Purpose
Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence, the purpose of this paper is to disentangle the effects of scientific capabilities and strategic R&D on KIE performance; and how the constituent elements of these dimensions can be configured to generate conditions for high performance.
Design/methodology/approach
The authors’ empirical setting involves companies that submitted projects to the Innovative Research in Small Businesses (PIPE) program in Brazil. The authors then run partial least square structural equation modeling to verify how scientific and strategic R&D capabilities influence the performance construct. Second, the authors apply fuzzy-set qualitative comparative analysis to identify configurations that are equifinal in terms of generating superior performance.
Findings
Findings indicate a strong association between scientific capabilities and KIE performance. The configurational approach outlines the existence of multiple paths to success, but human capital stands as a core condition throughout estimations.
Practical implications
The authors’ assessment has implications for how KIE firms are managed according to their organizational profiles and trajectories. Also, it advances the authors’ comprehension on how entrepreneurship policies can better target these distinct profiles.
Originality/value
The authors’ analysis provides new evidence on the inherent complexity behind the generation of high performance in KIE when addressing their portfolios of knowledge-related capabilities. More than that, the authors were able to identify the existence of heterogeneous profiles that can equally lead to higher levels of performance.
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Armando Papa, Roberto Chierici, Luca Vincenzo Ballestra, Dirk Meissner and Mehmet A. Orhan
This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative…
Abstract
Purpose
This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.
Design/methodology/approach
This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.
Findings
Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.
Originality/value
This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.
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Diego Alex Gazaro dos Santos, Aurora Zen and Bruno Anicet Bittencourt
Innovation ecosystems can emerge and grow organically, but the process can also be managed through conscious intervention. Therefore, this study observes different motivations and…
Abstract
Purpose
Innovation ecosystems can emerge and grow organically, but the process can also be managed through conscious intervention. Therefore, this study observes different motivations and expectations for each group of actors. The lack of alignment between actors could have a negative influence on the development of innovation ecosystems. This study aims to analyze the coordination strategies of the actors throughout the life cycle of innovation ecosystems.
Design/methodology/approach
This study develops and proposes a model for coordinating innovation ecosystems based on the theoretical backgrounds of the ecosystem life cycle and ecosystem coordination.
Findings
This study argues that each stage of an innovation ecosystem’s life cycle – inception, launching, growth and maturity – demands different coordination strategies. Initially, networks are simpler and thus the coordination issues are less difficult. However, as the ecosystem evolves and the complexity of the networks increases, a more sophisticated strategy, such as orchestration or choreography, is needed.
Research limitations/implications
This is a theoretical study that recommends further research to test this model.
Practical implications
The understanding of coordination and stages of the life cycle of an innovation ecosystem can guide actors in the design of strategies for developing of ecosystems.
Social implications
The proposed framework could support strategies to engage civil society in actions to develop innovation ecosystems.
Originality/value
This study presents a framework to understand the coordination strategies better, considering the stages of an innovation ecosystem’s life cycle.
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