Christian Zellner and Dirk Fornahl
Considers some external aspects influencing the dynamics of firms’ knowledge base. Argues that the successful management of the knowledge base in a fast changing innovative…
Abstract
Considers some external aspects influencing the dynamics of firms’ knowledge base. Argues that the successful management of the knowledge base in a fast changing innovative environment is closely related to three kinds of knowledge acquisition channels, these are: the recruitment of people; the external informal networks of employees; and formal cooperation of the firm with other institutional agents. Focusing on firms’ interaction with research institutions, suggests a typology of scientific knowledge which allows us to analyse how different types of knowledge are associated with different knowledge acquisition channels. Because of the close interlinkages among the channels, knowledge (unlike information) is not freely floating in the system. Rather, its effective transfer and commercial exploitation is contingent on the mobility of people, as well as the extent to which they draw on their informal networks. Spells out some of the implications for recruitment policy and firm location.
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Nils Grashof, Alexander Kopka, Colin Wessendorf and Dirk Fornahl
This paper aims to show the interaction effects between clusters and cluster-specific attributes and the industrial internet of things (IoT) knowledge of a firm on the…
Abstract
Purpose
This paper aims to show the interaction effects between clusters and cluster-specific attributes and the industrial internet of things (IoT) knowledge of a firm on the innovativeness of firms. Cluster theory and the concept of key enabling technologies are linked to test their effect on a firm’s incremental and radical knowledge generation.
Design/methodology/approach
Quantitative approach at the firm-level. By combining several data sources (e.g. ORBIS, PATSTAT and German subsidy catalogue) the paper relies on a unique database encompassing 8,347 firms in Germany. Ordinary least squares (OLS)-regression techniques are used for data analysis.
Findings
Industrial IoT is an important driver of radical patents, mediated positively by firm size. For incremental knowledge, a substitution effect occurs between a cluster and IoT effects, which is bigger for larger firms and dependent on cluster attributes and firms’ outside connections.
Research limitations/implications
The paper opens up new research paths considering long-term disruptive effects of the industrial IoT compared to short-term effects on the innovativeness of firms within clusters. Additionally, it enables further research enriching the discussion about cluster attributes and how these affect ongoing processes.
Practical implications
Linking cluster theory and policy with Industry 4.0 raises awareness for being considerate in terms of funding and scrutinising one-size-fits-all approaches.
Originality/value
Connecting the concepts of a cluster and advanced manufacturing technologies as a proxy for industrial IoT, specifically focussing on both radical and incremental innovations is a new approach. Especially, taking into account the interaction effects between cluster attributes and the influence of industrial IoT on the innovativeness of firms.
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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.