Learning and Innovation in Organizations and Economies

Nooteboom, Bart

doi:10.1093/acprof:oso/9780199241002.001.0001

Cited by 586 publications

(718 citation statements)

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“…The literature on competence building has neglected the governance of relational risk, and transaction cost theory has neglected issues of learning and innovation. A combination of the two perspectives should yield a more complete understanding of interorganisational relations (Williamson, 1999;Nooteboom, 2000Nooteboom, , 2004Dosi and Marengo, 2000).…”

Section: Social Network Theorymentioning

confidence: 99%

“…To substantiate this claim we need to differentiate between cognitive variety and cognitive distance. Cognitive variety refers to how many different individual cognitive frameworks are present in a network, cognitive distance refers to the difference between any of them (Nooteboom, 2000). The density of ties is relevant as it indicates the potential for cognitive variety present in a network.…”

Section: Who Is Right Under Which Conditions?mentioning

confidence: 99%

“…However, too many weak ties may increase the chance of misunderstanding when cognitive distance among the various ties is (too) large, ultimately leading to chaos (Nooteboom, 2000). Learning among heterogeneous actors can only happen when information and knowledge are transferred efficiently and when this knowledge and information can be retained (McKelvey, 1997).…”

Section: Implications For the Density And Strength Of Tiesmentioning

confidence: 99%

“…There is growing consensus in the academic literature that in such a setting, strategic alliances can be an extremely effective organisational form as they bring together complementary actors from different technological backgrounds. In the context of learning and innovation, exploration thrives on a diversity of knowledge which yields a potential for Schumpeterian novel combinations to emerge (Nooteboom, 2000). It is in this heterogeneity that actors are able to combine and integrate complementary knowledge and capabilities (Porter, 1990;Hamel and Prahalad, 1994;Grabher, 1993;Hagedoorn, 1993;Hagedoorn and Schakenraad, 1994;Smith Ring and van de Ven, 1994;Grandori, 1997;Spekman et al, 1995;Uzzi, 1997;Nooteboom, 1999Nooteboom, , 2004Ahuja, 2000;Rowley et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the literature is very clear about the change in focus from exploration to exploitation. However, the origins of exploration itself remain (Nooteboom, 2000). This issue on 'the origins of novelty' forms the central question in this paper and will be studied in the context of interfirm networks engaging in exploration, further referred to as 'exploration networks'.…”

Section: Introductionmentioning

confidence: 99%

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Understanding novelty creation in exploration networks—Structural and relational embeddedness jointly considered

2008

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In exploration networks the key-organisational question is not how to organise a division of labour but instead how to create novelty. The aim of this paper is to develop an understanding of how such novelty in exploration networks is created. Based on an empirical analysis of the multimedia and biotechnology industries in the Netherlands, this paper shows that exploration networks face a trade-off between diversity and selection. Moreover, the findings indicate that depending on the type of exploration task, exploration networks need to make a combination of density and tie strength in such a way that diversity and selection are aligned. The paper concludes, among others, that the views of Burt, Coleman and Granovetter should not be seen as contradictory, but rather as proponents of complementary views. r

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Section: Social Network Theorymentioning

confidence: 99%

Section: Who Is Right Under Which Conditions?mentioning

confidence: 99%

Section: Implications For the Density And Strength Of Tiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding novelty creation in exploration networks—Structural and relational embeddedness jointly considered

2008

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Proximity‐aware research leadership recommendation in research collaboration via deep neural networks

Qingpeng

2021

Asso for Info Science & Tech

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Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity‐aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership‐aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute‐aware skip‐gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state‐of‐the‐art collaborator recommendation models in research leadership recommendation.

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The importance of learning processes in wind power development

Kamp

2007

Eur. Env.

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This paper examines four learning processes that occur simultaneously during wind turbine development and diffusion: learning by searching, learning by doing, learning by using, and learning by interacting. It considers how these different learning processes affect technology development, which factors facilitate or impede these kinds of learning processes, and how they can be facilitated better to accelerate technological change. These questions are addressed to a historical case study of large-scale and small-scale onshore wind power development in The Netherlands, with some provisional conclusions drawn from recent research on the development of offshore wind. The most prominent learning process in onshore wind power development in The Netherlands was learning by searching. However, no reliable wind turbines, let alone a wind turbine industry that was able to compete at international level, were produced as a result of this learning process. Other learning processes were needed, like learning by doing and learning by interacting with the other actors involved. In the recently set up offshore wind-power innovation system, we see some learning from past mistakes. Wind turbine users are participating more actively and prominently than in the onshore innovation system, allowing more learning by using and learning by interacting. Furthermore, the focus is less on rapid up-scaling and high-tech innovation, but more on learning with existing technology in a different, offshore, environment.

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Learning and Innovation in Organizations and Economies

Cited by 586 publications

References 0 publications

Understanding novelty creation in exploration networks—Structural and relational embeddedness jointly considered

Understanding novelty creation in exploration networks—Structural and relational embeddedness jointly considered

Proximity‐aware research leadership recommendation in research collaboration via deep neural networks

The importance of learning processes in wind power development

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