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on Knowledge Management and Knowledge Economy |
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Issue of 2015‒07‒11
five papers chosen by Laura Ştefănescu Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Lorna Earl; Helen Timperley |
| Abstract: | In this working paper, Earl and Timperley argue that evaluative thinking is a necessary component of successful innovation and involves more than measurement and quantification. Combining evaluation with innovation requires discipline in the innovation and flexibility in the evaluation. The knowledge bases for both innovation and evaluation have advanced dramatically in recent years in ways that have allowed synergies to develop between them; the different stakeholders can bring evaluative thinking into innovation in ways that capitalise on these synergies. Evaluative thinking contributes to new learning by providing evidence to chronicle, map and monitor the progress, successes, failures and roadblocks in the innovation as it unfolds. It involves thinking about what evidence will be useful during the course of the innovation activities, establishing the range of objectives and targets that make sense to determine their progress, and building knowledge and developing practical uses for the new information, throughout the trajectory of the innovation. Having a continuous cycle of generating hypotheses, collecting evidence, and reflecting on progress, allows the stakeholders (e.g., innovation leaders, policymakers, funders, participants in innovation) an opportunity to try things, experiment, make mistakes and consider where they are, what went right and what went wrong, through a fresh and independent review of the course and the effects of the innovation. This paper describes issues and approaches to each phase of the cycle. It concludes by outlining the synergies to be made, building capacity for evaluative thinking, as well as possible tensions to be addressed.<BR>Dans ce document de travail, Earl et Timperley mettent en avant l’argument que la pensée évaluative est un élément indispensable à une innovation réussie, et qu’il ne s’agit pas seulement de méthodes de mesure et de quantification. Combiner évaluation avec innovation exige de la discipline dans l’innovation et de la souplesse dans l’évaluation. Les bases de connaissances pour l’innovation comme pour l’évaluation ont vu une évolution importante ces dernières années, permettant le développement de synergies entre ces deux domaines ; les différentes parties prenantes peuvent apporter la pensée évaluative à l’innovation, en tirant parti de ces synergies. La pensée évaluative contribue aux nouveautés en matière d’apprentissage en fournissant des preuves pour documenter, recenser et mesurer le progrès, les succès, les échecs et les obstacles dans l’innovation en cours. Il s’agit de réfléchir aux preuves qui seraient utiles au cours des activités de l’innovation, et donc d’établir un champ d’objectifs et de cibles propices à déterminer le progrès de ces activités, acquérir des connaissances et développer des usages pratiques des nouvelles informations tout au long de la trajectoire de l’innovation. La génération d’hypothèses en cycle continu, le recueil de preuves, et la réflexion sur le progrès permettent aux parties prenantes (par exemple, les leaders de l’innovation, les responsables politiques, les bailleurs de fonds, et les personnes prenant part à l’innovation) d’essayer, d’expérimenter, de faire des erreurs et de considérer où sont ces erreurs, ce qui s’est bien passé ou ce qui a mal tourné, grâce à un bilan nouveau et indépendant du déroulement et des effets de l’innovation. Ce document décrit les enjeux et les approches de chacune des phases du cycle. Il conclut en indiquant les synergies qu’il reste à accomplir, ouvrant le champ à la pensée évaluative, ainsi que des tensions éventuelles à traiter. |
| Date: | 2015–07–03 |
| URL: | http://d.repec.org/n?u=RePEc:oec:eduaab:122-en&r=knm |
| By: | Orman, Cuneyt |
| Abstract: | This paper develops a theory of the firm scope where not only research but also ordinary production employees can generate inventions. Separating research from production (“specialization”) solves the two-tier agency problem of inducing simultaneously research effort and managerial truthful-reporting but is costly when capital markets are imperfect. Improvements in capital markets, therefore, promote specialization, allowing a greater number of specialized firms to be established and also enabling them to undertake innovative projects with larger potential outcomes. Moreover, this capital market improvement effect is stronger for innovative activities that are less capital-intensive and that have weaker synergies with existing production activities. The model can help us understand the explosion of small company innovation in the U.S. since late 1970s and the contribution of venture capital to this change. |
| Keywords: | Innovation, Organizational form, Agency problems, Technological synergies, Financial imperfections. |
| JEL: | D86 D2 D82 O32 G24 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:65441&r=knm |
| By: | Vasaf, Esmaeil; Sanatkhani, Mahboobeh |
| Abstract: | Besides high policy-induced motivations for development of research activities in photovoltaic industry, there have been a few social network studies concentrating on the scientific publication in this field. This study tried to shed light on the structure and evolution of publication network in German PV industry from 1988 to 2013. For this purpose, using the centrality indices, I realized the most influential actors as potential source of knowledge and actors who play the central role in knowledge production and diffusion. In next step, I investigated the dynamic of co-authorship network of scientists. Results showed that against the downward trend of network’s cohesion, overall compared to the same size random generated network, German PV co-authorship network is characterized as a small world network which emphasizes the efficient diffusion of knowledge compare to other type of network. Finally, to disclose the drivers behind the evolution of co-authorship network, I hypothesized two different scenarios. First, using descriptive analysis, the existence of preferential attachment mechanism is investigated. Fitting power law distribution over degree of nodes rejected our hypothesis for all investigating time windows. Therefore, preferential attachment mechanism cannot significantly explain the evolution of the network and reveals that network is robust in response to removal of large nodes. Second, looking at the composition of knowledge on map of science provided strong evidence in support of interdisciplinarity nature of German PV industry. Our descriptive analysis shows that along with existence of leading macro-disciplines such as Materials Science and Physics Applied, new subject categories of science have found a significant position over the existing knowledge domain during the observed period. |
| Keywords: | publication network, PV industry, knowledge diffusion, preferential attachment |
| JEL: | O30 |
| Date: | 2014–11–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:65453&r=knm |
| By: | Rong Rong (Department of Economics, Weber State University); Daniel Houser (Interdisciplinary Center for Economic Science and Department of Economics, George Mason University) |
| Abstract: | Innovation occurs in network environments. Identifying the important players in the innovative  process,  namely  “the  innovatorsâ€,  is  key to understanding the process of innovation. Doing this requires flexible analysis tools tailored to work well with complex datasets generated within such environments. One such tool, cluster analysis, organizes a large data set into discrete groups based on patterns of similarity. It can be used to discover data patterns in networks without requiring strong ex ante assumptions about the properties of either the data generating process or the environment. This paper reviews key procedures and algorithms related to cluster analysis. Further, it demonstrates how to choose among these methods to identify the characteristics of players in a network experiment where innovation emerges endogenously. Length: 30 |
| Keywords: | cluster analysis, k-means algorithm, innovation, networks, laboratory experiment |
| JEL: | C46 C81 |
| Date: | 2014–10 |
| URL: | http://d.repec.org/n?u=RePEc:gms:wpaper:1049&r=knm |
| By: | Joachim Wagner (Leuphana University Lueneburg, Germany) |
| Abstract: | This paper uses a new tailor-made data set to investigate for the first time the links between innovation activities (measured by employees active in research and development) and the extensive margins of exports (number of destination countries; number of goods exported) for manufacturing enterprises in Germany, the third largest exporter of goods on the world market. It documents that more innovative firms outperform less innovative firms at both margins of exports – they export more goods and they export to a larger number of countries. All these differences are statistically highly significant and large from an economic point of view. |
| Keywords: | Extensive margins of exports, Germany, innovation, research and development |
| JEL: | F14 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:lue:wpaper:343&r=knm |