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on Knowledge Management and Knowledge Economy |
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Issue of 2020‒02‒03
four papers chosen by Laura Ştefănescu Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Avdimiotis, Spyros |
| Abstract: | Several researchers stressed out the importance of tacit knowledge underlying the fact that it is a type of knowledge, almost impossible to articulate, codify and thus to transfer. Based on the argument of Avdimiotis (2016) that tacit knowledge could be acknowledged, acquired and transferred through employees’ behavioral patterns, the present paper seeks to associate emotions- as determinant factor of behavior- with tacit knowledge management in hospitality establishments. To prove the association a quantitative research was held on a stratified sample of 128 hotel employees in Northern Greece. The research model was based on Nonaka and Takeuchi (1995) SECI knowledge transfer model and Salovey and Mayer Emotional Intelligence model. Findings indicate that both Emotional Intelligence and Tacit knowledge are strongly associated, leading to the inference that E.I. is a structural element of tacit knowledge. |
| Keywords: | Tacit Knowledge, Emotional Intelligence, Hotel, and Human Resources Management |
| JEL: | D83 D91 |
| Date: | 2019–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:98125&r=all |
| By: | Sanusi, Anuar; , yulmaini; Yusendra, Aria Eka |
| Abstract: | One important role of universities nowadays, is to develop the entrepreneurial culture. This article discusses on knowledge and technology transfer to society especially in developing entrepreneurship initiative and strategies (IPTEKS bagi Kewirausahaan/IbK), and also strengthening technology and business incubator (INKUBITEK) in IBI Darmajaya, one of leading university from Bandar Lampung, Indonesia to develop young entrepreneurs from among universities students and alumni. The program objectives are to improve entrepreneurship motivation of students and alumni, develop skills and proficiency for students and alumni in mastering management, business strategies, and manage the business environment, and also mentoring effective entrepreneurial business as the new business owner. The activities from the program consist of good corporate governance in business, entrepreneur training, training in an effective business plan, marketing apprentice and volunteer, field visit on developing business on Lampung, business execution and management consultancy, provision of capital for the business owner, and business mentoring from the well-established business player on Lampung. |
| Date: | 2017–12–10 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:uf5b2&r=all |
| By: | Diekhof, Josefine; Eckl, Verena; Krieger, Bastian; Licht, Georg; Nguyen, Thu-Van; Peters, Bettina; Rammer, Christian; Stenke, Gero |
| Abstract: | The exchange of knowledge between science and industry has been a focus of innovation research and policy for many decades. New developments in the way technologies are generated, shared, and transferred into new products, services, and business models are currently re-emphasising science-industry interactions. Main drivers are the emergence of open innovation models, the increased internationalisation of innovation processes, the rise of digital platforms, new modes of governance in public research, and the enlarged role of disruptive innovations. At the same time, the measurement of knowledge flows is still limited, and indicators on recent trends in science-industry interaction are lacking. This limits innovation policy in monitoring changes and addressing challenges. A conference in October 2019 in Berlin brought together industry representatives, researchers, and policy makers to discuss these developments and how the measurement of science-industry links could be improved. This policy brief summarises key trends in science-industry collaborations, presents existing indicators and discusses ways to improve our indicator system on knowledge flows between science and industry in order to better inform policy. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewpbs:92019&r=all |
| By: | Reinhold Kosfeld (University of Kassel); Timo Mitze (Southern University of Denmark) |
| Abstract: | Modern cluster theory provides reasons for positive external effects that accrue from the interaction of spatially proximate firms operating in common and related fields of economic activity. In this paper, we examine the impact of R&D-intensive clusters as a key factor of regional competitiveness on productivity and innovation growth. In analogy to the industry-oriented concepts of related and unrelated variety (Frenken, Van Oort, Verburg 2007), we differentiate between effects of cluster specialisation and diversity. The identification of R&D-intensive clusters is based on a hybrid approach of qualitative input-output analysis and spatial scanning (Kosfeld and Titze 2017). Our empirical study is conducted for a panel of German NUTS-3 regions in 2001-2011. To comprehensive account for specialisation and diversity effects of clustering we adopt a spatial econometric approach, which allows us to identify these effects beyond the geographical boundaries of a single region. After controlling for regional characteristics and unobserved heterogeneity, a robust ‘cluster strength’ effect (i.e. specialization) on productivity growth is found within the context of conditional convergence across German regions. With regard to the underlying mechanisms, we find that the presence of a limited number of R&D-intensive clusters in specific technological fields is most strongly linked to higher levels of regional productivity growth. While we also observe a positive effect of cluster strength on innovation growth once we account for spatial spillovers, no significant effects of ‘cluster diversity’ can be identified. This indicates that some but not all cluster-based regional development strategies are promising policy tools to foster regional growth processes. |
| Keywords: | Industry clusters, regional competitiveness, cluster specialisation, cluster diversity, correlated random effects model |
| JEL: | L16 R11 R15 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:mar:magkse:202001&r=all |