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on Knowledge Management and Knowledge Economy |
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Issue of 2017‒10‒08
five papers chosen by Laura Ştefănescu Centrul European de Studii Manageriale în Administrarea Afacerilor |
| By: | Chu, Angus C.; Shen, Guobing; Zhang, Xun |
| Abstract: | In an open-economy R&D-based growth model with two intermediate production sectors, we find that strengthening intellectual property rights (IPR) has a positive effect on innovation in the sector that uses domestic inputs but both positive and negative effects on innovation in the sector that uses foreign inputs. We test these results using an empirical analysis of matching samples that combine Chinese provincial IPR data with industrial enterprises database and customs database. |
| Keywords: | Intellectual property rights; imports; knowledge spillovers; innovation |
| JEL: | F43 O31 O34 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81706&r=knm |
| By: | Asongu, Simplice; Nwachukwu, Jacinta |
| Abstract: | Compared to other regions of the world, Africa is lagging in its drive toward knowledge-based economies. This study surveys the literature in order to highlight the policies and strategies with which African countries can accelerate their current drive towards knowledge economies. These are discussed in terms of the four pillars of the World Bank’s knowledge economy framework. They are the indices for: (i) education and skilled population, (ii) information and communication technology, (iii) economic incentives and institutional regime and (iv) innovation systems. |
| Keywords: | Knowledge economy; Development; Africa |
| JEL: | O10 O30 O38 O55 O57 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:81701&r=knm |
| By: | Raluca-Ioana Iorgulescu (Institute for Economic Forecasting, Romanian Academy); Carmen Beatrice Păuna (Institute for Economic Forecasting, Romanian Academy); Marioara Iordan (Institute for Economic Forecasting, Romanian Academy); Tiberiu Diaconescu (Institute for Economic Forecasting, Romanian Academy); Gabriela Bilevski (Institute for Economic Forecasting, Romanian Academy); Thomas Brekke (University College of Southeast Norway, Norway); Ole Henrik Gusland (University College of Southeast Norway, Norway); Lasse Berntzen (University College of Southeast Norway, Norway) |
| Abstract: | Addressing climate change through the reduction of fossil resources dependency requires the transition from fossil-based industrial production to a bio-based (green) industrial structure. The development of bio-based industry clusters might be part of the solution. This paper introduces the ‘bioeconomy’ concept and the Triple Helix model that are useful when examining the development of green industries clusters in the emerging digital era; the Smart City model might promote new ways to create profitable and sustainable businesses. Examples of good practices and clusters for green industries from Norway are provided and some success stories including Romanian firms are presented. |
| Keywords: | green industry, bioeconomy, bio-based industry cluster, triple helix model, smart cities, Romania |
| JEL: | L86 Q55 Q57 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:rjr:wpiecf:170701&r=knm |
| By: | Chetty, Krish; Qigui, Liu; Gcora, Nozibele; Josie, Jaya; Wenwei, Li; Fang, Chen |
| Abstract: | To promote digital transformation, equal emphasis needs to be placed on digital skills development as to infrastructure development. Integral to investment in digital skills development is the subsequent management and evaluation of digital training programmes. This paper assesses mechanisms to ensure digital training programmes are adequately managed using a standardized data collection framework to measure an internationally accepted digital literacy index. Such an index must be defined by an agile definition of digital literacy responsive to the fluid nature of the digital economy. The paper also explores the extent to which a G20 advisory body may inform a nationally representative data collection strategy within the context of a data collection process that is cognizant of the evolving demands of businesses and users alike. |
| Keywords: | Digital literacy,digital skills,digital divide,digitalization,information literacy,computer literacy,media literacy,communication literacy,technology literacy,agile policy making,representative sampling |
| JEL: | C83 J20 J22 J23 J24 I24 I25 O15 O19 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:201769&r=knm |
| By: | Martin Daniel Ackermann (Graduate School of Business Leadership (SBL), Unisa, Pretoria, Gauteng, South Africa Author-2-Name: John Andrew van der Poll Author-2-Workplace-Name: Graduate School of Business Leadership (SBL), Unisa, Pretoria, Gauteng, South Africa Author-3-Name: "Huibrecht Margaretha van der Poll " Author-3-Workplace-Name: Graduate School of Business Leadership (SBL), Unisa, Pretoria, Gauteng, South Africa) |
| Abstract: | "Objective – Business Intelligence has little bearing with graphs and dashboards of traditionally defined Business Intelligence. Rather it is all about experience and sound judgement of the person at the helm of the decision-making process. In line with this view, we evaluate and subsequently, reposition the current definition of Business Intelligence in the literature. Methodology/Technique – The initial development of the data, information, knowledge and wisdom (DIKW) hierarchy excluded intelligence and so it never questioned the accepted definition of Business Intelligence. The extended DIKIW hierarchy includes intelligence but we raise the question about the definition of intelligence in Business Intelligence. This paper positions the existing definition of Business Intelligence as Business Information instead, and so, it redefines traditional Business Intelligence. Findings – Applying the DIKIW hierarchy, the new definition of Business Intelligence is shown in equation as the transformation of “Business Data to Business Information to Business Knowledge to Business Intelligence to Business Wisdom”. Novelty – The impact of the new definition of Business Intelligence is that it changes its meaning from one that belongs to information science into one that is a human behavioural science and profiling concept. It does not do away with the existing work in literature but it redefines Business Intelligence as belonging to the realm of Business Information. " |
| Keywords: | Business Intelligence; DIKW hierarchy; DIKIW hierarchy; Knowledge Management; Wisdom |
| JEL: | L25 M10 |
| Date: | 2016–12–27 |
| URL: | http://d.repec.org/n?u=RePEc:gtr:gatrjs:jmmr115&r=knm |