A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey

The Community Structure of R&D Cooperation in Europe. Evidence from a social network perspective

The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 1998-2002, within the 5th Framework Program. The cooperations in this Framework Program give rise to a bipartite network with 72,745 network edges between 25,839 actors (representing organizations that include firms, universities, research organizations and public agencies) and 9,490 R&D projects. With this construction, participating actors are linked only through joint projects. In this paper we describe the community identification problem based on the concept of modularity, and use the recently introduced label-propagation algorithm to identify communities in the network, and differentiate the identified communities by developing community-specific profiles using social network analysis and geographic visualization techniques. We expect the results to enrich our picture of the European Research Area by providing new insights into the global and local structures of R&D cooperation across Europe

Business model requirements and challenges in the mobile telecommunication sector

The telecommunications business is undergoing a critical revolution, driven by innovative technologies, globalization, and deregulation. Cellular networks and telecommunications bring radical changes to the way telecom businesses are conducted. Globalization, on the other hand, is tearing down legacy barriers and forcing monopolistic national carriers to compete internationally. Moreover, the noticeable progress of many countries towards deregulation coupled with liberalization is significantly increasing telecom market power and allowing severe competition. The implications of this transition have changed the business rules of the telecom industry. In addition, entrants into the cellular industry have had severe difficulties due to inexistent or weak Business Models (BMs). Designing a BM for a mobile network operator is complex and requires multiple actors to balance different and often conflicting design requirements. Hence, there is a need to enhance operators’ ability in determining what constitutes the most viable business model to meet their strategic objectives within this turbulent environment. In this paper, the authors identify the main mobile BM dimensions along with their interdependencies and further analysis provides mobile network operators with insights to improve their business models in this new ‘boundary-less’ landscape

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Research on organic agriculture in the Netherlands : organisation, methodology and results

Chapters: 1. Organic agriculture in the Netherlands; 2. Dutch research on organic agriculture: approaches and characteristics; 3. Dutch knowledge infrastructure for organic agricultur'; 4. Sustainable systems; 5. Good soil: a good start; 6. Robust varieties and vigorous propagation material; 7. Prevention and control of weeds, pests and diseases; 8. Health and welfare of organic livestock; 9. Animal production and feeding; 10. Special branches: organic greenhouse production, bulbs, ornamentals and aquaculture; 11. Healthfulness and quality of products; 12. Economy, market and chain; 13. People and society. A publication of Wageningen UR and Louis Bolk Institut

Towards evaluation design for smart city development

Smart city developments integrate digital, human, and physical systems in the built environment. With growing urbanization and widespread developments, identifying suitable evaluation methodologies is important. Case-study research across five UK cities - Birmingham, Bristol, Manchester, Milton Keynes and Peterborough - revealed that city evaluation approaches were principally project-focused with city-level evaluation plans at early stages. Key challenges centred on selecting suitable evaluation methodologies to evidence urban value and outcomes, addressing city authority requirements. Recommendations for evaluation design draw on urban studies and measurement frameworks, capitalizing on big data opportunities and developing appropriate, valid, credible integrative approaches across projects, programmes and city-level developments