Emerging markets and U.S. horizontal merger guidelines: A Turkish competition law perspective

Developed economies have historically been a model for emerging market economies, particularly in the development and enforcement of competition laws. Modifications to competition law rules in developed economies, however, may not always be practical for emerging market economies to adopt. Insufficient knowledge, experience, and power of competition law authorities in emerging markets require a structure with greater legal certainty rather than one that provides a wide berth for interpretation. This article provides an overview of some of the significant developments in the 2010 U.S. Horizontal Merger Guidelines from an emerging market perspective. While taking into consideration the general characteristics of emerging market countries, the treatment of four specific topics under the new Guidelines will be scrutinized from a law and economics perspective: market definition, market shares and market concentration, market entry, and coordinated effects. This article also delves into discussions of Turkish competition law matters, as an example of emerging merger regime models, with respect to each of the four areas of discussion. © The Author (2014).Published by Oxford University Press

STEM education in the twenty-first century: learning at work-an exploration of design and technology teacher perceptions and practices

Teachers’ knowledge of STEM education, their understanding, and pedagogical application of that knowledge is intrinsically linked to the subsequent effectiveness of STEM delivery within their own practice; where a teacher’s knowledge and understanding is deficient, the potential for pupil learning is ineffective and limited. Set within the context of secondary age phase education in England and Wales (11–16 years old), this paper explores how teachers working within the field of design and technology education acquire new knowledge in STEM; how understanding is developed and subsequently embedded within their practice to support the creation of a diverse STEM-literate society. The purpose being to determine mechanisms by which knowledge acquisition occurs, to reconnoitre potential implications for education and learning at work, including consideration of the role which new technologies play in the development of STEM knowledge within and across contributory STEM subject disciplines. Underpinned by an interpretivist ontology, work presented here builds upon the premise that design and technology is an interdisciplinary educational construct and not viewed as being of equal status to other STEM disciplines including maths and science. Drawing upon the philosophical field of symbolic interactionism and constructivist grounded theory, work embraces an abductive methodology where participants are encouraged to relate design and technology within the context of STEM education. Emergent findings are discussed in relation to their potential to support teachers’ educational development for the advancement of STEM literacy, and help secure design and technology’s place as a subject of value within a twenty-first Century curriculum

A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprises

A core subfeld of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specifc. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifcally for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry

From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for several decades but is typically performed by a limited number of people in organizations with an affinity to modeling. What is captured in models is only a fragment of what ought to be captured. Thus, this research note argues that EM is far from its maximum potential. Many people develop some kind of model in their local practice without thinking about it consciously. Exploiting the potential of this “grass roots modeling” could lead to groundbreaking innovations. The aim is to investigate integration of the established practices of modeling with local practices of creating and using model-like artifacts of relevance for the overall organization. The paper develops a vision for extending the reach of EM, identifies research areas contributing to the vision and proposes elements of a future research Agenda

Meta Modeling for Business Process Improvement

Conducting business process improvement (BPI) initiatives is a topic of high priority for today’s companies. However, performing BPI projects has become challenging. This is due to rapidly changing customer requirements and an increase of inter-organizational business processes, which need to be considered from an end-to-end perspective. In addition, traditional BPI approaches are more and more perceived as overly complex and too resource-consuming in practice. Against this background, the paper proposes a BPI roadmap, which is an approach for systematically performing BPI projects and serves practitioners’ needs for manageable BPI methods. Based on this BPI roadmap, a domain-specific conceptual modeling method (DSMM) has been developed. The DSMM supports the efficient documentation and communication of the results that emerge during the application of the roadmap. Thus, conceptual modeling acts as a means for purposefully codifying the outcomes of a BPI project. Furthermore, a corresponding software prototype has been implemented using a meta modeling platform to assess the technical feasibility of the approach. Finally, the usability of the prototype has been empirically evaluated

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Existing classification techniques that are proposed previously for eliminating data inconsistency could not achieve an efficient parameter reduction in soft set theory, which effects on the obtained decisions. Meanwhile, the computational cost made during combination generation process of soft sets could cause machine infinite state, which is known as nondeterministic polynomial time. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of searching domain space using a developed Markov chain model. Furthermore, this study introduces an efficient soft set reduction-based binary particle swarm optimized by biogeography-based optimizer (SSR-BPSO-BBO) algorithm that generates an accurate decision for optimal and sub-optimal choices. The results show that the decision partition order technique is performing better in parameter reduction up to 50%, while other algorithms could not obtain high reduction rates in some scenarios. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms the other optimization algorithms in achieving high accuracy percentage of a given soft dataset. On the other hand, the proposed Markov chain model could significantly represent the robustness of our parameter reduction technique in obtaining the optimal decision and minimizing the search domain.Published versio