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Globally, urban planners and decision makers are pursuing place-based initiatives to develop and enhance urban infrastructure to optimise city performance, competitiveness and sustainability credentials. New discourses associated with big data, Building Information Modelling, SMART cities, green and biophilic thinking inform research, policy and practice agendas to varying extents. However, these discourses remain relatively isolated as much city planning is still pursued within traditional sectoral silos hindering integration. This research explores new conceptual ground at the Smart – Natural City interface within a safe interdisciplinary opportunity space. Using the city of Birmingham UK as a case study, a methodology was developed championing co-design, integration and social learning to develop a conceptual framework to navigate the challenges and opportunities at the Smart-Natural city interface. An innovation workshop and supplementary interviews drew upon the insights and experiences of 25 experts leading to the identification of five key spaces for the conceptualisation and delivery at the Smart-Natural city interface. At the core is the space for connectivity; surrounded by spaces for visioning, place-making, citizen-led participatorylearning and monitoring.The framework provides a starting point for improved discussions, understandings and negotiations to cover all components of this particular interface. Our results show the importance of using all spaces within shared narratives; moving towards ‘silver-green’ and living infrastructure and developing data in response to identified priorities. Whilst the need for vision has dominated traditional urban planning discourses we have identified the need for improved connectivity as a prerequisite. The use of all 5 characteristics collectively takes forward the literature on socio-ecological-technological relationships and heralds significant potential to inform and improve city governance frameworks, including the benefits of a transferable deliberative and co-design method that generates ownership with a real stake in the outcomes.

The case is based on a real supply chain outsourcing management decision at a major manufacturing company. The company has been disguised for confidentiality reasons. The case discusses different types of outsourcing, supply chain management, the benefits and risks of outsourcing, and various pricing models for outsourcing contracts. Students must make a management decision and answer these questions: Is supply chain outsourcing a viable option for DB Toys? What will the return on investment be? What is the best outsourcing model? What is the best pricing model?

Students learn the different types of outsourcing, supply chain management, the benefits and risks of outsourcing, and various pricing models for outsourcing contracts. Students also learn how to calculate the return on investment of supply chain outsourcing. Most important, the case enables students to understand the strategic context of outsourcing, and to decide which outsourcing model and pricing is appropriate.

International business

Undergraduate/graduate/executive education.

China has become the world's largest producer of automobiles, surpassing the USA and Japan. The Chinese auto industry differs quite significantly from those countries though. While the industry exhibits a substantial degree of concentration in the USA and Japan in early 2011, it remained highly fragmented in China. The Chinese Central Government had announced a desire for consolidation, yet it remained unclear whether a significant shakeout would occur in the near term.

Like many Chinese automakers, Chang'an partnered with well-known global auto makers to develop, produce, and distribute its products. In the coming years, Chang'an hoped to develop more independence from its foreign partners, including the production and distribution of self-branded cars. However, the company grappled with how it could strive for independence while managing its existing joint ventures. Executives worried too about how to compete with foreign automakers who had achieved global economies of scale.

The case provides a rich description of the evolution of the Chinese auto industry, and it documents how the Chinese industry differs from other global markets. Readers can analyze the extent to which they believe scale economies provide foreign firms an advantage over smaller Chinese rivals, and they can evaluate the conventional wisdom regarding the industry's minimum efficient scale. The case also provides a detailed account of Chang'an's rise to prominence. The case concludes by offering an in-depth description of the firm's key rivals, and it presents the key questions being considered by Chang'an executives in 2011.

Enables students to examine how and why an industry's structure can differ substantially across geographic markets.

Enables students to examine whether the need to achieve economies of scale may cause substantial consolidation in the Chinese auto industry.

Provides an opportunity to evaluate the pros and cons of the joint venture strategies employed in China.

Provides an opportunity to examine how a relatively small firm can position itself against large multinationals in a high-growth emerging market.

Teaching notes.

The purpose of this paper is to examine how refugees from a professional career domain restore a coherent narrative when confronting barriers to recognition of their former career status. It focuses in particular on the identity work in which they engage in order to reconcile tensions between their current status as refugees and their professional identity.

In total, 15 refugees to the UK who were professionally qualified in medicine or teaching in their country of origin took part in interviews or focus groups exploring career barriers, plans and future aspirations. Initial inductive thematic analysis identified recognition of professional identities as a primary concern. Further analytic iterations between theory and empirical material sharpened the focus on identifying the tensions in their professional identity work.

Participants struggled both to restore their former professional identity and to develop alternative identities. Professional identity work limited, but also sustained them in the face of barriers they encountered as refugees.

More support for refugee career development would facilitate adaptation to local job markets, thereby addressing gaps in education and health services in the UK.

The paper highlights the tensions in refugee professional identity work and particularly the challenges and rewards of professional identification in the face of employment barriers.

This chapter provides a conceptual framework for inclusive education for learners with low-incidence disabilities grounded in the argument that increased access and participation in socially valued roles, activities, and settings are both the most fundamental goals of the inclusive education process and also the primary means in which these goals are achieved. By challenging traditional views of learning development as merely the acquisition of skills, the proposed framework largely considers the social contexts in which the development of new skills takes place. Through the presentation of three case illustrations, the authors describe ways in which the framework may be relevant to designing and evaluating programs of inclusive education that are responsive to the needs of diverse communities, including those in a variety of international contexts.

The purpose of this paper is to explore independent small business owners’ perceptions of franchisees relationships with their franchisors, their fellow franchisees within the chain, their employees and their customers.

The authors use a qualitative approach and, more specifically, 26 in-depth interviews conducted with independent small business owners from various business sectors.

These independent small business owners perceive that franchisees have a dependency-based relationship with their franchisors; a competition-based relationship with their fellow franchisees; a rather complicated relationship with their employees; and a superficial relationship with their customers.

This study contributes to the franchising literature by presenting an outside-chain view of franchisees’ relationships with their franchisors, other franchisees, employees and customers.

The findings may have practical implications for franchisors, enabling them to better understand the concerns of independent small business owners as potential franchisee candidates.

The outside-chain view of franchisees’ relationships is innovative.

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.

After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.

This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.

The purpose of this paper is to demonstrate how signaling support services and contractual arrangements that create value for incumbent franchisees can help to create value for the whole network by attracting prospective franchisees.

Using data from Bond's Franchising Report the study analyses franchisors operating between 1994 and 2008 via a Generalized Method of Moments (GMM) model for an unbalanced panel of 2,474 franchisors.

Training, financial assistance, sub-franchising and restrictions against passive ownership, and the use of area development agreements are found to be valuable for prospective franchisees. Experience and the number of company-owned and franchised units also attract prospective franchisees.

Our findings imply that not all value-creating services and contractual arrangements are interpreted in the same way by prospective franchisees. Franchisors should offer training and financial assistance to new franchisees in the early stages of a franchise. They should also allow sub-franchising but restrict passive ownership and offer the possibility for area development agreements as contractual arrangements to appeal to new franchisees. Franchisors should focus not only on expansion, but should view the chain in a holistic manner by sustaining and growing both franchised and company-owned units.

The findings contribute to the franchising literature by providing new evidence on how offering and signaling some contractual arrangements and support services can help franchisors create value for incumbent franchisees and can attract new franchisees. Our research shows that value in franchising is created differently depending on whether the franchisees are incumbent or prospective.