A Mexican edutainment business model: KidZania
Subject area 
 
 
 
 
Management of technology, technological innovation, business innovation and new product development, innovation, design and strategy, entrepreneurship innovation and leadership strategic planning of technological innovation. 
 
 
 
 
Study level/applicability 
 
 
 
 
MBA. 
 
 
 
 
Case overview 
 
 
 
 
KidZania® is a Mexican company of family entertainment centers, founded in 1996 by Luis Javier Laresgoiti Fernandez and fully developed by Xavier Lopez Ancona. An innovative concept inspired in a fusion of nursery and theme park, KidZania®, brings together strong brands as partners to support their own and offer a complete entertainment and educational experience to kids between two and 16 years old. A unique business model, involvement of experts and a committed board of directors has been the key to the innovation of KidZania®. Its managers, by 2011, operate eight centers – two in Mexico, two in Japan and the rest in Indonesia, Portugal, United Arab Emirates and South Korea – and have plans to expand to more countries in Asia, Latin America and Europe in 2011 and finally to the USA. The future of KidZania® seemed bright, and the manager of the company believed that the growth appeared unstoppable because the purpose was to grow on 100 percent. The strategy appeared clear: dominate in the emerging and consolidated markets (big cities) in order to strengthen its competitive position and then, enter the US market with all the muscle and take the lead in the biggest market. But what was the competition going to do about it? What will the moves be for big players like Walt Disney – which had revenues of US$38.06 billion (USSEC, 2010), for example? Will the competitors try to buy the new entrant in order to build it up or to disappear it? Or will they try to imitate KidZania®? What would be the future of this new edutainment business model? 
 
 
 
 
Expected learning outcomes 
 
 
 
 
This case has been used in executive and MBA courses in creating and sustaining innovation, recognizing disruptive technologies, and in identifying effective methods of marketing a new innovative business model. Instructors can use the case to achieve the following two learning objectives: the KidZania® case helps students to refine their understanding of the model of disruption. They are forced to look closely at the product/service and decide whether it is a disruptive innovation or a sustaining innovation. This close examination becomes a helpful tool as students think about what decisions they would make to secure the success of the KidZania® in the entertainment market. The KidZania® case allows students the opportunity to develop their knowledge and understanding of the model “skating to where the money is”. Based on their analysis of the company and product students, must decide whether the KidZania® is a business that will produce sustained revenue and is worth investing in. 
 
 
 
 
Supplementary materials 
 
 
 
 
Teaching notes are available for educators only. Please contact your library to gain login details or e-mail support@emeraldinsight.com to request teaching notes.
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Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images
Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.