• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.4-14
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• 2010

Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. This study addresses the specific types of collaboration used in the fashion industry while also examining strategies that have been most successful for fashion companies and perceived benefits of collaboration from the customer perspective. In the present study we define fashion companies and brands as collaborators and their partners or stakeholders as collaboratees. We define collaboration as a cooperative relationship where more than two companies, brands or individuals provide customers with beneficial outcomes utilizing their own competitive advantages on an equal basis. Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. Through collaboration, fashion companies have pursued both tangible differentiation, such as design and technology applications, and intangible differentiation such as emotional and psychological benefits to customers. As a result, collaboration within the fashion industry has become an important, value creating concept. This qualitative study utilized case studies and in-depth interview methodologies to examine customers' attitudes concerning collaboration in the fashion industry. A total of 173 collaboration cases were identified in Korean and international markets from 1998 through December 2008, focusing on fashion companies. Cases were collected from documented data including websites and industry data bases and top ranked portal search sites such as: Rankey.com; Naver, Daum, and Nate; and representative fashion information websites, Samsungdesignnet and Firstviewkorea. Cases were collected between November 2008 and February 2009. Cases were selected for the analysis where one or more partners were associated with the production of fashion products (excluding textile production), retail fashion products, or designer services. Additional collaboration case information was obtained from news articles, periodicals, internet portal sites and fashion information sites as conducted in prior studies (Jeong and Kim 2008; Park and Park 2004; Yoon 2005). In total, 173 cases were selected for analysis that clearly exhibited the benefits and outcomes of collaboration efforts and strategies between fashion companies and stakeholders. Findings show that the overall results show that for both partners (collaborator and collaboratee) participating in collaboration, that the major benefits are reduction of costs and risks by sharing resource such as design power, image, costs, technology and targets, and creation of synergy. Regarding types of collaboration outcomes, product/design was most important (55%), followed by promotion (21%), price (20%), and place (4%). This result shows that collaboration plays an important role in giving life to products and designs, particularly in the fashion industry which seeks for creative and newness. To be successful in collaboration efforts, results of the depth interviews in this study confirm that fashion companies should have a clear objective on why they are doing the collaboration. After setting the objective, they should select collaboratees that match their brand image and target market, make quality co-products that have definite concepts and differentiating factors, and also pay attention to increasing brand awareness. Based on depth interviews with customers, customer benefits were categorized into six factors: pursuit for individual character; pursuit for brand; pursuit for scarcity; pursuit for fashion; pursuit for economic efficiency; and pursuit for sociality. Customers also placed more importance on image, reputation, and trust of brands regarding the cases shown in the interviews. They also commented that strong branding should come first before other marketing strategies. However, success factors recognized by experts and customers in this study showed different results by subcategories. Thus, target customers and target market should be studied from various dimensions to develop appropriate strategies for successful collaboration.

• Journal of Korean Home Economics Education Association
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• v.19 no.2
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• pp.1-12
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• 2007

This study analyzed the recognition and utilization regarding teacher's guidebook for middle school technology-home economics class in the 7th Educational Curriculum. The data were collected via e-mail to teachers teaching home economics in middle schools. These e-mail addresses were acquired from middle school web pages registered on the Educational Board. The 355 data were analyzed using the SPSS program. The results were as follows: First, teachers recognized highly the necessity of teacher's guidebook. However, as the actual guidebook was not adequately helpful, the overall degree of satisfaction was relatively low. Teachers utilizing guidebook had more positive recognition on teacher's guidebook than teachers who did not. And teachers majored in technology education thought teacher's guidebook more helpful compared with teachers majored in home economics education. Second, teachers referenced teacher's guidebook mostly for field practice guidance. Third, teachers who did not utilize teacher's guidebook used other reference materials such as Internet Web sites and audiovisual materials. They were most commonly used for the reason that the contents were ample and easy to access. Fourth, the followings were suggested to improve teacher's guidebook. The provision of learning contents that can be practically used in class, the various samples of teaching-learning method, the specified methods of planning and criteria for performance assessment, the adequate supplementations regarding textbook contents, and the improvement of the outward layout format of the guidebook.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.