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Gazi Üniversitesi İİBF Dergisi

Yıl 2010 , Cilt 12 , Sayı 1

Makale özeti ve diğer detaylar.

Makale özeti
Başlık :

Türk tekstil kümelerindeki bilgi tabanı farklılıklarının ölçümü ve yenilikçilik

Yazar kurumları :
Dokuz Eylül Üniversitesi, İşletme Fakültesi, İngilizce İktisat Bölümü1
Görüntülenme :
150
DOI :
Özet Türkçe :

Yapılandırılmış bölgesel avantajlar yaklaşımı teorik çerçevesini takiben, bu çalışmanın amacı İstanbul ve Denizli'de bulunan ve birbirinden farklı bilgi tabanları ve bölgesel yenilik sistemlerine sahip olan tekstil kümelerinin yenilikçilik kapasiteleri arasındaki farkları tespit etmektir. Bölgeler öğrenme, bilgi paylaşımı ve yenilik yaratma konularında önemli olmakla beraber, bölgesel politika girişimlerinin tasarımı ve başarısı; bölgesel yenilik sistemleri, yönetim biçimi ve politika yaklaşımlarına göre farklılık göstermektedir. Veriler, İstanbul ve Denizli'de bulunan firmaların (sırasıyla 22 ve 32 firma) yöneticileriyle yapılan görüşmeler sonucu elde edilmiştir. Görüşmelerde, ESF-ECRP Yapılandırılmış Bölgesel Avantajlar projesi bünyesinde oluşturulan anket kullanılmıştır. Elde edilen sonuçlar, bilgi oluşumu ve bilgiyi kullanma sürecinin, aynı sektörde dahi yaş, büyüklük, eğitim seviyesi, istihdam kaynakları, bilgi kaynağı ve yenilikçiliğe bağlı olarak anlamlı biçimde farklılaştığını göstermektedir. İstanbul firmaları daha karmaşık ve dinamiktir. Denizli firmaları ise çoğunlukla pratik yeteneklerine güvenen geleneksel firmalardır. Sonuç olarak, bu çalışma her bölgeye aynı politikaların uygulanamayacağı gerçeği ortaya koymuştur. Yapılması gereken farklı bilgi tabanlarına sahip alt sektörlerdeki iletişim ağlarının yapılarını araştırarak yenilikçilik kapasiteleri ve bilgi tabanları arasındaki ilişkileri ortaya çıkarmaya çalışmak olmalıdır. Ayrıca yenilikçilik politikaları farklı bilgi tabanlarına sahip olan bölgeler arasındaki farkları hesaba katarak oluşturulmalıdır.

Özet İngilizce :

Successful regional development requires the ability and potential of firms and other regional actors to have access to knowledge and to create innovation through learning. In addition to regions' importance in learning, knowledge and innovation, the plan and success of regional policy initiatives show distinctions with respect to regional innovation systems and policy approaches. Knowledge sharing and innovativeness differ across the types of regional innovation systems, modes of governance and policy approaches in different regions. Thus, the idea of "one size fits all" regional policy does not work (Tödtling & Trippl, 2005). Consequently, regional development policies should be structured to be are region-sensitive and accountable for diversities across sectors and regions (Asheim, Boschma, & Cooke, 2008). Moreover, successful regional policy initiatives should be able to transform knowledge into innovation for sustained competitiveness. Innovation requires that firms make use of internal and external resources. Innovation being an evolutionary process necessitates the generation, diffusion, application and exploitation of knowledge by regional actors. Regional innovation systems which consist of interaction of knowledge generation and exploitation subsystems are crucial for innovativeness of a region. In this context, the knowledge base of the firms stands as an important determining factor for their innovativeness. Knowledge base takes into account of the sectors' knowledge creation process in addition to the interplay between actors and the knowledge that is created, transmitted and absorbed (Modysson & Asheim, 2006). There are three types of knowledge bases (analytical, synthetic and symbolic) which include different mixes of tacit and codified knowledge. Differences in knowledge bases help in explaining firms' ability to create, apply and exploit knowledge for the end of innovation. Analytical knowledge base covers science-based and codified knowledge where new knowledge creation and learning is based on the interaction with the knowledge infrastructure. Traditional industries such as food, engineering and textile industry are examples of synthetic knowledge base which relies more on practical skills, learning by doing, experience and interaction. Symbolic knowledge base includes creative industries such as advertising, fashion design and industrial design. Following with structured regional advantages approach to its theoretical framework, the aim of this work is to determine the differences of innovation capacities of textile clusters in Istanbul and Denizli which has different knowledge bases and regional innovation systems. Istanbul is characterized as Metropolitan region with the problem of fragmentation while Denizli is a peripheral region with the problem of organizational thinness. In Istanbul, there are 21 private and public universities, 2 techno-parks, 560 non-governmental institutions whereas, there is only one public university, 40 non-governmental institutions and no techno park in Denizli. Although Istanbul is more developed in terms of knowledge generation infrastructure compared to Denizli, there is significant lack of interaction between firms; and between firms and public/private institutions. Furthermore, the textile clusters analyzed in this study have significantly different knowledge bases. Istanbul fashion design firms operate under symbolic knowledge base whereas Denizli bathrobe and home textile firms entail the properties of synthetic knowledge base. Data is obtained from interviews with firms in Istanbul and Denizli (22 and 32 respectively). The questionnaire for the interviews is established within ESF-ECRP Structured Regional Advantage Project. Results show that knowledge generation and usage differentiate within the same sector with respect to age, education level, employment and innovation. Istanbul firms are more complex and dynamic. Denizli firms usually are traditional firms that depend on practical ability. This study revealed that same policies cannot be applied to each region. What is needed to be done is by researching the different knowledge base of the structure of the communication networks in the sub-sectors, the relationship between innovative capacity and knowledge base should be uncovered. Additionally, innovative policies should be created by taking into account the differences between regions which have different knowledge base.

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