MSc. Thesis Defense: Mete Sevinç
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Mete Sevinç
Industrial Engineering, M.Sc. Thesis, 2014

Thesis Jury
Prof. Dr. Gündüz Ulusoy (Thesis Supervisor), Assoc. Prof. Kemal Kilic 
Prof. Dr. Lütfihak Alpkan

Date &Time: August 06th, 2014 – 10:00

Place: FENS G029

Keywords: Manufacturing Industry, Statistical Analysis, Clustering, Innovation Taxonomy, Drivers of Innovativeness, Data Envelopment Analysis


Innovation is an important competitiveness determinant and is studied extensively by both the academicians and the practitioners particularly in the last decades. Dichotomous (e.g., High vs. Low) research is widely available in innovation literature in terms of analyzing the innovative capabilities and the defined determinants of innovativeness. Our approach in this thesis extends the literature by providing a conceptual taxonomy for the capabilities and determinants of innovativeness. The results demonstrate that the investigation on differences among groupings of firms yields statistical significance and actionable insights.

The main objective of this study is to model and analyse the innovative capabilities and determinants of innovativeness for a firm through conducting statistical analysis and implementing information visualization on a dataset comprising the results of an innovation survey of 184 Turkish manufacturing companies. Innovative capabilities of firms are among the leading factors defining their competitiveness, thus it is of extreme importance to define and analyze these skills and conclude with insights related to the enterprise and the industry. For this purpose, clustering analysis, statistical testing and Data Envelopment Analysis (DEA) are performed and the resulting visualizations are provided. Four clusters are formed from the data, and these are labeled as the Leading innovators, Followers, Inventors and Laggers respectively. These clusters are investigated under the components of intellectual capital, organizational structure, organizational culture, barriers to innovation, monitoring and collaborations. DEA analyses provide productivity as well as benchmarking results through efficiency scores.  The end results obtained from the analyses are commented upon.