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Analytic Hierarchy Process Expansion for Innovation Performance Measurement Framework

DOI: 10.1155/2013/632845

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Innovation is a top strategic priority for the majority of companies. The need for innovation becomes more and more evident in the current corporate world, and the purpose of innovation is to create business value. The Analytic Hierarchy Process (AHP) is a structured technique for organizing and analyzing complex decisions. This paper is targeting the framework design of the innovation performance criteria and provides the general guidelines to evaluate the relationship between the criteria by using AHP expansion. 1. Introduction Innovation is a top strategic priority for the majority of companies. The need for innovation becomes more and more evident in the current corporate world, and the purpose of innovation is to create business value. Western companies in particular are facing high competition due to the continuous globalization. This brings focus on making innovation more comprehensive in order to be effectively able to manage and improve it. The value of innovation can take many different forms, such as incremental improvements to existing products, the creation of entirely new products and services, or reducing costs. Enterprise is driven to survive and to grow, and in a rapidly changing market the only way to do either is to innovate effectively. But most companies are failing to keep pace in a critical part of the equation: metrics and measurement. It may be called innovation uncertainty principle as many of the ways that companies might want to measure innovation can significantly impede the innovation process, because innovation involves a venture into the unknown, and if we try to pin these unknowns down too fast we may make them harder to recognize and realize. The Analytic Hierarchy Process (AHP) is a structured technique for organizing and analyzing complex decisions: selecting among competing alternatives in a multiobjective environment, the allocation of scarce resources, and forecasting. Although it has wide applicability, the axiomatic foundation of the AHP carefully delimits the scope of the problem environment. Based on mathematics and psychology, it was developed by Thomas L. Saaty in the 1970s and is quite often referred to as the Saaty method. The method is mathematically simple calculations but the very practical tool. There is the homogeneity axiom states that the elements being compared should not differ by too much in the property being compared [1–3]. If this is not the case, large errors in judgment could occur. When constructing a hierarchy of objectives, one should attempt to arrange elements in clusters, so that they

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