اندازه گیری بهترین شیوه نوآوری: بهبود شاخص نوآوری یکپارچه سازی آستانه و اثرات هم افزایی

Innovation, as a competitive economic factor, is a process that requires a continuous, evolving and mastered management. Therefore innovative companies need to measure their innovation capacity. Literature attests of research in the field of innovation measurement or the innovation abilities evaluation. One major theoretical problem consists in elaborating mathematical models that consider the threshold effect and synergy between innovation practices and verify their validity. In this article, mathematical approaches supplementing multi-criteria models are suggested.

Top management attention was formerly directed toward cost reduction, delivery time reduction and quality in order to become and remain competitive on the market. By extension, new criteria are emerging to successfully face competitors: among others—innovation. The ability of companies to meet consumer expectations depends deeply on their ability to innovate and deliver new products at competitive prices. Innovation is a key driver to achieve sustainable competitive advantages and, more particularly, becomes one of the key challenges for small and medium enterprises (SMEs) (O’Regan et al., 2006). Many definitions of innovation are proposed in the literature. According to Schumpeter (1934), it consists in the introduction of new products and production methods, the opening of new markets, the discovery of new raw materials and the implementation of new organizations. Innovation affects manufacturing and process industries, trade activities and social services. The Organization for Economic Co-operation and Development (OECD) states that innovation is “either the transformation of an idea into a new product or the improvement of an existing product or an operational process” (OECD, 1981). Taxonomies have been developed aiming at a better understanding of this complex process (Garcia and Calantone, 2002). Sternberg et al. (2003) established a classification through eight reference groups of companies. “Replication” represents the lower level of Sternberg's scale, while “Integration” is the higher. Other authors outline the cognitive dimension of the innovation process. According to Vandervert, one major aspect is the relationship between the short-term memory and the cognitive perception function, i.e. brain function, and more precisely the construction of new representations of the environment from perceptions. He demonstrates that the concept of generalization capacity and the establishing of dynamic cerebral models (commands that allow generalization capacities) are innovation key factors (Vandervert, 2003). As a consequence, value creation through innovation is depending on the restructuring of the cognitive dimension of those involved in the process. Moreover, innovation relates to a learning process. Furthermore, evidence of a necessary constructivist approach in innovation management was demonstrated, particularly within the SME's sector (Boly et al., 1999). Success of an innovation relies on the ability to identify and seize opportunities. Hence, top management has to: direct attention toward the definition of global development orientations, launch projects and organize an on-going improvement of innovative project management approaches. As a result evaluation of the innovation capacity becomes a major concern in order to ensure a continuous development of these management practices. This article focuses on the measurement of the innovation capacity of industrial firms. More precisely, innovation measurement integrates both the evaluation and the comparison of the innovation capacity of companies. This capacity is correlated to a set of competencies, knowledge, tools and financial resources. Our research is in concern with various aspects of metrology, including: criteria definition, data collection and treatment methods and reference model elaboration. In the field of metrology, “measuring” is defined as an operation allowing the determination of the value of a variable through various features. For instance, measuring is inseparable from social practices. Besides mathematical knowledge, measuring requires knowledge related to institutions, social practices (Vuola and Hameri, 2006) and measuring techniques (Jedrzejewski, 2002). Literature attests of researches in the field of innovation capacity evaluation (for a company or a country) (Furman, 2003). These approaches are generally based on the evaluation of the innovation process outcomes and of the resources devoted to it. All these statements may be considered through three analytical levels (setting aside the individual and collective cognitive level) (Boly, 2004): • Level A: The permanent and global innovation management of the company. This level integrates all the strategic tasks, the organization of new projects launching and the improvement of innovation management practices. • Level B: The outcomes or inputs of a particular project. This level is characterized by a limited period and is concerned with the transformation of an idea up until an innovative product. • Level C: The material characteristics of the innovative product resulting from the new product development process. This level represents the artefact of Level B. This approach suits our special interest in establishing links between evaluation and operational management tasks. The evaluation of Level C is very common in engineering through the definition of the future specifications of the innovative product and its relating performances. Literature is mostly concerned with Level B evaluation. Many authors propose approaches to determine the balance between the outcomes and inputs of innovation. Generally, financial and commercial variables are taken into account (Griffin and Page, 1996; Huang et al., 2004; Kangmao et al., 2005). Financial evaluations are based on classical ratio including financial margins and returns on investment (Crepon et al., 2000). Moreover, specific financial criteria dedicated to innovation resources are suggested: they generally measure time and cost development (Grant and Pennypacker, 2006). Marketing variables include qualitative and quantitative aspects, such as new market shares and customer satisfaction (this last example is dedicated more to product's Level C than to the project's Level B). Strategic considerations, such as competitive advantage, are integrated to evaluate the balance between outcomes and inputs. Several authors (Archibugi and Pianta, 1996; Abraham and Moitra, 2001) add technological criteria, such as the number of patents, to conduct this evaluation. Chiou et al. (1999) suggest a technology-oriented productivity measurement model (TOPMM), more suitable with global management Level A. TOPMM takes into account all the outcomes of the innovation process: the final new product but also intermediary results (prototypes, models, as well as competencies). From a systemic point of view a strong limitation exists: the activities linking the resources to the outcomes of the innovation process are not evaluated. Thus, our special focus is the innovation management activities evaluation at Level A. Chiesa et al. (1996) has elaborated a scorecard to evaluate four phases of a project (Level B): concept generation, product development and related production process elaboration and technology acquisition. We proposed the calculation of an index based on 13 innovation practices identified by Boly as the best practices for innovation (Boly, 2004; Corona Armenta, 2005). Our objective is to take into account threshold effects in the realization of the practices. Hence, innovation management activities are not independent from each other. Moreover, the choice of preference profiles has to be possible according to the fundamental needs of the evaluator.

This survey offers some thoughts regarding innovation evaluation. Although the study is exploratory, propositions are formulated in the field of evaluation criteria definition and data treatment. Innovation capacity evaluators gain useful insights by considering attributes of the process (practices) and directly observable phenomena (sub-practices). This approach grants a better similarity between the evaluations of different evaluators analysing the same company. In fact, the achievement of each sub-practice can be proved by facts or documents. The census of practices may be enriched without modifying the principles of the further data treatment steps. Nevertheless, sub-practices scoring still represents a limitation. Sub-practices scores are either one or zero (observation or not of sub-practices in the company). But score of a sub-practice being implemented remains problematic. Using fuzzy-sets logic may help solving this problem. Focusing on data treatment, two first indexes were proposed: the potential innovation index (PII) and the variable PII (PIIvar). The advantage of the latest lies in the attribution of different weights to each category of companies. Its dynamic character allows a more realistic classification of companies considering their innovation capacity. The typology is based on the realization of the specific group's key practices. A second proposition deals with the threshold effect PIIthreshold. The innovation capacity evaluation takes into account a threshold effect, which is a function of innovation practices. Several possible models expressing a threshold effect were suggested. An expertise phase (using a questionnaire) led to the selection of appropriate models, resulting in a new index. However, it does not provide significantly different results in comparison with index PII. Conducting a broader study, proposing other models and broadening companies’ sample size may correct this limitation. Finally, the third method is based on data mining, more precisely, the joint application of the Principal Component Analysis (PCA) and the hierarchical classification. This method does not lead to a quantitative classification like the PII. Nevertheless, it allows for the more innovative companies to be differentiated from the others, and to mathematically prove the existence of a synergy effect. It was demonstrated that to be innovative, companies may choose between two different visions and strategies in the field of information management. On a methodological point of view, this research is a contribution to an innovation metrology framework. It aims at the definition of an innovation scale. Software applications have to be developed aiming to an easier use of these approaches. Particularly, it would simplify the selection of the profile preferences (weighing definition) according to the objectives of the evaluators including: top management, experts, academics or public structures. On a practical point of view, evaluation approaches help to clarify outstanding activities and allow accurate research for better innovation management practices to be pursued. Complementary to in situ observation campaigns, evaluation contributes to a better understanding of innovation phenomena. Hence, synergy effects have been described. Evaluation approaches represent a rigorous way to assess efficiency of new management practices. Moreover the development of common evaluation methods within the management of technology academic community would contribute to enrich our empirical background.