رویکرد جدید برای ارزیابی فن آوری های کاندیدا با توجه به پتانسیل های نوآوری آنها : فرایند اطلاعاتی نوآوری سریع
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2369||2013||11 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 3, 15 February 2013, Pages 881–891
Technological innovation process starts with technical discovery of new things or new ways of doing things, i.e., invention. With the commercialization of invention, the term, innovation, takes place instead of invention. The process ends with (duplicative and/or creative) imitation by competitors. It is expected that maximum profits can be achieved in the time interval from commercialization of invention to its imitation because of monopoly power. Therefore, assessing the emergence of invention in accordance with ability for commercialization and resistance for imitation can generate winning innovation intelligence. Besides, for achieving sustainability, disruptive technologies should also be taken into account through evaluation of trendiness of candidate technologies. This study presents a novel assessment process that aims to evaluate and prioritize candidate technologies according to their innovation potentials by considering commercialization, imitation and trendiness factors all together. According to authors’ best knowledge, the technology assessment process presented in this study is the first attempt in the literature that is dedicated to winning innovation intelligence and takes above mentioned factors together into account. Main input resources of the process are patents, scientific publications and market research reports. While trendiness of technologies is evaluated with the help of a fuzzy inference system that combines patent data and publication data, commercial and imitation potentials are evaluated with the help of some marketing indicators and determinants in the proposed assessment process.
Numerous studies related with innovation start by using this cliché sentence; innovation is crucial to survival. When searched this sentence in a search engine, thousands of results can be found. We also agree with that innovation is an important issue. However, it is a fact that most new products fail to deliver on their objectives (Christensen, 1997). Raising innovation success is possible with being efficient and effective in innovation process. In the case of otherwise being an (early) imitator can be more appropriate strategy. Nevertheless, it is remarkable to state here that making and leading the innovation wind rather than being possessed are almost inevitable to have a large slice of market pie. In order to be efficient and effective in innovation process, developing and providing appropriate infrastructures, support systems and intelligent approaches are essential. In this regard, development of a new category of tools known as Computer Aided Innovation (CAI) is an emerging domain. Management of innovation process is getting easier and providing better solutions is being possible with computer-based applications thanks to the theoretical studies and advances in information and communication technologies (ICT). CAI aims to support the users during the innovation process. It is expected that changes in innovation paradigms will occur through the use of CAI methods and tools (Leon, 2009). Understanding fundamentals and life cycle of innovation process is decisive for developing appropriate frameworks that aim to improve success of innovation process. Technological innovation process starts with technical discovery of new things or new ways of doing things, i.e., invention. With the commercialization of invention, the term, innovation, takes place instead of invention. The market determines whether an invention becomes an innovation (Kusiak, 2009). Porter (1990) gives a description of innovation: “Innovation is a new way of doing things that is commercialized”. Therefore, an invention has first to be commercialized to be called as innovation (Lorenz, 2010). After commercialization, the process continues with adoption of introduced innovation from innovation perspective. However, the process ends with (duplicative and/or creative) imitation by competitors from companies’ perspective. One of definitions of intelligence given by Kurzweil (2000) says intelligence is the ability to use limited resources optimally to achieve goals. Innovation has its drawbacks and it is burdened by two sources of uncertainty: first, the time span between investments is realized and its financial return is obtained, and second because it could be easily copied without incurring in the cost of research and development (R&D) (Escribano & Giarratana, 2011). Innovation is a risky and expensive endeavor, which results in low success rates (Cormican & O’Sullivan, 2004). Therefore, robust assessment of innovation potential of technologies is essential before investment decision. Although the literature is scarce, there have been some preliminary attempts to assess the innovation potential. Justel, Vidal, Arriaga, Franco, and Val-Jauregi (2007) proposed a method for assessing the innovation potential of product concepts and selecting those with greater probability of success. This method takes into account the degree of novelty of product concepts and their potential for success in the market. In another study, Jayanthi, Witt, and Singh (2009) presented a data envelopment analysis (DEA) based application of innovation potential evaluation to a US photovoltaic industry. They measured the potential of innovation in terms of its relative efficiency with respect to a best practices frontier. Gupta, Garg, and Maheshwari (2011) proposed a method to decide innovation potential and type of innovation in a proposed design concept of a product. Innovation intelligence refers to intelligent approaches for assessment of technologies according to their innovation potential to make maximum profits. Innovation intelligence is about the provision of relevant information on innovations and the evaluation of their impact on the corporation (Golovatchev & Budde, 2010). The goal of innovation intelligence is to identify, qualify and evaluate technologies in order to develop a viable innovation strategy (Golovatchev & Budde, 2010). Innovation intelligence is necessary for doing the right things, i.e., effectiveness, on innovation. It is expected that performance on making money, i.e., profitability, is better during the time from commercialization of invention to imitation of innovation because of monopoly power. With the imitation, the share of innovator on the benefits obtained from innovation will be less. Therefore, innovation potential of technologies should be evaluated by considering ability for commercialization and resistance for imitation. Evaluating the innovation potential of technologies in accordance with ability for commercialization and resistance for imitation can generate winning innovation intelligence (see Fig. 1). For winning innovation intelligence, firstly, commercial potential and imitation potential should be evaluated. Then, the outputs of the evaluation processes should be combined (fused). Therefore, an innovation intelligence process needs determinants and/or indicators of commercialization potential and imitation potential of corresponding technologies which will be evaluated. Additionally, a data fusion methodology is needed to combine the obtained data. Computer support will also be helpful to get easier and for providing better solutions in the management of innovation intelligence process.In this study, a novel approach for assessment of candidate technologies with respect to their innovation potentials, namely; quick innovation intelligence process, is proposed. According to authors’ best knowledge, the technology assessment process proposed in this study is the first attempt in the literature that is dedicated to winning innovation intelligence and takes commercialization, imitation and trendiness factors all together into account. While trendiness of technologies is evaluated with the help of a fuzzy inference system that combines patent data and publication data, commercial and imitation potentials are evaluated with the help of some marketing indicators and determinants in the proposed assessment process. Besides, this process has been designed to be supported by computer technology. For that reason, proposed innovation intelligence process can also be considered within the scope of CAI methods. Therefore, proposed assessment process also contributes to CAI literature. The rest of the study is organized as follows: Section 2 gives research profile of the CAI domain to capture key attributes, leading actors, and to clarify the scope of the existing studies. In Section 3, proposed quick innovation intelligence process is presented. An illustrative application of the quick innovation intelligence is given in Section 4. Conclusion and future studies are given in the last section.
نتیجه گیری انگلیسی
Above and beyond its crucial importance to remain competitive and to survive in today’s business world, innovation is also risky and expensive endeavor which results in low success rates. For this reason, being efficient and effective in innovation process gets very important issue to raise the obtained success through innovation. Correspondingly, a novel technology assessment process that is dedicated to winning innovation intelligence was proposed in this study to improve the effectiveness of innovation process. The process aims to evaluate technologies in accordance with their innovation potential through consideration of commercialization, imitation and trendiness factors all together. Patent data, publication data and market research reports are the main input resources of the proposed process. Several major advantages of the proposed technology assessment process can be given as follows: • In the literature, although commercialization and trendiness factors have been taken mostly separately but rarely together into account, imitation has not been taken into account by the researchers for technology assessment in the early stage. Proposed technology assessment process considers these three factors all together. Therefore, the outcomes of the assessment results can be more beneficial for sustainable success of firms and national economies when the process is executed. • The proposed process uses determinants and indicators of innovation potential rather than opinions of experts. The framework reduces the overall duration of evaluations. Moreover, being independent from the opinion of experts gives an opportunity to develop computer aided tools. Therefore, quick innovation intelligence process developed in this study can also be considered within the scope of CAI methods. • In the trendiness evaluation step, patents data and publications data are matched through a fuzzy inference system. This matching provides to direct considerations to use-inspired basic researches, i.e. Pasteur’s quadrant (Stokes, 1997). Focusing use-inspired basic researches can enhance the science-based technological improvements those are more value added and crucial for sustainable success. Although fuzzy logic is an effective tool for fusing two different kinds of data sources, it has not found any application in the literature for fusing publication and patent data within a technology evaluation framework. This study is also the first attempt in the literature that proposes a trendiness evaluation that is based on fuzzy logic. However, this study uses type-1 fuzzy sets and systems. The use of type-2 fuzzy sets and systems can also be considered to handle more uncertainties in the future studies. Besides, it is a fact that the reliability of the produced output is naturally affected by the reliability of the input. For this reason, researching for more reliable input should be another topic for future studies. Moreover, in the last step of the quick innovation intelligence process, three different ranking lists are combined (fused) to classify candidate technologies. Fuzzy classification or developing different ranking fusion method can be addressed in the future studies. Making comparative analysis of the existing industrial regions through proposed assessment process and generating strategic roadmaps should also be addressed in the future studies.