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Innovation does not always mean employing the very latest cutting-edge technology. On the contrary, it is less a question of technology and more a way of thinking and finding creative solutions for business or social problems. Considering the above perspective, innovation management techniques (IMTs) can be seen as a range of tools, techniques and methodologies that help companies to adapt to circumstances and meet market challenges in a systematic way (European Commission, 2004).
The effectiveness of an Innovation Management Technique (IMT) is therefore the firm's ability for knowledge management, an ability that exists either internally or through external support. To this end, smaller enterprises (i.e., SMEs) are underprivileged because of two identified difficulties for successful innovation management: access to information and limited external assistance (Carayannis & Bakouros, 2010). In this paper we propose an innovative management technique to mitigate these problems. The technique proposed implements a multi-criteria methodology through an information system and it emphasizes the process of technology transfer, matching the innovation products and potential users. More specifically, this work is to recommend innovations to users, based on their specific profiles.
The prospect of the proposed innovation management technique, seems ever more relevant as there is a growing trend to shift the attention of companies from the closed, internal research and development model towards the open innovation model (Christensen, Olesen, & Kjer, 2005; Rigby & Zook, 2002). Moreover, in case that companies find a way to reconcile their internal Research & Development (R&D) efforts with external sources of knowledge, they complement a critical factor to increase their innovation performance (Faems, Van Looy, & Debackere, 2005). Especially as the complexity and rapidity of technological change is high and it keeps growing, it is extremely difficult for an organization to remain innovative relying solely to the pool of its internal knowledge. Indeed, the more radical is the type of innovation, the more difficult it becomes to remain innovative (Cassiman & Veugelers, 2006).
Acknowledging this need for organizations to quest external sources, information systems that maintain databases of technology supply and demand have been developed. However, the challenge of accessing external sources for technology transfer is tackled by two major drawbacks: the extent to which the supplied technologies meet personal requirements / preferences and the time that it is needed to filter the information. To demonstrate the amount of information, let us consider the information system of the Enterprise Europe Network (http://www.enterprise-europe-network.ec.europa.eu/services/technology-transfer), which currently manages more than 13,000 profiles. In the case of the patents databases the corresponding figure is much higher. In addition, the supplied technologies are described by a variety of factors, criteria besides their thematic categorization (e.g., maturity, type of cooperation requested, etc.), making the search for information even more complicated. Therefore, searching by just filtering the subject categories or by entering keywords (the popular search options that are offered), may not be sufficient and may deter organizations from conducting a thorough search. This work proposes a way to resolve both of these limitations: the potential user of a technology declares his personal preferences by ranking an indicative (and fictional) set of technologies. Furthermore, the quantification of the weights is estimated through an ordinal regression technique. Then these weights are used in an additive function which calculates the value of each supplied technology (alternative) for the user, which is eventually used to recommend a list of a finite (small) set of technologies.