Home Computer Science Technological Entrepreneurship: Technology-Driven vs Market-Driven Innovation
Modern technologies which are often multitechnological in nature, demand much higher levels of knowledge. This significantly complicates the innovation process (Narula 2004). Examples include industries such as automotive, aircraft, telecommunication, electrical equipment, computers, biotechnology and new materials. As the knowledge content of technological innovation has grown with complexity, it has become highly specialised, based on experience and often upon tacit information.
Successful innovation usually requires a firm to complement its customer orientation with a distinct technology-orientation that enables it to develop new solutions for already identified customer needs and to create new forms of customer demand. Narula (2004) posited the multitechnological nature of complex innovation demands that in addition to existing technological core competencies, new complementary competencies are often required in other areas of science or technology. However, due to the increased amount of knowledge and necessary competencies, few firms can afford to maintain R&D efforts to develop world-class competencies in all fields. Also, the knowledge required for complex innovations is generated from many different sources requiring demanding access to a dense network of connections involving institutions such universities, research institutes, suppliers, customers and other partners. Today many successful companies have joined to form worldwide innovation networks. The key advantages of these networks are the possibilities of sharing infrastructures, risk and resources. Exploitation of complementary competencies within a network permits each organisation to focus on optimisation of the use of their respective core capabilities (De Liu et al. 2010).
Bullinger et al. (2004) said that with co-operation and collaboration becoming more important than intense competition in certain situations, firms have joined several kinds of networks. These include vertical networks with suppliers and customers and horizontal networks with other companies at the same level with a supply chain. The importance of networks is increasing. This reflects the necessity for more intense co-operation in knowledge-driven economies in order to enhance and increase the speed of innovation.
Tell (2000) concluded that participation in innovation networks has several implications on the growth orientation of member companies. First, companies need to expand their exploitation of the complementary knowledge of the partners in the network. Second, involvement allows a sharing of ideas by network partners such that should an idea not fit with one company’s strategy, other members of the network may take the idea forward. Third, network membership can serve to broaden a firm’s strategic perspective as a consequence of access to knowledge assets located in different organisations, which often have different innovation priorities and strategies. For this outcome to be achieved, participating companies have to share a common vision of a problem-solving culture; thereby permitting a common purpose in relation to strategic intent. In the case of networks within high-tech sectors or dealing with complex multitechnology products, members typically need to integrate acquired new knowledge to ensure continuous renewal of the knowledge within the various members’ organisations.
Pleschak and Stummer (2001) proposed that companies need to develop the following abilities and competencies in order to optimise the benefits of network membership:
Social capital is the sum of the actual and potential resources (e.g. knowledge) that arise in network relationships (Nahapiet and Ghoshal 1998). Social capital exists in ties between people and networks, while human capital consists of individuals’ knowledge and competence (Zheng 2010) . Ahuja (2000) defined network structure as existing in three dimensions: (1) the number of direct ties a focal actor has to partners, (2) the number of indirect ties the actor has to the partners of other partners and (3) concerns about the ties between the focal actor’s partners and the extent to which they are bound to one another.
Innovation therefore cannot be regarded as the product of a company, but as the product of interaction between two or more actors in a network or networks (Frenz and Ietto-Gillies 2009). New knowledge generally develops where different areas of knowledge intersect. Technical solutions generated by one actor may be usable by another actor in another area. New ideas can thus be developed by combining the experience of various actors.
Technological innovation often requires various forms of knowledge be combined or used in complimentary ways. Knowledge may be codified or non-codified. Less complex knowledge can usually be codified or documented in manuals, books, articles and computer files. Knowledge that is more complex can rarely be codified or documented. Such non- codified knowledge is tacit knowledge, for example know-how or unique experiences. Tacit knowledge can only be disseminated or transferred when the actors involved meet and interact (Rost 2011). Goffin and Koners (2011) suggested tacit knowledge transfer is made easier when mutual relationships, shared values, trust, commitment and co-operation exist within the network.
Another source of interfirm co-operation can occur through the emergence of innovation clusters. Clusters have been defined as ‘a geographically proximate group of interconnected companies, specialised suppliers, service providers, firms in related industries, and associated institutions in particular fields that compete but also cooperate’ (Porter 1998, p. 78). One example of a regional innovation cluster is the tri-national BioValley cluster in the Rhine Valley, which comprises Alsace in France, South Baden in Germany and the area around Basel in Switzerland. BioValley is a cross-border cluster of entrepreneurial activities and research in the fields of life sciences and biotechnology. It includes more than 300 life science companies, among them several large international pharmaceutical companies such as Roche, Aventis and Novartis. Additionally, the cluster has four major universities, and more than 30 private and public research organisations (Biovalley 2004).
Bullinger et al. (2004) found that the characteristics of successful clusters include:
|< Prev||CONTENTS||Next >|