Managing Emerging Technologies for Socio-Economic Impact
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Managing Emerging Technologies for Socio-Economic Impact

Edited by Dimitris G. Assimakopoulos, Ilan Oshri and Krsto Pandza

The development of emerging technologies demands a rapidly expanding knowledge base and intensive collaboration across organizational, institutional and cultural borders. This book is the first of its kind to focus on the management of key emerging technologies and their social and economic impact in Europe. Split into four parts, across seventeen chapters, the scholars offer multiple levels of analysis concerning the management of emerging technologies across various sectors ranging from nanotechnology, renewable energy and cloud computing to synthetic biology and particle therapy for cancer.
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Chapter 14: Open innovation in family firms: a matter of socio-emotional wealth

Juan Pablo Madiedo M.


Competitive capabilities and resources shape the firm’s market position and competitiveness (Barney, 1991; Teece et al., 1997; Krajewski et al., 2010). Practitioners and academics have recognized cost, quality, speed and flexibility as key elements for success in dynamic market situations (Petersen et al., 2003; Aberdeen Group, 2004). Still, because of specialization and constrained resource endowments, for developing and delivering a new successful product or service offer, firms cannot work by themselves and need to rely on business partners (Benton and Maloni, 2005; Koufteros et al., 2007; Mitra and Singal, 2008). The literature has stated that working within an open innovation framework with suppliers and customers has a positive effect on the company’s performance (Chesbrough, 2003, 2006a; Enkel et al., 2009). High levels of openness and the corresponding integration with business partners are usually related to greater responsiveness and resilience due to the increase of information visibility and of operational knowledge, and lower costs of doing business because of the stronger links that allow firms to target their customer requirements effectively, while leveraging their resources for reducing the count of non-conformities and their magnitude (Dyer, 1996; Grant, 1996; Kim, 2009).

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