Leadership in Open Innovation: Examining the Influences of Open Innovation on Competencies, Control, and Behavior in R&D Environments

Leadership in Open Innovation: Examining the Influences of Open Innovation on Competencies, Control, and Behavior in R&D Environments

Frank Wippich (Henley Business School, UK)
DOI: 10.4018/978-1-61350-341-6.ch006
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Throughout the partnership interaction, leadership in Open Innovation becomes mainstream, so that everyone involved in the value creation process needs to exhibit skills along the proposed flexible leadership framework.
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The traditional model of innovation management sees internal research and development as a strategic asset, a barrier to entry of competitors as well as a means to achieve economies of scale and scope through first-mover advantage. The result has been a vertically integrated innovation model (Chandler, 1990; Hamel & Prahalad, 1994), which internalizes R&D activities and performs commercialization through internal development, manufacturing and distribution for sustainable competitive advantage.

Recent empirical evidence - through case studies at large multi-nationals such as Procter & Gamble (Huston & Sakkab, 2006), IBM (Chesbrough, 2003a) and Deutsche Telekom (Rohrbeck, Holzle, & Gemuenden, 2009) among others - suggests that this closed model might not be sustainable in the modern business world.

In the automotive industry as well, the traditional R&D model seems to be reaching its limits. As an example, the cockpit electronics business, which includes audio systems, instrument clusters and connectivity devices, is faced with high-speed advancement of consumer electronics technology in a long development and life cycle nature of the automotive industry, according to the president of the Visteon Corporation's Electronics Product Group, Steve Meszaros (Visteon, 2008). Furthermore, closed vehicle architectures limit reuse of components, which has an adverse effect on R&D costs. A summary by Dannenberg and Burgard (2007) in Figure 1 highlights the key influences suggesting that with the amount of challenges and pressure for increased efficiencies, companies are not able to innovate on their own anymore.

Figure 1.

Cost pressure on innovations in automotive (© 2007 Oliver Wyman / Dr Jan Dannenberg. Used with permission)


In a global study by IBM (Rishi, Stanley, & Gyimesi, 2008), executives of major automotive companies agreed that the biggest barrier to continued and efficient innovation is the creation of global standards. To overcome this barrier companies adopt a new open model of innovation focusing R&D on the business model and extensively collaborating throughout the value net with external players such as governments, universities and companies across industry boundaries with the aim of achieving higher innovation efficiency and customer satisfaction.

This open approach was first academically defined and promoted by Henry Chesbrough under the term “Open Innovation” (hereafter: “OI”) and according to Chesbrough (2003), the main reasons for the need in opening up the innovation process are:

  • Abundant availability and mobility of skilled workers

  • Existence and power of venture capital market

  • Availability of external options for ideas sitting on the shelf (i.e. licensing out)

  • Skilled and capable external suppliers

Based on these boundary conditions, Chesbrough (2003b) concludes that companies now need to be able to access technology either from the inside or outside depending on where it is available first and allow for competition in the research and development process. In addition, companies have the opportunity to generate additional revenue and profits from actively selling research outputs to other firms for patents and inventions that do not fit the company’s business model. Chesbrough (2003a) further argues that along with this open model, the skill set required for successful R&D activities are the identification, understanding and selection from, and connection to the wealth of available external knowledge. In addition, R&D staff will need to supply the missing pieces of knowledge not available externally and finally need to integrate internal and external knowledge for more complex new systems and architectures.

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