Generating Knowledge Resources for Innovation

The capability to continuously generate novel insights is often considered to a key strategic resource for firm engaged with innovation activities. The process of generating these resources are rife with complexities and difficult to sustain. For instance, there is a large body of literature that analyses the composition of knowledge based activities and the impact on variations on innovation outcomes. Polanyi(1966), for example, defines two specific states of knowledge, tacit and explicit. Explicit knowledge is defined as the knowledge that is easily codified and transferred among actors, such as the documents and data collection. This form of knowledge provides firms with benefits of standardization and quantifiable data (Nonanka, 1991). Tacit knowledge is defined as the form of knowledge that lies within the mind of the actors with in the network. Tacit knowledge is not as easily communicated, such as the routines, practices or highly specialized capabilities and serves as a conduit that facilitates efficient knowledge transfer between actors and can provide an impetus for innovation (Polyani, 1966).

Tacit knowledge is not easily replicated, communicated, or transferred and is likely to be embedded within the individual capabilities found within the human capital held by a firm (Nonanka, 1994; Subramanian & Venkatraman, 2001) and is the root of idea generation (Castiaux, 2007). Over time, members of firms acquire experience, learn through overcoming challenges, and collect knowledge stocks regarding how to efficiently and effectively operate with a form of knowledge that often becomes embedded in its documents or repositories, routines, processes, and practices (Price, 2007). This form of knowledge must be intentionally disseminated among organization members to encourage innovation (Tsai, 2001) to increase the breadth of knowledge necessary to stimulate innovation (Christensen, 1996; 2004) and relies on relational capabilities (Stasser, 1992).

The development of innovation is an interactive process (Edquist & Hommen, 1999). Knowledge sharing activities can provide opportunities to stimulate and contribute to their collective and individual abilities to innovate (e.g., Kogut & Zander, 1992; Tsai & Ghoshal, 1998; Tsai, 2001). It involves social complexities and human elements in opportunity identification, the development of new knowledge, and, often, the transfer of difficult to codify (tacit) knowledge and absorption (Cohen & Levinthal, 1990; Autio et al. 2000). Tacit has long

31 been acknowledge as a fundamental and strong impetus for innovation (Madhavan & Grover, 1988, Nonaka, 1994; Nonaka & Takeuchi, 1995), however it is difficult to communicate and often relies upon individuals developing strong relationships, high levels of motivation, and integration with partners (Tsai & Ghoshal, 1996; Uzzi, 1996, 1997; Fitcher, 2009; Belenzon & Schankerman, 2014).

There are several positions within the literature regarding how the process of innovation and new knowledge generation may emerge between individuals. Some researchers argue that the ‘knowers’ must be capable to absorb external knowledge and recognize the relevance to currently held knowledge stocks (Cohen and Levinthal, 1990; Hughes et al., 2014). The theory of absorptive capacity proposes that knowledge obtained in the absence of prior knowledge (or relevance to the core activities of the firm) will not possess innovation capabilities (Cohen & Levinthal, 1990). Additionally, there must be a level of distributed learning in open innovation and inter-organizational relationships (Lane & Lubtkin, 1998; Robertson, Casali, & Jacobson, 2012). Of the utmost importance, and as Kogut and Zander (1992) noted, learning has a relational component that makes it contingent on the value of social relations (e.g. social capital) that makes the combinative capabilities possible.

Research suggests that the conditions attributable to innovation (or new knowledge creation) requires the disparate connection of knowledge from disparate sources (Hargadon & Sutton, 1997). The knowledge pursued and transferred within the organization can be identified as a mixture of scientific expertise, organizational culture, and contextualised information and insight that allows members to incorporate new information into the firm’s operations, activities, or products (Davenport & Prusak, 2000; Price, 2007). Innovation performance and outcomes are associated with the capability to connect new knowledge to previously held knowledge stocks and making either radical or incremental new combinations (Nanapiet and Ghoshal, 1998). This becomes more complex within inter-organizational networks, as firms are apprehensive to share core elements of product design with external entities (Kline, 2003) and fear for knowledge spill overs (Athuana-Gima, 2005; Knott, 2006) (as related to the discussion on the resource based view in section 2.2.3) and create rigid process that undermine innovation performance. Alternatively, some research suggests that firms that adopt fully organic and complex responsive systems to enhance knowledge flow and innovation within networks (Stacey, 2001; Katz, 2006; Sorenson, Rivkin, & Fleming, 2006). Thus, supporting knowledge creation and sharing in networks (Cross et al., 2001) involves developing normative

32 behaviours and communities of practice that motivate learning and innovation (Brown & Duguid, 1991; Belenzon & Schankerman, 2014).

A strong relationship between actors in an inter-organizational network has been associated with the transfer of complex knowledge. Relational capabilities (e.g. trust and interaction) must be developed to address the desire to protect resources, yet foster the knowledge exchange required for innovation (Kale, Singh, & Perlmutter, 2000) and enable the heterogeneous knowledge to flow within the network (Knott, 2003). Therefore, fostering this act of knowledge sharing has been associated with the organizational climate (Bock et al., 1994) that promotes acceptance and acclimation of innovation (Myer & Goes, 1988) and knowledge combination capability within inter-organizational networks that relies upon relational capabilities (Carmeli & Azerouli, 2009) and appropriate responses to various external factors.

While studies provide evidence that strong social relationships (among embedded ties) are best suited for complex information diffusion, they also acknowledge a threshold of usefulness and a point at which ties become redundant for novel information (Uzzi, 1996, 1997; McEvily & Marcus, 2005). There are several arguments that suggest weak relational links provide diverse sources of information and improves the possibility for novelty to emerge (Granovetter, 1973; Burt, 1992). This theoretical tension indicates that knowledge emergence and novelty performance requires an innovation network may be characterised by a combination of strong and weak relationships. Developing the knowledge resources necessary for fuelling innovation activities becomes more complex, as the locally specific treatment and views of knowledge management varies per organization. These variations in views may directly impact the ways in which innovation activities are organized as well as the strategic approach to the acquisition of knowledge resources are obtained, either through internal or external processes (Nonanka, 1991). The next section considers the impact of the underlying theories of the firm adopted by the researchers or managers in the development of these strategies. It aims to illustrate the widely-adopted logic that is applied within firms and research, the resource based view. It aims to highlight how this logic poses implications for innovation strategy and knowledge management.

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