Distance Effects

In addition to high uncertainty and tacitness, it is particularly the notion of distance which requires a more accurate definition. Rosenkopf and Nerkar (2001) define radical knowledge as knowledge spanning both organizational and technological boundaries. This seems a somewhat limited notion of boundaries encountered when searching and transferring novel knowledge. When discussing processes for matching distant knowledge with existing routines, therefore, a finer definition is required. Since Rosenkopf and Nerkar’s (2001) seminal paper, literature from the innovation-oriented economic geographical school have advanced the understanding of distance away from mere “spatial distance” to a multi-facet concept (Hartig, 2009). This school argues that purely referring to spatial, geographic distance does not do justice to the actual, underlying sources of distance which are evoked by spatial distance, namely cognitive, organizational, social and institutional distances (Boschma, 2005). While the literature, and, in particular, Boschma’s contribution analyzes distance in terms of the four aforementioned dimensions, in addition to purely geographical, this study also introduces technological distance, while social distance and organizational distance are omitted. This is done for several analytical purposes. First, by adding technological distance, it introduces a variable of high relevance for the present study, which, due to the

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highly technical nature of the firms studied, requires careful consideration when establishing knowledge distance. Second, social distance refers to “socially embedded relations between actors at the micro-level”

(Boschma, 2005:66). Since the focus of this study is on knowledge where no prior social relations are supposed to be present, it is not included in the analysis of knowledge distance. Nevertheless, a possible outcome of this study may well be that social proximity needs to be created in order to enable trust-based, rich communication and exchanges, a notion widely accepted and researched in the organizational learning/knowledge management literature (e.g. Nonaka, 1991). Third, organizational distance is omitted since it is to some extent redundant in the context of this study. Although organizational proximity is treated broadly and defined in terms of common knowledge base, cognitive distance, and discrepancy in size, Boschma (2005:65) defines it as “the extent to which relations are shared within an organizational arrangement, either within or between organizations”, and more specifically, “the rate of autonomy and the degree of control that can be exerted in organizational arrangements”. To the same extent as issues of social distance, organizational distance is assumed to be high in the context of this study, and social and organizational proximity would contradict the notion of novel or distant knowledge applied in this study, therefore rendering both variables redundant. Thus, the following section distinguishes between technological, cognitive, institutional and geographic distance.

Technological Distance

Technological distance determines in how far knowledge is compatible with the existing technological trajectory. Technology close to the existing knowledge base builds on existing capabilities and provides incremental improvement. Since investment in technological knowledge is cumulative, organizations are inclined to search and invest in technological knowledge close to their existing knowledge base (Cohen and Levinthal, 1990; Rosenkopf and Almeida, 2003). Conversely, technologically distant knowledge fundamentally changes an organization’s technological trajectory and may have profound implications for the entire business model (Benner and Tushman, 2003). Distance can also be modeled along the dimension of systemic or autonomous innovation. Autonomous or modular innovation can be integrated without any repercussions for the overall product architecture. Systemic or architectural innovation involves changes in how modules are linked together (Henderson and Clark, 1990; Iansiti and Clark, 1994;

Chesbrough and Teece, 1996). The closer to the existing technological architecture novel knowledge is, the easier to identify and utilize. However, the more removed from the existing knowledge base, and the more influential on technological architecture, the more competence-destroying and conflicting knowledge is likely to be.

Cognitive Distance

“Cognition denotes a broad range of mental activity, including proprioception, perception, sense making, categorization, inference, value judgments, emotions, and feelings, which all build on each other”

(Nooteboom et al., 2007:1017). Cognitive proximity between actors makes knowledge sharing more

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efficient, especially inside an organization. This is captured in the concept of dominant logic proposed by Prahalad and Bettis (1986). In the interest of efficiency, managers inside the organization rely on heuristics which guide their decisions. Such cognitive schemas are also present on industry level, a phenomenon labeled “industry recipe” by Spender (1989). Large cognitive distance has the merit of novelty but may encounter misunderstanding and incomprehension (Nooteboom, 2000). The more rigid in their capabilities and cognition actors are, the higher the costs to bridge cognitive distance (Perez and Soete, 1988). Rigid cognitive structures and low cognitive distance between actors are also likely to increase competency traps (Levitt and March, 1988). Cognitive distance, conversely, may provide complementary knowledge, triggering creativity and fostering serendipity (Boschma, 2005).

Institutional Distance

Institutions consist of a formal component, embedded in laws and rules, as well as an informal one, such as cultural norms and values (North, 1990). Together, they are contained in “sets of common habits, routines, established practices, rules, or laws that regulate the relations and interactions between individuals and groups” (cf. Edquist and Johnson, 1997:46). Close institutional proximity between actors decreases the costs of transactions by providing a framework for effective contracting. In addition, shared institutional bases instill understanding and trust, complementing formal forms of transaction. As with other distance effects, close institutional proximity may lead to lock-in and inertia, and leave little room for novel knowledge and associated change. Conversely, high institutional distance may make transactions difficult.

Geographical Distance

Geographical distance can be understood as the “spatial or physical distance between economic actors”

(Boschma, 2005:69). Research in the Marshallian industrial district tradition stresses the importance of geographically proximate interaction with regard to innovation, due to the ability of actors for frequent face-to-face interaction, strong social networks and high localized labor mobility (Saxenian, 1994; Storper and Venables, 2004). Advances in ICT have raised doubts as to the continued relevance of geographic proximity, as increasing richness in communication is expected to replace the need for geographical proximity (Friedman, 2007). However, although the distance of novel knowledge and an organization’s formal and informal structure should not be used in the geographical sense, geographical proximity is nevertheless a factor in the mitigation of the effects of other aspects of distance, such as technological or institutional distance (Phene et al., 2006; Tallman and Phene, 2007). Geographical proximity facilitates frequent and cost effective rich forms of communication, such as face-to-face. Face-to-face enables the close socialization necessary to transfer tacit knowledge, which couldn’t otherwise be codified. At the same time, it fosters trust and mutual understanding and helps to solve incentive problems, an important element of transactions which may not be possible under formalized contracts and under conditions of incomplete information (Roberts, 2000; Storper and Venables, 2002). Since clusters tend to geographically

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center on a core set of technology, novel knowledge can therefore be expected to originate from geographically distant sources. Since, as Roberts (2000) pointed out, the scope for distant knowledge transfer is still severely limited and may be restricted to highly codified and universally understood knowledge, firms must devise appropriate means of establishing rich communication channels. Ideally, of course such channels consist of human carriers (Allen, 1977; Rosenkopf and Almeida, 2003; Singh and Agrawal, 2008). However, as discussed in chapter 2, the costs of hiring human resources in all relevant fields are prohibitive.

As Boschma (2005) highlights, there is a tradeoff between distance and the firm’s ability to integrate and utilize novel knowledge. Therefore, organizations must strike the right balance between knowledge distance, on the one hand, and their ability to utilize distant knowledge, on the other hand. The curvilinear relationship between knowledge distance and innovative performance is captured in figure 12. Table 6 provides an author summary for the theory discussed thus far in this chapter:

Figure - 12 Relationship Between Knowledge Distance and Innovative Performance¹³

Innovation Performance

Distance

13 Laursen and Salter (2004) found the same curvilinear relationship between searching widely and deeply and innovative performance in a large-scale sample of industrial firms.

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Table - 6 Author Summary

Dimension Explanation Supporting Authors

High Uncertainty

Technology may lose to other entrants in becoming dominant design. Market size and growth difficult to predict: Even if technology becomes standardized and market is sizable, business model may be ineffective.

Dosi, 1988; Teece, 1996; ; Chesbrough and Rosenbloom, 2002; Grant, 2005;

Teece, 2009; Tidd and Bessant, 2009

Tacitness At the early stages of a new technology development, knowledge will be difficult to codify and to transfer. This also creates difficulties in appropriability.

Polanyi, 1967; Teece, 1986; Nonaka and Takeuchi, 1994; von Hippel, 1994;

Brown and Duguid, 1996; Szulanski, 1996; Brown and Duguid, 2000;

Nooteboom, 2000 Technological

Distance

Technologically distant knowledge is less compatible with existing technology trajectory but has more innovative potential.

Henderson and Clark, 1990; Iansiti and Clark, 1994; Chesbrough and Teece, 1996; Benner and Tushman, 2003

Cognitive Distance

Cognitive distance between actors makes communication more difficult but promotes creativity.

Perez and Soete, 1988; Nooteboom, 2000;

Boschma, 2005; Nooteboom, 2007

Institutional Distance

Institutional distance, manifest in

differences in formal and informal norms and rules, creates scope for creativity. Too much distance, however, makes

communication impossible.

Nooteboom, 2000; Taylor and Osland, 2003; Boschma, 2005

Geographical Distance

Geographical distance per se no barrier in knowledge transfer. However, geographical proximity can facilitate rich communication and trust to buffer other distance effects.

Roberts, 2000; Storper and Venables, 2002; Boschma, 2005, Phene et al., 2006;

Tallman and Phene, 2007

Rosenkopf and Nerkar (2001:289) stress that “technological similarity actually implies a continuum, where some technologies are quite similar, others are somewhat similar, and still others are less similar”. They also highlight that “these distinctions are, to a large extent, socially constructed; furthermore, any such boundary between technologies is fuzzy and can evolve with time”. For the purpose of data collection and subsequent analysis, this study adopts both the notion that technological similarity, and thus, equally, distance, is not an absolute value in itself, but always relative. For the subsequent empirical analysis, in an effort to enable more thorough within-case and cross-case analysis, this notion is operationalized by

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attributing a subjective value to the degree of distance between a focal firm and the source of novel knowledge. This is expressed in a scale of one to five. Table 7 summarizes the logic applied to the scale:

Table - 7 Degree of Knowledge Distance/Novelty Dimension Scale Logic (1 = Very Low/ 5 = Very High)

High Uncertainty • Uncertainty of Dominant Design

• Opaqueness of Target Market

• Opaqueness of Business Model Tacitness • Ambiguity/Idiosyncrasy

• Language Difference

• Richness of Communication Channel Required

• Weakness of Appropriability Regime

Technological Distance • Competence Discrepancy in Underlying Scientific Discipline

• Discrepancy in Product Technology

• Non-Compatibility with Existing Production Facilities Cognitive Distance • Discrepancy in Mental Models and Thought Patterns

• Discrepancy in Problem Solving Approaches Institutional Distance • Discrepancy in Regulatory Framework

• Discrepancy in Informal Rules, Habits and Values Geographical Distance • Spatial Distance

• Time Difference

• Travel Time

For the later analysis, the distance of knowledge to be integrated can be visualized using a radar chart.

Figure 13 shows an example in which particularly geographic, cognitive and technological distance are moderate to high:

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Figure - 13 Radar Chart of Dimensions of Knowledge Distance

5

2

2

2 3

3

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5

Geographic Distance

Institutional Distance

Tacitness

Uncertainty Cognitive Distance

Technical Distance

Example

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In document Emerging Strategies for Matching Distant Knowledge with Existing Innovation Capabilities (Sider 56-63)