Social proximity

Social proximity originates from the concept of embeddedness (Granovetter, 1985), which stresses that most economic linkages are “closely embedded in networks of inter-personal relations” (Granovetter, 1985: 504). In other words, social relations affect economic outcomes. Furthermore, the embeddedness literature has emphasized that the level of social embeddedness positively influences the firm’s likelihood to benefit from interactive learning and innovation processes.³⁶ Two types of embeddedness affecting economic outcomes have been identified: dyadic (i.e. pairwise relationships), and structural (i.e.

groups of firms or overall network of relationships) (Granovetter, 1992; Boschma, 2005).

Still, a company’s embeddedness is often affected by the embeddedness of individuals (e.g. firm owners, managers and employees) and the firm’s embeddedness as a collective (Oinas, 1997).

Social proximity refers to socially embedded links between individuals at the micro-level, i.e. intertwined social networks of individuals of multiple organizations.³⁷ Socially embedded relations are characterized by trust and reputation based on friendship, family ties, as well as shared personal or work experiences and repeated contacts, for example, through cooperation in the past (Boschma, 2005).³⁸ Social proximity and individual’s embeddedness in a social network, respectively, determine the actor’s ability to access tacit and, sometimes even more or less, confidential knowledge, and, in turn, the likelihood to engage in interactive learning (Breschi & Lissoni, 2003).³⁹ Thus, the main argument behind social proximity is that trust-based social relations enable the exchange of tacit knowledge, which is considered fundamental for innovation. Shared trust enables a more open attitude towards sharing of knowledge between actors, instead of rational and calculated communication. Furthermore, social proximity is usually related to commited and reliable relationships as opposed to pure market and cost-minimizing relationships that may dissolve as soon as problems occur. Therefore, social connectedness also decreases

36 Lundvall (2006) has referred to the importance of know-who in terms of social settings as a specific catego-ry within the concept of knowledge. In general, four categories of economically relevant knowledge are dif-ferentiated: know-what referring to knowledge about facts, know-why referring to scientific knowledge, know-who referring to specific social relations and networking, as well as know-how referring to skills.

37 More macro-level similarities between actors, for example, shared ethnic and religious values, are incorpo-rated within the concept of institutional proximity (Boschma 2005).

38 Huber (2012) has described three dimensions of social proximity: 1) “knowing each other”, 2) “emotional closeness” and 3) “feeling of personal obligation” (Huber, 2012: 4).

39 For example, Bercovitz and Feldman (2011) have found that prior social ties in collaborative teams in-creases the team’s innovation performance.

the risk of opportunistic behaviour (Boschma, 2005). Moreover, social proximity also refers to the mediation of trust and trustworthiness between unrelated actors by trusted individuals or organizations (see Box 2). For example, employees, who were co-workers in the past, can connect unrelated firms. Hence, social proximity also is an important criteria for boundary-spanners and intermediaries (Mattes, 2012; Menzel, 2015).

Box 2: Trust

Almost all concepts related to knowledge exchange include the notion of trust. Trust appears to be a central prerequisite of knowledge interaction and learning. It affects how and what knowledge is exchanged (Lane et al., 2001; Cooke, 2002). Trust is in particularly important in respect to risks of freeriding, opportunistic behaviour and confidentiality.

Regarding knowledge exchange, economic actors usually prefer trust-based relationships over newly formed or anonymous ties (Broekel & Boschma, 2012).

Trust combines several dimensions. The confidence in a partner’s capabilities and motiva-tion to meet his commitment and obligamotiva-tions refers to the cognitive dimension of trust (Menzel, 2015). So-called intentional trust refers to the belief and attitudes towards the partner’s honest motivations, goals, commitments and fair actions, i.e. “that things will not go wrong” (Nooteboom, 2002: 192).⁴⁰ Trust is a feature of existing personal relationships, but it does not explain the creation of new ties, which are important to access new external knowledge. In this case, generally accepted institutions, norms and structures can help to reduce uncertainty, increase the controllability of first joint activities, as well as define realistic expectations and predict partners’ behaviours. Also, trust is an evolutionary process, as the its creation requires time and repeated personal interaction. For example, shared experiences increase trust (Dettmann & Brenner, 2010). Furthermore, structural embeddedness on the network level creates trust and reputation. Actors, who are trusted by many other network members, may benefit from networked reputation. Thus, in busi-ness networks, it is possible to transfer trustworthibusi-ness between actors with no prior rela-tionship. Such trust is typically higher in dense than in loose networks. In contrast, outsid-ers or loosely linked firms are more likely to suffer a lack of trust and reputation (Glückler &

Armbruster, 2003; Menzel, 2015).

Similar to the notion of social proximity and embeddedness, the concept of social capital (Coleman, 1988) also stresses the relevance of personal networks and related resources for the stimulation of cooperative behaviour, knowledge interaction and interactive learning.

40 Glückler (2005) has referred to the two types of trust as competence trust and goodwill trust.

Social capital refers to potentially available assets, which are embedded in the actor’s total set of social relations and networks. The collectively owned capital is predominantly related to feelings of gratitude and loyalty or even guaranteed rights associated with the network membership.⁴¹ A firm’s ability to create and exploit social capital is found to be an important determinant for its innovativeness. Also, enhanced social capital affects regional economic development positively due to enhanced knowledge and innovation externalities (Nahapiet

& Ghoshal, 1998; Huber, 2009).⁴²

However, too much social coherence may limit the learning capability of organizations.

Referred to as social lock-in, networks lacking permeability and openness for new actors, information and knowledge may be disadvantageous to firms’ innovative capacity. Also, economic relations based on friendship and kinship may lead to irrational decisions and underestimating opportunism. This may result in negative effects on a firm’s innovative performance, especially in markets that are characterized by high degrees of uncertainty due to regularly changing technologies and framework conditions, and where opportunistic behaviour is common. As a result, the relationship between social proximity and firms’

innovative performance is characterized by an inverted U-shape (see Figure 5).

41 Three dimensions of social capital are identified; 1) the structural dimension (network ties and network configuration), 2) the relational dimension (trust, norms and obligations) and 3) the cognitive dimension (shared codes, languages and interpretations) (Nahapiet & Ghoshal, 1998).

42 In contrast, the concept of network capital underlines the rational perspective of a homo oeconomicus.

While social capital is formed in social networks, network capital is created and developed through calcula-tive networks, which are developed and maintained to exploit assets and resources within inter-firm net-works based on economic expectations (Huggins, 2010).

Figure 5: Relationship between firm’s embeddedness and innovative perfor-mance (inverted U-shape)

Source: Boschma (2005, p. 67)

Thereafter, the positive relationship between social embeddednes and innovation holds up to a certain threshold, after which effects on interactive learning may become negative due to social lock-in, irrational preferences and underestimated risks of opportunistic behaviour (Uzzi, 1997; Boschma, 2005). Boschma and Frenken (2010) have coined this paradoxical situation the proximity paradox.⁴³

On the whole, social proximity in conjunction with trust is a critical criteria for knowledge interaction to take place. Socially embedded relations and shared trust between actors strongly contribute to a more open attitude towards the exchange of tacit and confidential knowledge, which is fundamental for learning and innovation.

43 The term proximity paradox refers to the paradoxical situation that a certain degree of proximity is consid-ered as prerequisite for the formation of knowledge relations between actors, while too much proximity does not necessarily lead to actors’ enhanced innovation performances, but even harm learning (Boschma

& Frenken, 2010). In regard to embeddedness in particular, Uzzi (1997) has coined this situation the para-dox of embeddedness.

Box 3: Strong and weak ties

Strong and weak ties refer to the degree actors are linked, i.e. whether they are either loosely coupled or tightly connected. This concept in social network theory, developed by Granovetter (1973), argues that strong ties formed within densely connected sub-networks (for example, close friends and departments within an organization) are critical for continuous knowledge exchange, while weak ties (e.g. acquaintances) connecting diverse sub-networks are important for accessing heterogenous knowledge. Thus, both kinds of ties are important for learning (Granovetter, 1973).

In the context of knowledge relations and networks, strong ties encourage trustful, reliable and long-term cooperation between similar actors also in times of uncertainty and environmental changes. Furthermore, they are more suited for transferring highly complex and tacit knowledge (Granovetter, 1973; Nooteboom, 2000a). Examining the strength of ties coupled with network density, McFadyen et al. (2009) have found that strong ties in combination with direct exchange contacts, who have few direct links to each other, show the best results regarding knowledge creation. Also, strong ties seem to be critical to enforce radical changes in organizations affecting the existing status quo and power constellations. Here, trust in the responsible change drivers is critical (Krackhardt, 1992).

In contrast, weak ties are found to be more likely to span boundaries between different sub-networks, thus facilitating the diffusion of diverse, but complementary knowledge. This sets enhanced potential for novel combinations of knowledge and, consequently, innovation. Weak ties, however, appear less effective for the transfer of tacit and complex knowledge (Granovetter, 1983; Hansen, 1999; Nooteboom, 2000a). Also, by connecting diverse knowledge areas in a network or unrelated sub-networks, weak links are critical for the cohesion of a network overall (Glückler, 2007).⁴⁴