Turning public into private: how geographically bound social

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It is well-known that entrepreneurs access resources and knowledge for innovation from external sources including users, suppliers, competitors, universities, etc. (Rosenkopf & Nerkar, 2001). Innovations most often require the simultaneous use of different skills and knowledge which may not be available within the firm boundaries (Rosenberg, 1982). The early Schumpeterian idea of the solitary entrepreneur producing innovations has been overtaken by representations of multiple actors working together on iterative processes of trial and error (Freeman & Soete, 1997; Schumpeter, 1942/87; Shan, Walker, & Kogut, 1994; von Hippel, 1988). As Van de Ven (1986:591) argues, 'while the invention or conception of innovative ideas may be an individual activity, innovation (inventing and implementing new ideas) is a collective achievement'. This argument is in line with the open innovation paradigm that states that openness fosters the introduction of new products by combining the efforts and resources of a large and varied pool of individuals with access to heterogeneous and complementary knowledge.

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Previous research shows that entrepreneurs use social capital to access resources and knowledge (Baron & Markman, 2003; Stam & Elfring, 2008) and capitalize on social ties to obtain bank loans (Uzzi, 1997) and private equity (Shane & Cable, 2002), or market information (Birley, 1985). There is also evidence that individuals with high levels of social capital are more likely to start new ventures (Aldrich, Elam, & Reese, 1996). Given the importance of social ties in the innovation context, the question arises about how entrepreneurs capitalize on social capital to develop innovation capabilities.

Social capital can benefit communities (a collective-good view), but it can also benefit individuals (a private-good view). Coleman (1990), Putnam et al. (1993) and Fukuyama (1995) argue that social capital is a public good and each individual in a given social community can exploit the social capital in their particular context. Bourdieu (1980), Burt (1992) and Lin (1999), on the other hand, stress the private nature of social capital, and underline the different access granted to different individuals.

This distinction determines the approach social capital, that is, either individual approach or structural. The individual approach identifies social capital as the product of individual investment in a network of relationships (Burt, 1992; Lin, 1999). Specifically, in this study, following the entrepreneurship literature (Larson and Starr, 1993; Stuart and Sorenson 2003; Shane and Stuart, 2002), I adopt an entrepreneur's perspective investigating the role of an entrepreneur's social capital for firm outcomes. The structural approach defines social capital as a public good embedded in the firm's external context. It considers social capital to be a geographically bound phenomenon that is a community rather than a single actor asset (Putnam et al., 1993). The concept of geographically bound social capital refers to Coleman's (1990:302) view of social capital, which conceptualizes it as an endogenous phenomenon within social relations, that varies from context to context. Places have unique histories and cultures and, therefore, unique social capital. Following this approach, this chapter aims to demonstrate that geographically bound social capital is a central external contingency for the firms located in a particular place.

This chapter systematically integrates these two approaches to social capital in order to construct a new and more comprehensive framework that relates social capital to the firm's degree of innovation. Using an original database, I investigate the effects of social capital on degree of innovation in a sample of Italian manufacturing firms. The Italian case is particularly appropriate for this study since Italian regions differ in their 'availability' of social capital. Italy figures prominently in the debate on social capital and was the context first studied by sociologists and political scientists interested in the effects of social capital (Banfield, 1958; Putnam et al., 1993).

I combine data on social capital for 6 provinces with a data set on innovative activities in a representative sample of 124 manufacturing firms to analyse the effect of entrepreneurs' and geographically bound social capital on the firm's degree of innovation in terms of the introduction of incremental and radical innovations (Dewar & Dutton, 1986; Elfring & Hulsink, 2003). Gatignon et. al (2004:1107) note that incremental innovation involves 'improving and exploiting an existing technological trajectory', while radical innovation refers to a 'disrupt[ion in] an existing technological trajectory'.

I find that entrepreneur's social capital positively affects the firm's degree of innovation, and that geographically bound social capital represents an external contingency that increases the effectiveness of entrepreneurs' social capital on the firm's degree of innovation. These results hold after controlling for a set of firm characteristics and local variables.

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The chapter is organized as follows. The next section reviews the literature on social capital and innovation, describes the theoretical constructs employed in my study, and presents the research hypotheses. In the subsequent sections, I present the research methods and empirical context, the sample selection process, and the measures and econometric techniques. The final sections set out the empirical findings and describe the contributions and implications of the analysis and some suggestions for future research.

THEORETICAL BACKGROUND

The importance of social variables for economic outcomes is well established. Granovetter (1973) describes how social connections are grounded in the history of markets and highlights their contribution to the outcomes of economic decisions.

The influence of social capital on performance has been studied at several levels of analysis, ranging from individuals and small groups (e.g. Moran, 2005) to larger organizations and inter-firm alliances (e.g. Gulati, 1995; Nahapiet & Ghoshal, 1998; Tsai, 2000), cities (e.g. Jacobs, 1961) and regions (e.g. Beugelsdijk & Schaik, 2005; Maskell, 2000; Putnam et al., 1993) and entire nations (e.g. Knack & Keefer, 1997; Zak & Knack, 2001).

At the beginning, the term social capital was used to describe the relational resources included in the personal ties exploited in the development of social organizations (Jacobs, 1961; Loury, 1977).The first important conceptualization of social capital was provided by Bourdieu (1980:2) who defined social capital as 'the sum of the resources, actual or virtual, that accrue to an individual or group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition'.

Lin and Dumin's (1986) and Burt's (1992) work focuses on the nature of the resources that are embedded in social networks. This work addresses the role of social interactions, and develops tools for identifying and quantifying network structure. Nahapiet and Ghoshal (1998:243) rely on this approach, which focuses on the resources embedded in networks and defines social capital as 'the sum of the actual and potential resources individuals obtain from their relationships with others'. While this research provides some useful tools for analysing entrepreneur networks, it does not consider the characteristics of the context in which individual networks are formed and ignores the social and cultural characteristics of geographical spaces.

The structural approach to social capital considers social capital as the existing stock of social relationships in a society where social capital is a multidimensional entity and a feature of the social structure (Coleman, 1988). Woolcock (1998) is one of the first attempts to design a unified conceptual framework for social capital. He analyses embeddedness as a form of social capital, highlighting that embeddedness can take different forms including social ties, cultural practices and political contexts, which position individuals in different circles. These circles or groups of individuals are powerful in the shaping opportunities and constraints in the pursuit of economic advancement (Zukin & DiMaggio, 1990).

The literature on economic geography recognizes the importance of places for firm behaviour. Several studies focus on analyzing local competitiveness, in which social capital is seen as a crucial element (Cooke, 2001; Hauser, Tappenier, & Walde, 2007; Saxenian, 1994). According to Marshall (1920), the factors that generate local competitiveness are held together by something 'in the air'. Marshall's intuition can be considered one of the earliest acknowledgements of the role of social capital.

Porter (2001) considered social capital to be a key determinant of cluster formation; Storper (1995) discusses the role of trust and cooperation, and local systems of norms and conventions.

Geographically bound social capital is used to explain several phenomena. Knack and Keefer (1997) relate social capital to economic growth, Guiso et al. (2004) exploit it to explain financial development.

In this chapter, I relate social capital to the firm's degree of innovation by considering the role played by: (i) entrepreneur's social capital, defined as a private asset that captures the entrepreneur's access to sets of individuals from heterogeneous knowledge domains; and (ii) geographically bound social capital, conceptualized as a public good, a collective asset that is available to all members of a given community.

HYPOTHESIS DEVELOPMENT

Entrepreneur's social capital and firm innovation

Firms are oriented towards the discovery of new opportunities that may involve the introduction of new products and exploring of new markets (Shane & Venkataraman, 2000). The success of these initiatives is attributable mostly to the capabilities and skills of entrepreneurs. Scholars try to understand why some entrepreneurs are more successful than others in terms of discovering opportunities.

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The entrepreneur's social network has become the subject of research and is developing into a dominant concept explaining entrepreneurs' performance (Aldrich & Zimmer, 1986). Entrepreneurs socially well connected to other actors, will be more likely to be able to access external resources. Batjargal's (2003) study of Russian entrepreneurs in the post-Soviet era shows that entrepreneurs who have access to numerous social ties and are able to exploit their contacts to obtain financial resources, for example, obtain the highest revenues. In the case of new firms, Hansen (1995) claims that personal social capital is one of the most valuable assets that the firm founder brings to the new firm. Florin et al. (2003) show that social capital provides important benefits by leveraging the productivity of a new venture's internal resources.

In the context of innovation, since entrepreneurs need to draw on several knowledge domains to produce new combinations of existing knowledge (Rosenkopf & Nerkar, 2001), social ties may be a definite advantage (Shane & Cable, 2002; Stuart & Podolny, 1996). The approach in this chapter is to consider entrepreneur's social capital in terms of network diversity, which enables access to distinct and different pools of knowledge (Reagans & McEvily, 2003) based on social ties encompassing a variety of knowledge pools. I identity three elements of entrepreneur's social capital: social interaction, social credentials and social obligations. These elements produce distinct benefits for the entrepreneur which facilitate firm innovation.

Social interaction promotes communication and enables the diffusion of information and knowledge. To communicate with strategically positioned actors guarantees better access to external knowledge domains in the short term (Adler & Kwon, 2002; Burt, 1992). For instance, entrepreneurs in frequent communication with university researchers are likely to learn earlier about new technologies that can be commercialized (De Carolis & Saparito, 2006). They may get access to potentially valuable technologies before they enter the public domain. Low levels of social capital make it more difficult to communicate with other actors including academics, based on the differences in languages, codes, goals and assumptions, for instance (Davidsson & Honig, 2003).

Social credibility is based on recognition that the individual is part of a particular social group, on the basis of which access to certain resources is enabled. For an entrepreneur, social capital is proof of social credentials, which are the passport to accessing knowledge and resources. Also, social credentials provide firms with legitimacy (Aldrich & Fiol, 1994).

Social capital creates social obligations between actors, which ultimately result in more generous behaviour (Gulati, 1995). Uzzi and Gillespie (1999:33) describe how social connections 'interject expectations of trust and reciprocity into the economic exchange that, in turn, activate a cooperative logic of exchange. This logic promotes the transfer of private information and resources and motivates [both parties] to search for integrative rather than zero-sum outcomes. In this way, embedded ties both create new collaborative opportunities and induced the mutual rather than selfish distribution of rewards'. Under conditions of uncertainty and information asymmetry, direct ties can be advantageous to those seeking financial and other resources (Podolny, 1994).

Based on the arguments rehearsed above, it can be seen that the entrepreneur's social capital is the primary source of new ideas and knowledge, which increase the firm's capacity to deploy various knowledge domains (Hill & Rothaermel, 2003) and which, in turn, could be expected to increase innovation. I conjecture that entrepreneurs that rely on a broad network of actors find easier to acquire external knowledge and recognize new opportunities. This leads to the hypothesis that:

Hypothesis 1. The broader the entrepreneur's social capital, the grater will be the firm's degree of innovation.

Geographically bound social capital and firms' innovation

As already mentioned, geographically bound social capital is a collective good embedded in the firm's environment (Knack & Keefer, 1997; Putnam et al., 1993). This is an acknowledgement that regional associations and informal ties constitute a fertile context for knowledge sharing (Putnam et al., 1993) and that social capital is a mechanism that operates locally.

In line with Laursen et al (2007), I would suggest that firms located in regions with high levels of social capital benefit from several advantages. First, social capital supports social interaction among the actors by connecting otherwise disconnected groups of actors. A high degree of connectedness increases the opportunities for informal talk and the possibility to access various knowledge sources (Jaworski & Kohli, 1993). These connections, which produce a rich set of communication channels, may enable boundary spanning search, often seen as necessary for successful innovation (Fleming & Sorenson, 2001; Rosenkopf & Nerkar, 2001; Sorenson & Audia, 2000). Connectedness increases the possibility to combine existing knowledge and develop new innovation capabilities (McFadyen & Cannella, 2004). Additionally, social capital reduces the tendency for economic actors to behave opportunistically since it contributes to modifying the system of individual incentives, exploiting the social component of actions (Dasgupta, 2005).

Innovation projects involve information asymmetries and require potential moral hazard problems to be taken into account (Pisano, 1990; Williamson, 1979). High levels of social capital increase transparency for the actors involved in innovation and render decision making less costly. In high social capital settings, entrepreneurs do not have to invest in checking the veracity of their partners claims (Zak & Knack, 2001), which frees up resources for investment in other economic activities. Social capital reduces information asymmetries by enabling the establishment of trust-based relationships (Adler & Kwon, 2002) through repeated social contact (Gulati, 1995). Thus, I posit that geographically bounded social capital positively affects firms' innovation capability.

Hypothesis 2. The higher the level of geographically bound social capital, the grater the firm's degree of innovation.

The moderating role of geographically bound social capital

Here, I suggest that geographically bound social capital increases the effectiveness of entrepreneur's social capital on firm innovation by increasing the benefit generated by the elements of entrepreneur's social capital.

Social interaction has been shown to be an element of entrepreneur's social capital that enables knowledge sharing. However, in the process of innovation, when partners interacts, the characteristics of the firm's environment, such as the market imperfections in terms of information asymmetries and potential moral hazard problems, have to be considered (Pisano, 1990; Williamson, 1979). High levels of geographically bound social capital facilitate communication among partners and increase their propensity to share valuable knowledge by reducing transaction costs and moral hazard problems in the firm's environment.

In terms of social credentials, since a wide variety of knowledge is essential for innovation (Metcalfe, 1994), greater credibility enables collaboration which provides access to a wider range of external knowledge. This enables innovations based on new combinations of technologies and knowledge (Laursen & Salter, 2006). Social credentials reduce the need to invest so heavily in searching external partners and checking their trustworthiness (Zak & Knack, 2001). Firms located in high social capital settings are more likely to engage in profitable collaborations since geographically bound social capital reduces the search and verification costs and make it easier for firms to signal social credibility.

Finally, geographically bound social capital may expand the range of reinforcement mechanisms related to social obligations. Coleman (1988) finds that geographically bound social capital prevents opportunistic behaviour based on awareness that such behaviour will become public knowledge and result in loss of reputation. High social capital settings increase the effectiveness of social obligations between partners by creating the conditions under which these obligations between are respected. These arguments lead to the following hypothesis:

Hypothesis 3. Geographically bound social capital positively moderates the effect of entrepreneur's social capital on firm's degree of innovation.

METHODS

Data

In order to test these hypotheses, I built an original database based on survey data and data collected by the Italian Institute of Statistics. The survey data were gathered from a sample of Italian firms in Abruzzo and Umbria, two regions in the centre of Italy. Regional expenditure on R&D as a percentage of these region's GDP is just below the average national expenditure of 1.1 per cent. In the five-year period 2000-2005, investment was relatively stable. Abruzzo's investment in R&D increased slightly from 0.9 per cent of GPD to 1.0 per cent of GPD, while investment in Umbria reduced slightly from 0.9 per cent in 2000, to 0.8 per cent in 2005. My choice of two regions with R&D expenditure slightly below the national average was to provide further support for my argument that social capital benefits firms, independent of the types of knowledge available in the region. The selected empirical contexts may be further confirmation that highly connected firms with high levels of geographically bound social capital may be more innovative regardless of the regional expenditure on innovation. Data collection was promoted and sponsored by both regions.

The sample includes a large group of firms, extracted from the list of firms registered at the local Chambers of Commerce, and industries, in order to maximize the variation in the variables and to increase the generalizability of the results. The following NACE industry codes are considered: 27, 28 and 29 (mechanical engineering); 15 (manufacture of food products and beverages); 24 and 25 (chemical industry); 72 and 74 (service firms) and 17 (manufacture of textiles). The sample is stratified by size based on the categories: 9 to 49 employees, 50 to 99 employees, and 100 to 250 employees. The final sample includes 300 firms.

Interviews were conducted in 2008. To increase response rates, letters were addressed to and mailed directly to the highest-ranking executive (usually the president) of each of the 300 firms. The letters briefly explained the purpose of the research and asked the recipient to contribute by completing the questionnaire. The letters gave assurance that the data collected would be protected by statistical confidentiality and personal data protection legislation. Respondents were told they would receive a personalized report benchmarking its firm's strategic decisions against a representative sample of firms.

Respondents were offered two ways of completing the questionnaires: a self reported online questionnaire, or a telephonic interview. Responding to the survey online involved answering the questionnaire that was made available on a purpose built website, for telephone interviews, a mutually convenient time was agreed.

Two weeks after the letters were sent, I made follow-up calls to provide some more information on the research. Respondents who agreed to use the online questionnaire were given a password to access the website. Telephone interview respondents were asked the questions over the phone in a call that lasted around 20 minutes. Most respondents (85%) opted for a phone interview. For the 15 per cent of respondents that completed the questionnaire online, I made a follow up calls to clarify certain points.

The total number of firms interviewed was 152, a response rate of 50 per cent. The final database was obtained by merging the survey data with secondary data collected by Italian Institute of Statistics. The final sample, after deleting for missing values, was 124 firms. The resultant data set contained very detailed information about social capital and innovation input and output.

To measure geographically bound social capital, I used data from a provincial level census collected by the Italian Institute of Statistics in 2001. The data were aggregated into 103 provinces, a level that corresponds to the Nomenclature of Territorial Units for Statistics level 3 (NUTS 3).

MEASUREMENT

Entrepreneur's social capital

The social capital of an individual can be described in terms of his or her social network and refers to the intrinsic resources in the network (Bourdieu, 1980; Coleman, 1988). Several methods are available to collect data on social networks, including whole network and egocentric approaches.

A whole network approach has several benefits, such as identification of the structural properties of the network (e.g. centrality, group, cliques). However, this method requires identification of the network boundaries. In reality, for the majority of networks, there is no identifiable boundary. Because measurement of entrepreneur's social capital requires identifying the knowledge accessed by the entrepreneur in large and not definable networks, an egocentric approach was preferred.

An egocentric network is a network focused on a central actor (ego) and consists of all the actors (alters) that the ego has direct relations with. In the case of the present study, the ego is the entrepreneur who was asked to describe the relations with alters with access to valuable knowledge (Burt, 1997; Rytina & Morgan, 1982).

In defining entrepreneurs' social capital, I adopted the position-generator technique, an original ego-network sampling technique proposed by Lin and Dumin (1986).

The position generator technique is used widely in sociological studies since it provides a measure of the respondent's network by asking whether he/she knows someone in a list of positions. This technique was implemented through a series of steps. The first consisted of identifying a list of relevant positions that the individual might have access to. I selected positions likely to provide access to information useful for innovation. Von Hippel (1988) highlights that innovation requires firms to form ties with customers, suppliers, universities and even competitors. Relying on previous studies exploiting the position generator technique to measure an entrepreneur's ego network (Batjargal, 2003; Chiesi, 2007), I selected the following positions: strategic suppliers, strategic clients, competitors, advisers, experts in technological innovations, university researchers/professors, policy makers, financial people/funders, people connected to public administrations, presidents of public or private industry associations, employment agencies.

Table A4.1 in the appendix illustrates how the position generator technique was used in this study.

[Insert Table A4.1 about here]

In the second step, respondents were asked to specify whether they knew someone in any of the positions listed. Since innovation is largely determined by the entrepreneur's ability to access multiple sources of knowledge (Renzulli, Aldrich, & Moody, 2000), I estimate entrepreneurs' social capital in terms of network diversity (Marsden, 1987). Using the position generator technique, I measured an entrepreneur's social capital based on the number of direct ties with individuals in the different positions and thus in different knowledge domains.

The possibility to access to varied sources of knowledge is one of the most incisive benefits of social capital (Lane & Lubatkin, 1998; Yli-Renko, Autio, & Sapienza, 2001; Zahra, Ireland, & Hitt, 2000).

Geographically-bound social capital

As discussed above, geographically bound social capital includes a range of interactions and social attitudes. The complex nature of the concept makes it difficult to measure. Nahapiet and Ghoshal (1998) acknowledged the multidimensional nature of social capital, suggesting that social capital should be articulated according to three main dimensions: structural, representing the structure of the network, relational, that is, the influence that a social relationship exerts on an economic action, and cognitive, which relates to the resources that enable common systems of representation and interpretation (Nahapiet & Ghoshal, 1998).

This chapter focuses on the structural dimension of social capital, and considers different aspects of the social structure. Geographically bound social capital is determined the measure proposed by Laursen et al. (2010) which is based on the selection of up to four provincial variables. The variables considered represent participation in social associations (number of not-for-profit firms, number of unpaid workers in not-for-profit organizations, number of employees in not-for-profit firms) and social inclusion (number of foreign residents). Table 4.1 presents the social capital variables. The four provincial variables were used for factor analysis, which is a widely used statistical method that enables the researcher to describe the variability among the observed variables in terms of a smaller number of unobserved variables, which are called factors. In this case, the variations in the four observed variables mainly reflect the variations in a single unobserved latent variable that I used to measure geographically-bound social capital. The factor analysis provides an estimation of the degree to which it is possible to identify a common underlying structure in the four variables selected. The analysis suggests that the variables to be interrelated can be expressed in a common dimension (eigenvalue=2.59). Table 4.1 shows the factor loadings of the factor analysis.

[Insert Table 4.1 about here]

I measure social capital at the minimum level of aggregation possible for my data (NUTS 3). This level of aggregation is useful in providing a local measure of geographically-bound of social capital, but limits the number of variables that can be selected to provide a more consistent measure of the phenomenon being investigated.

Explanatory variables

The dependent variable is an ordered variable (from low to high) that offers a measure of the firm's degree of innovation. In measuring firm level of innovation I distinguish between incremental and radical innovations. Radical innovations are major transformations of existing products or technologies that often make the prevailing product/service designs and technologies obsolete and change the old knowledge into something completely new (Chandy & Tellis, 2000). Incremental innovations are improvements to existing products and are based on existing knowledge. Incremental innovations are implemented to refine existing products or technologies (Ettlie, 1983).

In this analysis, the dependent variable takes the value 0 if, during the three years 2004-2006 the firm did not introduce any innovation, the value 1 if the firm introduced a new product that incorporates a change in the state of the technology in the company or a new product that enables the firm to obtain new clients, and 2 if the firm has introduced a new product that creates a new market or a new product that incorporates a substantially different core technology relative to the previous product generation.

Control variables

The degree of innovation depends on several factors, including the entrepreneur's characteristics, firm behaviour and strategic orientation, and other context-specific factors that may influence the firm's innovation propensity. Previous studies point to the importance of the entrepreneur's human capital in the process of identification, discovery and implementation of new opportunities (Mosey & Wright, 2007; Shane, 2000), a process that requires a set of individual skills and insights. Therefore, I control for entrepreneur's education, measured as first degree, college degree, university degree.

Prior studies show that the level of the entrepreneur's experience accumulated over the years, is an important determinant of his\her beliefs, values and cognitions (Stuart & Abetti, 1990). Thus, I control for entrepreneur's experience, with a variable that takes the value of 1 if the entrepreneur has worked in the company for less than 1 years, the value 2 if the entrepreneur has been in the company more than 1 year, but less than 5 years, 3 if the length of time in the company is between 5 and 15 years, and 4 if the time in the company is between 15 and 30 years. In all other cases it takes the value 5. I control also for entrepreneur's age.

I also include a set of firm level variables to control for firm size, firm age, firm human capital, firm group and firm export orientation. The extant empirical research states that the advantages of size for firm's innovation capabilities are ambiguous (Cohen, 1995). On the one hand, larger firms may have greater financial resources and be favoured by economies of scale and scope, which may increase the profitability of their innovation strategies; on the other hand, smaller firms are more flexible and less bureaucratic, which may increase innovation efficiency. I measure firm size by the number of employees in 2004.

I control for firm age since this variable is likely to influence innovation capabilities, even though its effects are difficult to define: on the one hand, older firms usually rely on a somewhat broader base of knowledge for innovation; on the other hand, younger firms' greater flexibility may enable them to be more innovative.

Firms with highly skilled employees with graduate education may be more innovative. Therefore, I control for firm human capital measured as the percentage of employees with a degree.

I include firm group to measure whether or not the firm is a member of a corporate group. Firms that belong to corporate groups are more likely to have experience in cooperating with other organizations and are likely to have access to more information.

I control for firm export measured as a dummy variable that takes the value of 1 if the firm has exported its products or services in the three years 2004-2006 and 0 otherwise. Firms that compete in foreign countries have more incentive to innovate (Basile, 2001).

I also control for level of entrepreneurial activities in the firm's province, measured as the differences between firms that enter the market and firms that exit, over the average total firms in the province. Finally, I control for population measured as the number of people in the firm's province. This gives an idea of the size of the province.

RESULTS

The dependent variable (firm's degree of innovation) is an ordered variable coded as consecutive integers from 0 to 2. Although the variable is coded it is not possible to analyse an ordinal outcome using a linear regression model (LRM) since it violates the assumptions of the LRM, which can lead to inaccurate conclusions (Long, 2006; McKelvey & Zavoina, 1975). With ordinal outcomes the use of models that do not assume that distances between categories are the same, is recommended. Therefore, I use an ordinal regression model (ORM) to test my hypotheses. ORM is nonlinear and the change in the outcome probability for a given change in one of the explanatory variables is influenced by the levels of all of the independent variables (Long, 2006).

Table 4.2 presents the descriptive statistics and the correlations among variables. The correlation matrix does not show any worrying collinearity among the variables.

[Insert Table 4.2 about here]

Models I and II in Table 4.3 present the results of the ordinal regression model. The parameter for entrepreneur's social capital is positive and significant, which suggests that entrepreneur's social capital influences the firm's degree of innovation. This finding provides support for Hypothesis 1 (the broader the entrepreneur's social capital, the grater will be the firm's degree of innovation.).

The parameter for geographically bound social capital is positive and significant in explaining the dependent variable. Therefore, Hypothesis 2 (the higher the level of geographically bound social capital, the grater the firm's degree of innovation) finds confirmation. This result is in line with the study by Laursen et al. (2007) validating the relevance of geographically bound social capital for firm innovation.

I also assume that geographically bound social capital should moderate the relationship between entrepreneur's social capital and firm's degree of innovation. The moderator term in Model II (geographically bound social capital*entrepreneur's social capital) is positive and significant in explaining firms' degree of innovation. This means that as the level of geographically bound social capital increases, so does the effectiveness of entrepreneur's social capital on the firm's degree of innovation. This result confirms Hypothesis 3 in this chapter (geographically bound social capital positively moderates the effect of entrepreneur's social capital on firm's degree of innovation).

[Insert Table 4.3 about here]

CONCLUDING REMARKS

This chapter examined the interaction effects between entrepreneur's social capital, defined as a private asset that capture the entrepreneur's access to a set of individuals that belong to heterogeneous knowledge domains, and geographically bound social capital, defined as a public good, a collective asset that is available to all members of a given community. The results of this study suggest that both entrepreneur's social capital and geographically bound social capital have a positive effect on the firm's degree of innovation. Also, geographically bound social capital positively moderates the relationship between entrepreneur's social capital and firm's degree of innovation. The empirical analysis was conducted on a sample of Italian firms. The discussion of results is presented under implications for theory and implications for practice.

Implications for theory

Overall, the findings in this chapter provide support for the premise that social capital influences firms' innovation.

This study expands the entrepreneurship literature by investigating the role of entrepreneur's social capital as the foundation of firm innovation. The entrepreneur's investment in a broad social network appears to have a positive effect on the firm's ability to acquire and exploit external resources. Entrepreneurs with broad social capital assume a central role in the generation innovations since social capital facilitates the identification of new technologies and recognition of the market potential of new products.

Also, this chapter contributes to the entrepreneurship literature by supporting empirically that geographically bound social capital positively moderates the effects of entrepreneurs' social capital on firms' innovation. Previous literature investigates the role of entrepreneur's social capital focusing on the benefits that the firm obtains from the entrepreneur's investment in a network of relationships (Larson & Starr, 1993; Stuart & Sorenson, 2003). While there is some consensus in the literature on the importance of entrepreneurs' social networks for firms' success, a set of good network relations on its own does not guarantee success. This study complements this literature clarifying the contribution of geographically bound social capital in shaping the relationship between entrepreneur's social capital and firm innovation. Results indicate that geographically bound social capital (in the form of a collective asset) increases the effectiveness of entrepreneur's social capital on firms' innovation, helping the entrepreneur to identify new opportunities and mobilize valuable resources.

The findings in this chapter have significant theoretical implications for the debate on the effects of regional social structure on firm innovation. Prior research argues that the nature of the external environment influences firm innovation (Zahra, 1996; Zahra & Bogner, 2000). I demonstrate that geographically bound social capital favours firm innovation by providing conduits for knowledge and expertise sharing.

In recognizing the value of connections, the results of this study are in line with the stream of research that explores the importance of various forms of communication and interaction within regions among the actors that contribute to the creation of innovation (Agrawal & Henderson, 2002; Rosenkopf & Almeida, 2003). This chapter contributes to this stream of research by suggesting that for innovating firms, geographically bound social capital is useful to increase the effectiveness of entrepreneur's social capital on the probability of introducing an innovation.

This chapter also adds to our understanding of the concept of firm context. Several studies rely on context to explain what other variables are not able to explain with regard to firms' behavior. Firm context is used as a residual variable. This study on the importance of geographically bound social capital provides empirical grounding to the social dimensions of geographical context.

Moreover, the findings of this study are in line with the open innovation paradigm, which states that in the context of innovation openness fosters the introduction of new products by combining the efforts and the resources of a large and heterogeneous pool of individuals with access to different and complementary knowledge. Therefore, new opportunities are increasingly the result of a process that breaks down the conventional boundaries to the firm and allows ideas, information and knowledge to flow (Chesbrough, Vanhaverbeke, & West, 2006). The literature on open innovation does not look at the interaction between geographically bound social capital and entrepreneurs' social capital in relation to the efficiency of introducing innovations. The contribution of this chapter is in highlighting that social capital matters for firms' degrees of innovation.

Implications for practitioners

Entrepreneurs' investment in social capital appears to have a positive effect on firms' innovation. To leverage this investment effectively, it may be imperative for firms to develop innovation capabilities.

This chapter provides empirical support for the importance of geographically bound social capital in promoting firms' innovation. Entrepreneurs and managers need to be aware of the role of geographically bound social capital for predicting innovation capabilities. This means that location in a context with high levels of social capital increases the chances that firms will be able to capture external knowledge to improve the quality of their products and services and enlarge their customer base. This finding has implications for entrepreneurs and managers, who need to take account of this variable in their location decisions. For the entrepreneur, location is a strategic choice that may enable access to diverse and valuable information.

Limitations of the study and direction for future research

This study has some limitations. First, I use cross-sectional data that do not allow controlling for the effects of experience. If firms have been engaged in innovation activities in the past, this is likely to affect their future strategies. Also, cross-sectional data make it difficult fully to substantiate causal arguments. Future research should analyse the relationship between social capital and innovation using longitudinal data.

Another limitation is that this study considers a sample of Italian firms. While most research on social capital considers Italy based on its evident regional differences in this regard, there may be country specificities that might limit the generalizability of the findings. Future research should test the hypotheses in this chapter using a sample of firms located in other countries, which would be informative about different country patterns.

Moreover, this chapter explores the relationship between social capital and firms' degree of innovation in terms of incremental and radical innovations. Future research should investigate how entrepreneurs' and geographically bound social capital affect the firms' probability to introduce product and process innovations. Product innovation involves the creation of technologically new products, whereas process innovation involves new elements introduced into an organization's production or service activities in terms of task specifications, work and information flow input factors, and machinery used to manufacture a product or deliver a service (Abernathy and Utterback 1975; Freeman and Soete 1997; Rosenberg 1976; Tushman and Anderson 1986). Product innovation requires extensive processes of explorative search and interaction with many different internal and external sources of knowledge (Brown and Eisenhardt 1995). In contrast, process innovation has been described by Tushman and Rosenkopf (1992: 313) as "the most primitive form of innovation" and typically requires less search outside the organization. Process innovations are often the result of learning-by-using and learning-by-doing, as a consequence of the organization's experience of using new technology (Hatch & Mowery 1998; Rosenberg 1982). Accordingly, process innovations are the outcome of managerial decisions about how best to organize the firm to optimize the efficiency of its internal procedures and routines. As a result, process innovations are often not licensed or sold to other organizations (Arora et al. 2001). Moreover, given the difficulties in defining process innovations precisely, secrecy is in general more effective to protect the return from process innovation rather than product innovation (Cohen et al. 2000; Levin et al. 1987). Obviously, the role of secrecy reduces the scope for external interaction in the innovation process. In sum, these arguments lead to the conjecture that social capital increases the likelihood of firms introducing product innovation more than process innovation.

Laursen et al. (2007) make an attempt to investigate the effects of geographically bound social capital on firms' product and process innovation. They find that process innovation is affected by geographically bound social capital as distinct from no innovation, and the parameter for product innovation is higher and distinct from no innovation and process innovation. However, in this study, the authors do not consider the role played by entrepreneur's social capital in influencing the firms' probability to introduce product and process innovations.

Additionally

, I focus in this chapter on the positive effects generated by social capital. It is important to underline that social capital may have some negative consequences if the underlying interactions become too intense. While social capital theorists, such as Coleman, highlight the benefits of social capital, very intense and exclusive social interactions could have negative effects including excluding outsiders, making excessive claims on group members, restricting individual freedoms, and constructing downward levelling norms (see Portes 1998: 15, for a detailed discussion of these effects). While the proposed measure of geographically bound social capital can be considered a proxy for regional social structure, I cannot empirically disentangle strong from weak ties. Future work might try to do this in order to examine the possible negative effects of social capital in the form of 'overembeddedness' (Uzzi, 1997). Such an analysis would be difficult due to the lack of availability of data that provide an accurate description of the phenomenon.

This study has important implications that enhance and refine the growing interdisciplinary work on social capital: it appears that the degree of a firm's innovation is linked inextricably to the entrepreneur's social capital. The contribution in this chapter consists of a two-level perspective on social capital. To the best of my knowledge, this is the first study that explore how entrepreneur's social capital and geographically bound social capital interact. The effects of interaction between these two variables needs to be explored further to increase our understanding of the role of social capital on firm performance.

TABLES

Table 4. 1 Description of the variables included in the principal component analysis at provincial level.

Variables

Description of the variables

Factor Loading

Not-profit firms

Number of non-profit organizations over total population

0.873

Number of unpaid workers in not-profit

Number of unpaid workers in non-profit firms over population

0.750

Number employees of not-profit firms

Number of employees in not-profit firms over population

0.685

Protests

Per capita legal protests for non respect of obligations

-0.704

Foreign Residents

Number of foreign residents in the province in 2001

0.547

Table 4. 2 Descriptive Statistics and correlation matrix.

 

 

Mean

Std Dev

1

2

3

4

5

6

7

8

9

10

11

12

1

Firm's degree of innovation

2.008

1.055

2

Entrepreneur's social capital

5.121

2.066

0.190

3

Geographically bound social capital

0.536

0.584

0.234

-0.202

4

Entrepreneur's education

2.194

0.521

0.130

0.099

0.006

5

Entrepreneur's experience

2.879

1.200

0.104

-0.024

-0.143

0.025

6

Entrepreneur's age

44.097

10.368

0.000

-0.018

0.090

-0.263

-0.225

7

Firm size

27.258

26.421

0.036

-0.067

-0.104

0.055

-0.015

-0.071

8

Firm age

15.056

9.701

0.027

0.007

0.121

-0.327

-0.025

0.516

-0.091

9

Firm human capital

8.919

11.674

0.065

0.044

0.011

0.059

-0.057

-0.097

0.647

-0.115

10

Firm group

0.113

0.318

0.022

-0.009

0.130

-0.084

0.036

0.123

0.301

0.009

0.287

11

Firm export

0.435

0.498

0.071

0.446

-0.115

0.017

0.171

-0.040

0.020

0.082

0.112

-0.056

12

Population of the province (in million)

0.433

0.144

0.190

-0.122

-0.087

-0.150

0.113

-0.047

0.117

-0.033

0.045

0.083

-0.101

13

Entrepreneurial activities in the province

0.002

0.005

0.293

-0.026

-0.041

0.151

-0.059

0.052

0.137

0.039

0.082

-0.107

0.059

-0.205

Table 4. 3 Econometric Models.

 

Firm's degree of innovation

Ordinal regression model

Model I

Model II

Coef.

 

Std. Err.

Coef.

 

Std. Err.

Entrepreneur's social capital

0.450

***

(0.112)

0.225

†

(0.167)

Geographically bound social capital

8.506

***

(1.645)

5.426

**

(2.340)

Entrepreneur's social capital*geographically bound social capital

0.626

*

(0.357)

Entrepreneur's education

0.810

*

(0.403)

0.746

†

(0.401)

Entrepreneur's experience

0.297

†

(0.170)

0.299

†

(0.172)

Entrepreneur's age

0.002

(0.022)

-0.002

(0.022)

Firm size

0.007

(0.009)

0.005

(0.009)

Firm age

0.012

(0.025)

0.012

(0.025)

Firm human capital

-0.011

(0.021)

-0.008

(0.021)

Firm group

-0.299

(0.692)

-0.255

(0.704)

Firm export

-0.627

(0.435)

-0.602

(0.437)

Population of the province (in million)

0.000

(0.000)

0.000

(0.000)

Entrepreneurial activities in the province

429.910

***

(74.676)

449.638

***

(75.268)

N. of obs.

124

124

Log likelihood

-117

-115

chi2

77.59

***

80.65

***

Pseudo R2

0.249

 

 

0.259

 

 

Notes: Two-tailed tests for controls, one-tailed tests for hypothesized variables. Coefficient significant at 0.1% *** . 1% **. 5% *. 10% †. Standard errors in parenthesis