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THE MEDIATING EFFECT OF COGNITIVE SOCIAL CAPITAL ON KNOWLEDGE ACQUISITION IN CLUSTERED FIRMS

Gloria Parra Requena (Gloria.Parra@uclm.es)

Pedro Manuel García Villaverde (Pedro.GVillaverde@uclm.es)

Departamento de Administración de Empresas UNIVERSIDAD DE CASTILLA-LA MANCHA

ÁREA TEMÁTICA: Distritos industriales / clusters industriales

RESUMEN: (máximo 300 palabras)

Recently relational perspective has fuelled the literature on industrial districts. Geographical and cognitive proximity among similar organizations in bounded contexts favors the creation of diverse forms of social capital (McEvily and Zaheer 1999). Although proximity generates beneficial dense and cohesive social networks, it has also been argued that this characterization of networks restrains the capacity to detect and access new ideas and other knowledge resources. The specific concern of this paper is to analyze the role played by the cognitive dimension of social capital on knowledge acquisition in firms belonging to industrial districts. The cognitive dimension refers to the degree to which people and organizations share goals and culture (Bolino, Turnley and Blodgood 2002). This cognitive dimension has received much less attention in the social capital literature, as acknowledged by Bolino et al. (2002). However, in our opinion, it is perhaps the most appropriate dimension to define the relational characterization of clustered firms. This cognitive proximity can be found in the notion of feeling of belonging in districts (Becattini 1979). In our view, the cognitive dimension of social capital offers a congruent explanation of firms’ capacity to acquire knowledge and consequently, to improve innovation in a context of geographical proximity. Therefore, in contrast to the assumption of direct and free access to common knowledge in territorial agglomerations (Storper 1992), we argue that knowledge access depends on firms’ capacity to share goals and culture with other members of the district. This research draws on an empirical survey in the Spanish footwear industry, based on a sample of 224 companies. The paper is structured as follows. First, we explain the

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theoretical framework and the derived hypotheses. We then describe the research method and findings. Finally, we outline its possible contribution and implications.

PALABRAS CLAVE: Industrial district, social capital, cognitive dimension

1. INTRODUCTION

Recently, social capital has been considered as an explanatory factor of firms’ behavior

and performance (Adler and Kwon 2002). Previous research, although from very

different perspectives, shares some common propositions. Specifically, it has been

argued that dimensions of social capital, that is, how and with whom organizations are

connected, have a significant effect on value creation (Nahapiet and Ghoshal 1998).

On the other hand, the relational perspective has fuelled the literature on territorial

agglomerations of firms, those referring to concepts of the industrial cluster or district.

Geographical and cognitive proximity among similar organizations in bounded contexts

favors the creation of diverse forms of social capital (McEvily and Zaheer 1999).

Drawing on these two perspectives, social capital could be expected to explain, to a

great extent, the value creation of clustered firms. However, this has been a

controversial argument in the previous literature. Although proximity generates

beneficial dense and cohesive social networks, it has also been argued that this

characterization of networks restrains the capacity to detect and access new ideas and

other knowledge resources. Among others, Grabher (1993), Uzzi (1997), Gargiulo and

Benassi (2000) have suggested that the same ties that serve as a filter of information and

knowledge resources may generate lock-ins, isolating organizations from the external

world.

The specific concern of this paper is to analyze the role played by the cognitive

dimension of social capital on knowledge acquisition in firms belonging to industrial

districts. The cognitive dimension refers to the degree to which people and

organizations share goals and culture (Bolino, Turnley and Blodgood 2002). This

cognitive dimension has received much less attention in the social capital literature, as

acknowledged by Bolino et al. (2002). However, in our opinion, it is perhaps the most

appropriate dimension to define the relational characterization of clustered firms. This

cognitive proximity can be found in the notion of feeling of belonging in districts

(Becattini 1979).

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In our view, and this is the possible contribution of the paper, the cognitive dimension

of social capital offers a congruent explanation of firms’ capacity to acquire knowledge

and consequently, to improve innovation in a context of geographical proximity.

Therefore, in contrast to the assumption of direct and free access to common knowledge

in territorial agglomerations (Storper 1992), we argue that knowledge access depends on

firms’ capacity to share goals and culture with other members of the district.

This research draws on an empirical survey in the Spanish footwear industry, based on a

sample of 224 companies. This industry is characterized by the presence of a relevant

number of districts, making it particularly appropriate for this kind of study.

The paper is structured as follows. First, we explain the theoretical framework and the

derived hypotheses. We then describe the research method and findings. Finally, we

outline its possible contribution and implications.

2. THEORETICAL FRAMEWORK

2.1. The concept of the industrial district

The industrial district has traditionally been defined as a socioeconomic entity which is

characterized by the active presence of both a community of people and a population of

firms in one naturally and historically bounded area (Becattini 1990: 39). An industrial

district presupposes the existence of a population of firms that are specialized in one or

more phases of the production process. The district is characterized as a group of firms

that work together, where the division of labor takes place on an inter-firm rather than

intra-firm basis.

Although on the whole, the relations that are developed as a result of geographical

proximity may vary considerably in their details, their underlying logic remains

constant. Thus, despite having their own specific characteristics, the organizational

principles underlying the districts in south-west Germany and north-east Italy are

widely applicable. Similar inter-firm cooperation is often found in economic activities

carried out on a regional/supranational scale (e.g. Scandinavia) or in local contexts, such

as Silicon Valley in the United States.

An initial justification of the benefits of industrial districts for firms comes from

Marshallian or agglomeration economies. The author of the original concept of the

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industrial district (Marshall 1925) identified a number of external economies deriving

from the pool of common factors that include qualified human resources, specialized

suppliers and technological spillovers (Krugman 1991). At the same time the notion of

industrial atmosphere can be translated in the existence of intangible resources based on

experience, knowledge and information that is common to all the firms belonging to the

district. In general, authors have argued that firms belonging to districts benefit from

intangible externalities such as mutual knowledge, repeated and long term relationships,

or common experience, which build trust and a cooperative attitude (Paniccia 1998).

Within the context of our work we understand the notion of the district in the broad

sense of the term, as referring to a physical and relational space where externalities are

generated for firms. Despite the different views expounded, a review of the literature

provides us with a set of common ideas and postures that are useful for our research and

which we have set out in the following points:

(1) Face-to-face contact and physical proximity between firms facilitates interaction and

the transfer of resources and knowledge, which would be difficult to achieve with

long-distance relations.

(2) The critical value of districts has more to do with social or relational resources than

with tangible externalities or physical infrastructures.

(3) Of those who participate in districts, the leading players are not only final firms but

also suppliers of the different products and intermediate services, as well as a wide

range of institutions, such as universities, trade associations, industrial policy agents

and other local or regional institutions.

Recently, authors have postulated different paths for district transformation. Most have

advocated opening the district up to external sources and carrying out substantial

internal restructuring (Belussi, Sammarra and Sedita 2008). This new model may affect

some district principles such as internal homogeneity. Firms may vary significantly in

terms of resources and outputs, leaving aside previous internal homogeneity Boschma

and ter Wall (2007). Giuliani and Bell (2005), Giuliani (2005) and Morrison and

Rabellotti (2005) have posited the existence of sub-networks inside the districts, with

significant differences in terms of network structure characterization. In fact, firms have

varied knowledge bases and in consequence they can perform different roles in

knowledge networks.

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2.2. The social capital perspective: the cognitive dimension

The social capital perspective considers the economic action embedded in the network

of relationships which firms maintain, including non-business relationships (Oliver

1996). Firms import knowledge through social capital, which indeed constitutes a

valuable resource for them (Bourdieu and Wacquant 1992). Some authors have argued

that social networks are a critical part of the learning process where firms find new

opportunities and obtain new knowledge, also improving their previously existing

knowledge through interacting with others (Tsai 2000).

In creating knowledge and building trust, social capital prevents or restrains

opportunism in relationships (Trigilia 2001). Moreover, social capital reduces

transaction costs and uncertainty (Dosi 1988). As Yli-Renko, Autio and Sapienza

(2001) have argued, the degree to which firms use external networks to acquire and

exploit knowledge is regulated by the amount of social capital they possess. Firms

improve the quality of mutual exchanges of knowledge through their social interactions

(Lane and Lubatkin 1998).

Some authors have presented and discussed different mechanisms and potential

outcomes associated with social capital. Analytically, social capital presents three

different dimensions (Nahapiet and Ghoshal 1998). First, the structural dimension

concerns the density or dispersion of the network of ties. On the other hand, the nature

of the ties is related to the relational (strength) and cognitive (shared goals and culture)

dimensions.

As Tsai and Ghoshal (1998) suggest, there are indubitably connections between all three

dimensions, particularly between the cognitive dimension and the other two. Shared

goals and culture and other elements such as shared values or vision as expressions of

cognitive social capital also favor the development of trusting relationships, associated

with strong ties. On the other hand, the association between structural and cognitive

dimensions is based on the premise that social interactions play a critical role in shaping

goals and values among the members of the network.

Shared goals represent the degree to which the members of the network share an

understanding of and perspective on the achievement of the network’s activities and

results. When members of the network share goals and have similar perceptions of how

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to act with others, exchange of ideas and resources is fostered (Inkpen and Tsang 2005).

On the other hand, common culture refers to the degree to which common behavioral

norms control the relationships, that is, the set of institutionalized rules and norms that

govern behavior in the network (Inkpen and Tsang 2005). In this respect, sharing the

same entrepreneurial culture implies sharing concepts such as objectives, concerns,

processes, routines, etc. (Rowley 1997). In consequence, common culture includes

many different aspects such as codes, language, histories, visions or goals. All these

elements permit and improve the understanding between parties involved in the

relationship, thereby facilitating knowledge transmission.

According to Tsai and Ghoshal (1998), the cognitive dimension is related to the shared

vision among network members and includes collective objectives and aspirations.

Members of the network thus have more opportunities for a free exchange of ideas and

resources. Moreover, common objectives and interests help to reveal the potential value

of the exchange and combinations of resources. In conclusion, cognitive capital can be

viewed as a relational mechanism that helps network members to integrate and

exchange resources.

2.3. Knowledge acquisition

Knowledge acquisition is understood as the process used by an organization to obtain

knowledge. This process takes place through the organization’s external and internal

relationships. The relationships that provide knowledge vary in nature, and include both

formal and informal daily activities, as well as others. Some authors have systematized

the processes through which organizations acquire knowledge. Huber (1991) and Grant

(2000) provide a categorization of the sources of knowledge generation and acquisition,

respectively. These are integrated in the present paper: first, the internal creation of

knowledge, obtained through internal R&D, together with the learning that derives from

mechanisms such as the inheritance of knowledge possessed by the founders or

additional knowledge that was acquired before the organization was created. Grafted

learning is also included, since organizations improve their knowledge thanks to new

members’ knowledge that was not available before they joined the firm. Second,

experimental learning, based on action, acquired through direct experiences: this

learning includes processes such as organizational experiments, training in work, and

simulations. Third, external knowledge: these processes include a great variety of

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actions from the attendance of conferences, courses, workshops, benchmarking with

other organizations, interaction with other actors or establishing strategic alliances.

Searching learning is also included, namely, the information acquired by exploring the

firm’s external environment.

External sources of knowledge have been increasingly attracting the attention of

researchers in recent times. External sources include a broad range of mechanisms such

as external R&D, patent and license acquisition, strategic alliances and other

cooperation modalities (see Mowery, Oxley and Silverman 1996; Simonin 1999;

Caloghirou, Kastelli and Tsakanikas 2004). External knowledge acquisition becomes

crucial for firms since the innovation process requires external knowledge flows to

enhance their innovative capacity as some authors have suggested (Dyer and Singh

1998; Lane and Lubatkin 1998). In fact, the positive effect of knowledge acquisition on

innovation has already been proved in the literature (e.g., Ahuja and Katila 2001; Yli-

Renko et al. 2001; Chen and Huang 2008).

3. HYPOTHESES

3.1. The industrial district and knowledge acquisition

The definition of an industrial district suggests that inter-organizational relationships

(firms and institutions) and proximity constitute the basic elements of clustered firms.

Inter-organizational relationships constitute an external source of knowledge since they

provide opportunities for acquisition and exploitation of knowledge (Dyer and Singh

1998; Lane and Lubatkin 1998). Therefore, these sources of knowledge would appear to

be more relevant in contexts of intense relationships between organizations. Some

researchers have argued and demonstrated that territorial agglomerations of firms permit

a greater exchange of information and knowledge (e.g. Utterback 1974; Jaffe 1989;

Jaffe, Trajtenberg and Henderson 1993). On the other hand, proximity produces and

favors spontaneous, social or non-business interactions between managers and

employees in the industry that also facilitates knowledge dissemination (Lazerson and

Lorenzoni 1999). In spite of the development of new technologies that improve

communication between distant actors, tacit or non-codified knowledge is mainly

transmitted between close actors (Uzzi 1996), since intense interactions are required

(Dyer and Nobeoka 2000). In conclusion, geographical proximity favors the natural

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exchange of ideas (Decarolis and Deeds 1999) and is an element that facilitates

knowledge flows and technological exchange between firms (Boschma and ter Wall

2007).

In the industrial district tradition, the concept of industrial atmosphere refers to the

existence of knowledge shared by all members inside the district. In Marshallian terms,

this knowledge is in the air (Marshall 1925). Becattini (2005) defines knowledge inside

the district as mainly contextual, that is, knowledge closely related to the underlying

activity in which the district is involved. This knowledge gains value within the specific

activity, but on the other hand, it loses value with alternative uses. Furthermore, this

knowledge is difficult to reproduce in other temporal, social and spatial contexts, since

it is basically tacit in nature and experience based. In fact, as Bellandi (1996) suggests,

the district is characterized by gradual learning from experience.

Additionally, one of the important elements of the district is the existence of local

institutions that provide supporting services to the firms in district. These entities

compile and disseminate knowledge among firms, thereby reducing their search costs

(Molina-Morales 2005; McEvily and Zaheer 1999). Specifically, Antonelli (2000)

emphasized the role of universities and public research centers, since they can provide

information on laboratory discoveries, which represent complex and tacit scientific

knowledge. In the same vein, technician and employee mobility inside the district offers

further possibilities to obtain knowledge (DeCarolis and Deeds 1999).

To summarize, there are diverse sources of knowledge in the district, due to

geographical proximity, and intense relationships between organizations. Both facilitate

formal and informal communication, supported by internal mechanisms such as

friendship or family relationships, internal mobility of human resources, a shared

education from local institutions or spin-off processes, amongst others.

From these arguments the following hypothesis can be posited:

H1: DISTRICT MEMBERSHIP WILL BE POSITIVELY ASSOCIATED WITH

KNOWLEDGE ACQUISITION IN FIRMS.

3.2. Industrial district and cognitive social capital

Since social capital refers to the structure and content of relationships, possible effects

can be analyzed at different levels, including individual, organizational, regional or

national levels. Many authors have considered social capital insights as inherently

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spatial (Martin 1994), since relations, particularly those which are informal in nature,

frequently evolve close to home (Malecki 1995). In fact, social capital has been rapidly

propagated in the territorial literature (see Trigilia 2001 or Wolfe 2002; among others).

According to Trigilia (2001), a territorial context can be said to be rich in social capital,

depending on the degree to which individuals and groups are involved in relationship

networks of greater or lesser scope. Previous research has explained how districts

represent local configurations made up of many small local enterprises with specialized

and complementary competences rich in social capital, characterized by mutual trust,

cooperation and entrepreneurial spirit (Dakhli and De Clercq 2004). In fact, trust is

more successfully built up through repeated interactions and personal contacts, such as

those developed under conditions of proximity (Gulati 1995). Various authors have

described particular mechanisms in districts that drive the creation of social capital,

such as internal human resources, social non-business relationships, spiff-off from

previous district firms, among others (DeCarolis and Deeds 1999).

Specifically proximity and interaction intensity, characteristic of districts, play a key

role in sharing goals and building common values between network members. In this

way, actors adopt common codes, values and practices through social interactions (Tsai

and Ghoshal 1998). Thus, as a consequence of their frequent relationships, clustered

firms in districts are more likely to share common cultural elements (Paniccia 1998).

Firms especially build a code of communication and common language that uses these

interactions (Nelson and Winter 1982).

In conclusion, districts can be described as groups of firms embedded in a strong local

network and sharing a relatively homogenous system of values and ideas (Becattini

1990; Barabel, Huault and Meier 2007). In this respect Molina-Morales and Martínez-

Fernández (2006) observed greater shared culture and values in firms belonging to

industrial districts as compared to external firms.

The above arguments lead us to formulate a positive association between district

membership and cognitive social capital.

H2: DISTRICT MEMBERSHIP WILL BE POSITIVELY ASSOCIATED WITH

COGNITIVE SOCIAL CAPITAL DEVELOPMENT IN FIRMS.

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3.3. Cognitive social capital and knowledge acquisition

Although previous research is limited on this specific point, some precedents do

establish a positive association between cognitive social capital and firm performance.

Krause, Handfield and Tyler (2007) have evidenced that shared values positively affect

firm results. In general, shared goals and objectives among members of a network foster

common understandings about what an improvement is, and how it should be

implemented, thus leading to better firm performance. In contrast, if they are

incongruent, misunderstandings and conflicts are more likely to arise, presenting an

obstacle to the exchange of knowledge resources (Inkpen and Tsang 2005; Krause et al.

2007).

Specifically, the cognitive dimension of social capital may favor knowledge acquisition

in firms. First, it can be argued that in a relational context where actors share a similar

culture, the acquisition of tacit knowledge will probably be easier (Storper 1997).

Hence, when partners possess the same working culture, knowledge communication,

transmission and acquisition become more effective. Compatibility between cultures of

partners is required to facilitate the understanding of norms and values among parties

(Lane, Salk and Lyles 2001; Mowery et al. 1996). In contrast, organizational distance

negatively affects knowledge flows. Cultural conflicts and misunderstanding can limit

acquisition of information and learning (Simonin 1999).

In the same vein as shared goals, shared expectations affect knowledge acquisition in

the context of intellectual capital creation. When firms have the same perceptions of

how to act, there are fewer misunderstandings in their communication processes. This

increases the opportunities for idea and resource exchange, and for understanding the

potential value of these exchanges (Tsai and Ghoshal 1998). In this way, shared vision

can be considered as a binding mechanism that helps different parts of the network to

integrate knowledge (Inkpen and Tsang 2005).

In consequence, we understand that the cognitive dimension not only has a positive

effect, but it is fundamental to the external knowledge acquisition in firms. Thus, in

contexts where the organizations involved attain a better alignment of their goals and

culture, they are likely to obtain access to external knowledge. We can express this idea

formally as follows:

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H3: COGNITIVE SOCIAL CAPITAL DEVELOPMENT WILL BE

POSITIVELY ASSOCIATED WITH KNOWLEDGE ACQUISITION IN FIRMS.

3.4. Mediating effect of cognitive social capital

As pointed out above, in industrial districts knowledge flows with a certain degree of

freedom (Brusco 1990). In this vein, some scholars have argued that accessing

knowledge is one of main externalities firms derive from belonging to a territorial

agglomeration. Additionally, this knowledge is rarely available to firms outside the

district (Krugman 1991; Storper 1992).

Nevertheless, geographical proximity is not a sufficient condition to enable firms to

access district knowledge. Firms vary in terms of their ability to understand, and in their

degree of commitment to the cultural context existing in the district (Storper 1997). The

vision and goals of an individual firm may differ from those of the other firms

belonging to the district (Inkpen and Tsang 2005). In consequence, firms vary in their

capacity to acquire and learn from the valuable knowledge in district.

We consider that cognitive social capital is a basic explanatory factor that links

industrial district membership and internal district knowledge acquisition. In this way,

firms that are able to develop shared representations, interpretations, goals, routines and

ways of acting are in the best position to take advantage of their membership of an

industrial district. We understand that belonging to an industrial district will have an

indirect effect on the firm’s knowledge acquisition through the development of

cognitive social capital.

In line with the above arguments, we formulate the following hypothesis:

H4 THE DEVELOPMENT OF COGNITIVE SOCIAL CAPITAL MEDIATES IN

THE ASSOCIATION BETWEEN A FIRM’S MEMBERSHIP OF A DISTRICT

AND ITS KNOWLEDGE ACQUISITION.

Figure 1 shows the theoretical model and proposed hypotheses representing the

relationship between the analyzed variables. As can be observed, in addition to the

hypothesized effects we have introduced size and age as control variables (Yli-Renko et

al. 2001).

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Figure 1. Model of the determinants of knowledge acquisition in districts

4. METHOD AND EMPIRICAL STUDY

4.1. Sampling

The empirical study focused on the Spanish footwear industry. This labor intensive

industry is characterized by the existence of small and micro enterprises (accounting for

99% of the total). These firms are concentrated in Spanish regions such as the Valencian

Community (65.9%), Castilla-La Mancha (9.94%), La Rioja (7.1%) and the Balearic

Islands (3.55%), among others. In 2007, the industry produced 108.4 million pairs of

shoes, with a value of 1,905 million euros. Most of the total production is exported

(93.7% of total production in 2007). Finally, the Spanish footwear industry is mainly

structured in industrial districts, as mapped by Boix and Galleto (2004, 2006).

In our opinion, such a mature and traditional industry is particularly appropriate for our

research proposals. First, social capital requires a certain period of time to develop

completely. Second, a highly competitive environment, characteristic of mature

industries, allows us to better analyze aspects related to the accumulation and diffusion

of knowledge. In addition, the geographical distribution of firms combines the presence

of industrial districts with a significant number of isolated or non-district firms.

We used two databases to establish the population of firms, in particular SABI1 and

Camerdata2, which provide descriptive and financial information about Spanish firms.

Once we had filtered the initial list of firms from different sources, we determined a 1 SABI is a directory of Spanish and Portuguese firms that gathers general information and financial data. In the case of Spain, it compiles information on more than 95% of the firms with total yearly revenues over 360,000-420,000 € from the 17 Spanish regions. 2 The Camerdata database compiles a directory of all Spanish firms from the network of local Chambers of Commerce.

H4

H1

H3

H2 Age

Size

Knowledge Acquisition

District Membership

Cognitive Social Capital

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population of 1,403 firms3. A questionnaire was distributed among these firms, of which

a final total of 224 valid complete questionnaires were returned, constituting a response

rate of 16.97%. This can be considered an acceptable rate in comparison with similar

surveys. The sampling error was 5.96% for a confidence level of 95%, and the least

favorable situation of p=q=0.5. Furthermore, when we tested for non-response bias, no

significant differences were observed between respondent and non-respondents on

structural characteristics.

4.2. Variables

Independent variables

District membership: To identify firms belonging to industrial districts, we asked for the

location of the firm. District membership was established when the firm was located in

one of the industrial districts identified by previous research. We therefore incorporated

a dummy variable to distinguish between district member and non-member firms,

similarly to other previous studies (Hundley and Jacobson 1998; Molina-Morales and

Martínez-Fernández 2004; among others)4. In order to reinforce the internal consistency

of the objective measurement of district membership, we included a perceptual variable

in the questionnaire to measure feeling of belonging. Following the criterion of Becattini

(1979), we used a 7-point Likert scale with only one item to measure this perception

(see appendix5).

Cognitive social capital: The variable shared goals was measured by a six-item Likert

scale. This scale is comprised of those used by Tsai and Ghoshal (1998), Young-Ybarra

and Wiersema (1999) and Yli-Renko et al. (2001). We adapted the scales to the

particular characteristics of our study. We used the Simonin (1999) scale to measure

shared culture and a second order construct to measure cognitive social capital. This

construct is formed by two first order constructs (shared goals and shared culture). 3 We excluded companies with fewer than 6 employees. This criterion was suggested by other studies because a minimal operative structure is required to define their behavior and performance (Spanos and Lioukas 2001). A similar criterion is also used in other industrial district studies, such as Boschma and ter Wall (2007). 4 We considered all firms that were members of any district to be in the same category when testing our hypotheses. Thus, in order to test for bias, we analyzed mean differences of the variables of the study between firms belonging to each of the industrial districts. We ran an ANOVA and a Scheffe’s test between pairs of groups and found no significant differences for variables. 5 After running an ANOVA on the feeling of belonging variable for firms both internal and external to the industrial districts, we observed the existence of a significant difference (p<0.001) between the two groups. This feeling of belonging is greater for firms belonging to industrial districts. These results reinforce the nomological validity of the objective criterion used to measure belonging to a district.

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Dependent variable

Knowledge acquisition. From precedents in the literature, we included the Kale, Singh

and Pelmutter (2000) and Maula, Autio and Murray (2003) scales. Since these scales

were used in the fields of strategic alliances and customer relationships, we adapted

them to our specific context. Thus, this construct allows us to measure knowledge

acquisition of one organization derived from the relationships with different agents.

Control variables. This study included two variables to control their effects on

knowledge acquisition. Previous studies strongly support the use of these variables (e.g.

Yli-Renko et al. 2001). Some studies suggest that a firm’s age can affect its ability to

acquire knowledge (e.g. Lane and Lubatkin 1998; Zahra, Ireland and Hitt 2000), as

older firms can gain advantages from their experience of knowledge acquisition (Autio,

Sapienza and Almeida 2000). Firm size can also affect knowledge acquisition (Autio et

al. 2000), since larger firms have more resources to spend on relationships (Yli-Renko

et al. 2001). Size was measured by number of employees and age was measured by the

number of years from the foundation of the company to the survey date (2008).

4.3. Analysis techniques

Structural equations analysis was used since it has some advantages over traditional

multivariate techniques (Haenlein and Kaplan 2004). Specifically, we used partial least

squares (PLS) with PLS-Graph software to analyze data. PLS is particularly suitable for

data analysis during the early stage of theory development where the theoretical model

and its measures are not well or definitely formed. The level of statistical significance of

the coefficients of both the measurement and the structural models was determined

through a bootstrap re-sampling procedure (500 sub-samples).

5. RESULTS

5.1. Measurement model

To evaluate item reliability, we controlled the value of the loadings ( ). All loading

values exceeded the recommended threshold of 0.7 (Carmines and Zeller 1979).

Construct reliability was assessed using the composite statistic of reliability ( c), which

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is similar to Cronbach’s alpha. As we can observe in Table 1, all constructs exceeded

the accepted value of 0.8. For instance, Nunnally (1978) suggested that values above 0.8

can be considered as strict reliability. To assess the convergent validity we used average

variance extracted (AVE). All constructs exceeded the recommended threshold of 0.5

(Fornell and Larcker 1981).

Table 1. Reliability

Construct Composite reliability AVE

Cognitive social capital 0.919 0.851 Knowledge acquisition 0.954 0.774

Finally, in order to control discriminant validity (Barclay, Higgins and Thompson 1995)

the mean extracted variance should be used (Fornell and Larcker 1981). We compared

the square root of the AVE (the diagonal in Table 2) with the correlations between

constructs (the off-diagonal elements in Table 2). We can observe that the square root of

AVE for both constructs is greater than the correlation between constructs, suggesting

that each construct relates more strongly to its own measures than others.

5.2. Structural model

We evaluated the structural model by examining the size and significance of the path

coefficients and the R2 values of the dependent variable. Figure 2 shows the results of

the model analysis and the explained variance. The results allow us to corroborate all

the research hypotheses.

Table 3 shows that district membership has a positive and significant effect on

knowledge acquisition ( =0.172; p<0.05). District membership also has a positive and

Table 2. Discriminant validity and correlations

Construct Cognitive S.C. Knowledge acq.

Cognitive S.C. 0.923 0.554 Knowledge acq. 0.554 0.880

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significant effect on cognitive social capital ( =0.218; p<0.001). These findings support

hypotheses 1 and 2.

Table 3. Direct effects of industrial district N= 224; **p<0,05; ***p<0,01; ****p<0,001

Knowledge

acquisition

Cognitive social

capital

Construct

Path T Path T

Industrial district 0.172 2.264** 0.218 3.677****

Hypothesis 3 proposed a positive effect of cognitive social capital on knowledge

acquisition. The results presented in Table 4 allow us to confirm this hypothesis

( =0.558; p<0.001).

Table 4. Effect of cognitive social capital on knowledge acquisition

N= 224; **p<0,05; ***p<0,01; ****p<0,001

Knowledge acquisition Construct

Path T R2

Cognitive social capital 0.558 9.105**** 0.316

Figure 2. Model of the results of the determinants of knowledge acquisition in districts

0.050ns

0.084ns 0.218****

0.549****

0.044ns

Size

Knowledge Acquisition

District membership

Cognitive Social Capital

Age

R2= 0.317

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In hypothesis 4 we proposed an indirect effect of industrial district on knowledge

acquisition through cognitive social capital. To confirm this hypothesis the four

conditions established by Baron and Kenny (1986) must be met. For this mediator

effect, the first condition is satisfied since the independent variable (district

membership) has a positive and significant influence on the dependent variable

(knowledge acquisition). The second condition establishes a positive relationship

between the independent variable and the mediator variable, that is, cognitive social

capital. This condition is satisfied through the corroboration of hypothesis 2. The third

condition requires a relationship between the mediator variable –cognitive social

capital- and the dependent variable –knowledge acquisition-. This condition is satisfied

by the confirmation of hypothesis 3. The fourth condition establishes that the

relationship between the independent variable and the dependent variable should be

eliminated —or at least reduced— when the mediator variable is included in the model.

When we introduced these three variables into the model, the effect of industrial district

on knowledge acquisition disappeared (from 0.172 to 0.044 and is not significant).

That means that cognitive social capital wholly mediates the relationship between

industrial districts and knowledge acquisition. Therefore, we can accept hypothesis 4

since we see that the industrial district has an indirect effect on knowledge acquisition

through cognitive social capital. This effect has a value of 0.1206.

The model shows a high consistency, since the value is over the 0.1 established by Falk

and Miller (1992). Thus, the model allows us to explain 31.7% of the total variance of

the dependent variable, in our case firms’ external knowledge acquisition.

6. DISCUSSION AND CONCLUSIONS

This paper analyzes how the cognitive dimension affects knowledge acquisition by

clustered firms. Firstly, findings show how firms belonging to an industrial district

acquire a significant amount of knowledge from contacts inside the district. In fact,

there is a positive and significant association between district membership and cognitive

social capital and also with knowledge acquisition. However, when we introduced all

the factors into an integrated structural model, we observed a significant indirect effect

6 This value is computed by multiplying the significant structural paths.

18

of district membership on knowledge acquisition through the development of cognitive

social capital. Moreover, the mediator effect of the cognitive dimension is particularly

strong. In fact, the significant association between membership and knowledge

acquisition now disappears under the effect of the cognitive variable.

Specifically, this paper has focused on the cognitive dimension of social capital, rarely

studied, yet indubitably related to the other two structural and relational dimensions

(Tsai and Ghoshal 1998). This dimension is particularly relevant to explain the

connection between location inside the district and valuable knowledge acquisition

through external contacts7. Therefore, our findings underline the decisive role played by

shared goals, values and culture in the capacities and knowledge acquisition process in

the context of the industrial district.

The main contribution of this research is the way it identifies and proves that the

cognitive dimension of social capital explains why firms take advantage of the common

knowledge generated in contexts of territorial proximity. It has been suggested that

contexts like industrial districts are appropriate for efficient knowledge acquisition;

however, this acquisition only occurs when firms are immersed in a common cultural

context, sharing visions and goals with other firms in the local neighborhood. In fact

individual firms vary in their access to knowledge and market power (Boschma and

Lambooy 2002). These findings support previous research suggesting that the degree to

which firms use external networks to acquire and exploit knowledge is conditioned by

the amount of social capital they possess (Yli-Renko et al. 2001). Our proposal provides

theoretical linkages between key concepts of three different theoretical

conceptualizations, namely the industrial district (Marshall 1925; Becattini 1979), social

capital (Putman 1993; Nahapiet and Ghoshal 1998) and the knowledge-based view

(Nonaka 1994; Grant 1996).

Our findings also at least partially contradict some of the industrial district literature that

focuses exclusively on the district-level or systemic advantages (Signorini 1994),

without considering the relevancy of the individual firm. In contrast, our findings are in

line with recent research emphasizing internal heterogeneity inside the district (Giuliani

2002; Giuliani and Bell 2005; Morrison and Rabellotti 2005). The social capital

7 We undertook exploratory tests on the indirect effect of the two other dimensions, with the result that the structural and relational dimensions have a minor significance.

19

perspective in particular provides a solid base from which to explain heterogeneity

among firm members in industrial districts in order to access common knowledge and

capacities.

Moreover, this research supports the conceptualization and delimitation of the industrial

district. Following Becattini (1990), we have used both objective elements to identify

the district and perceptual elements such as the feeling of belonging. In addition, by

considering the whole Spanish footwear industry we reduce risks in the generalization

of findings. This study therefore overcomes some of the traditional limitations of

empirical studies in the district field, such as potential specific case bias.

These research findings support the competitiveness of firms in mature industries such

as the footwear industry, since they can still offer potential knowledge and specific

abilities for member firms. However, a firm’s membership of a district is not sufficient

on its own to ensure advantages are harnessed. Firms must engage in actions and

develop specific strategies to exploit the opportunities districts offer. Particularly, firms

should address their efforts to building common norms, values and cultural elements

with their contacts to efficiently acquire relevant knowledge. In this vein, firms must

promote cooperative relationships and favor understandings with others in order to

facilitate knowledge transmission.

On the other hand, local institutions involved in districts, such as universities,

technological institutes, policy agencies, trade associations and others, must coordinate

their actions to encourage flows of valuable and non-redundant knowledge between

firms. These actions may be complemented with institutional efforts to boost collective

representation as well as common goals and vision (Keeble, Lawson, Moore and

Wilkinson 1999), in order to strengthen shared norms and values in the district. In this

way, the promotion of commercial and technological projects that bring together efforts

and interests between firms will foster the climate of trust necessary to integrate and

exchange abilities and knowledge.

One of the limitations of our cross-section analysis refers to its static nature. However,

longitudinal studies could be much more demanding because of the data and

information required for a study like this one. Moreover, in spite of our efforts to assure

robustness in the validation of data and constructs, potential bias cannot be dismissed.

Finally, the study focuses on the footwear industry in Spain, specificities that can

restrain possible generalization of the findings. However, similarities with other cases in

20

terms of maturity of the industry and social context permit, with obvious caution,

conclusions to be generalized.

As a final remark, our findings question the more simplistic approach sometimes found

in the literature on knowledge acquisition among clustered firms. A complementary line

of research may consist of analyzing the role played by relational and structural

dimensions of social capital to improve knowledge acquisition. Further research might

continue the analysis of the heterogeneity or asymmetric distribution of advantages

inside districts. In this vein, variables such as absorptive capacity, innovative capacity

or individual knowledge bases can provide a more precise explanation about why firms

vary in exploiting district externalities.

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Appendix I Feeling of belonging

In general, I strongly identify with organizations from my local area. Cognitive social capital (shared goals)

We share the same ambition and vision as our contacts. My firm is enthusiastic about pursuing the collective goals and missions of our relationships. We share our goals and objectives with our contacts. We understand our contacts’ strategy and needs. My firm’s employees and my contacts’ employees have positive attitudes toward a cooperative relationship. My firm and my contacts tend to agree on how to make the relationship work.

Cognitive social capital (shared culture) The business practices and operational mechanisms of your contacts are very similar to yours. The corporate culture and management style of your contacts is very similar to yours.

Knowledge acquisition Your company has learnt or acquired new or important information from your contacts. Your company has learnt or acquired critical capability or skill from your contacts. Your relationships or contacts have helped your company to enhance its existing capabilities/skills. Your contacts have been an important source of information/know-how for you on customer needs and trends. Your contacts have been an important source of information/know-how for you on competition. Your contacts have been an important source of information/know-how for you in technical issues.