Shared, Competitive, and Comparative Advantages Essay

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Shared, Competitive, and Comparative Advantages

Department of Business Administration and Marketing, Universitat Jaume I, Campus Riu Sec. ? 12071 Castellon, Spain; e-mail: [email protected] uji. es Received 26 February 2004; in revised form 29 April 2004 Abstract. The author’s aim is to construct and validate empirically a theoretical model that allows performance and competitiveness in firms located in industrial districts to be explained. From the strategic perspective adopted, economic revenues are explained by three types of advantage: shared advantages, competitive advantages, and comparative advantages.

Neither integration in the district, nor its attraction due to the shared competences within it, are significant predictors of performance. Empirical results indicate that organisational performance is largely explained by the joint effect of firm distinctive competences and cluster-shared competences. It was also found that the greater the degree of a firm’s embeddedness in an industrial district, the greater the effect of its distinctive competences on organisational performance.

This evidence suggests that firms which are better endowed with resources and capabilities find the development of sustainable competitive advantages easier when they locate in industrial clusters, as they are more capable of capitalising on the potential for economic rents that these clusters offer. Therefore, the internal heterogeneity of the cluster stems from the different patterns of appropriation of shared competences.

In addition, firm embeddedness in an industrial district is also revealed as a moderating variable in the relationship between shared competences and global performance/average return on assets, explained by the positive effects of the participation in models, values, and knowledge flows circulating within the cluster. Introduction My general purpose in this paper is to construct a theoretical model enabling an explanation to be given of the performance and competitiveness of firms located in industrial districts, and to confirm this explanation empirically through a sample of 835 industrial firms in 35 clusters in Spain.

This model of industrial-district effectiveness attempts to explain the reasons for the heterogeneity of results among intradistrict firms, among intradistrict compared with extradistrict firms, and among organisations located in different territorial agglomerations. The strategic perspective adopted attempts to explain firm economic rents stemming from three types of advantage: advantages based on competences shared by all companies located in a district; competitive advantages produced by individual distinctive competences; and comparative advantages stemming from the attractiveness of the general environment.

Industrial districts have increasingly been recognised as an organisational model that enables small and medium-sized enterprises (SMEs) to compete internationally (Becattini, 1987; Brusco, 1982; D’Aveni and Illinich, 1992; Krugman, 1991; Lazerson, 1995; Wiklund and Karlsson, 1994). Empirical studies such as those by Karlsson and ? Klaesson (2000), Becchetti and Rossi (2000), Paniccia (1999; 1998), Camison (2001), and Signori (1994) have provided evidence that firms belonging to an industrial district perform better.

However, the wealth of contributions cannot hide the weaknesses in this body of knowledge. I particularly wish to highlight three weak points: (a) the lack of a theoretical model to guide research into the sources of business success and into the possible contribution of territorial agglomerations to this success (Foss, 1996); (b) the problem of business-performance homogeneity or heterogeneity within the district (Becattini, 1990; Brusco, 1990; DeCarolis and Deeds, 1999; McEvily and 2228 ?

C Camison Zaheer, 1999); and (c) the lack of empirical studies providing significant statistical evidence on the suggested causal relationships, deriving from a series of methodological and conceptual problems (Paniccia, 1998; Parr, 2002). In this paper I provide theoretical and methodological innovations to overcome these problems. With respect to the first problem, I approach the study of industrial districts from the competence-based view (CBV). This view attempts to test the contribution of the individual portfolio of resources and capabilities of firms considered individually, and of the shared competences in the district to performance.

From this approach, the industrial district is understood as an external space containing resources and capabilities to which member firms have access. Second, this theoretical model enables the premise of internal homogeneity of intradistrict companies to be questioned. Shared competences in the industrial district exert pressure in favour of internal homogeneity, conditioning the behaviour of intradistrict firms in a way similar to the effect of industry structure in the paradigm `structure ^ conduct ^ performance’ of industrial economics.

But the internal environment of districts is heterogeneous, because the individual firms are free agents with different distinctive competences, and subnetworks with different protagonists are present. In this research, the hypothesis will be put forward that the competitive and performance-related asymmetries between firms within the district stem from their different patterns of appropriation of shared competences, which are, in turn, connected with their individual distinctive competences.

I also attempt to find a solution to the problem of the lack of statistically significant empirical evidence of the causal relationships established. The origin of this problem is to be found principally in the methodological deficiencies stemming from research based on industrial district case studies selected idiosyncratically by the researcher (Paniccia, 1998). A further important methodological problem is the very definition of agglomeration economies (Malmberg and Maskell, 2002; Parr, 2002), of an industrial district, and of the setting of its boundaries.

Becattini (1990, page 61) notes the need to conceptualise the industrial district to enable “the precision and conclusions derived from empirical studies to be improved. ” Controversy also surrounds the methods used for measuring industrial districts, with parallel approaches centred exclusively on economic variables (Bellandi, 1989) or on those enriched with cultural and social variables (Becattini, 1979; 1990; Lazerson and Lorenzoni, 1999). In an attempt to overcome these methodological problems, three specific objectives were determined to provide further innovations.

First, verification that the concepts used in the CBV to prove competitive advantages and the individual performance of firms obased mainly on information, knowledge, and learningoare widespread within the sphere of the industrial district. In this way, the concept of the industrial district, defined as an external space of shared competences to which member firms have access, is tested. Second, a rigorous measurement of two of the most difficult elements in defining an industrial districtoembeddedness and shared competences ois sought.

Third was the provision of statistical evidence of the strength of causal relationships: the hypotheses were tested statistically by means of multiple regression models. A theoretical model of industrial-district competitiveness The canonical concept of the industrial district coined by Becattini (1979; 1989; 1990) and Brusco (1982; 1990) is based on an updating of the original Marshallian concept. This definition of the industrial district profiles its stylised features as a socioterritorial entity.

It is not simply a geographical space with a high density of small and mediumsized enterprises specialising in certain activities: the concept also includes the frequent Shared, competitive, and comparative advantages 2229 existence of interfirm cooperation networks and a community of people with both a strong sense of belonging and common cultural characteristics. The effectiveness of this production model depends on the density of the cooperation network created, the degree of integration between firms, and the social context.

These factors determine the wealth of externalities and the volume of economies of agglomeration that can be accessed by all the cooperating firms. This concept is an attempt to define the fluidity and continuity of the flows of experience, information, and knowledge circulating, with few restrictions, in the district; the rapid informal dissemination and absorption of innovations and new skills; the existence of technology spillovers (Krugman, 1991; Rodr|? guez-Pose and Refolo, 2003); and an infrastructure of regionally based institutional strength (Henry and Pinch, 2001; Pinch and Henry, 1999).

(1) Economies of agglomeration found in the industrial district enable it to be identified as a `munificent environment’ (DeCarolis and Deeds, 1999). Externalities enable the establishment of transaction relationships at lower costs than the costs of internal coordination derived from a hierarchical form of organisation (D’Aveni and Illinich, 1992). These externalities are totally different from the comparative advantages (the country effects) generated by the attractiveness of the general environment in which the firm is located (Porter, 1990; Thompson, 2003).

Although both types of rent derive from location in a particular area (territorial agglomeration versus country/region), their determining factors are different. The theory of comparative advantage in international trade affects the differences in facilities and the relative cost of factors (Leamer, 1984), the institutional framework (Tezuka, 1997), the structure of the economic system (Arora and Gambardella, 1997), the regional culture (Whipp et al, 1989), and the quantity and quality of the human, technological, and social capital invested in the national environment by public action (Howes and Singh, 2000).

These same factors run through many agglomeration economies. In contrast, comparative advantages do not lead to the common patterns of strategic and organisational design observed in clusters. The presence of a community of people within the district has implications which go further than Marshallian agglomeration economies (Harrison, 1991). An industrial district is supported by a community of people who share a feeling of belonging, or common identity (Becattini, 1979)oa relatively homogenous system of values and ideasoin the same way as a cognitive community does.

Authors such as Paniccia (1998), Crewe (1996), and Harrison (1991) maintain that the existence of this community of people is related to the concept of embeddedness put forward by Granovetter (1985). The sociological notion of the district as a community of people (Grandori, 1999; Lazerson, 1995; Saxenian, 1994) attempts to explain the competitive advantages of companies integrated into a district based on values, institutions, and rules, interpreted as assets produced by interorganisational relationships between people and organisations in a particular social context.

In addition, for the social dynamics to work without hindrance, a set of shared social institutions and rules must develop in parallel with the value system to disseminate these values within the district, support them, transmit them to future generations, and regulate the system (Becattini, 1990, pages 39 ^ 40). From the community of people stems an atmosphere of cooperation, trust, and social sanction in which economic action is regulated both by explicit and by implicit rules (Lazerson and Lorenzoni, 1999).

In this way, trust through mutual knowledge and sustained reproduction of (1) In fact the strategic literature (Enright, 1998; Porter, 1990; 1998; Porter and Solvell, 1998) speaks « of regional clustersofocusing above all on regional research and development (R & D) institutions (DeCarolis and Deeds, 1999). However, in this paper, both concepts, cluster and industrial districts, are used synonymously. 2230 ?

C Camison cooperative relationships between the agents within the district means that there is only a limited threat of opportunism (Dei Ottati, 1994; Foss and Koch, 1996), which generates low transaction costs and supports individual and collective learning processes. From these economic and sociological approaches to the industrial district, it is deduced that the organisational performance of companies will be directly influenced by their location in a local production system. These theories focus on explaining the differences in performance between firms located inside and those located outside a territorial agglomeration.

The central hypothesis is: Hypothesis 1: There is a positive relationship between embeddedness in an industrial district and organisational performance. To test this proposition, empirical work published so far has been based on select case studies or samples in which the level of homogeneity in the endowment of externalities within the district is not known. This approach offers no clues either to understanding the competitiveness of certain industrial districts compared with others, or to the asymmetry of results inside the district.

The problems of measuring agglomeration economies and of achieving robust empirical evidence of the causal relationship in case studies suggest that a new approach needs to be sought. In the present paper a different theoretical framework is adopted, which allows the externalities inside the district to be measured, and a different methodological design, namely, testing the hypothesis with multiple linear regression modelsoexplained in the next section.

Following the suggestions in recent theories expounded by authors such as Malmberg and Maskell (2002), Lawson (1999), Lawson and Lorenz (1999), DeCarolis and Deeds (1999), Maskell and Malmberg (1999), and Foss (1996), the CBV is adopted as a theoretical framework. The literature (Foss, 1993; 1997; Foss and Knudsen, 1996; ? Sanchez et al, 1996) uses the label `CBV’ for all studies whose common denominator is a highlight on the importance of firm-specific competences orelated to tacit knowledge and shared inside the organisation ofor strategy and business success.

This theoretical model may be an attractive option if it is extended to the sphere of the district. Its emphasis on factors determining the achievement of sustained competitive advantage and economic rents at the micro level is useful for dissecting the internal structure of the district. This view attempts to test the contribution to performance derived from firm competences, as well as from industrial district shared competences. We are therefore differentiating three levels of competences.

(a) Personal competences are equivalent to what many authors call `skills’, and are defined as capabilities possessed by an individual or group of individuals within the organisationosuch as leadership or experience. (b) Corporate competences consist of combinations of knowledge and skills that belong to a firm. In contrast to the potentially migratory and largely tacit nature of individual knowledge, corporate knowledge tends to be independent of the individual and to remain within an organisation when individuals or particular groups leave it, as it permeates through organisational activities and structures.

(c) Shared competences in a industrial district include the assets of knowledge, information, and learning deposited in a territorial environment close to the firm; the flexibility in production achieved by the district as a whole in the style of almost vertical integration; and the industrial culture solidly established in the territory. These competences are based on network assets derived from stable, long-term cooperative relationships between the agents in the local environment.

An industrial district may thus be understood as an external space with resources and capabilities to which member companies have access. The importance of shared competences as a source of shared advantages over competitors outside a cluster, or from other clusters, who cannot access them, derives Shared, competitive, and comparative advantages 2231 precisely from their intangible nature. These intangible assets have the characteristics of tacit knowledge and can be referred to as district-specific tacit knowledge (Porter and Solvell, 1998).

In the literature (Grant, 1991; Hall, 1992; 1993) there is repeated « insistence on the value of intangible assets as a source of sustainable competitive advantage, particularly because of the barriers to duplication or their replacement by strategically equivalent assets. Additional difficulties in appropriating from, or imitating, competitors outside the district further reinforce their value in terms of competitiveness and as a source of economic rents. Shared competences are inserted into the processes, networks, and institutions existing within the industrial district.

These competences circulate within the district with a certain degree of freedom, as they are combinations of knowledge and skills that are not the legal property of any one firm. But they are not accessible to firms outside the cluster. They lack embeddedness in the community of people and networks within the district and hinder their access to the common space for resources and capabilities. Therefore, a second hypothesis may be outlined. Hypothesis 2: There is a positive relationship between the competences shared by organisations located in an industrial district and firm organisational performance.

This common space is a promoting factor for the internal homogeneity in the district (Becattini, 1990; Brusco, 1990), as it conditions the behaviour of firms located in it. The strong interdependence between people and firms within a relatively homogenous community with a shared value system works in favour of symmetry. The transmission of knowledge and reference models within the particular industrial atmosphere of the district is another force in favour of common behaviour patterns.

However, the evidence provided by observation and empirical research indicates that there is strong heterogeneity within industrial districts. Studies such as those by DeCarolis and Deeds (1999) and Lazerson and Lorenzoni (1999) have indicated that the organisations located in these territorial agglomerations continue largely to be free agents in determining their own development. It must therefore be supposed that there are different variables that define the internal heterogeneity of intradistrict firms.

Specifically, the CBV (Barney, 1986; 1991; Grant, 1991; Peteraf, 1993; Wernerfelt, 1984) postulates that firm individual distinctive competences are the basic source of competitive advantages and economic rents. Shared competences may be the origin of individual competitive advantages in enabling an intradistrict company to access other distinctive competences transferred from other organisations (Buckley and Casson, 1988; Hamel et al, 1989), encouraging the sharing and creation of knowledge and learning.

It can be argued that the effect of district-shared competences on performance is moderated by the asymmetry of individual distinctive competences in each organisation, which will regulate their specific capacity to internalise these shared assets. Each firm potentially can exploit the group of interdependences that together define the district in different ways, which is probably the origin of the performance differentials within it.

The earlier premise may now be modified to be expressed as follows: Hypothesis 3: The effect of the competences shared by the enterprises located in an industrial district on their organisational performance is moderated by the distinctive competences belonging to each intradistrict firm, or Hypothesis 3A: The richer the individual endowment with distinctive competences of an intradistrict firm, the greater will be the positive effect of the shared competences on its organisational performance. 2232 ? C Camison.

On the other hand, the likelihood that a firm is not fully linked to an industrial district must be considered. There are indeed organisations which, although entirely within a local production system, remain on the margins of the community and the industrial atmosphere that characterises it. In addition, there are also firms whose activities are carried out only partially within the districtoeither because they have facilities beyond its borders, as part of corporate groups or other external interorganisational networks, or because of a history of cooperation and commercial relationships with other agents.

The key to benefiting from the district-shared competences is in the feeling of belonging, which makes the firm go along with the common mental models, values, and networks and take part in the flows of information and knowledge derived from the economies of agglomeration. A firm located inside a district but maintaining independence and absolute reservation with respect to the other internal agents will be deprived of access to these shared competences. Such assets may be inert assets for an intradistrict firm which is not disposed to make its borders permeable in order to absorb the positive externalities freely circulating in the system.

It should be remembered that an internal firm can restrict its communication with the district. Consequently, it must be argued that the greater the embeddedness of a firm in a district, the more it can benefit from the shared competences deposited in it. The hypothesis may be outlined in the following terms: Hypothesis 4: The effect of the competences shared by the firms located in an industrial district on their organisational performance is moderated by each firm’s embeddedness in the cluster.

The rootedness in an industrial district may equally have effects on the generation of distinctive competences by internal firms. The district acts to a certain degree as a collective research and development (R & D) laboratory, in which innovation is constantly germinating. Increased embeddedness of a firm leads to its greater involvement in networks, in flows of knowledge, and in learning processes, which can act as catalysts for developing new individual distinctive competences.

We can therefore formulate a new proposition: Hypothesis 5: The effect of the individual distinctive competences of each firm located in an industrial district on its organisational performance is moderated by each organisation’s embeddedness in the district. Figure 1 schematically represents these causal relationships, which make up a theoretical framework of reference in studying industrial districts from a CBV. Methodology The empirical study was carried out on a sample of 835 industrial firms and 35 industrial districts in Spain.

The information on the firms which was required for empirical confirmation of the hypotheses was obtained from a primary study. All the information refers to 31 December 2001. The list of Spanish industrial sectors, excluding the energy sector, published by the Spanish National Statistical Institute (INE) in its Central Directory of Enterprises was taken as the universe. The choice of the initial sample was made by means of a stratified sampling procedure with optimum fixing of size and industry. Within each stratum, selection was carried out by simple random sampling.

The initial sample was fixed at 2000 firms to obtain a statistical margin of error of ? 2X2% with a 95. 5% confidence interval. The collection of data was carried out through a postal survey, Database Shared, competitive, and comparative advantages 2233 Distinctive competences Hypothesis 5 Embeddedness in district Hypothesis 1 Organisational performance Hypothesis 3 Hypothesis 2 Hypothesis 4 Intradistrict shared competences Figure 1. Direct, moderator, and interaction effects of the explanatory variables on performance. with the fieldwork undertaken between June and October 2002.

The questionnaires were sent to the top manager of each of the sample firms. A total of 964 questionnaire responses were received. After an initial statistical refinement of the questionnaires, 12 of them were eliminated for various reasons. With the aim of homogenising the unit of analysis, a total of 117 diversified companies were eliminated in order to avoid the additional consideration of the `corporation effect’. The sample considered for the empirical study was therefore made up of 835 nondiversified companies. This final sample had a statistical margin of error of ?

3X3% with a 95. 5% confidence interval. The sample may be interpreted as a real reflection of the current Spanish industrial structure. The average size of company surveyed was 301 workers, with average sales of 22. 87 million. The geographical location covered the whole Spanish territory, but was concentrated in the strongest industrial centres. Spain has a significant number of local production systems in various activities and geographical areas, which have involved notable decentralisation of production and ? industrial diffusion (Benton, 1992; Camison, 2001; Costa, 1993; Ybarra, 1991).

The majority of empirical studies have adopted a quantitative criterion for district identification, depending on how various factors come together to demonstrate a high density of SMEs specialising in certain activities (Becchetti and Rossi, 2000; DeCarolis and Deeds, 1999; Paniccia, 1998; 1999; Signori, 1994), although there are also studies based on panels of experts. The advantage of this approach is that it avoids the problems caused by the subjective choice of districts, which will not represent the whole range of potential situations (Harrison, 1991), and may be restricted, for example, to models of success (Paniccia, 1998).

The aim here was to select industrial districts without having to demonstrate that they are tied to the canonical Marshallian concept. Cases involving experiences of success, emergence, and decline were therefore all considered. The empirical procedure used to delimit an industrial district is based on the methodology employed by De Luca and Soto (1995) and Ybarra (1991). Information on employment and industrial sector firms (excluding energy) was taken from the INE’s Central Directory of Enterprises, updated on 31 December 2001, as the starting point.

For the purposes of profiling the limits of the district as accurately as possible, the statistical information collected had to be geographically segmented into the smallest possible areas, and hence was grouped not at regional, but at municipal, level. 2234 ? C Camison The information was broken down from two perspectives: on one hand spatially, by municipalities (identified from the 2001 INE Population Census), and on the other, economically, following the 1993 National Classification of Economic Activities (CNAE) down to three digits.

The 1994 input ^ output table for the Spanish economy was also used; this provides information about intermediate consumption and its origin for the various groups of activities included in the CNAE. The statistical procedure developed to detect objectively the existence of certain territorial concentrations of industrial activities followed the process detailed below. (1) Determination of the principal industrial activities (PIAs) in Spain. The first step consisted of choosing, from all the production sectors represented in the Spanish economy, the dominant PIA in a particular industrial area.

A local production system must be profiled which, it is assumed, corresponds to industrial districts because it shows ratios of industrialisation and specialisation in a dominant industry (as a percentage of the total industrial employment) over and above the regional average. It was also assumed that the PIAs fulfilled two requirements: they were industrial organisations dominated by independent, autonomous SMEs; and they were revitalising elements of the set of sectors integrated to its value system.

The second requirement implies that PIA output must be final goods (that is, with more than 50% of production destined for final demand), for them to be regarded as the promoters of interlinking in a firm network. It was therefore understood that the existence of a market is what stimulates the industrial production of final goods, and that this in turn has a pulling effect on the production of intermediate goods inside their territorial environment.

(2) Determination of the complementary industrial activities (CIAs) linked to each PIA, defined as the industrial sectors that provide the main inputs used by the PIA. Suppliers of raw materials were not included, as they were not of industrial origin and fell outside the scope of interest o namely, the spatial distribution of the industrial structure. The requirements of inputs whose production takes place outside the regional area were also excluded, as it was considered that the activity of these suppliers would not form part of the regional industrial organisation.

The result of this phase was the constitution of a series of groups constructed with strong functional links, known as `industrial functional groups’ (IFGs), which gather the most important interfirm relations established between a PIA and its CIA, inside a region. (3) Delimiting and location of the industrial districts. The final step consisted of estimating the intensity of IFG local presence. The underlying hypothesis in this procedure is that the economies derived from productive interdependences among the various activities making up an IFG will be promoted by spatial concentration. The estimation consists of several stages.

Calculation of the regional weight index (RWI) and municipal weight index (MWI) indicators for each municipality and each selected activity. As the aim was to estimate the importance of each municipality in each IFG activity, two informative variables were selected: an indicator of the participation of each industrial branch in each municipality; and a second that reflects the contribution made by the industrial municipal activity to the regional as a whole.

The analytical expressions of both indexes are as follows, where xi j represents the employment in municipality i in activity j: xi j RWIi j ?? 100 , m xl j xi j MWIi j ? ? 100 . n xil l? 1 l? 1 Shared, competitive, and comparative advantages 2235 The database created provided a great deal of information: two indicators for each activity making up the IFG, each of which consisted of 8108 observations oone for every Spanish municipality.

Given the magnitude of the database, and in order to make it more operative, the available information was integrated. The procedure consisted of integrating the information provided by the set of variables in a single indicator, and using this indicator to rank the municipalities.

The fusion of the information to construct the indicator was based on synthesis techniques that form part of the multiple attribute decisionmaking models (Hwang and Yoon, 1981). Specifically, the method selected was a `method of distance’, from the Minkowski family of distance metrics. An ideal municipality is one that presents the ideal values in the variables that form part of the analysis. The method of distance assigns a location indicator to every municipality, calculated as the distance that separates it from the ideal municipality.

The distance that separates a particular municipality from the ideal one is given by the expression: ! 1a2 ? 2 di ? wj ? yi j A yjA ? , j where di is the Euclidean distance (second order)? the municipality i from the ideal one; of wj is the weighting of each variable j with j wj ? 1; yi j is the value of the municipality i in the variable j; yjA is the value of the ideal municipality in the variable j. To guarantee the independence of the selected variables, and to avoid undesirable redundancies in their joint study, the variables were refined by removing the structures of dependence established among them.

Factorial analysis was used to solve this problem. In addition, as the variables can present different ranges of variation, they were standardised for purposes of comparison. The transformation was carried out using the following expression: yinj ? Fi j A Fj m , Fj M A Fj m where yinj is a standardised factorial score of activity j in municipality i; Fi j is the factorial score of activity j in municipality i; Fj m is the minimum factorial score of activity j; Fj M is the maximum factorial score of activity j. This procedure is a linear transformation of the scale of the values regi.

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