Relatedness and coherence in technological and product

Structural Change and Economic Dynamics11 (2000) 295–315

Relatedness and coherence in technological andproduct diversification of the world’s largest

firms

Lucia Piscitello *Dipartimento di Economia e Produzione, Politecnico di Milano, P.za L. da Vinci 32,

20133 Milan, Italy

Received 12 May 1998; received in revised form 24 February 2000; accepted 17 March 2000

Abstract

The present paper investigates relatedness and coherence with reference to both productand technological diversification. In particular, it is argued that: (i) relatedness can bedisentangled into three dimensions: industry-, technology- and firm-specific; and that (ii)coherence refers to both product and technological diversification. We provide empiricalsupport of our premises with a study of a large cross-firm panel of technological andeconomic activity for 248 large firms over the period 1977–1995. The results support theview that large firms’ diversification processes are characterised by product-based coherenceat the beginning of the period considered, and by technology-based coherence more recently.© 2000 Elsevier Science B.V. All rights reserved.

JEL classification: L2; O33

Keywords: Corporate diversification; Relatedness; Coherence; Competencies

www.elsevier.nl/locate/econbase

1. Introduction

Diversification has long been studied as a broad topic. The economic andmanagerial literature has paid extensive attention to corporate diversification,emphasising the benefits from diversification in terms of lower costs and risk-

* Tel.: +39-2-23992740; fax: +39-2-23992710.E-mail address: [email protected] (L. Piscitello).

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296 L. Piscitello / Structural Change and Economic Dynamics 11 (2000) 295–315

spreading, as would arise from the exploitation of economies of scale andscope by firms. Nonetheless, diversification has still largely focused upon thereasons for, and the nature of, product diversification (Rumelt, 1974; Bigadikke,1979; Didrichsen, 1982; Pavitt et al., 1989; Montgomery, 1994), for which theconcepts of relatedness and coherence have been specifically developed. Theseconcepts led to the perception that, by diversifying into related product markets orareas which lie ‘close’ into the firms’ existing profiles of competencies, thosefirms could grow and attain economies of scale and scope (Chandler, 1990). In thisway the firm is able to diversify and expand whilst corporate coherence ismaintained.

More recently, these issues have begun to be extended to the concept ofcorporate technological diversification (e.g. Granstrand and Sjolander, 1992;Granstrand et al., 1997; Pavitt, 1999), meaning the diversification of the firm’stechnological competencies. In accordance with Penrose’s resource-basedview of the firm as a collection of productive assets, and with the more recentcompetence-based theory of the firm (Richardson, 1972; Winter, 1987, 1988;Loasby, 1991; Nelson, 1991; Foss, 1993; Cantwell, 1994; Teece et al., 1994), inwhich the firm is seen as an institution that constructs capabilities throughinternal learning processes in the form of evolutionary experimentation, firmsextend their capabilities into ‘closely’ related fields of production and technology.Therefore, firms do not diversify in a random way. Instead, most of them displaya pattern and logic to both the product and the technological diversificationchoices.

Nonetheless, although several approaches have been so far suggested in theliterature in order to capture the ‘orientated patterns’ (Cainarca and Mariotti,1985), the ‘coherence’ (Dosi et al., 1992; Teece et al., 1994) or the ‘purposiveness’(Scott, 1993) of corporate product diversification, there remains great ‘fuzziness’over what the concept of relatedness does actually include.

In this context, the purpose of the present paper is twofold:1. to suggest an alternative multidimensional interpretation of relatedness which

distinguishes between: (i) industry-specific, (ii) technology-specific, and (iii)firm-specific aspects; and

2. to measure and investigate the coherence with reference to both the technolog-ical and the product diversification patterns pursued by large firms.

These are both quite novel contributions to the literature in the field, as — toour knowledge — previous studies have neither considered multi-dimensionalmeasures of relatedness, nor applied the concept of coherence to the diversifica-tion of technological activities by the firm.

Additionally, the assessment of what degree of closeness amounts to relatednesshas been so far highly subjective in most studies. The empirical contribution inthis paper formulates objective measures, based on actual data. Specifically, theanalysis makes use of a large panel of the largest European, US and Japanesefirms over the period 1977–1995.

297L. Piscitello / Structural Change and Economic Dynamics 11 (2000) 295–315

2. Relatedness and coherence: a theoretical framework

The theoretical and empirical literature has extensively investigated firms’ growththrough diversification. The most recent studies observe that the world’s largestfirms are not just multi-product, but typically and increasingly multi-technology too(Oskarsson, 1993; Patel and Pavitt, 1994; von Tunzelmann, 1995; Granstrand et al.,1997).

In both these contexts, a central issue is what determines the direction of firmdiversification, and in particular whether they diversify into related or unrelatedlines of business (Teece, 1982). Nonetheless, much of the discussion on relatednessand coherence has been so far applied to the level of products, and to the possibilityof expanding product ranges whilst maintaining coherence (Dosi, 1982, 1988; Dosiet al., 1992; Teece et al., 1994).

That a fundamental part of any firm’s corporate strategy is its choice of whatportfolio of businesses to compete in, and that related diversification in productterms outperforms unrelated diversification (Wernerfelt, 1984; Barney, 1991;Markides and Williamson, 1994; Robins and Wiersema, 1995), are well-establishedpropositions. However, there is still disagreement about what the concept ofrelatedness does precisely include.

According to Edith Penrose’s theory of diversified growth (Penrose, 1959),unused productive services are a selective force in determining the direction ofexpansion. Excess resources are most often employed in similar settings, i.e. firmsprefer to enter industries where the resource characteristics — the level of capitalintensity, the level of sales intensity, the level of R&D intensity — are similar totheir own resource profiles. More specifically, firms seem to choose to enterindustries that are close to their existing line of business, since the enterprise’sfirm-specific resources help drive its diversification strategy. However, Penroseherself recently argued that ‘there is no reason why a firm should see its prospectsof growth, its productive opportunities, in terms of its existing products only; thereare many reasons why it should see them in terms of its productive resources andits knowledge, and should search for opportunities of using them more efficiently’(Penrose, 1995). Other studies, characterising corporations by boundaries whichrelate to their shared firm-specific assets (Winter, 1987) and their multi-productscope (Teece, 1982), have shown that firms do not diversify in a random way (e.g.Teece et al., 1994).

Nevertheless, despite the noteworthy amount of theoretical studies, the empiricalmeasures of relatedness and coherence hitherto suggested provide only a partialpicture of the scope of the firm to exploit interrelationships between sectors andtechnologies. The focus is predominantly on industry- and technology-specificcharacteristics of sectors (Markides and Williamson, 1994). Pairs of sectors can bethought of as similar:1. if they are vertically related or integrated;2. if they have technology-based inter-sectoral linkages (or technological

spillovers);3. if they serve similar types of markets.


In other words, relatedness is considered as a concept exclusively associated withthe inherent properties of the sectors. As such, firms can know a priori and exploitthem in order to develop and maintain competitive advantages over their rivals.

More recently, a wider concept of relatedness which also embodies firm-specificaspects (capabilities and technological competencies) has been suggested (Dosi etal., 1992, 2000; Teece et al., 1994). It is based on the idea that a firm does not knowwhat is related until trying it out.

Relatedness and coherence are essentially cognitive and firm-specific concepts.Coherence is the result of a trial-and-error process, and what may be relateddiversification and coherent organisation for one firm, may not be so for anotherfirm. The firm becomes an experimental device for selection: by exploring newcombinations of capabilities, the firm incrementally and cumulatively learns whichcapabilities are related, and collects these together within its organisation as acoherent whole (Cantwell, 1998). Consequently, coherence could then be seen in adynamic context as the firm’s capacity to exploit and explore complementaritiesbetween a diversity of stocks of dispersed knowledge and localised learningprocesses (Foss and Christensen, 1996).

In this context, the present paper argues that:1. the concept of relatedness amongst sectors must include all the dimensions

separately suggested by the previous literature, as well as an additional firm-spe-cific dimension; and

2. the notion of coherence, as discussed with reference to product diversification, isapplicable in the case of technological diversification too. Indeed, as in the caseof product diversification where ‘coherence is exhibited when a firm’s lines ofbusiness are related in that there are certain technological and market character-istics common to each’ (Teece et al., 1994), one would expect technologicalcoherence to be exhibited when certain underlying scientific or engineeringknowledge is common to each relevant technological area (e.g. Breschi et al.,1998).

Other authors (e.g. Scott, 1993) have recently argued that the diversification ofR&D within the firm is not random, that is the firm diversifies its R&D effortpurposively to gain advantage of better appropriability, or because of cost advan-tages of common facilities or complementarities in the process of research acrossmultiple industries1. Moreover, identifying and integrating competencies essentialfor the corporation almost inevitably requires investment in in-house learning and

1 It is worth observing that the coherence of the technology base has been recently associated with thedegree of co-ordination and interrelatedness within the technology base as between the differenttechnological and innovative capabilities (Christensen, 1998a). Christensen (1998b) highlights threedimension of coherence: (i) external coherence, which reflects the degree to which competenciesconstituting the technology base match the requirements of the competition; (ii) contextual coherence,which reflects correspondence between the technology base and the broader firm or corporate context(the complementary assets, the operational and infrastructural firm context, the company’s strategy andculture); (iii) local coherence, which signifies the degree to which there are interdependencies andsynergies between different parts of the technology base.


patient experimentation, with the expectation of finding winning combinations oftechnologies, as where the technological relatedness lies may be unclear a priori2

(Granstrand et al., 1997).

3. The data

In order to analyse corporate diversification and to investigate the coherenceissue through the multi-dimensional type of relatedness suggested above, weconsidered both product and technological diversification straategies of the world’slargest firms. Several studies have extensively shown that large multi-divisionalfirms are the largest single source of the new technological knowledge. Indeed, theyperform most of the R&D activities, employ most of the qualified research scientistsand engineers, perform and publish most of the corporate basic research, andmaintain the closest links with academic research (Pavitt, 1999). They also con-tribute to the development of knowledge and products for their suppliers ofproduction equipment, components and software (Rosenberg, 1963; Patel andPavitt, 1994).

Corporate diversification has been examined using data on world’s largest firmssales and patenting activities in the United States for the period 1977–1995.Specifically, we considered 248 US, European and Japanese worlds’ largest indus-trial companies (as listed in Dunning and Pearce (1985) with a few additions ofcompanies apparently missed from the Fortune listings). The sample chosen isstratified in order to include firms from the whole sectoral and geographicalspectrum. The consolidated firms are allocated to their primary industry of outputaccording to the product distribution of their sales, so that firms have been dividedinto 18 industrial groups (see Appendix A). In order to analyse business diversifica-tion, product distribution of sales has been allocated over 42 sectors (26 manufac-turing and 16 services sectors, see Appendix B). Such data have been collected atPolitecnico di Milano.

Technological diversification of firms relates to their patenting activity in theUSA. The corporate patenting was divided into 56 technological fields (see Ap-pendix C), derived from the US patent class system.

Patent statistics present a potentially very rich source of empirical evidence onquestions related to technology. The advantages and disadvantages of using USpatents as an indicator of technological activity are well known and quite widelydiscussed in the literature (e.g. Schmookler, 1950, 1966; Basberg 1983, 1987; Pavitt,1985, 1988; Jaffe, 1986; Archibugi, 1992). Though recognising some potentiallimitations of the US patenting measure, mainly related to the fact that patentsmeasure codified knowledge whereas a high proportion of firm-specific competence

2 For instance, firms from certain chemical sectors sometimes move into areas of competenceassociated with the manufacture of specialist chemical equipment (a mechanical technology), to a greaterextent than into a more closely related science-based technologies within the chemical field (Cantwell andFai, 1999b).


is tacit (or non-codified) knowledge, studies like Patel and Pavitt (1997) have shownthat other measures that embody tacit knowledge (such as R&D expenditure, orjudgement of technological peers) give results that are very similar to thoseobtained using US patenting.

Sectoral and geographical characteristics of firms constituting our sample arereported in Table 1. The comparison with the relevant characteristics of theFortune 500 firms (also reported in Table 1) allows to assess the balance of thesample.

4. The three dimensions of relatedness

In order to investigate firms’ diversification patterns in the period considered, andparticularly whether they moved into related or unrelated areas of activity, wedeveloped a survivor measure of relatedness. According with Teece et al. (1994),this measure is based on the principle that economic competition will lead to the

Table 1Sectoral and geographical characteristics of the sample considered vs. Fortune 500 firms

Industry Sample Fortune 500 (1993)

No. firms % No. firms %

475.9 9.415Food18 3.610Drink 4.0

Tobacco 1.051.64539.1 10.623Chemicals

5.223 9.1 26Pharmaceuticals25 9.9Metals 53 10.6

Mechanical engineering 15 5.9 26 5.218 8.844Electrical equipment 7.1

3.6Office equipment 185.1136.316 43Motor vehicles 8.6

3.014 5.5 15Aircraft9 1.83.6Textiles and clothing 9

13Paper products 4.6235.1123.69Printing and publishing 2.4112.4 2.2Rubber and plastic products 6

4.3 2311 4.6Non-metallic mineral products16 6.3 56 11.2Coal and petroleum

2.08 3.2 10Professional and scientific instruments

Total 248 100.0 492 98.4

Geographical area86 31.0Europe 15534.0

159 31.8109United States 43.120.953Japan 27.0135

449100.0248Total 89.8


disappearance of relatively inefficient organisational forms (Stigler, 1961). There-fore, the underlying assumption is that the activities in sectors which are morerelated will be more frequently combined within the same firm. Thus, if corpora-tions which engage activity in sector i almost always engage activity also in sectorj, then these two sectors are likely to be related.

Accordingly, considering the universe as constituted by K firms (= 1,..., k,..., K),it is possible to define:

Cik=1 if firm k is active in sector i, and 0 otherwise;mk=�i Cik= the number of activities3 of firm k ;ni=�k Cik= the number of firms active in sector i

Therefore:Jij=�k CikCjk= the number of firms which are active in both sector i and sectorj ;mij=E(xij)= the expected number of firms active in both sector i and sector j,under the hypothesis that diversification is random.In order to operationalise the random hypothesis, we consider the number xij of

firms active both in sector i and j as a hypergeometric random variable. Therefore,with population K, and specific participants ni and nj, random diversification wouldimply4:

mij=E(xij)=ni nj/K

s2ij=mij [(K−ni)/K)][(K−nj)/(K−1)]

If the actual number Jij of linkages observed between sector i and sector j greatlyexceeds the expected number mij, then the two sectors are highly related. Therefore,the measure of relatedness between sector i and sector j is:

RELATij= (Jij−mij)/sij

which, by analogy with a t-statistic, measures the degree to which the observedlinkage between the two sectors exceeds that which could be expected if theassignments of activities were simply random.

In order to avoid subjectivity problems, the data employed to evaluate related-ness between industries refer to the data obtained from the 1985 Trinet LargeEstablishment data set5. The values of RELATij range from a minimum of −18.85

3 Following the notation adopted in Teece et al. (1994), activity has been defined as the operation ofa firm in a sector.

4 It is perhaps worth reminding that the hypergeomentric distribution is derived from a samplingwithout replacement, which is the most appropriate for the case under study. Indeed, it can beassociated to an experiment in which we are interested in the probability of drawing a firm k active insector i after drawing the same firm k active in sector j from the universe of pairs (firm, sector).

5 The Trinet data set provides information on every establishment in the United States with over 20employees; each of the approximately 400,000 records constituting the data set describes an establish-ment, recording its name, address, telephone number, employee count, activities in each four-digit SICsector, estimated value of shipments, and corporate company, if any. In order to apply such arelatedness measure to our sample of firms, we aggregated the four-digit SIC sectors into the 42sectors considered.


(between Holding services and Distribution services) to a maximum of 46.22(between Holding services and Financial services). The average relatedness is 3.20and the standard deviation is 6.47.

The hypothesis developed in this paper is that relatedness between sectors is onlypartially explained by industry-specific (i.e. vertical integration between sequentialphases in the production chain, and market similarity) and technology-specific(technological spillovers) characteristics. In fact, the remaining unexplained vari-ance has mostly to do with firm-specific characteristics, that is with individual firms’capabilities and competencies.

In order to test the multi-dimensional character of relatedness, we develop aneconometric model. According whith previous theoretical and empirical studies, thevariables considered as proxy for the different dimensions are the followings.

The industry-specific aspects of relatedness have been proxied by vertical integra-tion and market similarity. Specifically, as far as vertical integration is concerned,we evaluated the linkages between sectors as obtained from input/output tables6

(Eurostat, 1980). Accordingly, the variable VERTij is a dummy which assumes thevalue 1 if the two sectors i and j are sequential in the productive chain, and 0otherwise.

The market similarity has been proxied by the variable MKTij, which is a dummyequals to 1 if two sectors i and j are both oriented to the industrial market, and 0otherwise.

The technology-based inter-sectoral linkages (or spillovers) have been proxied byan input–output measure of the interlinkages in terms of R&D flows (Scherer,1982). Such a measure is consistent with a two-way interaction between sectors(Laursen and Meliciani, 1999). Thus, the variable TECH–LINKij is a dummywhich assumes the value 1 if the two sectors i and j have high technological linkagesin terms of R&D flows7.

The firm-specific aspect has been proxied by the affinity or proximity in theunderlying technological competencies8. The firm’s idea is that firms require spe-

6 We considered as the critical value a share of 5%. Other studies (e.g. Orecchia, 1998) consider acritical value of 3%, but the results obtained are very similar in both cases.

7 The cut-off point considered, in accordance with a sensitivity analysis run for different values, isUS$15 millions in Scherer’s table. Our choice of R&D flows as indicators for technological spilloversobviously implies that we take a limited perspective on the issue. Spillovers are much broader than whatis captured by our indicator. Griliches (1979) distinguishes between two types of spillovers, i.e. rentspillovers and pure knowledge spillovers. Like all the studies considering R&D as a neasure of spillovers,we are within the interpretation of rent spillovers (Coe and Helpman, 1995; Laursen and Meliciani,1999).

8 Other approaches have been suggested. Among the most recent, Engelsman and van Raan (1994)based their approach on co-classification maps built through patents; Verspagen (1997) suggested amethod which is based upon the distinction between the main (or primary) classification code assignedto a patent document, and the supplementary (or secondary) classification codes.


cialised technological competence in order to produce attractive productsby efficient methods in their respective industries. The firm’s competencies con-struction activity proceeds purposively (Markides, 1995, 1996) through an evolu-tionary learning process, based on trial and error which is gradual and path-dependent. Therefore, the affinity between the technological profiles of sectorsdetermines firms’ diversification into them. Consequently, it helps explain theirrelatedness.

According to the survivor principle, innovative activities (and therefore, compe-tencies) in related technological fields would be more frequently combined withinthe same firm. In order to build a proxy for this dimension of relatedness, theanalytical framework used in Teece et al. (1994) has been applied to the firms’technological competencies. By measuring the firms’ technological competenciesthrough their patenting activity in the relevant technological fields, it is possible todefine9:

Pik=1 if firm k has been granted at least one patent in technological field i, and0 otherwise;rk=�i Pik= the number of technological fields in which the firm k patents;si=�k Pik= the number of firms patenting in technological field i

Therefore:Yij=�k PikPjk= the number of firms patenting in both technological field i andj ;nij=E(xij)= the expected number of firms patenting in both technological field iand j, under the hypothesis that technological diversification is random.Considering the number xij of firms which patent in both technological field i and

j as a hypergeometric random variable, it will be:

gij=E(xij)=ni nj/K

s2ij=mij [(K−ni)/K)][(K−nj)/(K−1)]

Therefore, if the actual number Yij of linkages observed between technologicalfield i and j greatly exceeds the expected number nij, the two technological fields arehighly related. The measure of the technological competence-based relatednessbetween fields i and j, is:

COMPETij= (Yij−gij)/sij

The values of COMPETij range from a minimum of −2.53 (between field 40, i.e.motor vehicles and components, and fields 20, food products, and 21, tobaccoproducts) to a maximum of 27.57 (between food products and tobacco products).The average value is 6.06 and the standard deviation is 3.94.

9 Again, in order to limit subjectivity problems, the data refer to the whole data set on patentsdeveloped at the University of Reading. In order to respect homogeneity with the data employed forproduct diversification and, particularly, for the calculation of the relatedness between sectors, the dataconsidered refer to 1985.


Table 2Correlation matrix

VERT MKTTECH–LINKRELAT COMPET

RELAT 1COMPET 0.456 1VERT 0.403 0.188 1TECH–LINK 0.139 0.149 0.124 1

0.276 0.093MKT 0.398 10.230

Table 3Results of the econometric models (dependent variable=RELAT)a

Modelc1 Modelc2

−0.804 (−1.484)Constant −0.853 (−1.560)0.574*** (7.582)COMPET 0.568*** (7.436)

0.523 (0.707)TECH–LINK5.254*** (5.696) 5.311*** (5.785)VERT3.236*** (5.123) 3.253*** (5.157)MKT

Model statistics324No. observations 324

61.490***Regression, F 46.171***0.358Adjusted R2 0.359

a Notes: the t-statistics are reported in parentheses (all are two-tail tests).*** Significant at PB0.01.

4.1. The econometric model

In order to investigate whether the relatedness between two sectors i and jdepends on industry-, technology- and firm-specific dimensions, we modelled thedependent variable RELATij as a function of the variables described in the previoussection10. Such a model has been estimated by OLS.

Before running the regression, we performed a correlation analysis in order todetect if any problem of multicollinearity amongst the variables existed (see Table2). RELAT shows a noteworthy correlation with COMPET (r=0.456), but alsowith VERT (r=0.403) and MKT (r=0.398). The independent variables do notshow any multicollinearity problems, as the highest simple correlation is thatbetween MKT and VERT (r=0.276).

The results of the regression are reported in Table 3. In particular, Model 1illustrates the regression in which all the independent variables are jointly tested,

10 It is worth observing that i and j concern here only industrial activities (i.e. the 26 sectors describedin Appendix B). Accordingly, we adopted a higher level of aggregation for the original 56 technologicalfields (see Appendix C) into 26 fields (see Appendix D). The author wish to thank Bart Verspagen andan anonymous Referee for the stimuli which led to clarify this delicate point.


while Model 2 represents the best specification obtained. The econometric resultssupport the hypothesis that relatedness among sectors depends on:� industry-specific factors. The proximity in the productive chain, VERT, shows a

positive coefficient significantly different from zero at PB0.01 in both themodels. Likewise, the proxy for the market similarity, MKT, is always significantat PB0.01;

� the firm-specific dimension. The variable COMPET is always significant atPB0.01 in both the models.Interestingly, the technology-specific dimension proxied by TECH–LINK does

not seem to influence the dependent variable significantly. Nonetheless, that mightwell reflect shortcomings in the measurement of technological specificity.

5. Coherence in technological and product diversification

The analysis of relatedness among sectors paves the way for the empiricalinvestigation of coherence in the diversification patterns pursued by the firm.

Since coherence has been defined as the presence of relatedness in firms’ linesof business, it increases as the number of common characteristics found in eachproduct line increases. Therefore, a firm fails to exhibit coherence when com-mon characteristics and competencies are allocated randomly across its lines ofbusiness.

Following Teece et al. (1994), coherence in the firm diversification patterns ofproduct activities is defined as the firm’s co-presence in related sectors. A possiblemeasure of coherence can be the weighted average relatedness in products (WARP).Specifically, WARPi is defined as the relatedness of sector i to all the other sectorsin which the firm is active:

WARPi=%j RELATij sj/%j sj

where sj represents the firm’s sales in sector j.Likewise, we could define the coherence for the firms’ technological diversifica-

tion as the weighted average relatedness in technologies (WART). Specifically,WARTi is defined as the relatedness of the technological field i to all the other fieldsin which the firm patents. WARTi is defined as follows:

WARTi=%j COMPETij pj/%j pj

where pj is the number of patents granted to the firm in technological field j.In order to investigate the dynamic aspects of coherence in firms’ diversification

patterns, both WARPi and WARTi have been calculated in three different years(1977, 1986, 1995).

Concerning the firms’ product diversification, the average value of coherencedecreases over the three periods. Indeed, Table 4 shows that the average value ofWARP decreases from 11.12 to 5.09 over the whole period. Additionally, the


number of firms showing an index above the average also decreases, thus confirm-ing a decreasing coherence over time.

On the contrary, technological diversification in the same period remains a rathercoherent process. The average value of WART tends to remain rather stable overthe whole period, while the number of firms with a coherence index above theaverage slightly increases.

This preliminary descriptive statistical analysis highlights that corporate coher-ence is increasingly associated with the firms’ moves into new technological areasmore than into new product activities.

Additionally, the analysis can offer suggestions about the dynamics of coherencein the decades considered. The analysis of the correlation matrix for those variablesin each period (see Table 5) suggests that coherent strategies pursued by firms arecumulative (or path-dependent). In particular, this result holds more strongly at thebeginning of the whole period considered for the diversification of product activities(the correlation coefficient between WARPt1 and WARPt2 is 0.742, while it is only0.206 between WARPt2 and WARPt3). Conversely, coherence in the firms’ diversifi-cation patterns of technological activities seems to be higher in the most recentperiod, though it is also considerable in the previous one (the correlation coefficient

Table 4Coherence in product diversification (WARP) and in technological diversification (WART), in thethree periods considered

Technological diversification (WART)Product diversification (WARP)

t2t1 t3t2 t3 t1

243 243 240No. observations 200 213 21711.57 10.08 10.39Mean 11.12 10.79 5.09

2.672.236.426.91S.D. 7.558.402.78 1.00 0.00Min −2.86−6.74 −3.82

56.95 31.48 29.00 79.52Max 21.39 27.57

Coherence\mean 6alue7795 133100No. of firms 14199

50.00 55.42% 58.0244.60 41.4235.48

Table 5Correlation matrix: product and technological coherence in each period

WARPt1 WARPt2 WARPt3 WARTt1 WARTt2 WARTt3

WARPt1 1WARPt2 10.742

0.2060.258WARPt3 1WARTt1 0.020 0.030 0.270 1

0.433 1WARTt2 0.026−0.032 0.2150.413 0.758 1WARTt3 −0.161 −0.137 0.192


Table 6Results of Kolmogorov–Smirnov testsa

WARPt3 WARTt2 WARTt3WARPt2

0.083** (0.424)WARPt10.421 (0.000)WARPt2

0.235 (0.000)WARTt10.042** (0.987)WARTt2

a Notes: the adjusted P-value are reported in parentheses (two-sided test).** Significant at PB0.05.

is 0.758 between WARTt2 and WARTt3, and 0.433 between WARTt1 andWARTt2).

In order to strengthen this result, we run the Kolmogorov–Smirnov test11 for theequality of coherence patterns over the period considered (see Table 6). It emergesthat the coherence in diversification of product activities is more persistent at thebeginning of the period considered. The equality of the two distributions WARPt1

and WARPt2 can not be rejected, while it is rejected between WARPt2 and WARPt3

at PB0.05. On the contrary, coherence in the firms’ diversification patterns oftechnological activities is persistent in the most recent period. The observed dataprovide some evidence in support of the null hypothesis (that is, the equality of thedistributions WARTt2 and WARTt3), while they do not seem to sustain it in thefirst period (in which the null hypothesis is rejected at PB0.05).

The findings support the view of the firm as a repository of accumulatedcompetencies which are developed by firms themselves through a gradual learningprocess. This accumulation process takes place within the firm and provides it withthe characteristics of technological persistence or path dependency (Fai, 1998;Cantwell and Fai, 1999a,b).

6. Conclusions

This paper has suggested a multi-dimensional concept of relatedness, and it hasexplored coherence in diversification processes pursued by large firms.

The literature has to date extensively studied relatedness as a concept exclusivelybased on industry and market similarities between sectors. On the contrary, thepresent study argued that relatedness must involve a firm-specific dimension too.Indeed, the firm acts as a selection mechanism, thus determining ex interiore what

11 The Kolmogorov–Smirnov test (which is a non parametric test of two sample to determine aconfidence interval that the two samples are chosen from the same distribution) has been preferred to thechi-square test because, although the latter is somewhat more popular as goodness-of-fit test, it alsorequires data that have been grouped into categories while the former works with individual data andit is therefore more appropriate for the case under examination. The author is grateful to an anonymousReferee for this suggestion.


is related and what is not. First empirical results confirmed the significance of thefirm-specific dimension in reducing the proportion of unexplained variance.

As far as coherence is concerned, it has been traditionally investigated only withreference to product diversification. Instead, we argue that coherence is an impor-tant feature also for technological diversification. The analysis of a sample of theworld’s largest firms over the last decades, confirmed that diversification does notproceed in a random way but coherently (Teece et al., 1994), purposively (Scott,1993), in an ‘oriented’ domain (Cainarca and Mariotti, 1985) where firms are drivenby competencies allowing them competitive advantages over their rivals.

Importantly, the empirical results confirmed this hypothesis both at the businesslevel and at the technological level. However, technological diversification andproduct diversification followed coherent patterns in different periods. Specifically,product diversification undertaken by large firms was more coherent in the firstperiod considered (roughly, late 1970s to mid-1980s). Technological diversificationstrategies along coherent paths predominate only more recently (from later 1980s tomid-1990s).

Additionally, the coherent patterns pursued by the firms in the diversification oftheir technological activities do not necessarily reflect a similar behaviour indiversification of products. Product diversification refers primarily to the exploita-tion of accumulated resources, usually to attain lower marginal costs. As far astechnological coherence is concerned, whilst lowering the cost of developing acompetence in a particular technology through economies of scope in the applica-tion of the underlying scientific or engineering principles remains, firms are evenmore concerned with which technologies can be used together strategically, howthey might be combined to make better current goods and to produce them moreefficiently, as well as which technologies could be used to produce new productsand how. Technological diversification by firms has changed over time from broadto more focused diversification, thus implicitly recognising the coherence embodiedin the process (Fai, 1998).

Finally, it is possible to draw some hints about the interrelationship betweenproduct and technological diversification. Recent empirical evidence (Markides,1995; Gambardella and Torrisi, 1998) already showed that firms have recentlyreduced their product diversity but they have continued to increase the diversity oftheir technological base. Accordingly, our results suggest a growing independenceof technological diversification from product diversification.

This constitutes a noteworthy step forward in the research field since, at least toour knowledge, no previous empirical attempts have tested ‘dynamic corporatecoherence’ (Christensen, 1998b) at the technological competencies level. The presentfindings thus underpin a widespread but hitherto largely unsubstantiated beliefabout a major aspect of structural change in economic dynamics.

The issue deserves certainly more future research. More sophisticated panel datatechniques could be adopted to pursue this line of study. Additionally, longerstreams of observations on firms’ product and technological diversification wouldallow to test for their cointegration over the longer term.


Acknowledgements

The author wishes to thank John Cantwell, Giovanni Dosi, Nick von Tunzel-mann, two anonymous Referees, and the participants in the Schumpeter SocietyConference and the TSER Conference held in June 1998, in Vienna and Urbino,respectively, for helpful comments and suggestions on earlier versions of this paper.The usual disclaimer applies.

Appendix A. Primary industries of production

DescriptionIndustry no.

Food products1Drink products2Tobacco products3Chemicals and allied products4Pharmaceuticals5Metals6Mechanical engineering7Electrical equipment8Office equipment9Motor vehicles10Aircraft11Textiles products and clothing12

13 Paper productsPrinting and publishing14Rubber and plastic products15Non-metallic mineral products16Coal and petroleum products17Professional and scientific instruments18

Appendix B. Description of the 42 sectors

ServicesManufacturing

Sectors (definitions)No. Sectors (definitions) No.

ConstructionsMining 2712 28 TransportationsFood products3 CommunicationsTobacco products 29

Electric and gas energy4 30Textile, mill products and clothing


Furniture and fixtures 31 Distribution5Lumber, wood and paper products 32 Catering and hotels6

Finance and investment337 Printing and publishing34Chemicals and allied products Brokerage8

Insurance359 Petroleum and coal products36Rubber and plastic products Real estate1037Leather and leather products Holding offices11

Business services3812 Non-metallic mineral products13 39 InformaticsPrimary metal industries

Leisure servicesFabricated metal products14 40Electronic and other electrical15 Health care services41

equipment 42 Miscellaneous serviceindustriesScientific instruments16

17 Other transportation equipmentMotor vehicles and components18Aircraft19

20 Pharmaceuticals21 Cosmetics and detergents22 Computer and office equipment23 Mechanical engineering24 Leisure products25 Drink products

Miscellaneous manufacturing26industries

Appendix C. Description of the 56 technological fields

Technological fields No. Technological fieldsNo.

Other general industrial29Food and tobacco products1Distillation processes equipment2

30 Mechanical calculators and3 Inorganic chemicals4 Agricultural chemicals typewriters

Power plants31Chemical processes5Nuclear reactors326 Photographic chemistry

33Cleaning agents and other Telecommunications7Other electrical communication34compositionssystemsDisinfecting and preserving8Special radio systems9 35Synthetic resins and fibresImage and sound equipment36Bleaching and dyeing10

Other organic compounds 37 Illumination devices11Electrical devices and systems38


39 Other general electrical12 Pharmaceuticals andbiotechnology equipment

4013 SemiconductorsMetallurgical processes4114 Office equipment and data pro-Miscellaneous metal products

Food, drink and tobacco15 cessing systemsequipment Internal combustion engines42

Motor vehicles16 Chemical and allied equipment 4317 44Metal working equipment Aircraft18 Paper making apparatus Ships and marine propulsion45

Railways and railway equipment19 Building material processing 46equipment Other transport equipment47

48Assembly and material handling Textiles, clothing and leather20equipment 49 Rubber and plastic products

21 Non-metallic mineral productsAgricultural equipment 5022 Other construction and excavat- Coal and petroleum products51

ing equipment Photographic equipment52Mining equipment23 Other instruments and controls53

24 Electrical lamp manufacturing Wood products5425 Textile and clothing machinery Explosive combustions and55

Printing and publishing26 chargesmachinery 56 Other manufacturing and non-

27 Woodworking and machinery industrialOther specialised machinery28

Appendix D. Aggregation of 56 technological fields according to the 26manufacturing sectorsa

No. Technological fields Sectors

Food products+tobacco productsFood and tobacco products1Distillation processes Chemicals and allied products+2

drinks productsChemicals and allied productsInorganic chemicals3Chemicals and allied products4 Agricultural chemicalsChemicals and allied productsChemical processes5Chemicals and allied productsPhotographic chemistry6Cosmetics and detergentsCleaning agents and other7

compositions8 Disinfecting and preserving Chemicals and allied products

Chemicals and allied productsSynthetic resins and fibres9Chemicals and allied productsBleaching and dyeing10

11 Chemicals and allied productsOther organic compounds


PharmaceuticalsPharmaceuticals and biotechnology12Metallurgical processes Primary metal industries13

14 Fabricated metal productsMiscellaneous metal productsMechanical engineeringFood, drink and tobacco equipment15Mechanical engineeringChemical and allied equipment16Mechanical engineeringMetal working equipment17Mechanical engineeringPaper making apparatus18

Building material processing19 Mechanical engineeringequipment

Mechanical engineeringAssembly and material handling20equipment

21 Agricultural equipment Mechanical engineeringMechanical engineeringOther construction and excavating22

equipment23 Mining equipment Mining+mechanical engineering

Electrical lamp manufacturing Electronic and other electrical24equipment

25 Mechanical engineeringTextile and clothing machineryPrinting and publishing machinery Printing and publishing+mechanical26

engineeringWoodworking and machinery Mechanical engineering27

Mechanical engineeringOther specialised machinery28Mechanical engineeringOther general industrial equipment29

30 Computer and office equipmentMechanical calculators andtypewriters

Electronic and other electricalPower plants31equipment

Nuclear reactors Fabricated metal products32Telecommunications Electronic and other electrical33

equipment34 Other electrical communication Electronic and other electrical

systems equipmentSpecial radio systems35 Electronic and other electrical

equipmentImage and sound equipment36 Scientific instruments

Electronic and other electricalIllumination devices37equipment

38 Electronic and other electricalElectrical devices and systemsequipment

39 Electronic and other electricalOther general electrical equipmentequipment

40 Electronic and other electricalSemiconductorsequipment

41 Computer and office equipmentOffice equipment and data process-ing systems


Mechanical engineeringInternal combustion engines4243 Motor vehicles and componentsMotor vehicles

Aircraft44 AircraftOther transport equipmentShips and marine propulsion45

46 Other transport equipmentRailways and railway equipmentOther transport equipmentOther transport equipment47Textile, mill products and cloth-Textiles, clothing and leather48

ing+leather and leather products49 Rubber and plastic products Rubber and plastic products

Non-metallic mineral products50 Non-metallic mineral productsPetroleum and coal productsCoal and petroleum products51Scientific instrumentsPhotographic equipment52Scientific instrumentsOther instruments and controls53Lumber, wood and paper prod-Wood products54

ucts+furnitures and fixtures55 Chemicals and allied productsExplosive combustions and charges

Leisure products+miscellaneousOther manufacturing and non-56industrial

a When necessary, technological field has been split and incorporated intoaggregation of two different sectors.

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Relatedness and coherence in technological and product