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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Using data from the Fourth UK Community Innovation Survey this paper explores the diffusion of a range of innovative activities (encompassing process, product, machinery, marketing, organization, management and strategic innovations) across 16,383 British companies in 2004. Building upon a simple theoretical model it is shown that the use of each innovation is correlated with the use of all other innovations. It is shown that the range of innovations can be summarized by two multi-innovation factors, labelled here ‘organizational’ and ‘technological’, that are complements but not substitutes for each other. Three clusters of firms are identified where intensity of use of the two sets of innovations is below average (56.9% of the sample); intermediate but above average (23.7%); and highly above average (19.4%). Distinctive characteristics are found to be common to the companies in each cluster. Finally, it is shown that innovativeness tends to persist over time.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Although much past research has focused on the productivity gap that exists at the macro level between the UK and its major international competitors, including Germany, France and especially the USA (O'Mahony and De Boer, 2002), it is clear that a productivity gap of some substantial size also exists at the sectoral level within the UK and even between firms in given sectors. It is to this latter, micro, literature that this paper contributes and to which an increasing interest is now being paid.

Within the research on productivity there has always been an emphasis upon the role played by technological innovations. More recently research has increasingly emphasized that differences at the firm level may also be a function of how companies are managed. This is in line with Porter and Ketels (2003) who, in their review of the state of UK competitiveness, suggest that one explanation for productivity gaps is the use and the effectiveness of modern management practices in UK firms. Authors such as Berman, Bound and Griliches (1994), Berman, Bound and Machin (1997), Bloom and Van Reenen (2007), Cappelli and Neumark (2001), Edwards, Battisti and Neely (2004) and Wengel et al. (2000) have also explored the role of new work and management practices in the performance of the firm. They have then argued that the simple adoption of technological innovations alone is not sufficient to gain competitiveness; the full benefit of those technologies is only achieved if they are accompanied by a cluster of related innovations in production, organization, customer and supplier relationships and new product design. This is equivalent to stating that there are positive synergistic gains to be realized from simultaneous innovation on several fronts. Consequently, any study of the impact of the adoption and use of an innovative practice should not be carried out in isolation from the adoption of other such practices, as this would neglect the potential for synergies and extra gains derived from joint adoption of complementary innovations (see Ruigrok et al., 1999, or Whittington et al., 1999).

An extensive literature has explored the diffusion of technological and managerial innovations in isolation. Most of this literature has also concentrated on one innovation at a time. Robust empirical evidence on the existence of complementarity across innovations is still quite scarce. As a result, our knowledge of the combined use of, and synergies among, the range of strategic, organizational or managerial innovations is limited, let alone the relation of such innovations to the more traditionally considered technological innovative activity.

At least partly, the lack of prior research in this field is due to poor data availability. Innovation that has not involved changes in processes and products has traditionally merited little effort in data collection. In addition, the occasional ad hoc surveys that have been undertaken rarely incorporated information on a full spectrum of management as well as technological innovations. In this paper, we overcome such limitations by using the individual firm level returns data1 from the Fourth UK Community Innovation Survey (CIS4)2 which provides information on the use of a wide range of innovative activities carried out by 16,383 British companies between 2002 and 2004. This data set is quite unique in that it contains information on strategic, management, organizational and marketing innovations as well as on innovations of a more traditional technological nature (such as new machinery, new processes and new products). We use this information to explore the simultaneous use of a wide set of innovations in an attempt to (i) map out the patterns of use across firms; (ii) explore the determinants of these patterns; (iii) isolate the synergies; and (iv) explore the impacts of joint adoption on firm performance.

The theoretical framework employed here is a simple, decision theoretic, innovation adoption model, based upon profitability considerations, which we extend to allow for synergistic gains derived from the joint adoption of complementary (or potentially substitute) innovations. The model conceptually belongs to a class of equilibrium models used in the literature on the economics of technological diffusion (see Stoneman, 2002, for a review). The resulting model is essentially distribution free, in line with the work of Perrow (1967) and Leseure et al. (2004), does not superimpose certain combinations of innovations as desirable so that ‘one fits all’, and does not assume that the optimal level of adoption is universally 100%. Rather, driven by profitability considerations, it allows that what is optimal for the firm is firm specific and, as conditions internal and external to the firm change, so does profitability and the desired level and combination of the use of the innovations.

When we apply this interpretative framework to our data, we find that significant complementarities arise from the joint use of the different innovations. These complementarities are reflected in the identification of two main sets of innovative factors that we name ‘organizational’ innovation and ‘technological’ innovation. The former encompasses innovation involving new management practices, new organization, new marketing concepts and new corporate strategies. The latter encompasses technological innovation such as the traditionally measured process and product innovations.

Further to the mapping out of the patterns of use across firms and to isolate the synergistic effect, we are able to identify three clusters of adopting firms which we classify as intensive, medium and low users. We explore the characteristics of firms in each cluster and the impact of their adoption decision upon their performance. We believe that in this way this study makes a valuable contribution to the understanding of the complexity of the innovation path of UK firms and their performance.

The paper is structured as follows. The next section introduces the data set, the key variables of interest and some initial indicators of new technology usage. The following sections provide the theoretical model and explore revealed synergistic gains in the data. Then principal components analysis is used to identify key factors, and the clustering of the use of these factors across the sample and the impact of firm characteristics on usage are explored. The impact of innovative activities upon firm performance is examined, and persistence in innovation is investigated. A final section concludes.

The CIS4 data set and measures of innovative activity

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

The Community Innovation Survey (CIS) is a pan-European survey carried out every four years3 by each EU member state and is designed to gather information on the extent of innovation in European firms across a range of industries and business enterprises. CIS4 is the fourth round of data collection, was carried out in 2005 and relates to innovative activities carried out in the three-year period from 2002 to 2004. In the UK this survey was administered by the Office of National Statistics on behalf of the Department of Trade and Industry (DTI). The survey was addressed to enterprises (which we here call firms, although this is misleading for multiplant firms) with more than ten employees, in both manufacturing and service industries, with response being voluntary. We have been given privileged access by the DTI to the individual returns although we are unable to identify respondents.

From an original sample of 170,735 companies the questionnaire was sent to a stratified (by industry, firm size and geographical region) sample of 28,000 enterprises and 16,383 responses (about 50% response rate) were eventually registered, which represent the sample for the work reported here.4 The salient point for our purpose is that the data set contains information on a wide range of innovative activities carried out by firms. In particular it contains information on whether, between 2002 and 2004, the sample companies had introduced new product innovations (PRODINOV); new process innovations (PROCINOV); and any technological innovation such as new machinery, equipment and computer hardware or software to produce new or significantly improved goods, services, production processes or delivery methods (MACHINE). Further to these traditional indicators of innovative activities, responses to CIS4 question 23 contains information on whether the enterprises have made major changes in the areas of business structure and practices during the three-year period 2002–2004 concerning the implementation of new or significantly changed corporate strategy (STRATEGY); implementation of advanced management techniques (MANAGEMENT); implementation of major changes to the organization structure (ORGANIZATION); and implementation of changed marketing concepts or strategies (MARKETING).

Out of the 16,383 enterprises who responded to the CIS4 questionnaire, about 20% have adopted at least one of the innovations, the exception being MACHINE, which has been adopted by about half of the sample. Table 1 reports the variable definitions and the percentage of adopting firms in the sample.

Table 1. Definition of innovation variables and sample adoption (%)
Innovation variable labelDefinitionAdopting firms
PROCINOVWhether a product innovation (new to the enterprise or to the market or a significantly improved good or service) has been introduced on the market between 2002 and 2004 (see Q7–Q8)20%
PRODINOVWhether a process innovation (new to the enterprise or to the market that significantly improved methods for the production or supply of goods and services) has been introduced between 2002 and 2004 (see Q11)29%
MACHINEWhether advanced machinery, equipment and computer hardware or software to produce new or significantly improved goods, services, production processes or delivery methods has been acquired between 2002 and 2004 (see Q13)47%
STRATEGYWhether a new or significantly changed corporate strategy has been implemented between 2002 and 2004 (see Q23.10)19.9%
MANAGEMENTWhether advanced management techniques, e.g. knowledge management systems, Investors in People etc., have been implemented between 2002 and 2004 (see Q23.20)17.6%
ORGANIZATIONWhether major changes to the organizational structure, e.g. introduction of cross-functional teams, outsourcing of major business functions, have been implemented between 2002 and 2004 (see Q23.30)22.6%
MARKETINGWhether changes in marketing concepts or strategies, e.g. packaging or presentational changes to a product to target new markets, new support services to open up new markets etc., have been implemented between 2002 and 2004 (see Q23.40)23%

In Table 2, using the CIS4 data summarized in Table 1, we explore the extent to which firms introduced multiple innovations. We report Kendall's tau-b correlation coefficient (a non-parametric measure of association based on the number of concordances and discordances in paired observations) for the seven innovation variables listed above in order to indicate the extent to which the sample firms between 2002 and 2004 undertook simultaneous innovation practices. For all the variables the pairwise degree of association is significantly different from zero, showing that adopting one innovative practice or technology is not independent of adopting another innovative practice or technology and that the adoption of all practices is correlated with the adoption of all others. However, the degree of association differs in intensity and varies from innovation to innovation.

Table 2. Correlation matrix Kendall's tau_b correlation coefficient (N=15657)
 PRODINOVPROCINOVMACHINESTRATEGYMANAGEMENTORGANIZATIONMARKETING
PRODINOV1.000      
PROCINOV0.4291.000     
MACHINE0.3190.3601.000    
STRATEGY0.2750.2530.1981.000   
MANAGEMENT0.2140.2380.2200.4071.000  
ORGANIZATION0.2750.2550.2040.5430.4121.000 
MARKETING0.3380.2930.2520.4480.3810.4451.000

Theoretical groundings

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

The existence of significantly positive pairwise correlations between the adoption of different innovative practices is not necessarily proof of complementarities and/or synergies. The correlations may in fact be the result of other background factors. In this section we therefore approach the issue theoretically in order to provide some grounding for our analysis. The theory in this section is largely built upon approaches standard in the economic analysis of technological diffusion (see Stoneman, 2002, for a review) that for the purpose of this study we extend to the diffusion of non-technological innovations (see, in addition, Battisti and Iona, 2007).

Assume an industry (or sector) with N heterogeneous profit-maximizing firms, i=1, …, N, each of which initially can adopt a new practice or technology y in time t with the expected present value of the gross profit gain from adoption of innovation y being πit(y). Assume that πit(y) is distributed across the N firms according to F(πit(y)), the distribution being invariant with respect to time and the extent of use of the innovation (an assumption made for simplicity but which could at the cost of greater complexity be relaxed).

The cost to firm i of acquiring the innovation in time t, cit(y), is assumed to have a component common to all firms, ct(y), reflecting, say, the charge for buying equipment, plus a firm-specific component, eit, reflecting perhaps installation costs, such that cit(y)=ct(y)+eit(y).

Assume also that firms are myopic in their expectations formation processes and expect πit(y) and cit(y) to remain constant over time. Under such assumptions the profitability and arbitrage conditions for the adoption of an innovation coincide. This assumption removes expectations effects from the model but these could be included and have been in the literature (Ireland and Stoneman, 1986). Firm i will then be expected to adopt innovation y at the first date at which πit(y)−cit(y)≥0. More formally, define a dummy variable Dit(y) as equal to 1 if firm i has adopted (only) innovation y in time t and zero otherwise; then Dit(y)=1 if πit(y)≥cit(y).

The net gain from adoption, πit(y)−cit(y), may increase over time due to either πit(y) increasing or cit(y) decreasing. The latter for example may happen if there are reductions in acquisition/adoption costs; the former may happen if, for example, there are quality improvements in innovations over time or externalities derived from use by other companies. However, at a point in time, as ct(y) is the same for all firms, the cross-section usage pattern5 will only reflect differences across firms in πit(y) and eit(y). Thus for example, at time t, firms for whom πit(y) is large will be more likely to introduce the innovation than firms for whom πit(y) is small. This is particularly relevant as we only have cross-section and not time series data.

Recent theoretical and empirical research has increasingly recognized that to look at the adoption of stand-alone innovations may be misleading since firms often tend to adopt clusters of innovations rather than individual practices and innovations in isolation. The supposition is that joint adoption of complementary innovations can significantly improve productivity, increase quality and often result in better corporate financial performance relative to isolated instances of innovation. Milgrom and Roberts (1990, 1995), indeed, explicitly claim that bundling more innovative practices together is not an accident. Rather, it is the result of the adoption by profit-maximizing firms of a coherent strategy that exploits complementarities. Similarly, Battisti, Colombo and Rabbiosi (2005), within a causality framework, find the existence of extra profit gains from the joint rather than individual adoption of different work practices. Complementary innovations are essentially innovations where the overall net gain from joint adoption is higher than the sum of the net gains from individual adoption (see for example Battisti and Iona (2007), Ichniowski, Shaw and Prennushi (1997) and Whittington et al. (1999) for examples of super-additivity and clusters of innovations, or the formalized models of Battisti, Colombo and Rabbiosi (2005) or Stoneman (2004) for substitute and complementary technologies etc.).

To consider such complementarities, assume that there is a second innovation k that is available at the same time as technology y.6 This innovation k may be adopted in time t by firm i at a cost cit(k), made up, as for y, by a general and a firm-specific effect such that cit(k)=ct(k)+eit(k). If innovation k alone is adopted by firm i in time t then the gross payoff is πit(k). If both innovations y and k are introduced the payoff πit(y and k) is assumed to be πit(y)+πit(k)+μyk, where μyk reflects synergies between the two innovations.

The firm has four possible strategies:

  • 1
    Adopt neither innovation in which case the net profit gain is zero.
  • 2
    Adopt only innovation y with a gross present value payoff of πit(y).
  • 3
    Adopt only innovation k with a gross present value payoff of πit(k).
  • 4
    Adopt both innovations y and k with a gross present value payoff πit(y and k).

Of particular interest here is what will encourage firms to adopt several innovations jointly rather than just single innovations, i.e. to pursue strategy 4 as opposed to strategies 2 or 3 (or even 1). A profit-maximizing firm will adopt both innovations if joint adoption is profitable and if the net benefit from adopting an extra innovation having already adopted the other is positive. Thus joint adoption will result if (i) it is profitable to own both innovations, i.e. πit(y)+πit(k)−cit(y)−cit(k)+μyk≥0; (ii) having got innovations y it is profitable to also install k, i.e. πit(k)−cit(k)+μyk≥0; and (iii) having got k it is profitable to also install y, i.e. πit(y)−cit(y)+μyk≥0. Ceteris paribus, the greater is μyk the greater is the chance of these conditions being met and thus the probability of joint adoption increases with μyk.

One may interpret μyk as reflecting the synergies between the two innovations, and in particular if the innovations are complements then μyk≥0 and if they are substitutes then μyk≤0. If they are not connected then μyk=0. The more it is the case that the payoff to one innovation is greater when the other innovation is in use, the more one would expect both innovations to be used together (although the conditions show that the innovations do not have to be complements to be jointly in use, as long as they are not too strict substitutes).

Defining the dummy variable Dit(k) in line with Dit(y) as reflecting use of innovation k, and for simplicity assuming that μyk is not firm specific, we may now extend the above single innovation conditions for the use of an innovation to state that firm i will be using innovation y in time t if πit(y)+Dit(k)μyk≥cit(y) and be using innovation k in time t if πit(k)+Dit(y)μyk≥cit(k). If μjk is positive then these conditions imply that complementary effects will increase the likelihood of adoption of the second innovation.

Individual innovative activities can be defined to be complementary (exhibiting synergies) if the adoption of one raises the marginal payoff of others (see also Battisti and Iona, 2007; Battisti, Colombo and Rabbiosi, 2005; Ruigrok et al., 1999; Whittington et al., 1999). In this context, Arora and Gambardella (1990) and Arora (1996), following the revealed preference approach, show that this is equivalent to saying that the second-order cross-derivative of the expected gain between innovation y and innovation k (μyk as modelled above) is positive. Such marginal payoff effects will be shown when, in the econometric modelling of the probability of adopting by firms of any one innovation, the conditional covariance between the adoption of any two innovations y and k is positive, after controlling for the impact of a number of firm and environmental characteristics which might act as potential lurking factors.7 In the next section we undertake such an exercise to isolate patterns of complementarities and synergies across the seven identified innovations in the database.

The theory above suggests that the cross-section pattern of usage at a moment in time will reflect (i) the stand-alone payoffs to individual firms from adoption, which in turn will depend upon the firm-specific cost eit for the innovation; (ii) the stand-alone firm-specific gross profits to be earned from the innovation πit; and (iii) any synergies available from joint adoption (μky). The greater the synergies the more one might expect adoption of multiple rather than single innovations.

Complementarities in innovative activities

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Having shown above that the CIS4 data reveal significant pairwise correlations in the use of new technologies and practices, we now explore whether, on the basis of the theory detailed earlier and the CIS data, we are able to make any empirical inferences on synergies (by seeing, as suggested, whether the conditional covariance between the adoption of any two innovations is positive in the econometric modelling of the probability of adopting by firms of any one innovation).

The key to operationalizing the model to explore usage of innovative activities is in specifying the determinants of the differing returns to the use of innovative activities πit(.) and also the different firm-specific cost effects eit(.), i.e. the different net gains. The rationale behind our approach is that firms are different and as a result get different returns from the use of innovations. These returns reflect different gross profit gains and different firm-specific costs. As one cannot necessarily separate cost and revenue effects we will discuss below just different returns without being specific as to whether these result from the cost or revenue side. We define the determinants of the different returns as a vector of firm-specific and environmental factors θi. It is assumed that the characteristics that determine the differences in returns are not themselves affected by the firm's own innovation adoption.

There is an extensive theoretical and empirical literature that looks at what the relevant characteristics might be (see Geroski, 2000). The firm and environmental characteristics that we have included have been dictated partly by the economic analysis of technology diffusion and partly by data availability. They are listed below and summarized in Table 3 (as we are here primarily interested in analysing cross-sectional data and thus differences across firms at a point in time, from this point on we drop the t subscript and, where not necessary, also the i subscript).

Table 3. Control variables, conditional adoption probabilities
LabelDefinition
SIZENumber of employees
GROUPWhether part of a group (1) or independent establishment (0)
INTERNATWhether the market is international (1=yes; 0=no)
AGEWhether established after 2000 (1=yes; 0=no)
R&DWhether the enterprise engages in R&D activities (1=yes; 0=no)
SCdegreePercentage of the enterprise's employees educated to degree level or above in science and engineering subjects
OTHdegreePercentage of the enterprise's employees educated to degree level or above in other subjects
SUPPORTPUWhether received any public financial support (1=yes; 0=no)
SICjIndustry to which the establishment belongs: j=1 to 14, wide SIC 92 classification, dummy variables
  • (i)
    Firm size (SIZE) measured by the number of employees. Size may pick up a number of other firm characteristics such as efficiency, management abilities (see Åstebro, 1995) and perhaps past innovations and may also reflect any scale economies that there might be in the use of innovations. It may also pick up whether the unit cost of innovation varies with firm size. Firm size has a long history as a deterministic factor in diffusion studies (see for example Åstebro, 2002; Colombo and Mosconi, 1995; Hannah and McDowell, 1984; Karshenas and Stoneman, 1993; Mansfield, 1968; Saloner and Shephard, 1995), it generally being found that size of the establishment exerts a significant and positive impact upon innovation adoption.
  • (ii)
    R&D intensity (R&D), which takes the value one if the firm reports R&D activity in the period 2002–2004 and zero otherwise. This variable reflects the Schumpeterian hypothesis that formalized R&D exerts a positive impact upon the use of innovations, in line with Cohen and Levinthal (1989).
  • (iii)
    The covariates SCdegree and OTHdegree measuring the percentage of employees with a degree in science or other degrees in 2004. The importance of skills has been emphasized by, for example, the pioneer work of Finegold and Soskice (1988) who first defined the concept of low skills/low quality equilibrium or more specifically by the work on links between innovation and skills by Bartel and Lichtenberg (1987), Bresnahan, Brynjolfsson and Hitt (2002), Caroli and Van Reenen (2001) etc.8
  • (iv)
    Whether the firm was established after 2000 (AGE). The age of the establishment is included according to the view that older plants generally have more experience that allows them to assess costs and benefits of any changes better than younger plants (see for example Noteboom, 1993). Nevertheless, older plants might also be less flexible in introducing innovations due to the nature and complexity of their organizational structure (see Battisti, Colombo and Rabbiosi, 2005; Little and Triest, 1996) or the resistance of employees to the introduction of innovations (see Ichniowski and Shaw, 1995). In the CIS4 questionnaire there is a question on whether the company was established after 1 January 2000. We use it as a proxy for young and old establishments.
  • (v)
    Three other dummy variables that have been linked to early adoption of innovations in previous literature (see Battisti, Canepa and Stoneman, 2009; Stoneman and Battisti, 2008) capture whether the firm belongs to a group (GROUP), whether the market for its final product is international (INTERNAT) and whether the company received any public financial support (SUPPORTPU).
  • (vi)
    We also include a series of 12 industry dummy variables to reflect different industry (wider subgroup) conditions, markets, and types of innovations and payoffs to firms in different industries. The industrial classification follows SIC 92 as defined in the Appendix.

To econometrically model the probability of adoption by firms of single innovations we undertook seven probit model estimations, one for each innovation, that relate adoption/non-adoption of the innovation by the firm to the firm characteristics in the vector θi. The estimates yield the results presented in Table 4.

Table 4. Control factors and the probability of adoption, probit estimates
 PROCINOV coeff.PRODINOV coeff.MACHINE coeff.STRATEGY coeff.MANAGEMENT coeff.ORGANIZATION coeff.MARKETING coeff.
  • *

    Coefficients significant at 5% in bold.

ONE−1.274−0.972−0.630−1.329−1.271−1.311−1.244
GROUP0.2080.2360.042*0.3700.3000.4910.282
INTERNAT0.0270.0050.1480.0600.0190.0600.178
AGE20000.0010.0730.0190.2130.0400.0860.065
R&D0.8071.1350.9530.6040.5680.6240.813
SCdegree0.0010.0010.0010.0010.0000.0010.001
OTHdegree0.0010.0020.001*0.0020.0010.0020.002
SUPPORTPU0.5220.6160.3960.3870.3840.3100.404
EMPLOY.0.0000.0000.0000.0000.0000.0000.000
D10.084−0.7730.0200.0420.1500.102−0.465
D20.170−0.1100.323−0.151−0.235−0.133−0.122
D30.0770.0540.228−0.160−0.1530.092−0.321
D40.0600.1920.1540.085−0.2190.064−0.227
D50.0560.0160.2240.0710.0880.098−0.453
D60.0100.0170.227−0.185−0.2840.126−0.234
D8−0.367−0.4690.031−0.1630.0820.105−0.323
D10−0.272−0.417−0.231−0.272−0.328−0.317−0.276
D11−0.406−0.523−0.287−0.355−0.178−0.337−0.355
D120.078−0.1860.1900.121−0.1240.092−0.219
D130.2340.0840.1560.2360.0280.2610.072
D140.184−0.1090.0130.0750.0560.1220.087

The coefficient estimates are largely in line with our prior expectations as far as sign and significance are concerned (but these are not our main interest). The main interest is in the results on the significance and signs of the off-diagonal elements of the covariance matrix of the standardized residuals of the probit specifications (R_j, where j=process, product, machinery, marketing, organization, management and strategic innovations), and these are reported in Table 5. The degree of association, i.e. the extent of the complementarity effect μyk, is significant and positive for all pairwise comparisons although it varies and differs in intensity from pair to pair of innovations (e.g. management and strategy illustrate greater synergy than product and strategy). This suggests that there exist important synergies generated by joint adoption although some innovations are more influential and versatile than others. The implication is that to concentrate on the analysis of the adoption of single innovations in isolation would be misleading, and it is far preferable to consider the joint adoption of complementary innovations.

Table 5. Non-parametric Kendall's tau_b correlations of the residualsa
 R_ProcessR_ProductR_MachineR_StrategyR_ManagementR_OrganizationR_Marketing
  • *

    Correlation is NOT significant at the 0.01 level (p=0.0067).

  • a

    Listwise N=15,082.

R_Process1.0000.1610.1310.2120.1780.1920.253
R_Product0.1611.0000.2970.0700.0250.1060.140
R_Machine0.1310.2971.0000.015*0.0270.0550.070
R_Strategy0.2120.0700.0151.0000.3770.4890.392
R_Management0.1780.0250.0270.3771.0000.3290.296
R_Organization0.1920.1060.0550.4890.3291.0000.422
R_Marketing0.2530.1400.0700.3920.2960.4221.000

The intensity and clustering of innovative activity

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Thus far we have proceeded by analysing the seven different technologies as separate, but involving synergies. This is a cumbersome procedure and there are considerable analytical advantages if the number of innovation variables to be analysed can be reduced. Principal components analysis is a commonly used tool for dimensionality reduction in data sets while retaining those characteristics of the data set that contribute most to its variance by keeping lower-order principal components and ignoring higher-order ones. Here we perform iterated principal factor analysis (IPFA) based upon the decomposition of the tetrachoric correlation matrix of the pairwise adoption decision for the firms in the CIS4 sample. This identifies the underlying pattern of intensity of use of different innovative practices by the sample of UK firms in 2004. We do not make any presumptions as to what is the ‘best’ combination of innovations (see, for example, Perrow, 1967). We instead let the data inform on the variability and the intensity of use of the different practices based upon the extent of their natural association.

IPFA models the correlations amongst the innovations adopted and linearly transforms them to obtain a smaller set of variables uncorrelated with (orthogonal to) each other and defined so that the first factors are the vectors of coefficients (loadings) of the linear combination that explains the largest proportion of variance. In other terms, IPFA allows one to summarize the heterogeneity of use of the set of seven innovations via a reduced number of latent factors capable of picking up the underlying pattern of use that can explain the largest proportion of variability of the joint adoptions and so identify the innovative practices that play the major roles in the overall innovative activities of the firm.

In Table 6 we report the tetrachoric correlation matrix for the use of different innovations. The highest correlations are found between process and product innovation and among new strategy, management, organization and marketing practices. The Kaiser–Meyer–Olkin measure of overall sampling indicates whether the sum of partial correlations is large relative to the sum of correlations. Its value of 0.8652, being close to 1, indicates that patterns of partial correlations are relatively compact; thus factor analysis should yield distinct and reliable factors. The Bartlett measure of sphericity is significantly different from zero at the 1% significance level indicating that the original correlation matrix is not an identity matrix and thus that factor analysis is appropriate for these data.

Table 6. Tetrachoric correlations (obs=15,657)
 PRODINOVPROCINOVMACHINERYSTRATEGYMANAGEMENTORGANIZATIONMARKETING
  1. Note: All coefficients are significant at 5%.

PRODINOV1.0000      
PROCINOV0.66431.0000     
MACHINE0.50330.61161.0000    
STRATEGY0.46170.43780.34971.0000   
MANAGEMENT0.37730.42120.40000.65031.0000  
ORGANIZATION0.45400.43590.35000.78640.65471.0000 
MARKETING0.54120.48960.42660.68860.61740.67921.0000

In Table 7 we report the rotated9 factor loadings and their uniqueness. While the former are the coefficients of the linear combination of the original variables that decreasingly explain the largest part of the variability, the latter measure the proportion of variance of the variable that is not accounted for by all the factors taken together.10 The first factor (Factor 1) accounts for 83.5% (57% if rotated) of the total variability in firms's innovative activity and it is driven by the extent of use of strategy, management, organizational and marketing innovations. These are labelled in CIS4 as wider innovations (defined as ‘new or significantly amended forms of organization, business structures or practices, aimed at step changes in internal efficiency of effectiveness or in approaching markets and customers’) but we prefer the label organizational innovations.

Table 7. Rotated factor loadings
VariableFactor 1Factor 2Uniqueness
PRODINOV0.33000.67780.4316
PROCINOV0.24770.85140.2137
MACHINERY0.23900.64390.5283
STRATEGY0.84420.25770.2209
MANAGEMENT0.68840.29500.4391
ORGANIZ0.84220.25390.2262
MARKETING0.69970.40440.3470
%var83.5%16.5% 
(57% R)(43% R) 

The second factor (Factor 2) in Table 7 explains 16.5% (43% if rotated) of the remaining variability in the heterogeneity of use of innovative activities by the firms in the sample and it is driven by product, process and technological innovations, which we generally label technological innovations. The overall pattern can be better seen in Figure 1 which reports the rotated factor loadings on the two axes. On the x axis the principal factor shows the importance of organizational innovations, while on the y axis the second factor shows the importance of technological innovations.

image

Figure 1. Rotated factor loadings

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For all the variables used in the IPFA analysis the uniqueness statistic indicates that most of their heterogeneity of use is largely related to the other extent of use variables. Interestingly, MACHINE is the innovation that has the least shared variance and is the most adopted (in fact about 47% of the firms in the sample employ this innovation). As MACHINE incorporates software and PCs it may be that information technology has become so widespread that it no longer yields a competitive advantage to adopters. This is consistent with the observation that MACHINE is the dominating factor load in the third factor extracted by the IPFA analysis but the percentage of variance explained is just 6.7%.

The IPFA analysis in summary suggests that, although the innovation literature has been mainly concerned with ‘traditional’ or technological innovations, ‘wider’ or organizational innovations play a predominant role in the innovative activity of UK firms.

Having identified the two factors, in order to identify the existence of clusters of firms based upon the intensity of use of the seven innovations we have carried out a two-step cluster analysis over the projection of the firms standardized factor scores (the latter being the summary information on the intensity of use of each factor). This has resulted in three clusters being identified containing 9317 (cluster 1), 3881 (cluster 2) and 3185 (cluster 3) firms/enterprises respectively. In Figure 2 we report the calculated 95% confidence intervals for the average intensity of use (i.e. the average standardized factor score) of Factor 1 and Factor 2 for each of the three clusters.

image

Figure 2. Confidence intervals for the mean of Factor 1 (on the left) and Factor 2 (on the right)

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From Figure 2 one may observe that cluster 1 firms use organizational innovations at levels below the sample average (the average standardized factor score) represented by the straight horizontal line. The other two clusters are firms that use organizational innovations progressively more intensively. The same can be said for the differences across the clusters in the second factor illustrating the intensity of use of technological innovations (see Figure 2(b)) with usage increasing as one moves from cluster 1 through to cluster 3.

Table 8 reports the percentage of the firms within each cluster that have introduced each of the seven innovations. As predicted by the factor analysis the intensity of use of the practices is highest in cluster 3 where a majority of the firms have adopted each of the seven innovations. Cluster 1 contains the least ‘innovative’ firms. Within this cluster less than 2% of the firms report having carried out organizational innovative activities, although about 22% have introduced technological innovations. Although not shown, 6% have developed new products but only 2.3% of those products (against 42% in cluster 3) were new to the market rather than just new to the firm.

Table 8. Within-cluster percentage of firms who report having introduced the innovations
 MANAGEMENTSTRATEGYORGANIZATIONMARKETINGPRODINOVPROCINOVMACHINE
Cluster 11.51.22.01.66.01.322.3
Cluster 218.720.725.827.848.232.471.5
Cluster 359.269.073.574.976.162.984.1

Interestingly, the extent of technological innovation as measured by MACHINE is comparatively high in each of the three clusters, although its intensity is less than proportional to the extent of overall firm innovativeness. This may confirm that technological innovations can more easily be introduced and assimilated than organizational innovations or a product new to the market, which require flexibility and cognitive skills that not all firms might possess (see Battisti, Colombo and Rabbiosi, 2005; Black and Lynch, 2004; Bresnahan, Brynjolfsson and Hitt, 2002; Brynjolfsson and Hitt, 1994, 2000; Colombo and Delmastro, 2002; etc.).

Given that cluster 1 has the largest number of firms and cluster 3 has the smallest, to the extent that CIS4 is representative of the UK population, this suggests that about 19.4% of UK firms operate well above average in terms of innovative activity while 56.9% perform below the average.

Interestingly, across the clusters we find that Factor 1 innovation is positively associated with Factor 2 innovation, suggesting that organizational innovations and technological innovations do not represent substitute, alternative or competing innovation strategies, but rather are complements with positive synergistic effects. If the factors had been substitutes we would expect to have seen some firms using organizational innovations intensively but not technological innovations and other firms using technological innovations intensively but not organizational innovations. We do not observe such patterns and thus may reliably adduce that the organizational and technological innovations are complements.

Innovation and firm characteristics

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

The theoretical framework we have proposed suggests that in addition to synergistic effects that encourage simultaneous use of innovations, there are many firm-specific and environmental effects that can explain differences in the use of technologies across firms in a cross-section. We have summarized them in the components of the vector θi. Having identified three clusters of firms in the data, in this section we explore apparent associations within the data to the elements of that vector. We are well aware that in a single cross-section one cannot imply causality and that the methods that we rely upon thus only indicate association. Positive associations are necessary but not sufficient to show that the characteristics impact upon use.

The first column of Table 9 reports the average size of the firm in each cluster, measured by the number of employees in 2004. The extent of firm innovativeness seems to increase with firm size, with cluster 1 firms being mostly small (trimmed mean=76.84; median=27), cluster 2 being mainly medium sized firms (trimmed mean=140.93; median=52) and cluster 3 being medium to large firms (trimmed mean=219.30, median=81.5). However, the standard deviations are very large suggesting that the averages can be highly misrepresentative. In order to visualize the within-cluster distribution of firm size, in Figure 3 we group the firms in each cluster into three classes: small (10–49 employees), medium (50–249) and large (250 or more). Figure 3 shows that cluster size compositions are quite heterogeneous; the relative importance of large firms is highest in the third cluster, and the majority of small firms tend to populate the first cluster.

Table 9. Firm characteristics by cluster: descriptive statistics
 Size (employees)Age (whether est. after 2000)R&DTraining% with science degree% with other degreePart of a groupPublic financial supportInternational market for its productService sector
Cluster 1
Mean168.75*0.150.120.212.88*4.93*0.260.040.980.62
5% trimmed mean76.840.110.080.180.882.110.24010.63
Median27000000011
SD756.150.360.330.4111.0314.600.440.190.130.5
Min9000000000
Max326551111001001111
Cluster 2
Mean304.39*0.140.460.587.34*8.90*0.410.140.980.55
5% trimmed mean140.930.100.460.594.185.820.400.1010.56
Median52001020011
SD1281.230.350.500.4917.0617.780.490.350.150.50
Min10000000000
Max483871111001001111
Cluster 3
Mean470.68*0.160.680.7611.00*11.46*0.530.250.970.55
5% trimmed mean219.300.120.700.797.718.280.530.2210.56
Median81.5011251011
SD2148.330.370.470.4320.5219.340.500.430.170.50
Min10000000000
Max604981111001001111
image

Figure 3. Intra-cluster firm size composition

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We find that the proportion of establishments that carry out in-house R&D11 is lowest in cluster 1 and highest in cluster 3 reflecting the Schumpeterian hypothesis that formalized R&D exerts a positive impact upon the use of an innovation. The proportion of employees with a degree in science and engineering subjects or other subjects both increase progressively from cluster 1 to 3 confirming the importance of the link between innovation and skills emphasized by, among others, Caroli and Van Reenen (2001) and Bresnahan, Brynjolfsson and Hitt (2002). The percentage of firms that received public support increases with the extent of innovative activity carried out by the firm, reaching a peak of 25% in the highly innovative group (cluster 3). The proportion of firms that are part of a group (versus independent establishments) is higher in cluster 3 than in the other clusters. No significant differences across clusters have been found with respect to (i) whether the market for the firm's final product is international or (ii) the age of establishments.

In Table 10 we report the distribution of firms across industrial sectors by clusters. We observe that in every sector cluster 1 contains the largest number of firms, suggesting that the distribution of firm innovativeness is skewed. Second, firms operating in the service sector are no more likely to belong to cluster 3 than firms in other sectors. Third, within the production sector, perhaps unsurprisingly, firms in mining and quarrying, electricity, gas and water supply and construction are the least intensive innovators. By contrast, firms in high technology sectors such as manufacturing of electrical and optical equipment, manufacturing of transport equipment, followed by manufacturing of fuels, chemicals, plastic metals and minerals are more intensive innovators.

Table 10. Distribution of firms (%) across sectors by clusters
SIC classificationDefinitionCluster 1Cluster 2Cluster 3Total (count)
Production
 10–14Mining and quarrying60.924.414.7(197)
 15–22Mfr of food, clothing, wood, paper, publish and print48.628.822.6(1432)
 23–29Mfr of fuels, chemicals, plastic metals and minerals48.627.723.7(1897
 30–33Mfr of electrical and optical equipment34.831.733.5(663)
 34–35Mfr of transport equipment44.527.428.1(402)
 36–37Mfr not elsewhere classified47.430.322.3(515)
 40–41Electricity, gas and water supply68.620.011.4(35)
 45Construction72.917.010.1(1603)
Services
 50–51Wholesale trade (including cars and bikes)59.623.716.7(1341)
 52Retail trade (excluding cars and bikes)73.617.49.1(1543)
 55Hotels and restaurants74.915.79.5(983)
 60–64Transport, storage and communication63.321.215.5(1386)
 65–67Financial intermediation44.624.431.0(668)
 70–74Real estate, renting and business activities50.825.323.9(3718)
 Total 56.923.719.4(16383)

The two sectors with the highest percentage of low intensity users are in services. They are retail trade and hotels and restaurants. These are two sectors previously noted in the literature as exhibiting a particularly wide productivity gap relative to other sectors (see for example Griffith et al., 2003).

These results are essentially a picture at a moment in time of the innovative state of UK industry where innovation is essentially represented by two factors (one organizational and the other technological) enabling us to divide the population of firms into three clusters, 1, 2 and 3, in which the intensity of use of both factors increases as one moves from cluster 1 through to cluster 3. The analysis suggests that the number of firms in each cluster reduces as one goes through clusters 1 to 3 and that firms in the higher clusters do R&D, employ graduates, receive public support and are in higher tech sectors.

It is not possible with the data at our disposal to consider cause and effect. Thus we are unable to say whether firms are large because they are innovative or innovative because they are large. Similar statements can be made with respect to spending on R&D, employment of graduates and receipt of public support. We are thus unable to say whether only 19.4% of UK firms operate well above average in terms of innovative activity while 56.9% perform below the average because of their character or their characters are precisely because they do so perform. The real contribution of this analysis is that the findings relate to both technological and organizational innovations and their use in parallel. Past analysis has concentrated on technological innovation but these results extend to both technological and organizational innovations jointly.

Intensity of innovation and firm performance

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

The impact of firm innovativeness upon firm performance has been the concern of an extensive literature (see for example Hall, 2004). In particular, within the economics of innovation and technological change and within the endogenous growth literature one can find several theoretical and empirical studies that have demonstrated the role played by technological innovations in promoting competitiveness at both micro and macro levels. The evidence on the impact of the adoption of organizational innovations, for a number of reasons, tends to be less consistent (see for example Battisti and Iona (2006) for a review of the literature on the impact of a range of such practices upon firm performance). In both cases, however, most of the existing studies tend to analyse the impact of individual innovative practices in isolation. If, as claimed in this paper, complementarity effects exist, such an approach can be highly misleading, and only an integrated approach will be able to capture synergistic effects and the (extra) profit generated by joint adoption.

Due to the nature of the CIS4 data and the strong potential endogeneity of several of the variables, we have not been able (or willing) to specify any causal relation in order to explore the relation between innovation and firm performance or to test its statistical significance. However, we have looked at differences in the performance of the companies in the three clusters. In the absence of independent data on sample firm performance, we measure performance by using indicators available from the responses to the CIS4 questionnaire (although they are mostly based upon a view of innovation as product innovation) to do this. An obvious starter for measuring impact on performance is the impact of innovation upon firm value added. Unfortunately, we do not have direct measures of the value added due to each or any of the innovative activities investigated above.12 However, CIS4 contains a question (Q1290) on the establishment's own estimate of the effect of the introduction of product and processes in increasing value added. The responses are reported in Table 11 and diagrammatically in Figure 4.

Table 11. Degree of importance of product and process innovation in generating value added: within-cluster composition (column %)
 Cluster 1Cluster 2Cluster 3
Not relevant54.5519.757.09
Low11.4913.239.84
Medium22.5038.3838.94
High11.4628.6444.13
Total100.00100.00100.00
Total (count)817838173159
image

Figure 4. Inter-cluster distribution of the degree of importance of product and process innovation in generating value added

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The responses to Q1290 clearly show that the largest share of those who reported ‘high importance’ (44.13%) are in cluster 3 while the largest proportion of the ‘not relevant’ (54.55%) can be found in the least innovative cluster (1) which is also the largest cluster.

We have undertaken similar analysis on responses detailing firms's views as to the impact of innovation upon turnover (Q8). These we do not report in detail but the results are similar to the above. Innovation gets to be more important to firms as a determinant of performance as one moves from cluster 1 to cluster 2 to cluster 3 firms.

These results indicate that firms' own view of the importance of innovation as a determinant of firm performance increases as one moves from clusters 1 to 3. However, in the absence of appropriate data we cannot say whether firms are in cluster 3 because innovation is important or whether innovation is important because the firm is in cluster 3. What we can say, however, is that cluster membership depends on both technological and organizational innovative behaviour and thus any links are not restricted to technological innovation alone – organizational innovation also matters.

Innovation persistency: evidence from CIS4 and CIS3

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

In this section we explore whether firms that are innovative are also continuously innovative. This has two purposes. The first is to explore whether, just as performance may result from multiple innovations rather than isolated individual innovations, so it may be the case that, intertemporally, continuous innovation is required to improve performance rather than isolated instances of innovation. Second, our data only indicate whether firms introduced particular innovations in the 2002–2004 period and do not distinguish within the non-innovator group those who introduced innovations at other times from those who never innovate. Persistency analysis may overcome this problem.

We compare the extent of innovative activity reported by the cohort of firms in CIS4 (16,383 establishments) and CIS3 (8172 establishments). While CIS4 covers innovative activity carried out between 2002 and 2004, CIS3 covers innovative activity carried out between 1998 and 2000 (for details see http://www.berr.gov.uk/files/file9657.pdf). Due to the nature of the sample design of the two surveys there are only 959 establishments for which we have information in both surveys.13

In the first two columns of Table 12 we report the proportion of establishments that have introduced each of the studied innovations in the two time periods (2002–2004 and 1998–2000). This provides us with an overview of the intertemporal dimension of the intensity of use of each of the seven innovations under scrutiny. Although the extent of product and process innovation remains significantly unchanged in the two time periods (test statistics for equality of proportions: zPRODINOV=−4.1394, p=0.00, and zPROCINOV=−2.1446, p=0.016), the intensity of use of organizational innovations has almost doubled. Also the introduction of MACHINE has increased dramatically but this is likely to be due to the changed definition adopted in CIS4 which included software and a wider definition of supporting innovative activities which were not previously included in the CIS3 version of the questionnaire.14

Table 12. Degree of persistency of innovative activity: CIS3–CIS4 panel (proportions)
 (1) Proportion of innovators in CIS4(2) Proportion of innovators in CIS3(3) Test of association χ2v=1 (p value)(4) Proportion of CIS4 innovators that introduced the same innovation also in CIS3(5) Establishments that introduced no innovation in either CIS3 or CIS4
  1. Note:a The two proportions cannot be compared as the variable's definition in CIS3 has been changed in the CIS4 survey.

PRODINOV0.300.3980.24 (0.000)0.460.50
PROCINOV0.250.3049.09 (0.000)0.410.57
MACHINEa0.72a0.57a4.68 (0.030)0.750.12
STRATEGY0.570.269.44 (0.002)0.660.34
MANAGEMENT0.470.2520.57 (0.000)0.610.42
ORGANIZATION0.560.3353.63 (0.000)0.730.35
MARKETING0.570.2919.21 (0.000)0.690.33

The third column of Table 12 reports the χ2 test of association between the introduction of an innovation in either, both or neither period. For all the innovations under scrutiny the test indicates that introduction of an innovation is not independent of introduction in the previous period. This can be better seen in the fourth column which reports the proportion of the establishments that introduced the same innovation in the period 2002–2004 as well as in the period 1998–2000. The degree of persistency of innovative activity is particularly high for organizational innovations. The proportion of establishments that introduced a product or process innovation in both periods is lower.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Although there exists a large literature on the adoption and diffusion of innovations, only a very limited part considers the joint adoption of a range of innovations. In this study we have used the information contained in CIS4 to explore the pattern of use of innovations in UK industry and to test for the existence of complementarities among seven types of innovations, i.e. process, product, machinery, marketing, organization, management and strategic innovations.

Using a profitability based decision model, by means of statistical and econometric tools we were able to test the existence of complementary effects across the seven innovations. The results suggested widespread synergies among the identified innovations. Decomposition of the payoffs from joint adoption has led us to identify two major sets of innovations. The most important includes the wide or organizational innovative activities (marketing, organization, management and strategic innovations); the second set comprises more traditional or technological activities (machinery, process and product innovations). This finding is of particular importance in that, despite the extensive focus of the innovation literature on technological innovations, ‘wide’ or organizational innovations are found to play a major role in the innovative activity of UK firms. This indicates that innovations based around the technical aspect of the delivery of the final product (the process, the product per se or the machinery used), although important, tell only part of the story of the innovative effort of a firm.

A two-step cluster analysis based upon the intensity of organizational and technological innovative activities was carried out leading to the identification of three clusters of firms, each reflecting the intensity of use of the two sets of innovations. One cluster was found where intensity of adoption of the two sets of innovations was below average. This is the largest cluster containing about 56.9% of the firms in the sample. A second cluster (about 23.7% of the sample) was found with intermediate but above average adoption of both innovative activities. Finally a third cluster (containing about 19.4% of the sample) was found, made up of highly intensive adopters seemingly capable of fully exploiting the synergistic effects generated by joint adoption of organizational and technological innovations.

This is a very new picture of the pattern of innovative activity in the UK economy, simultaneously reflecting both technological and organizational innovations and showing that organizational innovations and technological innovations are complements and not substitutes for each other. The empirical evidence thus suggests that companies that are innovative in one dimension tend to be innovative, although with different intensity, in all dimensions, irrespective of the nature of the innovation.

When looking at the characteristics of the firms populating each cluster we found that the majority of small firms tend to populate the cluster of below average users. We found no significant differences across the three clusters in the percentage of recently established firms, but the proportion of establishments that carry out in-house R&D, the proportion of enterprises that carry out regular training, the percentage of firms that received public support, the proportion of firms that are part of a group and the proportion of employees with a degree all increase progressively going from cluster 1 to 3 (and therefore with the intensity of use of the two major innovations). The data do not, however, enable conclusions on directions of causality.

We found that establishments operating in the service sector are no more or less intensive users of innovations than firms in the production sector. Within the production sector high technology sectors such as manufacturing of electrical and optical equipment and manufacturing of transport equipment are the sectors with the highest relative number of intensive adopters of new technologies. By contrast, mining and quarrying, electricity, gas and water supply, and construction are the least intensive innovators. Overall the highest percentage of low intensity users are in two service sectors, retail trade and hotels and restaurants. Interestingly these are the same sectors that current literature has found to exhibit a wide productivity gap (see for example Griffith et al., 2003).

In terms of the impact of innovation upon firm performance, due to the lack of a time dimension to the data and the strong potential endogeneity of several of the variables in the CIS4 questionnaire, we cannot explore causality, nor do we have objective data on firm performance indicators. We have thus looked at the establishments's own estimates of the effect of the introduction of product and processes in increasing value added and restrict the analysis to association. Despite this measure being biased toward the technical aspects of innovation, the results clearly show that the largest share of those who reported ‘high importance’ for impact upon performance (44.13%) are in cluster 3 while the largest proportion of the ‘not relevant’ to company performance (54.55%) can be found in the least innovative cluster which is also the largest cluster. This does not allow us to say whether firms in the third cluster rank innovation high or because they rank innovation highly they are in the third cluster. However, what we can say is that both technological and organizational innovations are interlinked and any links to performance are not restricted to technological innovations alone: organizational innovation also matters.

In order to investigate whether firms that are innovative are also continuously innovative we have compared the extent of innovative activity reported by the cohort of firms included in both the CIS4 and the earlier CIS3 survey. The findings reinforce a view that intertemporal persistence is important to performance. Although the extent of product and process innovation remains largely unchanged in the two time periods, the intensity of use of organizational innovations has almost doubled.

In terms of contribution, we believe, first, that our results make a significant contribution to the mapping of innovation in the UK, simultaneously taking into account all types of innovation. The complementarity of innovations and the simultaneous introduction of different innovations suggest that future mapping exercises will need to pay much more attention to synergies and complementarities than has been the case in the past. Second, although our finding that 56.9% of UK firms are in an underperforming low innovation cluster is worrying, the characteristics of firms in that cluster (small, no in-house R&D, no regular training, no public support, few graduate employees etc.) may indicate where, and on what, innovation policy should be targeted if the innovative performance of these firms is to be improved. Third, the finding that organizational and technological innovations are complements suggests that the theoretical literature that suggests that technological innovation in the absence of organizational innovation alone cannot drive competitiveness has empirical validity and implications for corporate behaviour. Finally the findings suggest that future research on firm innovative behaviour and performance should give greater emphasis to the integration of technological and organizational factors. In a more limited vision, it would also appear that following on from this paper there are opportunities to, for example, explore other diffusion models based upon information acquisition and uncertainty as alternatives to the profitability based models. More innovation survey data will also soon be available that may well enable better testing of the causal relation between the extent of multi-innovation adoption and firm characteristics.

Appendix

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References

Appendix: 1992 SIC codes by wide industry grouping

Code Industry
10 Mining of coal
11 Extraction of oil and gas
14 Other mining and quarrying
15 Food and beverages
16 Tobacco
17 Textiles
18 Clothes
19 Leather
20 Wood
21 Paper
22 Publishing
23 Coke, petroleum and nuclear fuel
24 Chemicals
25 Rubber and plastic
26 Other non-metallic mineral products
27 Basic metals
28 Fabricated metal products
29 Machinery and equipment
30 Office machinery and computers
31 Electrical machinery
32 Radio, television and communication
33 Medical/optical instruments
34 Motor vehicles
35 Other transport
36 Furniture
37 Recycling
40 Electricity, gas and water supply
41 Collection, purification and distribution of water
45 Construction
51 Wholesale
60 Land transport
61 Water transport
62 Air transport
64 Post and telecommunications
65 Financial intermediation
66 Insurance and pensions
67 Financial intermediation (activities auxiliary)
70 Real estate
71 Renting of machinery and equipment
72 Computer and related activities
73 Research and development
74 Business activities
Footnotes
  1. 1For the provision of which we would like to thank the Department of Trade and Industry (DTI), recently relabelled the Department for Business Enterprise and Regulatory Reform (BERR).

  2. 2There is some confusion over nomenclature, in that BERR now label the UK CIS4 as the 2006 UK Innovation Survey.

  3. 3In the UK another Innovation Survey (labelled the 2007 UK Innovation Survey) with results expected mid-2008 has now been carried out, only two years after the CIS4 exercise, so the four-year timing is not adhered to strictly.

  4. 4Further details on the UK CIS4 including the questionnaire, the data collection process, sampling, the extraordinarily high response rate etc. can be found elsewhere (see http://www.berr.gov.uk/innovation/innovation-statistics/cis/cis4-sample/page11777.html).

  5. 5In such a case the number of users of the technology y, M(t) at time t, will be given by M(t)=N{1−F[ct(y)+eit]} and will be related to the distribution of returns across the N firms, the firm-specific costs and the cost of acquisition.

  6. 6Once again the cross-sectional nature of our data makes it unnecessary to ask what would happen if j and k became available at different times for our data do not reveal intertemporal differences between firms in the pattern of adoption.

  7. 7A lurking factor is a factor highly correlated with each innovation so that an increase (decrease) in its level increases (decreases) the adoption of each of the two innovations without the two innovations being necessarily complementary.

  8. 8We also experimented with other firm-specific variables present in the data set such as a dummy reflecting export activity and therefore competitive pressures but this considerably reduced the sample size.

  9. 9The extraction of principal components amounts to a variance maximizing (varimax) rotation of the original variable space. The rotated factor loadings, by stretching the loadings to their extremes (+1 or −1), improve the interpretative capability of the factors, without changing their nature or that of the model.

  10. 10A very high uniqueness can indicate that a variable may not belong with any of the factors. Uniqueness is 1 – communality where communality reflects the common variance in the data structure, i.e. 56.8% of the variance associated with PRODINOV is common, or shared, variance.

  11. 11One might argue that R&D be included among the seven innovations under scrutiny. We decided not to go down that route as we wanted to concentrate on innovation outputs and not on innovation inputs.

  12. 12Moreover, even if we did the nature of the data set is such that it would be difficult to establish the direction of the causal relations between adoption timing and payoff from adoption.

  13. 13We have tried to build a panel merging the information in CIS2, CIS3 and CIS4. Unfortunately this reduces the sample to 101 establishments making any statistical analysis totally unrepresentative of the UK establishment population.

  14. 14In CIS4 the relevant question is question 13.30 on whether in the three-year period 2002–2004 the enterprise engaged in the following activity: ‘Acquisition of advanced machinery, equipment and computer hardware or software to produce new or significantly improved goods, services, production processes or delivery methods’. In the CIS3 similar information was asked in question 9.1 where it was asked whether in 2000 the enterprise engaged in the following activity: ‘Acquisition of machinery and equipment (including computer hardware) in connection with process or product innovation’. Also the response rates for the two questions were different. 939 responses were recorded in the CIS4 round while only 459 were recorded in the CIS3 round. Slightly different definitions were given in the four questions concerning the introduction of wider innovations (e.g. examples of practices especially in organizational and management innovations) during the three-year period preceding the survey. However, in the context of this study we do not see these changes as particularly significant or impeding the comparison over time.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. The CIS4 data set and measures of innovative activity
  5. Theoretical groundings
  6. Complementarities in innovative activities
  7. The intensity and clustering of innovative activity
  8. Innovation and firm characteristics
  9. Intensity of innovation and firm performance
  10. Innovation persistency: evidence from CIS4 and CIS3
  11. Conclusions
  12. Appendix
  13. References
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Giuliana Battisti is an Associate Professor and Reader in Industrial Economics at Nottingham University Business School. She is also an ESRC-AIM (Advanced Institute of Management) Fellow in Services, an AIM scholar and Principal Investigator on an EPSRC-AIM project (Ref. EP/D503965/1) on the role of management practices in closing the UK productivity gap. Her research interests are in applied statistics/econometrics and the diffusion of innovations. She has published in the International Journal of Management Reviews, Research Policy, International Journal of Industrial Organization, Oxford Economic Papers etc.

Professor Paul Stoneman is Research Professor in the Marketing and Strategic Management Group and Head of the Technological Innovations Research Unit in Warwick Business School. He has held visiting positions at Stanford and Nuffield College, Oxford. He has published widely on the economics of innovation especially as regards the diffusion of new technology and technology policy. He is author of one of the earliest studies of computerization in the UK as well as of texts on technological change and technology policy, with a latest volume on technological diffusion. Past papers have appeared in inter alia the Rand Journal of Economics, the Economic Journal and the Journal of Industrial Economics.