Demand And Supply Factors In Explaining The

Economics of Innovation and New Technology

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Demand And Supply Factors In Explaining The Innovative Activity Of Swiss Manufacturing Firms Spyros Arvanitis & Heinz Hollenstein To cite this article: Spyros Arvanitis & Heinz Hollenstein (1994) Demand And Supply Factors In Explaining The Innovative Activity Of Swiss Manufacturing Firms, Economics of Innovation and New Technology, 3:1, 15-30 To link to this article: http://dx.doi.org/10.1080/10438599400000001

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Econ. Innov. New Techn., 1994, Vol. 3, pp. 15-30 Reprints available directly from the publisher Photocopying permitted by licence only

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DEMAND AND SUPPLY FACTORS IN EXPLAINING THE INNOVATIVE ACTIVITY OF SWISS MANUFACTURING FIRMS

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An analysis based on input-, output- and market-oriented innovation indicators * SPYROS ARVANITIS and HEINZ HOLLENSTEIN Centerfor the Research of Economic Activity, Federal Institute of Technology, Zurich, Weinbergstrasse 35, CH-8092 Zurich, Switzerland (Final version received 9 November 1993) The old question of the relative importance of supply and demand factors in explaining the innovation behaviour of firms still remains only partially answered, above all because of an unsatisfactory specification of the supply side variables, which primarily reflects data deficiences. Our investigation directcd to this problem is based on a simple neoclassical model of the firm's innovation decision. The empirical estimates with various input-, output- as well as market-oriented innovation indicators yield a quite robust basic pattern of explanation. It is concluded that - in the Swiss case - supply factors play the dominant role. In addition, it is shown that ordered categorical measures of innovation can successfully be employed to evaluate theoretical propositions on the innovative activity of firms.

KEY WORDS: Innovation behaviour of firms, supply vs. demand factors, appropriability, technological opportunity, several types of innovation indicators, qualitative response models

1. INTRODUCTION There is a wide consent in economic literature that demand growth potential, type and intensity of competition, market structure as well as factors governing the production of knowledge (appropriability of the returns on innovations, technological opportunities in the relevant fields of activity) are the main determinants of the innovation activity of the firm (Dasgupta, 1986; Dosi, 1988; Cohen and Levin, 1989). However, most of the empirical evidence does not allow to determine the relative importance of the groups of factors which are associated with the "demand-pull" (Schmookler, 1966) and the "technologypush" hypotheses (Phillips, 1966; Rosenberg, 1976) respectively. Empirical work directed to this matter typically suffers from an unsatisfactory specification of the supply

*The research reported on in this paper was supported by the Swiss National Science Foundation under National Research Program No. 28 and the Federal Ministry of Economic Affairs. The comments of an anonymous referee and the Journal Editor on a previous version of the paper are gratefully acknowledged.


16

S. ARVANITIS AND H. HOLLENSTEM

factors, primarily reflecting a lack of suitable data.' Hence, the first objective of this paper is to investigate empirically the relative merits of these two propositions. Most studies on the determinants of innovative activity are based on a single indicator, predominantly R&D-expenditures or the number of patents.' In contrast, our work includes a whole set of innovation variables, which is intended to measure the generation of new products and processes at different stages (input-, output- and market-oriented indicators). This procedure should allow to separate robust from shaky relationships; in addition, significant differences of the model estimates with respect to specific innovation variables could help to define an empirically validated range of applicability of the underlying model. The empirical investigation of these two topics - relative importance of supply and demand factors in explaining innovative activity and sensitivity of the results to the chosen innovation measures - is based on a simple model of a firm optimizing its innovation output with respect to products as well as processes. It takes account of demand and market conditions, the appropriability of knowledge and technological opportunities. The model is of neoclassical type and builds primarily on the work of Dasgupta and Stiglitz (1980), Stoneman (1983) as well as Levin and Reiss (1988). It primarily envisages incremental innovations, i.e. improvements and modifications of existing basic technologies or products with a certain range of basic characteristics. - The data for the empirical tests are from a survey on innovative activity of Swiss manufacturing firms conducted in November 1990, which was based on a stratified random sample (three firm size classes). About 2700 firms were asked to fill up a questionnaire about their innovating activities in the period 1988-90. We have received valid answers from 687 firms (of which I67 non-innovators) representing 23.4% of total employment in the Swiss manufacturing sector. The final data set includes enterprises from all fields of activity and size classes and may be considered as representative of the Swiss industry mix; however, the data show a certain bias towards larger firms. The information collected is mainly qualitative (primarily ordinal) in nature. This holds for the innovation indicators as well as the explanatory variables. Accordingly, the cross-section analysis of the individual firm data is predominantly based on qualitative response models. The set-up of the paper is as follows: Section 2 sketches the underlying theoretical background. The empirical model is presented in Section 3; in addition to the specification of the different types of innovation and explanatory variables, two versions of the empirical model which differ with respect to the treatment of the technological opportunities are defined. Section 4 presents a selection of the model estimates; a complete documentation is given in Amanitis and Hollenstein (1992), to which we refer in what follows as AH92. In the final section we draw some conclusions with respect to the relative importance of the different determinants of innovative activity, the robustness of the explanatory pattern found ancl the usefulness of empirical tests based on qualitative data.

'See Scherer (1982), Bosworth and Westaway (1984). Kleinknecht and Verspagen (1990). Griliches el al. (1991); exceptions are Cohen er 01. (1987). Cohen and Levinthal(1989) and Jaffee (I 988, 1989). =Othervariables such as the number of innovations (Acs and Audretsch, 1988). the dichotomous variable "innovation nolyes" (Zimrnerrnann, 1989) or the sales share of new products (Kraft, 1990) have been used only sporadically.

DEMAND AND SUPPLY FACTORS IN INNOVATION

17

2. THEORETICAL BACKGROUND


We used as theoretical framework a simple static deterministic model of a firm optimizing its innovation output separately for new products and new processes under monopolistic competition; the model comprises the most important determinants of innovative activity as seen in the literature.' The omission of an explicit treatment of dynamic and uncertainty aspects seems to be a reasonable simplification for the theoretical setting of an empirical investigation based solely on cross-section data. The model contains the usual production and sales functions complemented by an innovation cost and a knowledge production function with intra- and extramural knowledge as inputs; in this context the appropriability of knowledge or - viewed the other way round - know-how spillovers are taken into account. The innovation output is conceptualized as costreducing in the case of new processes and demand-creating for new products. For process innovations, the demand for the firm's output q is represented by

The shift parameter s stands for pure income effects as well as demand influences resulting from inter-firm differences with respect to the intensity of non-price competition. The production costs c (no fixed costs, constant returns to scale) are given by ~ = b w - ~ q

1 > a > O and b > O

(2)

and depend - in addition to the quantity produced - on the innovation output w used as an input in the production process. The parameters b and a, together with r to be introduced below, are considered to reflect dimensions of (cost reducing) technological opportunities. The innovation costs k for new processes (as well as for new products) are simply a linear function of the innovation output w : ~

For product innovations, the demand for the firm's output is modelled as follows:

q = s t p-e

e>O

with t=bwa

l > a > O and b > O

The demand schedule contains, as an additional (demand shifting) element, the product quality t, which is improved by product innovations. Production costs are represented simply by

Next, a knowledge production function is added to the model. The innovation output w is produced with two inputs, "raw" (intramural) knowledge x generated in the firm itself and "raw" (extramural) knowledge xe acquired from outside. The inputs are "raw" in the 'See Arvanitis (1991) for a detailed description of the model. 'Assuming increasing marginal costs does not change the basic results given below.

S. ARVANITiS AND H. HOLLENSTEIN

18

sense that they cannot be directly used for marketable new products or processes. For the present purpose it is sufficient to assume a Cobb-Douglas-function?


w = x -' (h xe)'

O < r < l and O < h < l

(7)

with r as the elasticity of innovation output with respect to extramural knowledge (representing a further dimension of technological opportunity) and h as the portion of external knowledge that can be absorbed by the firm. The degree of appropriability of the firms' own knowledge 1-h has an impact on the innovation activity by its influence on the incentive structure of the enterprise. Profit maximization given (I), (2), (3), (7) for process and (3), (4), ( 5 ) , (6),(7) for product innovations yields first order conditions with respect to q and x, which allow to write x as depending on s, a, b, e, v, r and h. A further important determinant to be considered is, according to one of the Schumpeterian hypotheses, market concentration. It is reasonable to assume in a cross-section study, that the market structure, represented here by the inverse number of firms in the market 1111, is fixed in the short rum6 Taking account of all elements described so far the firm's innovative activity x is determined by the following factors: x = f (s, e, l/n, 1-1, b, a, r, v)

(8)

Comparative statics yield the signs indicated in (8);' wherever two signs are given, the first one refers to process, the second to product innovations. The signs seem economically plausible, although there are certain problems with respect to the price elasticity of demand e, which basically result from the static character of the model. It would be more appropriate to treat e as endogenous, i.e. determined among other things by the innovative activity. In a static model, the impact of e can be rationalized by interpreting it as the elasticity anticipated by the firm.In this sense, process innovations are positively affected by the price elasticity (a high e induces cost-reducing innovations); product innovations are inversely related to e ( a low elasticity is an incentive for the generation of new products). A specific feature of this theoretical approach is the absence of firm size as an explanatory variable. Firm size can be used as a proxy for various economic effects connected with the innovation process, but the literature does not offer a standard economic interpretation of the size effects. Therefore, it seems reasonable to concentrate on the explanatory power of variables with a clear theoretical background. An alternative research strategy could be to investigate directly hypotheses relating to different types of such size effects, something we do not do in this paper.s 'For the implications of other functional forms see Arvanitis (1991). to include concentration in a formal model of the type discussed above, but for slightly different market conditions, that is under the assumption of a Cournot-oligopoly; it can be shown, that the results obtained so far remain valid also for that case. T h e character of the model allows only to establish the sign and not the magnitude of the individual effects. BRegressionestimates with firm size as an additional explanatory variable not presented here show, that it correlates rather strongly with most innovation variables. However, there is also sufficient correlation with some of the explanatory variables to support our suspicion of firm size being an unspecific proxy for too many things (for details see AH92). 61t is possible

DEMAND AND SUPPLY FACTORS 1N INNOVATION

Table 1 lnnovation Variables Variable

Definition

1. Input-oriented measures INNIN R&D input requirements

RDEQ

R&D expenditures to sales ratio (%)


2. Output-oriented measures INN0 Innovation nolycs

Measurement Scale/ Response Levels

Value Range

ordinal 1 : const~ction/design 2: development 3: research

0.3

metric

0, 100

nominal

0, 1 0.3

IASST

Technological assessment of innovations

ordinal 1: low importance 2: intermediate importance 3: high importance

IASSE

Economic assessment of innovations

ordinal (see IASST)

INNEW

Novelty of innovations

ordinal 1 : substantially improved product 2: firm novelty 3: Swiss novelty 4: world novelty

ITECH

Technical features of product innovations

ordinal I: use of new matererials or intermediate products 2: new functional solution based on existing product 3: fundamentally new product ordinal 1: new production technique 2: automation 3: fundamentally new production system

Technical features of process innovations

3. Market-oricnted measures NEW Sales share of products in in the introduction stage of the life cycle (%) NEWGROW Salcs share of products in the introduction and growth stage of the life cycle (%)

0, 3

metric metric

All variables except INNO. NEW and NEWGROW have been measured scpantcly for p d u c t and process innovations. The value range is complemented for all qualitative variables by the response level 0,i.e. the non-innovalinglirms. The response levcls for ITECH were chosen on the basis of considewtions put f o m d by Schmalholz and Scholz (1985).

3. THE EMPIRICAL MODEL

A characteristic of this paper is the parallel use of input-, output- and market-oriented indicators of innovative activity. Table 1 lists the innovation variables used, most of them

S. ARVANITIS AND H. HOLLENSTEIN

20

Table 2 Specification of the Explanatory Variables Vuriuble

Description /Economic Interpretation

Sign

1. Demand Medium-term expected change of demand in 1985 (four point scale: decrease, no change, growth, strong growth)


2. Market Conditions PP

Intensity of price competition in the product market: negative (positive) sign expected for product (process) innovation

NPP

Intensity of non-price competition in the product market (scores of a factor analysis with six dimensions of non-price competition)

CONC

Concentration measure based on the number of competitors in the product market (three point scale: more than 50, between 16 and 50, less than 15 competitors)

-I+

3. Appropriability APPRPDPC

Extent to which innovations can be protected from competition (stronger effect expected for new products)

4. Technological Opportunities

TPOT

Technological potential,le. scientific and technological knowledge relevant to the firm's innovative activity

EXTINTPDK

Contribution of external knowledge to the firm's own innovative activity (for model 1 only)

+

EXTINTPDPC is replaced in model 2 by measures for the relevance of the following external sources of knowledge:

+

USERPDPC

Users of the firm's products; positive effect expected for product innovations

SUPPPDIPC

Suppliers of materials and cquiprnent; positive effect expected for process innovations

COMPPDPC

Competitors

?

UNIVPDPC

Universities/research laboratorics, scientific journals

+

ASSOCPDPC

Business associations, conferences

?

EXPERTPDPC

Recruitment of' experts

PATPDPC

Patents, licences

+ +

COOPPDPC

Cooperating firms (ioint ventures, subsidiaries, etc.): dummy variable

?

If not otherwise specified. the variables reflect asscssmcnts of thc survcyed firms measured on a five point scale. The postfix P D K indicates a specific measurement of the explanatory variables for product and p w e s s innovations respectively.

differentiated for product and process innovations, and shows how they are measured. Some of these indicators have been used in previous studies (RDEQ, INNO, NEW, NEWGROW). Other measures (INNIN, INNEW, ITECH) have been included in innovation surveys for certain countries, but - to our knowledge - they have not yet served as dependent variables in empirical models; and the two overall assessment variables IASST and IASSE seem to be novelties.



21

Table 2 summarizes the relevant information with respect to the four groups of explanatory variables included in the empirical model. Demand and market conditions are measured by D, PP, NPP and CONC, where D and NNP reflect different kinds of demand shifts s, PP is used as a proxy for the price elasticity e, and CONC stands for the inverse of the number of competitors lln. The degree of appropriability of the firm's knowledge 1-h is measured by APPR. One of the technological opportunity factors distinguished in the theoretical model is represented by the technological potential TPOT (corresponding to cost or quality shifts b); the innovation elasticity of external knowledge r, another dimension of technological opportunity, is proxied by either an overall measure for the contribution of external knowledge to the firm's own innovative activity EXTINT (model 1) or a set of external knowledge sources USER, ..., COOP (model 2).9 Most of the variables shown in Table 2 reflect assessments of the surveyed firms on a five point scale, which is assumed to be an interval measurement. Some other variables are ordinal (D, CONC) or nominal (COOP) in nature, and NPP is the outcome of a factor analysis based on firms' assessments of six dimensions of non-price competition. The sign expectations as given in the last column are derived from the theoretical model, except those for the knowledge sources USER, ..., COOP, which reflect either the results fromempirical work for the USA (see next paragraph) or ad hoc considerations. The basic structure of the empirical model is similar to that used in the few other studies which also cover the whole range of (potentially) relevant determinants of innovative activity. In this respect Cohen et al. (1987) and Cohen and Levinthal (1989) are the investigations best suited for a comparison; they draw - as some other papers such as Levin et al. (1985), Nelson (1987) or Levin and Reiss (1988) - on a data base developed at Yale University (Levin et al. 1987). However, there are some differences in model specification which should be mentioned here: whereas the American authors aggregate the data of the surveyed firms on the level of "lines of business" to get a correspondence with official R&D-data, we estimate models with firm data. As a consequence, the US studies include existing estimates for industry specific income and price elasticity measures to catch the demand factors, whereas we construct proxies of these measures on the basis of the survey results (D, PP). In addition to these demand and price effects we take into account the intensity of non-price competition (NPP). Besides, the firm-specific number of principal competitors on the relevant (international) markets (CONC) is used as an indicator of market concentration, which seems to be more appropriate than such traditional measures as the concentration ratio or the Herfindahl-index referring exclusively to the home market. With respect to the supply-oriented variables, several differences are worth mentioning; by asking directly for the degree of protection of innovations against competitors, we use a less sophisticated measure of appropriability (APPR) than the US authors, who define appropriability measures based on the effectiveness of specific strategies to preserve an innovatory lead. The technological opportunities are represented in both countries by two dimensions, the relevance of a series of science fields and sources of knowledge in the US case, the technological potential as assessed by the firm (TPOT) and a set of knowledge sources in model 2 of our approach (USER, ..., COOP), whereas model 1 takes account of the external knowledge only in an aggregate form (EXTINT). An important reason for not relying on the relevance of science fields lies in the presumption, that this would produce a misleading measure in the Swiss case; in a small country, know-how production is necessarily selective in nature and concentrates in 9No measures are available in our data set for a, the elasticity of costs (demand) with respect to innovation output, and for v, the unit costs of innovation.

22

S . ARVANITlS AND H. HOLLENSTEIN

many fields on the absorption of knowledge produced in large countries, which have the capacity to move on the frontier of technological progress over the whole range of scientific fields. Depending on the innovation variables used, different estimation methods have been used. Censored models (tobit) were estimated for the metric variables, to take account of the fact that non-innovating firms are included in the saniple. In the casc of the qualitative variables, we employed the probit procedure for the dichotomous response variable INN0 and the ordered logit model for the various polychotomous variables.1° 4. EMPIRICAL RESULTS


4.1 The Basic Pattern of Model Estimates

The estimates with the dichotomous innovation variable INN0 reveal a pattern which is confirmed by the findings for many of the more differentiated measures. Accordingly, we define a "basic pattern" using the results for INN0 (Table 3) and take it as a reference for the discussion of the estimates with the other innovation indicators (Section 4.2). According to the results for both model versions and for product as well as process innovations, the four groups of explanatory variables hypothesized by the theoretical model are all important. The impact of the variables representing the appropriability (APPR) and the two dimensions of the technological opportunities - TPOT as well as EXTINT and USER, ..., COOP resp. - is strikingly strong. Whereas the medium-term demand expectations D are also highly significant, the effect of the variables describing the structural conditions on the product market - price and non-price competition (PP, NPP) as well as market concentration (CONC) - is weak and not very robust. On the whole, the more supply-oriented determinants of innovations seem to be more relevant than the variables to be attributed to a primarily demand-oriented explanation. A more detailed inspection of the results of Table 3 leads to further interesting findings. First, the impact of the appropriability is surprisingly not less pronounced for process than for product innovations. Second, with respect to the technological opportunities, the contribution of external knowledge proves to be stronger than that of the technological potential in both model versions, presumably owing to the less (firm-)specific nature of the latter dimension (the effects of the different sources of external knowledge are discussed further below). Third, some remarks have to be made concerning the impact of the market variables. The intensity of non-price competition yields the expected positive sign for both types of innovation. On the other hand, the results for the intensity of price competition contradict the model implication in the case of product innovations where the estimated coefficient is significantly positive. This outcome could be rationalized on an ad hoc basis: the average firm seems to evade intensive price competition through innovations, which lead to low price elasticities in the short run; these become large again under the pressure of competition (diffusion of innovation). Concentration in the product market has no significant impact on the innovation behaviour; however, this effect may be somewhat underestimated because of a certain 'The estimation procedure chosen is strictly appropriate only if the explanatory variables are measured on an interval scale, which is, as mentioned, not the case for some of them. Therefore, we produced alternative estimates for selected innovation variables, where each independent variable was specified as a set of dummy variables; however, the results do not differ much from those presented in this paper. The robustness of the estimates is also confirmed by tests with rescaled independent variables (collapsing the number of measurement categories).


23

Table 3 Probit Estimates with INN0 (Model 1.2) Explanatory Variables

Product I

INN0

2

Process 3

4

Intercept D PP NPP CONC APPRPDPC


TPOT EXTINTPDIPC USERPDrPC SUPPPDA'C COMPPDPC UNIVPDPC ASSOCPDlPC EXPERTPDPC PATPDPC COOPPDPC

N Mc Fadden R2 Schwartz SC Somers' D

55 1 .I03 568 ,439

The equations I and 2 were cbtimsted with data for product innovations for the explanatory variables, the equations 3 and 4 with pnxess innovation data. Each column includes the estimated parameters and the standard errors in brackets: the statistical significance of the parameters (Wald ChiSquare-test) is indicated with ***. **. * resp. representing the I%-, s%-, and 10%-level.

amount of correlation between concentration and appropriability." Fourth and last, favourable demand prospects seem to stimulate process innovations even more than the generation of new products. "This correlation may reflect measurement effects, because it cannot be excluded, that the respondents of our survey did not distinguish clearly between the knowledge protection based on factors related to technology (first mover or learning curve effects, patents, etc.) and those connected with market power. However, additional model estimates with either CONC or APPR alone showed, that appropriability in the proper sense clearly dominates.


24

S. ARVANlTlS AND H. HOLLENSTEIN

Not all of the external sources of information seem to be of equal importance for innovative activity (model 2 estimates). In the case of product innovations, user contacts are clearly the most relevant ones; in addition, universities and patentsllicences contribute significantly to the generation of new products. For new processes, the knowledge acquired through relations with suppliers and experts is highly important, whereas contacts to universities play only a marginal role. Other potential knowledge sources -competitors, professional and business associations, cooperations with other firms or other units of the same enterprise - are insignificant1*. Complementary estimates of both model versions with two-digit industry dummies as additional regressors improve somewhat the model fit. However, these dummies practically do not interact with the firm-specific explanatory variables (with the exception of D), leaving the basic pattern almost unaltered. Hence, there does not seem to exist an omitted variable bias of any importance. A comparison with similar US studies (see above) shows analogous results with respect to demand (positive impact), appropriability (positive, but seemingly stronger and more robust effect in the Swiss case), and technological opportunities (positive, presumably stronger impact in our study). As far as the individual external knowledge sources are concerned, one notes that user contacts are highly relevant in both countries, whereas university knowledge seems to be of greater importance in the USA; this is not surprising given the low weight of public research laboratories and programmes directly geared to industry needs in Switzerland and the different innovation strategies of the two countries." The other sources of external knowledge of importance to Swiss manufacturing are insignificant (supplier contacts) or not analysed (expert knowledge, use of patents and licences) in the US case. With respect to market conditions, the US studies show a negative impact of the price elasticity of demand, whereas we found a positive sign (though only for product innovations); the comparison is inconclusive (concentration) or impossible (no variable for non-price competition in the US case) for the other market structure variables. In sum, given the limits of comparability, the results are not very different for the two countries: the dissimilarities seem to be concentrated on the market variables. 4.2 Differences as to the Type of Innovation Variables

Estimates for all polychotomous or quantitative innovation indicators of model 1 and model 2 are available in AH92. In what follows, we present selected results of model 2 estimates (Table 4a and 4b).I4 Product innovations Table 4a shows model 2 estimates for input- (cols. 1, 2) , output- (cols. 3 to 6 ) and market-oriented (cols. 7, 8) indicators of product innovation, some of them quantitative (cols. 2, 7, 8), others qualitative in nature. In general, the resulting pattern of explanation does "Significantly negative signs for specific knowledge sources are interpreted as an indication of "no impact". "Dominance of fundamental, sciencc-based innovations in the USA vs. preponderance of incremental skillbased developments using existing basic knowledge in Switzerland, with the notable exception of the chemical industry (Ergas 1987; Hotz-Hart and Kiichler 1992). I4It is shown in AH92 in detail that estimates of separate slope parameters for each response level of the polychotomous innovation variables yield some additional useful information not discussed here. Such estimates can be used, for example, to determine empirically the appropriate number of response levels of such variables and to evaluate (given the underlying theoretical modcl) the innovation performance of a firm more reliably than just using the dichotomous variable INNO.


25

Table 4a Product Innovations: Tobit and Ordered Logit Estimates with Input-, Output- and Market-oriented Measures (Model 2) -

Explanatory Variables

INNIN I

RDEQ

49 1

343 141 606

2

-

-

IASSE 3

IASST 4

INNEW 5

ITECH 6

NEW 7

NEWGROW 8

494

490

494

485

483 111 1607

483

Intercepts

D


PP NPP CONC APPRPD TPOT USERPD SUPPPD COMPPD UNIVPD ASSOCPD EXPERTPD PATPD COOPPD

N Censored -2 log L McFadden R2 Schwartz SC Somers' D

,087 1189 ,408

,073 1311 ,365

.097 1270 ,422

,080 1519 ,391

59 2102

,101 1253 ,439

Censored models were estimated for RDEQ, NEW and NEWGROW, whereas the ordered logit procedure was used for the other variables. For the definition of the significnnce levels see table 3.

not vary too much among the input- and output-oriented variables used, whereas the market-oriented indicators yield different and statistically less satisfying results. The findings for input-oriented measures are closer to those for INN0 than the estimates for output indicators.


26

S . ARVANITIS AND H. HOLLENSTEIN

The results for input-oriented indicators correspond to a large extent to the basic pattern described in the previous section.I5In addition, the results for qualitative and quantitative measures of innovation (col. 1 vs. 2) do not differ very much. Additional estimations with left-hand variables for the individual types of input requirements (research, development, design/ construction) not reported here show that research is explained primarily by the appropriability and the demand variable (in this case interpreted as a proxy for the availability of research funds), whereas the other two input requirements follow closely the basic pattern; this result is not surprising in view of the casual observation of research, the first stage of the innovation process, being weakly connected with the market-oriented activities of a film. The findings for output-oriented variables show a stronger influence of the supply-side determinants of innovative activity; this holds most clearly for ITECH. The similarity across various types of output measures with respect to the core variables seems to be even higher than for the input measures, although the variables represent quite different measurement concepts. The model estimates are better for the technology-oriented indicator IASST than for the performance-oriented measure IASSE. The results for INNEW are somewhat blurred by an unconvincing a priori-ranking of the response levels; according to detailed estimates reported in AH92, the variable INNEW could be recoded into four (instead of five) ordinal groups ranked as follows: world novelty, Swiss novelty and substantial improvement of an existing product, firm novelty, no innovation. The results for the two market-oriented indicators used are quite different. Whereas NEW shows some similarity to the basic pattern, the findings for NEWGROW contradict clearly the explanatory model. The estimation results for the latter variable (positive demand, negative price effect), which is based on the sales not only in the introductory but also in the growth stage of the life cycle, show that it is no adequate innovation indicator; it seems mainly to catch market development effects. The quality of the estimates with these market-oriented indicators, whose usefulness is emphasized, for example, by OECD (1992) and Eurostat (1992), is not convincing. This reflects not only conceptuai problems but may also indicate that the relationship between a (technical) innovation and the success on the market place is not straightforward. Process innovations The results for process innovations (Table 4b) correspond even stronger to the basic pattern than those for new products. An exception is the estimate for ITECH, where the results indicate a misspecification with respect to the a priori ordering of the response levels. The quality of the estimates is somewhat lower than that of the equivalent equations for product innovations. This difference is presumably attributed to the fact that the introduction of new processes quite often does not mean much more than buying and installing new machinery; in this case the innovation decision becomes more or less an ordinary investment decision. A detailed analysis based on the individual response levels not presented here shows that this is partly the case. However, there are also types of process innovation for which the intramural innovation input of a firm is substantial; examples are processes new for the world or new for the Swiss market as measured by the variable INNEW. Therefore, the qualitative categories used for process innovations may not be as convincing as in the case of new products. 'sEstirnates based on a wider concept o f input requirements R&D-expenditures - yield no convincing results.

- addition

of various follow-up costs to the


27

Table 4b Process Innovations: Tobit and Ordered Logit Estimates with Input- and Output-oriented Measures (Model 2) p p

-

INNIN I

RDEQ 2

IASSE 3

IASST 4

INNE W 5

ITECH

Intercepts

-1.8** (.73) -2.0*** (.73) -3.8*** (.75)

-5.3*** (1.3)

-3.8*** (.73) -2.4*** ~72) -1.7** (.7 1)

-3.6*** ~72) -2.6*** (.72) -2.0*** (.71)

-2.0*** (58) -0.5 057) 0.4 (.67)

D

.37*** (.13) .03 (.09) .16* (.lo) .04 (. 13) .35*** (.09) .19* (.lo) -.17* (.09) .03 (.08) -.02 (.09) .14* (.OW -.I I (.@) .17** (.OW .05 (.lo)

.65*** (.22) .20

.30** (. 12)

-.I3 (.lo) .05 (. 18)

.37*** (. 12) .09 ~09) .18* (.09) .03 (. 13) .29*** (.08) .I I (.09) -.20** (.09) .20** (.08) -.04 (.09) .09 (.08) -.01 ~09) .18** (.OW -.09 (.lo) .22 (.19)

-1.7** (.69) -2.9*** (.70) -4.7*** (.71) -5.5*** (.73) .30***

480 .064 1302 .356

PP


- -

Explanatoiy Variables

NPP CONC APPRPC TPOT USERPC SUPPPC COMPPC UNIVPC ASSOCPC EXPERTPC PATPC COOPPC

.09 (.20)

N Censored -2 log L McFadden R2 Schwartz SC Sorners' D

447 ,067 1 1 13 .365

.32* (.17)

.20 (25) .39*** (.14) .23 (.17) .O 1 (. 14) .2 1 (.14) -.35** (. 16) .06 (.16) .03 .09 (.14) -.08 (. 17) .45 (.33) 322 134 517

.00 ~09) .11 (.09) -.01 (. 13) .30*** (.OW .29*** (.@) -.15' (.08) .05 (.08) -.03 (.13) .15* (.08) -.02* (.09) .29***

.07 (.ow .I2 ~09) .08 (. 13) .32*** (.OW .I3 (.09) -.I3 (.08) .12* ~07) -:07 (.08)

6

.I2 (.I21 .06 (.ow .23** t.09) -.09 (.I21 .09 c.08) .05

(. 18)

-.07 (.08) -.07 (.08) -.06 (.08) .I1 (.OW -.I3 ~09) .26*** (.07) -.I4 ~09) .15 (.18)

487

482

486

,059 1262 ,343

.05 1 1465 .322

.03 1 1391 ,246

.04

(.OW -.12

(.09) .22*** (.07) .04 .13

Censored models were estimated for RDEQ, whereas the ordered logit pmedure was used for the other variables. Fur the definition of the significance levels see table 3.

A final remark refers to the indicators IASST and IASSE. The model fit for both variables is about the same for new processes, whereas the technology-oriented indicator performs better in the case of product innovations. This difference may reflect the fact that for new products there is not necessarily a high correspondence of technological change

S. ARVANITIS AND H. HOLLENSTElN

28

Table 5 Decomposition of the Explained Variance by Group of Variables (as a percentage of explained variance) Group of Variables

Product innovation output tneusures input tneusures I 2 I 2

Process it~novation output measures it~puttneasures I 2 I 2

Demand

9

6

4

4

17

13

21

15

Market Conditions

9

3

16

7

8

4

8

5

Appropriability

52

45

63

58

28

29

46

50

Technological Opportunities

30

46

17

31

47

54

25

30


The figures for output-oriented estimates are based on the cqualions for INNO. IASSE. IASST, INNEW, ITECH (unweighted mean of lhc yercmlages), for input-orientedmeasures on INNIN. Equivalent information from tobit estimates (IUEQ, NEW. NEWGROW) an- not available for iechnical reasons. Both model versions. indicated by I and 2, are u.wd in the calculations for each type of measurement.

and economic return.I6 In contrast, new processes are quite directly linked to higher returns through cost reductions andlor higher production flexibility.

5. CONCLUSIONS The innovation behaviour of Swiss manufacturing firms with respect to new products as well as new processes is characterized by a basic pattern which seems to be quite robust across various model estimations. The main deviations from the general pattern show up in estimates for innovation indicators related to the very first (basic research) and the last stage (market creation and development) of the innovation process. Except for these two types of activity, where a separate specification may be necessary, the underlying model postulating the relevance of four groups of variables - i.e. demand, market conditions, appropriability, technological opportunities - seems appropriate. It has to be reminded that the usefulness of the model is limited to an explanation of (regular) movements along a given technological path (incremental innovations); an explanation of fundamental technological breakthroughs lies beyond its scope. The empirical work reported on in this paper shows, that categorical measures of innovation, which have not been used very often, can successfully be employed to evaluate theoretical propositions on the innovation behaviour of firms. The experience with (ordered) multi-response models turned out to be encouraging. The empirical basis for the analysis of innovation processes can be enlarged substantially by using categorical data, because it is easier to get this type of information. What are the conclusions with respect to the relative importance of supply and demand factors in explaining innovations? Table 5 points to a clear dominance of the supply-oriented factors (appropriability, technological opportunities) as against the impact of demand and market conditions. This result is more pronounced for product than for process innovations and is quite independent of the type of innovation measure as well as "Technologically new products may not (yet) be of much econon~icimportance (small markets, existence of narrow substitutes); on the other hand, even minor product changes can lead to economically significant improvements of the firms' market position.


29

the model version used. In sum, the results are more in accordance with the "technologypush" than the "demand-pull" hypothesis. A strict comparison with other work directed towards the analysis of the relative merits of these two hypotheses is hampered by differences of model specification. The majority of such studies is inconclusive because of an unsatisfactory specification of the supply-oriented factors reflecting in most cases data deficiencies. According to the US studies taking account of the whole range of (potentially) relevant determinants of innovative activity, supply factors play a more important role than demand and market conditions; however, the dominance of the supply variables seems to be more pronounced in the Swiss than in the US case.


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30

S. ARVANITIS AND H. HOLLENSTEIN


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