Absorptive capacity—one size fits all? A firm-level analysis of absorptive capacity for different kinds of knowledge

MANAGERIAL AND DECISION ECONOMICS

Manage. Decis. Econ. 31: 1–18 (2010)

Published online 28 May 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/mde.1423

Absorptive Capacity}One Size Fits All? AFirm-level Analysis of Absorptive Capacity

for Different Kinds of Knowledge

Tobias Schmidt*

Center for European Economic Research (ZEW), Department of Industrial Economics and International Management,P.O. Box 10 34 43, D-68034 Mannheim, Germany

This paper empirically analyzes the effect of R&D activities, human resource and knowledge

management, and the organization of knowledge sharing within a firm on the absorptivecapacity of innovative firms for three different types of knowledge, namely absorptive capacity

to use knowledge from a firm’s own industry, knowledge from other industries and knowledge

from research institutions. Using data from the German innovation survey, we investigate how

firms are able to exploit knowledge from external partners for successful innovation activities.The estimation results show that the determinants of absorptive capacity differ with respect to

the type of knowledge absorbed for innovation activities. In particular, we find that the R&D

intensity does not significantly influence absorptive capacity for intra- and inter-industryknowledge. Additionally, our results suggest that absorptive capacity is path dependent and

firms can influence their ability to exploit external knowledge by encouraging individuals’involvement in a firm’s innovation projects. Copyright # 2009 John Wiley & Sons, Ltd.

INTRODUCTION

Since Cohen and Levinthal (1989, 1990) publishedtheir seminal work on ‘absorptive capacity’ (AC),a lot of empirical and theoretical work has beendevoted to analyzing the AC of firms. However,the use of the concept of AC has not been limitedto the firm level; it ranges from the level of theindividual to that of entire nations (see Van DenBosch et al., 2003; Narula, 2004). These levels areintertwined, as a nation’s AC depends on that ofits organizations and the AC of an organizationdepends on that of its individuals (Cohen and

Levinthal, 1990). The AC concept has proven tobe flexible enough to be used not only for differentunits of analysis but also in many fields ofresearch, e.g. industrial organization, strategicmanagement, international business and technol-ogy management (see Zahra and George, 2002, foran overview). Despite this wide application of theconcept and various modifications of its specificfeatures,1 AC has been used in most cases as afirm’s ability to ‘identify, assimilate and exploitknowledge from the environment’ (Cohen andLevinthal, 1989, p. 569).2

The empirical operationalization of the concepthas not been focused, although, mainly because itis hard to construct good measures of AC from theavailable information.3 One reason for this is thelack of a paper trail for the acquisition of externalknowledge which could be tracked and used by

*Correspondence to: Center for European Economic Research(ZEW), Department of Industrial Economics and InternationalManagement, P.O. Box 10 34 43, D-68034 Mannheim,Germany. E-mail: [email protected]

Copyright # 2009 John Wiley & Sons, Ltd.

researchers. A solution to this problem is the useof surveys. Surveys can and have been used forresearch on AC at the firm level. Even withsurveys, however, researchers are not able tomeasure AC directly because it is}despite itsrelatively simple definition}a fuzzy concept;practically no one can give a straightforwardindication of his or her level of AC. Using surveysthus requires developing an empirical concept ofAC. Popular proxies that have been used tocapture AC in recent empirical studies on theinnovation and cooperation behavior of firmsinclude R&D budgets, stocks and intensities(Stock et al., 2001; Cassiman and Veugelers,2002; Oltra and Flor, 2003; Belderbos et al.,2004), following up on the arguments presentedby Cohen and Levinthal (1989). Other proxies andmeasures (primarily used by researchers from thefield of business administration) include organiza-tional structure and practices, such as incentivesystems and human resource and knowledgemanagement (Van Den Bosch et al., 1999;Vinding, 2000; Lenox and King, 2004) and‘production line performance in terms of laborproductivity and conformance quality’ (Mukherjeeet al., 2000, p. 157).

The lack of a direct empirical measure of AChas not only caused some problems with thecomparability of research results;4 it has also led tolittle research ‘on the process by which AC isdeveloped’ (Lane et al., 2002, p. 5). This researchshortage on the determinants of AC was stressednot only by Lane et al. (2006), who reviewed about280 papers citing Cohen and Levinthal (1998,1990) but also by Veugelers (1997, p. 314).She writes that ‘More work is needed to identifyspecific firm characteristics generating this AC’.Mahnke et al. (2005) also state that there is alack of empirical literature on how a firm canincrease its AC.

In this paper, we try to fill the gap by empiricallyanalyzing the determinants of AC of innovativefirms. In order to be able to construct a moredirect measure of AC than previous studies, wepropose a focus on the results of AC instead of onthe inputs that are assumed to build AC. Usingdata from the German innovation survey(‘Mannheim Innovation Panel’ (MIP)), we areable to assess whether firms’ innovations incorpo-rate or are based on knowledge obtained fromexternal partners. We argue that firms thatintroduce innovations, which are based on ex-

ternal knowledge, necessarily have the ability toexploit knowledge from external sources, thusevincing ACs. We are therefore able to investigatethis component directly and separately from theother two components of AC (identification andassimilation of knowledge).5 However, a firmwhich is able to exploit external knowledge usuallyalso has the ability to identify and assimilate it.

This paper also contributes to the existingliterature by including measures for the existenceof human resource and knowledge managementand the organization of knowledge sharing withina firm. We also analyze the differences amongthese and other measures with respect to exploitingknowledge from within a firm’s industry, knowl-edge outside its industry and knowledge generatedby research institutes.

The following section will give a more detailedreview of the literature on the determinants of AC.In the third section we will outline the data andempirical setup used, before presenting the resultsin the fourth section. The paper ends with adiscussion on the findings and suggestions forfuture research.

AC IN RELATED LITERATURE

In this section, we review the relevant literature onabsorptive capacity of firms.6 We will first take acloser look at the definitions of AC, in particular,with respect to the three components of AC(identification, assimilation and exploitation).Afterwards we will focus on the determinants ofAC found in the literature and then discuss someof the findings for the acquisition and exploitationof different kinds of knowledge.

The Components of AC

AC at the firm level is relatively simple to define.Essentially, it is a firm’s ability to deal withexternal knowledge. According to the highlyinfluential definition offered by Cohen and Le-vinthal (1989), it is firms’ ability to ‘identify,assimilate and exploit knowledge from the en-vironment’ (p. 569). Other authors have used somemodifications of the concept (see Zahra andGeorge, 2002, for an overview) but have stillretained the notion that AC is not a one-dimensional concept, consisting rather of various

T. SCHMIDT2

Copyright # 2009 John Wiley & Sons, Ltd. Manage. Decis. Econ. 31: 1–18 (2010)DOI: 10.1002/mde

skills and dimensions. Lane and Lubatkin (1998)use the three components proposed by Cohen andLevinthal (1990) for their study on the prerequi-sites of a firm’s ability to learn from another. VanDen Bosch et al. (2003) also suggest defining ACas having three components ‘the ability torecognize the value of external knowledge, assim-ilate it, and apply it to commercial ends.’ (p. 280).

Determinants of AC7

The application of the AC concept in various fieldsand at various levels of analysis has led to theidentification of a whole array of factors which areassumed to influence AC. Most of these determi-nants come from theoretical considerations andempirical studies on the usage and management ofknowledge in R&D or innovation processes. Thesefactors can be assigned to the following threegroups:8

(a) R&D activities: Cohen and Levinthal (1989)focus mainly on the role of R&D expenditures inbuilding AC and point to the dual role R&D playsin the innovation process of firms: building ACand generating new knowledge and innovations.Many other scholars have thus used R&D-relatedmeasures and approaches to model AC at the firmlevel.9 Among them are:

* R&D expenditure: R&D intensity (R&Dexpenditure/total sales) (Cantner and Pyka,1998; Rocha, 1999; Stock et al., 2001) andlevel of R&D investment (Grunfeld, 2003;Leahy and Neary, 2007).

* Continuous R&D activities (Becker andPeters, 2000; Oltra and Flor, 2003).

* Existence of an R&D lab (Veugelers, 1997;Becker and Peters, 2000).

(b) Related prior knowledge and individuals’skills: In their 1990 paper Cohen and Levinthal,expand the concept and argue that AC is pathdependent because experience and prior knowl-edge facilitate the use of new knowledge. As aconsequence, AC is cumulative. This cumulativenature of AC has not been taken into account bymany empirical studies but has been extensivelydiscussed in the literature on knowledge andspillovers,10 which are closely related to AC.

The cumulative nature of knowledge may alsobe related to another determinant of AC: employ-ees’ level of education. The more education and

training an employee receives, the higher his or herindividual ability to assimilate and use newknowledge will be. As firms’ ACs depend on thoseof their employees, the general level of education,experience and training their employees have, hasa positive influence on firms’ level of AC. Rothwelland Dodgson (1991) found that (small) firms needwell-educated technicians, engineers and techno-logical specialists to access knowledge from out-side their boundaries. Frenz et al. (2004) take thisinto account in their analysis by including theshare of scientists and engineers in total employeesas well as training expenditures in their model.

In this context the presence of the so-called‘gatekeepers’ play an important role in determiningAC. Vinding (2000) submits that gatekeepers,whose role is to create a language that can beunderstood by different departments and to act as‘boundary spanner’ within the firm or as aninterface between the firm and its environment(Cohen and Levinthal, 1990), improve a firm’s ACthrough knowledge sharing.

(c) Organizational structure and human resourcemanagement practices: A firm’s AC is not thesimple sum of its employees’ abilities as Cohen andLevinthal (1990) argue. According to them, itdepends on the ability of an organization as awhole to stimulate and organize the transfer ofknowledge across departments, functions andindividuals. This aspect of AC has been incorpo-rated into many studies: It has been shown that theAC of a firm is determined by its expertise instimulating and organizing knowledge sharing(Van Den Bosch et al., 1999) and the similarityof any two cooperating firms’ systems for doing so(Lane and Lubatkin, 1998). Daghfous (2004)review yields that the organizational structure ofa firm and cross-functional communication havebeen found to improve AC if they lead toimproved knowledge sharing among departmentsand individuals within a firm (see also Lane andLubatkin, 1998; Van Den Bosch et al., 1999, 2003;Welsch et al., 2001). In addition, according toDaghfous (2004), organizational culture has apositive influence on the level of AC if it providesincentives for knowledge diffusion through theempowerment of employees and managers. Grad-well (2003) points to the strong influence of closenetworks and relationships within firms in stimu-lating the transfer of tacit knowledge.

Closely related to organizational structure andknowledge sharing are human resource and

ABSORPTIVE CAPACITY FOR DIFFERENT KINDS OF KNOWLEDGE 3


knowledge management. It can certainly helpfacilitate the flow of knowledge with a firm (Cohenand Levinthal, 1994; Jones and Craven, 2001;Mahnke et al., 2005). Human resource manage-ment can also help to stimulate learning throughreward systems and training (Daghfous, 2004;Mahnke et al., 2005). These actions lead to higherindividual ACs and, consequently, to a highercapacity of the organization as a whole. William-son (1967) argues that information gets lost or atleast distorted if it is transferred through differentlayers of hierarchy. Thus, direct contact amongemployees from different departments, units andthe like should lead to a more efficient transfer ofknowledge and a subsequently higher AC.

The review of the literature leads us to formulatethe following hypothesis, which will be testedempirically:

H1: R&D activities are not the only buildingblocks of AC. The organization and stimulation ofknowledge transfer within a firm as well as theemployment of qualified personnel play a criticalrole in determining the AC of firms.

AC for different kinds of knowledge

The determinants of AC discussed above focus onthe firm at the receiving end of the knowledgeexchange and how its structure and activitiesincrease or decrease AC. This is, however, onlyone side of the coin. Lane and Lubatkin (1998)make the case that a firm might not be able tolearn equally from each external firm, arguing thatcertain characteristics of the ‘student firm’, i.e. thefirm absorbing the knowledge, and of the ‘teacherfirm’}the firm providing the knowledge to betransmitted}have to be similar in order for thestudent firm to be able to learn. According to theseauthors, the ability to learn from an externalpartner (‘teacher’) depends, among other things,on ‘the specific type of new knowledge offered bythe teacher’ (p. 462). Dussauge et al. (2000) as wellas Cohen and Levinthal (1990) conclude that afirm is better able to acquire and use externalknowledge from areas it has some prior experienceor related knowledge in (path dependency of AC).Becker and Peters (2000) and Nelson and Wolff(1997) argue that firms need higher ACs forscientific knowledge than for other types ofknowledge. Mangematin and Nesta (1999) confirmthis result. They found that higher ACs increasethe ability to use more fundamental (as opposed to

applied) external knowledge and firms with higherAC have more contacts with research institutesthan firms with lower ACs.

All of these findings suggest that there aredifferent ACs or varying levels of AC required fordifferent kinds of knowledge, one distinction beingbetween science-based knowledge and knowledgefrom the private sector.

Our second hypothesis for the empirical part ofthe paper is thus given as follows.

H2: Different kinds of knowledge are associatedwith different ACs.

DATA AND EMPIRICAL

IMPLEMENTATION

To test the hypotheses mentioned above, we usedata from the German innovation survey, theMIP. This annual survey is conducted by theCenter for European Economic Research (ZEW)on behalf of the German Federal Ministry ofEducation and Research. The methodology andquestionnaire of the survey, which is targeted atenterprises with at least five employees, are thesame as those implemented in the CommunityInnovation Surveys. For our analysis we use the2003 survey, in which data were collected on theinnovation behavior of enterprises during thethree-year period 2000–2002. About 4500 firms inmanufacturing and services responded to thesurvey by providing information on their innova-tion activities.11 Almost 2000 enterprises indicatedthat they had introduced at least one product orprocess innovation in the reference period. Werestrict our analysis to firms which introducedinnovations between 2000 and 2002 because mostof the questions we use to construct our variablesare only available for innovating firms, in parti-cular the questions used to construct the depen-dent variables.

The 2003 MIP questionnaire provides us withdata with which we can analyze the factorsinfluencing the ACs of firms. Since AC cannot bemeasured by a single indicator, we construct ourmeasure using questions regarding impulses fromexternal actors12 used by firms to develop innova-tive products and processes.13 We argue thatsuccessfully using such external sources of innova-tion is a rather direct measure of the exploitationcomponent of AC. A firm which is able to pick up

T. SCHMIDT4


impulses from external parties and turn them intoinnovations is certainly able to exploit externalknowledge; it thus possesses ACs as well.

The firms were also asked to indicate whichindustries the suppliers and customers, providingthese innovative impulses, represented during thereference period. We use this information toconstruct different measures of AC for intra- andinter-industry knowledge. To test the hypothesisthat knowledge stemming from research institu-tions and universities requires specific ACs, wealso included a measure of exploitive ACs14 forknowledge from research institutes and universi-ties used for developing product and/or processinnovations.

The dependent dummy variables were con-structed in the following way:

* AC (Absorp)

One, if one of the ACs below equals one.

* AC for intra-industry knowledge (Absorp intra)

One, if at least one of the firm’s innovations (inthe period 2000–2002) has been developed andsuccessfully implemented because of impulsesfrom customers, suppliers or competitors fromthe firm’s industry (NACE 2).

* AC for inter-industry knowledge (Absorp inter)

One, if at least one of the firm’s innovations (inthe period 2000–2002) has been developed andsuccessfully implemented because of impulsesfrom customers or suppliers from industries otherthan its own (NACE 2).

* AC for scientific knowledge (Absorp science)15

One, if at least one of the firm’s innovations (inthe period 2000–2002) has been developed andsuccessfully implemented because of impulsesfrom universities or other public research insti-tutes.

The literature review yielded three main groupsof determinants for AC. In order to analyze theirinfluence on our dependent variables we includedthe following independent variables in our mod-el:16

R&D activities: We measure the R&D activitiesof firms along two dimensions: the continuity(continuous vs occasional) of their R&D engage-ments and their R&D spending as a share of totalturnover. With the first measure (R&Dcon) we tryto capture the path dependence of AC, as firms

which are continuously involved in R&D activitiesshould have developed skills and experience intheir specific fields of research. For firms whichengage in R&D only occasionally the amount ofrelated prior knowledge can be assumed to belimited or at least less than that of firms perform-ing R&D continuously. We thus expect that firmswith continuous R&D are more likely to have ACsthan other firms.

R&D intensity (R&D int), measured as share ofR&D expenditure in total turnover, is to a largedegree a measure of the scope of a firm’s R&Dcommitment. We assume the AC of firms to behigher the more they spend on R&D in a givenyear. We also include R&D intensity as a squaredterm (R&D int2). This variable is included be-cause we think that a firm which approaches thetechnological frontier, thereby researching at theforefront of its field, is no longer able to learnsubstantially from external parties. Hence, a largerpart of R&D spending is targeted at knowledgegeneration rather than for improving AC. Inessence, we think that R&D intensity does notnecessarily influence AC linearly, but might ratherexhibit a non-linear effect such that firms with veryhigh R&D intensities are less dependent onexternal impulses with respect to their innovationactivities.

Related prior knowledge and individuals’ skills:The existence of related prior knowledge withinfirms is hard to operationalize with the data wehave at hand, as we cannot determine in whichspecific field each firm does its research andpossesses previously accumulated skills and ex-perience. This determinant will thus only berepresented by the aforementioned variable forcontinuous R&D (R&Dcon).

Employee skill level can be fairly easily mea-sured by the amount of employees with highereducation degrees as a share of total employees(grads).

Organizational structure and human resourcemanagement practices: The literature providesevidence that the organization of knowledgetransfer inside a firm has a positive influence onAC. In order to be able to test our first hypothesiswe included several indicators of the way knowl-edge exchange is organized within a firm. Thesecan be divided into two groups: measures intendedto stimulate innovation activities and individuals’involvement in knowledge sharing, and collabora-tion between different departments. Both groups



can be seen as determinants of AC. While theformer provides information on a firm’s will-ingness and efforts to increase knowledge transferand exploitation (incentives), the latter is a bettermeasure of the actual knowledge transfer occur-ring between departments (organization); fromour point of view, this is the more importantdeterminant of AC. We thus include a singleindicator of the importance of measures meant tostimulate the involvement of individuals in knowl-edge sharing (stim index)17 and seven variablesfocusing on different means of collaborationbetween departments. Two different aspects ofcollaboration will be investigated: hierarchical andsporadic information provision (mostly involvingmanagers) as well as broad information provision(involving all employees). The former comprisesjoint development of innovation strategies(col jointstrat), regular meetings of departmentheads to discuss innovation-related topics(col heads), seminars and workshops for innova-tion projects involving several departments(col seminar), mutual support of other depart-ments with innovation-related problems(col mutsup) and temporary exchange of person-nel between departments for innovation projects(col exchange). The latter is represented byinformal contact among employees (col infor),open communication of ideas and conceptsbetween departments (col opencom).

Control variables: We also include a number ofcontrol variables, most importantly two measuresof size: number of employees (ln emp) andnumber of employees, squared (ln emp2). Thesetwo variables are meant to capture differences inAC among small and large firms. Small firmsmight not have the same means and opportunitiesto exploit external knowledge, simply because theycannot risk betting on the wrong horse. Largerfirms, on the other hand, often have multipleinnovation projects running at the same time andcan thus potentially exploit external knowledgebetter.

An additional dummy variable is includedindicating whether a firm is situated in EasternGermany (east), as Eastern German firms’ innova-tion behavior still differs significantly from that ofWestern German firms.18 It is thus reasonable toassume that AC also differs between the tworegions.

To control for industry influences that are notpicked up by other variables in the model, we

include six industry group dummies,19 with ‘othermanufacturing’ being the reference group.

The construction of the dependent variables hastwo noteworthy implications for the empiricalsetup: first, we are not able to observe the level ofAC directly, but rather only the existence of AC.We argue that we will nonetheless be able to atleast obtain a proxy for the level of AC byestimating a probit model. We further hold thatfirms more likely to have exploitive ACs actuallydo evince higher levels of AC. The secondimplication is that we have to consider theinterdependence among the three measures ofAC for the different types of knowledge. Sincewe are allowing firms to have more than one typeof AC, we have to assume that their possession ofone has an influence on the others. What is more,it is reasonable to assume that the determinants ofAC}R&D expenditure, for example}contributeto the accumulation of AC for knowledge fromuniversities and businesses alike. This is especiallytrue if the usage and exploitation of different kindsof external knowledge require the same or verysimilar competencies and experience. In order totake this interdependence into account in ourmodel, we estimate a trivariate probit model, i.e. asimultaneous system of three equations, instead ofthree separate probits.20 This will allow us toincrease the validity of our estimates.

The full empirical setup then looks like this:We first estimate a probit model for AC in

general:

Absorpn ¼ b0X þ u with Absorpi

¼1 if Absorpni > 0

0 otherwise

(

where X is the column vector representing theindependent variables outlined above.

In the next step we estimate the followingtrivariate probit model for the three different typesof AC:

Absorp intern ¼ b01X þ E1

with Absorp interi ¼1 if Absorp interni > 0

0 otherwise

(

Absorp intran ¼ b02X þ E2

with Absorp intrai ¼1 if Absorp intrani > 0

0 otherwise

(

T. SCHMIDT6


Absorp sciencen ¼ b03X þ E3

with Absorp sciencei¼1 if Absorp scienceni > 0

0 otherwise

(

where the pair-wise correlation of the error termsis not equal to zero:

CovðE1; E2Þ ¼ r1; CovðE1; E3Þ ¼ r2CovðE2; E3Þ ¼ r3

This model can be solved by employing amaximum-likelihood procedure. To evaluate thelikelihood of a certain outcome, the probability ofan observation has to be calculated using atrivariate normal probability density functionwhich takes into account E1; E2; E3;r1;r2 and r3:This poses some problems: It has been shown thatstandard numerical calculation techniques cannotbe used if the normal density function is of anorder higher than 2.21 A way to solve this probleminvolves using simulation techniques. One, whichis now implemented in many statistical packages,is the so-called ‘GHK simulator (Geweke–Haji-vassiliou–Keane simulator)’ for multivariate nor-mal distributions.22 For our model estimation weuse a procedure developed by Antoine Terracolfor the STATA statistical software package(triprobit), which relies on the GHK simulationprocedure.23

For the model estimation we were able to use1650 firms which indicated that they had carriedout innovation activities during the three-yearperiod 2000–2002.

Of the 1650 observations, 1177 (71%) haveat least one type of AC. Five hundred and seventy-five have at least intra-industry, 956 at leastinter-industry and 248 at least scientific AC.24

For those firms that only show one type of ACwe find a similar distribution. Just 47 firms, or2.7% of the enterprises in our sample, have onlyscientific AC, while 156 and 463 have only intra-industry and inter-industry ACs, respectively.Ninety-one of the firms have all three types. Thefairly large number of firms having more than onetype of AC (43% of all firms with AC) providesfurther evidence that the manners in which thethree types of AC are accumulated are somewhatrelated and a trivariate probit estimation proce-dure should be used.

As far as the independent variables are con-cerned, we find a few differences between the

sample mean and the mean of firms withACs. Differences are observed for the share ofemployees with higher education, which is about 2%points higher for firms with AC, and the R&Dintensity, which is 1% point higher for the lattergroup. Additionally, the share of firms with con-tinuous R&D is 6% points higher for firms with AC.The index for stimulating knowledge exchangeand innovation activities is also significantly higherfor firms with AC, but only by 2% points.

Within the group of firms with at least onetype of AC, the same variables make a differencebetween those with scientific AC and the othertwo types. Here, the differences are more pro-nounced. Firms with scientific AC have an averageshare of employees with higher education of42% and an R&D intensity of 14.8%. Forthe mean firm with intra-industry AC thenumbers read 28 and 7.7%, and for theaverage firm with inter-industry AC they are 30and 8.7%.

RESULTS

The results of our estimation are presented inTable 1. Let us first turn to the standard probitestimation of firms’ ACs.

A first striking result is that continuous R&Dis significant and positive, while R&D intensity,which is widely used in the related literatureas a proxy for AC, is not. This indicatesthat continuous R&D engagement (and notnecessarily the level of R&D expenditure) isrelevant to AC. A firm’s current25 expenditureon R&D is usually not primarily targeted atbuilding AC but rather at accumulating newknowledge and developing new products andprocesses, which might explain our findings.Our results suggest that a firm’s currentR&D expenditure does not make an ad hoccontribution to the assembly of AC; instead,it helps to develop the skills and knowledgenecessary to source external knowledge over time.In this sense, AC is cumulative. We will arguebelow that current R&D expenditure can alsoimmediately contribute to exploitive ACs, but onlyfor specific types of knowledge.

Another explanation for the insignificance ofR&D intensity variables is that firms with higherR&D intensities have a lower demand for external



Table1.

RegressionResultsfrom

Probit(1)andTrivariate

Probit(2)–(4)Estim

ations

Definition

Variable

AC

(1)

Intra-industry

AC

(2)

Inter-industry

AC

(3)

Scientific

AC

(4)

R&D

activities

Share

ofR&D

expenditure

inturnover,in

%R&D

int

0.004

�0.002

0:007n

0:014nnn

(0.004)

(0.004)

(0.004)

(0.004)

Share

ofR&D

expenditure

inturnover,squared

R&D

int2

�0.00001

0.000005

�0:000028n

�0.00005

(0.00001)

(0.00001)

(0.000014)

(0.0002)

ContinuousR&D

activities

R&Dcon

0:306nnn

0:127n

0:222nnn

0:451nnn

(0.078)

(0.076)

(0.073)

(0.089)

Skill/size

Share

ofem

ployeeswithhigher

educationin

%grads

0:003nnn

0.001

0.002

0:010nnn

(0.002)

(0.002)

(0.002)

(0.002)

Number

ofem

ployees(logarithm)

lnem

p�0:199nn

�0:134n

�0.079

�0.099

(0.090)

(0.079)

(0.076)

(0.087)

Number

ofem

ployees,squared(logarithm)

lnem

p2

0:023nnn

0:021nnn

0.010

0.012

(0.008)

(0.007)

(0.007)

(0.007)

Collaboration/stimulation

Inform

alcontactsamongem

ployees

colinfor

0:159nn

0:218nnn

0.113

0.096

(0.076)

(0.072)

(0.144)

(0.088)

Open

communicationofideasandconcepts

amongdepartments

colopencom

0.030

0.102

�0.074

�0:238nn

(0.086)

(0.081)

(0.078)

(0.098)

Jointdevelopmentofinnovationstrategies

coljointstrat

�0.105

�0:138n

�0.022

�0.050

(0.085)

(0.080)

(0.080)

(0.100)

Mutualsupport

ofother

departments

withinnovation-relatedproblems

colmutsup

0.090

�0:180nn

0.122

�0.089

(0.084)

(0.079)

(0.077)

(0.096)

Regularmeetingsofdepartmentheadsto

discuss

innovation-relatedtopics

colheads

�0.010

0.031

�0.032

�0.123

(0.080)

(0.076)

(0.075)

(0.096)

Tem

porary

exchangeofpersonnel

betweendepartments

forinnovationprojects

colexchange

�0:275n

�0.092

�0.127

0.117

(0.150)

(0.147)

(0.140)

(0.160)

Sem

inars

andworkshopsforinnovationprojectsinvolvingseveraldepartments

colseminar

�0.009

�0:341nnn

0.013

0:243nn

(0.116)

(0.110)

(0.105)

(0.220)

Index

forim

portance

ofmethodsofstim

ulatinginnovationandknowledgetransfer

stim

index

0:683nnn

0:293n

0:602nnn

0:798nn

(0.173)

(0.170)

(0.166)

(0.205)

T. SCHMIDT8


Controlvariables

Firm

locatedin

EasternGermany

east

0:152nn

0:154n

0:122n

�0.050

(0.076)

(0.072)

(0.070)

(0.093)

Low-techmanufacturing

ind

lt�0.092

0.143

�0.003

�0.124

(0.245)

(0.225)

(0.078)

(0.291)

Medium–low-techmanufacturing

ind

mlt

�0.193

�0.315

0.167

�0.183

(0.239)

(0.222)

(0.205)

(0.288)

Medium–high-techmanufacturing

ind

mht

�0.114

0.217

�0.005

�0.120

(0.237)

(0.215)

(0.201)

(0.275)

High-techmanufacturing

ind

ht

�0.111

0.038

0.055

�0.092

(0.248)

(0.226)

(0.211)

(0.282)

Low-techservices

serv

lt�0.311

�0.177

�0.009

�0.291

(0.232)

(0.214)

(0.197)

(0.277)

High-techservices

serv

ht

0.008

0.094

0.112

�0.147

(0.259)

(0.235)

(0.221)

(0.295)

Constant

0.417

�0:540n

�0.305

�1.655

(0.316)

(0.293)

(0.279)

(0.366)

Observations

1650

1650

w2132.73

347.89

Loglikelihood

�2663.48

Aldrich–NelsonpseudoR

20.137

0.231

rð2;3Þ:0:27nnn

ð3;4Þ:0:19nnn

ð2;4Þ0:11nn

nSignificantat10%;nnsignificantat5%

;nnnsignificantat1%;robust

standard

errors

inparentheses.AC,exploitiveabsorptivecapacity.



knowledge than firms with lower R&D intensities.The more R&D is done in-house, the moreknowledge is generated internally and the lessexternal knowledge is required. It is alsoquite likely that firms with large in-house knowl-edge pools would generate more impulsesand ideas from within and use less externalimpulses and ideas for their innovations. In thatcase firms would have the capacity and capabilityto use external knowledge in their innovationprocesses, but simply do not need to do it.26 Ifthis negative effect on demand for external ideasand knowledge dominates the effect of R&Don AC, the R&D intensity would still have apositive effect on AC, but it would not show in ourestimations.

To investigate whether our results are driven bythe choice of measure of R&D intensity, weestimate several additional model specifications.The results, which are reported in column 1of Table 2, show that regardless of the measureused for R&D intensity and the inclusionor exclusion of one of the two R&D-relatedvariables, the coefficients of R&D intensity remaininsignificant and continuous R&D remainshighly significant, providing further evidencethat current R&D intensity does not immediately

determine the ability of firms to exploit externalknowledge.27

A large body of literature has pointed to thepositive influence of collaboration and stimulationof knowledge sharing on AC, as described in thesecond section. Our results do not totally contra-dict these findings but raise some doubts abouttheir importance in exploiting external knowledge.Similar to other studies we find that collaborationbetween departments has an impact on AC,lending support to our first hypothesis. However,only informal contacts have a positive andsignificant effect on AC. The variables representinghierarchical information provision are not or onlyvery marginally significant. This suggests that it ismore important to create a culture and organiza-tion that leads to informal knowledge transferrather than a culture in which informationprovision is more centralized. One reason for thismight be that the diffusion of new knowledge isfaster and less prone to distortions throughinformal networks compared with formal systems(see Williamson, 1967). The insignificance of theother variables is puzzling still, as one wouldexpect that every knowledge exchange regardlessof the method used is helping firms to exploitexternal knowledge.

Table 2. Coefficients of Five Different Probit and Trivariate Probit Estimationsa

Variable Absorptive capacity Intra-industryabsorptive capacity

Inter-industryabsorptive capacity

Scientificabsorptive capacity

Model 1: R&Dconþ R&D intR&D int 0.004 �0.002 0:007n 0:014nnn

R&D int2 �0.00001 0.00005 �0:00003n �0:00005nnn

R&Dcon 0:306nnn 0:127n 0:222nnn 0:451nnn

Model 2: R&Dconþ R&D int emplR&D int empl 0.130 0.211 0.629 1:343nn

R&D int empl2 �0.167 �0.862 �0.625 �0.708R&Dcon 0:318nnn 0:135n 0:255nnn 0:504nnn

Model 3: R&DconR&Dcon 0:320nnn 0.121 0:245nnn 0:504nnn

Model 4: R&D intR&D int 0.007 �0.001 0:009nn 0:017nnn

R&D int2 �0.00002 0.00001 �0:00003nn �0:0001nnn

Model 5: R&D int emplR&D int empl �0.229 0.085 0.378 0.950R&D int empl2 0.680 �0.682 �0.276 �0.153

nSignificant at 10%; nnsignificant at 5%; nnnsignificant at 1%; robust standard errors in parentheses.aThe models all include the full set of independent variables not related to R& D as well as the variables shown. The full estimationresults are available upon request.

T. SCHMIDT10


The measure for methods that try to stimulateemployee participation in innovation activities ispositively associated with AC. This suggests that itis not only necessary to increase the knowledgeflows between actors inside the firm (mainlythrough informal contacts), but also to leveragethe knowledge of each individual in the innovationprocess. Almost all the stimulation methodsincluded in the factor analysis are aimed at theinvolvement of the employees and managers in theinnovation process. These findings confirm that afirm’s AC is related to that of its employees.

Further evidence for this result is providedby the importance of higher education forexploitive AC. As expected, the share of employeeswith higher education positively influencesthe ability of firms to exploit externally availableknowledge. This is also true for continuous R&Dactivities, which we use as an indicator of relatedprior knowledge. We confirm other studies’ find-ings that both indicators influence firms’ ACs.Our interpretation is that both the relatedprior knowledge of individuals}gained througheducation}and that of firms, which they havedeveloped through steady R&D investment, posi-tively influence the ability to exploit externalknowledge. The positive influence might havesomething to do with the fact that this relatedprior knowledge is also necessary to identify andassimilate external knowledge.

We find a significant U-shaped effect of thenumber of employees on the likelihood that firmshave exploitive AC. This is surprising, as onewould expect the number of employees to alwayshave a positive effect on firms’ ACs: Individuals’AC is, after all, part of the firms. Because the U inquestion (turning point: 75 employees) is very flat,this finding should not be over-interpreted, how-ever. One explanation for the U-shaped relation-ship might be that very small firms depend moreon external knowledge than medium-sized ones.After the turning point of 75 employees is reached,the expected size effect sets in, i.e. every additionalemployee increases the ability of firms to exploitexternal knowledge. The demand effect suggestedby the insignificance of the R&D intensity (seeabove) might also be able to explain the U-shapedrelationship between the number of employees andAC. Very small firms need external knowledge tofurther exploit their (only) inventions and innova-tions or to get new ideas for applications of theirexisting knowledge. After they have reached a

certain size (and gained some experiencein the market), they are more likely to focus onthe exploitation and commercialization of theirinnovations and internal knowledge rather thanlook for new ideas outside their boundaries. Thisseems to lower the demand for external knowledgeby medium size enterprises. If their internal‘potential’ has been exploited they have to usemore external knowledge to grow further.

In essence, we find support for our hypothesisthat not only R&D is relevant for the ability toexploit external knowledge. Besides R&D, highlyskilled labor as well as knowledge managementtools that stimulate the involvement of employeesin innovation projects seem to be important.However, only informal contacts, and not ashypothesized almost all collaboration methods,influence the ability to exploit external knowledgepositively.

Our results provide evidence supporting oursecond hypothesis, as we can clearly show that theability to exploit different types of external knowl-edge is influenced differently by the factorsconsidered.28 Most pronounced is the differencebetween the exploitation of scientific knowledgeand knowledge from the business sector. Theyparticularly differ with respect to R&D variables.As mentioned above, there are differences betweenthe effects of R&D intensity on the three differenttypes of AC. For scientific knowledge we find ahighly significant effect of current R&D intensity;no such effect is evident for the other two types ofknowledge. Firms that spend a large amountof turnover on research are usually in greater needof external knowledge and are thus more likely toexploit that knowledge. Additionally, as the shareincreases they become more and more similar topublic research institutes and universities.The more similar firms are, the better they canlearn from each other, as Lane and Lubatkin(1998) argue. Their ability to exploit knowledgelearned from similar partners also increases, asour results suggest. This similarity argumentcannot be made for the other two types of actorsproviding knowledge, which might explain why wedo not find significant signs for intra-industry ACand only marginally significant signs for inter-industry AC. In the long run, however, R&Dseems to be relevant for inter-industry knowledgeas well, as the positive effect of continuous R&Dsuggests. For intra-industry knowledge this vari-able is also significant, but only slightly.



We again tested the robustness of our model byincluding different measures of R&D intensity inthe estimations. The results remain quite similar toour original model. For scientific AC the onlymore pronounced change we found is the sig-nificance of the squared R&D intensity when usingthe share of R&D employees instead of the shareof R&D expenditure. This can be explained by thevalues the two variables can take. While the R&Dintensity based on expenditure is not constrainedto the interval [0;1],29 that which is based onemployees is. If we drop firms with R&D intgreater than 1, we obtain the same result as forR&D int empl. For intra-industry AC the resultsdiffer more than for the other two types. Theprominent role of continuous R&D can at least beconfirmed in all models.

The results for the three types of AC differnot only with respect to R&D-related variables;we also find differences with respect to the extentof collaboration among departments. Thesefactors are only significant for intra-industry andscientific AC. This is surprising, as one wouldexpect the exploitation of inter-industry know-ledge to also be influenced by collaborationamong departments. We argue that the exploita-tion of inter-industry knowledge for innovationsmight require less collaboration because alarge amount of that knowledge is embodied inproducts from suppliers and each employeecan take the knowledge needed for his or herinnovation activities directly from the product.The insignificance and negative significance ofsome collaboration variables in the equations forintra-industry and scientific knowledge point tothe fact that collaboration among departments, asbeneficial as it might be for certain enterpriseactivities, does not necessarily contribute toexploitive AC. On the contrary, a firm has tochoose how it organizes collaboration with respectto the knowledge it wants to absorb and balancethe need to exploit external knowledge with itsother needs and goals.

Intra-industry AC, for example, is negativelyinfluenced by mutual support among departments.The latter reduces the probability of intra-industryknowledge being successfully exploited in innova-tions, suggesting that this method does not fit thetype of knowledge to be exploited. Mutual supportwith innovation-related problems is likely to beassociated with significant difficulties if differentdepartments are configured distinctly or use

procedures not known to others outside ofthe department, leading to an increase in costsand necessary efforts without leveraging theexploitation of external knowledge. Seminarsinvolving actors from different departments andthe development of joint innovation strategiesalso influence the likelihood that a firm exploitsintra-industry knowledge negatively. The methodbest suited to exploiting intra-industry knowledgeis to generate informal contact among employees.This suggests that it is especially beneficial tospread knowledge throughout the whole firmrather than distribute it through formal andmore targeted mechanisms. One reason for thismight be that knowledge from a firm’s ownindustry can easily be understood by everyonewithin the firm. Broad dissemination shouldthus increase the potential use of information forinnovation activities. For scientific knowledgethe opposite is true: It cannot be easily understoodand processed by all actors in the company buthas to be ‘translated’ into a form that is usable byeveryone in the firm. The positive influence ofseminars and workshops in the equation forscientific knowledge supports the notion that moretranslated knowledge implies a higher probabilitythat it can be integrated into the existing knowl-edge base and utilized in the innovation process.This underscores the role of gatekeepers in theprocess of building AC. In contrast, broad knowl-edge diffusion reduces the probability of scientificknowledge being exploited. This method is notbeneficial since only very few actors inside the firmare able to profit from a more widespreaddissemination of knowledge and considerable(opportunity) costs might be involved.

Stimulating employees to get involved in theinnovation process as well as knowledge acquisi-tion and distribution is of great importance indetermining AC for all three kinds of knowledge,as our results suggest. The explanation is straight-forward: The more knowledge is screened and thehigher the incentives are to use acquired knowl-edge in the innovation process, the higher thepotential to exploit external knowledge.

The differences between the three typesof AC are not limited to the collaboration andR&D variables, however. While we find a positiveand significant coefficient for the share of employ-ees with higher education in the scientific knowl-edge equation, it is insignificant in the other twoequations. Naturally, one would assume that it is

T. SCHMIDT12


easier for employees who have attended universityto use knowledge from this domain. They knowhow to use the knowledge as well as how andwhere to get it. The level of education does notsignificantly influence the ability to exploit intra-and inter-industry knowledge. For intra-industryAC, size is more important. As in the case of ACsin general, we find a U-shaped relationshipbetween the number of employees and intra-industry AC (turning point: 25 employees). Forinter-industry AC neither the share of high-skilledlabor nor size matters. The differences with respectto high-skilled labor can be explained by varyingrequirements for the exploitation of externalknowledge. One can argue that in order to exploitknowledge from one’s own industry, experience ismore relevant than a high level of education. Evenwithout a large share of highly educated personnel,firms should be able to exploit knowledge fromwithin their own industries. On the other hand, theexploitation of very sophisticated methods andknowledge produced by public research institutescertainly require a similar kind of advancedtraining in a particular field. To absorb inter-industry knowledge more general skills in structur-ing problems and gathering information on pre-viously unknown subjects might be moreimportant than the initial education level of firms’employees.

The positive and significant effect of the dummyfor Eastern Germany for intra-industry AC is inline with what Sofka and Schmidt (2004) find inanalyzing firstmover and follower strategies forGerman firms: Eastern German firms are moreoften followers than leaders. Eastern Germanfirms are thus more dependent on innovationand knowledge from their market rivals and areconsequently more focused on exploiting knowl-edge from their own industry than WesternGerman firms.

CONCLUSIONS

In this paper we investigate the determinants ofdifferent types of ACs in innovating firms, usinginformation on external sources that firms havesuccessfully used in developing and introducinginnovations. Our first hypothesis was thatthe existence of exploitive AC is not onlydetermined by R&D activities, but also by the

organization and stimulation of knowledge trans-fer within a firm as well as the employment ofqualified personnel. In particular, the stimulationof innovation activities and knowledge transferhas proven to be an important building blockof exploitive AC. This holds for AC in general,but also for all three types of AC. The same isnot true for collaboration among departments oninnovation activities. Here we find significantdifferences between the three types of AC andidentify methods that hinder the developmentof ACs rather than support it. For intra-industryknowledge it seems best to broadly distributeacquired knowledge through informal networksinstead of formal channels. The exploitation ofscientific knowledge, on the other hand, requires aless broad distribution, but depends on thetranslation of this knowledge before it is dissemi-nated through seminars and workshops.The specific kind of knowledge from inter-industrysources seems to require less collaboration, asnot a single one of the collaboration variables issignificant. It is thus feasible to conclude thatfirms can manage and build AC by implementingmethods that stimulate knowledge transfer andby providing an organizational frameworkwhich improves the flexibility and efficiency ofknowledge transfer within their ranks. However,not all types of mechanisms to transfer knowledgeare equally suited for the exploitation of specifickinds of knowledge. Owing to this, there mightbe a conflict of goals not only with respect toexploiting external knowledge and other firmgoals but also with respect to the knowledge tobe acquired. The influence of stimulation andmanagement of knowledge sharing on AC alsoprovides proof of the fact that individuals con-tribute significantly to the ACs of firms. Further-more, it looks like there is indeed a differencebetween potential and realized AC, as proposed byZahra and George (2002), and that firms canactively try to increase both their general andparticular ACs.

Current R&D expenditure as a share of turn-over does not influence AC, regardless of themeasure we use. We find, however, that it helps tobuild a knowledge stock which contributes sig-nificantly to exploitive AC. It looks like one face ofthe ‘two faces of R&D’ (Cohen and Levinthal,1989) is dominating the other in the shortrun, i.e. R&D expenditures are predominantly ameans of developing new knowledge and innova-



TableA1.

Descriptive

Statistics(m

eans)

Definition

Variable

Sample

ACa

Intra-industry

ACa

Inter-industry

ACa

Scientific

ACa

Number

ofobservations

1650

1177

575

956

248

Percentageoftotal(%

)68

33

56

14

Share

ofR&D

expenditure

inturnover,in

%R&D

int

7.46

8:69n

7.70

8:66n

14:80nnn

(22.27)

(24.75)

(23.00)

(23.17)

(28.50)

Share

ofR&D

expenditure

inturnover,squared

R&D

int2

551.19

687.52

587.58

611.30

1028.27

(5187.4)

(6004.8)

(5756.2)

(5243.9)

(6371.9)

Share

ofem

ployeeswithhigher

educationin

totalem

ployees,in

%grads

27.70

29:57n

27.67

29:66n

42:15nnn

(26.83)

(27.41)

(25.50)

(27.54)

(29.69)

ContinuousR&D

activities

R&Dcon

0.53

0:59nnn

0:60nnn

0:60nnn

0:77nnn

(0.50)

(0.49)

(0.49)

(0.49)

(0.42)

Number

ofem

ployees,logarithm

lnem

p4.59

4.65

4:95nnn

4.66

4.68

(1.89)

(1.97)

(2.11)

(1.97)

(2.26)

Number

ofem

ployees,logarithm,squared

lnem

p2

24.61

25:49n

28:93nnn

25:58n

26:95n

(20.52)

(21.96)

(24.78)

(22.03)

(26.95)

Firm

locatedin

easternGermany

east

0.32

0.34

0.33

0.34

0.33

(0.47)

(0.47)

(0.47)

(0.47)

(0.47)

Index

forim

portance

ofmethodsofstim

ulatinginnovationactivities

andknowledgetransfer

stim

index

0.46

0:48nnn

0:48nnn

0:48nnn

0:52nnn

(0.23)

(0.22)

(0.22)

(0.22)

(0.21)

Inform

alcontact

amongem

ployees

colinfor

0.47

0:50n

0:51nn

0:50n

0:55nnn

(0.50)

(0.50)

(0.50)

(0.50)

(0.49)

Open


amongdepartments

colopencom

0.46

0:49n

0.47

0:48n

0.47

(0.50)

(0.50)

(0.50)

(0.50)

(0.50)

Jointdevelopmentofinnovationstrategies

coljointstrat

0.32

0.34

0.31

0:35n

0.37

(0.47)

(0.47)

(0.46)

(0.48)

ð0:48Þn

Mutualsupport

ofother

departments

withinnovation-relatedproblems

colmutsup

0.43

0:46n

0.41

0:47nn

0.46

(0.50)

(0.50)

(0.49)

(0.50)

(0.50)

Regularmeetingsofdepartmentheadsto

discuss

innovation-relatedtopics

colheads

0.36

0.37

0.39

0.38

0.36

(0.48)

(0.48)

(0.48)

(0.49)

(0.48)

Tem

porary

exchangeofpersonnel

betweendepartments

forinnovationprojects

colexchange

0.06

0.06

0.06

0.06

0:10nn

(0.24)

(0.24)

(0.23)

(0.24)

(0.30)

Sem

inars


colseminar

0.12

0.13

0.11

0.14

0:21nnn

(0.33)

(0.34)

(0.31)

(0.34)

(0.40)

AC,absorptivecapacity;standard

errors

inparentheses.

aMeanisdifferentfrom

thesample

meanat

n10%;nn5%

;nnn1%

.

T. SCHMIDT14


TableA2.

VariablesIncluded

intheModel

Variable

Type

Construction

Absorp

Dummy

One,ifin

thethree-yearperiod2000–2002atleastoneinnovationofafirm

wasdeveloped

because

ofim

pulses

from

atleastoneofthefollowing

sources:customers,suppliers,competitors,universities,researchinstitutions

Absorp

intra

Dummy

One,ifin


wasdeveloped

andsuccessfullyim

plementedbecause

ofim

pulses

from

customers,suppliersorcompetitors

from

thefirm

’sindustry

(NACE2)

Absorp

inter

Dummy

One,ifin


wasdeveloped

andsuccessfullyim

plementedbecause

ofim

pulses

from

customersorsuppliersfrom

industries

other

thanitsown(N

ACE2)

Absorp

science

Dummy

One,ifin


wasdeveloped

andsuccessfullyim

plementedbecause

ofim

pulses

from

universities

orother

publicresearchinstitutes

Grads

%Share

ofem

ployeeswithhigher

educationin

totalem

ployees

R&D

int

%Share

ofR&D

expenditure

inturnover,2001

R&D

int2

%Share

ofR&D

expenditure

inturnover,2001,squared

R&D

intem

pl

%Share

ofR&D

employeesin

totalem

ployees

R&D

intem

pl2

%Share

ofR&D

employeesin

totalem

ployees,squared

R&Dcon

Dummy

One,

iffirm

wasengaged

inR&D

activitiescontinuously

colinfor

Dummy

One,

ifinform

alcontact

amongem

ployeeswerehighly

important

coljointstrat

Dummy

One,

ifjointdevelopmentofinnovationstrategieswashighly

important

colopencom

Dummy

One,

ifopen


amongdepartments

washighly

important

colmutsup

Dummy

One,

ifmutualsupport

ofother

departments

withinnovation-relatedproblemswashighly

important

colheads

Dummy

One,

ifregularmeetingsofdepartmentheadsto

discuss

innovation-relatedtopicswerehighly

important

colexchange

Dummy

One,

iftemporary

exchangeofpersonnel

betweendepartments

forinnovationprojectswashighly

important

colseminar

Dummy

One,

ifseminars


werehighly

important

stim

index

Index

Index

basedonaquestionregardingtheim

portance

(four-pointLikertscale:‘0’notim

portant;‘4’highly

important’)ofninedifferentmethodsof

simulatinginnovationandknowledgetransfer

(stim1–stim

9),rangingfrom

monetary

incentives

forleadingem

ployeesto

incentives

todevelop

one’sownideas(fulllistavailableuponrequest).Resultofprincipalcomponentfactoranalysissuggestsasinglefactorwithaneigenvaluegreater

than1(5.75).Factorloadingsafter

Varimaxrotationrescaledbetween0and1

Lnem

pLog

Naturallogarithm

ofnumber

ofem

ployees

Lnem

p2

Log

Naturallogarithm

ofnumber

ofem

ployees,squared

east

Dummy

One,

ifafirm

islocatedin

easternGermany

ind

ltDummy

One,

ifafirm

isfrom

alow-techindustry

ind

mlt

Dummy

One,

ifafirm

isfrom

amedium–low-techindustry

ind

mht

Dummy

One,

ifafirm

isfrom

amedium–high-techindustry

ind

ht

Dummy

One,

ifafirm

isfrom

ahigh-techindustry

serv

ltDummy

One,

ifafirm

isfrom

low-techservices

serv

ht

Dummy

One,

ifafirm

isfrom

high-techservices



tion rather than of building AC. Nonetheless,R&D activities help in the long run to buildabsorptive capacities. Our results also suggestthat R&D activities contribute differently to theaccumulation of the three types of AC. While it isvery important for scientific knowledge, its short-run contribution to exploitive AC for businesssources is less pronounced. Following the previousargument, this may very well be due to the factthat R&D expenditure contributes faster to thebuild-up of one type of absorptive capacity thanthe other, regardless of the intentions behindspending it.

The differences between intra-industry, inter-industry and scientific ACs are striking and shouldbe taken into account in future studies dealingwith AC. Researchers should think about whatproxies and determinants of AC to use, dependingon the kind of knowledge firms, individuals,regions or nations have to deal with in theirstudies. Our results suggest that R&D intensitymight not always be the best proxy. What is more,we show that differences exist between knowledgefrom different industries and from science, com-plementing other studies by Gradwell (2003) andMangematin and Nesta (1999), which suggestdifferences for tacit and codified knowledge.

Future work might also try to further distin-guish the types of knowledge to be acquired (and,consequently, the ACs required). One possibledirection would be to investigate the differences inAC for domestic and foreign knowledge.

Acknowledgements

The author would like to thank Uwe Cantner, Georg Licht,Christian Rammer, Wolfgang Sofka, Guy Gellatly, MicheleCincera, Jens Frolov Christensen, Fiorenza Belussi, MartinKuckuk, Tyler Schaffner, Andrew Flower and two anonymousreferees for valuable comments. This paper was presented at the

Augustin Cournot Doctoral Days (2005), the 39th AnnualMeeting of the Canadian Economics Association (2005), theDRUID Summer conference (2005), the 2nd ZEW Conferenceon Innovation and Patenting (2005) and the Annual Congressof the Verein fur Socialpolitik (2005).

APPENDIX

Table A1 provides descriptive statistics (means) for the sample

used in the empirical analysis. Table A2 gives the exact

definitions of all the independent variables included in the

empirical model. A list of the correspondence between the six

industry dummies (control variables) and the NACE2 is given

in Table A3.

NOTES

1. Zahra and George (2002), for example, cite Moweryand Oxley (1995) who define AC as a set of skillsneeded to deal with tacit knowledge.

2. The concept may have been ‘reified by manysubsequent scholars’, though (Lane et al., 2006, p. 834).

3. Among others, Becker and Peters (2000, p. 11) state:‘The empirical measurement of absorptive capacitiesof firms is difficult’.

4. Zahra and George (2002, p. 186) highlight thisproblem by writing: ‘ . . . it is unclear if thesemeasures [of absorptive capacity] converge tocapture similar attributes of the same construct, . . . ’

5. Zahra and George (2002) argue: ‘Substantial differ-ences exist among these dimensions, which allowthem to coexist and be measured and validatedindependently.’ (p. 199), when talking about thethree dimensions of AC.

6. More extensive reviews have been compiled byDaghfous (2004), Van Den Bosch et al. (2003) andZahra and George (2002).

7. Daghfous (2004) gives an overview on the determi-nants of the components of AC.

8. There is a fourth group of determinants that will notbe discussed in this paper. It includes networks andalliances with external partners and the knowledge

Table A3. List of Industry Dummies Included in the Model

Industry group NACE code

Other manufacturing 40, 41, 45Low-tech manufacturing 10–22, 36, 37Medium–low-tech manufacturing 23, 25–28, 351Medium–high-tech manufacturing 24 (excluding 244), 29, 31, 34, 35 (excluding 353)High-tech manufacturing 244, 30, 32, 33, 353Low-tech services 50, 51, 52, 55, 60–63, 65–67, 70, 71, 74, 75, 90, 92High-tech services 64, 72, 73

Note: Only those NACE codes that were present in our sample are listed in the second column.

T. SCHMIDT16


environment in general (see, for example, Nonakaand Takeuchi, 1995; Caloghirou et al., 2004; Lim,2004). Our focus is on internal factors only.

9. Lane and Lubatkin (1998) offer one of the fewstudies that calls the use of measures of AC based onR& D spending into question.

10. Some of this literature is cited in Cohen andLevinthal (1990) and Daghfous (2004).

11. For a more detailed description of the MIP survey,see Janz et al. (2001).

12. Separate questions regarding impulses from custo-mers, competitors, research institutes and universi-ties and suppliers were asked.

13. The questions were phrased as follows: ‘Were any ofthe innovations introduced by your enterpriseduring the three-year period 2000–2002 triggeredby new research results?’; ‘Were any of the innova-tions introduced by your enterprise during the three-year period 2000–2002 triggered by competitors’innovations?’, etc.

14. The term ‘exploitive AC’ refers to the ability of firmsto exploit external knowledge for their innovationactivities.

15. We use the term ‘scientific AC’ for this concept inthe remainder of the paper.

16. Exact definitions of the variables can be found in‘‘Table A1 in the Appendix’’.

17. This index is the result of a principal componentsfactor analysis of the importance of nine differentmethods of stimulating innovation and knowledgetransfer. The methods could not be includedseparately in the estimation since they were highlycorrelated with each other and with some ofcollaboration variables.

18. See, for example, Rammer et al. (2004) and Sofkaand Schmidt (2004).

19. See Table A3 in the Appendix for details.20. See Greene (2002) on ‘multivariate probit models’.21. See Glasgow (2001) for a discussion of the topic.22. Other simulators could also be used. Hajivassiliou

et al. (1996) review 11 simulators and find that theGHK is the most reliable method for multivariatenormal distributions.

23. The method is known to be sensitive to the numberof observations drawn in each iteration. We thustested several different settings. The results onlychanged as far as the size of the coefficients isconcerned, the significance levels and qualitativeresults stayed the same. The model presented belowuses 200 draws instead of the default number ofdraws which is 25.

24. Additional descriptive statistics can be found in theAppendix.

25. In order to reduce a possible endogeneity bias, weuse R&D intensity in the year 2001 instead of that of2002.

26. They might also not want to do it, if the sourcing ofexternal knowledge were connected with certaincosts.

27. We also tested the joint significance of the twoR&D intensity variables, but were always able toreject the joined H0 that both are equal to zero.

28. Note that the test for the interdependence of thethree equations in the trivariate probit shows thatthe equations are, in fact, not independent, as allRhos are positive and significant at least at the 95%confidence level.

29. Note that some firms in our sample out-spend theirturnover in financing R&D. They are all fromNACE 73, ‘Research and Development’. For firmsin that field it is not unusual to have R&D intensitygreater than 1.

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Absorptive capacity—one size fits all? A firm-level analysis of absorptive capacity for different kinds of knowledge