Functional versus Capability-oriented Innovation Management

in Multinational Firms

Walter Kuemmerle - Richard S. Rosenbloom

Harvard Business School

June 16, 1999

Please send correspondence to: Walter Kuemmerle, Harvard Business School, Morgan Hall T45, Soldiers Field, Boston, MA 02163 Tel. (617) 495-6353, Fax (617) 496 4066, e-mail: wkuemmerle@hbs.edu Keywords: International R&D Management; Firm Capabilities; Laboratory Networks We would like to thank seminar participants at Harvard Business School, MIT and at the 1998 Strategic Management Society Annual Meeting in Orlando, FL, for comments. We would also like to thank Art Shleifer and Bill Simpson for very helpful comments on the statistical analysis. The financial support of the Harvard Business School Division of Research is gratefully acknowledged.

2

Functional versus Capability-oriented Innovation Management

in Multinational Firms

This paper argues that innovation management in multinational firms will be

enhanced if managers distinguish between alternative roles for geographically dispersed sites: capability-augmenting and capability-exploiting. The paper first compares the new role-based distinction with the conventional functional distinction of ‘research’ and ‘development’ through an analysis of 8 matched case studies. Based on a statistical analysis of 156 sites the paper then shows that the new role-based distinction is a superior predictor of strategic choices made when laboratories are established.

3

This paper argues that innovation management in multinational firms will be

enhanced if managers distinguish between alternative roles for geographically dispersed

facilities: capability-augmenting and capability-exploiting. The paper shows that this

distinction is superior to the conventional functional categories of ‘research’ and

‘development’ as a predictor of some strategic choices made when laboratories are

established. An analysis of 8 matched case studies as well as a statistical analysis of 156

R&D sites supports this argument.

The role of subsidiaries is an important theme in research about management of

innovation in multinational corporations. Recently there has been an increasing focus on

how subsidiaries contribute to firm-specific capabilities and how a network of

subsidiaries can be coordinated (Bartlett and Ghoshal 1990; Birkinshaw et al. 1998;

Cantwell 1989; Gupta and Govindarajan 1994; Nonaka and Takeuchi 1995). Some of this

research has focused on the conceptualization of subsidiary roles, but most empirical

research has been limited to manufacturing and sales subsidiaries.

It is clear that technological subsidiaries, typically research and development

laboratories, also play varying roles. This paper extends the existing literature by

highlighting the innovative function and also by proposing a different way of thinking

about the roles of the institutions involved in innovation. Specifically, the paper

addresses the following question: When managers are responsible for a geographically

dispersed network of innovative organizations, how can they most usefully classify the

roles of those establishments?

To begin, we argue that the conventional functional categories that distinguish

“research” from “development” organizations are not the most useful for purposes of

4

strategic planning or organizational control. Instead, we propose an alternative conception

that distinguishes between activities intended primarily to augment established corporate

capabilities and those intended primarily to exploit them more fully.1

The paper is in four sections. The first section discusses the functional

characterization of ‘research’ and ‘development’ and explains the alternative distinction.

The second section provides an analysis of matched case studies, while the third presents

a statistical analysis examining the predictive power of functional and capability-based

categorizations with regard to important laboratory management indicators. The final

section discusses the findings and their implications for theory and practice.

THEORETICAL AND EMPIRICAL BACKGROUND

Categorizing innovation-oriented subsidiaries: research versus development

Scholars and managers both use the term ‘R&D’ as a comprehensive label for

creative work undertaken on a systematic basis to increase the stock of knowledge and to

devise new applications for this knowledge. The literature also uses the term ‘R&D’ as an

encompassing expression for all public and private innovative activities at the firm,

industry and national levels (Florida 1997; Mansfield 1991; OECD 1981). Similar terms

are used in different countries and languages: e.g. Japan (kenkyukaihatsu), Germany

(Forschung und Entwicklung), and France (recherche et developpement).

1 This notion grows out of the finding that R&D laboratories established internationally can usefully be divided into similar categories: those which seek to acquire new knowledge for the firm and those intended to exploit knowledge that already exists within its boundaries (Kuemmerle 1997). This research builds on earlier work about motives for foreign direct investment (Wesson 1993).

5

In common parlance, and in formal definitions, ‘research’ generally refers to more

basic, discovery-oriented activities while ‘development’ refers to those specifically

geared toward the design and improvement of a particular product or process (OECD

1981). But the line between the two is drawn variously in different quarters. For scholars

who use ‘R&D’ as an aggregate term, any ambiguity in the distinctions between ‘R’ and

‘D’ does not matter. For those who seek to carve up the complex processes of invention

and innovation, however, the distinction does matter.

Not surprisingly, many authors concerned with the economics and management of

technology and innovation have long argued that the conventional dichotomy between R

and D is not necessarily the best and certainly not the only way to analyze the process of

industrial innovation (Brooks 1994; Freeman 1982; Mansfield 1984; Nelson and Winter

1982). A major shortcoming of the terms ‘R’ and ‘D’ is their ambiguity. Scholars use

these terms in different ways and there is only limited adherence to the standardized

meanings proposed by the OECD in its Frascati Manual (OECD 1981). A survey that

considered whether the National Science Foundation’s classification of industrial R&D

into three categories (basic research, applied research and development) covers the scope

of R&D financed by private firms, found that many managers in large firms were also

dissatisfied with this scheme (Link 1996).

Despite their dissatisfaction with the distinction, scholars continue to characterize

particular innovative activities as either "research" or "development." Several factors

motivate this practice despite the inherent ambiguities. First, the familiar distinction is

just that: familiar through long usage. It is also a parsimonious, easily remembered

category scheme. Finally, no suitable alternative has ever gained general acceptance.

6

A Capability-oriented typology

We believe that innovation managers, as well as scholars, are searching for a

better way of distinguishing laboratory units than by categorizing them under ambiguous

functional labels like ‘research’ and ‘development’. For the firm’s portfolio of current and

future laboratories, managers should welcome an analytical lens that facilitates resource

allocation decisions and intra-firm communication.

Interest in new typologies to categorize and analyze innovative activities has been

motivated, among other reasons, by the increasing dispersion of corporate R&D activities

into geographically separate R&D locations (Dalton and Serapio 1997; Florida 1997;

Hakanson and Nobel 1993; Kuemmerle 1999). Increasing dispersion arises both from the

increasing international dispersion of R&D within firms, and the increasing level of

outsourcing of R&D (DeMeyer 1993; Kuemmerle 1997; Pearce and Singh 1990; Westney

1993). According to one estimate, multinational companies from major OECD countries

in 1996 carried out, on average, 15 to 20 per cent of their R&D abroad (OECD 1997).

Another survey (Kuemmerle 1996) of all new R&D sites established by 32 multinational

companies showed that these firms established more new R&D sites abroad during the

last 10 years (1986 to 1995) than during the previous 30 years (1956 to 1985) combined.

In 1995 these 32 firms carried out 26% of their R&D efforts outside their home countries.

At the same time, large firms are entering into an increasing number of alliances with

universities and small firms for discovery and development purposes. This phenomenon

is particularly salient in the drug and electronics industries (Thomke et al. 1997).

In innovative firms with geographically diversified operations, evaluation of the

strategic and operational effectiveness of independent units depends upon clarity in

7

definition of the roles of those units within the larger corporate system. To define a unit’s

mission as “research” is to imply a certain framework for evaluation. But prolific

outpourings of research product may or may not be salient to the firm’s innovative

performance.2 In the search for better classification schemes, several researchers have

proposed more complex and multi-faceted frameworks than the traditional

research/development distinction. Table 1 summarizes some of the more interesting

proposals.

Our purpose in what follows is to outline a different sort of distinction, one that

emphasizes the role of an innovative unit, rather than the functional character of activities

carried out within it. We propose a simple test: in relation to the innovative system within

which it operates, is the unit primarily intended to augment or to exploit the established

capabilities of the system? Hence, in place of terms like ‘research’ or ‘development’, we

propose the categories of ‘capability-augmenting’ and ‘capability-exploiting.’ The

alternative distinction relates more naturally to established lines of inquiry in the

management of innovation, in strategic management, and in international management.

Scholars in management of innovation have shown the importance of managerial

attention to information flows and knowledge management (Clark and Fujimoto 1991;

Leonard-Barton 1995; von Hippel 1988; von Hippel 1994). One could argue that our

distinction represents a focused approach to knowledge processing that can help firms

2 See, for example: (Smith and Alexander 1988).

8

structure the complex process of industrial innovation along the dimension of intra-firm

knowledge flows.3

By calling attention to the creation and exploitation of corporate capabilities rather

than research and engineering functions, our scheme fits well with the ‘dynamic

capabilities’ perspective on strategic technology management (Helfat 1997; Teece et al.

1997). The balance between creation and exploitation in a firm’s activities has also been

emphasized by March (March 1991). March argues that firms with outstanding

innovative capabilities are skilled at the creation of new knowledge and the conversion

of this knowledge into products and, eventually, profits.

In the domain of international management, research has shown that the decision

to enter foreign countries depends on existing firm capabilities at the firm’s home base as

well as on the firm’s absorptive capacity for knowledge in the respective foreign country

(Bartlett and Ghoshal 1990; Kogut 1991; Kogut and Chang 1991). Furthermore, Hedlund

and Rolander (Hedlund and Rolander 1990), much along the lines of March (March

1991), distinguish experimentation and exploitation activities in foreign countries. 4

In the context of the literatures cited above, a role-based category scheme has

evident attractions. But the reader may well ask: Is there a real difference to go with this

conceptually attractive distinction? To be sure, research organizations are in the business

of creating knowledge and hence would seem to fit the definition implied by the term

‘capability-augmenting.’ But analytically, the relevant matter is the strategic role a unit is

3 One of the present authors used the terms ‘home-base-augmenting’ and ‘home-base-exploiting’ instead of ‘capability-augmenting’ and capability-exploiting’ in an exploratory empirical survey underlying this paper. 4 Experimentation in this context refers to the effort to create new products by independent research activities carried out by the firm in a foreign environment.

9

intended to play within a dispersed network of technological units.5 For a non-trivial

number of research laboratories, the primary corporate role is exploitation rather than

enhancement of established capabilities. The converse is true of some development

organizations; while inherently more suited to exploitation, some are charged,

nevertheless, with a stronger role in enhancing capabilities.

This point is illustrated by the examples given in the following section. We

describe four matched pairs of units from two companies in different industries. Section 4

(Showing That the Distinction Makes a Difference) then examines data from 32

companies to test the proposition that role (as defined above) is better than function at

predicting four key management choices.

ILLUSTRATING THE TWO DISTINCTIONS THROUGH CASE STUDIES

The following eight examples describe investments in R&D sites abroad by two

firms, Hoechst and IBM. Data describing these units were collected through archival

research, a detailed questionnaire survey, eleven interviews at IBM and more than twenty

interviews at Hoechst, where one of the authors spent two months studying resource

allocation processes in the pharmaceutical division. The case studies illustrate the

differences between the functional classification of R vs. D and the capability-based

classification of capability-augmenting and capability-exploiting. We selected one

investment in each field of a 2 x 2 matrix for each of the two firms. (See Figure 1.) The

5 Note that we assume that a firm operates a number of geographically dispersed R&D sites and that the firm has a home base site, where innovation activities are coordinated and where technology strategy is

10

case studies in the lower left part and the upper right part of the matrix are clearly the

most interesting since they concern situations in which the two distinctions are not

congruent. All eight case studies are described below.

Capability-Augmenting & Research

In 1963 IBM established a research laboratory in Zurich whose main objective

was basic research related to computing. Zurich was selected because of the quality of its

technical university and the availability of a large pool of talented international scientists

who had settled in Switzerland during and after World War II. IBM’s vision was to

transfer knowledge from this site to IBM’s home-base laboratory in Yorktown Heights.

Within IBM the Zurich laboratory is considered a successful investment by several

measures. Not only did the lab generate two Nobel Prizes in physics, it had an impact on

IBM’s product portfolio and, among graduate students, improved the firm’s reputation as

potential employer.

In 1985 Hoechst established a laboratory for biotechnology research on the

premises of Massachusetts General Hospital in Boston, a decision triggered by a hostile

regulatory environment and a negative public opinion towards biotechnology in Germany.

Hoechst felt that biotechnology would have an increasingly strong impact on drug

discovery techniques; Boston was one of the premier locations for medical and

biotechnology research in the U.S., and Hoechst had established close contacts with a

number of scientists there prior to building the laboratory. The laboratory clearly had a

capability-augmenting mission aimed at acquiring biotechnology-related knowledge for

formulated.

11

use in the discovery process of pharmaceuticals around the world. While the Boston site

was staffed exclusively with MGH researchers, Hoechst required site leaders to provide

detailed progress reports about research to central R&D in Frankfurt. Furthermore,

Hoechst created an employee rotation program whereby employees from Hoechst’s

central R&D would come to Boston for periods ranging from six months to a year to

learn new research techniques and to interact with researchers at MGH.

Capability-Exploiting and Development

In the early 1960s IBM provided both mainframe hardware and customized

software for corporate clients in a rapidly growing market. In 1965 it established an

engineering site in Stockholm with the purpose of better adapting IBM’s products to

Scandinavian markets. The site had become necessary because the geographical distance

between Scandinavia and IBM’s European headquarters in London proved too large to

serve existing and new customers effectively with software applications and

documentation in the relevant languages.

Before 1970, Hoechst had been selling pharmaceuticals in Japan through a

minority owned subsidiary as well as through licensing some of its products to Japanese

pharmaceutical companies that marketed these drugs exclusively in Japan. In order to

signal to both doctors and the Japanese Ministry of Health and Welfare that it was

committed to the Japanese market for prescription drugs, Hoechst established a drug

development site in Tokyo in 1970. The purpose of this site was to better exploit the

potential of its indigenous drug portfolio by developing and marketing more drugs

12

through Hoechst’s Japanese subsidiary rather than through joint-venture partners or

licensing agreements.

Capability-Augmenting & Development

In 1961 IBM established a development site in Boeblingen close to Stuttgart,

Germany. The site was designated to focus on development of computer peripherals and

application software for mainframe computers. The output of the site was designated to

be used primarily in the German market but also in other European markets. Germany

was chosen as a location not only because of the significance of the German market

within Europe, but also because IBM could tap into outstanding engineering talent

available in the Boeblingen region. Several of the peripherals and some of the software

components developed at that site combined a number of novel technical features. These

peripherals were subsequently adopted and further refined by IBM’s home base site in the

US and sold on a worldwide level. Over time the Stuttgart site grew in importance and

although it still focused primarily on hardware and software development it became an

important contributor to IBM’s overall technology base. Incidentally, the four founders of

the world’s fifth largest business software firm, SAP, all worked as engineers at IBM in

Boeblingen for a number of years before starting their firm.

In 1993, Hoechst acquired a small biotechnology company in Australia that

concentrates on drug delivery systems (these systems transport chemical substances to

relevant receptors in the human body). Drug delivery is part of the drug development

process, which begins only after a new chemical entity has been identified as safe and

effective; knowledge about drug delivery systems is generally shared across different

13

therapeutic areas within a pharmaceutical firm. Although Hoechst decided to make the

acquisition because one of its pharmaceutical divisions needed additional drug delivery

capabilities, senior R&D managers also determined that knowledge concerning drug

delivery systems created in Australia could be used most effectively if it was accessible

for all therapeutic areas within Hoechst. Thus, while the laboratory in Sydney is part of

drug development activities, it clearly has a capability-augmenting mission.

Capability-Exploiting & Research

Having been in the Japanese market for several decades, IBM decided, in 1981, to

intensify its sales effort there, particularly for customized hardware in the mainframe

segment. To better exploit its knowledge base in hardware design and manufacturing,

IBM deemed it necessary to better understand the differences in basic information-

processing patterns between English and Japanese. Thus, it set up a research site closely

linked to a number of linguistics researchers at the University of Tokyo. This site carried

out research concerning questions specific to the Japanese language and market in order

exploit IBM’s general knowledge base on information processing. The site was

capability-exploiting in nature, as most of the knowledge for new products for the

Japanese market was flowing to Japan from IBM’s home base sites in the US.

Griliches (Griliches 1957) observed that researchers seeking to develop new plant

varieties established laboratories at sites where specific climate conditions existed. In

1975, Hoechst made a similar decision concerning its herbicide business. India had

become increasingly important as a market for herbicides, and Hoechst wanted to better

exploit its large knowledge base as one of the world’s leading producers of herbicides. In

14

order to develop a very detailed understanding of climate and soil conditions in India,

Hoechst established a research site in Mulund. Like IBM’s site in Tokyo, this site was

geared toward basic research that would enable Hoechst to better exploit its portfolio of

herbicides on the Indian subcontinent.

SHOWING THAT THE DISTINCTION MAKES A DIFFERENCE

In a population of 156 R&D laboratories operating away from the “home base” of

multinational companies in the electronics and pharmaceutical industries, and describable

in terms of both sets of categories -- research/development and capability

augmenting/exploiting -- those classified as having similar roles resemble each other

more than do those performing similar functions. We shall demonstrate this by testing for

similarities and differences on three key managerial dimensions: leadership, staffing, and

local relationships.

Differences in the expected character of knowledge flows between “home base”

and these dispersed sites are suggested schematically in Figure 2. When the intended role

is “capability-augmenting,” the establishing firm hopes to ‘tap’ into the work of others in

the community by capturing some of the spillovers from universities and competitors.6

To facilitate that, we believe that such a unit is more likely to be led by someone recruited

from the country in which it is situated and whose prior experience took place outside the

parent company – in other words, someone with existing ties to other local technological

6 The significance of spillovers to productivity in pharmaceutical research is emphasized by Henderson and Cockburn (Henderson and Cockburn 1996).

15

institutions, whether academic or industrial. Similarly, we conjecture that a higher

proportion of staff at the unit will hold the Ph.D., a proxy for easier access to the

technological community outside the unit. Finally, managers in such a unit will encourage

some joint activity with local academic institutions.

For their part, capability-exploiting sites are established in close proximity either to

factories or markets or both. These sites facilitate the transfer of knowledge from the

locus of knowledge creation to the locus of production and revenue generation. In other

words, capability-exploiting sites help the firm identify opportunities for existing

knowledge as well as the need for creating new knowledge. For those purposes, Ph.D.

staff and collaboration with universities should be less important, while leadership with

ties within the company, especially in relation to home base, will be more important.

Data, Dependent, Independent Variables and Hypothesis

In an effort to understand the global dispersion of R&D activities, one of the

current authors had earlier collected data on all foreign locations at which 32

pharmaceutical and electronics companies carry out R&D activities. These two industries

were chosen because a number of independent surveys identified them as the most active

in terms of geographical dispersion of R&D (MIRI 1991; OECD 1993). The sample

comprises 13 pharmaceutical firms and 19 electronics firms; 10 firms have their home

base in the US, 12 in Japan, and 10 in Europe. All firms are large; 19 of the 32 firms are

Fortune Global 100 companies. Altogether, there are 156 R&D sites abroad in the

sample. Data were collected in late 1994 and in 1995.

16

The data collection effort included archival research, a detailed questionnaire, and

at least two, normally three, interviews with senior managers in R&D and top line

managers at each of the companies. A senior R&D manager who was based at the firm’s

home base generally completed the questionnaire. In the questionnaire, managers were

asked to quantify how much of each laboratory’s total capacity (in terms of full-time

equivalent researchers) was allocated to research versus development. Almost all

laboratories clearly fall into one of the two categories. The questionnaire also asked

managers to classify laboratory sites as either capability-exploiting or capability-

augmenting.7 Questionnaire results were confirmed during the subsequent interviews.

Figure 3 shows the classification of R&D sites: 19.9% of the cases fall into

quadrants 2 and 3; in 80.1% of the cases ‘research’ overlaps with ‘capability-augmenting’

and ‘development’ overlaps with ‘capability-exploiting.’ The largest share of sites

(46.1%) falls into the category of capability-exploiting/development, while a somewhat

smaller share (34.0%) falls into the category of capability-augmenting/research.

The four independent variables used in this paper are among the most important

variables that managers consider when starting a laboratory. One author identified the

significance of these variables in a pilot study, and in the main study found that all the

firms in his sample had made explicit managerial choices regarding these variables for all

of the sites in the sample. As noted earlier, the three key dimensions are leadership,

staffing, and local relationships.

7 In the survey respondents were provided with a definition of these concepts. Capability-augmenting activities were defined as ‘experimental or theoretical work undertaken to create and acquire new knowledge that your firm considers important for future products.’ Capability-exploiting activities were

17

Leadership: The technical leader sets directions for the lab and assures that it is connected

appropriately to both the local environment and the firm’s home base. The parent often

faces a choice between a local resident and a corporate insider. Our first measure on the

leadership dimension is to test for local nationality. In addition, the parent often must

choose between hiring a person who has worked for the company before and someone

who has worked for a competitor, a public research institution, or a university. We

classified a laboratory leader as an outsider if she had held a position outside of the

company immediately prior to being appointed leader of the laboratory site.

Staffing: Each firm also has to make decisions about the human resource profile of each

laboratory site. We use the percentage of personnel with Ph.D. degrees as an indicator for

the role orientation of a laboratory.

Local relationships: Firms also have to decide whether they want to carry out joint

research projects with local universities. Because such projects generally consume

considerable financial and human resources on the firm’s side, if a laboratory commits to

doing joint research with a local university, it is generally an indicator that the laboratory

is oriented towards capability-augmenting activities. We classified laboratories as

positive on this dimension if they had carried out at least one research project with a local

university that lasted for at least one year during the preceding five years.

defined as ‘systematic work, drawing on existing knowledge within the firm and directed toward producing products in the near future or adapting existing products.

18

Our basic hypothesis is as follows: The role-oriented distinction of capability-

augmenting versus capability-exploiting sites is a more powerful predictor regarding

important laboratory management characteristics than the functional distinction of

research versus development. This hypothesis is supported by the relationships indicated

in the analysis below.

Analysis and Results

Table 2 shows a correlation matrix and Table 3 displays results from the four

independent regressions. We included variables for the location of the firm’s home base

in either Japan or Europe, with the US as a base case. We also included a control variable

for industry. Table 2 shows the product moment correlation coefficients; none of them

exceed 0.65. This indicates that the independent variables are not measuring identical

constructs. Regression 3 is an OLS regression where the dependent variable is the

percentage of laboratory personnel with Ph.D. degrees. Regressions 1,2 and 4 are logistic

regressions where the dependent variable is binary. Regressions 3 and 4 rely on 151 and

155 observations, respectively, because of missing data.

Table 3 shows that on four of the four management tools CAPAUG (capability-

augmenting versus capability-exploiting) is significant. RESEARCH (research versus

development) is significant only on one management tool (location of laboratory leader),

but it is less significant (10% level) than CAP (1% level).8 Table 3a presents an extended

8 We also included an interaction term between RESEARCH and CAP and found that it was not significant in any of the regressions.

19

version of Table 3, where RESEARCH and CAPAUG are entered successively into

regression models. Results show that the entry of CAPAUG leads to a much stronger

increase in explained variance than RESEARCH in all four models. For the percentage of

personnel with Ph.D. degrees, for example, the adjusted R2 is 37% in model 3a (using

CAPAUG as independent variable) while the adjusted R2 in model 3b (using

RESEARCH as independent variable) is only 19%.

We also examined just the 31 sites that are capability-augmenting/development

and capability-exploiting/research (quadrants II and III in Figure 3). For this sub-sample

of sites, CAPAUG and RESEARCH have opposite values and therefore make opposite

predictions. When entering CAPAUG into models 1, 2 and 4 we find that odds ratios for

CAPAUG are always larger than 1 (Table 4). For model 3, the regression coefficient is

positive for CAPAUG. These results show that even if one examines only the cases where

the independent variables CAPAUG and RESEARCH make differing predictions, the

CAPAUG coefficient carries the expected sign in the OLS regression and is larger than 1

in the logistic regressions.

In summary, we find strong support for all our hypotheses.9 The distinction of

capability-augmenting versus capability-exploiting has more powerful effects on the four

dependent variables examined, than the distinction of research versus development.10 We

ran the four models with CAPAUG and RESEARCH in the same regressions. In all

9 Our results are based on the assumption of a correct classification of laboratory sites along each of the two distinctions. During the data gathering process we made a strong effort to prevent any measurement errors. The existence of such errors would weaken the results of our statistical analysis in section 4 (Showing That the Distinction Makes a Difference.) 10 The results also show that pharmaceutical laboratory sites have a higher percentage of scientists and engineers with Ph.D. degrees than electronics labs and a higher probability of having an outside leader than electronics labs.

20

cases, the magnitude of the coefficient on CAPAUG was greater than on RESEARCH.

We then also tested the null hypothesis that coefficients for CAPAUG and RESEARCH

are identical. This hypothesis could be rejected for models 1, 2, 3 and 4 at the 5%, 10%,

1% and 1% confidence levels, respectively.

CONCLUSION

The analysis of matched case studies and the statistical analysis in this study

demonstrate the validity of distinguishing between capability-augmenting and capability-

exploiting roles for laboratory units. The distinction focuses attention on the mission of a

laboratory site within a network of innovative units, while the conventional distinction

between ‘research’ and ‘development’ focuses on its function. We have shown that a

mission-oriented classification is a stronger predictor of key managerial choices about

leadership, staffing, and local relationships than is a functional classification. Real

differences are embedded in the distinction we have advanced.

Because we did not analyze the effectiveness of the staffing and leadership

decisions in the organizations we studied, we cannot speak to the normative value of

using the capability-based distinction. Anecdotal evidence from ongoing research by one

of the authors, however, suggests that some firms are using the new distinction in their

strategic planning and innovation management efforts. Particularly when new laboratories

are established, the new distinction is used to define and to communicate the mission of

the new site.

21

The capability-based distinction offers some potential practical advantages to

managers. It is less abstract than the functional distinction (research versus development)

and states a clear business objective for each organization within the network of

innovative units. It might prove easier for a scientist or engineer to contribute to the

organization’s mission if she clearly understands what it means to be part of a capability-

augmenting unit. Finally, the capability-based designation may make communication

within the firm easier for day-to-day operations and also may help strategic planners in

designing an organizational structure along the dimension of knowledge flows that meet

the firm’s communication needs.

One question that arises from our analysis is whether one set of categories should

replace the other. One could argue that with increasing geographical dispersion of

innovative activities and in the light of the existing ambiguity about the boundary

between research and development, it would be better to stop using the conventional

functional categories. We do not share this view. Instead, we would suggest that the two

distinctions can be used in a complementary fashion. We advocate the capability-oriented

distinction for classifying organizational units within a dispersed network of activities.

We believe that it can offer a clear ‘sense of direction’ and prove useful as a tool for

resource allocation, strategic planning and corporate communication. The functional

distinction between research and development should continue to be a useful tool for the

analysis of a portfolio of research programs. Furthermore, many governmental entities are

committed to this framework for purposes of statistical data collection.

Our analysis addressed only a few of the salient management choices related to

human resource management and technology integration of laboratories. Future research

22

could be extended to include a larger number of critical management decisions in the

laboratory start-up and management process.

While the categories we studied are particularly applicable to innovation

management, they may also prove useful for the analysis of organizational networks

dedicated to other functions. Some manufacturing sites for example, focus on products

that experience low growth or even decline. Other manufacturing units may focus on

product areas with high potential growth. The first type may need primarily to exploit

existing firm capabilities to the fullest degree, while the latter may be charged with to

building up new capabilities. Empirical research using these ideas could test the

significance of the distinction in other business functions.

As future research builds on the findings of this paper it seems worthwhile to

explore how resources are allocated to capability-augmenting and capability-exploiting

activities. This research will lead to a better understanding of the evolution of firm

capabilities over time and across borders.

23

Table 1 Proposed Classification Schemes

Source Categories Comments (Dalton and Serapio 1993) • Basic research

• Technology sourcing • Product design • Product customization

A mix of functional and capability-based dimensions

(Brockhoff and Schmaul 1996)

• Headquarters dependent • Locally dependent • Shared R&D decision-making

autonomous

Two related dimensions: Dependency and autonomy

(Joly and Mangematin 1996) • Research centers for the profession

• Designers of generic tools and methods

• Basic and specialized laboratories

Public sector only. Focuses particularly on contractual relationships and appropriability of output.

(Mansfield 1984) • Product technology R&D • Process technology R&D

Capability-oriented rather than site-oriented

(Pearce 1989) • Locally integrated • Internationally integrated • Support

Support sites are small and neither locally nor globally integrated

(Eto 1991) Eleven different types according to several criteria, including geographic and functional focus

(Medcof 1997) Eight types along three dimensions: • Spatial focus (local versus

international) • Functional focus (research,

development, support) • Intra-firm service recipient

(manufacturing, marketing)

(Crow and Bozeman 1987) Nine types of laboratories according to their degree of publicness regarding ownership and output

Embraces public sector as well as private; useful for distinguishing public from private R&D sites but does not focus specifically on information flows within the firm.

Table 2: Correlation Coefficients CAPAUG RESEARCH PCTPHD LEADNAT OUTSLEAD UNIRESJV PHARMA

CAPAUG 1.00 RESEARCH 0.61* 1.00 PCTPHD 0.59* 0.42* 1.00 LEADNAT 0.64* 0.50* 0.41* 1.00 OUTSLEAD 0.31* 0.19 0.36* 0.35* 1.00 UNIRESJV 0.60* 0.41* 0.45* 0.57* 0.30* 1.00 PHARMA 0.04 0.14 0.14 0.05 0.12 0.08 1.00 EUROPE 0.23* 0.24 0.06 0.11 -0.0 0.16 0.28* JAPAN -0.02 -0.14 0.07 -0.16 -0.0 -0.07 -0.26

EUROPE JAPAN

EUROPE 1.00 1.00 JAPAN -0.46*

* - Signif. 0.01

Table 3: Regression Results

Model 1 2 3 4

Dep. Variable LEADNAT OUTSLEAD PCTPHD UNIRESJV

Description lab leader is host country national

lab leader hired from outside

% personnel w/ Ph.D.

Lab has Research Agreement With university

Regression Logistic

Logistic OLS Logistic

Sample full full full full

Indep. variables CAPAUG 24.50*** 5.08*** 0.11*** 17.77*** RESEARCH 2.55* 0.95 0.02 1.3 PHARMA 0.94 2.01* 0.03** 1.34 JAPAN 0.23*** 0.82 0.03* 0.74 EUROPE 0.53* 0.35** -0.02 0.86 Intercept 0.04***

N 156 156 151 155 F-value 19.04 Adjusted R2 0.38 Chi-Square 82.94 22.14 63.56 % of cases classified correctly

80.8% 69.9% N.A. 80.0%

Pseudo R2 0.39 0.11 0.30 * significant at 10% confidence level ** significant at 5% confidence level *** significant at 1% confidence level Models 1, 2 and 4 report odds-ratios, model 3 reports coefficients. Note: statistical software Stata 6.0 was used for all statistical analysis in this paper.

26

Table 3a: Regression Results (Table 3 including models for separate entry of CAPAUG and RESEARCH)

Model 1 1a 1b 2 2a 2b 3 3a 3b 4 4a 4b

Dep. Variable LEADNAT LEADNAT LEADNAT OUTSLEAD OUTSLEAD OUTSLEAD CTPHD PCTPHD PCTPHD UNIRESJV UNIRESJV UNIRESJV

Regression Logistic Logistic Logistic Logistic Logistic Logistic LS OLS OLS logistic logistic logistic Sample full full full full full full full full full full full full Indep. Variables CAPAUG 24.50*** 39.81*** 5.08*** 4.92*** 0.11*** 0.12*** 17.77*** 20.88*** RESEARCH 2.55* 9.50*** 0.95 2.44** 0.02 0.08*** 1.3 5.58*** PHARMA 0.94 1.11 0.84 2.01* 2.00* 1.77 0.03** 0.04** 0.03* 1.34 1.37 1.09 JAPAN 0.23*** 0.22*** 0.51 0.82 0.82 1.03 0.03* 0.02 0.04** 0.74 0.72 1.09 EUROPE 0.53* 0.28* 0.71 0.35** 0.35** 0.52 0.02 -0.01 0.00 0.86 0.87 1.52 Intercept 0.04*** 0.04*** 0.05***

N 156 156 156 156 156 156 151 151 151 155 155 155 F-value 19.04 23.31 10.29 Adjusted R2 0.38 0.37 0.19 Chi-Square 82.94 79.60 43.42 22.14 22.13 9.75 63.56 63.29 29.05 % of cases classified correctly

80.8% 82.1% 75.0% 69.9% 69.9% 64.8% N.A. N.A. N.A. 80.0% 80.0% 71.0%

Pseudo R2 0.39 0.37 0.20 0.11 0.11 0.05 0.30 0.30 0.13 * significant at 10% confidence level ** significant at 5% confidence level *** significant at 1% confidence level Models 1, 1a, 1b, 2, 2a, 2b, 4, 4a, 4b report odds-ratios, models 3, 3a and 3b report coefficients.

27

Table 4: Regression Results for Sample of Capability-Augmenting/Development and Capability-Exploiting/Research Sites Only

Model 1c 2c 3c 4c

Dep. Variable LEADNAT OUTSLEAD PCTPHD UNIRESJV

Regression Logistic Logistic OLS Logistic Sample Sub-sample Sub-sample Sub-sample Sub-sample Indep. Variables CAPAUG 12.85** 15.61** 0.12** 21.23** PHARMA 11.58** 17.89** -0.04 11.94 JAPAN 1.33 -0.04 4.44 EUROPE 0.35 0.16 0.01 1.81 Intercept 0.13**

N 31 31 30 30 F-value 1.52 Adjusted R2 0.07 Chi-Square 9.26* 10.70** 9.86** Pseudo R2 0.22 0.27 0.24 * significant at 10% confidence level ** significant at 5% confidence level *** significant at 1% confidence level Models 1c, 2c, 4c report odds-ratios, model 3c reports coefficients. The dummy variable for JAPAN was dropped in model 2c because of multicollinearity. Probability > F for model 3c: 0.22.

Figure 1: 8 Case Studies

Capability-Augmenting

Capability-Exploiting

Research

Development

I

•IBM in Zurich•Hoechst in Boston

III

•IBM in Tokyo•Hoechst in Mulund

II

•IBM in Boeblingen•Hoechst in Sidney

IV

•IBM in Stock- holm•Hoechst in Tokyo

29

Figure 2: Direction of Primary Knowledge Flows Between Home Base and Geographically Separate R&DSites

Home-baseR&D Site

Capability-augmenting

site

Capability-exploiting

site

Capability-augmenting

site

Capability-exploiting

site

Technology Related InformationMarket and Manufacturing Related Information

30

Figure 3: Number of R&D Sites in Each Category

CapabilityAugmenting

CapabilityExploiting

Research

Development

I III

II IV

53(34.0%)

24(15.4%)

7(4.5%)

72(46.1%)

Total number of sites: 156

References

Bartlett, C. A., and Ghoshal, S. (1990). “Managing Innovation in the Transnational Corporation.” Managing the Global Firm, C. A. Bartlett, Y. Doz, and G. Hedlund, eds., Routledge, London.

Birkinshaw, J., Hood, N., and Jonsson, S. (1998). “Building Firm-Specific Advantages in Multinational Corporations: The Role of Subsidiary Initiative.” Strategic Management Journal, 19(3), 221-241.

Brockhoff, K. K. L., and Schmaul, B. (1996). “Organization, Autonomy, and Success of Internationally Dispersed R&D Facilities.” IEEE Transactions on Engineering Management, 43(February), 33-40.

Brooks, H. (1994). “The Relationship Between Science and Technology.” Research Policy, 23, 477-486.

Cantwell, J. (1989). Technological Innovation and Multinational Corporations, Basil Blackwell, Oxford.

Clark, K., and Fujimoto, T. (1991). Product Development Performance: Strategy, Management and Organization in the World Auto Industries, Harvard Business School Press, Boston, MA.

Crow, M., and Bozeman, B. (1987). “R&D laboratory classification and public policy: The effects of environmental context on laboratory behavior.” Research Policy, 16, 229-258.

Dalton, D. H., and Serapio, M. G. (1993). U.S. Research Facilities of Foreign Companies, US Department of Commerce - Technology Administration, Washington, DC.

Dalton, D. H., and Serapio, M. G. (1997). Foreign R&D Facilities in the United State (Draft), , Washington DC.

DeMeyer, A. (1993). “Management of an International Network of Industrial R&D Laboratories.” R&D Management, 23(2), 109-120.

Eto, H. (1991). “Classification of R&D Organizational Structures in Relation to Strategies.” IEEE Transactions on Engineering Management, 38(2), 146-156.

Florida, R. (1997). “The Globalization of R&D: Results of a Survey of Foreign-affiliated R&D laboratories in the USA.” Research Policy, 26(1), 85-103.

Freeman, C. (1982). The Economics of Industrial Innovation, MIT Press, Cambridge, MA.

Griliches, Z. (1957). “Hybrid Corn: An Exploration into the Economics of Technical Change.” Econometrica, 25(4), 501-522.

Gupta, A. K., and Govindarajan, V. (1994). “Organizing for Knowledge within MNCs.” International Business Review, 3(4), 443-457.

32

Hakanson, L., and Nobel, R. (1993). “Foreign Research and Development by Swedish Multinationals.” Research Policy, 22, 373-396.

Hedlund, G., and Rolander, D. (1990). “Action in Heterarchies - New Approaches to Managing the MNC.” Managing the Global Firm, C. Bartlett, Y. Doz, and G. Hedlund, eds., Routledge, London.

Helfat, C. E. (1997). “Know-how and Asset Complementarity and Dynamic Capability Accumulation: The Case of R&D.” Strategic Management Journal, 18(5), 339-360.

Henderson, R., and Cockburn, I. (1996). “Scale, scope, and spillovers: The determinants of research productivity in drug discovery.” The Rand Journal of Economics, 27(Spring), 32-59.

Joly, P. B., and Mangematin, V. (1996). “Profile of public laboratories, industrial partnerships and organisation of R&D: the dynamics of industrial relationships in a large research organization.” Research Policy, 25, 901-922.

Kogut, B. (1991). “Country Capabilities and the Permeability of Borders.” Strategic Management Journal, 12(Special Issue), 33-47.

Kogut, B., and Chang, S. (1991). “Technological Capabilities and Foreign Direct Investment in the United States.” The Review of Economics and Statistics, 401-413.

Kuemmerle, W. (1996). Home Base and Foreign Direct Investment in Research and Development (Unpublished Doctoral Dissertation), Harvard University, Boston.

Kuemmerle, W. (1997). “Building Effective R&D Capabilities Abroad.” Harvard Business Review(March/April), 61-70.

Kuemmerle, W. (1999). “Foreign Direct Investment in Industrial Research in the Pharmaceutical and Electronics Industries - Results from a Survey of Multinational Firms.” Research Policy, 28(2-3), 179-193.

Leonard-Barton, D. (1995). Wellsprings of Knowledge: Building and Sustaining the Sources of Innovation, Harvard Business School Press, Boston.

Link, A. N. (1996). “On the Classification of Industrial R&D.” Research Policy, 25, 397-401.

Mansfield, E. (1984). “R&D and Innovation: Some Empirical Findings.” R&D, Patents and Productivity, Z. Griliches, ed., University of Chicago Press, Chicago.

Mansfield, E. (1991). “Social Returns from R&D: Findings, Methods and Limitations.” Research and Development Management(Nov./Dec.), 24-27.

March, J. G. (1991). “Exploration and Exploitation in Organizational Learning.” Organization Science, 2, 71-87.

Medcof, J. W. (1997). “A Taxonomy of Internationally Dispersed Technology Units and its Application to Management Issues.” R&D Management, 27(4), 301-318.

33

MIRI. (1991). Grobaraizeishon no shinten to sangyo ni kan suru chosakusho, Mitsubishi Research Institute, Tokyo.

Nelson, R., and Winter, S. (1982). An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge.

Nonaka, I., and Takeuchi, H. (1995). The Knowledge-creating Company: how Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York, NY.

OECD. (1981). The measurement of scientific and technical activities: proposed standard practice for surveys of research and experimental development, OECD, Paris.

OECD. (1993). “Globalisation of Industrial Activities: Sector Case Study of Globalisation in the Pharmaceutical Industry.” DISTI/IND(93)4, OECD.

OECD. (1997). Globalisation of Industrial Research: Background Report (DSTI/STP/TIP (97)6, OECD, Paris.

Pearce, R. (1989). The Internationalization of Research and Development by Multinational Enterprises, St.Martin's Press, New York.

Pearce, R., and Singh, S. “Internationalization of R&D among the World's leading Enterprises: Survey Analysis of Motivation, Organizations and Implications.” Conference on Technology Management and International Business, Stockholm.

Smith, D. K., and Alexander, R. C. (1988). Fumbling the Future: How Xerox invented, then ignored, the first personal compute, WilliamMorrow, New York.

Teece, D. J., Pisano, G., and Shuen, A. (1997). “Dynamic capabilities and strategic management.” Strategic Management Journal, 18(7), 509-533.

Thomke, S. H., von Hippel, E. A., and Franke, R. R. (1997). Modes of Experimentation: An Innovation Process - and Competitive - Variable, Harvard Business School, Boston.

von Hippel, E. (1988). The Sources of Innovation, Oxford University Press, New York.

von Hippel, E. (1994). “"Sticky Information" and the Locus of Problem Solving: Implications for Innovation.” Management Science, 40(4), 429-439.

Wesson, T. (1993). An Alternative Motivation for Foreign Direct Investment (Unpublished Dissertation), Harvard University, Cambridge, MA.

Westney, D. E. (1993). “Cross Pacific Internationalization of R&D by US and Japanese Firms.” R&D Management, 23(2), 171-182.