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Technological Acquisitions and the Innovation Performance of Acquiring Firms: A Longitudinal Study

March 2001
Strategic Management Journal 22(3):197 - 220

March 2001
22(3):197 - 220

Authors:

Stanford University

This paper examines the impact of acquisitions on the subsequent innovation performance of acquiring firms in the chemicals industry. We distinguish between technological acquisitions, acquisitions in which technology is a component of the acquired firm's assets, and nontechnological acquisitions: acquisitions that do not involve a technological component. We develop a framework relating acquisitions to firm innovation performance and develop a set of measures for quantifying the technological inputs a firm obtains through acquisitions. We find that within technological acquisitions absolute size of the acquired knowledge base enhances innovation performance, while relative size of the acquired knowledge base reduces innovation output. The relatedness of acquired and acquiring knowledge bases has a nonlinear impact on innovation output. Nontechnological acquisitions do not have a significant effect on subsequent innovation output. Copyright © 2001 John Wiley & Sons, Ltd.

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Strategic Management Journal

Strat. Mgmt. J., 22: 197–220 (2001)

TECHNOLOGICAL ACQUISITIONS AND THE

INNOVATION PERFORMANCE OF ACQUIRING

FIRMS: A LONGITUDINAL STUDY

GAUTAM AHUJA

* and RIITTA KATILA

College of Business Administration, University of Texas at Austin, Austin,

Texas, U.S.A.

Robert H. Smith School of Business, University of Maryland, College Park, Mary-

land, U.S.A.

This paper examines the impact of acquisitions on the subsequent innovation performance of

acquiring ﬁrms in the chemicals industry. We distinguish between technological acquisitions,

acquisitions in which technology is a component of the acquired ﬁrm’s assets, and nontechnologi-

cal acquisitions: acquisitions that do not involve a technological component. We develop a

framework relating acquisitions to ﬁrm innovation performance and develop a set of measures

for quantifying the technological inputs a ﬁrm obtains through acquisitions. We ﬁnd that within

technological acquisitions absolute size of the acquired knowledge base enhances innovation

performance, while relative size of the acquired knowledge base reduces innovation output.

The relatedness of acquired and acquiring knowledge bases has a nonlinear impact on

innovation output. Nontechnological acquisitions do not have a signiﬁcant effect on subsequent

innovation output. Copyright 2001 John Wiley & Sons, Ltd.

In this paper we examine the impact of acqui-

sitions on the subsequent innovation performance

of acquiring ﬁrms. Studying the impact of acqui-

sitions on postacquisition innovation performance

is important from at least three perspectives. First,

this evaluation is important from the perspective

of organizational learning and innovation, and

helps us understand how organizations absorb and

use external knowledge. Firm-level theories of

technical change suggest that a ﬁrm’s inno-

vativeness is an outcome of increases in its

knowledge base (Griliches, 1984, 1990; Pakes

and Griliches, 1984; Henderson and Cockburn,

1996). While a ﬁrm’s knowledge base can grow

through a series of knowledge-enhancing invest-

ments by the company over time, ﬁrms can also

grow their knowledge through acquiring or ‘graft-

ing’ of external knowledge bases (Cohen and

Levinthal, 1989; Huber, 1991). Interestingly,

Key words: innovation; acquisitions; knowledge

*Correspondence to: Gautam Ahuja, College of Business

Administration, University of Texas at Austin, Austin, TX

78712-1174, U.S.A.

Final revision received 12 October 2000

while the relationship between ﬁrms’ investments

in knowledge and their innovation output has

been studied extensively (Hall, Griliches and

Hausman, 1986; Griliches, 1990), relatively little

research has focused on the role of acquisitions

in growing the ﬁrm’s knowledge base (Granstrand

and Sjolander, 1990; Huber, 1991; Gerpott, 1995).

This lacuna is all the more surprising given ﬁnd-

ings which indicate that obtaining technological

know-how and developing technical capabilities

are increasingly important motives for acqui-

sitions (Link, 1988; Granstrand et al., 1992;

Chakrabarti, Hauschildt, and Suverkrup, 1994;

Wysocki, 1997a, 1997b).

Scholars studying the market for corporate con-

trol have also examined the relationship between

acquisitions and innovation output (Hitt et al.,

1991, 1996; Hoskisson, Hitt, and Ireland, 1994).

However, in contrast to the ﬁndings of the inno-

vation literature, studies in the corporate control

tradition have generally found that acquisitions

have a negative impact on the postacquisition

innovation output of acquiring ﬁrms. Agency

problems, reduction in managerial commitment to

198 G. Ahuja and R. Katila

innovation, and the absorption of managerial

energy in the acquisition integration process at

the expense of routine management have been

posited as possible explanations for these results

(Hitt et al., 1991, 1996). This conclusion, how-

ever, is puzzling since acquisitions continue to

be a popular strategy for corporate growth. In

recent years more dollars have been invested in

acquisition activity than in any other equivalent

time period in history (Curry, 1997). Evaluating

the postacquisition innovation output of acquiring

ﬁrms provides one indicator, albeit an indirect

one, of the returns to corporate investments in

acquisition activity.

A third reason for studying the impact of acqui-

sitions on the postacquisition performance of

acquiring ﬁrms comes from the growing literature

on the resource-based view of the ﬁrm. According

to this perspective acquisitions are an important

part of the business process of redeploying

resources into more productive uses (Anand and

Singh, 1997; Capron, Dussauge, and Mitchell,

1998; Capron, Mitchell, and Swaminathan, 1998).

Through acquisitions ﬁrm-speciﬁc assets housed

within one organization are merged with assets

in another organization to improve the productiv-

ity of the combined assets (Haspeslagh and Jemi-

son, 1991; Anand and Singh, 1997). Evaluating

the postacquisition performance of ﬁrms provides

evidence on the efﬁciency of this asset-matching

and combining process.

In this paper we draw upon theories of techno-

logical innovation, learning, and the resource-

based view to develop a theoretical model and

predictions relating acquisition characteristics to

the innovation performance of acquiring ﬁrms

(Cohen and Levinthal, 1989; Grant, 1996). Inno-

vation performance can be measured in terms of

innovative inputs such as R&D expenditures, or

innovation outputs such as patenting frequency

(Griliches, 1984; Henderson and Cockburn,

1996). Acquisitions can affect both innovative

inputs and innovative outputs (Hitt et al., 1991).

For instance, a ﬁrm’s R&D expenditures can

decrease after it conducts an acquisition as the

ﬁrm eliminates certain streams of research or as

managers become more risk averse (Hitt et al.,

1991). Yet, even while research efforts decrease,

the productivity of those efforts can increase as

the two hitherto separate research teams combine

their skills and knowledge. In this research we

focus on the impact of acquisitions on innovation

outputs as measured by the patenting frequency

of the acquiring ﬁrm. Accordingly, we adopt an

innovation production function framework and

model patenting frequency as the output of a

production function (Griliches, 1984). The

hypotheses of this study are statements about

the relationship between this patented knowledge

output and the ﬁrm’s stocks of owned and

acquired knowledge. In estimating the impact of

acquisitions on the innovative output of a ﬁrm

we statistically control for the levels of innovative

inputs such as R&D expenditures, but leave their

substantive examination to future work.

HYPOTHESES

In this research we focus on two contingencies

that may be critical to clarifying the relationship

between acquisitions and the postacquisition inno-

vation performance of acquiring ﬁrms. First, we

draw attention to the fact that technological

reasons do not motivate all acquisitions. For

example, acquisitions can be motivated by the

desire to obtain access to distribution channels,

to gain entry into new markets, or to obtain

ﬁnancial synergies or market power (Lubatkin,

1983; Balakrishnan, 1988; Chatterjee, 1991; Has-

peslagh and Jemison, 1991; Capron, Dussauge,

and Mitchell, 1998). Such acquisitions may pro-

vide no technological inputs to the acquiring ﬁrm

and therefore cannot be expected to improve its

innovation output. Second, we note that even

among technologically motivated acquisitions the

impact of acquisitions may depend on the charac-

teristics of the relationship between the knowl-

edge of the acquired and acquiring ﬁrms

(Lubatkin, 1983; Singh and Montgomery, 1987;

Lane and Lubatkin, 1998).

In summary, we argue that the impact of acqui-

sitions on the acquiring ﬁrm’s innovation output

can be understood in the context of the techno-

logical inputs provided by the acquisition. We

argue that acquisitions that provide no technologi-

cal inputs cannot be expected to have a positive

impact on ﬁrm innovation output (Hypothesis

1). Second, within acquisitions that do provide

technological inputs, we predict that the impact

of an acquisition on the postacquisition innovation

output of the acquiring ﬁrms is likely to vary

positively with the absolute size of the acquired

ﬁrm’s knowledge base, negatively with the rela-

Technological Acquisitions and Innovation 199

tive size of the acquired and acquiring knowledge

bases, and curvilinearly with the relatedness of

the acquired and acquiring knowledge bases

(Hypotheses 2, 3 and 4).

Technological vs. nontechnological

acquisitions

Acquisitions can affect the acquirer’s subsequent

innovation output through two possible mecha-

nisms. First, an acquisition of another ﬁrm can

be viewed as an absorption of the acquired ﬁrm’s

knowledge base into the acquiring ﬁrm’s knowl-

edge base. Such a union can potentially expand

the acquirer’s knowledge base and increase its

innovation output by providing economies of

scale and scope in research and by enhancing the

acquirer’s potential for inventive recombination

(Henderson and Cockburn, 1996; Fleming, 1999).

Since nontechnological acquisitions add less to

the knowledge base of the acquirer they are less

likely to lead to such innovation output-enhancing

effects.

However, an acquisition can also disrupt

the established routines of the acquiring ﬁrm and

those of its newly acquired component, and

thereby reduce productivity (Jemison and Sitkin,

1986; Haspeslagh and Jemison, 1991). Prior

research suggests that acquisition integration

entails far-reaching disruption, and involves sig-

niﬁcant managerial attention and transactions

costs (Pritchett, 1985; Haspeslagh and Jemison,

1991; Hitt et al., 1991, 1996; Hoskisson et al.,

1994). However, whether this disruption related

to nontechnological acquisitions will produce a

negative impact on innovative productivity is not

clear, a priori. On one hand it is possible that

as management focuses more on acquisitions and

their integration, decision making on routine tech-

nological matters can be delayed, activities such

as product championing can suffer, and a crisis

mentality on the management of the acquisition

can lead to only residual energies being supplied

to day-to-day operations even in the technological

core of the company (Thompson, 1967; Pritchett,

1985; Hitt et al., 1996). Alternately, it is pos-

sible that since such acquisitions do not

involve a technological component by deﬁnition,

they may not affect the technological subsystem

As suggested by one of the reviewers, in some cases

nontechnological acquisitions can increase innovation output,

for instance by providing contacts with new customers.

(Thompson, 1967) and innovation routines of the

ﬁrm, and therefore have no impact on innovation

output at all. The ﬁnal impact of an acquisition

depends on the degree to which these effects

come into play.

Accordingly, we present the following base

hypothesis:

Hypothesis 1: Nontechnological acquisitions

will affect the postacquisition innovation output

of acquiring ﬁrms either negatively or nonsig-

niﬁcantly.

Technological acquisitions and the absolute

size, relative size, and relatedness of

knowledge bases

Technological acquisitions are acquisitions that

provide technological inputs to the acquiring ﬁrm.

Thus, they potentially expand the acquirer’s

knowledge base and provide scale, scope, and

recombination beneﬁts (Henderson and Cockburn,

1996; Fleming, 1999). However, technological

acquisitions can also entail a disruption in organi-

zational routines. Further, this disruption is most

likely in the set of routines that are closest to

the innovation arena, the technological subsystem

of the ﬁrm. Thus, technological acquisitions can

also have a negative impact on the innovation

output of the acquiring ﬁrm. On balance,

assessing whether technological acquisitions will

have a positive or negative impact on postacqui-

sition innovation output is likely to depend upon

the quantity and nature of knowledge elements

that they bring to the acquiring ﬁrm. To evaluate

whether the scale, scope, and inventive recombi-

nation beneﬁts of acquisitions outweigh their

negative effects on organizational routines we

compare the knowledge bases of acquired and

acquiring ﬁrms along three key characteristics

that have also been prominently used in prior

acquisition research: absolute size, relative size,

and relatedness (Lubatkin, 1983). In the following

sections we examine and develop distinct hypoth-

eses for each of these characteristics.

Absolute size of acquired knowledge base

The absolute size of an acquired knowledge base

can affect the post acquisition innovation output

of the acquiring ﬁrm through at least two mecha-

nisms, both indicating a positive effect of larger

200 G. Ahuja and R. Katila

knowledge bases. First, the integration of the two

hitherto separate knowledge bases may enable

enhanced economies of scale and scope

(Henderson and Cockburn, 1996). For instance, a

long tradition of research in technology suggests

that new innovative outputs are often the result

of recombining existing elements of knowledge

into new syntheses (Schumpeter, 1934; Henderson

and Clark, 1990; Kogut and Zander, 1992; Tush-

man and Rosenkopf, 1992; Utterback, 1994;

Fleming, 1999). From this combinatorial perspec-

tive, the number of direct combinations that a

ﬁrm can create from its own knowledge elements

increases with the size of the acquired knowledge

base. While a ﬁrm with ﬁve units of knowledge

can generate 10 combinations using two elements

at a time, acquiring another ﬁrm with three units

of knowledge increases the number of combi-

nations that become available to 28. Similarly,

the merger of two knowledge bases can also

provide scale or scope economies by reducing

duplication in research efforts or by providing a

larger research base to defray costs.

Second, acquiring a larger knowledge base may

enhance a ﬁrm’s absorptive capacity. Prior

research indicates that as a ﬁrm expands its inter-

nal knowledge base and technological capability,

it also enhances its ability to absorb and utilize

external knowledge (Cohen and Levinthal, 1989;

Cohen and Levinthal, 1990). Thus, when a ﬁrm

acquires a knowledge base it obtains access not

only to the acquired ﬁrm’s internally created

knowledge but also to a larger external domain

of knowledge that is understood and used by

the acquired ﬁrm. Thus, acquisitions increase the

number of elements of both internal and external

knowledge that are available to the acquiring ﬁrm

and hence increase its potential for inventive

recombination. Hence, we hypothesize:

Hypothesis 2: The greater the absolute size of

the acquired knowledge base, the greater the

subsequent innovation output of the acquiring

ﬁrm.

Relative size of acquired knowledge base

The arguments above focused on the increased

scale, scope, and recombination beneﬁts possible

through the acquisition of a knowledge base. Yet,

several steps must be completed before newly

acquired knowledge can improve the acquirer’s

performance. The acquirer needs to recognize the

value and content of the acquired knowledge,

assimilate it, and apply it (Cohen and Levinthal,

1990). The degree to which these tasks can be

successfully accomplished is likely to vary with

the relative size of the acquiring and acquired

knowledge bases. The larger the relative size of

the knowledge base to be integrated, the more

difﬁcult these stages are likely to be, and the

more negative the impact on postacquisition inno-

vation output.

Knowledge is primarily transferred through

interactions between the acquired and acquiring

units, and entails both teaching and learning on

both sides (Haspeslagh and Jemison, 1991). Inte-

gration teams, meetings within and between the

two R&D units, and extensive face-to-face com-

munication are integral parts of the process by

which the merging units learn about each other’s

technology and processes (Gerpott, 1995). Since

every communication needs both a sender and a

receiver, the relative size of the knowledge bases

in the merger becomes relevant. If the merged

knowledge bases are relatively equal in size, most

of the knowledge resources of the combined ﬁrm

will be devoted to the task of integrating the two

knowledge bases. As the two approximately equal

groups educate each other, fewer resources will

be available for conducting the actual business

of innovation. On the other hand, if the two

knowledge bases are relatively dissimilar in size,

the absorption and assimilation activity will

occupy only a part of the larger group’s resources

even if it entails the preoccupation of the smaller

of the two groups.

A second dimension of the relative size effect

is the disruption of existing organizational rou-

tines (Haspeslagh and Jemison, 1991; Singh and

Zollo, 1997). For successful assimilation and

application of the newly acquired knowledge,

many changes have to be introduced into the

functioning of the organization. Pathways of com-

munication, routing of work and authority, and

formal and informal organizational structures all

have to be adapted to incorporate the acquired

unit’s knowledge (Gerpott, 1995). If the acquired

ﬁrm’s knowledge base is small relative to the

acquirer the modiﬁcations required are likely to

be minor, and therefore not be very disruptive.

However, if the acquired ﬁrm’s knowledge base

is large relative to the acquiring ﬁrm, fairly major

changes would have to be made in the acquiring

Technological Acquisitions and Innovation 201

ﬁrm, leading to a signiﬁcant disruption of existing

processes. Accordingly, we predict:

Hypothesis 3: The greater the relative size

of the acquired knowledge base, the less the

subsequent innovation output of the acquiring

ﬁrm.

Relatedness of acquired and acquiring

knowledge bases

The third critical dimension in the uniﬁcation of

two knowledge bases is their relatedness

(Lubatkin, 1983; Singh and Montgomery, 1987;

Lane and Lubatkin, 1998). While the previous

arguments concerned the magnitude of the

acquired and acquiring knowledge bases the

relatedness argument concerns the content of

these knowledge bases. We predict that

relatedness between the acquired and acquiring

knowledge bases is likely to have a nonmonotonic

inﬂuence on the subsequent innovation perfor-

mance of acquiring ﬁrms. Innovation output will

increase with increasing relatedness, but beyond

some optimum innovation output will decrease

with increasing relatedness.

The absorptive capacity argument suggests that

the ability to use new information to solve prob-

lems is enhanced when the new knowledge is

related to what is already known (Cohen and

Levinthal, 1990). Elements of similar knowledge

facilitate the integration of the acquired and

acquiring knowledge bases (Kogut and Zander,

1992; Grant, 1996). Common skills, shared lan-

guages, and similar cognitive structures enable

technical communication and learning (Cohen and

Levinthal, 1989; Lane and Lubatkin, 1998).

Further, the recipes for conducting research, or

the innovation routines of the acquired and

acquiring ﬁrms, are also likely to be different if

the ﬁrms come from distant realms of technology

(Kogut and Zander, 1992; Spender, 1989). In

such circumstances the integration of knowledge

bases can be resource consuming, or even

counterproductive as routines inappropriate to

either or both knowledge bases can be adopted

(Haspeslagh and Jemison, 1991; Singh and

Zollo, 1997).

On the other hand, an acquired knowledge base

that is too similar to the acquiring knowledge

base may also contribute little to subsequent inno-

vation performance. From an absorptive capacity

perspective acquired knowledge can help improve

performance through two effects. First, acquired

knowledge can provide a cross-fertilization effect

as old problems can be addressed through new

approaches, or by a combination of old and new

approaches (Cohen and Levinthal, 1990). Second,

new acquired knowledge can serve as the basis

for absorbing additional stimuli and information

from the external environment. If an acquisition

brings in knowledge that is too closely related to

the existing knowledge base of the acquiring ﬁrm,

both these beneﬁts might be limited.

Knowledge bases with moderate degrees of

relatedness provide the beneﬁts of enhancing the

variety of possible combinations that the ﬁrm can

use, while maintaining the elements of com-

monality that facilitate interaction between the

acquired and acquiring knowledge bases. Based

on the above arguments we suggest that acqui-

sitions that are characterized by a moderate

degree of overlap in the knowledge bases are

likely to provide the most signiﬁcant positive

impact on the acquiring ﬁrm’s subsequent inno-

vation output. Accordingly, we predict:

Hypothesis 4: The relatedness of the acquired

knowledge base will be curvilinearly (inverted

U) related to the subsequent innovation output

of the acquiring ﬁrm.

DATA AND METHODS

Organizational knowledge base

In the previous section we argued that an acqui-

sition can be viewed as the union of two knowl-

edge bases. Measuring an organizational knowl-

edge base is then the key operational issue in

testing the hypotheses. Speciﬁcally, we need to

identify empirical measures that capture the abso-

lute size, relative size, and relatedness of knowl-

edge bases. In the arguments below we suggest

that an organization’s patent portfolio provides a

means for capturing these dimensions and map-

ping an organization’s knowledge.

A patent, by deﬁnition, represents a unique and

novel element of knowledge. A set of patents

then represents a collection of discrete, distinct

units of knowledge. Identifying a set of patents

that a ﬁrm has demonstrated familiarity with, or

mastery of, can be a basis for identifying the

‘revealed’ knowledge base of a ﬁrm, the distinct

202 G. Ahuja and R. Katila

elements of knowledge with which the ﬁrm has

revealed a relationship (Kim and Kogut, 1996).

The patents owned by a ﬁrm represent the

knowledge that the ﬁrm is acknowledged as hav-

ing created (Jaffe, Trajtenberg and Henderson,

1993). Such patents are naturally elements of the

ﬁrm’s knowledge base. However, the ﬁrm’s pa-

tents also build on prior patents, the knowledge

created by the same ﬁrm in the past or by other

ﬁrms which have preceded it in that line of

inquiry. These prior patents are cited in the pa-

tents as recognition of their contribution to the

knowledge embodied in the focal patent. By cre-

ating a patent that builds on these prior patents,

the ﬁrm provides evidence that the knowledge

contained in those past patents is a part of the

ﬁrm’s knowledge set. Thus, the patents cited by

the ﬁrm should also be included in its knowledge

base. The Appendix describes in more detail the

logic underlying this approach to measuring

organizational knowledge bases.

Using patent data to measure organizational

knowledge bases corresponds closely to the con-

ceptual abstraction of a ﬁrm’s knowledge base as

a set of knowledge elements (Grant, 1996). The

number of cited and obtained patents provides a

measure of the size of the knowledge base. The

individual patents in the ﬁrm’s knowledge base

provide the basis for comparing the ﬁrm’s knowl-

edge base with other knowledge bases. Since

each patent number uniquely identiﬁes a distinct

component of knowledge, the higher the number

of patents that are common across two knowledge

bases, the higher the relatedness between those

knowledge bases (see also Stuart and Podolny,

1996; Mowery, Oxley and Silverman, 1998).

Methods

A panel data design was used to test the hypoth-

eses. We selected a sample of ﬁrms from the

global chemicals industry independent of their

acquisition behavior, and traced the acquisition

behavior of these ﬁrms over a 12-year period,

from 1980 to 1991 (more details on the sample

are provided later). Several of the sample ﬁrms

were very active in the acquisitions arena, while

others conducted few or no acquisitions at all.

We attempted to collect data on every acquisition

conducted by these ﬁrms. The acquisition charac-

teristics of these ﬁrms were modeled as time-

varying inﬂuences on the subsequent innovation

performance of these ﬁrms. A distributed lag

analysis was used to identify the effects of an

acquisition on innovation performance in the 4

years succeeding the acquisition.

This approach addresses several methodological

problems that arise in evaluating the impact of

acquisitions on the postacquisition performance

of acquiring ﬁrms. First, examining a single

industry over a common period controls for

industry and period effects that have been cited

as a problem with prior acquisition research

(Fowler and Schmidt, 1988). Second, this research

design naturally includes both ﬁrms that are active

in acquisitions and those that are inactive. In the

absence of the latter group of ﬁrms it is difﬁcult

to refute the argument that good or bad perfor-

mance by acquiring ﬁrms was not shared by

similar nonacquiring ﬁrms (Fowler and Schmidt,

1988). Third, this approach resolves the problem

of handling ﬁrms with multiple acquisitions. To

reduce the problem of confounding caused by

ﬁrms making several acquisitions over the study

period, some researchers have omitted such ﬁrms

from analyses (Fowler and Schmidt, 1988). How-

ever, omitting such ﬁrms may lead to biased

ﬁndings. With our research design such ﬁrms can

be retained in the analysis by including acqui-

sition and ﬁrm characteristics as time-varying

covariates in a panel regression. Finally, our panel

data set includes distributed lag effects. The dis-

tributed lag technique enables us to use multiple-

period lagged values of the independent variables

as additional regressors in the estimated equation

(Judge et al., 1988). By using this approach, we

can, in principle, trace the effects of the acqui-

sition on the performance of the acquiring ﬁrm

for several periods after the acquisition.

Model speciﬁcation and econometric issues

We now describe our econometric approach, fol-

lowed by a discussion of the industry setting and

the variables used to test the hypotheses. The

dependent variable of the study, innovation out-

put, as measured by patent counts, is a count

variable and takes only non-negative integer

values. The linear regression model’s assumptions

of homoskedastic, normally distributed errors are

thus violated. A Poisson regression approach is

appropriate for such data (Hausman, Hall, and

Griliches, 1984; Henderson and Cockburn, 1996).

Accordingly, we speciﬁed the following Poisson

Technological Acquisitions and Innovation 203

regression model:

=exp (X

it−1

γ+A

it−1

it−2

it−3

it−4

) (1)

where P

is the number of patents obtained by

ﬁrm iin year t,X

it⫺1

is a vector of control

variables affecting P

, and A

it-year

is the lagged

vector of the acquisition variables for years t⫺

1tot⫺4.

Intuitively, this speciﬁcation implies that the

number of patents obtained by any ﬁrm in any

year is randomly distributed following a Poisson

process, where the covariate vectors X

it⫺1

and

it⫺1

it⫺2

it⫺3

, and A

it⫺4

determine the mean

of this process. Changes in the value of individual

covariates thus inﬂuence patenting frequency by

affecting the mean of the Poisson distribution

from which observations are drawn, in a manner

identical to an ordinary regression.

Since the impact of an acquisition is likely to

be felt over a number of years, rather than

entirely in any one year, we used a distributed

lag model (Judge et al., 1988). To capture the

lag effects, the one-period, two-period, three-

period, and four-period lagged values of all acqui-

sition related variables were included as covari-

ates in the above model. This distributed lag

model tests the impact of acquisitions for up to

4 years after the year the acquisition was orig-

inally made. Thus, illustratively speaking, in these

models the acquiring ﬁrm’s innovation perfor-

mance in 1986 is potentially inﬂuenced by acqui-

sitions made in 1982, 1983, 1984, and 1985. In

sensitivity tests (results available from the

authors), instead of using four lags we also esti-

mated the models using three and ﬁve lags and

found results substantively identical to those

reported here.

The use of distributed lags provides two bene-

ﬁts. First, it enables us to examine the time

pattern of the impact of acquisitions on ﬁrm

innovation output. For instance, if an acquisition

contributes to improved performance for the ﬁrst

2 years but thereafter leads to no further improve-

ment in innovation output, the 1- and 2-year

lagged acquisition variables will be positive and

signiﬁcant, while the 3- and 4-year lagged acqui-

sition variables will be nonsigniﬁcant. Second,

since the total impact of an acquisition is likely

to be distributed over several periods following

the acquisition and may be statistically incon-

sequential in any one period, the regression coef-

ﬁcients on the distributed lags can be summed to

obtain the total impact of an acquisition across

time (Gujarati, 1988: 507). By using the variances

and covariances of the individual lag coefﬁcients

from the regression output the variance of the

summed coefﬁcient can be calculated. The

summed coefﬁcient can then be used for hypoth-

esis testing. For instance, the hypothesis that the

total impact of acquisitions summed across all

years is zero can be tested by computing the

following t-statistic and checking whether it is

statistically signiﬁcant at the desired conﬁdence

level (Greene, 1993; Gujarati, 1988).

t=(β

t−1

+β

t−2

+β

t−3

+β

t−4

)

/√Variance (β

t−1

+β

t−2

+β

t−3

+β

t−4

)

where the β

t⫺i

’s are the regression coefﬁcients

for the ith period lagged acquisition variable

(Judge et al., 1988). The variance for the summed

coefﬁcients can be computed using the following

relation (Gujarati, 1988: 507):

Variance (β

t−1

+β

t−2

+β

t−3

+β

t−4

)

=[Var(β

t−1

)+Var(β

t−2

)

+Var(β

t−3

)+Var(β

t−4

) (2)

+2Cov(β

t−1

t−2

)+2Cov(β

t−1

t−3

)

+2Cov(β

t−1

t-4

)+2Cov(β

t−2

t−3

)

+2Cov(β

t−2

t−4

)+2Cov(β

t−3

t−4

)]

The conditioning vector Xin Equation 1 helps

us to control for alternative explanations. For

instance, since an acquisition represents the

absorption of one ﬁrm by another, a simple expla-

nation for an increased postacquisition innovation

output would simply be increased innovative

inputs—the one postacquisition ﬁrm reﬂects the

combined efforts of the two preacquisition ﬁrms.

By including innovative inputs as time-varying

covariates in the conditioning vector X, we can

directly control for such effects. Other possible

determinants of innovation outputs such as ﬁrm

size, diversiﬁcation, nationality, and time are also

controlled for through this vector.

This speciﬁcation does not account for unob-

served heterogeneity, or the possibility that obser-

vationally equivalent ﬁrms may differ on unmeas-

ured characteristics. For instance, ﬁrms may enter

204 G. Ahuja and R. Katila

the sample with inherently different innovation

generating capabilities. Such unobserved hetero-

geneity, if present and not controlled for, can

cause estimation problems. First, it can lead to

overdispersion in the data. For the Poisson distri-

bution the variance is restricted to equal the

mean. If this restriction is false and the data are

overdispersed in that the variance exceeds the

mean, the computed standard errors in a Poisson

regression are understated. Second, unobserved

ﬁrm effects can lead to serial correlation among

the residuals of observations from the same ﬁrm.

Under overdispersion or serial correlation, com-

puted regression coefﬁcients remain consistently

estimated; however, the standard errors are inac-

curate. Thus, hypothesis testing and inference can

be invalidated. To address the possibility of unob-

served heterogeneity we used the Presample Panel

Poisson approach (Blundell, Grifﬁth, and Van

Reenen, 1995).

In the Presample approach, unobserved hetero-

geneity is modeled as an additional covariate in

the basic Poisson model. The values of the depen-

dent variable in the periods immediately preced-

ing the study period are used to construct an

instrumental variable. This instrumental variable

serves as a ‘ﬁxed-effect’ for the ﬁrms in the

panel and helps to partial out unobservable differ-

ences across ﬁrms. Thus, presample information

on the ﬁrms provides the basis for controlling

for unobserved heterogeneity. In the context of

the current research the Presample variable can

be interpreted as a measure of the unobserved

differences in knowledge stocks between the sam-

ple ﬁrms.

Although the presample approach can help in

the reduction of overdispersion and serial corre-

lation, a more complete treatment of these poten-

tial problems would be to use an estimation

approach that accounts for any remaining over-

dispersion and serial correlation even after the

inclusion of a presample variable. The Gen-

eralized Estimating Equations (GEE) method-

ology provides a direct approach to modeling

longitudinal Poisson data with serial correlation

in a regression context (Liang and Zeger, 1986).

The GEE estimation procedure involves two

stages. In the ﬁrst stage of the procedure beta

coefﬁcients are estimated with the assumption of

independence across observations. This process

yields consistent estimates of the beta parameters

and the residuals from this regression provide an

estimate of the ‘working correlations’ between

the errors of different observations. This working

correlation matrix is then used as an input in a

second regression. The beta coefﬁcients and stan-

dard errors from this second regression provide

consistent estimates of the underlying parameters

while accounting for the observed correlation

between observations. To ensure that all residual

correlation was accounted for, we used the GEE

procedure to estimate all models. Even for the

GEE approach instead of using the model-based

standard errors we used the more conservative

(i.e., larger) empirical standard errors. This

ensures that other potential misspeciﬁcations of

the variance function, such as any residual over-

dispersion, are also accounted for.

Sample and data

We tested the hypotheses on a longitudinal data

set comprising the acquisition and patenting

activities of 72 leading ﬁrms from the global

chemicals industry. Focusing on the largest ﬁrms

of the industry was necessary to ensure the avail-

ability and reliability of data. Obtaining infor-

mation on the key variables for smaller ﬁrms is

extremely difﬁcult. This focus on large ﬁrms is

also consistent with prior research on acquisitions

(Hitt et al., 1991, 1996). We identiﬁed the 100

leading players in the chemicals industry from

lists of the largest chemicals ﬁrms that are pub-

lished annually by trade journals such as Chemi-

cal Week and C&E News. To avoid survivor bias,

the selected sample was drawn from the lists at

the beginning of the study period. In these pub-

lished lists subsidiaries were often listed sepa-

rately from parent ﬁrms. After combining subsidi-

aries with parent ﬁrms, a sample of 82

independent ﬁrms was identiﬁed for inclusion in

the sample. However, for 10 ﬁrms data could not

be reliably obtained and they were subsequently

dropped from the analysis. The remaining ﬁrms

include the key ﬁrms in the industry over the

study period and comprise of 30 European, 26

American, and 16 Japanese ﬁrms. The panel is

unbalanced as some of the ﬁrms were acquired

by other ﬁrms or restructured in a fashion that

made comparison difﬁcult beyond a particular

year. Even though the sample was focused on

the largest ﬁrms in the chemicals industry the

inclusion of 72 ﬁrms provides signiﬁcant depth

to the sample and ensures that there is consider-

Technological Acquisitions and Innovation 205

able variety within the sample on the key vari-

ables of this study. For instance, the number of

employees for ﬁrms in the sample varies from a

minimum of 2300 to a maximum of more than

181,000. Similarly, the number of patents

obtained yearly varies from 0 to 760. Financial

ﬁgures and personnel data on these ﬁrms were

obtained from Compustat, Worldscope, Japan

Company Handbooks, Daiwa Institute Research

Guides, and trade publications and company

annual reports. For all ﬁrms, ﬁnancial data were

converted to constant (1985) U.S. dollars to

ensure standardization within the sample. A full

list of the sample ﬁrms is available from the

authors.

The chemicals industry is an appropriate setting

for several reasons. First, technology-based acqui-

sitions have been a signiﬁcant feature of this

industry (Chemical Week, 1983; Gibson, 1985).

Second, patents are generally regarded to be

effective, and used widely and consistently in the

chemicals industry (Levin et al., 1987; Arundel

and Kabla, 1998). For the ﬁrms in the sample

we obtained yearly patent counts from 1975 to

1992 and acquisition and ﬁrm attribute data for

the years 1980 to 1991. The need to use lagged

relationships between patent counts and the other

variables and to construct a control for unob-

served heterogeneity reduced the ﬁnal panel for

regression analysis to 9 years. We describe the

data collection and coding procedures for the

three sets of variables in some detail below.

We used U.S. patent data for all ﬁrms, includ-

ing the foreign ﬁrms in the sample. This was

necessary to maintain consistency, reliability, and

comparability, as patenting systems across nations

differ in their application of standards, system of

granting patents, and value of protection granted.

The United States represents one of the largest

markets for chemicals, and ﬁrms desirous of com-

mercializing their inventions typically patent in

the United States if they patent anywhere. Studies

by Dosi, Pavitt and Suete (1990) and Basberg

(1983) show empirically that U.S. patent data

provide a good measure of foreign ﬁrms’ inno-

vativeness. Prior research using patent data on

international samples has also followed this strat-

egy of using U.S. patent data for international

ﬁrms (e.g., Stuart and Podolny, 1996; Patel and

Pavitt, 1997).

Data on acquisitions were obtained through

detailed archival research on the chemicals sec-

tor. Three main types of data sources were used

to identify acquisition activity and to collect

data on acquisition transactions: (1) commercial

data bases, including general business news

mediasuchastheDowJonesNewsRetrieval

Text Index, and chemicals sector-speciﬁc data

bases such as Metadex, (2) general business

print media such as the Frost and Sullivan Predi-

casts Index (United States, International, and

Europe), Mergers and Acquisitions Journal and

Moody’s Manuals, and industry-speciﬁc publi-

cations such as Chemical Week and Plastics

Technology, and, (3) government publications

and consultant reports for the chemicals indus-

try. We were able to identify 1287 acquisition

announcements for the sample ﬁrms over the

period of 1980 to 1991.

To identify the technological acquisitions

within this larger sample two approaches were

used. First, we obtained detailed news stories

associated with each acquisition announcement.

We were able to obtain news stories providing

details for 516 of the 1287 acquisitions identiﬁed

for the sample ﬁrms. For the remaining acqui-

sitions no further details, or inadequate details,

were available. Second, we searched the U.S.

Patents Data Base to determine if the acquired

ﬁrms had obtained any patents in the 5 years

preceding the acquisition. 165 of the acquired

ﬁrms had obtained patents during the 5 years

preceding their acquisition. For all subsequent

analyses we retained only those acquisitions for

which we were able to ﬁnd either corroborating

news stories or patenting activity (534 acqui-

sitions in total). Acquisitions for which we were

able to obtain no further information were omit-

ted. The paucity of information on these acqui-

sitions suggests that these acquisitions were very

small or unimportant. Therefore, our analysis is

based on the 534 acquisitions for which we have

relatively complete information.

We used two criteria to distinguish technologi-

cal acquisitions from all other acquisitions. First,

we examined the news stories to establish if the

acquiring ﬁrm reported technology as a motivat-

ing factor for the acquisition or if technology

was a part of the transferred assets. We classiﬁed

the acquisition as technological if either of these

conditions was met. Second, we classiﬁed the

acquisition as technological if the acquired ﬁrm

had any patenting activity in the 5 years preced-

ing the acquisition. Of the 534 acquisitions on

206 G. Ahuja and R. Katila

which we had information, 283 met at least one

of the two above criteria and were classiﬁed

as technological acquisitions. The remaining 251

acquisitions were classiﬁed as nontechnological.

This classiﬁcation scheme reﬂects a fairly

inclusive deﬁnition of technological acquisitions.

Firms need not have patented to be classiﬁed as

technology acquisitions. Further, acquired ﬁrms

obtaining even a single patent in the 5 years

prior to the acquisition are classiﬁed as techno-

logical acquisitions.

This classiﬁcation scheme provides two bene-

ﬁts. First, it uses two indicators to identify tech-

nological acquisitions and therefore enables more

complete identiﬁcation of technological acqui-

sitions than either indicator by itself would. For

instance, our patent-based measures cannot be

computed if only a part of a ﬁrm is acquired,

rather than the complete entity, or if the tech-

nology has not been patented at all. In such

circumstances the news story could provide an

indication of whether the transaction entailed

technology or not. Similarly, if the acquisition

was motivated by multiple factors of which tech-

nology was not necessarily the one mentioned in

the news story, the patents measure provides an

indicator of whether technology was involved.

Thus, using the input from the news stories sup-

plements our patent-based measures.

Second, using this scheme of classiﬁcation

makes our statistical tests more conservative.

With this scheme we are likely to capture any

acquisition that includes a technological compo-

nent. Further, we are more likely to err in the

direction of deﬁning acquisitions as technological

even when they have a relatively small tech-

nology component. If such misclassiﬁcations

occur, we reduce our likelihood of ﬁnding

a positive impact of technological acquisitions

on the innovation performance of acquiring

ﬁrms.

Finally, we need to select a time period or

window for measuring a ﬁrm’s knowledge base.

At one extreme only the current year’s patents

could be considered relevant. At another extreme

any patent obtained by the ﬁrm in the past could

be included in computing its current knowledge

base. Prior research suggests that knowledge capi-

tal depreciates sharply, losing signiﬁcant value

within 5 years (Griliches, 1979). Although the

depreciation rate for knowledge capital is likely

to vary across industries, a boundary of 5 or 6

years seems reasonable and has been used by

other researchers (Podolny and Stuart, 1995;

Henderson and Cockburn, 1996). In this research

we use the patents obtained by a ﬁrm in the

preceding 5 years to compute our knowledge

base measures. We also computed some of the

measures using patents for the preceding 6 years.

These measures were highly correlated with the

5-year measures, suggesting that the construct is

not unduly inﬂuenced by changes in the time

period used to compute it.

Variable deﬁnitions and operationalization

Dependent variable

Patents Innovation output, the dependent vari-

able, is measured through the patenting frequency

of ﬁrms, that is, the number of successful patent

applications by a ﬁrm in a given year. Patents

have both signiﬁcant strengths and weaknesses as

measures of innovation output. First, patents are

directly related to inventiveness: they are granted

only for ‘nonobvious’ improvements or solutions

with discernible utility (Walker, 1995). Second,

they represent an externally validated measure of

technological novelty (Griliches, 1990). Third,

they confer property rights upon the assignee and

therefore have economic signiﬁcance (Kamien and

Schwartz, 1982: 49; Scherer and Ross, 1990:

621).

Patents also correlate well with other measures

of innovative output. Empirical studies ﬁnd that

patents are closely related to measures such as

new products (Comanor and Scherer, 1969), inno-

vation and invention counts (Achilladelis,

Schwarzkopf and Cines, 1987), and sales growth

(Scherer, 1965). Expert ratings of corporate tech-

nological strength are also highly correlated with

the number of patents held by corporations

(Narin, Noma, and Perry, 1987). Further, surveys

of patent holders indicate that the rate of utili-

zation of patents is reasonably high, with esti-

mates indicating that between 41 percent and 55

percent of all patents granted are put to commer-

cial use for at least a limited time (Griliches,

1990). Similarly, about 50 percent of all patents

granted are still being renewed and a renewal fee

is being paid 10 years after the patents had

originally been applied for (Griliches, 1990;

Schankerman and Pakes, 1986). Given a nonneg-

Technological Acquisitions and Innovation 207

ligible renewal fee, this indicates a signiﬁcant

usefulness for the majority of patents for a sig-

niﬁcant time period.

However, the use of patents as a measure

of innovative output also has limitations. Some

inventions are not patentable, others are not pa-

tented, and the inventions that are patented differ

greatly in economic value (Cohen and Levin,

1989; Griliches, 1990; Trajtenberg, 1990).

Research, and the logic of appropriability, indi-

cate that the degree to which the ﬁrst two of

these factors are a problem varies signiﬁcantly

across industries (Cohen and Levin 1989; Levin

et al., 1987). Limiting the study to a single

industrial sector or a few closely related sectors

minimizes such problems as the factors that affect

patenting propensity are likely to be stable within

such a context (Basberg, 1987; Cohen and Levin,

1989; Griliches, 1990).

We measure Patents

, as the number of suc-

cessful patent applications, or granted patents, for

the acquiring ﬁrm iin year t. The granted patent

carries the date of the original application. We

use this date to assign a granted patent to the

particular year when it was originally applied

for. This procedure permits consistency in the

treatment of all patents and controls for differ-

ences in delays that may occur in granting patents

after the application is ﬁled (Trajtenberg, 1990).

Note that the patent count for the dependent

variable is based on the patents of the acquiring

ﬁrm obtained 1–4 years after the acquisition in

all cases. These patents are therefore different

from the patents on which the independent vari-

ables are based. The independent variables

described below are based on patents obtained

by the acquired and acquiring ﬁrms, respectively,

in the 5 years before the acquisition.

Independent variables

Number of nontechnological acquisitions As

noted earlier, acquisitions were coded as techno-

logical acquisitions if either of the following two

criteria are met: ﬁrst, if the acquiring ﬁrm

reported technology as a motivating factor for the

acquisition or if technology was reported as a

part of the transferred assets of the acquired ﬁrm;

second, if the acquired ﬁrm had any patenting

activity in the 5 years preceding the acquisition.

Acquisitions that did not meet either of the above

criteria were coded as nontechnological acqui-

sitions. We used four lagged versions of this and

all other acquisition related variables listed below.

Absolute size of acquired knowledge base To

obtain this variable we used the following pro-

cedure: for each acquiring ﬁrm for each year, we

prepared a list of the patents its acquisitions had

obtained in the preceding 5 years. These patents

were then combined with the patents cited by

them. Thereafter, all duplicates were removed to

ensure that a patent number appeared only once

on this list. The acquired knowledge base was

then computed as the number of patents (i.e.,

knowledge elements) on this list.

Relative size of acquired knowledge base This

variable was obtained by dividing Absolute

size of acquired knowledge base by the size of

the acquiring ﬁrm’s knowledge base. The size of

the acquiring ﬁrm’s knowledge base was

computed using the same procedure as the size

of the acquired ﬁrm’s knowledge base (see

above). In a few cases the acquired knowledge

base was larger than the acquiring knowledge

base. In these cases we used the larger number

as the denominator. Since our theoretical mech-

anism is concerned with the relative proportion

of the merged ﬁrms’ resources that are likely to

be occupied with integrative rather than inventive

activity, a number greater than 1 is not meaning-

ful.

Relatedness of acquired knowledge base To

measure the relatedness of the acquired knowl-

edge base, the following procedure was followed.

First, the list of patent numbers that appeared in

both the acquired ﬁrm’s knowledge base and in

the acquiring ﬁrm’s knowledge base was pre-

pared. Then, the number of elements on this

list was divided by Absolute size of acquired

knowledge base.

Number of technological acquisitions where pa-

tents unavailable This is the number of acqui-

sitions for which news stories indicated that tech-

nology was a component of the transferred assets

but where no patents could be identiﬁed with the

acquisition. This would occur if either the

acquired unit had obtained no patents or if the

acquired unit was a part of a larger parent ﬁrm

and patents were not assigned separately to the

acquired unit and therefore could not be separated

208 G. Ahuja and R. Katila

from the parent ﬁrm’s patents.

Controls

We included several control variables in the mod-

els. These control variables include yearly R&D

expenditures (R&D), ﬁrm size as measured by

natural log of number of employees

(Logemployees), ﬁrm diversiﬁcation as measured

by entropy (Diversiﬁcation), and a measure of

national cultural distance between the acquired

and acquiring ﬁrms (Foreign acquisitions). The

following formula was used to calculate the

Diversiﬁcation measure: Entropy =⌺P

⫻

ln(1/P

), where P

is deﬁned as the percentage of

ﬁrm sales in segment jand ln(1/P

) is the weight

for each segment j(Palepu, 1985). Foreign acqui-

sitions was computed as

Foreign acquisitions =

冘

i=1

{(I

−I

)

}/4 (3)

where I

stands for the index of the ith cultural

dimension, jand uare subscripts indicating the

countries jand u, and V

is the variance of the

index of the ith dimension (Hofstede, 1980). The

index thus indicates the cultural distance between

the acquirer’s country (j) and the acquired ﬁrm’s

country (u) (Kogut and Singh, 1988). We antici-

pate that foreign acquisitions might be more dif-

ﬁcult to integrate than domestic acquisitions.

Finally, in all models we included the ﬁrm hetero-

geneity control variable Presample patents (the

sum of patents obtained by a ﬁrm in the 3 years

prior to the ﬁrm’s entry into the sample) and

dummy variables for acquirer nationality and cal-

endar year.

RESULTS

Table 1 provides descriptive statistics and corre-

lations. The table indicates the diversity of ﬁrms

included in the sample. Even though the sample

involves the prominent players in the industry,

there is considerable variance on all the key

variables such as Patents,R&D,Logemployees,

and the acquisition variables. The variables

reﬂecting the hypothesized effects are not very

highly correlated among themselves or with the

control variables. However, the correlations

between some of the control variables are high,

notably the correlation between R&D and Logem-

ployees (0.72), and the correlation of the ﬁrm

ﬁxed-effect variable Presample patents with

R&D (0.89) and Logemployees (0.72). Robustness

tests (reported at the end of this section), how-

ever, indicate that the results on the hypothesized

effects were strong and unaffected by these high

correlations between some of the control vari-

ables.

Table 2 provides results for all models using

GEE Poisson estimators (reported with empirical

standard errors). The variables reﬂecting the

hypothesized effects were entered into the

regression individually and likelihood ratio tests

are reported for all models. Model 1 in Table

2 presents the base model with ﬁrm- and acqui-

sition-related control variables. Models 2–6

include the Number of nontechnological acqui-

sitions,Absolute size of acquired knowledge

base,Relative size of acquired knowledge base,

Relatedness of acquired knowledge base and

Relatedness of acquired knowledge base

vari-

ables entered successively. We use the full

model to discuss the results of the hypothesis

tests. The summed coefﬁcients and the

associated standard errors for the lagged acqui-

sition variables for these models are given at

the bottom of the table.

In Hypothesis 1 we predicted a negative or

nonsigniﬁcant relationship between the number of

nontechnological acquisitions conducted in a year

and the subsequent innovation output of the

acquiring ﬁrm. The coefﬁcients of Number of

non-technological acquisitions are nonsigniﬁcant

for all four periods individually. The summed

coefﬁcient, which represents the total effect of

acquisitions across the 4 years, is not statistically

signiﬁcantly different from zero. Thus, we do not

ﬁnd an appreciable impact of acquisitions without

a technological component on the innovation out-

put of the acquirer for the four periods following

the acquisition.

The coefﬁcients of the four Absolute size of

acquired knowledge base variables in Model 6 in

Table 2 are positive and signiﬁcant, supporting

Hypothesis 2. The summed coefﬁcient reﬂecting

the total impact of the acquisition is also positive

and signiﬁcant. However, as the summed coef-

ﬁcient indicates (0.0004), the absolute magnitude

of this effect is low. A one-unit increase in the

knowledge base of the acquired ﬁrm leads to a

0.04 percent increase in the acquirer’s innovation

Technological Acquisitions and Innovation 209

Table 1. Means, standard deviations, minimum and maximum values and bivariate correlations for all variables

Variable Mean S.D. Min. Max. 1 2 3 45678910111213141516171819202122232425262728293031323334

1Patients

93.90 137.65 0.00 760.00

2No. of nontechnological 0.35 0.79 0.00 6.00 0.12

acquisitions

t−1

3No. of nontechnological 0.34 0.78 0.00 6.00 0.12 0.26

acquisitions

t−2

4No. of nontechnological 0.29 0.68 0.00 5.00 0.11 0.29 0.23

acquisitions

t−3

5No. of nontechnological 0.25 0.69 0.00 7.00 0.12 0.21 0.29 0.30

acquisitions

t−4

6Absolute size of acquired 26.08 275.94 0.00 6227.00 0.13 0.07 −0.03 0.01−0.02

knowledge base

t−1

7Absolute size of acquired 34.59 332.94 0.00 6227.00 0.15−0.03 0.05 −0.03 0.00 0.02

knowledge base

t−2

8Absolute size of acquired 34.83 338.80 0.00 6227.00 0.18−0.02 −0.03 0.01 −0.03 0.03 0.01

knowledge base

t−3

9Absolute size of acquired 33.20 337.48 0.00 6227.00 0.19 0.00 −0.02−0.03 0.02 0.51 0.03 0.01

knowledge base

t−4

10 Relative size of acquired 0.02 0.08 0.00 0.90 −0.04 0.12 0.00 0.00−0.05 0.44−0.01 0.00 0.16

knowledge base

t−1

11 Relative size of acquired 0.02 0.09 0.00 0.90 −0.01−0.01 0.12 −0.01 −0.01 0.00 0.56 −0.01 0.00 −0.02

knowledge base

t−2

12 Relative size of acquired 0.02 0.09 0.00 0.90 0.01 0.04 0.00 0.09 0.00 0.04 0.00 0.55 −0.01 0.05 −0.02

knowledge base

t−3

13 Relative size of acquired 0.02 0.08 0.00 0.90 0.02−0.02 0.03 0.03 0.05 0.37 0.03 0.00 0.56 0.12 0.05 0.00

knowledge base

t−4

14 Relatedness of acquired 0.01 0.06 0.00 1.00 0.03−0.02 0.02 −0.03 −0.03 0.04 0.00−0.01 0.01 0.09 −0.01 −0.02 0.27

knowledge base

t−1

15 Relatedness of acquired 0.01 0.06 0.00 1.00 0.04 0.00 −0.01 0.02 −0.03 −0.01 0.03 0.00 −0.01 0.00 0.08 0.00 −0.01 −0.02

knowledge base

t−2

16 Relatedness of acquired 0.01 0.06 0.00 1.00 0.05 0.03 0.00 −0.01 0.03 0.07 −0.01 0.03 0.00 0.07 0.00 0.07 0.00 −0.02 −0.02

knowledge base

t−3

17 Relatedness of acquired 0.01 0.06 0.00 1.00 0.06 0.02 0.03 0.00 −0.01 0.09 0.06 −0.01 0.03 0.02 0.06 0.00 0.07 0.04 −0.01 −0.01

knowledge base

t−4

18 Relatedness of acquired 0.00 0.05 0.00 1.00 −0.01−0.03 0.02 −0.03 −0.03 0.01 −0.01 0.00 0.01 0.04−0.01 −0.01 0.36 0.92 −0.01 −0.01 0.00

knowledge base

t−12

19 Relatedness of acquired 0.00 0.05 0.00 1.00 0.00−0.02 −0.03 0.02 −0.03 0.00 0.00−0.01 0.00 0.01 0.03 −0.01 0.00 −0.01 0.92 −0.01 −0.01 −0.01

knowledge base

t−22

210 G. Ahuja and R. Katila

Table 1. Continued

Variable Mean S.D. Min. Max. 1 2 3 45678910111213141516171819202122232425262728293031323334

20 Relatedness of acquired 0.00 0.05 0.00 1.00 0.00 0.00 −0.02 −0.03 0.03 0.02 0.00 0.00 −0.01 0.03 0.01 0.03−0.01−0.01 −0.01 0.92 −0.01 −0.01−0.01

knowledge base

t−32

21 Relatedness of acquired 0.00 0.05 0.00 1.00 0.01−0.01 0.00 −0.02 −0.03 0.05 0.02 0.00 0.00 0.01 0.03 0.01 0.04 0.02 −0.01−0.01 0.93 0.00 −0.01 −0.01

knowledge base

t−42

22 No. of technological 0.17 0.47 0.00 3.00 0.19 0.22 0.16 0.17 0.20 0.03−0.03 −0.02 0.02 0.06 −0.04 0.00 −0.02 0.01 0.01 0.03 0.00−0.01 −0.02 0.00 −0.02

acquisitions where

patents unavailable

t−1

23 No. of technological 0.16 0.46 0.00 3.00 0.21 0.10 0.23 0.18 0.17−0.01 0.01 −0.03 −0.02 0.00 0.05−0.03 0.01 −0.03 0.01 0.02 0.04 −0.03 −0.01−0.01 0.00 0.28

acquisitions where

patents unavailable

t−2

24 No. of technological 0.14 0.43 0.00 3.00 0.22 0.15 0.11 0.23 0.22 0.01−0.01 −0.01 −0.03 0.00 −0.02 0.02−0.02 0.05 −0.03 0.02 0.00 0.03 −0.02 −0.01 −0.02 0.19 0.31

acquisitions where

patents unavailable

t−3

25 No. of technological 0.13 0.41 0.00 3.00 0.19 0.15 0.15 0.08 0.20−0.02 0.00 −0.02 −0.02 −0.02 0.00 −0.02 −0.01 0.02 0.06 −0.03 0.00 −0.01 0.03−0.02−0.01 0.14 0.21 0.31

acquisitions where

patents unavailable

t−4

26 Foreign acquisitions

t−1

0.20 0.86 0.00 14.66 0.02 0.23 0.06 0.07 0.04 0.15−0.02 0.00 −0.01 0.33 −0.03 0.11 0.01 0.12 −0.02 0.17−0.01 0.08 −0.02 0.09 −0.01 0.39 0.14 0.12 0.04

27 Foreign acquisitions

t−2

0.19 0.85 0.00 14.66 0.03 0.06 0.24 0.05 0.08 0.00 0.12−0.01 0.00 0.01 0.29 −0.03 0.12 0.01 0.13 −0.02 0.19 0.00 0.08 −0.01 0.09 0.10 0.40 0.16 0.14 0.15

28 Foreign acquisitions

t−3

0.15 0.60 0.00 6.86 0.04 0.24 0.10 0.15 0.10 0.08 0.00 0.01 −0.01 0.11 0.00 0.15−0.02−0.01 0.02 0.19 −0.01 −0.02 0.01 0.12 −0.02 0.18 0.17 0.41 0.22 0.42 0.23

29 Foreign acquisitions

t−4

0.15 0.69 0.00 8.97 0.03 0.10 0.21 0.07 0.11 0.01 0.06 −0.01 0.01 0.01 0.08 −0.01 0.16 −0.01 0.00 0.01 0.17 −0.01 −0.01 0.00 0.10 0.05 0.15 0.14 0.43 0.15 0.37 0.25

30 R&D

t−1

19.32 30.24 0.23 161.25 0.89 0.18 0.19 0.16 0.16 0.08 0.09 0.10 0.11 −0.04 −0.02 −0.01 −0.01 0.03 0.05 0.07 0.07 0.00 0.00 0.01 0.02 0.24 0.30 0.30 0.26 0.07 0.11 0.15 0.12

31 Logemployees

t−1

2.76 1.14 0.83 5.20 0.70 0.31 0.31 0.28 0.28 0.13 0.15 0.15 0.15 0.00 0.03 0.05 0.04 0.05 0.05 0.06 0.05 0.01 0.01 0.01 0.00 0.29 0.28 0.28 0.24 0.13 0.14 0.18 0.17 0.72

32 Diversiﬁcation/Entropy

t−1

0.53 0.41 0.00 1.60 0.04 0.10 0.08 0.07 0.06 0.10 0.12 0.11 0.10 0.05 0.09 0.10 0.10 0.01 0.01 0.00 0.01 0.02 0.02 0.01 0.02 −0.03 −0.07 −0.08 −0.12 0.01 0.00−0.06 −0.08 −0.05 0.14

33 U.S.A. 0.35 0.48 0.00 1.00 0.10−0.04 −0.04 0.00 0.02 0.04 0.07 0.09 0.09 −0.01 0.03 0.04 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.05 0.04 0.07 0.08 −0.13 −0.12 −0.12 −0.11−0.02 0.08 0.13

34 Japan 0.24 0.43 0.00 1.00 −0.19−0.24 −0.23 −0.22 −0.20 −0.04−0.05−0.05 −0.05 −0.08 −0.09 −0.09 −0.09−0.07−0.07 −0.07 −0.06 −0.05 −0.04 −0.04−0.04−0.17 −0.17 −0.15 −0.16 −0.05 −0.07 −0.07−0.07 −0.21 −0.53 −0.10 −0.42

35 Presample patents

t−1

2.28 3.26 0.01 15.80 0.94 0.11 0.12 0.09 0.10 0.07 0.08 0.11 0.11 −0.07 −0.05 −0.03 −0.03 0.05 0.05 0.05 0.05 0.01 0.01 0.01 0.01 0.20 0.21 0.21 0.18 0.00 0.02 0.05 0.04 0.89 0.72 0.02 0.10 −0.20

Technological Acquisitions and Innovation 211

Table 2. GEE presample Poisson regression with distributed lag analysis predicting patents

variable

Variable 1 2 3 4 5 6

Intercept 1.906*** 1.916*** 1.994*** 2.055*** 2.041*** 2.048***

[0.333] [0.334] [0.296] [0.293] [0.295] [0.292]

No. of 0.004 0.009 0.009 0.008 0.004

nontechnological [0.017] [0.017] [0.016] [0.016] [0.017]

acquisitions

t−1

No. of −0.002 −0.0003 0.004 0.005 0.004

nontechnological [0.018] [0.017] [0.016] [0.016] [0.016]

acquisitions

t−2

No. of −0.001 −0.006 −0.002 −0.003 −0.002

nontechnological [0.023] [0.023] [0.023] [0.023] [0.024]

acquisitions

t−3

No. of −0.010 −0.012 −0.008 −0.010 −0.008

nontechnological [0.014] [0.014] [0.013] [0.013] [0.012]

acquisitions

t−4

Absolute size of 0.0001** 0.0001*** 0.0001*** 0.0001***

acquired knowledge [0.00004] [0.00002] [0.00002] [0.00003]

base

t−1

Absolute size of 0.0001*** 0.0001*** 0.0001*** 0.0001***

acquired knowledge [0.00003] [0.00002] [0.00002] [0.00002]

base

t−2

Absolute size of 0.0001*** 0.0001*** 0.0001*** 0.0002***

acquired knowledge [0.00002] [0.00002] [0.00002] [0.00002]

base

t−3

Absolute size of 0.0000 0.0001** 0.0001** 0.0000*

acquired knowledge [0.0000] [0.00004] [0.00004] [0.0001]

base

t−4

Relative size of −0.393** −0.387** −0.348*

acquired knowledge [0.165] [0.167] [0.156]

base

t−1

Relative size of −0.447*** −0.442*** −0.455***

acquired knowledge [0.144] [0.142] [0.144]

base

t−2

Relative size of −0.388*** −0.405*** −0.488***

acquired knowledge [0.125] [0.125] [0.121]

base

t−3

Relative size of −0.479** −0.430* −0.369*

acquired knowledge [0.196] [0.198] [0.209]

base

t−4

Relatedness of −0.207 0.221

acquired knowledge [0.176] [0.558]

base

t−1

Relatedness of 0.002 0.821*

acquired knowledge [0.112] [0.384]

base

t−2

Relatedness of 0.158 0.847*

acquired knowledge [0.111] [0.371]

base

t−3

Relatedness of 0.151 0.836**

acquired knowledge [0.121] [0.335]

base

t−4

Relatedness of −0.820

acquired knowledge [0.911]

base

2t−1

212 G. Ahuja and R. Katila

Table 2. Continued

Variable 1 2 3 4 5 6

Relatedness of −1.580**

acquired knowledge [0.572]

base

2t−2

Relatedness of −1.301*

acquired knowledge [0.572]

base

2t−3

Relatedness of −1.251**

acquired knowledge [0.501]

base

2t−4

No. of technological 0.023 0.034 0.016 0.018 0.009

acquisitions where [0.026] [0.024] [0.023] [0.023] [0.024]

patents

unavailable

t−1

No. of technological 0.011 0.025 0.016 0.016 0.014

acquisitions where [0.024] [0.022] [0.021] [0.021] [0.022]

patents

unavailable

t−2

No. of technological 0.039 0.053* 0.043 0.047 0.045

acquisitions where [0.030] [0.027] [0.026] [0.025] [0.025]

patents

unavailable

t−3

No. of technological 0.063* 0.068* 0.066* 0.073** 0.077**

acquisitions where [0.020] [0.027] [0.026] [0.026] [0.027]

patents

unavailable

t−4

Foreign 0.034** 0.031 0.026 0.035 0.038* 0.040*

acquisitions

t−1

[0.012] [0.016] [0.017] [0.019] [0.017] [0.017]

Foreign 0.020* 0.019 0.014 0.023 0.022 0.024

acquisitions

t−2

[0.010] [0.013] [0.012] [0.015] [0.015] [0.015]

Foreign −0.020 −0.042* −0.051** −0.042* −0.046** −0.043*

acquisitions

t−3

[0.013] [0.017] [0.016] [0.017] [0.016] [0.017]

Foreign 0.009 −0.008 −0.010 −0.004 −0.008 −0.011

acquisitions

t−4

[0.013] [0.015] [0.015] [0.017] [0.017] [0.017]

R&D

t−1

−0.0004 −0.001 −0.001 −0.001 −0.001 −0.001

[0.001] [0.002] [0.002] [0.002] [0.001] [0.001]

Logemployees

t−1

0.554*** 0.553*** 0.534*** 0.517*** 0.522*** 0.516***

[0.105] [0.109] [0.099] [0.097] [0.097] [0.096]

Diversiﬁcation/ −0.176* −0.187* −0.217* −0.180* −0.183* −0.174*

Entropy

t−1

[0.076] [0.083] [0.085] [0.080] [0.080] [0.076]

US ﬁrm 0.553*** 0.549*** 0.526*** 0.509*** 0.507*** 0.514***

[0.128] [0.130] [0.120] [0.118] [0.117] [0.114]

Japanese ﬁrm 0.997*** 1.006*** 0.991*** 0.942*** 0.950*** 0.951***

[0.233] [0.229] [0.219] [0.217] [0.217] [0.216]

Presample patents

t−1

0.135*** 0.137*** 0.145*** 0.142*** 0.143*** 0.142***

[0.022] [0.024] [0.024] [0.023] [0.023] [0.022]

output. If we consider a hypothetical acquisition

in which the acquired ﬁrm has a knowledge

base with 50 elements (50 elements corresponds

approximately to a ﬁrm with 10 owned patents

plus 40 cited patents) then the above coefﬁcient

suggests that this acquisition should lead to a 2

percent increase in innovation output (0.0004 ⫻

50 =0.02) for the acquiring ﬁrm after the acqui-

sition, other things being equal.

In Hypothesis 3, a negative relationship was

proposed between the relative size of an acqui-

sition and the subsequent innovation performance

of the acquiring ﬁrm. This prediction was also

borne out. Speciﬁcally, the individual and

Technological Acquisitions and Innovation 213

Table 2. Continued

Variable 1 2 3 4 5 6

N598 598 598 598 598 598

Pearson chi sq./d.f. 10570/579 10491/571 9923/567 9454/563 9448/559 9280/555

−2 log-likelihood vis 158*** 1136*** 938*** 12*** 336***

`vis the preceding

model

Summed coefﬁcients (Model 6)

No. of nontechnological −0.001

acquisitions [0.054]

Absolute size of 0.0004**

acquired knowledge [0.000]

base

Relative size of −1.660**

acquired knowledge [0.661]

base

Relatedness of 2.725**

acquired knowledge [1.332]

base

Relatedness of −4.951**

acquired knowledge [2.020]

base

No. of technological 0.145*

acquisitions where [0.081]

patents unavailable

Foreign acquisitions 0.011

[0.049]

*p⬍0.05; **p⬍0.01; ***p⬍0.001 (one-tailed tests for hypothesized variables, two-tailed tests for controls)

The table gives parameter estimates; standard errors are in brackets. Year dummies are included, but not shown.

summed coefﬁcients for Relative size of acquired

knowledge base are negative, indicating that

acquiring ﬁrms that are large relative to the

acquirer leads to a decline in postacquisition inno-

vation output for the acquirer. We also ﬁnd sup-

port for Hypothesis 4. As shown in Model 6 of

Table 2, the coefﬁcients for Relatedness of

acquired knowledge base are positive, and that

for its squared term, Relatedness of acquired

knowledge base

, are negative. These ﬁndings

support the argument that the relatedness of

acquisitions has a curvilinear impact on the sub-

sequent innovation output of acquiring ﬁrms. The

summed coefﬁcients at the bottom of Table 2

support the same results.

Among the control variables, the coefﬁcients

on Number of technological acquisitions where

patents unavailable for the 1-year, 2-year, and 3-

year lags are positive but not signiﬁcant. How-

ever, the 4-year lag is positive and signiﬁcant.

The summed coefﬁcient is positive and signiﬁ-

cant. Thus, technological acquisitions for which

we were unable to identify patents also improve

postacquisition innovation output.

Foreign acquisitions, which represents the cul-

tural distance between acquired and acquiring

ﬁrms, has a nonsigniﬁcant impact on innovation

output. Although the 1-year and 3-year lagged

variables are marginally signiﬁcant but in

opposite directions (positive and negative,

respectively), the summed coefﬁcient is positive

but not statistically signiﬁcant (Table 2). Although

these results are not conclusive it appears that,

on balance, foreign acquisitions neither help nor

hurt innovation output. This ﬁnding is consistent

with recent research on international acquisitions,

which ﬁnds that acquisitions in which the acquirer

and the acquired are from different countries do

not result in greater postacquisition conﬂict, and,

in fact, often lead to superior postacquisition

performance (Weber, Shenkar, and Raveh, 1996;

Very et al., 1997). Nevertheless, further research

is required to understand this issue more com-

pletely.

214 G. Ahuja and R. Katila

Logemployees, the control measuring the size

of the acquirer, was found to be positively

associated with patenting frequency. Diversiﬁ-

cation is negatively associated with patenting fre-

quency. Prior results on the impact of diversiﬁ-

cation on innovative activity have been mixed;

studies ﬁnd that diversiﬁcation has both a positive

and a negative impact on innovation (Cohen and

Levin, 1989). Diversiﬁcation can either encourage

innovation by providing a stimulus of multiple

knowledge bases within a single ﬁrm and by

leading to cross-fertilization of ideas, but it can

also imply a loss of focus in a given technological

area as research efforts are spread in multiple

directions. The results of this research support

the latter interpretation.

The Presample patents variable and several of

the year dummies (not reported) were also sig-

niﬁcant in all the models. This indicates that it

was important to control for unobserved ﬁrm

effects as well as period effects in these data.

The Year dummies for 1983–86 were negative

and signiﬁcant, while all the other year dummies

were nonsigniﬁcant, relative to the omitted cate-

gory—1991. These results indicate that patenting

had signiﬁcantly increased for this set of ﬁrms

in the later years (1987–91), relative to the earlier

years (1983–86). The nation dummy variables

reﬂecting acquirer nationality, Japanese and U.S.,

were also positive and signiﬁcant, indicating that

Japanese and U.S. ﬁrms were likely to obtain

more patents than European ﬁrms.

Sensitivity analyses

We also ran several sensitivity tests to check the

robustness of the results. We reconstructed all

the knowledge base measures after separating the

patents obtained by a ﬁrm (Own Patents) from

the patents cited by the ﬁrm (Cited Patents), and

ran two distinct sets of models. In the ﬁrst set

we used knowledge-based measures for acquired

and acquiring ﬁrms based on only the Own Pat-

ents of the ﬁrm (i.e., we excluded Cited Patents).

In the second set we computed all knowledge-

based measures for acquired and acquiring ﬁrms

based on only the Cited Patents of the ﬁrms (i.e.,

we excluded Own Patents). The results of these

analyses are presented in Table 3. In the interest

of brevity, we only report the summed coef-

ﬁcients and associated t-tests for the models (the

full regression table is available from the authors).

Table 3. GEE presample Poisson regression predicting

patents

. Own-cited patents measures. Summary of the

summed coefﬁcient results for all lagged variables

Variable Own Cited

patents patents

No. of nontechnological −0.007 0.0006

acquisitions [0.057] [0.054]

Absolute size of acquired 0.002***

knowledge base (Own [0.001]

patents)

Relative size of acquired −1.632*

knowledge base (Own [0.708]

patents)

Relatedness of acquired 2.411

knowledge base (Own [3.562]

patents)

Relatedness of acquired −3.568

knowledge base

(Own [7.533]

patents)

Absolute size of acquired 0.0004***

knowledge base (Cited [0.000]

patents)

Relative size of acquired −1.653***

knowledge base (Cited [0.465]

patents)

Relatedness of acquired 2.752*

knowledge base (Cited [1.266]

patents)

Relatedness of acquired −4.759**

knowledge base

(Cited [1.865]

patents)

No. of technological 0.196* 0.141

acquisitions where patents [0.083] [0.081]

unavailable

Foreign acquisitions −0.003 0.011

[0.003] [0.048]

Reported coefﬁcient estimates are the sum of the four lagged

coefﬁcients (

␤

t−1

␤

t−2

␤

t−3

␤

t−4

) for each variable.

*p⬍0.05; **p⬍0.01; ***p⬍0.001 (one-tailed tests for

hypothesized variables, two-tailed tests for controls)

The summed coefﬁcients in Table 3 indicate that

with knowledge base measures based only on

Own Patents (Model 1), the hypotheses (2 and

3) on absolute size of acquired ﬁrm and relative

size of acquired ﬁrm were strongly supported.

However, the coefﬁcients for the relatedness

hypothesis (4) carried the right signs (+for

relatedness and ⫺for its square term), but they

were statistically nonsigniﬁcant. In the analysis

based on Cited Patents only (Model 2) all hypoth-

eses (2, 3, and 4) were strongly supported. Thus,

using only patents, or only citations to measure

knowledge bases, provides rather similar results

to using both patents and citations for the absolute

Technological Acquisitions and Innovation 215

size and relative size effects. However, in terms

of measuring relatedness, the patents-only meas-

ures do not appear to capture relatedness as well

as the citations-based measures. A comparison of

the results in Table 3 (based on Own Patents or

Cited Patents taken individually) with the results

in Table 2 (based on combining Own and Cited

patents to provide a single measure of knowledge

elements), however, suggests that the relatedness

measure based on both own and cited patents

collectively is more predictive than the measures

based on either Own Patents or Cited Patents

only.

In the results discussed above we used the

Presample Patents variable as a measure of unob-

served differences in the knowledge bases of

ﬁrms. The patent production function literature

provides several alternate approaches to construct

indices, reﬂecting differences in ﬁrms’ knowledge

stocks (Griliches, 1984; Hall et al., 1988; Hender-

son and Cockburn, 1996). We constructed several

such indices using different assumptions and input

data. First, we used capitalized historical R&D

expenditures to construct a knowledge stock index

(Hall et al., 1988).

Second, we constructed an

index based on the moving average sum of R&

D expenditures for the previous nyears. We

used a depreciation rate of 0.20 (consistent with

Henderson and Cockburn, 1996, and other studies

in this tradition) for the ﬁrst approach and n=

5 years for the second approach, and estimated

models using these indices in lieu of the R&D

variable. We also computed the knowledge stock

index using cumulative lagged depreciated patent

stocks rather than cumulated lagged depreciated

R&D, and used this as a control variable in our

regression models in place of the Presample Pa-

tents measure. In additional estimation we used

the one period lagged values of the dependent

variable in lieu of the presample variable. The

results for all these approaches (available from

the authors) are very similar to the earlier

reported results.

As noted earlier, the correlations between the

Speciﬁcally, for the ith ﬁrm and tth period, knowledge stock

) was computed using the formula K

=1⫺δ(K

it⫺1

)+R

where δstands for depreciation rate for knowledge capital,

and R

represents the current-period knowledge ﬂow addition.

The initial stock of capital was computed by dividing the

ﬁrst observed year’s ﬂow by δ+g, where gis the ﬁrm’s

historic rate of growth of real expenditures on R&D

(Henderson and Cockburn, 1996).

three control variables, R&D,Logemployees and

Presample patents were high. Although this high

collinearity does not bias coefﬁcient estimates, it

can affect the stability of the estimated coef-

ﬁcients. Consequently, omitting even a few obser-

vations can change the sign or the signiﬁcance

of the affected variables (Greene, 1993). To

ensure that our results were robust, we drew 50

random samples of 90 percent of the observations

and estimated the full model for each of these

samples. The results of this sensitivity analysis

indicated that the results of our hypothesis testing

were robust.

DISCUSSION

The results of the study indicate support for

our theoretical predictions. Speciﬁcally, separating

nontechnological acquisitions from technological

acquisitions and distinguishing between techno-

logical acquisitions on the basis of the absolute

size, relative size, and relatedness of the knowl-

edge bases of the acquired ﬁrm helps in predicting

postacquisition innovation output. We do not ﬁnd

any statistically signiﬁcant impact of nontechno-

logical acquisitions on subsequent innovation out-

put. Within technological acquisitions, absolute

size of the acquired knowledge base has a posi-

tive impact on innovation output, while relative

size of the acquired knowledge base reduces inno-

vation output. The relatedness of acquired and

acquiring knowledge bases has a nonlinear impact

on innovation output, with acquisition of ﬁrms

with high levels of relatedness and unrelatedness

both proving inferior to acquiring ﬁrms with mod-

erate levels of relatedness. These ﬁndings have

implications for theory, research, and practice.

We discuss these below.

Implications for theory and research

Recent research has highlighted the role of acqui-

sitions as a mechanism for the redeployment of

resources that are subject to market failure

(Anand and Singh, 1997; Capron, Dussauge, and

Mitchell, 1998). In this study we investigated the

effectiveness of such redeployment in the case of

one set of resources that are very susceptible to

market failure, knowledge-based or technological

resources. Our results provide both good and

bad news.

216 G. Ahuja and R. Katila

The positive outcome of this evaluation is that,

when the characteristics of acquisitions are

accounted for, acquisitions improve the techno-

logical performance of the acquiring ﬁrm.

Although prior research concluded that acqui-

sitions reduce innovative outputs, this study sug-

gests that under the appropriate circumstances,

even after controlling for innovative inputs such

as R&D, acquisitions can introduce a positive

shock onto innovation output. This ﬁnding is also

consistent with the sheer volume of acquisition

activity in the high-technology sector that sug-

gests that managers also view acquisitions as a

mechanism for accessing technology.

The results of this study are also important

from a broader economic perspective. The rapid

growth of technical knowledge in the past few

decades has meant that building and maintaining

expertise in multiple technologies is difﬁcult for

even the largest corporations (Granstrand and Sjo-

lander, 1990; Arora and Gambardella, 1994). Yet,

bringing together different streams of knowledge

is becoming an important precondition to success-

ful innovation in many industries (Grant, 1996;

Powell, Koput, and Smith-Doerr, 1996). This

need for increased differentiation in the develop-

ment of knowledge and increased integration in

its application has given new importance to tech-

nology markets (Demsetz, 1991; Grant, 1996).

Our results indicate that the process of obtaining

technological assets from external sources and

matching them with internally developed assets

to enhance their productivity can work, at least

insofar as the frequency of innovation output

is concerned.

The above arguments highlighted the brighter

aspects of the results. Another perspective on

these results, however, draws attention to their

less positive facets. Prior research has identiﬁed

three kinds of deﬁciencies in the context of

organizational learning: hubris, or underestimating

the likelihood of failure or the difﬁculty of the

task at hand; overexploration, or venturing into

domains of completely new knowledge; and,

overexploitation, or focusing only on the immedi-

ate neighborhood of the well known and under-

stood and overlooking more distant options

(Levinthal and March, 1993). Our data and results

indicated evidence of all three kinds of errors. To

the extent that relative size serves as a measure of

the relative difﬁculty of integration, it appears

that underestimating the magnitude of the inte-

gration task is not uncommon. Further, the vari-

ation on relatedness, and the curvilinear relation-

ship that we identiﬁed between acquisition

relatedness and innovation output, suggests that

in selecting acquisitions managers err in both

directions, acquiring both businesses that are only

distantly related or unrelated to the existing busi-

ness, as well as those that are too closely related

to the current business. The problems of acquiring

unrelated businesses are well documented and our

results further reinforce those lessons (Singh and

Montgomery, 1987). Additionally, our ﬁndings

help to highlight errors in the opposite direction.

Managers do make mistakes in picking acqui-

sitions that are too closely related to their extant

domains, and these mistakes are penalized with

poorer performance. Thus, we ﬁnd some empiri-

cal evidence of ‘competency traps’ (Levinthal and

March, 1993).

Implications for measurement

Our ﬁndings also have implications for the litera-

ture on acquisitions in general. Managerial hubris

and agency problems (Roll, 1986; Hayward and

Hambrick, 1997), imitation effects (Haunschild,

1993), inappropriate application of learning

(Haleblian and Finkelstein, 1999), and underesti-

mating the process impediments to postacquisition

integration (Jemison and Sitkin, 1986; Hitt et al.,

1996; Singh and Zollo, 1997) are all valid and

complementary explanations for why acquisitions

consistently fail to help the acquiring ﬁrm. The

ﬁndings of this paper suggest an additional expla-

nation, one that is perhaps more sympathetic to

managerial motivations and decisions. Evaluating

all acquisitions on the same performance metric,

for instance ﬁnancial performance, may not be

appropriate. Acquisitions motivated by different

objectives may differ in their timing and mode

of impact on ﬁrm performance.

The recent literature has provided several excit-

ing ways of using patent data to measure the

construct of knowledge (Mowery, Oxley, and Sil-

verman, 1998; Jaffe et al., 1993; Stuart and

Podolny, 1996). We build on these studies by

presenting an additional set of measures and

applying them to the context of acquisitions. The

patent-based approach to knowledge measurement

used by this study, and similar approaches used

in prior studies (e.g., Stuart and Podolny, 1996),

have several strengths. A primary strength of

Technological Acquisitions and Innovation 217

such approaches is that by using information on

individual elements of knowledge these

approaches make possible a ﬁne-grained assess-

ment of a ﬁrm’s knowledge base. Research has

argued that a ﬁrm’s knowledge base should ide-

ally be reﬂected as an asset on its balance sheet

(Granstrand and Sjolander, 1990). Although that

remains unattainable at present, these kinds of

measures do provide some basis for quantifying

this asset and enabling further analysis. A second

strength of these approaches is that they enable

continuous, and highly informative and detailed,

measurement of difﬁcult constructs such as tech-

nological relatedness. Finally, another virtue of

these approaches is that they permit quantiﬁcation

of knowledge bases from publicly available data,

in a longitudinal context.

The use of distributed lags to study the dynam-

ics of postacquisition performance represents

another potential contribution of this study.

Although the distributed lag technique has been

used by economists, relatively few researchers in

the strategy area have used this methodology.

This approach could be extremely useful in a

variety of strategic contexts that entail studying

the impact of an event or strategy on the perfor-

mance of a unit across several periods.

Implications for managers

The ﬁndings of this study draw managers’ atten-

tion to several paradoxical aspects of the acqui-

sition selection and integration process. This study

indicates that larger absolute size and smaller

relative size of acquisitions are associated with

superior postacquisition performance for the

acquiring ﬁrm. Further, a moderate level of

relatedness is preferable to acquiring a ﬁrm that

is very closely related, or distant to the acquiring

ﬁrm. Thus, from the acquisition selection stand-

point this research suggests that a balance on both

size and relatedness of acquisitions is favored.

Limitations and future research

Several limitations of this study are worth noting.

The restriction of the sample to a single industrial

context reinforces the need for conducting the

study in other industries. Additionally, the rel-

evance and utility of the patent-based measures

of knowledge are likely to be limited to industries

in which patents are themselves meaningful indi-

cators of innovation. In addition to the chemicals

sector some of the other contexts in which these

measures could be applied include biotechnology,

semiconductors, industrial machinery, and

advanced materials.

Another limitation of this study that suggests

an area of future research is the use of patents

to measure innovative output. Although patents

are reasonably good indicators of innovative out-

put, they are best regarded as intermediate out-

comes between acquisitions and value creation.

While our results indicate that acquisitions accel-

erate the creation of technologically new combi-

nations and innovations, they do not allow us to

directly measure the value generated by these

innovations. Examining the economic value of

postacquisition innovations would be a natural

extension of the work in this study and would

enable a more complete assessment of the contri-

bution of technological acquisitions to technologi-

cal development.

Finally, in this paper we used only a simple

count measure for nontechnological acquisitions.

However, such acquisitions could themselves vary

on many dimensions and reﬂect many different

objectives. The development of a schema to mea-

sure the various dimensions of nontechnological

acquisitions and relating them to different types

of ﬁrm outcomes would be another fruitful direc-

tion of further research.

ACKNOWLEDGEMENTS

We thank Jim Fredrickson, George Huber, Jim

Westphal, the editor, and two anonymous

reviewers for comments that helped us to reﬁne

our thinking. We especially thank Risto Miikku-

lainen for assistance and advice on the computer

programs used to compute the patent citation

variables. We gratefully acknowledge the ﬁnan-

cial support of the University of Texas at Austin.

The ﬁrst author also acknowledges the support

of the Center for International Business Education

and Research at the University of Michigan.

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APPENDIX

The list of patents obtained by a ﬁrm, and the

patents cited by the ﬁrm in the patents obtained

by it, is used as the measure of the ﬁrm’s knowl-

edge base. Since a ﬁrm’s own patents represent

knowledge created by the ﬁrm, their use as

components of the ﬁrm’s knowledge base is natu-

ral. However, the use of patents cited by the ﬁrm

as a component of the ﬁrm’s own knowledge

base may require further explanation.

Patent citations represent an acknowledgement

of the contributions of prior work in the develop-

ment of the current invention. As Jaffe et al.

(1993: 580) note:

The granting of the patent is a legal statement

that the idea embodied in the patent represents

a novel or useful contribution over and above

the previous state of knowledge, as represented

by the citations. Thus, in principle, a citation of

Patent X by Patent Y means that X represents a

piece of previously existing knowledge upon

which Y builds.

A citation thus represents a restriction on an

owner’s rights to exploit her own patent, and is

therefore not likely to be made if the cited patent

Weber Y, Shenkar O, Raveh A. 1996. National and

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does not truly represent knowledge underlying

the current patent.

The legal aspects of the patenting process

also ensure that patents that do truly represent

preexisting knowledge are cited with accuracy

in patents that build upon them. First, all appli-

cants are required by law to acknowledge all

‘prior art’ through citations. Second, to oversee

this process, each patent and its citations must

be cleared by a patent examiner before the

patent is actually granted. The patent examiner,

who is usually an expert in the technological

domain under which the patent falls, conducts

a search of the prior art in the area and adds

any citations that the patent applicant’s own

search may have missed. Since citations circum-

scribe the exploitable domain of the applicant’s

patent, the patent applicant is likely to contest

these citations if they do not accurately rep-

resent prior related knowledge. Together, this

system represents a set of checks and balances

that ensures that patent citations are relatively

robust indicators of knowledge ﬂows

(Trajtenberg, 1990; Jaffe et al., 1993). Thus,

the assignee of a patent is likely to know the

contents of the patents cited by her, at least to

a level of familiarity, and perhaps to mastery.

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Apr 2024

Nadia Hanif

Purpose Drawing on organizational design theory and organizational learning theory, this paper aims to examine component technology (CT) and the interaction between CT and experiential learning (EL) effects on the degree of integration (DI) of cross-border technological acquisitions. Design/methodology/approach Using a sample of 267 firms consisting of 229 acquirer firms who started cross-border technological acquisitions from developed economies and 38 acquirer firms who initiated cross-border technological acquisitions from emerging economies over the period of 1993–2016, this study adopts a value chain framework to measure the acquirers’ acquisition integration degree for the investigation of the effects of CT and the interaction between CT and EL. Findings First, this paper finds CT in cross-border technological acquisitions exerting a positive influence on the acquirer firm’s likelihood of the DI implementation, in line with the organizational design theory. Second, in view of organizational learning theory, this study finds EL and the combined effect of CT and EL to have an inverse influence on the DI. Practical implications The results imply that the moderating role of EL significantly optimizes decision choices for an acquirer firm for integration implementation strategies in the form of DI, such as full integration (structural integration), partial integration and no integration (structural separation), which appears to be crucial for cross-border technological acquisitions. Originality/value This study contributed to international business strategies by shedding light on the importance of the DI for an acquirer firm that undertakes a cross-border technological acquisition with a CT target firm. This study explains why structural integration might be necessary in cross-border technological acquisitions regardless of the costs of disruption it imposes, as well as the contexts in which it becomes less important or unnecessary. The study disclosed that the increase in the likelihood of DI because of CT depends on the EL of the acquisition company in the host country environment and fluctuates with the prior acquisition knowledge and EL of the host country. Combining two cross-border technological acquisition’s literature streams, such as CT and EL, this study enlightens the importance of organizational learning theory and theory of organization design strategic direction making on acquisition integration implementation strategies.

University absorptive capacity: Normative and perceived assessments

Article

May 2024

Knowledge management in universities is a dual process combining continuous learning and development of academic staff with the simultaneous transfer of knowledge to students. Collective capabilities and motivation of university employees to absorb and effectively apply knowledge underlie the so-called university absorptive capacity. However, characteristics perceived by workers and the level of absorptive capacity may contrast with the assessments based on university performance standards. The paper proposes and tests a dual method for judging on university absorptive capacity based on normative and perceived assessments. Methodologically, the study rests on knowledge management, the fundamental theoretical framework that covers the concept of absorptive capacity of an organization. Among the research methods are comparative analysis of university performance indicators and economic statistics. Empirical evidence includes data from the survey of 187 academic staff members of Ural Federal University (Ekaterinburg, Russia) and the Monitoring of Efficiency of Higher Educational Institutions in the RF in 2022–2023. Having performed content analysis, we identified the following evaluation parameters: the current level of employees’ knowledge and the potential for knowledge absorption in the organization. The first part of the methodology presents an algorithm for comparing the growth rates of normative performance indicators of the university in question with those of analogous universities. The second part deals with assessing absorptive capacity based on academic staff’s perceptions. The testing results indicate that there is a discrepancy between the normative and perceived assessments, which allowed us to propose a set of measures for adjusting the HR management model in a university.

Relative absorptive capacity and interorganizational learning

Article

Full-text available

May 1998

Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the Jinn-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical-biotechnology RED alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. (C) 1998 John Wiley & Sons, Ltd.

The Theory of the Firm Revisited

Chapter

May 1991

Harold Demsetz

In 1937, Ronald H. Coase published The Nature of the Firm, a classic paper that raised fundamental questions about the concept of the firm in economic theory. Coase proposed that the comparative costs of organizing transactions through markets, rather than within firms, are the primary determinants of the size and scope of firms. This volume derives from a conference held in 1987 to commemorate the fiftieth anniversary of the publication of Coase's classic article. The first chapter gives an overview of the volume. It is followed by a re-publication of the 1937 article, and by the three lectures Coase presented at the conference. These lectures provide a lively and informative history of the origins and development of his thought. Subsequent chapters explore a wide range of theoretical and empirical issues that have arisen in the transaction cost economic tradition. They illustrate the power of the transaction cost approach to enhance understanding not only of business firms, but of problems of economic organization generally. Contributors: Ronald H. Coase, Sherwin Rosen, Paul Joskow, Oliver Hart, Harold Demsetz, Scott Masten, Benjamin Klein, Oliver Williamson, and Sidney Winter.

Effects Of Acquisitions on R&D Inputs and Outputs

Article

Sep 1991

Market Structure and Innovation.

Article

Dec 1982

The Effects of Acquisitions and Restructuring (Strategic Refocusing) Strategies on Innovation

Chapter

Jan 1994

The decline in global competitiveness among US firms has been well documented (Franko 1989; Hill, Hitt and Hoskisson 1988; Young 1985). Accompanying this decline has been an increase in merger and acquisition activity that occurred in two surges — one in the late 1960s and one in the 1980s (Ravenscraft and Scherer 1987). Research has shown that acquisition strategies may involve trade-offs. For example, while acquired firms’ shareholders often gain significant abnormal returns, acquiring firms’ shareholders, on average, receive zero or slightly positive returns (Jensen 1988). The purpose of this paper is to examine the potential link between the decline in competitiveness and acquisition and divestiture (refocusing) activity.

The theory of the firm revisited

Article

Jan 1993

H. Demsetz

Market Structure and Innovation

Chapter

Jan 1987

Morton I. Kamien

The study of the relationship between market structure and innovation by economists is a relatively recent phenomenon that can be traced back to the mid-1950s. Prior to that time the bulk of economic analysis took the number of products and their means of production as determined exogenously, just as consumers’ tastes were taken as an exogenous given. With a few exceptions, economists appeared to be unconcerned with the economic incentives that determined the pace and direction of innovation despite the fact that this activity had begun to be institutionalized about 1876, when industrial research laboratories began to be established both in the United States and in Europe. The exceptions include Taussig (1915), Hicks (1932), Galbraith (1952), and most importantly Schumpeter (1961, 1964, 1975). It was Schumpeter who argued most persuasively that it was competition through introduction of new products and methods of production that was far more important than price competition, in the long run. For it was through innovative activity that economic development that resulted in higher per capita income took place.

Corporate Acquisitions: A Process Perspective

Article

Jan 1986

Historically, acquisition scholars and practitioners have adopted a choice perspective which portrays the corporate executive analyzing acquisition opportunities as a rational decision maker. This paper suggests that the choice perspective be supplemented with a process perspective which recognizes the acquisition process itself as a potentially important determinant of activities and outcomes. A series of research propositions is offered suggesting how four impediments present in the process itself might affect acquisition outcomes.

Longitudinal data analysis using generalized linear models

Article

Jan 1986
BIOMETRIKA

Dynamic count data models of technological innovation

Article

Mar 1995

Technological Acquisitions and the Innovation Performance of Acquiring Firms: A Longitudinal Study

Abstract and Figures

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