.

Do Corporate Mergers Bring about
New Combinations of Knowledge?
- Empirical Evidence from Patent Data -Atsushi INUZUKAThe University of Tokyo
Research Center for Advanced Science and Technology,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904 JAPANOriginal Source:International Journal of Knowledge Management Studies
Vol.3, Nos.1/2, 2009, pp.40-59.

Abstract. The aftereffects of corporate mergers, particularly whether corporate mergers
facilitate new combinations of knowledge, were investigated using patent application data fortwo corporate mergers in the Japanese chemical industry. The results show that new
combinations are more likely to occur after mergers and that the choice of a “newcombinations” strategy can differ between companies. This strategic choice has an impact on
the aftereffects of corporate mergers, and the difference in strategic choice might depend onwhether power and initiative reside in the same premerger firm.
Keywords. corporate merger, new combination, patent application, inventor, after-effect,
efficiency, effectiveness, collaboration, technology map, selection and concentration.
Reference to this paper should be made as follows: Inuzuka, A. "Do corporate mergers bringabout new combinations of knowledge?: Empirical evidence from patent data," International
Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59.
Biographical Notes: Atsushi Inuzuka received a Ph.D. in Knowledge Science from Japan
Advanced Institute of Science and Technology. He is currently a project associate professor at
Research Center for Advanced Science and Technology, The University of Tokyo. He hasover five years of industry experience in product development. His research interests include
knowledge management, organizational behavior, and organizational psychology.

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

1. INTRODUCTION

Recent literature on the role of firm-specific resources has indicated that knowledge is themost strategically significant resource of a firm. A particular view of a firm, namely, theknowledge-based view, emphasizes the capacity of a firm to integrate tacit knowledge(Nonaka, 1994; Grant, 1996; Conner and Prahalad, 1996). As this resource is usually difficult
to imitate and is socially complex, heterogeneous knowledge bases comprise the major
determinants of sustained competitive advantage and performance. However, as Grant (1996)
and other researchers have shown, the “knowledge-based view” is not yet a theory of a firm atsome respects.
If knowledge and its management are of such great importance, knowledge strategies
should play a critical role in a firm’s strategic planning. Of the numerous strategic choices thata firm can make, one of the most drastic ones is whether to merge with another firm. However,a corporate merger can be likened to a double-edged sword. If it is carried out successfully, afirm can gain access to new resources and, by the redeployment of resources, increase its
revenue and reduce costs, which will result in its performance improving significantly.
However, as a worst case, a corporate merger simply creates turmoil and results in poor
performance. In fact, many empirical studies have shown that there is no guarantee that theaftereffects of a corporate merger will be positive (Lubatkin, 1983; Mueller, 1997).
From a knowledge-based perspective, mergers can be viewed as combinations thatbroaden a firm’s knowledge base. A broader knowledge base may provide many opportunities
for recombining the existing elements of knowledge into new syntheses. In other words, theemergence of new combinations of knowledge that lead to innovation is one of the important
motives for suffering the stress of a corporate merger. However, empirical findings suggested
that the impact of mergers depends on the characteristics of the knowledge of the merger andmerged firms (Ahuja and Katila, 2001; Mowery, Oxley, and Silverman, 1996, 2002; Sampson,
2004). If the merger is successful, the firm should be able to generate new knowledge that
could not have been generated in its absence. If it is unsuccessful, the firm’s knowledge basecould be ruined. Although knowledge is seen as the most important asset of a firm, until now,
hardly any research from the knowledge-based view has been conducted on how theaftereffects of corporate mergers have been successfully achieved, especially at the microlevel. It is possible that the lack of empirical work documenting the aftereffects of mergers ispartly due to the difficulty of measuring the technological and other capabilities of firms(Mowery, Oxley, and Silverman, 1996).
Owing to the difficulty involved in measuring firms’ technological capability, thispaper focuses on the usefulness of patent application data. Patents have three particularadvantages: they are systematically compiled, they have detailed information, and they are1

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

available continuously across time (Almeida, 1996) 1. Considering these advantages, manyscholars have obtained useful results about firms’ innovative activities by using patent data
(Pavitt, 1985). Patents, because of their nature, represent technological advances and include
speculations about how a particular technology may be applied. Therefore, parents enable adetailed understanding of how and to what extent each technology was used before and after a
corporate merger. Therefore, I investigated the aftereffects of corporate mergers, using patentdata, and put forth two questions: (1) Do corporate mergers facilitate new combinations ofknowledge? (2) If they do, how and why does a firm choose a “new combination” strategy?
To answer these questions, I created a new measure for evaluating new combinations ofknowledge and tested three hypotheses.
The remainder of this paper is organized as follows. In the next section, I briefly
survey the academic contributions with respect to this issue and present the three hypotheses.
The following section describes the analytical procedure and the variables used for testing the
hypotheses. The results are presented and discussed in the next two sections. The last section
summarizes the key points, describes several limitations, and provides directions for future
research.

1

It should be noted that patents are not perfect indicators of firms’ innovative activity.
However, they may serve as an interesting monitoring device to identify the main lines andtrends, and even under specific conditions, enables us to analyze R&D processes in greater
detail (Engelsman and Raan, 1992). Three potential limitations of patents were pointed out byPatel and Pavitt (1997): (1) patents do not measure the extent of the firm’s external
technological linkages, (2) patents measure only codified knowledge, and (3) patenting does
not fully measure competencies in software technology.

2

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

2. BACKGROUND

New Combinations Evidenced by Patent Data
Perhaps the most significant contribution of Schumpeter was to define innovation as “newcombination” (Schumpeter, 1934). Innovation is considered to be accomplished by creatively
combining two (or more) previously unrelated pieces of knowledge to form a qualitatively
new piece of knowledge. For a new idea to come into existence, different pieces of knowledge
have to come together. This implies personal interaction between individuals because mostknowledge is held within them.
In this respect, a corporate merger provides many new opportunities for different
pieces of knowledge to come together. It is expected to reduce the psychological and/or
economic cost of interaction between the members of the two formerly separate firms.
Additionally, as each merging firm has a specific technological portfolio, the merger should
facilitate diversification and the filling of gaps between the portfolios. This implies that thepossibility of new combinations occurring in a firm should be higher after the merger.

H1: Corporate mergers facilitate new combinations of knowledge.

To test this hypothesis empirically, a reliable measure for new combinations ofknowledge is required. If a narrow definition of knowledge is permitted, patent statistics
provide a proxy for knowledge and are a good resource for investigating this problem2. The
patent systems of major countries use the International Patent Classification (IPC) code,
which had its origins in the Council of Europe’s 1954 European Convention on theInternational Classification of Patents for Invention. Under this scheme, each patent is
assigned one or more IPC codes corresponding to the patent’s contents. Since the codes
correspond to technology areas, the relatedness between fields of technology can be estimatedby analyzing the co-occurrences of the codes assigned to each patent (Jaffe, 1986; 1989;
Engelsman and van Raan, 1992; Breschi, Lissoni, and Malerba; 2003, 2004). For example, if
two codes co-occur frequently, the combination of the two corresponding technology fields isprobably not new (i.e., the combination is central in the firm’s total portfolio). Similarly, if
they co-occur infrequently, their combination is thought to be new (i.e., the combination is
marginal in the firm’s total portfolio). We can thus estimate the extent of newness of a
combination of knowledge on the basis of the frequency of the IPC code pairs assigned to
2

There is a discussion that patent statistics show different aspects of industrial innovation(Pavitt, 1982).

3

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

patents.

New Combinations as a Strategic Choice
A firm’s strategic choices strongly affect the behaviors of the inventors it employs; therefore,
focusing on the inventors is a reasonable way to assess the effects of a strategic choice. Jaffe,
Trejtenberg, and Henderson (1993), for example, analyzed patent citation data by the U. S.
patent office and found that citations were more likely to occur among patents invented bypersons from the same region. Such a localization effect of knowledge was also confirmed bymany scholars (Almeida, 1996; Almeida and Kogut, 1999; Stolpe, 2002; Song, Almeida, andWu, 2003; Thompson and Fox-Keanm, 2004).
Narin and Breitzman (1995) and Ernst, Leptien, and Vitt (2000) used patent data toverify Lotka (1926)’s law, i.e., the number of highly productive scientists is a relatively small
fraction of all scientists. In a firm, there is a relatively small number of higher-performance
inventors and a large number of lower-performance inventors. The higher-performance
inventors are assumed to be the key person in the firm’s research activities. Besides
developing new inventions, they also interact with each other to find promising combinationsof the knowledge that they are creating. Since their role includes developing a large numberof inventions as well as those that are unique (pursuing unique combinations of knowledge),
we are led to the following hypothesis.

H2: Inventors pursuing new combinations of knowledge are characterized as higher
performers.

However, the pursuit of new combinations involves greater risks. First of all, a firm
confronts higher uncertainty because the probability of the occurrence of new combinations isrelatively low. In other words, new combinations generally do not occur in the core area but inthe fringe areas of the firm. Since new combinations are unexpected, the probability ofcommercial success would be lower. Due to this low probability, a firm will encourage its
inventors to develop many inventions regardless of the probability of their success. This canbe designated as a “he who shoots often, hits at last” policy that compensates for lower
effectiveness. This discussion leads to the following hypothesis.

H3: A firm pursuing new combinations of knowledge as a strategic choice after amerger can be characterized as having higher efficiency but lower effectiveness.

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Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

3. ANALYTICAL PROCEDURE

Case Selection
The three hypotheses posited above were tested using data on patents filed at the Japanese
Patent Office (JPO). Although Japanese patent data may be biased, they have someadvantages that permit valid analyses. First and foremost, the Japanese two-step procedure for
granting patents enables a reasonable assessment of an inventor’s performance. Additionally,
owing to the detailed and integrated system, they are very helpful for a long-term analysis(both characteristics are fully described in later). Owing to these reasons, the Japanese
patenting system provides extensive and reasonable data for the following analysis.
The cases for the testing of the hypotheses were systematically selected. First, thetotal number of patent applications filed during the five years prior to the merger was
extracted of all horizontal M&As (a type of merger of two companies that are in directcompetition and share almost the same product lines and markets) from 1988 to 2002,
summarized in the “Databook of M&As in Japanese Firms” by RECOF Co. Ltd., in the Tokyo
Stock Exchange Market. The identification and patent data used hereafter was taken from the
PATOLIS database. The database contains extensive information about patent applications
filed in Japan, e.g., (a) the name and address of the applying firm and inventors, (b) the dateon which the application was filed at the JPO and information on its after status, and (c) the
IPC/FI code(s) assigned by the patent examiner.
After identifying the number of patent applications of each corporate merger, thecases were selected as follows. Considering this paper’s purpose, corporate mergers that are
not in technological areas, for example, retail or the banking sector are not consideredappropriate for the case. Also, in order to obtain accurate results, the case should have many
patents filed by the merger and merged firms, and their proportion should preferably be close
to equal. Therefore, I set three criteria for case selection: (1) the total number of patentapplications filed by both firms during the five years prior to the merger should be more than
5000, (2) the ratio of the number of applications filed between the two firms should notexceed 1:2, and (3) the name of the inventor’s firm was mostly confirmed on the patentdocuments (described below). Two cases met all the three criteria: Mitsubishi Chemicals and
Mitsui Chemicals (Table 1). Both cases were in the same chemical industry. Fortunately, thisenables a comparative analysis based on almost the same conditions. Although both cases
occurred in large Japanese business groups, the merger and merged firms did not have astrong business relationship prior to the merger. In fact, they were rivals with a contentious
relationship.

5

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

Table 1. Cases for analysis
firm name(after merger)

merger-side firm

merged-side firm

date ofmergernumber of patents filed during the number of patents filed during thefive years prior to the merger
five years after the merger

Mitsubishi Chemicals

Mitsubishi Kasei Kogyo

Mitsubishi Yuka

1994/10/1

7654(1989/10/1-1994/9/30)

6650(1994/10/1-1999/9/30)

Mitsui Chemicals

Mitsui Sekiyu
Kagaku Kogyo

Mitsui Toatsu Kagaku

1997/10/1

6072(1992/10/1-1997/9/30)

4981(1997/10/1-2002/9/30)

Identifying Inventors
Under the patent filing system in Japan, applications are published in a patent publication
bulletin (a patent journal) without a search report 18 months after the date of filing theapplication. The patent journal contains information about inventors, as shown in Figure 1.
From the example, we can determine that inventor3 Taro Suzuki worked for firm X and that
Hanako Tanaka worked for firm Y on the date of filing the application.

(71) applicant(s) 000123456
X-company
1-2-3 Shinjyuku-ku, Tokyo.
(72) inventor(s) Taro Suzuki
In X-company, 1-2-3 Shinjyuku-ku, Tokyo.
Hanako Tanaka
In Y-company, 4-5-6 Shibuya-ku, Tokyo.

Figure 1. Example of patent information in Japanese patent journal

Using this information, I identified which of the firms (merger firm or merged firm)each inventor worked at before the merger. Hereafter, I will use “A” to represent the merger
firm and “B” to represent the merged firm (i.e., Mitsubishi Kasei Kogyo and Mitsubishi Yuka
are referred to as firm A and firm B, respectively). I examined all the patent documents filed
by both the firms during the five years before the merger and listed the inventors who could
be linked to either of the firms. Two lists were created. One was created for the merger firm
(list A) and the other, for the merged firm (list B). I excluded the inventors who had provided
information other than the firm’s name in the address field (the cases where the addresses ofinventor’s home, affiliate firm, joint research firms, university, etc. were mentioned).
3

In Japanese patent law, inventors are defined as the person(s) who practically contributed tothe invention, excluding just managers, assistants, and sponsors.

6

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

However, the inventors whom I could link to any one firm through one or more patents andnot by other patents, were regarded as having worked for that firm and their names wereadded to the list4.
Thereafter, using the same data, I checked whether each person on list A and each
person on list B had filed at least one patent application during the five years after the merger
and eliminated those who had not filed any patent application. The lists, then, contained only
those inventors who had filed one or more patent applications during the five years both
before and after the merger. The following analysis uses the patent applications filed by theinventors on the two lists.

Variables of Inventors
I introduced six variables to designate inventors’ characteristics. Hereafter, S is the set ofpatent applications filed during the target period (five years before or after the merger).
Further, P, belonging to set S, is the set of patent applications on which inventor p is named.

(1) New combinations index for each inventor (new combinations)
Since the “newness” of combinations corresponds to a lower probability of co-occurrence
between the technology fields, the degree of innovativeness is considered to be the inverse of
the number of combination occurrences. The assumption made is that the frequency withwhich two classification codes are jointly assigned to the same patent document corresponds
to the strength of the knowledge relationship (Breschi, Lissoni, and Malerba, 2003). As
described above, IPC codes, which are assigned to patent documents by patent examiners, areuseful for estimating this frequency. However, since the codes are reviewed periodically(modified slightly every five years), they are not suitable for long-term studies. Therefore, I
used the FI code, which is the Japanese original code having its origin in the IPC, and having
almost the same classifications as the IPC, as an alternative. The main difference between IPC
and FI code is that the latter is updated frequently by the JPO in order to be consistent withother patent codes to enable long-term searches. Therefore, the FI is more suitable for tracing
firms’ technological areas over the long term.
The FI has the same hierarchical structure as the IPC, i.e., class, subclass, main group,4

This procedure has certain limitations. First, if there were inventors with the same family
and personal names in both firms A and B, I could not strictly identify which firm theinventors belonged to. This problem was almost negligible because there was only one
instance for each case (the inventor was eliminated from both the lists). However, it ispossible that one inventor had been listed twice because the inventor might have changed hisor her family name due to marriage during the target period.

7

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

and subgroup. I used the higher level, which is represented by an alphabetical “section
symbol” followed by a two-digit “class symbol” number (e.g., “C07” corresponds to “organic
chemistry”). Using this classification, I first estimated the degree of “newness” by thefrequency of co-occurrence between the FI class codes for the patent applications filed during
the target period. The formula used for the calculation is derived from Shannon’s information
theory, according to which, the less likely the event (i.e., co-occurrence) is to occur, thegreater the value. The degree of “newness” between FI classes j and k and the one of FI class j
are estimated by the following equation.

 fi j, k  ficount  ficount  1newness j , k    ln
1
i

i

iS

i

iS

if

 fi j ,
ficount  1, newness j    ln
1
i

iS

i

iS

where ficounti represents the number of unique FI classes for patent i. fi(j,k)i, andfi(j)i takes binary values: 1 means co-occurrence between FI class j and k ( ficounti  1 ) andoccurrence of FI class j ( ficounti  1 ) for patent-i.: 0 means no (co-)occurrence.
After calculating the “newness” between the FI classes, I assigned a new
combination index to each inventor. It is a weighted average that accounts for “exam ratio”(described below). The index is larger for inventors with patent applications in technologicalfields with a low probability of (co-)occurrence. In other words, the less the probability of(co-)occurrence in technological areas in which the inventor generates patents, the larger isthe index for him or her.

new _ combinations p 








  newness( j, k )   fi  j, k  n  ficount  ficount  1   newness(l )   fil  n ,
1 n
j k , jk

iP

i

i

i

i

l

i

iP

where ni represents the number of unique inventors for patent-i.

8

iP

i

i

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59(2) Inventors’ performance (patents, partial patents, exam ratio)
Inventors’ performance is measured in terms of efficiency and effectiveness.
Efficiency is related to the number of patent applications for the inventor. Credit isassigned in two ways: An inventor gets full credit (a “patent”) for an application and getspartial credit (a “partial patent”) for an application if more than one inventor is named; forexample, if three inventors are named, each is credited for one-third of the patent (Narin andBreitzman, 1995).
Effectiveness is related to the patent examination ratio for the inventor (“exam ratio”).
In Japan, a request for examination must be made to the JPO (the request must be madebefore three years have passed since the application filing date5). Such a request is not madefor all applications for several reasons. It may be decided that the patent is not important after
the application is filed, or that disclosing the patent’s contents is sufficient for budding thenovelty of related patents to be granted. This judgment process by a firm implies that the ratioof patents for which a request is made can be used as a measure of an inventor’s performanceon the basis of quality. Hence, I considered this ratio for each inventor as a proxy of his or her
effectiveness and calculated it in terms of a weighted average of the number of co-inventors.
For example, suppose an inventor has one patent application for which a request has been
made along with one other person and another patent application for which a request has notbeen made along with two other people. In this case, the exam ratio for the inventor is
calculated as a weighted average: (1/2 + 0/3) / (1/2 + 1/3) = 0.6, not simply 0.5.
Although some might argue that the ratio of patent registrations is a better indicatorof an inventor’s performance, because registering of a patent takes many years after theapplication is filed (e.g., Jaffe, et al., 1993; Hall, 2002), most of the patents in the two cases
mentioned here still have their examinations pending. Similarly, many researchers havepointed out that a patent’s strength is best measured by the number of patent citations to it(e.g., Narin, Carpenter, and Woolf, 1984; Albert et al., 1991), however, it also takes
considerable amount of time to measure the number of targeted patent citations. Therefore, I
used only exam ratio as the metric for an inventor’s effectiveness.

5

The period was changed from seven to three years as of 2001/10/1. Since I am interested inapplications filed from 1992/10/1 to 2002/9/30 in the Mitsui Chemicals case, some of themmight not have had a request for registration by the firm.

9Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

patents p   1iP

partial patent s p   1 ni
iP

d n
exam ratio 
1 n
iP

i

i

p

i

iP

(3) Average number of co-inventors per patent for each inventor (co-inventors)
I calculated the average number of co-inventors (including the targeted inventor) for eachpatent by inventor p, using the following equation.

nco _ inventors 
1
iP

i

p

iP

(4) Weighted average number of FI classes per patent for each inventor(weighted FIs)
Similar to Lerner’s (1994) work, I calculated the weighted average number of different FI
classes––which is weighted by the number of co-inventors––per patent using the following
equations: ficount nweighted FIs 
1 n
i

iP

p

iP

10

i

i

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

4. RESULTS

Aftereffects of a Corporate Merger
The average of (partial) patents and the exam ratio of an inventor during the five years before
and the five years after a merger, and the difference between them with t-statistics6 aresummarized in Table 2. The table also shows the index of new combinations and weighted FIs.
The latter two are higher for after the merger than before the merger in both cases, implyingthat a corporate merger tends to broaden the perspective of research activity. In both cases, themerger appears to have played a positive role in promoting the encountering of new
knowledge within the company, which can lead to innovation. This supports H1. It implies
that mergers increase the probability of encountering people within the firm who haveknowledge of different technologies or different ways of thinking. Consequently, inventors
are more likely to find new ways of coordinating or combining technologies.
In terms of inventor performances, the number of (partial) patents declines
significantly after a merger (Mitsui Chemicals case is not significant but at the level p < .1)
probably due to the reassignment of technology areas for researchers after the merger as a partof the restructuring. In contrast, inventors’ effectiveness (exam ratio) improves significantlyfor Mitsubishi Chemicals as shown by the higher exam ratio, while it declines slightly for
Mitsui Chemicals.

Table 2. Characteristics of inventors
firm name
Mitsubishi Chemicals(N=1522)
Mitsui Chemicals(N=1306)

period patents filed

patents

partialpatents

exam
ratio

5 years before merger
5 years after merger
difference (after-before)
5 years before merger
5 years after merger
difference (after-before)

9.597
8.131
-1.466 ***11.86710.969-0.8983.334
3.051-0.283 *
3.348
3.078
-0.2700.539
0.584
0.045 ***
0.607
0.577
-0.029 *newco-inventors
combinations
4.621
4.855
0.235 ***
4.490
4.702
0.212 ***

3.445
3.455
0.010
4.173
4.279
0.106 *

weighted
FIs
1.591
1.666
0.075 ***
1.668
1.757
0.089 ***

Note : A paired sample t-test is used ( * p < .05, *** p < .001).

Effect of New Combinations as a Strategic Choice
I used three regression models to estimate whether the difference in the inventor’s
6

Owing to the large sample, the average of paired difference for the exam ratio is also
regarded as t distributed.

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Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

effectiveness (exam ratio) between the two firms could be confirmed after controlling theeffects of other variables and determine the extent to which these characteristics affect theinventors’ performance. The first model defines three characteristics as independent variables:
new combinations, co-inventors, and weighted FIs. The second and third regression models
have one or two dummy variable(s), respectively for inventor classification.
The classification of inventors was carried out by the following procedure. Using list
A and list B, which I had prepared, I checked each patent application filed during the five
years after the merger to see whether it named inventors from firm-A and firm-B. Then,inventors who shared at least one patent application with someone from the other firm wereclassified as collaborators; the remaining inventors, as non-collaborators. On the basis of thisclassification, the dummy variable in model 2 was set to 1 for non-collaborators and to 0 forcollaborators. In Model 3, “dummy A” was set to 1 for non-collaborators from the merger
firm, and “dummy B” was set to 1 for non-collaborators from the merged firm, and both were
set to 0 for collaborators. I used the ordinary least squares (OLS) regression for partial patents
as the dependent variable (I took the natural log of the number of partial patents because the
distribution of partial patents was highly skewed) and used the probit regression for exam
ratio as the dependent variable. The results are summarized in Tables 3 and 4.

Table 3. Results of regression analysis (Mitsubishi Chemicals)
dependent variable

ln partial patents
ln partial patents
exam ratio(5 years before merger)(5 years after merger)(5 years before merger)
model 2model 3model 1model 2model 3model 1model 2model 3independent variable model 1-1.282-2.051 *new combinations -0.147 *** -0.119 *** -0.098 ** -0.169 *** -0.104 *** -0.112 *** -1.247
co-invetors-0.312 *** -0.315 *** -0.325 *** -0.425 *** -0.428 *** -0.422 *** -0.640-0.634-0.123weighted FIs
0.111 *** 0.093 **0.091 **
0.165 *** 0.112 *** 0.117 *** 2.943 **2.962 **
3.051 **
dummy A+B―
-0.175 ***――-0.300 ***
―
―
0.359
―
―
―
-0.358 ***
―
―
1.521
dummy A――
-0.248 ***――-0.336 ***――-2.175 *dummy B――
-0.114 ***intercept
1.775 *** 2.192 *** 2.179 *** 1.733 *** 2.444 *** 2.433 *** -0.457-0.562-0.473
0.105
0.135
0.142
0.192
0.279
0.281
―
―
―
R2
59.1 ***
59.1 ***
50.2 *** 120.2 *** 146.8 *** 118.2 *** 838.5
838.5
830.9
F / χ2

exam ratio(5 years after merger)
model 1model 2model 3-4.710 *** -4.615 *** -4.990 ***-0.060-0.0600.372
3.155 **
3.102 **
3.349 ***―
-0.123
―
―
―
0.650
―
―
-1.6922.988 **
2.748 **
2.620 **
―
―
―
892.6
892.4
889.6
*

Note : N=1522. Numerical variable represents standarized coefficient estimated by OLS regression analysis / probit analysis ( p < .05,**

p < .01,***p < .001).

Table 4. Results of regression analysis (Mitsui Chemicals)
dependent variable

exam ratio
exam ratio
ln partial patents
ln partial patents(5 years before merger)(5 years after merger)(5 years before merger)(5 years after merger)
model 2model 1model 3model 3model 2model 3model 1model 2model 3model 1model 2independent variable model 1
new combinations -0.202 *** -0.178 *** -0.186 *** -0.211 *** -0.148 *** -0.156 *** -5.186 *** -5.166 *** -5.083 *** -2.305 * -2.667 ** -2.206 *
***
***
***
***
***
***
***
***
co-invetors-0.244-0.227-0.265-0.217-0.223-0.243
1.278
1.263
1.733
3.645
3.694
4.630 ***
weighted FIs
0.254 *** 0.227 *** 0.230 *** 0.157 *** 0.079 **0.086 **
4.640 *** 4.619 *** 4.577 *** 2.464 *2.895 **
2.551 *
dummy A+B―
-0.182 ***――-0.413 ***
―
―
0.087
―
―
2.968 **―――
-0.401 ***
―
―
1.098
―
―
4.354 ***
dummy A――
-0.239 *********dummy B――
-0.145――-0.419――-0.562
―
―
1.445
0.185
-0.203-1.368-2.292 * -3.043 **intercept
1.132 *** 1.402 *** 1.552 *** 1.316 *** 2.157 *** 2.233 *** 0.214
2
0.092
0.124
0.135
0.073
0.239
0.241
―
―
―
―
―
―
R
43.7 ***
46.1 ***
40.5 ***
34.0 *** 101.9 ***82.5 *** 576.6
576.6
575.8
618.7
617.8
611.7
F / χ2*

Note : N=1306. Numerical variable represents standarized coefficient estimated by OLS regression analysis / probit analysis ( p < .05,

12**

p < .01,***p < .001).

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

The results indicate that new combinations and co-inventors had a significantly
negative effect on inventors’ efficiency (i.e., partial patents), while weighted FIs had asignificantly positive effect both before and after the merger. This implies that inventors who
work relatively independently and cover many but related technological areas are likely to be
more efficient. All the results show that new combinations negatively affect inventors’
efficiency, which contradicts H2. Furthermore, the negative signs for the dummy variables
mean that non-collaborators are less efficient than collaborators. In other words, collaborators
are more efficient than non-collaborators, which imply that collaborators must play key roles
in developing innovation. Moreover, the increase in the absolute values of the dummy
variables after the merger means that collaborators play an even more important role after themerger.
The results for effectiveness (i.e., exam ratio) differ somewhat between the two cases.
For example, the sign for co-inventor is negative for Mitsubishi Chemicals, while it is positive
for Mitsui Chemicals. Moreover, for Mitsui Chemicals, the coefficients for co-inventors are
significantly greater after the merger. This suggests that Mitsui Chemicals emphasized moreon collaborative work among researchers after the merger. More importantly, the values ofdummy variables (non-collaborators) for Mitsui Chemicals were significantly greater after themerger, which is in contrast to for the case of Mitsubishi Chemicals. This indicates thattechnological collaboration between the merger and merged firms was less successful after themerger for Mitsui Chemicals than for Mitsubishi Chemicals.
The signs of the new combinations are all negative, which means that pursuing new
combinations does not result in effective inventions. This, together with the results describedabove, leads to the conclusion that H2 is not supported in terms of both efficiency andeffectiveness. In addition, careful analyses of the results confirm the differences between thetwo cases. In the Mitsubishi Chemicals case, the values for new combinations are lower after
the merger, while in the Mitsui Chemicals case they are higher. This indicates that Mitsubishi
Chemicals followed a “selection and concentration of technology” strategy after the merger interms of effectiveness. In contrast, Mitsui Chemicals emphasized “new combinations” rather
than “selection and concentration” of technology.
This strategic contrast affects the roles of collaborators as well as non-collaborators.
From Table 3, the effectiveness of Mitsubishi Chemicals’ collaborators and non-collaborators
did not change much after the merger. In contrast, as shown in Table 4, the collaborators of
Mitsui Chemicals worked less effectively than the non-collaborators after the merger. These
cases imply that if a firm makes “new combinations” its strategic choice, effective inventions
might not be expected for collaborative work. Also, the results shown on the left side of Table
4 imply that the aftereffect for collaborators in terms of efficiency is higher after the merger(the values for non-collaborators significantly decline after the merger), showing thatcollaborators are likely to produce more inventions than non-collaborators after a merger. In
short, Mitsui Chemicals apparently adapted a “he who shoots often, hits at last” policy forcollaborators to compensate for their lower effectiveness, which supports H3.

13

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

5. DISCUSSION

Thus far, I have discussed the aftereffects of corporate mergers, using patent application data.
The findings show that Mitsubishi Chemicals and Mitsui Chemicals made different strategic
choices for filing patent applications. In this section, I consider how their technology areas
affected their strategic choices because this choice is strictly rooted in the technology area inwhich a firm stands.
Although new combinations after a merger demand a certain level of technologicalchange, in general, such changes cannot be easily realized. In fact, there is considerable
evidence that a firm’s innovative activities are cumulative in the sense that its technological
specialization tends to remain stable over lengthy periods of time (Cantwell and Anderson,
1996; Malerba, Orsenigo, and Peretto, 1997; Malerba and Orsenigo, 1999; Cefis and Orsenigo,
2001; Cefis, 2003; Breschi, Lissoni, and Malerba, 2003). This is because there is inertia thatreinforces specialization and technical interdependence (Dosi, 1982; Patel and Pavitt, 1997);
thus a firm’s technology area generally changes gradually over time. This means that people
must take strong initiative for new combinations to occur.
I used a technology map to identify who took the initiative that led to new
combinations. I created the map, using correspondence analysis, a method of factoring
categorical variables and displaying them in a feature space. First, I determined the total
number of patent applications7 in each FI class for each filing year, using the formula given
below (I counted the number of partial patents for each FI class for each filing year).
Subsequently, correspondence analysis was conducted with FI class as a row category and theclassification of inventor (list A, list B, or both) for each filing year for both cases as a column
category. Figure 2 shows a scatter plot of points for each FI class. The distance between FIclasses is shorter when they have relatively the same ratio of patent applications amongcolumn categories. It must be emphasized that the axes do not represent specific parameters;
the relative positioning itself is essential.

contribute number of patents  j , t   fi( j) n  ficount iP t iii

P(t) : Set of patent applications filed by target inventors in period t

7

Since the purpose is to analyze the properties of patent data as a proxy of the firm’s
innovative activities, patent applications were chosen instead of registered patents.

14

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

2.4
B27
E03

E02
D21

E21

F24
H05 E01
F21
A44
B07 B67
B26
C04B41
D03
B28
A01
C09
A45 F17 F25
C07F23
G12
G02
B42
A24
C01 F28B81
E04F02 F27B82
A42 C11C23
B68 B24A61
B32 A63G09 B01B05 E06C03 G11
B66 F26
B30 D06 G06 C05
C08
C02
C25
B60 B29
0H01 H04B22 F01 B02G07
2.4
B25 G05 G03 A23
A41 D02 B43 B65
G21
B04
B03
G01 B08
F16 C12
B09
F03
C22
F04 D01
G08
A47
B44 C10C13
H02 C30
B23
A21
D04
C21 B62
A46
F15
G04
B21
B31
D07
B61

C40
A43
B63
A62

-2.4

H03

B64

B06

-2.4

Figure 2. Technology space

Next, I plotted the inventors’ technological centroid positions in the same technology
space (Figures 3 and 4). The patent applications filed before the merger by inventors for file A(merger firm) and file B (merged firm) are labeled A and B, respectively. The applications
filed after the merger by inventors for both file A and file B are labeled C. The numbers inparentheses indicate the filing period, i.e., (–1) implies filing in the year prior to the merger,and (1) implies filing in the year after the merger. I added ellipses to cover all the filing
periods for the inventors’ classification.

15

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

1.2

Mitsubishi
Chemicals(after merger)
C(3)
C(4)

-1.2

0

B(-5)

C(1)
C(5)

B(-3)

B(-2)

C(2)
A(-3)

A(-1)
A(-2)
A(-4)

1.2

A(-5)

Mitusbishi
Kasei Kogyo(merger-side)

B(-4)
B(-1) Mitsubishi Yuka

(merged-side)-1.2

Figure 3. Technological position (Mitsubishi Chemicals)

1.2
Mitsui Toatsu Kagaku(merged-side)

B(-1)
B(-3)
B(-4)
C(1)

B(-2)

B(-5)

C(3) C(5)C(4) Mitsui Chemicals
C(2)(after merger)

-1.2

0

Mitsui Sekiyu
A(-5) Kagaku Kogyo(merger-side)A(-4) A(-3)
A(-2) A(-1)-1.2

Figure 4. Technological position (Mitsui Chemicals)

16

1.2

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

Using these figures, we can estimate the firm that took the initiative in terms ofcollaboration after the merger. For example, Figure 3 shows that patent applications from themerger firm (A), are located in the area plotted in the fourth quadrants, and those from themerged firm (B) are located in the third quadrant. The applications filed by inventors fromboth firms after the merger (C) are located in the first and fourth quadrant and closer to thosefrom the merger firm (A) than those from the merged firm (B). This implies that the initiative
after the merger was taken mainly by the inventors from the merger firm in the Mitsubishi
Chemicals case. In contrast, in the Mitsui Chemicals case (Figure 4), the positions of theapplications for the merged firm (B) and for both firms after the merger (C) partly overlap,
indicating that the initiative was taken mainly by the inventors from the merged firm.
Supporting this, the famous Japanese business newspaper, Nikkei, reported the following on
the day of the merger, “producing a cash flow by the stable petro-chemical business of Mitsui
Sekiyu Kagaku Kogyo (the merger firm), and investing it in Mitsui Toatsu Kagaku (themerged firm)’s fine-chemical business that has high growth potential –– the newly formed
Mitsui Chemicals develops such a scenario.”
This finding provides one explanation for making a different strategic choice for new
combinations. Usually, the merger firm has much strong power than the merged firm.
Therefore, the results for the Mitsubishi Chemicals case imply that a merger firm can adopt a
top-down approach to implement a “selection and concentration” strategy because the merger
firm is more powerful and takes strong initiative. On the other hand, the results for the Mitsui
Chemicals case indicate that if initiative is lacking in the merger firm, the resulting
maldistribution of power and initiative can make the adoption of a top-down approachdifficult. This forces the firm to choose a “new combinations” strategy instead of a “selection
and concentration” one. These cases imply that if the “selection and concentration” strategy is
to be applied, both power and initiative should reside in the same premerger firm.

17

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

6. CONCLUSION

I began this paper by posing two questions: (1) Are new combinations of knowledge
facilitated by corporate mergers? (2) How and why does a company make a strategic choicefor new combinations of knowledge? My analysis of the two cases indicated that new
combinations were more likely to occur after mergers. However, the focus on the “newcombinations” strategy revealed a different story. Mitsubishi Chemicals concentrated on theirtechnology area for filing patent applications after the merger. In contrast, Mitsui Chemicals
encouraged its inventors, especially the collaborators, to produce many inventions regardlessof their effectiveness. Further analyses revealed that the choice of a “new combination”
strategy depends on whether or not power and initiative reside in the same premerger firm.
Moreover, strategic choice seems to have a significant impact on the aftereffects of acorporate merger.
These evidences have some managerial implications. First of all, although a
corporate merger facilitates new combinations of knowledge, pursuing new combinations ininnovative activities does not necessarily result in higher performance. This implies that if a
firm considers “new combinations” as the strategic purpose for a merger, the firm must endure
low performance for research activities. To compensate for the lower performance, a firmmight need some schemes that will encourage research activities, especially in terms of thenumber of research activities. The second point is that the possibility of choosing anappropriate strategy for a firm depends on the overall portfolio of the firm after the merger. If
maldistribution of power and initiative exists, a firm might face the difficulty of following the“selection and concentration” strategy rather than the “new combinations” strategy. Contrary
to this, if maldistribution does not exist, the “selection and concentration” strategy might bebetter because the empirical results exhibit significant improvement in effectiveness after themerger.
From a human resource management perspective, two implications can be drawn.
The first is the fact that there is a difference in the conditions with regard to the creation of
innovation before and after a merger. Therefore, a firm should create a distinct organizational
environment for research activities before and after the merger. Second, the classification ofcollaborators and non-collaborators implies that effective innovation might occur in the fringearea of a firm’s technical portfolio as well as of human resources (although the efficiency of
inventions is mainly led by collaborators, it is not always achieved effectively by them).
Effective inventions can be developed by “unexpected” inventors of a firm, implying thatsuch people should not be treated casually.
While these findings are insightful, there are several significant limitations of thisstudy that should be noted. Perhaps the greatest one is the limited number of cases and thelimited geographical area covered by the study (in Japan). The small number of corporate
mergers with a sufficient number of filed patent applications in Japan limited the analysis to18

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

two cases. The limited conditions in Japan as well as in the chemical industry make thegeneralization of the results too narrow. To develop the theoretical underpinnings of how todevelop innovations, more empirical works examining the difference between firms,
industries, or countries is required. Thus, so that the cases can be compared on an equal basis,
some weights that reflect the technical and/or commercial importance of patents need to bedevised because each merger is different in its size, type, purpose, etc.
This study also has limitations that have resulted oeing to the analytical method thispaper has taken. For example, this paper measured an inventor’s performance by his or her
patenting activity, whereas the quality of the resulting patents is not explicitly taken into
account (I assumed that each patent has an equal weight). In addition, since patent data is nota perfect indicator of research activities, I neglected many other aspects that are important for
generating innovation.
This study is just an initial step. The following questions could be investigated infuture studies.

(a) Who are the real contributors in a corporate merger? This paper classified inventors as
collaborators or non-collaborators, and assumed that collaborators took an initiative forthe collaborated work. However, the real contributors could exist in both the categories.
More meaningful classification should be devised for detailed analyses.
(b) What kind of organizational environments can a firm offer to achieve the purpose of amerger? Since this paper focused only on the variables of inventors’ activities, thevariables at the organizational (team, division, etc.) level were neglected. Including thesevariables provides a clearer list of condition that a firm should prepare in order to merge
successfully.
(c) How are internal research activities related to a firm’s outcome? There is a missing link
between each inventor’s performances and his/her real contributions to a firm. An
endeavor for bringing together many measures relating to an inventor’s activities in a
firm will provide us with a more detailed understanding of real contributions of patenting
activities.
Despite several limitations, I believe that this study might be the first empirical testof the effects of corporate mergers at the micro level with the purpose of identifying theconditions that may affect the success of corporate mergers. As the analyses revealed, theaftereffects of corporate mergers did not occur in a void. It should be stressed that they
depended largely on the firms’ strategic choices.

19

Atsushi Inuzuka,"Do corporate mergers bring about new combinations of knowledge?: Empirical evidence from patent data,"
International Journal of Knowledge Management Studies, Vol.3, Nos.1/2, 2009, pp.40-59

Acknowledgement
This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid
for Scientific Research (B), 17330085.

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