Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Chapter 3
Technology Development and Similarities*

Koichiro KIMURA†, Hiroshi MATSUI‡, Kazuyuki MOTOHASHI§,
Shun KAIDA**, and Janthorn SINTHUPUNDAJA††

Abstract: Firms must choose which technologies or technological fields to focus on
relative to their competitors when building their competitive technology positions. To
examine the similarities of firms in terms of their technological fields, we conduct a casestudy on the similarities between a fast-growing Chinese robotics firm and a Japanese
first-mover firm within the same industry. We show that as the number of patentapplications by the Chinese firm increases, the technologies of the two firms become
more similar in terms of the level of technological positions but more diverse in terms of
the level of patent documents. In other words, as the Chinese firm develops technologies
in major technological fields of robotics, it works on a variety of technologies within the
major fields.
Key Words: patent; technology position; technological similarity; robotics industry;China

1. Introduction
An increasing number of firms in emerging countries are focusing on technology
development for competitive survival and further growth. As a result, innovation centers
have dispersed geographically from traditional developed countries to emerging countries.
Moreover, recent technological changes arising from the Fourth Industrial Revolution
have led to a rapid increase in business opportunities for firms to develop new

56

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

technologies. This has allowed firms in emerging countries to increase their competitive
advantages through aggressive technology development and innovation activities. １
Patent documents play a key role in researching and understanding thetechnology of firms. Patents have often been used as an indicator of innovation because
they contain useful technological information and expand the availability of databases invarious countries (Nagaoka et al., 2010). Much research has been conducted on patents,
such as studies on the factors of patent production and how the number and quality ofpatents affect business performance. Consequently, patent research has revealed a great
deal about the background and impact of technology development and innovation
activities.
Moreover, patents can indicate technological similarities or differences amongfirms through International Patent Classification (IPC) codes and other technology
classifications assigned to patent documents. The IPC is a hierarchical classification ofpatents according to technological fields. Using the IPC, the technology position of eachfirm can be identified according to a vector defined as a firm’s number of patents in atechnological field as a fraction of its total number of patents. Firms face choices in thetechnologies or technological fields they focus on and acquire to build their competitivetechnology positions relative to industry competitors. In other words, the choice oftechnological distance from rivals is critical for firms under fierce competition.
Therefore, we will analyze how the number of patent applications made by afirm in an emerging country changes its technology position compared with that of a firstmover firm in the same industry. Much research has already accumulated on technological* The authors would like to thank Professor Gary Jefferson (Brandeis University) and Mr. Zhenyu
Gong (Brandeis University) for the useful discussion at an online meeting held by the Institute ofDeveloping Economies on December 18, 2020.
† Associate Senior Research Fellow, Institute of Developing Economies, JETRO, Japan and IDE
Bangkok Research Center, JETRO Bangkok‡ Expert, ABeam Consulting (Thailand) Ltd.
§ Professor, The University of Tokyo** Senior Consultant, ABeam Consulting Ltd., Japan†† Consultant, ABeam Consulting (Thailand) Ltd.
１
Competitiveness depends on not only technology but also business models, knowledge in a
broader sense, and other factors. Moreover, innovation is a broad concept not limited totechnology, as it refers to bringing new value to society through channels such as new productdevelopment, reorganization, and others.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

similarity; we will utilize it here as well. However, the concept of similarity has often
been used to verify whether technological spillover exists among industries or firms withadvanced levels of technological similarity (Bloom et al., 2013; Jaffe, 1986). In contrast,
we will focus on the formation pattern of the technology position itself—that is, howtechnology position changes with an increasing number of patent applications. ２
Additionally, we will use natural language processing (NLP) to calculatetechnological similarity. ３ Technology positions based on the IPC are determined fromall or several of a firm’s patent documents, not from individual patent documents.
Therefore, if we can obtain a technology position from each patent document, we can also
calculate the technological similarities at the level of individual patent documents.
Consequently, it will even be possible to show differences between technologies
classified within the same technological field. To accomplish that, we will vectorize each
abstract of patent documents based on NLP in addition to vectorizing each technology
position at the firm level based on the IPC. We will then compare the vectors generated
by NLP with those based on the IPC and examine the potential use of NLP-generated
vectors for future research on technological similarities or differences and changes inthose characteristics.
Therefore, the purposes of this case study are, first, to show the technologicalsimilarities between a latecomer (a rapidly growing Chinese robotics firm) and firstmover (a Japanese firm within the same industry) and, second, to examine the availabilityof vectors generated by NLP. Specifically, we will first show that as the number of patentapplications increases, the technologies of the two firms become more similar in theirlevel of technological positioning, but the technologies for which they submit patentdocuments become more diverse. In other words, the Chinese firm, while developing
technologies in major robotic technological fields, works on a variety of technologies inthose major fields. As for the second purpose, this study will be fundamental research todevelop a means to show technological similarities using NLP.
The structure of this article is as follows. Section 2 introduces the industry
covered here and the approach used in our analysis. Section 3 reports the results of ouranalysis. Finally, we summarize and conclude the analysis in Section 4.

２

Kimura (2020) also showed some characteristics of the technology positions of Chinese firms
and their formation processes.
３
Term frequency–inverse document frequency (TF-IDF) is another way to vectorize documents.
It is a method of evaluating the importance of a word based on the frequency of its occurrence.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

2. Method
2.1. The CaseIn this study, we focus on a Chinese industrial robotics firm as a case study. In the global
industrial robotics market, a few global firms hold a large market share, but indigenous
firms are expanding their production volumes and sales rapidly in China, where themarket is growing rapidly (Cheng et al., 2019). In 2019, approximately 144,000 units ofindustrial robots were sold in China, with sales consisting of approximately 99,000 units
by foreign firms and 44,600 units by indigenous firms (China Robot Industry Alliance,2020). With market expansion, Chinese firms have increased the number of patentapplications they file and are actively engaged in mergers and acquisitions to enhancetheir competitive advantages. In addition, robots are increasingly used in various
applications beyond traditional automotive and electronics production lines at factories,
expanding the opportunities to develop new technologies.
Specifically, we compare a fast-growing Chinese firm, SIASUN Robot &Automation Co., Ltd. (hereafter, Siasun) in Shenyang with a Japanese first-mover,
YASKAWA Electric Corporation (hereafter, Yaskawa) in Kitakyusyu. Siasun wasestablished in 2000 and belongs to the Chinese Academy of Sciences. ４ Yaskawa,founded in 1915, started its robotics business in the 1970s based on its motor and servo
motor business. ５
Before showing the number of patent applications filed by each firm, we first
introduce the Bureau van Dijk (hereafter, BvD) Orbis Intellectual Property (hereafter,
Orbis IP) database used in this study. BvD, a Moody’s Analytics firm, is a provider offirm information worldwide. Orbis IP is a combination of accounting information, Orbis,
and intellectual property information linked by BvD’s unique firm codes. Using Orbis IP,
we construct an analytical data set as follows. We download patent documents filed by
the firms at the patent offices in their home countries. The patent documents are valid asof their download times in August and September 2020.

４

Source: Siasun’s official website: http://www.siasun.com/ (accessed on January 25, 2021).
Source: Yaskawa’s official website: https://www.yaskawa.co.jp/ (accessed on January 25,2021).

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

The patent applications filed by Siasun and Yaskawa based on this data set aresummarized in Table 1. The cumulative number of patent applications by Siasun has
rapidly increased since the 2010s, and the technological fields covered by their filings
have become increasingly diversified over time, as shown in Table 1(a). Moreover, theirbusiness has expanded in line with the number of patent applications, as shown in Table
2. ６ Sales have grown at a rapid compound annual growth rate of approximately 19%,
and the gross profit rate has also steadily improved. In addition, we find that they haveincreased their R&D spending at a faster pace than their growth in sales. In contrast,
Yaskawa already has many patents and has established the technological fields on which
they focus, as shown in Table 1(b). In other words, their technology position has notchanged significantly in recent years. Yaskawa’s sales in 2019 were 3.8 billion USD, orapproximately 10 times more than Siasun’s sales, according to Orbis IP. Therefore, byextension, Chinese firms are growing rapidly within the market, but major existing firms
are still a significant presence.

６

However, there is a two-way causal relationship between business expansion and increased
R&D expenditures, and there is a time lag for patents to materialize into business advantages.
Therefore, care should be taken when discussing the relationship between business performanceand the formation of technology positions.
60

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Table 1: Summary of the Data Set(a) Siasun
2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Application Number

11

2

13

7

71

31

14

44

41

20

187

Cumulative Number

20

22

35

42

113

144

158

202

243

263

450

A

Human Necessities

0

0

0

0

0

4

5

8

11

12

27

B

Performing Operations; Transporting

7

7

13

18

52

66

70

86

114

127

212

C

Chemistry; Metallurgy

2

2

3

3

7

7

7

7

7

7

9

D

Textiles; Paper

0

0

0

0

0

0

0

0

0

1

1

E

Fixed Constructions

6

6

6

6

6

7

7

7

9

9

11

F

M echanical Engineering; Lighting; Heating;
Weapons; Blasting

0

0

0

0

6

6

6

7

8

8

37

G

Physics

5

7

10

11

23

30

37

50

54

58

97

H

Electricity

0

0

3

4

19

24

26

37

40

41

56

Source: The authors’ creation based on the Orbis IP.

(b) Yaskawa
2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Application Number

498

349

484

546

442

518

265

235

96

118

69

Cumulative Number

5,185

5,534

6,018

6,564

7,006

7,524

7,789

8,024

8,120

8,238

8,307

A

Human Necessities

87

89

89

89

90

107

130

156

159

162

166

B

Performing Operations; Transporting

924

1,027

1,184

1,394

1,550

1,772

1,844

1,906

1,933

1,985

2,013

C

Chemistry; Metallurgy

116

122

124

128

128

128

132

140

141

141

141

D

Textiles; Paper

9

9

9

9

9

9

9

9

9

9

9

E

Fixed Constructions

16

19

23

23

23

23

23

23

23

23

24

F

M echanical Engineering; Lighting; Heating;
Weapons; Blasting

135

143

153

165

175

187

189

194

194

197

198

G

Physics

1,387

1,448

1,511

1,556

1,599

1,674

1,722

1,757

1,766

1,779

1,793

H

Electricity

2,511

2,677

2,925

3,200

3,432

3,624

3,740

3,839

3,895

3,942

3,963

Source: Same as that of Table 1(a).

61Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Table 2: Siasun’s Business Performance, 2010–2019
Item (Unit) \ Year

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Sales

(1,000 USD)

81,392

121,783

162,673

213,015

245,868

256,554

290,020

371,941446,265

389,064

Gross Profit(1,000 USD)

25,37638,926

48,77975,01991,887

91,73596,016150,857

165,134

144,467

Gross Profit Rate

(%)

31.2

32.0

30.0

35.2

37.4

35.8

33.1

40.6

37.0

37.1

R&D Expenses(1,000 USD)

761

5,390

5,904

6,171

11,127

9,461

10,15917,21921,29722,214The Ratio of R&D(%)
Expenses to Sales

0.9

4.4

3.6

2.9

4.5

3.7

3.5

4.6

4.8

5.7

Number of
employees

815

1,191

1,479

2,117

2,480

3,097

3,611

4,150

4,513

4,559

(Persons)

Source: Same as that of Table 1(a).

2.2. Preprocessing and Vectorization
Next, we take the following steps for data cleaning and preprocessing of the abstracts inthe downloaded patent documents. First, we eliminate signs in the abstracts—e.g., “,”, %,and ?—and we lowercase all of the alphabetical characters. In addition, the headings in
the abstracts of the patent documents filed in Japan, such as “Problem to be solved” and“Solution,” were eliminated because although they are useful for classifying the issues
and trends in technology development emphasized by each firm, they do not directly
indicate the technological field itself. Second, we use only nouns, verbs, and adverbs andconvert them into stems with the natural language toolkit. Then, we vectorize each
abstract and make it a 100-dimensional vector with Doc2Vec.
Finally, we calculate the technological similarity between Siasun and Yaskawa.
Specifically, we calculate the cosine similarity, 𝑠𝑠𝑡𝑡 , between the cumulative patentapplications of Siasun until the year, 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡𝑆𝑆 , and those of Yaskawa until the same year,
𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡𝑌𝑌 :𝑠𝑠𝑡𝑡 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡𝑆𝑆 , 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑡𝑡𝑌𝑌 ),where t is time. The cumulative patent applications at each year are the accumulation ofeach component of the document vectors. The similarity here is obtained by the following
calculation:

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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

𝑠𝑠𝑡𝑡 =∑𝑛𝑛𝑖𝑖=1 𝑆𝑆𝑖𝑖 𝑌𝑌𝑖𝑖

�∑𝑛𝑛𝑖𝑖=1 𝑆𝑆𝑖𝑖2 �∑𝑛𝑛𝑖𝑖=1 𝑌𝑌𝑖𝑖2,

where Si and Yi are components of each vector of the patent documents of Siasun andYaskawa, respectively. It should be noted that the similarity is based on the fraction ofeach component, so it does not reflect the size of each component. In other words, thenumber of patent applications does not affect technological similarity. Additionally, thedegree of similarity is based on technological fields and features in terms of words.
Therefore, the similarity does not consider the quality of patent applications.

3. Results
3.1. Similarity
Figure 1 shows the technological similarities between Siasun and Yaskawa. It includes
not only the similarity based on the vectors obtained by NLP but also the similarities
based on the single- and four-digit levels of the IPC (hereafter, IPC1 and IPC4,
respectively) to compare the NLP-based similarity. Because the number of technologicalfields is 8 in IPC1 and 203 in IPC4 in our data set, the number of components in thevectors for IPC1 and IPC4 are also 8 and 203, respectively. ７

７The single-digit level of the IPC originally has a total of eight categories.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Figure 1: Similarities between Siasun and Yaskawa, 2008–2018
1.00
0.90
0.80

NLP

0.70
0.60
IPC1
0.50
0.40
IPC4

0.30
0.20
0.10
0.00
2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Source: The authors’ calculation.

The NLP-based similarity shows the technological similarity of Siasun’s
technology position based on that of Yaskawa. The similarity increases fromapproximately 0.40 in 2008 to approximately 0.80 in 2018. After a rapid increase insimilarity as the number of patent applications begins to increase, the similarity flattens
out around the mid-2010s, and there is not much change even when the number of patentapplications in each year is relatively high. Even if a primary business of each firm is theindustrial robotics business, Siasun is also developing factory automation-related
technologies, and Yaskawa has a large business in motors which are a core component ofrobots. Therefore, as the number of patent applications increases to a certain extent, even
if the two firms are in the same industry, the differences in their technological positions
become clear.
As mentioned in the previous section, the similarity here is based ontechnological fields, so we also compare the NLP-based similarity with that based on
IPC1 and IPC4. Figure 1 shows the trends in similarity among the three different vectors
as a whole. However, there are at least two characteristics when we compare thesimilarities in detail. First, the IPC1-based similarity is consistently greater than the IPC4based similarity during the observation period. This is likely because the differences in
the fraction of patent applications among broader classifications such as those of IPC1
generally tend to be smaller than the differences among narrower ones such as IPC4 for64

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

firms in the same industry. Second, the NLP-based similarity was located between the
IPC1- and IPC4-based similarities from 2009 to 2015, after which the levels between the
IPC1- and NLP-based similarities reversed because the IPC1-based similarity decreased,
although that based on NLP did not change much. In other words, it is possible that thedifferences in technological fields in terms of IPC became greater than the differences inthe technologies themselves in terms of the words in the abstracts.８
Thus, although there are differences between the three similarities, their trends
are broadly similar as mentioned above. To understand the background, we compare thefractions of technological fields based on IPC1 in 2008 with those a decade later. Figure
2 illustrates the fractions of the number of patent applications in Table 1. Both Siasun andYaskawa had higher fractions in Section B related to manipulators and in Section G
related to controls in 2008 and 2018, both of which are major technologies for robots. In
contrast, the main difference in 2008 was that Siasun and Yaskawa had higher fractionsin Section E related to drilling operations, which were not for robots, and in Section H
related to servo motors for robots, respectively. However, over the next 10 years, Siasun
did not increase the number of its patent applications in Section E as much as it had
previously, but it increased its patent applications in Section H related to servo motors
and power supply. As a result, the technological similarity of Siasun to Yaskawa wascloser than it had been in 2008.

８

In addition, changes in the writing style of the abstracts should be accounted for in the NLP
analyses.
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Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Figure 2: Fractions Based on IPC1 as of (a) 2008 and (b) 2018(a) 2008
100
90
80
70
60
50
40
30
20
10
0
A

B

C

D

E

Siasun

F

G

H

F

G

H

Yaskawa

Source: Same as that of Table 1(a).

(b) 2018
100
90
80
70
60
50
40
30
20
10
0
A

B

C

D

E

SiasunYaskawa

Source: Same as that of Table 1(a).

3.2. Diversity
The technology position of Siasun approaches that of Yaskawa as described in 3.1, butthere is also a great deal of diversity among the patent applications. If we calculate andcompare the similarities between Siasun’s new patent applications in a given year andYaskawa’s technology position in that year, we find a wide range in the similarities.
Figure 3 shows the range of maximum and minimum similarities between Siasun’s and66

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

Yaskawa’s individual patent applications from 2008 to 2018. During the period when theannual number of patent applications was low (until 2011), the difference between the
maximum and minimum similarities ranged from 0.46 in 2011 to 0.78 in 2009. In contrast,
as the number of patent applications in each year increased, the differences widened from0.81 in 2017 to 1.12 in 2018. A large number of patent applications tend to develop intoa wide variety of technologies.
Figure 3: Range of Maximum and Minimum Similarities, 2008–2018
1.00
0.80
0.60
0.40
0.20
0.00
2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

-0.20
-0.40
-0.60
-0.80
-1.00

Source: The authors’ calculations.

As we mentioned, however, because firms generally develop and sell a variety
of products, we calculate the width only in “B25J9: Programme-controlled manipulators,”
which has the highest number of patent applications in terms of the IPC. Similar to thetrend of all the patent applications taken as a whole, even within the same code of B25J9,
the greater the number of patent applications is, the greater the range of similarity.
Specifically, it was just 0.19 in 2010 with two applications but 0.69 in 2018 with 17
applications. ９ As technological development in a certain technological field increases, avariety of technologies are developed even within the same field.

９

The range cannot be calculated for 2011 because there was only one patent application in
B25J9.
67

Koichiro KIMURA, ed. (2021) Impacts of Innovation on Firm Performance and Industrial Development in East Asia
(BRC Research Report), Bangkok: Bangkok Research Center, JETRO Bangkok.

4. Discussion
We conducted a case study on the technological similarity between Siasun and Yaskawa
and showed that as the number of patent applications increases, the technologies of thetwo firms become more similar in the level of their technological positions but at the sametime more diverse in terms of the technologies reflected in their patent documents.
Consequently, Siasun can be seen as differentiating and improving the quality of its
products by developing a variety of technologies within the main technological fields ofrobotics. Thus, through this case study on similarity, we were able to understand how afirm in an emerging country develops its technologies in comparison with its competitors.
Therefore, we need to accumulate more cases on the technological similarity
among firms in other industries. By doing so, we can extract more general characteristics
about the patterns of technological similarities or differences among firms in the processof industrial development. Further research is also needed on the relationship betweenchanges in technological similarity and business performance. This would allow forfurther discussion of the effective technological development of firms in terms oftechnology position. Through the study of technological similarities, we will be able togain a better understanding of the relative market advantages of firms.

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