ISSN 2189-9592

Journal of Japanese Management
Vol. 1, No.2, May 2017

JFMRA
Japan Federation of Management Related
Academies

Editor-in-Chief
Fangqi Xu (Japan Creativity Society, Kindai University)
Editors
Yukio Takagaki (Japan Academy of Management, Surugadai University)
Nobuchika Kaido (Japan Academy of Business Administration, Kwansei Gakuin University)
Editorial Board
Shintaro Abe (Japan Academy of Consumer Education, Josai International University)
Noriyuki Hamaoki (Japan Academy of Enterprise and Business Administration, Chuo Gakuin
University)
Masataka Hashimoto (Japan Logistics Society, Meiji University)
Masayuki Horie (Japan Auditing Association, Nihon University)
Yasuo Hoshino (The Japanese Association of Administration Science, International Pacific
University)
Yoshiharu Hyakuta (Japan Association for the Comparative Studies of Management,
Komazawa University)
Taketoshi Ikeda (Association for the Study of Industrial Management (Japan), Chiba Universityof Commerce)
Isuke Kohtoh (Japan Academy of Management Philosophy, Chukyo Gakuin University)
Hideo Narimichi (Japan Tax Accounting Association, Seikei University)
Kosuke Ogawa (Japan Institute of Marketing Science, Hosei University)
Shigeo Ohtsuka (Japan Accounting Association, Chiba University)
Hironori Otsuka (Japan Accounting Association, Saitama Gakuen University)
Nobuhiko Sato (Japan Accounting Association, Kumamoto Gakuen University)
Osam Sato (Japan Society for Information and Management, Tokyo Keizai University)
Makoto Takahashi (Japan Creativity Society, Japan Professional School of Education)
Masayasu Takahashi (Japan Academy of Business Administration, Meiji University)
Hideki Ueki (Knowledge Management Society of Japan, Tokyo Keizai University)
Masamichi Yoshioka (The Japanese Association of Management Accounting, Tokyo Universityof Science)
Advisory Board
Kazuo Hiramatsu (Japan Accounting Association, Kwansei Gakuin University)
Nobutaka Kazama (Association for the Study of Industrial Management (Japan), Meiji
University)
Takayuki Nonoyama (Japan Association for Management Systems, Aichi Sangyo University)
Takao Nuki (Association for the Study of Industrial Management (Japan), Musashi University)
Koji Okubayashi (Japan Society of Human Resource Management, Osaka International
University)

ISSN 2189-9592

Journal of Japanese Management
Vol.1, No.2, May 2017

JFMRA

J

Japan Federation of Management Related
Academies

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Contents

The Future of the Fair Trade Market in Japan:
What motivates consumers to purchase fair trade products? ・・・・・・・・・・・・ p.1
Yoshiaki Takahashi & Ayuri Omoto

Factor Analysis on Software Piracy

・・・・・・・・・・・・p.17

Lianfeng Yang
Open Innovation University-Industry Collaboration:
Student Idea Contests and Exit Strategy in Japan
Hiroko Kawai

・・・・・・・・・・・・p.31

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

The Future of the Fair Trade Market in Japan: What motivates
consumers to purchase fair trade products?
Yoshiaki Takahashi

The Institute for International Policy Studies
Japan Academy of Consumer Education E-mail:ytaka2001@hotmail.co.jp
Ayuri Omoto

The Japan Research Institute, Limited
Abstract:
The purpose of this study is to identify factors that affect consumers’ decision making
regarding purchase of fair trade products in Japan. Fair trade is an approach that guarantees
that producers of goods from developing countries are not economically exploited. However,few models have been tested in Japan. Therefore, in this paper, referring to Shaw et al. (2000),
which analyzes factors to purchase fair trade products in the UK based on the Theory ofPlanned Behavior (TPB), we examine the effect of knowledge about fair trade and others onpurchase intention in Japan.
The data used here is from a survey in Ibaraki Prefecture (males and females aged between 20 and 79; n = 213). The results showed that knowledge alone did not directly affectpurchase intention. Rather, the more important factor influencing purchase of fair trade products was whether it was favorable or easy for consumers to do. Knowledge was related to theoutcome evaluation that purchasing fair trade products would be important for society. Suchpersons did not only obtain the knowledge, but also conduct altruistic behavior frequently.
They believed that altruism is socially important in daily life. Therefore, besides simply raising awareness about fair trade, education is important to understand the social significanceof altruistic behavior.
We tested only models based on TRA and TPB in this study. On the other hand, there
are many possible models to include factors that may explain consumers’ decision making forfair trade products. In addition, in order to generalize the results as a model of fair trade
products in Japan, it is necessary to carry out another survey targeting the whole of Japanese
population.
Keywords
Fair Trade, Consumer Education, Decision Making, Theory of Planned Behavior, Japan(1) Introduction

The purpose of this study is to identify

1. Fair Trade Market
1

Journal of Japanese Management Vol.1, No.2, May 2017

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factors that affect consumers’ decision mak-Global sales of products with this designa-

ing regarding purchase of fair trade products

tion reached 5.9 billion euros in total (JPYin Japan. The development of international

830 billion).

trade has raised various ethical issues. For

The Fair Trade logo was introduced ininstance, workers may be employed at low

Japan in 1993. However, awareness of thewages and be economically exploited in de-system in Japan was only 14.7% according toveloping countries. The most important is-a result from the Cabinet Office’s Nationalsue is that workers in developing countries

Life Preference Survey in 2007. There areoften lack bargaining power against import-

very few opportunities to find fair tradeers and producers of final products. Poverty

products in grocery stores. The market size

also leads to child labor issues. One of thein Japan is increasing and as of 2015 itways to solve these problems is the fair tradereached about JPY 10.0 billion (Figure 1).

system. Now, business pays greater atten-

This is equivalent to 0.53 euros per person.

tion to fair trade because consumers and in-

The market is still much smaller than thosevestors take more interest in corporate social

in other developed countries (Figure 2).

responsibility than they did previously.

What are important factors to promote

Awareness of fair trade products is now morefair trade products in Japan? A key is con-than 90% in the United Kingdom, Austria,

sumer behavior because consumer prefer-and Ireland (Fairtrade International &ence develops a market. If we understand

GlobeScan, 2015). It has become one deci-

the consumers’ decision-making process tosion-making criteria when consumers choose

buy fair trade products, tips to promote fair

a company’s product. As of 2014, 1.5 million

trade can be discovered.

producers and workers had joined the

In recent years, the relationship be-

Fairtrade International Certification system.

tween consumption and self-identity is one

Figure 1. Fair Trade Market in Japan (2010-2015)
Source: Fairtrade Label Japan (2015)2

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592(Euros)
60

50

49.54
46.98

40
32.17

30
20
10
0.53

Brazil

Kenya

India

Lithuania

South Korea

Hong Kong

South Africa

Japan

Spain/Portugal

LatviaSlovenia

Czech Republic

Italy

USA

Estonia

CanadaFranceAustralia/NZ

Germany

Belgium

Netherlands

NorwayDenmark

Luxembourg

Austria

SwedenFinlandUK

Ireland

Switzerland

0

Figure 2. Fair Trade Market in 2014
Note: The figures were calculated with information from Fairtrade International
Annual Report 2014-15 and United Nations World Population Prospects

ucts not only for economic value (prices) butof the drivers to promote consumption. Ac-

also relating to social values. Therefore, we

cording to Tamaki (2008), the formation of

can interpret such consumer behavior as ir-

identity through consumption is a process

rational behavior because consumers spend“to find themselves among others, explain

more money for “others,” i.e., workers in de-

their own as a consistent presence, and toveloping countries. In this paper, we focus

maintain a sense of self” (p. 52). Watanabe

more on the effect of the preference for oth-(2007) concludes that the awareness of fairers and education on purchase intention of

trade influenced the development of fair

fair trade products, using survey data fromtrade markets in Western countries. In fact,

Ibaraki, Japan.

since the mid-1990s, fair trade organizations
and other related associations in European

2. The definition of Fair Tradecountries have launched awareness raisingThree international fair trade organi-

campaigns and then increased sales of des-zations, the Fairtrade International (FLO),

ignated products. Therefore, raising aware-

the World Fair Trade Organization (WFTO),

ness through consumer education and in-and the European Fair Trade Association

creasing a sense of self-identity will be pil-

(EFTA), established the Fair Trade Network.

lars of promoting fair trade products in Ja-

The Network defined fair trade to be “a trad-

pan. Moreover, Watanabe (2007) also noted

ing partnership, based on dialogue, trans-

that the meaning of consumption has

parency and respect, that seeks greater eq-changed over time. Consumers choose prod-uity in international trade”. The Network3

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

states that “(i)t contributes to sustainable

in the international market. Even when thedevelopment by offering better trading con-price increases due to drought, small coffee

ditions to, and securing the rights of, mar-plant producers may not have any bargain-

ginalized producers and workers—especiallying power and get enough income to coverin the South. Fair Trade Organizations,

their production costs. On the other hand,backed by consumers, are engaged actively

the fair trade standard sets minimum prices,in supporting producers, awareness raisingregardless of the fluctuations of the interna-and in campaigning for changes in the rules

tional market prices (Figure 3). Importers

and practice of conventional internationalmust ensure that the minimum price is paid

trade”. According to the Fairtrade Label Ja-

to producers’ associations. The Japan Fairpan, fair trade was begun by Ten Thousand

Trade Labeling mentions that the price ofVillages of America for purchase of embroi-

approved coffee beans should be more thandery in Puerto Rico in 1946.

USD1.40 per pound (454 g). If it is organic,

In 2001, the Fair Trade Network de-

USD0.30 is added. Moreover, importers

cided its strategy as follows.

should pay USD0.20 per pound as a premium (incentive) to producers’ associations.

１．To work together so that marginalized

The Fair Trade system works to support sus-

producers and workers can break out ofvulnerable conditions and obtain economic independence and security;
2. To empower the producers and workers to organize themselves and become
significant stakeholders in a global market;
3. To actively play a wide variety of roles
in international trade and make it fair.
(Watanabe, 2007, p. 5)

tainable production and stability of the livesof the workers, and to facilitate trade on amore equal footing.
(2) Literature review
1. Behavioral model of fair trade products
The theory of reasoned action (TRA) isoften used as a model explaining consumer
behavior (Azjen & Fishbein, 1980). The theory of planned behavior (TPB) is anothermodel to be used (Azjen, 1985; Azjen, 1991).

Approved products with the fair trade

According to the TPB, when a consumer

marks follow the standards throughout thewants to buy something, the consumer has

process from raw material production, im-

an intention to buy before action. “Attitude

port, manufacture, through shipment. For

toward the behavior,” “subjective norm,” andexample, coffee prices fluctuated drasticallyFigure 3. The Framework of Fair Trade
4

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Figure 4. Modified TPB Model (Shaw, et al., 2000)“perceived behavioral control” affect inten-

ical issues. The results showed that ethicaltion. Attitude means whether one’s evalua-obligation and self-identity affected the be-tion toward the purchase is favorable. Sub-havioral intention to buy fair trade products.

jective norm is measured by the question

Some research papers tested models towhether most people who are important to

explain decision-making of environmentallyme think I should purchase the product. Be-

friendly behavior and ethical consumption inhavioral control asks whether the purchase

Japan. For instance, Toyota (2008) examinedof fair trade grocery products is easy or diffi-

several kinds of models for five types of eth-cult.

ical consumer behavior: the purchase of soShaw, et al. (2000) tested several mod-lar panels, the subscription of a magazine

els including TRA and TPB to interpret eth-dealing with poverty issues, saving in a vol-ical consumer behavior to purchase fairuntary deposit, the boycott of disgraced cor-trade goods. The data was from 1,400 fairporate products, and socially-responsible de-trade magazine subscribers in the United

cision-making for consumption. However,Kingdom. It was more appropriate for thethis paper didn’t include fair trade products

fair trade market to use the TPB model be-

as one kind of ethical consumption. Because

cause it had more explanatory power than

GFI and AGFI in all five cases were greater

the TRA model. Shaw, et al. (2000) proposed

than 0.9 in the TPB model, it concluded thatto modify the TPB model (hereinafter “mod-the TPB explained the consumer behavior

ified TPB model”) to include ethical obliga-

more effectively than others. Among severaltion and self-identity (Figure 4). Ethical ob-

factors, the paper found that the subjective

ligation means that a respondent feels annorm had strong effects on consumers’ deci-

ethical obligation to purchase fair tradesion making.

products. Self-identity is a variable to meas-

As shown above, a variety of types ofure if a person feels any concern about eth-ethical consumption models was discussed
5

Journal of Japanese Management Vol.1, No.2, May 2017

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regarding the determinants of consumer be-nants of altruism are well known, the spe-

havior such as the TRA and the TPB. On thecific mechanism that fair trade uses is stillother hand, there is no study in Japan to test

an under researched issue” (p. 370). Kikuchi

the TPB model or others for fair trade. In(1988) define it as a behavior to try to en-this study, referring to the modified TPB

hance the relationship with others and de-model in Shaw et al. (2000), we would like tosire it to become more appropriate. It may

explore what kinds of factors make effects on

cause self-sacrifice and costs to the person

fair trade purchasing intention.

who takes such pro-social behavior. He distinguished between pro-social behavior and2. Personal factors

behavior that expects returns or financial re-

Consumption has various meanings towards from others. On the other hand, it in-consumers. One important factor is atten-

cludes behavior expected to get intrinsic re-tion to one’s identity. Sugawara (1984)

wards such as satisfaction and pride. Kiku-

stated that, “when others look at you or youchi (1988) developed a pro-social behavior

look at yourself in a mirror, you are con-scale based on the altruistic behavior scalescious of yourself.” He called this self-con-developed by Rushton (1981). In his paper,

sciousness. In his paper, he developed a Jap-

he examined the correlation between self-

anese version of a self-consciousness scaleconsciousness and pro-social behavior. The

based on those developed by Fenigstein et al.

results showed no statistical correlation be-(1975). The results of a survey on 438 stu-

tween them. He concluded that it happened

dents show that there were two main factors

because people behave socially when they dofor self-consciousness: public and private

not expect to receive rewards but does re-self-consciousness. Public self-consciousness

spect others’ situations.

means that a person paid attention to how

The other important factor relating toothers evaluate her. Private self-conscious-

fair trade can be education. The main aim ofness means that the individual asks her own

education is that people achieve the capacity

mind who she is. We decided to include theto improve their lives. The capacity includes

self-consciousness scale to test the relation-

how people communicate and collaborate

ship between self-consciousness and con-with each other to make society better.

sumers’ decision making for fair trade.

Knowledge is also important to understandOne of the main objectives of fair trade

why some issues would be a problem andis to help and protect producers and workers

how they can be solved. For instance, thein developing countries. Therefore, theCabinet Office of Japan (2008) found thatmindset of showing respect to others can be

the longer a person had attended consumer

an important driver for fair trade. Psycholo-

education classes, the higher the test scores

gists measure this mindset as altruism oron environmental issues.

pro-social behavior. However, Koppel andIn our research, we will include three

Schulze (2013) posit, “while the determi-

factors into our model: knowledge, self-con-

6

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

sciousness, and pro-social behavior (or altru-

knowledge about fair trade can enhance be-ism).

havior intention (H2).
Because fair trade is a framework to(3) Methodology

support workers in developing countries,

1. Hypothesis

consumers cannot obtain extrinsic rewards

In order to clarify what purchasing in-from them. As Koppel and Schulze (2013)

tention of fair trade products looks like in Ja-

noted the under-researched issue, we test

pan and what kinds of factors make effects

the relationship between self-consciousness,on decision-making to buy fair trade prod-

pro-social behavior and purchase intention

ucts, we follow the modified TPB model de-toward fair trade products. We expect that,veloped by Shaw, et al. (2000). Although thisif a person gets a higher score on the self-model was used to get the results in the

consciousness scale and the pro-social be-

United Kingdom, we selected it to determine

havioral scale, he or she is more likely to buy

whether the same results would be found infair trade products (H3).

Japan.

The hypothetical model is illustrated

Based on Shaw, et al. (2000), H1 is if

in Figure 5. To compare to this model, we

attitudes, subjective norms, behavior control,

would also test other models such as TRA

ethical obligation and self-identity affect be-

and TPB. We used the logit model and struc-havioral intention directly. Behavioral belief

tural equation modeling (SEM) to test theand outcome evaluation affect attitude. Nor-hypothesis.

mative belief and motivation to comply affectsubjective norm. Moreover, based on the

2. Data

findings about the role of education on con-

Data used in this paper is from the Sec-sumer behavior by the Cabinet Office (2008),

ond Life Condition Survey in Ibaraki Prefecture (hereinafter “second Ibaraki Survey”),

Figure 5. Hypothetical model
7

Journal of Japanese Management Vol.1, No.2, May 2017

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which was conducted by Yoshiaki Takahashi

When we check if the sample is a rep-

Laboratory Group, University of Tsukuba,

resentative sample of population in Ibaraki

from November to December 2015. A total ofPrefecture in 2015, we found that the elderly

2,000 people from 20 to 79 years old named

was overrepresented (see Table 1). For in-on voters’ lists in Ibaraki Prefecture (1,042

stance, males and females aged 60-69 should

males and 958 females) were randomly se-

be 7.58% and 7.71%, respectively, of the total

lected as the sample of the first Ibaraki Sur-

samples. The percentages of male and fe-

vey. The first survey was conducted in No-

male respondents in their 60s were 15.76%

vember and December 2014. The respond-and 13.30%, respectively. On the other hand,ents to the second Ibaraki Survey were per-males and females aged 20-29 comprisedsons who agreed in the first survey to coop-

only 1.97% and 2.46%, respectively, of the re-erate again in the next survey. Students vis-

spondents, though they should consist of

ited those 305 persons to ask questions in

5.07% and 4.40% of the sample, respectively.

the second Ibaraki Survey questionnaire. A

Therefore, we must conclude that the data

total of 213 persons accepted and sent thewas not representative of demographics ofcompleted questionnaire back by post (re-the general population in Ibaraki. However,sponse rate: 69.8%).

because there is not any research to examine

The respondents were asked the ques-consumer behavior for fair trade products intions about intention, attitudes, and otherJapan, and the size of sample was sufficient

subjects regarding fair trade products. Most

to conduct statistical analysis, we decided toof the measurements were applied fromuse it in this paper.

Shaw, et al. (2000). The knowledge wasmeasured by 11 questions as a quiz (see Ap-(4) Empirical Analysis

pendix. The correct answers are circled). For

1. Opinions and attitudes to fair trade

instance, we asked the respondents to

A few of the respondents recognizedchoose the correct definition of fair trade. We

what fair trade was. In Figure 6, two-thirds

also asked them to identify the logo markof the respondents in Ibaraki were not at all

shown on a package of fair trade products.

familiar with fair trade. Another 20% had

We used several questions from a quiz devel-

heard of the term, but didn’t know muchoped by the Fairtrade Foundation in theabout it. Further, 24% believed that mostUnited Kingdom. One question was if

people who are important to the respondents

Fairtrade beauty products have ever beenthought that they should purchase fair tradetested on animals. Self-consciousness wasproducts (“subjective norm”), and 33% saidmeasured by the Japanese version of self-that it was easy for them to buy fair trade

consciousness scale developed by Sugawara

products in Japan (“perceived behavioral(1984). When we measured altruism, we

control”). Moreover, 36% agreed that they

modified the pro-social behavior scale devel-

feel an obligation to purchase fair trade

oped by Kikuchi (1988).

products (“ethical obligation”), and 20% con-8

Journal of Japanese Management Vol.1, No.2, May 2017

Know fairly well
9%
Have heard theterm, but don't
know meaning
20%

ISSN 2189-9592

Know very well
4%

Don't know at all
67%

Figure 6. Awareness about Fair Trade
Source: The second Ibaraki Survey

Table 1. Evaluation of representativeness (gender and age)Age group

20-29
30-39
40-49
50-59
60-69
70-79
Total

Expected sample
male
female109(5.07)(4.40)
13
12(6.27)(5.73)
15
14(7.30)(6.79)
1312(6.28)(6.14)
15
16(7.58)(7.71)
11
12(5.35)(5.78)
101
102(49.88)(50.12)

Ibaraki Survey
male
female45(1.97)(2.46)
710(3.45)(4.93)
11
21(5.42)(10.34)18
26(8.87)(12.81)32
27(15.76)(13.30)25
17(12.32)(8.37)
97
106(47.78)(52.22)

Note: Expected sample is calculated with the percentage of population by
age and gender in Ibaraki Population Survey in July 2015.

sidered themselves as a person who is con-purchase of fair trade goods is favorable andcerned about ethical consumer issues (“self-

only 10% considered that fair trade was not

identity”). In this situation, only 28% wasfavorable (“attitude”). Finally, 56% said that

more likely to purchase fair trade products

fair trade was important for our society

the next time they went to a grocery(“outcome evaluation”).

store(“intention”). On the other hand, morethan half (53%) of the respondents said that

2. Knowledge about fair trade9

Journal of Japanese Management Vol.1, No.2, May 2017

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35
30
25
20
15
10

5
0
0

1

2

3

4

5

6

7

8

9

10

11

(points)

Figure 7. The distribution of test scores of fair trade
Source: The second Ibaraki Survey

Figure 8. Percentage of correct answers about fair trade
Source: The second Ibaraki survey

The mean score of the quiz about fairestion with the most correct answers is Q7,

trade in the second Ibaraki Survey was

asking about female ownership of land in Af-

4.627 out of 11 (Figure 7).

The highest

rica; 68.7% answered it correctly. The second

score was 9 and the lowest was 0. Standard

question most correctly answered was Q5,

error was 2.016.

which asked the effect of fair trade; 64.9%

The percentage of correct answers is

answered it correctly.

different for each of the 11 questions (Figure

On the other hand, the question with

8). The Appendix shows all questions in thethe lowest rate of correct answer was Q9quiz. Each correct answer is circled. The qu-about a product in which 100 million rural
10

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

households in developing countries are in-ethical obligation on intention was statisti-

volved. Only 17.7% chose the correct answer,

cally significant at 10% level. Therefore, thecotton. That may be because there are a very

results in this paper were slightly different

few fair trade certificated cotton products infrom those in Shaw, et al. (2000), showing

Japan.

that attitude, behavioral control and ethicalThe second lowest correctly-answered

obligation had effects on the purchase inten-

question was Q10 about the market share oftion.

fair trade cacao in the global cacao market:

We then tested the H1, H2 and H3 with27.3% answered 1% as the correct answer.

the structure equation modeling (SEM) of

The third lowest correctly-answered ques-

full information maximum likelihood. Thetion was the fair trade sales values per cap-

first results are shown in Figure 9. GFI and

ita in Japan (Q3). The correct answer was

CFI were not above 0.9. However, RMSEA

around JPY100 and 28.5% selected it cor-

was lower than 0.1 (0.075). Similar to the re-rectly. In contrast, 33.1% and 24.6% chose

sults of the logit model, only attitude andaround JPY500 and JPY1,500, respectively.

perceived behavioral control affected inten-

Respondents may not have imagined thattion to buy fair trade products. The behav-

Japanese spend such a small amount ofioral belief and outcome evaluation influ-

money on fair trade products.

enced attitude. Normative belief and motivation to comply affected subjective norm.

3. Regression analysis

However, knowledge about fair trade, self-

The results of the logit model for theconsciousness, and pro-social behavior didn’t

proposed models are detailed in Table 2. For

seem to matter for fair trade at the 5% sig-

the TRA, attitude and subjective norm had

nificant level.

significant effects on purchase intention for

In order to check indirect effects offair trade. However, when we added per-knowledge and pro-social behavior on pur-ceived behavioral control as the TPB model,chase intention, we developed an alternative

subjective norm was not statistically signifi-

model (model 2) shown in Figure 10. We as-cant. The pseudo R2 (.204) was also improved.

sumed that knowledge had effect on outcome

Moreover, when we added ethical obligation

evaluation and pro-social behavior had effectand self-identity as the modified TPB modelon attitude. In addition, we deleted subjec-

shown in Shaw et al. (2000), the AIC andtive norm because the results of logit model

pseudo R2 were improved more. As a result,and SEM for the Model 1 didn’t show any re-

we are able to conclude that attitude and be-lationship between subjective norm and pur-havioral control were important factors forchase intention. When we looked at the re-consumers in Japan when they decide to buy

sults, GFI was higher than 0.9. AIC was im-

fair trade products. What we must note here

proved from Figure 9. However, CFI was notis that, when we delete subjective norm andabove 0.9. RMSEA was not also lower thanself-identity among the explanatory varia-

0.1 (0.174). According to the indicators, thebles in the modified TPB model, the effect ofmodel is not perfect. However, we can look at
11

Journal of Japanese Management Vol.1, No.2, May 2017

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Table 2. The results of multivariable analysis (Logit model)

TRA
Coefficient S.E.
(Intercept)-5.487 (1.092) ***Attitude0.670 (0.192) ***Subjective norm
0.260 (0.182)
Behavioral control
Ethical obligation
Self identity
n
144
AIC
153.03
pseudo R2
0.152

TPB
Coefficient S.E.
-6.857 (1.278) ***0.504 (0.208) **0.230 (0.197)
0.526 (0.186) ***144
146.000.204

Revised TPB1
Coefficient S.E.
-7.718 (1.529) ***0.389 (0.214) *0.182 (0.213)
0.526 (0.196) ***0.208 (0.209)
0.314 (0.244)
140
143.62
0.241

Revised TPB2
Coefficient S.E.
-6.934 (1.272)
0.478 (0.205) **0.556 (0.190) ***0.325 (0.186) *140
143.830.217

Note: ( ) shows standard errors. *** p< 1%, ** p<5%, * p<10%

Figure 9. The results of Model 1 (SEM)

the relationship between know knowledge,

on purchase intention of fair trade products.

pro-social behavior, and attitude. Knowledge
affected outcome evaluation at 1% signifi-(5) Discussion and Conclusioncant level and then outcome evaluation af-

At first, H1 was partially supported.

fected attitude at 1% significant level. Pro-

The results of the logit model and the struc-social behavior also influenced attitude atture equation modeling showed that it was

5% significant level. Therefore, knowledge

important whether purchasing fair tradeand pro-social behavior had indirect effects

products would be desirable (attitude) or
12

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

Figure 10. The results of Model 2 (SEM)easy to purchase (behavioral control). There-

decision making for fair trade products. For

fore, the availability of fair trade products ininstance, referring to discussion by Basu &shops mattered in Japan.

Van (1998), we can also understand that theOn the other hand, knowledge of fairpromotion of fair trade is a form of subsidies

trade and altruism did not directly affectto wage. The economics of child labor sug-purchase intention. Rather, knowledge wasgests that subsidies to wage cannot stop sup-related to outcome evaluation which meas-

plying child labor in developing countries.

ured if purchasing fair trade products would

Rather the promotion may create a black

be important for our society. Moreover, per-market of child laborers if governments insons who want to buy fair trade did not only

developing countries are not able to regulate

obtain knowledge, but also conducted pro-so-such black markets effectively. However, wecial or altruistic behavior frequently. They

could not take into account the relationshipbelieved that altruism is socially significant

on consumers’ decision-making in this study

in daily life. Therefore, we concluded that H2

explicitly.

and H3 were supported. Besides simply rais-

Lastly, the data used here was not a na-

ing awareness about fair trade in promoting

tionwide sample. The number of the sample

the purchase, education is an important

was also limited. The percentage of elderly

measure to encourage understanding the so-people among the respondents was very high.

cial significance of altruistic behavior.

In order to generalize our results, it is neces-

We only tested several models based onsary to carry out surveys targeting the whole

TRA and TPB in this study. On the otherpopulation of Japan. We need further re-hand, there are many possible models to in-search to compare our models with other po-

clude factors that may explain consumers’

tential models in a nationwide survey.
13

Journal of Japanese Management Vol.1, No.2, May 2017

Acknowledgement

ISSN 2189-9592_v5.ashx (Access on January 2, 2016)

We would like to express our great ap-

Fairtrade

International

andGlobeScan

important research. We would also like to ex-

Fairtrade
International
GlobeScan Consumer Study.
Fairtrade Label Japan (2016). Annual Report 2015 (2015 nendo Jigyo Hoko-

tend our thanks to support from staff in 41

kusho).http://www.fairtrade.jp.org/aboumunicipalities in Ibaraki Prefecture. We aret_us/data/2015_katsudo.pdf (Access on

also grateful to two anonymous reviewers for

November 30, 2016)

preciation to respondents in the second(2015).

Ibaraki Survey. Without their kind cooperation, we would not have been able to do thishelpful comments.

Fenigstein, Allan., Michael F. Sheier and Arnold H. Buss (1975). Public and Private

References

Self-Consciousness: Assessment andAjzen, Icek (1985). From intentions to ac-

Theory. Journal of Consulting and Clin-tions: A theory of planned behavior. In

ical Psychology, 43(4), pp. 522-527.

Ajzen, I., Action control. Berlin Heidel-

Koppel, Hannes and Gunther G. Shulze

berg: Springer, pp.11-39.

(2013). The Importance of the Indirect

Ajzen, Icek (1991). The Theory of Planned

Transfer Mechanism for Consumer

Behavior. Organizational Behavior andWillingness to Pay for Fair Trade Prod-

Human Decision Processes, 50, pp.179–

ucts: Evidence from a Natural Field Ex-

211.

periment. Journal of Consumer Policy,

Ajzen, Icek and Martin Fishbein (1980). Un-

36, pp.369-387.

derstanding attitudes and predicting
social behavior, Englewood Cliffs:

Kikuchi, Akio (1988). Omoiyari wo Kagaku

suru – Kou-Shokai-teki Kodo no Shinri
to Sukiru (The Science of Consideration

Prince Hall.
Basu, Kaushik and Pham Hoang Van (1998).

– Mentality and Skills of Prosocial Be-

The economics of child labor. American

havior). Tokyo: Kawashima Shoten.

economic review, 88(3), pp.412-427.
The Cabinet Office of Japan (2008). White
Paper of National Lifestyle: Prospects
for consumer citizenship Toward a comfortable and mature society (Kokumin

Rushton, J. P., R. D. Chrisjohn and G. C.

Seikatsu Hakusho: Shohisha Shimin

Shaw, Deirdre, Edward Shiu and Ian Clarke

Shakai heno Tenbo). Tokyo: Jijigaho(2000). The Contribution of Ethical Ob-

Sha.

ligation and Self-identity to the Theory

Fekken (1981). The altruistic personality and the Self Report Altruism Scale.

Personality and Individual Differences,2, pp.293-302.

Fairtrade Foundation, Fair Trade at 20.

of Planned Behaviour: An Exploration

http://www.fairtrade.org.uk/~/mdia/fairof Ethical Consumers. Journal of Mar-

tradeuk/get%20involved/docu-

keting Management, 16(8), pp.879–894.

ments/curent%20campaigns/

Sugawara, Kensuke (1984). Attempt to con-fairtrade%20at%2020/fairtrade20_quiz

struct the self-consciousness scale for14

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

Japanese. The Japanese Journal of Psy-

havior by Responsible Consumers: Datachology, 55(3), pp.184–188.

Analysis of Behavior Theory Model).

and Product Evaluation (Shohisha no

The Japan Society of Household Economics Annual Meeting 2009, pp.1–16.

Identity Keisei Ishiki to Seihin Hyoka).

Watanabe, Ryuya (2007). Trajectory of the

Journal of Business and Economics,

Fair Trade Movement: Challenging the54(3), pp.379–228.

International Trade System (Fair Trade

Tamaki, Satoru (2008). Identity Formation

Toyota, Shogo (2009). Sekinin Aru Sho-hishano Keisei to Tenkai: Kokusai Boeki Sys-

no Ishikettei to Shohi Kodo ni kansuru

tem heno Chousen). Tokyo Keizai Law

Kozo Bunseki (The Structural Analysis

Review, 14, pp.3–72.

of Decision Makings and Consumer BeAppendix: Fair trade quiz in the Ibaraki Survey
Q1: The definition of fair trade is .......
① a trading partnership, based on dialogue, transparency and respect, that seeks greater
equity in international trade;
2. a free trade by producers and vendors without government’s intervention like customs
duty;
3. financial aids that governments provide to assist economic development in developing
countries;
4. loans without interest to contribute to economic development and welfare in developing
countries.
Q2: The global sales values of fair trade in 2014 was JPY .....
1. 8 billion

2. 80 billion③ 800 billion

4. 8 trillion

Q3: Fair trade sales values in Japan per capita was around JPY ......
1. 16

② 114

3. 525

4. 1,621

Q4: The main product of fair trade in Japan was .......
① coffee

2. banana

3. cosmetics

4. chocolate

Q5: One of effects of fair trade is ........
1. cost-cutting to improve efficiency in business;
2. training technicians to convey technology in Japan to developing countries;
3. promoting competition in trade;④ improving working conditions in developing countries.
Q6: Which of these is the Fairtrade mark?
15

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

1. the logo of Consumer Agency of Japan
2. the logo of foods for specified health uses
③ the logo of Fairtrade International
4. the Woolmark logo
Q7: Women produce 60-80% of the food in most developing countries, but how much land in
Africa is owned by women?
① 1%

2. 9%

3. 16%

4. 21%

Q8: Smallholder farmers often receive low returns from their produce. According to a
Fairtrade Foundation report in 2013, what percentage of the retail value of a chocolate bar
are cocoa smallholders likely to receive?
1. 21-26%

2. 10-15%③ 3-6%

4. 1%

Q9: These statements are all true for which fair trade product?


Developing countries produce two thirds of this product

100 million rural households in developing countries are involved in its productionWest Africa produces about 4% of its total global production

1. rice

2. coffee③ cotton

4. cacao

Q10: Close to 50 million people depend on cocoa for their livelihoods. What percentage of cocoa
is sold on fair trade terms globally?
① 1%

2. 6%

3. 11%

4. 21%

Q11: Have fair trade beauty products ever been tested on animals?
1. Yes② No(Received: December 1, 2016)(Accepted: April 16, 2017)16

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

Factor Analysis on Software Piracy
Lianfeng Yang

Nagoya City University, Japan
Japan Society for Information and Management
E-mail:yourenhou06@yahoo.co.jp
Abstract
Many empirical researches of software piracy have focused on the domestic factors, suchas corruption, democracy, economic freedom et al. However, this paper assesses the extent of
the international factors contributing to software piracy. It is founded that Patent
Cooperation Treaty (PCT) international applications have a significant impact on it. Thatmeans international intellectual protection is a good way to control the piracy. Additionally,
corruption is not a significant factor on piracy as previously believed.
Keywords
software piracy, corruption, PCT International application．
(1) Introduction

nonlinear the decrease in economic growth

It is well known that the issue of

diminishes with piracy (Andrés and Goel,software piracy has been growing more2012).

severe worldwide in recent years. “software

While numerous previous studies havepiracy” can be defined as the unauthorized use

looked at the issue of software piracy, sinceof computer software or the unauthorized

it is a complex issue (Goel and Nelson, 2009;

distribution of copies of software without

Lau, 2003; Reinig and Plice, 2011). It has not

permission being given by theowner 1 .”been

elucidated

fully.

Studiesthat

According to the Software Alliance’s Ninthempirically analyze factors in software

Annualpiracy from a macroeconomic approach do so

BSA

(The

Business

Alliance) Global Software

Software
Piracyusing models based on one or more of the five2011 damages caused by piracy

following factors: economic factors, cultural

totaled USD63.456 billion worldwide andfactors, technological factors, legal factors,

both piracy and the amount of related

and educational factors (Gomes, Cerqueira

damages were in an increasing trend.

and Almeida, 2014).

Study 2 , inSoftwarepiracy2011

affects

economic

Gross Domestic Product (GDP) per-growth. Greater piracy of computer software

capita often is used as an economic factor.

lowers economic growth. However, theHarbi, Grolleau and Bekir (2012) and Panas

relationship between piracy and growth is

and

1

2

See Lau (2003).

17

Ninni

(2011)

http://www.bsa.or.jp/

find

a

relationshipJournal of Japanese Management Vol.1, No.2, May 2017

resembling a Kuznets curve between the two.

ISSN 2189-9592

2011; Goel and Nelson, 2009).

If there is a Kuznets curve relationshipIn analysis of legal or systemic factors,

between piracy rate and GDP per-capita,

economic freedom and freedom of the press

then protection of intellectual property (IP)are used frequently. The concept is sociallyrights should be conducted in accordance

accepted that the higher the degrees ofwith the stage of economic development, but

economic freedom and freedom of the press

over the long term there would be a negative

the lower the piracy rate. In addition, many

correlation between piracy rate and GDP

empirical analyses use corruption as a

per-capita.

systemic factor (Andrés and Goel, 2011;

Other

as

Gomes,

Bagchi, Kirs and Cerveny, 2006; Banerjee and(2014)

have

Williams, 2013; Goel and Nelson, 2009;

developed toReinig and Plice, 2011). Analysis of data on

serve as an indicator of national character,the piracy rate and indices of corruption

as a cultural factor, indicating that nationalshows a very high correlation between thecharacter do affect the piracy rate. Such as

two.

Cerqueirastudiesandsuch

Almeidaemployed Hofstede’s

index 3 ,Rafee and Rouibah (2010) found that

Regarding

educational

factors,aawareness treatment was significant factor

negative correlation has been identified

in reducing intention toward digital piracy.

between the literacy rate of people aged 15

Of course, the level of technology is

and above and the piracy rate (Goel andlikely to impact the piracy rate. It is

Nelson, 2009). However, it also be argued

conceivablethat this result is unclear (Andrés and Goel,that

strongprotectionofauthorized software would increase the costs2011).

of piracy, and as the costs of piracy rise, theThus, each of the papers discussed

piracy rate would decrease. However, since

above focuses solely on domestic factors

protection of authorized software would

when examining piracy. Certainly, a strong

decrease the number of users of the software,correlation has been demonstrated between

in some cases it would not be desirable todomestic factors and the

producers of authorized software 4 . The

suggesting that improvement of domestic

number of Internet users is used as afactors would be an effective way to reduce

technological factor.

Bagchi, Kirs andpiracy. However, since the issue of software

Cerveny (2006) show that Internet users had

piracy is complex, it is conceivable that itpartial influence on software piracy. Market

cannot be resolved fully through domestic

size is another technology factor often befactors alone. The global age is characterized

used in empirical analysis (Andrés and Goel,by all countries advancing in cooperation

3

Hofstede’s index was developed in 1980 by the Dutch
social scientist Geert Hofstede in order to measurequantitatively and index the national character ofmultiple countries. It consists of the following six
indices: (1) Power distance index, (2) Individualism,(3) Uncertainty avoidance index, (4) Masculinity, (5)Long-term orientation, and (6) Indulgence versus

piracy rate,

restraint.
4Since in general software involves network externalities,

the more the number of users increases the more thevalue of the software rises. For this reason, producersof authorized software aim to increase the number ofusers of their software.

18

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

with others. When advancing in cooperation

power. The reason this paper uses in its

with other countries, it is possible to protect

analysis the four factors other than cultural

one’s own country’s rights through mutual

factors is because such analysis has beenrecognition by countries of each others’common traditionally (Andrés and Goel,rights. In general, Patent Cooperation

2011; Goel and Nelson, 2009), and because it

Treaty (PCT ) international applications

is not easy to control the cultural factor ofplay an important role in getting othernationalcountries to protect one’s own country’s

through policy. The results show that ourscience and technology. A producer can

new variable of the number of applications

secure exclusive interests on a global basis

for PCT international applications does havethrough the exclusivity of PCT international

explanatory power with regard to the piracy

applications. For this reason, the increase in

rate. This can be considered to suggest the

the

PCT

possibility that countries’ recognition of eachinternational applications by producers in aothers’ PCT international applications could

country can be considered to relate to

be an effective way to reduce piracy.

numberofapplications

forstrengthening that country’s government’s

Thecharacter(Hofstede’s

paper employs theindex)

following

motivation to crack down on patent-related

structure. Section (2) presents a literaturepiracy. It is conceivable that recognizing

review. Section (3) illustrates research data

each other’s patents would have the effect ofon software piracy. Section (4) describes theprotecting IP rights. Accordingly, it can beanalytical method and models. Section (5)considered important and meaningful toillustrates the results of empirical analysis.

study the impact on the piracy rate ofSection (6) provides some considerations.

numbers

PCT

Section (7) concludes.

Based on the above perspective, this(2) Literature review

ofapplicationsforinternational applications.
paper analyzes the piracy rate through a

The issue of software piracy is aregression model in which the explanatorycomplex one to begin with, one impacted bypower of technological factors in particular isnumerous factors. Previous studies argue

strengthened, as one attempt at examiningthat it would not be possible to resolve piracy

factors such as those above. In addition toissues without improvements on all the

independent variables related to the four

factors of economic, systemic, social, andfactors used in Andrés and Goel (2011):

technological factors.

systemic, economic, social, and technological

The following is a description of thefactors, the model employs number of

specific indices impacting the piracy rateapplications

international

used as independent variables in theapplications as a new independent variable

following regression analysis, along with therelated to technological factors, developing areasons for using them, for each of the above

regression model to explain the piracy rate

four factors and in accordance with abased on these and using it with real-worldprevious study (Andrés and Goel, 2011).

data to seek out variables with explanatory

Differences from Andrés and Goel (2011) are

for

PCT

19

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

the facts that this study uses number ofstrategy of adding to authorized software

applications

international

high-quality value capable of differentiating

applications, which it did not use, and thatit from pirate software then consumers

this study does not use the size of thewould be likely to demand high-quality

software market, which had little impact on

authorized software as they enjoy greater

the piracy rate.

returns from its use. This is thought to be

for

PCT

As systemic factors, this study uses thethecorruption index, freedom of the press, andgrounds

for

the

above

negativecorrelation.

economic freedom. It is likely that in a

As a social factor, this study uses thecountry with a high corruption index pirates

national literacy rate. Gomes, Cerqueira andwould be able to secure a degree of toleration

Almeida (2013) show that more schoolingfor their acts or piracy through means such

years have the opposite effect to piracy. But

as bribery of government officials, increasing

Depken and Simmons (2004) have not

the piracy rate at a result. That is, it isidentified a significant relationship between

conceivable

literacy

that

there

is

a

positive

rate

and

piracy

rate.

It

is

correlation between the corruption index

conceivable that the national literacy rate

and the piracy rate. On the other hand, there

could have some impact on the piracy rate

is likely to be a negative correlation between

since as literacy increases the number of

freedom of the press (Andrés and Goel, 2011;users of software (both pirate and authorized

Salahodjaev Odilova and Andrés, 2016) andsoftware) would increase. However, it is not

economic freedom (Bezmen and Depken,

necessarily possible to determine clearly

2006; Fraj, 2015) on one hand and the piracy

whether this correlation would be positive or

rate on the other. It is conceivable thatnegative.

improving

determine the sign of the correlation based

these

would

increasesocialtransparency, making it easier to uncover

Accordingly,

this

paper

will

on the results of individual data analysis.

illegal acts by government and pirates andAs a technological factor, this study

increasing the risks of piracy as a result, souses number of Internet users. Goel andthat the piracy rate would decrease.

Nelson (2009) showed that internet users

This study, like numerous previous

will mitigate the software piracy. It is

studies, uses GDP per-capita as an indicator

conceivable that as this number increasesof economic factors. This is considered tothe piracy rate would decrease because it

have a negative correlation to piracy (Andrés

would become easier to detect acts of piracy.

and Goel, 2011; Chen, Chen and Yeh, 2010;

In other words, there could be a negative

Goel and Nelson, 2009). In fact, piracy iscorrelation between the number of Internet

unlikely to decrease if authorized software

users and the piracy rate.

and pirate software are identical in value

The independent variables used in theand consumers expect the same benefits

regression model, reflecting consideration of

from both authorized software and piracy.

previous studies, are as described above.

On

This paper also uses the number of

the

other

hand,

if

producersofauthorized software were to adopt theapplications
20

for

PCT

international

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

applications as an independent variableapplications

related to technological factors. By taking

applications would serve to make them moreoutaconscious of the need to demand an

producer can secure exclusive interests on aenvironment in which protection of IP rights

global basis through the exclusivity secured

strengthens, improving the piracy rate as aby patents. For this reason, an increase in

result. Accordingly, in the following analysis

the

as

PCT

international

numberofapplications,applications

for

PCT

international applications by producers in a

well

for

this

PCT

paper

international

focuseson

thisindependent variable in particular.

country can be considered to relate to astrengthening

of

that

country’s(3) Research data

government’s motivation to crack down on

As described above, the regression

patent-related piracy. At the same time, it

analysis used in this paper employs thealso can be considered to raise awarenesspiracy rate as the dependent variable andconcerning elimination of piracy as thevariables included in four categories as seen

producers

in

who

apply

for

the

PCT

AndrésandGoel(2011)

theinternational applications attempt to protect

independent

the value of the technologies that they have(indicators of corruption, freedom of thedeveloped themselves. For the above reasons,press, and economic freedom), economic

the

PCT

factors (GDP per-capita), social factors

international applications is expected to(national literacy rate), and technologicalhave a negative correlation to the piracy rate.

factors (number of Internet users, number of

numberofapplicationsforvariables: systemicasThis paper considers the final variableapplications

numberPCT

applications). The sources and meanings ofinternational applications to be of particular

the data used in this analysis are outlined

importance. This is because since thebelow.

ofnumberofofapplications

applications

for

for

PCT

technological progress, an increase in theofinternational

applications
applications

for
also

innovation.

In

fact,

PCT

organization is considered highly credible,

involvesitand the results of its studies on piracy andprotection of IP rights have been cited inisnumerous

conceivable that if this independent variable
does

affectproducers5

topiracyincreasethenthepapers.

Thepiracyrate

iscalculated as shown below:

encouraging
numberinternational

Software Alliance (BSA) 5 is used. This

aspects that make it possible to maintainsustained

PCT

1. Piracy rate (Piracy, abbreviated pir)
For piracy rate, data published by theinternational applications is related tonumberforfactorsof

http://www.bsa.or.jp/index.html. The BSA measures

Andrés and Goel, 2011; Goel and Nelson, 2009; Harbi,

the piracy of commercial software. These estimates are

Grolleau and Bekir, 2012; Lau, 2003; Reinig and Plice,some of the most reliable ones and have been used2011),

largely in empirical papers (for instance Andrés, 2006;

unreliable.

21

in spite ofcriticism that the data areJournal of Japanese Management Vol.1, No.2, May 2017

points, with a higher figure indicating apiracy rate=

lower degree of media freedom. For this

Quanitity of pirated PC software
Total quanitity of PC software installedreason, this figure can be described as anindicator of lack of freedom of the press. This

The denominator above is derived by

figure is made up of three elements: (i)multiplying the number of PCs on which

influence of laws and regulations on thesoftware is installed by the quantity of PC

media (ranging from 0 to 30 points), (ii)software per computer, while the numerator

political pressure and media internal control

is derived by subtracting the quantity of(ranging from 0 to 40 points), and (iii)

authentic PC software from the denominator,

economic impact of media (ranging from 0 toor the denominator minus total units of PC

30 points). A total score in the range of 0-30

software sold divided by the average price ofis considered to indicate a state of freedom,

PC software.
2.

while one in the range 31–60 indicates the

Corruption

index

presence of freedom to some degree and one(Corruption,adbreviated cor)in the range 61–100 indicates a lack ofAs an index of corruption, this study

freedom.

uses the estimated value of Control of

4. Economic freedom (Freedom of Economic,

Corruption 6 published on the Web by theabbreviated ef)

World Bank. This is an indicator of theDatagovernance ability of government, ranging

corruption),

corruption. These data are collected fromfree markets (freedom of trade, freedom ofinvestment, financial freedom). Each oforganizations,these elements is graded as a score in theinternational agencies, and private-sector

range 0–100, and the average of these scoresorganizations, among other sources.
the

press

is used as the value of economic freedom.
The higher this value, the higher the(Democracy,

assessed degree of economic freedom.

abbreviated dec)
This study uses data published by

5.

Freedom House7 concerning freedom of the7GDP

Per-capita(GDP

Per-capita,

abbreviated gdp)

press. The data range in value from 0 to 100
6politics

labor freedom, currency freedom), and (iv)through research organizations, think tanks,ofconstitutional(iii) regulatory efficiency (business freedom,on governance, based on data collected

Freedom(ii)(political freedom, government expenditure),companies,

citizens, and experts concerning their views

nongovernmental

Heritage(i) politics (property rights, freedom fromhigher the score the lower the degree ofnumeroustheelements from the following four categories:

the trustworthiness of government, and the

with

by

freedom. Economic freedom is made up of 10

corresponds to a 90% confidence interval forinterviews

published

Foundation 8 are used to indicate economic

in value from −2.5 to 2.5 points. This range

3.

ISSN 2189-9592

http://www.worldbank.org/
http://www.freedomhouse.org/report-types/freedom-

8

22

press
http://www.heritage.org/index/download

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

For GDP per-capita, this study uses theof R&D in each country the higher thisdata published on the Web by the Center for

number the greater the incentive to protect

Internationalthe

its own IP rights is likely to be. For this

University of Pennsylvania9. These data arereason, this paper focuses on the number ofconsidered the most trusted and most widely

applications

usedapplications. Furthermore, to eliminate thedataComparisons

on

GDP

at

per-capita

inmacroeconomic analysis.
6.

National

literacyratenumber(Literacy,

for

PCT

for PCT international applications is theWorld Intellectual Property Organization12,

follows their precedent. The data used arewhile data published by the World Healththe latest data published on Wikipedia 10 .

Organization (WHO) were referred to for

While for most countries these data covereach nation’s population13.

ages 15 and older, for a few countries
different ages are used.

Data adjustments
Actually, the countries for which data

7. Number of Internet users (Interneton the above dependent variable (piracy

user, abbreviated net)rate) and seven independent variables are

For number of Internet users, data

published vary by variable. For this reason,

published on the Web by the International
Telecommunication Union11 are used. These

Table 1. Meaning of each variable
Variable
pir
cor
dec
ef
gdp
lit

data indicate the number of Internet users
in a country per 100 citizens.
ofapplications

international

applications

for

PCT

(Patent,

abbreviated pat)
Another independent variable used inthis model is the number of applications fornet

PCT international applications. While there
are various methods of totaling patentpat

applications by companies, since the number
forapplications

source for data on numbers of applications

an independent variable, and this paper

applicationsofthe number per million population. The

analysis have used national literacy rate asofinternational

international applications are converted toMany previous papers on empirical

NumberPCT

effects of population, for all countries theabbreviated lit)

8.

for

PCT

international

applications is considered to reflect the level912

https://pwt.sas.upenn.edu/
http://en.wikipedia.org/wiki/List_of_countries_by_literacy_rate
11 http://www.itu.int/en/Pages/default.aspx
10

Meaning
Piracy rate
Corruption index
Freedom of the press
Economic freedom
GDP Per-capita
Literacy rate of population
aged 15 and older
Number of Internet users
per 100 population
Number of PCTinternational applications
applied for per million
population

http://www.wipo.int/pressroom/en/articles/2012/article_0001.html

13http://memorva.jp/ranking/unfpa/who_2010_

population_total.php

23

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

the analysis in this paper is limited to the 86

of independent variable to piracy is as shown

countries for which data are available for all

in Table 4.

eight variables (see Appendix). Lastly, theTable 3. The result of simplest model
Variables
Estimated result
0.897
constant(13.19)-0.090cor(-4.67*)0.002
dec(2.62*)-0.003net(-4.41*)
̅2
R
0.770

table below reviews the variables used:(4) Analytical method and models
This section will use the independent
variables described at the end of theprevious section in a regression analysis
with piracy rate as the dependent variable.
Table 2 shows the variation in size and otherinformation for each variable.

Note: t-values are in parentheses. t- values aligned to
the right* above are those for which estimates are
statistically significant at the 1% significance level.

Table 2. Range of variation in size of eachvariable
Variable Mean
Max.
Min.
pir

0.58

0.93

0.20

Table 4. The correlation coefficient of

cor

0.30

2.38

−1.27

independent variable and logarithmic value

dec

43.56

94.00

10.00

of independent variable to piracy

gdp

19250

136248

1247

ef

64.42

86.10

37.10

cor

-0.836lcor-0.836

net

49.71

93.39

3.70

dec

0.708

ldec

0.761

lit

0.92

1.00

0.56

ef

-0.711lef-0.697pat

53.17

521.830.01

gdp-0.639lgdp-0.788lit-0.425llit-0.412net-0.792

lnet-0.698pat-0.663lpat-0.885pir

Notes: net is Number of Internet users per 100
population, pat is number of applications for PCTinternational applications per million population.

pir

Note: Variables with l at the beginning denote thelogarithmic value of variable.

Regression analysis with the simplest
model (All the coefficients in this paper are

The table indicate that the logarithmic

non-standardized coefficients).

value especially GDP and patent has apir = c1 + b1 cor + b2 dec + b3 ef + b4 gdp

higher+ b5 lit + b6 net + b7 pat + u1

correlation

than

originaldata.

The result will be as shown in Table 3

Regression analysis with changing variable

when remove the no significant independent
variable by t-test (the absolute value of t test

and logarithmic value of variable, three
result of model with the best 𝑅̅ 2 value is theis less than 1.96).

following.
𝑝𝑖𝑟 = 𝑎1 + 𝑎2 𝑙𝑐𝑜𝑟 + 𝑎3 𝑑𝑒𝑐 + 𝑎4 𝑙𝑔𝑑𝑝

The result shows that corruption is+𝑎5 𝑒𝑓 + 𝑎6 𝑙𝑖𝑡 + 𝑎7 𝑛𝑒𝑡 + 𝑎8 𝑙𝑝𝑎𝑡 + 𝑢1

significant to piracy as many previously(1a)studies. But the correlation coefficient of
𝑝𝑖𝑟 = 𝑏1 + 𝑏2 𝑙𝑐𝑜𝑟 + 𝑏3 𝑑𝑒𝑐 + 𝑏4 𝑙𝑔𝑑𝑝

independent variable and logarithmic value+𝑏5 𝑒𝑓 + 𝑏6 𝑙𝑖𝑡 + 𝑏7 𝑙𝑛𝑒𝑡 + 𝑏8 𝑙𝑝𝑎𝑡 + 𝑢2
24

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592(5) Results of empirical analysis(1b)

Table 5 shows the results of estimation𝑝𝑖𝑟 = 𝑐1 + 𝑐2 𝑙𝑐𝑜𝑟 + 𝑐3 𝑑𝑒𝑐 + 𝑐4 𝑙𝑔𝑑𝑝

using regression model (1a), model (1b) and+𝑐5 𝑙𝑒𝑓 + 𝑐6 𝑙𝑖𝑡 + 𝑐7 𝑙𝑛𝑒𝑡 + 𝑐8 𝑙𝑝𝑎𝑡 + 𝑢3
(1c)model (1c). A look at the results of VIF for

Each of the coefficients 𝑎1 ~𝑎8 , b1 ~b8 ,each independent variable in Table 5 shows

𝑐1 ~𝑐8 will be estimated using this model. The

that each is substantially below 10, so thatterms 𝑙𝑐𝑜𝑟 , 𝑙𝑔𝑑𝑝 etc are abbreviations forthere is nothing similar to multicollinearity

the logarithms of the variables defined at the

between any of the independent variables,

end of the preceding section, ln(𝑐𝑜𝑟) ,and for this reason the results for theln(𝑔𝑑𝑝) etc, while 𝑢1 , 𝑢2 and 𝑢3 are error

regression coefficients can be surmised to beterms. Estimation is conducted using therelatively robust to variations in data values.

ordinary least squares (OLS) method. Since

Next, judgment of the statistical significancethe values of the corruption index rangeof the explanatory power of each regression

from −2.5 to 2.5, this paper uses a revisedcoefficient and each independent variable

corruption index in which 2.55 has beenusing t values shows that even at a

added to that value. All of the data used are

significance level of 10% the explanatory po-

for the year 2011.
Table 5. Results of estimation using regression model (1a), model (1b) and model (1c)Variable
Model (1a)Model (1b)Model (1c)constant
dec
ef
net
lit
lcor
lgdp

Result0.752(4.284)0.002(3.983*)-0.001(-0.601)-2.87 E-04(-0.417)0.520(4.755*)-0.007(-0.110)-0.065(-3.385*)

VIF

2.433
3.189

Result0.754(4.422)
0.002(4.123*)4.123(-0.543)

VIF

2.381

2.385

3.915
1.790
6.764
4.593

lnet

𝑅̅ 2

VIF

3.181

0.543
(4.855*)-0.009(-0.152)-0.062(-3.329*)

1.882
6.766
4.426

lef

lpat

Result0.883(2.084)0.002(4.113*)-0.0435.627(-5.788*)0.865-0.0182.898(-0.904)-0.042
5.237(-5.946*)
0.866

0.542
1.882
(4.843*)-0.0136.707(-0.220)-0.062
4.481(-3.322*)-0.0433.056(-0.431)-0.0182.922(-0.903)-0.042
5.234(-5.935*)
0.866

Notes: t-values are in parentheses, and t-values aligned to the right* above are those forwhich estimates are statistically significant at the 1% significance level.

25

Journal of Japanese Management Vol.1, No.2, May 2017

wer of the three variables corruption,

ISSN 2189-9592

at the same time).

economic freedom, and Internet users could

A look at the results in Table 6 shows

not be considered statistically significant.

that the values of all regression coefficients

By eliminating the lowest t value ofare statistically significant at the 1%

variables, the result is as the following insignificance level and that the corresponding

Table

independent variables have explanatory

6

(the

result is thesame byeliminating a variable at a time).

power vis-à-vis the dependent variable.
̅ 𝟐 hasComparing with the results ahead, 𝑹

Table 6. Results of regression analysis after
eliminating the lowest t values frommodel (1a), model (1b) and model (1c)
Estimated
Variables
VIF
result0.710
Constant(4.748)
0.003
1.727
dec
(5.354*)−0.071
3.344
lgdp(−4.518*)
0.536
1.675
lit
(5.226*)−0.0454.505
lpat(−7.050*)
𝑅̅ 2
0.869

been greatly improved, so the latter model isa superior model. Additionally, the common
explanatory variable is democracy only.
Other variable will be shuffled all. Notably,
corruption is not remained as independence
variable. As a result, this model combines
the original data and replaced logarithmic
data. In this model, corruption index will bedeleted by using 𝑙𝑔𝑑𝑝 and 𝑙𝑝𝑎𝑡 . That
means

𝑙𝑔𝑑𝑝

and

𝑙𝑝𝑎𝑡

are

betterindependence variables than corruption.
Incidentally, it is known that in thecase of regression analysis using cross sectional data as used in this study, the tNote: t-values are in parentheses, and t valuesaligned to the right* above are those for which
estimates are statistically significant at the 1%
significance level.

value often does not necessarily function
correctly as an indicator of statisticalsignificance due to a lack of uniformity in

Comparison of Table 5 and Table 6

distribution (Matsuura and McKenzie, 2012).

shows that the adjusted coefficient ofdetermination ( 𝑅̅ 2 ) remains largely

Accordingly, a check for such lack of
uniformity in distribution in this case wasunchanged and there was no difference in

conducted.

the fact that the explanatory power ofA White χ2 test with a null hypothesis

corruption, economic freedom and Internetof uniformity of distribution yields a χ2

users. These variables are not statisticallystatistic of 11.352, for which the p value issignificant. Accordingly, a regression model

0.658, so that the null hypothesis ofanalysis was conducted after eliminating theuniformity of distribution is not rejected.

variables corruption, economic freedom andInternet users as well. The results are shown(6) Consideration of resultsin Table 6 (There was no change in the

The results above can be summarized

results of the F test and the χ2 test between

as follows: First, the statistical significancethe case of eliminating economic freedomof each independent variable’s explanatory

alone and that of eliminating both variables

power will be verified. A look a Table 6 shows26

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

that all four of the independent variables ofcontrol piracy through improving theseindex of freedom of the press, GDP per-variables, since they involve large-scale

capita, national literacy rate, and number ofissues.

applications

for

PCT

international

This paper showed that the number ofapplications had statistically significant

applications

explanatory power with regard to the piracy

applications, which was not addressed inrate, indicating that all did affect piracy.

previous

While this finding is consistent with those of

explanatory factor with regard to the piracy

previous studies, it should be noted that

rate. What’s more, controlling this variableconsideration based on t value in particulardoes not involve unnatural issues with social

shows that the independent variable ofethics like those above and is effectively

numberPCT

correlated with social innovation, so that itinternational applications, introduced forcan be considered extremely significant. In

the first time in this paper, had the highest

fact, it is conceivable that as economic

degree of statistical significance.

globalization advances incentives will act onofapplications

for

for

studies,

PCT
isinternationalanimportantFirst of all, with regard to the nationalcountries and companies that have few

literacy rate, it should be clear that reducing

patents, encouraging them to obtain PCT

this rate even below its current level in orderinternational applications, and the resulting

to reduce the piracy rate would not be

increase in awareness of protection of

appropriate from economic considerations,patents (and copyright) could lead tobecause it would lead to a decrease in thestronger crackdowns on piracy. Accordingly,

number of users of software—not to mention

progress on creating an environment that

ethical reasons. It is conceivable that thewould encourage companies to apply for PCT

reason the piracy rate increases with aninternational applications can be considered

increase in the literacy rate is because of theas an effective means of decreasing theresultingincreaseinpiracy rate.

consumersusingpiratethenumberofsoftware,as

On the other hand, this study showed

individuals gain access to methods ofthat corruption, economic freedom, andevading the regulatory net. Realistically, thenumber of Internet users had little influencenational literacy rate is high in moston the piracy rate. While there is no problem

countries, so that the national literacy rate’swith this finding with regard to the latter

impact is not likely to be very pronounced.

two because previous studies also showed

The index of freedom of the press andthat they had no influence, it does conflict

GDP per-capita used in this paper both havewith the results of most previous studies

been used as variables explaining the piracy

that showed that corruption had a strong

rate in previous studies as well, and suchinfluence.

studies have identified them as having

corruption itself show that it can bestatistically significant explanatory power.

explained by factors such as freedom of the

However, it must be said that realisticallypress,

speaking it would in fact be difficult tofreedom (with coefficients of determination
27

Some

GDP

previous

per-capita,

studiesandon

economicJournal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

of 82% or above). While correlation between

consumers

indices of corruption and the piracy rate areindividuals gain access to methods ofvery high, and as a result an index ofevading the regulatory net.

corruption alone can show a high degree ofusing

piratesoftware,as

4. The number of PCT international

explanatory power regarding piracy rate, theapplications

appliedaddition of numerous independent variables

population also affects piracy. An increase in

as in this study results in the elimination of

the number of patent applications leads to a

the explanatory power of the index ofdecrease in the piracy rate. It is conceivablecorruption. In light of these considerations,that this is because governments andindices of corruption, considered important

producers

in many previous studies, like piracy rate, be

protecting IP rights and more strongly

dependent variables explained by variables

motivated to protect their own property.

such as freedom of the press and GDP per-Results of each of the analyses in this study

capita rather than causes of piracy rate.

showed that the number of applications forbecome

for

more

per

millionconsciousof

PCT international applications had a strong(7) Conclusion

degree of explanatory power vis-à-vis the

The main purpose of this paper is toverifytheeffectapplications

for

of

increasing the number of applications forinternational

PCT international applications would beapplications on the piracy rate. Using data

meaningful in both economic and policy

from 2011, it analyzed the factors impacting

terms because it enables a country to carrypiracyout sustainable innovation.

while

applications

of

PCT

adding
for

the

the

PCT

numberpiracy rate, and it is conceivable that

numberofinternational

5. This paper showed clearly thatapplications as a variable for the first time.

corruption, considered an important factor

The results showed that GDP per-capita,in many previous studies, is not a factor.

index of freedom of the press, national

Considerationofofapplications

PCT international applications affect theapplications, introduced for the first time inpiracy rate. These findings are summarized

this study, showed that if countries with low

below:

numbersof

PCT

numberliteracy rate, and number of applications for

1. The piracy rate decreases with an

for

the

applications

international

for

PCT

international applications put effort into

increase in GDP per-capita.

suchfactors

relatedtotechnological

2. The piracy rate decreases with aprogress they would be able to address

decrease in the index of lack of freedom ofpiracy more efficiently, and such efforts also

the press (i.e., an increase in freedom of thewould facilitate the long-term technologicalpress).

progress of such countries. Accordingly,

3. The piracy rate increases with an

puttingeffortinto

PCT

numberofincrease in the national literacy rate. It isapplications

conceivable that this is because an increase

applications would be recommended for suchin the literacy rate increases the number ofcountries. It is hoped that these findings will
28

for

the

international

Journal of Japanese Management Vol.1, No.2, May 2017

Bezmen, T. L. and Depken, C. A. (2006).
Influences on software piracy:
Evidence from the various UnitedStates, Economics Letters, 90, pp.
356–361.
Chen, C, C., Chen C, P. and Yeh, C, Y. (2010).
Determinants of software piracy:
Evidence from far east countries.

prove useful in policymaking.
Acknowledgements
The author would like to thank threeanonymousreferees

for

their

ISSN 2189-9592

usefulcomments. Also, thanks Prof. Shinichi
Kamiyama, Prof. Tetsuya Misawa and Dr.

Journal of International and global
Economic Studies, 3(2), pp.53-62.

Nianzhi Guo for their helpful comments andsuggestions at early stage of the paper.

Cox, J. A., Collins and Drinkwater, S. (2010).
Seeders, leechers and social norms:
Evidence from the market. Information
Economics and Policy, 22, pp.299–305.
Depken, C. A. and Simmons, L. C. (2004).
Social construct and the propensity forsoftware piracy. Applied Economics
Letters, 11(2), pp.97-100.
Fraj, S. H. (2015). The determinants ofsoftware piracy approach by panel data
andinstrumental
variables.

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copyright software protection andpiracy:Evidence
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European J. of Law and Economics,
21(1), pp.29–51.
Andrés, A. R., and Goel, R. K. (2011).
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Bagchi, K., Kirs P and Cerveny R. (2006).
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Banerjee, D., Khalid, A. M. and Sturm J.E.
(2005). Socioeconomic development
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Banerjee, D. S. and Williams, N. G. (2013).
Digital Piracy and Corruption: A Tale of
Two Crimes. http://econ.biu.ac.il/
files/economics/seminars/corruption_and_piracy_a_tale_of_two_crimes.pdf.

International Journal of Research in
Business Studies and Management,
2(2), pp.16-29.
Goel, R. K. and Nelson, M. A. (2009).
Determinants of software piracy
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Gomes, N. D., Cerqueira, P. A. and Almeida,
L. A. (2013). Determinants ofworldwide software piracy losses.
http://gemf.fe.uc.pt/workingpapers/pdf/
2013/gemf_2013-19.pdf.
－(2014). Software Piracy: A Critical Surveyof the Theoretical and Empirical
Literature.
http://www.uc.pt/feuc/gemf/working_pa
pers/pdf/2014/gemf_2014–05.
Harbi, S. E., Grolleau, G. and Bekir, I. (2012).
Is There A Piracy Kuznets Curve.
Review of Law & Economics, 8(2).
Hinduja, S. (2007). Neutralization theory
and online software piracy: An
empirical
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Information Technology, 9, pp.187–204.

experiment. Telematics and Informatics,

Lau, E. K. W. (2003). An empirical study ofsoftware piracy. Business Ethics: A
European Review. 12(3).
Matsuura, K. and McKenzie, C. (2012).

27, pp.283-292.
Reinig, B. A. and Plice, R. K. (2011). Toward
an Understanding of Software Piracy
in Developed and Emerging
Economies. International J. of Social
and Organizational Dynamics in IT,
January-March.
Salahodjaev, R., Odilova, S and Andrés, A.

Eviews ni yoru keiryo keizai bunseki(“Econometric analysis using Eviews”),
2nd edition, Toyo Keizai Inc.
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curve?.

R. (2016). Intelligence and crime: A

Appliednovel evidence for software piracy,

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https://torrentfreak.com/images/MPRA_

Rafee, S, AI and Rouibah, K. (2010). The
fightagainstdigitalpiracy:paper_71569.pdf.

An

Appendix: Countries for which data were used in this paper
Albania
Austria
Belgium
Cameroon
Croatia
EcuadorGeorgia
Iceland
Italy

Algeria
Azerbaijan
BosniaChile
CyprusEgypt
GermanyIndia
J apanArgentina
Bahrain
Botswana
China
Czech Republic
Estonia
GreeceIndonesia
Kazakhstan

Armenia
Bangladesh
BrazilColombia
Denmark
Finland
GuatemalaIreland
Kenya

Australia
Belarus
Bulgaria
Costa Rica
Dominican
FranceHungary
IsraelKorea

LatviaLebanon

Libya

Lithuania

Luxembourg

Malaysia
Nigeria
Peru
RussiaSlovenia
S witzerland
United Kingdom
ZambiaMalta
NorwayPolandSaudi Arabia
South Africa
Thailand
United States

Mauritius
Oman
Portugal
SerbiaSpain
Tunisia
Uruguay

MoroccoPakistan
Qatar
Singapore
Sri Lanka
TurkeyVenezuela

Nicaragua
PanamaRomania
Slovakia
SwedenUAE
Vietnam(Received: November 23, 2016)(Accepted: May 8, 2017)30

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

Open Innovation University-Industry Collaboration: Student
Idea Contests and Exit Strategy in Japan
Hiroko Kawai

Takasaki University of Commerce, Japan
Nippon Academy of Management E-mail: h-kawai@uv.tuc.ac.jp
Abstract
Open Innovation in the University-Industry Collaboration (OI-UNIC) is increasingly
important globally for researchers and practitioners. As a coupled-type OI, students contests
have become one of the most important elements for the creation of regional business in Japan. OI-UNIC, lab teams of university students from all over Japan, use patents from large
firms, exchange knowledge, brush the ideas up with the help of SMEs and regional institutions, and compete to commercialize their product. However, the existing literature has notrevealed how the collaborative team stakeholders can promote an “exit strategy” and commercialization through knowledge exchange channels. This study fills in the gaps. Applying
creative problem solution theory, intellectual property management, patent management,
and exit strategy methods, this study extends the concept of OI to the research frame. The
survey method is a qualitative analysis based on observations by the author, reflections by
the students, and interviews with the stakeholders. The research result shows that the teamthat commercialized their product had properly used the multiple channels adequately, but
the teams that created only prototypes, while they understood the importance of the channels,
did not take full advantage of them. The main conclusion of this study is formulated as ahypothesis and suggests further study of the effectiveness of innovation education and thepossibility of small-scale innovation of regional revitalization.
Keywords:
Open Innovation University-Industry Collaboration, Creative problem solving, Patent, Exit
strategy, Student idea contest(1) Introduction and Overview
1. Open Innovation University–Industry Collaboration
Open Innovation University–Industry
Collaboration (OI-UNIC), which is oriented
toward student contests, is recognized as
one of the most important elements for thecreation of regional business in Japan.
Many relevant initiatives and knowledge
channels that support innovative activities
in various ways have been developed. The
student idea contest (2015/2016),“Fostering
student’ ideas using published patents andthe OI platform,”1 aimed to develop a physical and virtual environment for generating, developing, and commercializing university students’ innovative ideas throughrelevant training and mentoring. Following
the entrepreneurial or open innovationroute, it fosters collaboration between universities and enterprises, enhances the employment potential of graduates and promotes innovation in companies.
2. The rising role of university in the

1 Detail

information: http://opi.innovations-i.com/idea/report/

31

Journal of Japanese Management Vol.1, No.2, May 2017

knowledge–based society2
As Peter Drucker noted about innovation and entrepreneurship3 in the mid-1980s,
the pervasiveness of information and communication technologies, globalization, and thedevelopment of knowledge society have led to
the growth of knowledge and its redistribution and supply of knowledge-absorbing
worker. In the era of continuous developmentof the knowledge–based society, where thevalue of university-industry collaboration(UNIC) gained a greater recognition 4 , theUNIC consequently became a more significant subject of study5.
For large sized firms that are looking toenhance and maintain their competitiveness,knowledge has become a decisive factor. The
foundation of competitiveness in the world is
now more dependent on valuable knowledge
resources that are widely distributed acrossthe globe, across the value chains, acrossR&D and across individuals with highknowledge absorption in multi organizations.
Against this situation, the paradigm of open
innovation (OI) has emerged as a new response to manage the increased amount ofboundary–knowledge flows both in and out of
the innovation process.
To overcome the limitations of closed innovation processes, such as increasing R&D
costs, insufficient resources, and unsatisfactory levels of competence, companies may
choose to outsource their innovation work
and invite external contributors to develop
ideas or solutions to specific, predefined problems.

ISSN 2189-9592

results of a complex interaction of various actors and institution. Universities (students’idea), large firms (licenser), SMEs (licensee),City industry policy divisions and financial
institutions (“Shinkin” credit bank or localbank) play crucial roles in accelerating innovation. Technological innovation is a result ofinteraction and feedback among all elements
within the system, rather than an occurrence
in a complete linear system (OECD, 1977)6.
The core of the UNIC system is corporations and universities, which manages andorganizes knowledge channels accessing thesource of external knowledge. However, several factors can lead to the failure of collaboration projects. Universities emphasizes not
only research, but also human resource development and the significance of knowledgetransfer and application to a large extent.
Corporations, however, emphasize patent
exit strategies.
However, while many innovation education programs have focused on proposing
creative ideas, the methodology of innovation
education in an “OI” is still controversial. Innovation education plays a crucial role in developing “creative problem-solving skills 7 ”
and “collaborative behaviors8”for students tocreate a new path towards a desirable future.
On the other hand, companies evaluate
utility that is intended to create profits rather than education. Japanese firms have external novelty and fresh knowledge sources
for their IP exit strategy. According to theSurvey on R&D Collaboration by RIETI9, theshare of firms with R&D collaboration withother firms, universities, or public research
institutions have increased in the past five
years, and is expected to increase in future as
well. (RIETI, 2004)10.

3. Dilemma of innovation education and corporate strategy in Japan
Japanese regional open innovation is theomy, Trade and Industry refers to as fundamental
competencies for working persons and what the Ministry of Education Culture, Sports, Science and Technology refers to as bachelor’s competencies and “21st century skill.
8 Ibid.
9 RIETI Research Institute of Economy, Trade and Industry)
10 Motohashi (2011), p16, p.23.

2 P.F.Drucker(1985).Innovation and Entrepreneurship

Principles and Practices, pp.58-80.
3 Ibid.
4 Studies increased in development of industry aca-

demic society, Japan Society for Intellectual Production, etc.
5 Region creation policy by METI, etc.
6 OECD, 1977 innovation report.
7 This skill is manifest in what the Ministry of Econ

32

Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

The study of open innovation has primarily focused on the benefits and consequences of inbound or outbound innovationexchange between firms; however, increasingly firms may be engaged in simultaneous
inbound and outbound exchange forming a“coupled11” approach.
Furthermore, firms increasingly collaborate with university and regional SMEs assources of innovation, but how does such coupled open innovation affect idea create, information flows, commercialization? Is a coupled open innovation applicable as a formal
university education program?
Through an exploratory study of student idea contest, this paper shows how thisapproach incorporates not only the patentand idea but also the provision of technology
and market information using multiple
knowledge channels.
For the above reasons, the author believes that incorporating the practice of collaborations between Japanese universities
and industries into the open innovation literature is important from the aspect of firm patent exit strategies and the aspects of university education for innovation, both theoretically and practically can lead to regional economic development.
Section 2 of this paper reviews the relevant literature about the concept of outbound, inbound and coupled types of OI, success factors, university-industry collaboration, and patent exit strategy. Section 3 presents an analytical framework of the OIUNIC. Understanding the knowledge channels in the UNIC context of the entire innovation process requires a framework for understanding the structure of team’s innovation activities. Section 4 explains the methodology: A) CPS project; and B) OI-UNIC
project with qualitative analysis ofknowledge channels. Finally, this paper conclude and discusses the managerial and educational implications.

11 One of Open innovation modes. Section 2 discusses

14 One of the core of the innovation approach, and it focuses

more details.
12 Schumpeter presented the following five types as
innovation, rather than innovation, (neue Kombination)"new combination” in the first edition of his greatclassic, The Theory of Economic Development (1911).
13 The definition here means an innovation is the result of the successful commercialization of an invention.

on human experience.

(2) Literature review
In this section, I briefly review the literature on the open innovation evolutionary
approach, the success factors, the openness ofintellectual property, and collaborative innovation
1. Open innovation evolutionary approach
Innovation12 is defined here as “creating new value by adding values through new
ideas, methods, directions, opportunities,
and solutions that meet new needs throughmore effective products, processes, services,and business models up to commercialization13”. Innovation also, means not only conventional technology-driven innovation, butalso “human-driven14” innovation, which focuses on creating products that people want
to use.
The concept of “open innovation” is defined as “the use of purposive inflows andoutflows of knowledge to accelerate internalinnovation and expand the markets for external use of innovation, respectively”(Chesbrough, 2003, p1; Chesbrough,2006a) 15 . Open innovators have a specificmindset and disposition to co-evolve ideas
and co-create new products and services inelaborateinnovation
ecosystems(Chesbrough, Vanhaverbeke and West,2014) 16 . Open innovation requires a supportive environment. (Salter, Crscuolo, and
Ter Wal, 2014)17. It was observed that, although an organization may encourage its
staff to be more open, individuals often shy
away from these efforts (Salter, Crscuolo,
and Ter Wal, 2014). In addition, a recent em-

15 Chesbrough(2003).Open Innovation: The new im-perative for creating and profiting from technology.
16 Chesbrough, Vanhaverbeke and West(2014)17 Salter, Crscuolo, and Ter Wal (2014)33

Journal of Japanese Management Vol.1, No.2, May 2017

pirical research implies that students theoretically understand the advantage of openness, but do not apply it to their own behavior (Oganisjana, 2015)18.
Following these initial insights, more
research from different settings arose, causing the definition of OI to be refined. Following the original and more recent conceptualizations (Chesbrough, 2003 p.43, 2006a;
Gassmann and Enkel, 2004; Dahlander and
Gann, 2010; West and Bogers, 2014),
Chesbrough and Bogers (2014) define OI as
a distributed innovation process based on
purposively managed knowledge flows
across organizational boundaries, using
pecuniary and non-pecuniary mechanisms in line with the organization’s
business model. These flows ofknowledge may involve knowledge inflows to the focal organization (leveraging
external knowledge sources through internal processes), knowledge outflows from a
focal organization (leveraging internal
knowledge through external commercialization processes), or both (coupling external
knowledge sources and commercialization
activities). The latter perspective of coupled
OI has recently been used to also connect
the literature on OI with research on user
innovation (Piller and West, 2014), a stream
in the literature focusing on the contributions of users and customers for the innovation process.
Open innovation means that valuableideas can come from inside or outside thecompany and can go to market from inside oroutside the company as well. This approach
places external ideas and external paths tomarket on the same level of importance asthat reserved for internal ideas and paths(Chesbrough, 2006, p.1).

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Figure 1. Three Archetypes of Open innovation processes19
Source: Towards a Theory of Open Innovation: Three Core Process Archetypes by
Gassmann & Enkel (2004), author applied

may engage with them—which the open innovation literature has termed inbound,
outbound, and coupled processes. (West andGallagher, 2006a; West and Lakhani 2008;
Vanhaverbeke et al., 2008; Enkel et al.,2009; Tucci et al., 2016).
Through the inbound process, firms
import external knowledge or resources todevelop internal innovations. This is, themost commonly researched process (Westand Bogers, 2014) building on related bodies
of work such as absorptive capacity (Cohenand Levinthal, 1990). In the outbound process, firms’ patent license or transfer internally developed innovations outside the firm
(Chesbrough, 2003). Finally, the coupledprocess is defined as “coupling the inboundand outbound processes by working in alliances with complementary partners”(Gassmann and Enkel, 2004). That is, it has
been assumed students idea contest assumed as Coupled innovation mode.

2. Three types of OI
Figure 1 shows the three archetypes ofopen OI.
As firms collaborate with universities,
there are three different ways in which they

3. Success factors of OI
Systematically reviewed the successfactors of the open innovation process asof Open Innovation: Three Core Process Archetypes'
done by Gassmann and Enkel, University of St.
Gallen, Switzerland.

18 Oganisjana (2015).A study at Riga Technical Uni-

versity in Latvia, p.1.
19
Gassmann & Enkel(2004), p7. Gassmann & Enkel(2001). According to the research 'Towards a Theory

34

Journal of Japanese Management Vol.1, No.2, May 2017derived from the empirical research literature (2003-2012) 20 . These success factors
are grouped into nine factors: 1) the people
involved in the process, 2) partners relationship aspect, 3) facilitators, 4) supplies andequations of resources, 5) Leadership, 6)
process management, 7) culture, 8) governance, 9) IP patent management.

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knowledge spillovers or increased demand forthe firm’s own goods and services.
Intellectual property managing can beused in two ways to help manage open innovation. First, the necessary codification of an
invention or technology which occurs when apatent application is successful, helps tostructure collaboration agreements (Hall,2010, p.4). Although, uncertainty and imprecision are inherent in the definition and scopeof any piece of knowledge or technology thatis to be licensed to another party, this imprecision can be mitigated if the description isalready subject to the standards imposed bypatent offices. In addition, because the patent is a legal document, in principle the language of the document is already suitable foruse referenced in a different legal document,such as a license. Second, IP rights can beused defensively to negotiate cross-licenses
with others in the industry that hold complementary technologies, thus avoiding mutual
litigation. Many firms in the semiconductor
and computing hardware/software industries
pursue this strategy (Hall, 2010, p.5).

4. Discussion on published patents for exit
strategyIntellectual property rights21 (IPRs) are
generally designed to exclude others from using a firm’s ideas and inventions. Hall (2010)
argued that, at first glance, open innovation
and IPR protection are irreconcilable. Openinnovation implies a willingness to allowknowledge produced within the firm to spillover to others (possibly with the expectationof receiving knowledge spillovers from others
in return) whereas IPR protection enables afirm to exclude others from using thatknowledge (Hall 2010, p1).
Despite this contradiction, some of Japan’s largest patent holders, including Fujitsu, Panasonic, and Osaka Gas, have embraced the open innovation model. Motohashi(2006)22 investigated the role of patent system in innovation at the firm level, and foundthat open strategy firm with active licensing
spends more on R&D.
Some SMEs doubt that the IP being
given up by these large firms is very valuable
to them. Indeed, the technology they offer to
the OI will not be their most valuable. Instead, it will be a technology for which they
have no further development plans but thatthey think can be developed by others in amanner that may ultimately benefit them via

5. Creative Problem Solving 23 and Analogical Thinking24
Fisher argued that creative problem
solving is a proven model for driving innovation when implemented as an organization
wide business process (Basadur, 2001; Basadur & Gelade, 2003). Figure 4 is an adaptation of Basadur’s Simplex model25 (Basadur,
2001). In the Simplex framework, creative
problem solving follows three distinct
phases: problem formulation, solution find

20 To determine the current research status and an-

22 Motohashi (2006), p.1.

swer the research question, the criteria for inclusionand exclusion was selected. The inclusions were published in 2006-2017 as empirical papers and peer reviewed ProQuest ABI/Inform, Web of Science and EBSCO were accessed, and searched for materials using
the keywords “open innovation process,” “open innovation activities” “open innovation,” combined with “University–Industry collaboration” or patent”. This wasdone from June 2016 to January 2017.
21 Source: Japan Paten Office Asia-Pacific Industrial
Property Center, JII

23 Fisher (2011),p.71
24 Kima &Horiia (2016). A Study on an Assessment

Framework for the Novelty of Ideas Generated by Analogical Thinking. Their research method was threeworkshops and a questionnaire survey.
25 This model has been used successfully in hundredsof innovation workshops at Procter & Gamble’s innovation studio; the GYM.

35

Journal of Japanese Management Vol.1, No.2, May 2017

ing, and execution (Fisher, 2010).

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and Horiia argued that as an ideation tool,
analogical thinking enables conceptualchange, which is seen as a crucial aspect of
creativity. In this regard, the use of analogy
can be an important instrument to facilitate
novel idea generation (Kima and Horiia,
2016, p.201).
6. Research questions
Previous theoretical and experimental
literature lead the following questions.
 To what degree do university students
use knowledge channels of collaboration
in OI-UNIC? OI? (Knowledge channels
are places where internal or external
ideas or knowledge flow)
To what degree do the students thinkknowledge channels of collaboration in OIUNIC are valuable for innovation elaboration with open innovation?
The goal of this research was to determine if certain teaching and learning
methods and approaches-students not
only create ideas and write business plans
but also make prototypes of new products,
and test the prototypes with potential
business partners-can be applied to university study practices.

Figure 2. Creative Problem Solving
Source: Basadur, Basadur’s Simplex model 2001.
Adapted by author.

Each phase includes a divergent step(in which all options are explored) followed bya convergent step (in which the most promising options are carried forward to the next
step in the process). Basadur’s researchshows that organizations with a culture ofcontinuous problem finding, problem solving,and implementation — along with requisite
attitudinal, behavioral, and cognitive skills— have the greatest long-term innovation
success (Basadur and Gelade, 2003). Kima

Figure 3. Research framework
Source: Author illustrated and adapted the concept of coupled type innovation to the student idea
contest. “Three Archetypes of Open innovation processes” (Gassmann & Enkel, 2004) andChesbrough’s Open Innovation Model (2003a, 2003b,2006）to the OI-UNIC.

36

Journal of Japanese Management Vol.1, No.2, May 2017(3) Research framework
Figure 3 shows the research framework for study of the knowledge exchange
channels between students and collaborators.
Japanese regional open innovation is
the results of a complex interaction of various actors and institutions. Universities(students) large firms (licenser), SMEs (licensee), City industry policy divisions, andfinancial institutions (“Shinkin” credit bankor local bank) play crucial roles in accelerating innovation. Human-driven innovation
using ICT and patent is a result of interaction and feedback among all elements
within the system rather than an occurrence
in a complete linear system (OECD, 1977).
Project (A) 2015-2016: University –Industry
collaboration was an innovation teaching including creative problem solving and analogicalthinking.
Then
Project(B)
2015/2016: Open Innovation- University–
Industries collaboration was an innovation
training: knowledge channels, performance
exit strategy and commercialization

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The CPSs operated as follow:

Figure 4. CPSs works
Figure 4. The 5 steps of CPS
Source: Author (2016,p.74)

2. How CPS series work28 2016
CPSs began with class instruction (1class), followed by lectures about ICT (4 lectures), CPS lectures and workshops using
the Active Learning method (10 lectures/workshops). The participants were
Sophomore enrolled in TUC and auditors.

Step 1: A pre-task given to participants
one week before. The pre-task consisted ofanswering questions about a current issue
related to the innovator’s business mode,
viewing YouTube videos and searching for
information on websites.
Step2: During group discussions
about the business mechanism, small
teams of students (five or six students) absorbed information and opinions from others.
Step 3: The innovation tried to visualize the sophisticated complex issues inherent in their business and explained thepath to solutions incorporating the viewpoints of various users. A method of designing a product or service system withthe latest technical system that provides
new functions from creative problem solvers were presented. In some classes, the Innovation Project Leader explained the
management of complex and mediumsized projects with the stakeholders. In order to cope with uncertainty and high-liquidity environment and social problems,innovative business system development
was explained.
Step 4: After each lecture, the students"CPS" project started in 2013 and ran for 4 years.
ICT means Information Communication Technologies
28 The CPSs implemented at the Takasaki Universityof Commerce, during the months of April-July in theyears 2013-2016 (Number of Participants were including
auditor of class who registered from high school etc.
N=80 in 2013, N=80 in 2014, N=190 in 2015, N=160
in 2016). 2 credit course, 15 hours classes for 2ndyear students and above.

(4) CPS Project 2013-201626
1. Overview
The CPS project was implemented
from 2013 to 2016 under the theme "Welfare
business and ICT27". The CPS project provided training in innovating the design ofproducts, services and other systems using
the Creative Problem Solving model, analogical thinking, and innovation management
approaches developed collaboratively TUC
University and private companies or public
institutions.

26
27

37

Journal of Japanese Management Vol.1, No.2, May 2017

submitted self-assessment and reflection
sheets.
Step 5: As a final assessment, students
selected one topic, identified the problem,and proposed a new business utilizing the
CPS method and analogical thinking.

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Table 1. Innovators/Themes/ 2015-201629
2016
May 9
May 18

3. Proposal of a new business solution by analogical thinking

Jun 6
Jun 13Innovators
IPA, Mr. Komon
Nomura HD, Mr.Sakai
DentsuPR ,Dr.Kitami
System alfa,
Mr.Okada
Nihon Kohden,
Mr.Tanaka
Yomiuri, Mr. Orita

As shown in Table 1., CPS examinedinnovation examples in three area: 1) products and processes (anti-aging cosmetics,
pharmaceuticals, automobiles and equipment, AED, biotechnology, etc.); 2) operation
system (efficient production management bytablet, cabin service, smartphone security);and 3) social issues (such as disaster / emergency medical care, tablet education, entertainment, communication, and food in spacedevelopment, forest management, etc.).The
final task for the students was to apply analogical thinking, creative problem solving
skills, and the proposed business solutions.
Data was collected from the 60 to 80 participants who participated in this study.

Jun 20

JAXA, Dr.Nakazara

Jun 27Maebashi red crosshospital, Dr.Nakamura
Fujitsu, Mr. Mizutani
ANA, Ms.Kawamoto

4. Method and results
Data was collected from the student’s
reflection sheets and final test according to
the 5-steps. The qualitative contents analysis was based on the CPS learning model(Figure 4).
The following four questions were used
to evaluate the students in the course．
・Did you fully understand the issue?
・Did you improve your ability to find andresolve problems?
・Did you actively work on the CPS task?
・Did you create an idea using information
technology?
The student reflection papers received

May 23
May 30July 4
July 112015
May 11

Theme
Smartphone security
Net equity investment
Digital PR& Social
Media Flames
Cognitive elderly
wandering with GPS
Automatic external
defibrillator R&D
Newspaper new Technology
Space food & Satellite
Communication
Disaster, Life Saving
by Doctor Heli
Sensor system in automobile
Cabin Service

Innovators
Cyber Police agt. expert
Senior Life Association, Mr.Hirai

Theme
Cyber Criminal

May 25Gain, Mr.Miura

Jun 1
Jun 8

Panasonic, Mr.Takeyasu
Shiseido, Dr. Amano

Internet Businessrisk & chance
New Business ModelJun 15NARO, Mr. Hayashi

Jun 22

LINE Mr. Eguchi

Jun 29Maebashi red crosshospital, Dr.Nakamura
Fujitsu, Mr. Kouketsu
Mitsubishi UFJ
Lease & Cyubu Forest Management,
Mr.Kizuka et al.

May 18July 6
July 13

Web platform of Senior Life

Anti-aging cosmetics
Automated Agriculture Machine with
GIS
Communication &security
Emergency, Life Saving by Doctor Heli
Leaner centric education
Forest Business

Source: Author.

high scores (4 or 5 on a 5-point scale). However, they chose problems that were closely
related to everyday life experiences because
it was difficult to apply an analogy to business problems in areas where they lacked"social media analysis" by "NTT Comware".
2013 Cases: TV commercial data transfer system by"IMD Japan", Operation of doctor helicopter in case ofdisaster. Hospital network system by NEC, user experience value products by "LION Corp.", production
management with WIFI tablet by EXE, large-scaledata analysis of smart society by OKI, information security system of Canon MJ, regenerative medicine ofAdvanced Medical Ltd. etc.

29 Themes in the past: 2014 Cases: Regeneration of re-gional economy by comedy project of "Yoshimoto
Kogyo". A robust solution for cybercriminal by IPA.
Promotion of local souvenirs by "International Tourism Association". "NARO" solution of agriculturalbusiness with GPS equipped agricultural machines,
World Heritage and tourism policy of Tomioka City.
Doshisha University exercise for anti-aging. Medical
efficiency by SAP Japan. Tourism insight solution by38

Journal of Japanese Management Vol.1, No.2, May 2017business knowledge and experience.

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promoting 'co-innovation' using our open patents selected among from own holding approximately 95,000 patents32 to support new
businesses through the SMEs”33.

(5) OI-UNIC 2015/2016
1. Overview
Open innovation in the university–industry collaboration is one of the most important elements for fostering innovation inregional economies. Many initiatives exist to
support these activities in different ways.
This section presents OI–UNIC cases in Japan, in 2015 and 2016. It presents the idea,
published patent, multiple channels ofknowledge flows and first results in open collaborative innovation particularly those related to student’ contests.

Author: What is the significance of using patents?
Fujitsu: The mission of the Fujitsu Limited
Business Development Department is to develop the business of the SMEs through patent licenses as an published patent that are
not utilized within our company, or newly
use her own business model.34
Author: How far can you offer support?
Fujitsu: Even without introducing new technologies, small businesses can create highly
competitive products and services in themarket with innovative concepts. Nonetheless, the use of new technology increases thepossibility of creating innovative products
and services that are not in the world, and if
the new technology is patented as a patent,
the market competitiveness of the product ismarkedly improved. Therefore, it is possible
to prevent a competitor from entering themarket in an uncontrollable way35”

2. Innovation's-i30” idea platform
The core idea behind the idea contest
project is to create innovative ideas by proactively exploiting the company's publishedpatents and the student's intellectual potential. “Innovations-i” office launched the Open
Innovation Gateway (OIG) for the Student
Idea Contest on the website in 2015. The
OIG is a platform where organizations as patent license), student’ teams (as idea creators), and SMEs (as entrepreneurs) utilize
patent information and applied product.

Author: What is different from technology
transfer?
Fujitsu: We consistently support what weneed for commercialization until commercialization. Such a technology transfer
method is distinguished from an act ofsimply selling patent licenses and placing alist of published patents on the public
shelf36.

3. Interview with Fujitsu mission and patent division exit strategy
Author: What is the mission of Fujitsu, specifically, the patent division?
Fujitsu: Through the OIG, Fujitsu Inc. 31
works to activate innovative drivers faster,
both inside and outside the company, and,based on collaborations between internaland external partners, verifies and transforms them into practical businesses. We are

Author: Can the business plans of university
students be commercialized?
Fujitsu: Business proposals and consistent

30 Its’ role was context organizer and intermediary.

32 Intellectual Property Report for FY 2014.

Interview with Mr. Kudo, innovation director (January, 4, 2017)31 Fujitsu Inc. headquarter is located in Kawasaki
city, is a leading ICT total-solution company in Japanthat provided a published patent. Fujitsu Ltd.
launched Open Innovation Gateway (OIG) in Kawasaki city in Japan. It is a platform to connect andgrow ideas, and based on the collaboration of internaland external partners, to verify and transform theminto practical businesses.

33 Author Interviewed Masatoshi Nishida, Vice Presi-dent, (January 4, 2017) Intellectual Property innovation Division, Headquarters of Fujitsu Legal, Compliance and Intellectual Property Unit.
34 Ibid.
35 Author Interviewed Masatoshi Nishida, Vice President, (January 4, 2017)36 Ibid.

39

Journal of Japanese Management Vol.1, No.2, May 2017support are products rather than patent licenses. By utilizing Fujitsu’s patents to create a business plan for university students,
universities, local governments, financial institutions jointly create a place to create new
businesses that are not bound by existing
concepts. I believe that human resources canbe raised through social learning.37”

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4) To propose the following related
ideas leading to the business of the SMEs.
*Product or service must be related to: a) Revitalization support. b)Tokyo Olympic
Games, Paralympic Games 2020. c) To contribute to the regional economy.
5) To utilize one of the six Fujitsu patents.
6) To assume an annual turnover ofabout 10 million yen to 100 million yen.

4. Contest start

6. Type of patent
The OI-UNIC 2015 began in June witha total of 71 student teams from around Japan and 12 types of published patents offered by three organizations (JAXA, NHK
E.S., and Fujitsu38), and finished the national competition in January of the following year. The OI- UNIC 2016 project started
in June with19 types of published patents offered by five organizations (Fujitsu, NHK
E.S., AIST, Fujitsu-ten, and Panasonic39,andended in December 2017 in Tokyo. Table 2
and Table 3 list the available types of patentand team selections.

Figure 5. Participants Location of the OpenUNIC 2016

7. OI gate, knowledge route and knowledge
exchange channels

Source: The author edited data from http://opi.innovations-i.com/idea/purport/

Figure 5 shows images of the locationof participants and the TUC University,
SMEs in Maebashi City of Gunma Prefecture in the northern Kantō region of Japan.
5. Rules of contest
Six requirements and problems to solve
were given to each team.
1) To create idea from the viewpointthat you want to purchase yourself, or you
want to use (useful / interesting / surprised).
2) To identify differentiation from similar products.
3) To add ideas as patents are a part ofthe information on some products and services.

Figure 6. OI 6 gate and assessments
Source: Author.

Figure 6 describes the gates and assessment of the OI. During the kick-off mee-

JAXA= Japan Aerospace Exploration Agency
39 AIST =National Institute of Advanced Industrial
Science and Technology.

37 Ibid
38 Formal name is as follows. Fujitsu = Fujitsu Lim-

ited, HKE E.S.= NHK Engineering System Inc.,

40

Journal of Japanese Management Vol.1, No.2, May 2017

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Table 2. Number of Team and Published Patents in 2015.

Type of published patent

License
ProviderNumbers ofTeamsFujitsu

9

FujitsuFujitsuFujitsuFujitsu

5
11
8
8

2
3
4
5

Sales support technology using transmission type liquid crystal and half
mirrorScreen display control technology of vending machines according to users
Product suggestion technology considering remaining budget
Capture and play equipment technology
Article inspection technology by hammering analysis

6

Contact sensor technology using air bag

Fujitsu

6

7

Metallic coloring technology without paintFujitsu

14

8

CG character control technology
Image retrieval technology by drawing requested image using image elements in database
Tactile presentation technology to convey diagrams and graphs
Flexible solar cellTwo-dimensional actuator
Total

NHK E.S.

1

NHK E.S.

1

NHK E.S.
JAXA
JAXA
3

0
5
3
71

1

9
10
11
12

Source: Author edited data from http://opi.innovations-i.com/idea/purport/

Table 3. Number of team & Published Patents in 2016
Type of published patent
1
2
3
4
5
6
7

License
ProviderFujitsuFujitsuFujitsuFujitsuFujitsuFujitsu
NHK E.S.

Numbersof Teams
28
24
11
5
11
19
0

NHK E.S.

0

NHK E.S.
NHK E.S.
AIST
AIST
AIST

0
0
1
3
0

9
10
11
12
13

Code embedding technology using light emitting diode: LED
Gaze detection technology
Behavior state detection technology
Direction guidance technology
Temperature difference power generation technology
Advertisement related technology
Three-dimensional sound reproduction technology with headphones
Television system applying the AR (Augmented Reality) technology "Augmented TV"
CG character animation production technology
Inflection conversion technology
Two-dimensional code easy to understand with dot picture
Two-dimensional actuator
Switchable light control mirror with gas

14

Photo synthesis system considering size

AIST

1

15

Electric switched dimmer mirror

AIST

1

16

Door auto-lock technology

Fujitsu-ten

017

Door unlock warning technology

Fujitsu-ten

018

Mike picking up tweets

Panasonic

0

19Super directional speaker

Panasonic

3

Total

3

107

8

Source: Author edited data from http://opi.innovations-i.com/idea/purport/ting (Gate 1) the team carefully chose a published patent, defined the current problem,
created and brushed up an idea (Gate 2),
proposed a business model (Gate 3), createda prototype and tested it with users (Gate 4),

further brushed up the idea (Gate 5), andpresented their plan in the business contest(Gate 6). The team can work with their collaborators through multiple knowledge

41

Journal of Japanese Management Vol.1, No.2, May 2017

route (Figure 7) based on their businessmodel through the project.

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Table 4. Knowledge Exchange Channels

1

Figure 7. Kowledge Route with Collaborators

2

studentteacher

3

team otherteam4

team-mentor coordinator outside5

team-firm 1
engineerteam-firm 2
engineerteam-firm 3
partner

Source: Author.

6
8

Figure 8. OI- UNIC licensing 2016
Source: Author illustrated (2016) and adapted
Chesbrough’s Open Innovation Model (2003a,
2003b,2006）to the OI-UNIC students idea contest.

9

team-stafflicenser

10

team-staff
city administrator
team-staff
investor
teamfinal users

11
12

Figure 8 ilustrates team innovation
performance on OI process. Blue marked
team ~ Top Winner team has exit.

Knowledge
multi-channel
within teamType of collaboration (Ba)Kick off, brushing up, selecting published property. Students divided their responsibilities for product concept,
design, solution, finance,
market research by exchanging findings and discussing
further activities with theirteammate.
Teacher open to discuss, advice and facilitate students
learning theory and practiceboth at individual and teamlevel. (encourage)
Kick off, brush up, intermediate phase. Team present
their idea done and discuss
the challenges faced with theother teams.
Coordinator visiting, telephoning, or meeting at city
office, recommending particular SMEs to realize andneed specific experience andknowledge.
Presentation, discuss, advice
regarding hardware
presentation, discuss, advice
regarding software
Presentation, discuss, advice
regarding business model,
marketing
Brash up, intermediatephase, Team present theiridea done and discussed thechallenges faced with the license
Advice regarding contact toSMEs, scheduling.
Advice regarding financial
information
Interview, questionnaire, observation

Source: Author’s observations40 (2016)degree do university students use multiple
channels of collaboration in OI-UNIC?” wasexplored based on qualitative analyses (author’s observations, student reflection papers, and interviews with potential partners

8. Methods and results
8.1. RQ 1 methods and results
The first research question, “To what
40 Author researched type of collaboration channels

are researched, revised, and modified based on thework of Oganisjana (2015).

42

Journal of Japanese Management Vol.1, No.2, May 2017

outside the university), according to theknowledge exchange channels (Table 4), OIUNIC steps (Figure 6) and the knowledgeroute (Figure 7). Figure 8 compares winner
team.
Results of R1: Partly applicable but not
all (Table 5). The proposed assessment
framework for collaborative behavior is inapplicable to the “one size fits all” type ofstudents. It does overcome some of the limitations of current evaluation methods.

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Table 6. Value Lists and Categories

Table 5. RQ1: Result 412015 and 2016

Source: Author (2016)43.

8.2. RQ 2 methods and results
The second research question, “To
what degree do the students think multiple
channels of collaboration have value for
their practical studies of OI,” was exploredaccording to the Value List (Table 6).
Unlike in previous studies (Oganisjana,2015), the author considered technical information about the published patent. Although Oganisjana (2015) categorized (IN, OUT,

IN-OUT-IN) as a “fragment of the qualitative content analysis and labeling of the categories,” the author included technology information about the published patent andredesigned the study.
The qualitative content analyses accounted for the three possible categories ofaction.
A) IN (inflow): students get an idea (theyjudge useful) from collaboration partners related to the category.
B) OUT (outflow): students give an idea
(they judge useful) to collaboration partners related to the category.
C) IN-OUT-IN (inflow–outflow): students
exchange ideas (they judge useful) withcollaboration partners related to the category. The student reflection papers and

41 The 2015 top winner was Showa Women's Univer-

42 Type of collaboration channels revised and modi-

Source: Author’s observations42 (2016).

sity team-"Small Drone Gate Passing Game supported by Showa Shinkin (Tokyo). Team A created a
prototype and just won at regional stage. http://opi.innovations-i.com/feature/idea/re/20151217/.
The 2016 top winner was university of Shizuoka
team-“Spot with spots!” supported by Seishin Shinkin. http://opi.innovations-i.com/idea/report/.
Team B lost game at the regional competition.

fied the work of Oganisjana (2015).
43 Compared Team A (prize) with Team B (no prize).
Team A was participated in CPS Project (innovation
training), value information patent, and criticalthinking more than team B.

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Journal of Japanese Management Vol.1, No.2, May 2017

ISSN 2189-9592

as shows how study courses and projects
can foster greater innovation by students.
The study revealed several problems forfurther research.
The main conclusion of this study formulated as a hypothesis, is that: when clear
guidelines for innovation management andindication of innovation performance, in OI
are prepared, university students can become more collaborative and motivated towork with potential business partners outside the university, the effect of active learning can be further enhance.

the author’s observations of student behaviors revealed that the students highly
value the potential for intergenerational
collaboration outside formal university
frames.
However, the student reflection also revealed that while some students are aware of
the value of studying in collaboration channels, they did not active and full use the open
channels.
(6) Findings
In this section, we present the ﬁndings
from the systematic review.
Management point of view:① Reliable goals and time schedules seem
necessary to build the credibility of the collaboration.
②Initial ideas don’t generate much value inthemselves. There needs to be professional
guidance for creative problem solving
and discussion of the idea.
③ Companies often have tended to claim
openness, while knowingly concealing certain information, such as patents or possible sources of competitive advantage.
④ Clear prizes for innovation challenges
are necessary. For example, a career-related reward is much more fulfilling andmotivating. Other rewards include internships, monetary prizes, and presenting the winner’s name on a website.
⑤Face-to-face is good for the initial phase
where different concepts and processes
are tested, but in the long run, online
channels are more useful for the entire
innovation process.
⑥Building trust and collaborating on an
idea takes times. Financial institutions
play a role in building trust.

(8) Limitations and future researchThis study of innovation education in
an OI required students to pose a theoreticalframework of innovation education in OI andtest it empirically. Future investigations
should note the limitations of this study.
First, the data source was limited to the author’s university students. Second, the research conducted using qualitative content
analysis.
Acknowledgements
Project (A) was partly supported by the
Telecommunications Advancement Foundation (2013-2016). Project (B) was partly assisted by the Community Center at Takasaki
University of Commerce, the Industry Policy
Division of Maebashi City and the staff of related organizations and facilities in Gunma.
Special Lecture and workshops were assisted by many industries. I deeply appreciate their cooperation.
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Publisher: JFMRA
May 2017