Annals of Business Administrative Science 10 (2011) 1–12
Available at www.gbrc.jp

Online ISSN 1347-4456 Print ISSN 1347-4464©2011 Global Business Research Center

Exploitation Reduces Novelty:
An Empirical Analysis of the Japanese Video Game Industry
Takeaki WADA

Abstract: Firms accumulate knowledge through product development activities suchas efficient product design, various experimental data, and know-how of project
management. By exploiting this knowledge, firms are able to improve product quality,
reduce cost, and shorten the development lead time. The more firms accumulateknowledge, the more they are able to gain competitiveness by exploiting theirknowledge. However, because of efficiency gained by exploitation, firms cansometimes excessively exploit their knowledge, thus making their products too
similar to and less attractive than earlier products and, as a result, losecompetitiveness. In this paper, we employ a time series analysis to show that excess
exploitation of know-how reduced product creativity in the Japanese video gameindustry.
Keyword: exploitation of know-how, novelty, Japanese video game industry

1. Introduction
This study aims to empirically show that the exploitation of know-how in productdevelopment leads to a decline in the novelty of products in the Japanese video gameindustry.
This study is based on the concepts of exploitation and exploration in the context of anorganization. An organization studies and acquires efficient problem-solving routines
through continual problem solving. However, if an organization relies heavily on
problem-solving routines aimed at providing efficiency under the present conditions, it
becomes difficult to implement these routines in different environments (Levitt & March,
1988). Organizations have to exploit current problem-solving routines to be efficient;simultaneously, they have to explore new routines corresponding to environmental
changes.
However, on a short-term basis, exploitation of effective problem-solving routines
offers its own set of advantages; thus, organizations tend to choose exploitation and losecompetitiveness in the long run (Levinthal & March, 1993; March, 1991).
A firm’s product development process is also a cyclic problem-solving process (Clark& Fujimoto, 1991), and the same can be said of product development activities. Firms
acquire a variety of know-how through previous problem-solving activities whiledeveloping products, such as well product design, various experimental data, and

School of Business, Aoyama Gakuin University, wada@busi.aoyama.ac.jp
1

Takeaki Wada

project management know-how. By exploiting their know-how, firms are able to improve
product quality incrementally, reduce cost, shorten development lead time, and makeproduct development activity more efficient. However, if they exploit their know-how
excessively, the new product will offer nothing different from and will not betechnologically advanced than the previous version. Therefore, from the long-term
perspective of maintaining the appeal and competitiveness of their products, firms haveto explore the new attribute of a product and new technology.
Benner and Tushman (2002) analyze the photography and paint industries and show
applying for a patent becomes passive in the firms that meet the ISO 9000
standards. They insist that to meet these standards, firms need to improve their
management process, for which they need to consider exploiting the management
process instead of the patent application process. Benner and Tushman (2003) explained
productivity dilemma (Abernathy, 1978) as a trade-off between the exploitation of thecurrent production processes and the exploration of new products.
Like Benner and Tushman (2003), Ikuine (2006) discussed excessive exploitation in
product development, but indicated that excessive exploitation tends to occur in thesoftware industry, which has almost no production process to exploit.
Ikuine (2006) pointed out that in the Japanese video game industry, firms
accumulated development know-how and the merits of exploitation became far more
substantial. Subsequently, firms focused on developing sequels like Dragon Quest(Dragon Warrior in the U.S.) II and III. In contrast, firms display a passive attitude while
exploring novel video game concepts that makes video games unappealing to consumers.
Ikuine (2006) termed this trade-off between exploiting know-how and exploring a new
product as productivity dilemma in development. Further, he indicated that excess
exploitation in development reduces the novelty of video games and shrinks the Japanese
video game market.
This paper empirically shows how exploitation of development know-how reduced
novelty in the Japanese video game industry. For this purpose, it analyzes the effect of
productivity dilemma in development.

2. Overview of the Japanese Video Game Industry andEffect of Excessive Exploitation in Development
This section gives an overview of the Japanese video game industry and the gamedevelopment process, and explains, with the help of previous studies, how excessive
exploitation affects sales when development takes place.
2.1. Overall condition of the Japanese video game industry
The video game industry in Japan started with the import and copy of American
consoles. Subsequently, the Japanese firms accumulated the development know-how
gradually through the imitation process (Sunagawa, 1998).
The Japanese video game industry grew substantially in 1983 when Nintendo
introduced the Family Computer (Nintendo Entertainment System).
Nintendo practiced a quasi-closed policy strategy: it supplied hardware exclusively
2Exploitation reduces novelty

Figure 1. Growth of the Japanese console video game software market (number)Million

90
80
70
60
50
40
30
20
10
0
1983

1985

1987

Family Computer (NES)
PS2

1989

1991

1993

1995

Super Famicom (SNES)
Other console hardware

1997

1999

2001

PlayStation

Source: Tokyo gangu ningyo donya, Toy Journal [to-i ja-naru], each year.

(closed), but openly announced that it developed video games for software developers(Asaba, 1995). As a result, many software developers entered the industry, resulting in
the development of various video games and the subsequent rapid growth of themarket. Figure 1 shows the market growth (number base) of the console video gamesoftware in Japan after 1983.
Figure 1 shows that the Japanese video game market matured in the late 1980s anddematured in the 1990s with the release of a new hardware, Super Famicom (Super
NES). Subsequently, it matured again in the late 1990s in spite of the release of a new
hardware, Sony PlayStation 2 (PS2).
2.2. Development process of Japanese video game developing firms
Japanese video game firms developed their products through an integrally
coordinated process and not through a waterfall model (Sunagawa, 1998; Tatsumoto,2003).
Initially, they decided on a simple, vague concept for the video game project. They
defined specifications during the developing, testing, tuning, and revising stages of thegame program, and in this process, the game content was improved and enriched(Ikuine, 2000; Sunagawa, 1998; Tatsumoto, 2003).
Firms accumulate know-how and assets—for example, appealing characters like
Mario from Super Mario, technique of programming, and know-how of project
management—by developing video games. By exploiting some specifications fromearlier games, firms can obtain concrete specifications and thus shorten development
lead time. By exploiting their knowledge, they can solve problems in the tuning andrevising stages expeditiously. Therefore, exploitation of knowledge makes thedevelopment process more efficient.
Nevertheless, if firms exploit their know-how excessively, they will develop new video
games that are almost similar to previous ones. Video game users (or gamers) wantinnovative, original, and interactive games (Ikuine, 2000), and exploitation makes video
games less attractive.
As mentioned in section 1, Ikuine (2006) termed this trade-off between gain in3

Takeaki Wada

productivity and loss of attraction by exploitation as productivity dilemma indevelopment. Ikuine (2006) investigated the Japanese video game market and verified thatJapanese video game makers were inclined to exploit their know-how in the late
1990s. Thus, he concluded that excessive exploitation was the reason that the Japanese
video game market shrunk (Figure 1).
2.3. Aim of this paper
However, Ikuine’s (2006) analysis has a limitation in that it measures the degree of
exploitation using sequels of games. Ikuine showed that there was an increase in thenumber of video game series and that the sales of sequels in a series were significantly
higher than the sales of non-series games.
Developing sequels as part of a series typically involves exploiting characters, thevirtual world, game platforms, and programs from earlier parts in the series. However,certain know-how like program technique and project management, which are notspecific to a series, can also be exploited. This can be further explained using a morefamiliar product, namely, an automobile; Toyota manufactures the Corrolla series, andthey exploit know-how obtained through the development of earlier models. For
example, when they started manufacturing another series, the Lexus, they could exploit
know-how obtained from the development of the Corolla series.
Therefore, to measure the degree of exploitation more precisely, indexes of exploitation
other than that of the game series is required. Therefore, this paper presents
methodology and indexes that measure the degree of exploitation using productdiffusion curve.

3. Methodology
3.1. Sales curve of software product and effect of exploitation
Shibata (1997) indicated that the sales curve of software products, for example, CDs
and movies (movie theater attendance),
indicates a “high initial value and declining
pattern,” whereas the sales curve of hardware products produces a bell curve that
gradually increases, peaks, and declines (Figure 2).
Naruke (1996) provided the sales curve of the packaged version of Microsoft
Windows 95 OS in Japan; the curve has a high initial value and declining pattern. He
also explained that because of the previous version of the Windows OS series (Windows
3.1), users can understand the value of the new version OS, look forward to it, and makea purchase decision as soon as it is released.
According to Rogers (2003), if the difference between new and existing products issmall and users can gauge the value of new products, adoption time (time spent on thedecision to buy) becomes shorter.
If firms heavily exploit their know-how and develop video games that lack originality,
game users may easily gauge its value and the adoption time may become very
short. Moreover, a sales curve indicates a higher initial value and a more rapid declining
pattern. However, if firms explore new attributes for games and develop video games
that have originality, users may understand the value of such novel games gradually
through word of mouth. Therefore, it can be assumed that the adoption time or the
initial sales to total sales ratio may represent the degree of exploitation and creativity. If
4Exploitation reduces novelty

Figure 2. Sales curve of software and hardware

Figure 3. Relation between sales curve anddegree of exploitation

Sales

Sales curve of software
Sales

Sales curve of software withhighly exploited know-how
and which is less different
from existing software

Sales curve of hardware

Time

Sales curve of software with
newly explored attribute andnoveltyTimethe average adoption time and the initial sales to total sales ratio of a video game are
high, then that game is considered to be a result of highly exploited know-how, and lessnovel (Figure 3).
3.2. Measuring average time and initial sales to total sales ratio using Fourt-Woodlock model(1960)In this paper, we use a product diffusion model proposed by Fourt and Woodlock(1960) to measure the degree of exploitation of video games.
Fourt-Woodlock model has the following components:(1) The total sales of product N depends on its appeal.
(2) The product is not purchased repeatedly in a period.
(3) At period t, p (p is constant, 0 < p < 1) number of persons who have decided tomake a first-time purchase.
(4) From the above assumption, sales in period t is
f (t) = {number of persons who have not yet made a purchase} × p= {N × (probability of not making a purchase t - 1 times)} × p= N (1 - p)t - 1p
Figure 4 illustrates Fourt-Woodlock model.
This model shows a high initial value and a declining pattern. Further, it has thefollowing features:(a) The constant p in the model implies initial sales to total sales ratio.
(b) (1 - p)t - 1p is a binominal distribution function, and by virtue of being one, theaverage of the function is 1/p. Therefore, p is a reciprocal number of the average
adoption time.
As assumed in section 3.1, the initial sales to total sales ratio and the average adoption
time represent the degree of exploitation. Therefore, by adopting Fourt-Woodlock model
for analyzing real sales data and estimating the p for each video game, we can5Takeaki Wada

Figure 4. Fourt-Woodlock model (1960)

Sales

Figure 5. Transformed Fourt-Woodlock model

Sales atSettings: N = 1,000 P = 0.3
t

Settings: N = 1,000 P = 0.3

350
300
250
200
150
100
50
0
1

3

5

7

350
300
250
200
150
100
50
0

9

N
inclination = ‐p
= ‐0.30Period500

1000

Cumilative sales at t-1

distinguish between a relative
explorative/more novel game.

exploitative/less

novel

game

and

a

relative3.3. Model transformation for analysis
Nonlinear regression is required for estimating p in Fourt-Woodlock model. To
estimate p more easily, in this paper, the model is transformed as given below.
f (t) = {number of persons who had not yet made a purchase} × p
and, it can transform as
f (t) = {N – cumulative number of persons who had made a purchase at thestart point of period t} × p= {N - Σ1t - 1f (t)} × p= Np - pΣ1t - 1f (t)Then, Fourt-Woodlock model can be described as linear function of cumulative andperiodical sales, as shown in Figure 5.
By this transformation, p can be estimated by a simple linear regression analysis.

4. Analysis
4.1. Analysis procedure
First, using a linear regression analysis with weekly sales data, we estimate the p in
Fourt-Woodlock model of each video game.
Then, by setting p as a dependent variable and setting variables related to exploitation
as independent variables, we perform a multiple regression analysis.

6Exploitation reduces novelty

4.2. Estimating p in Fourt-Woodlock model
4.2.1. Data set
To estimate p, the weekly sales data of video games, provided by Media Create Co.,
Ltd., were used for analysis in this paper. Media Create estimated the weekly sales by
surveying sales in retail stores and divided them by the market share of the stores.
This paper screened these weekly sales data according to the criteria given below:
a. A certain number of data is required to maintain the robustness of linear regression
analysis. Thus, video games that have data for 10 consecutive weeks’ data upon
release are selected and their data were used for analysis.
b. In the Japanese video game market, an indication of commercial success is the
1-million-sale mark. Therefore, video games that sold more than a million copies
were selected.
c. Video games for consoles released in 1997 to 2000 were selected.
As a result of this screening, data sets of 434 video games remain.
4.2.2. Estimating p
Next, using these data sets, we estimate p by a linear regression analysis. Figure 6
illustrates the results of applying the model of Final Fantasy VII. In this case, theestimated p is 0.684.
Some video games have a sales curve that does not display a high initial value anddeclining pattern. In this case, the estimated p is invalid (Figure 7).
If p is estimated as a negative value, that video game is considered the least exploitative
one, and p is set as 0.
4.3. Independent and control variables
To explain p, four independent variables and five control variables are set.

Figure 7. Fourt-Woodlock model (1960) applied

Figure 6. Four-Woodlock model (1960) applied

to IQ: Intelligent Qube

to Final Fantasy VII
Weeklysales

Weeklysales

2,500,000

60,0002,000,000

y = ‐0.684 x +1,953,335.528
R² = 0.975

1,500,000

50,00040,0001,000,000

30,000500,000

20,00000y = 0.095 x + 19,588.415
R² = 0.643

10,0002,000,000

00Cumilative sales

100,000

200,000

Cumilative sales
7

300,000

Takeaki Wada

4.3.1. Independent variables(1) The number of years since the Japanese video game market was established
In due course, firms develop many video games by exploiting (or possibly imitating)ideas from existing video games, for example, game genres and platforms. As it isassumed that the video game will be released years later, the p estimated by
Fourt-Woodlock model increases.
The variable that represents the industry-wide know-how accumulation is set as
below.
The number of years passed since the market was established = Published year of thegame – 1983 + 1(2) The number of years since the video game publisher entered the video gameindustry
Firms accumulate knowledge assets like appealing characters, program technique,and project management know-how through previous development activities. In
addition, more experienced firms have the incentive to exploit theirknowledge. Therefore, we assume that if the year since the development firm of thevideo game was established is longer, the p estimated by Fourt-Woodlock modelincreases.
The variable that represents intra-firm know-how accumulation is set as below.
The number of years since the publisher entered the video game industry = Year inwhich the game was released – Year in which the publisher published its first video
game + 1
To obtain the year in which firms published their first video game, this paper uses agame “bibliography,” Kougien, published by Ambit Corporation as data source.
(3) The cumulative number of video games released by the video game publisher
As mentioned above, firms accumulate knowledge assets through past development
activities. Moreover, if two firms have been conducting business for the same amount oftime, then the amount of knowledge assets would differ by the number of video games
they published. It is assumed that the cumulative number of video games released by
the publisher is larger, considering that the p estimated by Fourt-Woodlock modelincreases.
The variable that represents intra-firm know-how accumulation is set as below.
The cumulative number of video games released by the publisher until the previous
year when the game was released
To obtain the number of video games that firms have released, this paper uses Kougien
as a data source. However, this variable includes number of video games for portablehardware; regular and limited editions of games are counted as two, which does notgive a precise value.
(4) The number of years since the hardware of the video game was released
Ikuine (2006) indicated that, by releasing new hardware, it is possible to develop more
expressive games that utilize high-end platforms and that have to adapt to new
specifications of new hardware. Thus, it is assumed that the shorter the time since the8Exploitation reduces novelty

release of the video game hardware, the more explorative video game developing
activities and the smaller the estimated p.
The variable that represents know-how in terms of becoming adept at hardware is set
as below.
The number of years since the release of the game’s hardware
4.3.2. Control variables(i) Price
There are two price zones in the video game industry, one is the regular price zonethat ranges from ¥4,800 to ¥7,800, and the second is the economic price zone that ranges
from ¥1,500 to ¥2,800. Regular-priced games have a sufficient budget for advertisement
and promotion before release, but economically priced games are not generally
advertised.
Promotion educates users about video games, assists purchase decisions, andincreases the estimated p. To control this promotion effect on p, the price of games is set
as a control variable.
(ii) Series dummy
If a video game has a sequel, it is assumed that much know-how is exploited becausesuch an exploitation of previous games in a series is easy and makes the development
process efficient.
To control for the difficulty of exploitation, dummy variables (= 1 if sequel is made,and 0 otherwise) are set as control variables.
(iii) Popular edition dummy
Popular video game editions are set at a lower price than a hit video game released
earlier, similar to how the soft-cover edition of a book is priced lower than the hardcoveredition.
Consumers who purchase popular video game editions are considered as laggards inthe product diffusion process. Thus, the estimated p is assumed to be lower in popular
editions.
To control this difference in user characteristics, dummy variables (= 1 if popular
edition, and 0 if regular edition) are set as control variables.
(iv) Hardware dummy
In Japan, apparently, Nintendo’s hardware is popular among teenagers and families,
and Sony’s hardware, among consumers in the 20–30 age group.
To control this difference in user demographics, a hardware dummy variable(Nintendo 64 game or not, Sega Saturn game or not, Dreamcast game or not, PS2 gameor not, and PlayStation dummy is excluded) are set as a control variable.
(v) Technology-driven game dummy
Ikuine (2003) indicated that some video games need an advanced program and aricher interface; he called them technology-driven games. In these video games, exploitation
is important.
Following Ikuine (2003), the technology-driven game dummy variable (= 1 if thegame’s genre is action, fighting, shoot 'em up, racing, sport, or board, and 0 otherwise) is
set as a control variable.
9Takeaki Wada

4.4. Multiple regression analysis and results
Table 1 shows the correlation between independent variables.
The results for models 1, 2, and 4 show that the independent variables (1) number ofyears since market was established, (2) number of years since the firm entered theindustry, and (4) number of years since hardware released are positively correlated withp. These results support the assumption that the more the firms accumulate know-how,
the more they exploit it.
Table 1. Correlation between independent variables
Independent
Variable (1)Independent
Variable (2)Independent
Variable (3)Independent Variable (1)1Independent Variable (2)0.2641

1

Independent Variable (3)0.23370.4937

1

Independent Variable (4)0.2114-0.0033-0.1419

Independent
Variable (4)1Table 2. Results by multiple regression
model 1model 2

number of data

model 3model 4model 5

434

R2

0.491

0.498

0.459

0.502

0.548

adjusted R2

0.481

0.488

0.447

0.491

0.535

-0.434***(-3.741)0.036***(5.302)0.116***(3.807)0.158***(5.137)-0.049(-1.084)1.309***(3.187)-0.12***(-3.356)
0.009***(5.366)constant(1) number of years since
the market was established(2) number of years since
the firm entered the industry(3) cumulative number ofvideo games the firm
published(4) number of years since
the hardware released

0.009***(5.847)0.000(0.95)-0.000***(-3.065)0.040***
0.151***(6.11)(4.313)0.000***
0.000***
0.000***
0.000***
0.000***(i) price(8.078)(6.403)(7.606)(7.915)(5.761)0.074***
0.061***
0.077***
0.072***
0.058***(ii) series dummy(5.957)(4.767)(5.908)(5.863)(4.785)-0.168***-0.181***-0.183***-0.156***-0.130***(iii) popular edition dummy(-4.971)(-5.42)(-5.278)(-4.629)(-3.923)-0.069***-0.089***-0.063***0.011
0.215***(iv) Nintendo 64 dummy(-3.081)(-3.909)(-2.699)(0.431)(2.902)0.188***
0.163***
0.149***
0.192***
0.210***
Sega Saturn dummy(8.746)(7.983)(6.835)(9.032)(9.708)0.113***
0.142***
0.140***
0.269***
0.741***
Dreamcast dummy(4.969)(6.550)(5.764)(9.191)(5.248)-0.0400.012
0.022
0.194***
0.870***
PS2 dummy(-1.232)(0.408)(0.718)(4.765)(4.094)(v) technology-driven game
-0.049***-0.061***-0.057***-0.048***-0.046***dummy(-3.854)(-4.844)(-4.310)(-3.773)(-3.770)
Note: In each column, the first number is the regression coefficient and the second number is the t-value. ***:
1% significance, **: 5% significance10Exploitation reduces novelty

The result for model 3 shows that the independent variable (3) cumulative number ofvideo games firm published is not correlated with p, and it is contrary to theassumption. Some video game publishers outsource the development of video games
and only specialize in publishing. Thus, the number of video games published by thefirm may not represent the number of video games developed by it.
The series dummy variable is positively correlated to p in all the results across models,and it is consistent with Ikuine’s (2006) result. Moreover, independent variables are
significantly correlated with p even if the series dummy variable is controlled. It meansthat there is exploitation of know-how between video games that do not have sequels.
In the result for model 5, the independent variable (1) number of years since themarket was established is negatively correlated with p. From Table 1, we assume a weakmulti-collinearity between independent variables. In addition, a positive correlation
effect on then p of the independent variable (1) might be absorbed by the independent
variables (2) and (4). Leaving aside whether or not this assumption is true, independent
variables (2) and (4) still have a significant and positive effect on p and accumulation of
know-how have a negative effect on novelty.

5. Conclusion and Discussion
This paper proves that, gradually, firms accumulate know-how, exploitation of
know-how increases, and products become more exploitative but less novel in theJapanese video game market; this finding is based on an analysis of a product diffusion
model.
The Japanese video game market declined in 1997–2000, the period for which theanalysis was carried out (Figure 1). Ikuine (2006) attributed this decline to the
exploitation of know-how between game sequels; the results of this paper support thisfinding.
The analysis method proposed in this paper is based on a general discussion of
product diffusion. Therefore, to confirm the general applicability of this method and to
expand the findings, it is necessary to analyze another industry.

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12

ABAS: Annals of Business Administrative ScienceVol. 10 December 2011

CHIEF EDITOR

Junjiro Shintaku, the University of TokyoEDITORIAL BOARD

Makoto Abe, the University of Tokyo
Takahiro Fujimoto, the University of Tokyo
Makoto Kasuya, the University of Tokyo
Nobuo Takahashi, the University of Tokyo
Technical Editor: Maki Nishida, Global Business Research Center

ABAS: Annals of Business Administrative Science is published annually by Global
Business Research Center, Hongo, Bunkyo-ku, Tokyo, JAPAN. http://www.gbrc.jp