bang ju - wol , guest researchers of national institute...
TRANSCRIPT
PESY: Print ISSN 2231-1394, Online ISSN 2278-795X Vol. 4, No. 4 10.5958/2278-795X.2014.00003.4 October-December 2014
AN INTERNATIONAL COMPARATIVE STUDY ON THE EVALUATION
OF PROFESSIONAL BASEBALL TEAMS BETWEEN JAPAN AND KOREA;
BASED ON FAN SUPPORT INDEX (FSI)
LEE, SUNG-MIN, Graduate School of Health and Sports Science, Juntendo University.
YU, JEA-GU, Hanyang University
BANG JU-WOL, Guest Researchers of National Institute Fitness and Sports in
KANOYA
NOGAWA HARUO, Juntendo University
ABSTRACT
Baseball has been the most popular professional sports in Japan and Korea. In
order to make further and sustainable marketing strategies, the professional sports
leagues and teams need to be evaluated in finance and customer satisfaction by
qualitative as well as quantitative ways. This research was designed to find out the
validity of the Fan Support Index (FSI) by empirical comparison way between
Japan and Korea. The expected results of this study were to establish the
professional baseball team’s value evaluation method that focused on the fan
support.
The FSI formula used in this study was composed of the average number of
spectators(ANS)，the average number of spectators per winning game(ANSW),
average number of spectators per 10,000 population(ANSP) and ratio of the seat
11
occupancy(RSO). On the basis of the previous studies and experimental
projects，the FSI formula was designed as follows;
FSI = 40(ANS / max ANS) + 20(ANSW / max ANSW) + 20(ANSP / max ANSP) +
20(RSO / max RSO)
The main findings of this study were as follows. Regardless of the league and
nation, all FSI factors such as ANS, ANSW, ANSP and RSO had significant
relationships with each other. While all FSI factors had significant effect on the FSI
value, ANS had influenced on the FSI value most regardless of the league and
nation.
Due to the stadium capacity and the size of population, Japanese teams showed
higher scores than Korean counterparts in all FSI factors except for RSO. Due to
the lengthy popularity, Central League had the highest FSI, whereas Pacific League
showed higher FSI than Korean League.
Keyword: Fan Support Index， The Professional Baseball， Team value，
Index comparative， Japan-South Korea comparative study
INTRODUCTION
Professional sport is a main business of spectator sports among which
professional baseball has the most popularity in the both of Korea and Japan. It is
time to consider and construct marketing strategies to develop continuously sport
industry through professional baseball. The professional team value evaluation is
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one of ways to develop professional sport by comparision among teams(Kihl, A. L.,
Babiak, K., & Tainsky, S., 2014)..
The methods of evaluating teams have a tendency to examine teams in terms
of finance or quality. Thomas(1999) evaluated professional teams by team
performances and fan attendance in USA. In addition, looking into comparison
researches on evaluation of professional sport teams, most researches were
concentrated on brand value and fan satisfaction (Kim, 2001; Lee & Cho, 2003).
However, there have not been a wind range of researches on the brand value
comparing to reasearches on the media exposure and economic effect(Baade, R., &
Matheson, V., 2000). In terms of the brand value, recognized quality, which can be
considered fan support, should be clear. Considering the fact that a sport fan is a
customer, the fan support index gets more important when establishing management
strategies. However, related researches have not been conduected. Therefore, the
evaluation size and criterion for team valaue deserves to be built. The research was
designed to find the level of support by baseball fans which can lead the growth of
professional baseball in both Korea and Japan. In this research, Fan Support
Index(FSI) of each team was developed and comparision between Korean teams
and Japanese teams was conducted. In order to establish relative criteria, the
maximized fan support index was 100% indicating 1 because the evaluation was
measured relative ly based on the best team’s index as the standard. The rest teams
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were ranked according to the ratio of the higest socred team.
Researches for cross national comparision can be committed to the better
understanding of local and cultural differences including comprehensive
trends(Houlihan, 2007). However, researches on professional sport fans have been
insufficient. Therefore, this research was intended to increase the quality of both
Korean and Japanese baseball through the comparision of FSI. The research was
design to lead overall discussion on prosports league since the differences should
be understood in global market. The reason the baseball was selected among
professional sports to compare FSI of Korea and Japan was that it has the most
popularity in both nations. The expectation of the study was to find out the
information to increase FSI and it was meaningful that this study can be utilized as
the creteria on the value evaluation of probaseball league. The purpose of the study
was to make the differences of both national pro baseball leagues clear by the
comparision of FSI.
RESEARCH METHOD
Thomas(1999)’s formula for Fan Support Index calculation was used. Official
records; total spectators, the ratio of seat occupancy, population and winning game
were utilized and secondary information analysis was selected as the research
method. Those data would be base sources of FSI calculation. Theoretically,
interanational relative comparisions were conducted through the relative ratio that
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considered the quantitve differences of the national population, and economy size.
ANALYSIS METHOD
The subjects of this study were professional baseball teams in Japan and Korea.
The data from 2010 to 2012 recorded by NPB and KBO were collected. Only home
game records were selected such as the number of spectators, the stadiums’
capabilities, and the population in the based city, which were used for the FSI
calculation. Among 12 teams in NPB, the half number of teams belongs to Centeral
League(CL) and the rest belongs to Pacific League(PL). KBO, in 2010, ran one
league of which 8 teams consisted. Average home games were 72 in Japan and 67 in
Korea.
Extraction of Input-Output Factors
The following index was utilized in this research.Table 1 is representing the
basic data to extract FSI of Korean professional baseball teams.
Table 1 Statistical data of KBO
Kor. Season
Number of Home Game
Number of Winning
Home match Spectator
Average number of Spectators per winning game
Average number of Spectators
Satdium Capacity
Average number of Spectators per 10,000 Population
Ratio of seat occupancy
FSI
Kia Tigers
‘10
67 59 436,285
7,395 6,512
13,400
45.75 0.49 29.35
‘11
66 70 592,653
8,466 8,980
13,400
63.08 0.67 40.87
15
‘12 67 62 502,01
6 8,097 7,493
13,400 52.64 0.56 33.2
5
LG Twins
‘10
67 57 1,010,078
17,721
15,076
30,500
14.41 0.49 37.56
‘11
66 59 1,191,715
20,199
18,056
30,500
17.26 0.59 44.29
‘12
67 57 1,259,480
22,096
18,798
30,500
17.96 0.62 45.35
SK Wyverns
‘10
66 84 983,886
11,713
14,907
27,800
55.01 0.54 42.43
‘11
67 71 998,660
14,066
14,905
27,800
55.00 0.54 44.47
‘12
66 71 1,069,929
15,069
16,211
27,800
59.82 0.58 46.73
Nexon Herose
‘10
67 52 399,496
7,683 5,963
14,000
5.70 0.43 20.50
‘11 66 51
441,427 8,655
6,688
14,000 6.39 0.48
23.08
‘12 67 61
599,381 9,826
8,946
14,000 8.55 0.64
29.14
Doosan Bears
‘10 66 73
1,070,673
14,667
16,222
30,500 15.50 0.53
38.13
‘11 67 61
1,253,753
20,553
18,713
30,500 17.88 0.61
45.74
‘12 66 68
1,291,715
18,996
19,571
30,500 18.70 0.64
45.51
Lotte Giants
‘10 66 69
1,175,665
17,039
17,813
28,500 49.27 0.63
49.01
‘11 67 72
1,358,322
18,866
20,273
28,500 56.08 0.71
56.30
‘12 66 65
1,368,995
21,061
20,742
28,500 57.38 0.73
56.61
Samsung Lions
‘10
66 79 455,246
5,763 6,898
10,000
27.45 0.69 30.32
‘11
67 79 508,645
6,439 7,592
10,000
30.21 0.76 34.33
‘12
66 80 544,859
6,811 8,255
10,000
32.86 0.83 35.88
Hanwha Eagles
‘10
67 49 397,297
8,108 5,930
10,500
39.86 0.56 29.93
‘11
66 59 464,871
7,879 7,044
10,500
47.34 0.67 35.65
‘12
67 53 519,794
9,807 7,758
10,500
52.14 0.74 38.24
Average
66.50
65.04
828,952
12,791
12,473
20,650
35.00 0.61 38.86
Table 2 is representing the basic data to extract FSI of Japanese professional
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baseball teams in CL and PL separately.
Table 2 Statistical data of NPB
Jpn Season
Number of Home Game
Number of Winning
Home match Spectator
Average number of Spectators per winning game
Average number of Spectators
Satdium Capacity
Average number of Spectators per 10,000 Population
Ratio of seat occupancy
FSI
SoftBank Hawks (PL)
‘10 72 76 2,164,430
28,479 30,062 38,561
60.12 0.78 72.16
‘11 72 88 2,293,899 26,067 31,860
38,561 63.72 0.83
75.20
‘12 72 53 2,447,501 46,179 33,993
38,561 67.99 0.88
85.88
Nippon- Ham Fighters (PL)
‘10 72 74 1,945,944
26,297 27,027 53,796
47.73 0.50 59.55
‘11 72 72 1,990,338 27,644 27,644
53,796 48.81 0.51
61.38
‘12 72 48 1,858,524
38,719 25,813 53,796
45.58 0.48 61.35
Seibu Lions(PL)
‘10 72 78 1,591,303
20,401 22,101 33,921
31.78 0.65 52.37
‘11 72 68 1,591,651 23,407 22,106
33,921 31.79 0.65
53.98
‘12 72 53 1,526,028
28,793 21,195 33,921
30.48 0.62 53.07
Orix Buffaloes (PL)
‘10 72 69 1,443,559 20,921 20,049
36,627 23.19 0.55
46.76
‘11 72 69 1,400,961
20,304 19,458 36,627
22.51 0.53 45.31
‘12 72 46 1,330,676 28,928 18,482
36,627 21.38 0.50
46.03
Rakut ‘10 72 62 1,141, 18,414 15,856 23, 67.47 0.69 52.
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en Golden Eagles (PL)
640 026 53
‘11 72 66 1,168,188
17,700 16,225 23,026
69.04 0.70 54.12
‘12 72 60 1,177,793 19,630 16,358
23,026 69.61 0.71
53.51
Chiba Lotte Marines (PL)
‘10 72 75 1,546,105
20,615 21,474 30,082
35.97 0.71 54.06
‘11 72 54 1,332,815 24,682 18,511
30,082 31.01 0.62
49.95
‘12 72 56 1,239,168
22,128 17,211 30,082
28.83 0.57 44.78
Chunichi Dragons (CL)
‘10 72 79 2,193,124 27,761 30,460
40,500 43.51 0.75
68.59
‘11 72 75 2,143,963
28,586 29,777 40,500
42.54 0.74 67.96
‘12 72 47 2,080,530 44,267 28,896
40,500 41.28 0.71
71.28
Yakult Swallows (CL)
‘10 72 72 1,332,928
18,513 18,513 45,000
15.43 0.41 39.54
‘11 72 70 1,348,259 19,261 18,726
45,000 15.61 0.42
40.04
‘12 72 41 1,322,678
32,260 18,371 45,000
15.31 0.41 44.03
Yomiuri Giants (CL)
‘10 72 79 2,966,626
37,552 41,203 55,000
34.35 0.75 82.28
‘11 72 71 2,716,974 38,267 37,736
55,000 31.46 0.69
76.81
‘12 72 64 2,903,947
45,374 40,333 55,000
33.62 0.73 82.14
Hanshin Tigers (CL)
‘10 72 78 3,005,633 38,534 41,745
47,757 75.07 0.87
93.39
‘11 72 68 2,898,432
42,624 40,256 47,757
72.39 0.84 92.93
‘12 72 58 2,727,790 47,031 37,886
47,757 68.13 0.79
88.12
Hiroshima Toyo Carp (CL)
‘10 72 58 1,600,093
27,588 22,224 33,000
77.41 0.67 64.80
‘11 72 60 1,582,524 26,375 21,980
33,000 76.56 0.67
64.21
‘12 72 47 1,589,658
33,823 22,079 33,000
76.90 0.67 65.62
Yokohama DeNA BayStars
‘10 72 48 1,209,618 25,200 16,800
30,000 112.92 0.56
62.05
‘11 72 47 1,102,192
23,451 15,308 30,000
102.89 0.51 57.70
‘12 72 44 1,165, 26,498 16,194 30, 108.84 0.54 59.
18
(CL) 933 000 60 Average
72.00 63.14 1807817
28952 25109 38939
51.15 0.64 61.35
Table 1 and Table 2 are containing the category which can be compared relatively
against each other.
FSI and Calculation
FSI is the elaborate index based on the market factors of Forbes model. FSI is a
tool to understand how much fans support a team which is the key of the market
factors when the team value is evaluataed. In addition to the market factors, the
attendaces, the ratio of seat occupancy and the population in the based city were
considered to calculate FSI.
Kim&Kim(2003), Kim&Seo(2005), and Lee(2012) studied the brand property
factors of professional sport teams. Researches on the market value of professional
sports teams were performed(Kim, 2006; Shin, 2012). In particular, the FSI model
used in this research has been developed continuously by Kim2)13)14)(2001, 2003,
2004). In order to extract FSI, 40% weight was granted on the average number of
spectators and the rest 60% weight went to the average number of spectators per
one winning game，the average number of spectators per population of 10,000 and
the ratio of seat occupancy rate by 20% each. The FSI analysis was orginated from
the Thomas’ research on the effect of MLB to the minor league1). Thomas
19
recognized that the different proportioned weight should be granted on facilities,
service, population of the minor league to calculate FSI comparing to MLB.
According to researches by Kim(2001, 2003, 2004)2)13)14) and Lee(2003)3, Korean
professional baseball in 2000 showed the similar factors with the minor league in
1990 in terms of fan attenances at stadiums. The examination of FSI effectiveness
subjecting to MLB and KBO was conducted for 4-year. As a result, it was found out
that each factor should have different weights.
This research was designed to understand the comparision between NPB and
KBO by FSI. FSI was originated by Thomas(1999) to evaluate the minor league
team value. It was adjusted to the Korean professional sport by
Kim(2001，2002，2004). The FSI formula used in this study was composed of the
average number of spectators (ANS)， the average number of spectators per
winning game(ANSW), average number of spectators per 10,000 population(ANSP)
and ratio of the seat occupancy(RSO). On the basis of the previous studies and
experimental projects，the FSI formula was designed as follows;
FSI = 40(ANS / max ANS) + 20(ANSW / max ANSW) + 20(ANSP / max ANSP) +
20(RSO / max RSO)
Explanation of FSI
FSI utilized four differnet factors. The factors were the result of ratio calculation
based on the related game records. The ratio calculation could be understood as not
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only a absolute standard, but also relative priorities.
1) Average number of spectators(ANS): Total number of home game spectators in
a season was divided by total number of home games.
2) Average number of spectators per 10,000 population(ANSP): Average number
of spectators was divided by population of cities that home games were held. Then,
the value is multiplied by 10,000.
3) Ratio of the Seat Occupancy(RSO): Average number of spectators was divided
by the total number of seats in stadiums.
The average number of spectators, the population of based city, and the ratio of
the seat occupancy were weighted in different portions considering the purpose of
index and practicality. FSI is one of methods to evaluate the value of professional
sport clubs and it is mainly decided by the market factor saying how many fans
teams have. Hence, 40% weights were placed on the average number of spectators
because the fan size had more impact on Korean baseball market than the team
economic value, broadcasting income and other related industries.
Max means pointed out the team rated the highest in each categories, rest of the
teams’ points were calculated based on the top score. The size of population was
limited into the based city population. In other words, although a fan outside of the
based city visited the stadium, he/she was counted as a home fan. In addition, the
average number of spectators per winning game(ANSW) meant the total number of
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home game spectators was divided by total number of home games. It meant when
the the number of winning games were less and the average number of spectator is
high, the index increased. Therfore, ANSW can be understood as a fan loyalty
because fans stay at the stadium evet at the losing games.
Analysis Method
The way of data processing was used EXCEL and SPSS version 18 for Windows.
The Data analyses were conducted by correlation analysis，multiple regression
analysis，t-test，and One-way ANOVA.
RESULT : Correlation of FSI Constituent Factors
The correlation analysis about FSI constituent factors was carried out based on
data collected in Korea and Japan.
Table 3. Pearson Correlations of FSI Constituent Factors in Korea
ANS ANSW ANSP RSO
ANS 1
ANSW .954**
(.000) 1
ANSP .063
(.770) .267* (.039)
1
RSO .066
(.758) .331** (.010)
.337** (.009)
1
Table 4. Pearson Correlations of FSI Constituent Factors in Japan
ANS ANSW ANSP RSO ANS 1
ANSW .790** (.000)
1
22
ANSP -0.023 (.894)
0.100 (.560)
1
RSO .662**
(.000) .469** (.004)
.316 (.061)
1
According to Pearson’s product moment correlation coefficient analysis, FSI, a
dependent variable, and FSI constituent factors had significant relationships.
Among them, ANSW had the highest significant relationsips with ANS, which
could support that ANS had the most weights in the FSI cacluation. However, there
was not significant relationship between ANS and ANSP. In terms of the
professional sport management, the based city population size is considered
important, but even such a big franchise like Yomiuri Giants and Yakurt Swallows
in which over 13millions reside had only about 15~30 spectators per 10,000
residents and did not show any relations with ANS, That is why ANSP should be
reconsidered whether it can be used as the variable or not.
It could be analyzed in two ways that ANS did not have any significant
relationship with ANS. In statistical way, there was a difference in a degree. ANS
generally approached to the franchises which had over 1 million residents. However,
ANSP was based on 0.01 million which was 1/100 degree comparing to ANS. Large
gaps between ANS degree and ANSP degree and less cases of clubs could be
reasons for insignificant relationship. Important thing is not the correlation among
constituent factors but the correlations between constituent factors and FSI.
Additionally, there were differences of the level of fan support and passion
23
depending on the based cities. Given that, it could be possible that there was no
significant relationship between ANS and ANSP. Even though ANS could be
involved with the size of population in the based cities, the level of passion became
more important considering the number of spectators per 10,000 population. In the
case of Saint Cardinals, the number of seasonal spectators was higher than
population in the based city. In this case, those who were outside of town visited to
the stadium. Therefore, ANSP was not calculated in proportion to the size of
population because of the fan support level.
Regressional Effect of FSI Constituent Factors to FSI
Multiple regression analysis was conducted with FSI as a dependant variable and
ANS, ANSW, ANSP and RSO as independent variables. The analysis was done
apart from the different wetight on each category. FSI, the dependant variable, was
calculated with weight added. Therefore, the result that ANS had high regression
coefficients proved the calculation formula was acceptable.
Table 5. Regression Analysis of FSI Constituent Facots and FSI in Korea
Variables B SE B F P
ANS .001 .000 .601 10.666
.000
ANSW .000 .000 .245 4.361 .000
ANSP .186 .008 .387 22.702
.000
RSO 22.019 1.49 .236 14.782
.000
F=1122.756, sig=.000, adg R2=.995
24
Table 6. Regression Analysis of FSI Constituent Facots and FSI in Japan
Variables B SE B F P
ANS .001 .000 .628 50.025 .000
ANSW .000 .000 .184 18.22 .000
ANSP .195 .004 .346 50.415
.000
RSO 21.634 1.034 .189 20.915
.000
F=12292.144, sig=.000, adg R2=.999
It was found out that FSI, the dependant variable, was affected by all
independent variables. ANS was the most effective variable in both Jpaan and
Korea, which supported the theory of Kim(2001, 2003, 2004) and Lee(2003) that
ANS had more weights than other variables in terms of the FSI calculation.
Average Difference of FSI and Constituent Factors in Japan and Korea
In order to compare Japanese teams and Korean teams, comparisions of FSI
and constituent factors were conducted.
ANS, ANSW, ANSP and FSI represented statical differences, but there was
no statical difference on RSO. Japanese FSI was higher than Korean one which
could be explained in that the attraction of fan attendances in Japan was stronger
than in Korea. The result that RSO did not have any statical difference could mean
the popularity of baseball in Korea was as high as in Japan. Moreover, comparing to
MLB, ROS did not show statical difference. When 65% of seat occupancy can be
25
considered as a standard in Japan where 5 dome stadiums have existed, 61% of seat
occupancy in Korea could not be considered absolutely low because there has not
been a dome stadium that is not affected by external factors such as wether
conditions.
Table 7. Result of T-test among FSI Constituent Factors and FSI
Variables
Nation N Avg. SE t Sig
ANS Japan 36 25108.67 8350.67
7.072 .000 Korea 24 12472.75 5488.73
ANSW Japan 36 28952.03 8750.56
8.767 .000 Korea 24 12790.63 5523.34
ANSP Japan 36 51.15 26.44
2.680 .010 Korea 24 35.26 19.42
RSO Japan 36 .65 .13
1.042 .327 Korea 24 .61 .10
FSI Japan 36 62.31 14.90
7.499 .000 Korea 24 38.87 9.31
FSI in Japan and Korea showed the statical difference for the capacity of
stadiums Japanese stadiums can accommodate 2 times more fans than stadiums in
Korea. ANSP was 35 in Korea and 51 in Japan according to Table 7. It would be
said that in order for Korean teams to increase FSI as much as Japanes FSI, the
stadium capacity and spectators should be extended.
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FSI Comparision between Japanese League and Korean League
One-way ANOVA was performed to understand the differences among Central
League(CL), Pacific League(PL) and Korea Baseball Organization(KBO) including
the cross national comparision between Japan and Korea. As a result of analysis,
ANS, ANSW, ANSP and FSI had statical diffences among leagues. It was found out
that there was not the statical difference on ANS between PL and CL by Scheff’s
test, but there was the low statical difference with KBO. When it comes to ANSW,
CL represented the highest index and PL and KBO followed sequencely. There was
no statical difference on ANSP among KBO, CL and PL. FSI did not have statical
differences between CL and PL, but the statical difference was shown between
Japanese teams and Korean teams. The interesting fact was that CL was higher than
PL in terms of ANSW which meant the attendances were strongly affected by the
win in CL. In other words, it would be better for teams in CL to build marketing
strategies focusing on positive results of matches.
Table 8. Result of One-way Anova between Japanese and Korean Leagues
N Avg. SE F sig Scheffe
ANS
PL 18 22523.61 5606.74 25.020
.000 Kor<PL, CL
CL 18 27693.72 9898.60
Kor 24 12472.75 5488.73
Total 60 20054.30 9595.88
ANSW
PL 18 25517.11 7272.58 40.345
.000 Kor<PL<CL
CL 18 32386.94 8931.58 Kor 24 12790.63 5523.34 Total 60 22487.47 11002.96
27
ANSP
PL 18 44.28 17.90 4.833
.012 Kor<CL CL 18 58.01 31.91 Kor 24 35.26 19.42 Total 60 44.79 24.96
RSO
PL 18 .64 .12 .542 .585 -
CL 18 .65 .14 Kor 24 .61 .10 Total 60 .63 .12
FSI
PL 18 56.78 11.10 29.467
.000 Kor<PL, CL
CL 18 67.8383 16.40 Kor 24 38.8612 9.31 Total 60 52.9292 17.31
DISCUSSION
The Validity of FSI
Since the index analysis was utilized in this research, the vailidy and concept of
index analysis should be examined. The index is the representative data saying
specific contents and trends. In addition, the index is objective and reasonable in
comparisions under the same condition. It can be established from reasonable
formaula using various information and data(Lee, 1998). The more glogalization
has been expanded all over the world, the more indexes have been developed
because the index is one of the most effective way to reflect the global trend in
terms of the cross national comparisions.
「Index standardization」, 「Weight calculation」 and 「Index development」 are
required for the index comparision over nations(Cho, 2001). It could be said that
FSI met 「Index standardization」 because FSI was extracted from official records
that happened in both leagues. The official method of 「Weight calculation」 has
28
not been established yet. However, it has usually been calculated based on current
trends, expert opinions and random surveys. Setting weight reflects the entire
rate(Song, 2010). In this study, the examination of previous studies and the
investigation were used to set how much weight should be distributed on each
factor.
Lastly, the multiple regression analysis was utilized to verify statistically the
feasibility and index development. According to Kim(2002), the index researches
have been studied in the field of American sport. In particular, index researches on
fan supports have been performed(Depken, 2000). In Korea, the cost index was
introduced into spectator sport(Kim, 2001). FSI has been generally extended to the
various sports.
In general, sport consumer behavior was decided by sport fan need, information
seeking, experience reevaluation, a purchase decision, and a personal feature.
Additionally, social culture, a stadium location, and a economic factor can have
effects on consumer behavior(Fujimoto, 1996). However, this study was not about
the sport spectator behavior but about the cross national comparisions based on
sport spectators’ data. In this study, FSI was compared between Japan teams and
Korean teams under the factors of the size of stadiums, the population, the team
performance. Therefore, it could be said that the same protocol was proceed with in
terms of contents and theories dealt with in the previous studies.
29
As a result, the comparisions were found out between Japanese professional
baseball teams and Korean teams through the average number of spectators(ANS),
the average number of spectators per winning game(ANSW) and the average
number of specators per 10,000 population except for the ratio of seat occupancy,
which could be applied for the comparisions within Japanes leagues. According to
this study, ANSW was the strongest factors on FSI, which meant the team
performance was emphasized. The average attendances were strongly related to the
capacity of stadiums.
The remarkable differences between professional baseball leagues in Japan and
Korea were the average number of spectators per 10,000 population and stadium
capacity. Therefore, in order to raise FSI in Korea, the improvement of sport
facilities and the expectation of winning can be utilized effectively. Buildig
marketing strategies is difficult through the research method in this study but
suggesting a target figure is acceptible by the index comparision.
The Value Evaluation of Professional Sport Teams
This study directed the attention to the fan supports as one of market factors
Gladden(1998) suggested. The result of this study supported theories insisted in the
previsous researches done by Kim and Lee et al. It also proved that relative
comparision and quantitive comparision among professional sport teams could be
performed through FSI. However, there is a theoretical limitation on the fact that
30
the professional sport team value evaluation was conducted through only sports
spectators' behavior as one of the market factors.
Since most professional baseball teams in Japan and Korea are owned by
conglomerates, the team brand image is also important to the parent companies. It
would be more clear cosiderting that Masahiro Tanaka who transferred to New York
Yankees with the large amount fee form Rakuten Golden Eagles. When the
professional sporte team value is evaluated, combining the brand image and royalty
suggested by Hirose, Ishizaka, Manoyoshi and Lee with FSI would be more
persuasive.
Cross National Integrated Research
This research was a cross national integrated research. It was meaningful to
understand the possibility for the criteria on integrated value evaluation targeting to
professional baseball teams in Japan and Korea which have differences on social
culture and industrial features against each other. Therefore, the research
approached with the acknowledgment of differences between two nations from the
start. However, in this study, fan support was considered as the property belonging
to all teams. In addition, it was proved that the integrated discussion on fan support
was valid statistically. According to the result, comparisions on team value through
fan support reflected reality stastically, which was the similar with the previous
studies. However, the integrated discussion concentrating on fan support requires
31
explanatory background.
Sport fans are reinforced through such a process; the recognition of needs,
seeking information, option evaluation, spectating decision, spectating experience,
and experience reevaluation(Funk, C. D., 2008). Spectating experience makes sport
spectating better. Subsequently, visiting stadiums and in seeking information would
be more repeated. Moreover, the opportunities of spectating and communication
with friends would be arisen. Spectator’s decision making are influenced by
individual and environmental factors(Schiffman , L.G. , & Kanuk , L.L., 2001 ).
The individual factors consist of physical factor, learning, recognition, motivation,
attitude, individual and family life style and self concept. On the other hand, the
environmental factors consist of strangers, natural and reagional features, sport
marketing activities, cultural rule and value, class and race, and sport opportunities.
Spectating decisions would be different in any time when the individual is
influenced by strangers, cultural features or even stadium location(Trail, G.T., &
James, J. D., 2001). However, the customer analysis on sport spectating behavior is
usually processed with the categorization of internal and external factors. Therefore,
the comparisions on fan support with the acknowledgment of differences is a
normal research type. The inductive research methods premise all factors as the
same that cannot be under control and focus on difference factors. In this research,
various differences were set aside and only constituent variables were considered to
32
understand FSI. In other words, this research had the same theoretical system as the
previous studies because the comparisions based on key variables such as the
stadium size, the population in the based city, and team performance. Therefore, the
validity of this study could be found out in terms of the research results and
theoretical background.
CONCLUSION
This study was to calculate FSI of professional baseball leagues in Japan and
Korea based on the secondary information analysis. The FSI formula was
established based on official professional baseball match records in Japan and
Korea. Results were as follow.
(1) Regardless of nations and leauges, FSI constituent factors had a close
correlation among each other. In particular, the average number of spectators per
winning game had a high correlation with the average number of spectators. This
meant the positive result of matches is the most important factor to attract fans to
stadiums.
(2) It was found out that FSI constituent factors had significant relationships with
FSI that was influenced by the attendances in Japan and Korea. In the view of the
cross national comparisions, professional baseball teams in Japanese league
represented higher FSI than in Korea. It could be understood as the size of stadiums
and the average number of spectators had a strong influence on FSI.
33
As for FSI, Central Leagues’ FSI represented the highest, followed by Pacific
League and KBO which meant FSI was in accordance with the baseball popularity.
It was said that the value evaluation of professional sports teams was possible
through the index extracted based on the quantitive data which was adopted in this
study. In other words, FSI can be utilized to evaluate the value of professional
sports teams and leagues. However, FSI constituent factors and weight shoud be
reconsidered in the next research.
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