Spillover effect of sport team performance on the value of corporate sponsors and affiliated firms.
Sung, Hojun ; Nam, Changi ; Kim, Minki 等
Introduction
Sport sponsorship has increased the value of sponsoring firms by
becoming a key factor in the communication mix with customers and
investors that can enhance corporate image and stimulate the sales of
products and services (Javalgi, Traylor, Gross, & Lampman, 1994).
Several studies show that sport sponsorship can lead to positive stock
returns, which is consistent with modern corporate finance's
ultimate goal of maximizing firm value (Chen & Chen, 2012; Cornwell,
Pruitt, & Clark, 2005; Miyazaki & Morgan, 2001; Pruitt,
Cornwell, & Clark, 2004; Reiser, Breuer, & Wicker, 2012). In
line with such findings, spending on sport sponsorships has also
increased globally over the last several decades in conjunction with the
thriving sport industry, as indicated by the popularity of major
sporting events such as the World Series and Super Bowl. (1) It is also
worth noting that compared to sport sponsorship announcements, team
performance has a more direct impact on the value of corporate sponsors
(Ashton, Gerrard, & Hudson, 2003; Chen & Chen, 2012; Dobson
& Goddard, 2011). To the best of our knowledge, however, none of the
empirical studies has attempted to investigate whether the team
performance is spilled over to affiliated firms of sponsoring firm
(i.e., subsidiaries), and furthermore, what causes the heterogeneity in
the impacts of team performance on affiliates of sponsoring firms'
value. In this study, we therefore attempt to answer these questions by
utilizing several unique features of the South Korean professional
baseball league, known as the Korean Baseball Organization (KBO).
First, unlike in Major League Baseball (MLB) in the United States,
in which franchise teams are privately owned, publicly listed companies
own KBO teams. More specifically, large Korean family-owned
conglomerates called chaebols (2) support (own and sponsor) KBO
teams/clubs (3) as affiliates, with chaebols being composed of
interconnected affiliated companies through cross-holdings under
pyramidal structures (Almeida, Park, Subrahmanyam, & Wolfenzon,
2011). The chaebols that sponsor the teams advertise at the company
level by having the chaebol's name appear on the team uniform or
through sponsorship marketing activities (Reiser et al., 2012). If a
chaebol-sponsored team wins a postseason game, there could be a
transmission of value from sporting success to financial success in the
sense that good performance in sports could create value that affects
other chaebol-affiliated firms, leading to a positive effect on stock
returns (Meenaghan, 1991; Pruitt et al., 2004).
Second, the KBO offers an appropriate "natural
experiment" in which to analyze the determinants of heterogeneous
spillover effects across affiliated firms. Since some affiliated
firms' names do not contain the chaebol's name, we are able to
test whether team performance will have different effects on the values
of affiliated firms. Lastly, the postseason bracket structure of the KBO
culminates in its championship series (Korean Series), in contrast to
the common structure of professional sports leagues in other countries,
due to the KBO's single-league system. The KBO playoff system works
very differently from any other tournament bracket. The #1 seed is not
paired with an opponent team and automatically advances to the Korean
Series (championship); the #3 and #4 seeds play each other in the
quarterfinals; the winner of the quarterfinals plays the #2 seed in the
semifinals, and the winner of the semifinals plays the #1 seed in the
championship series. This structure results from the fact that the
league only has nine teams. Such a structure provides a unique setting
to examine the synergistic effects between the franchise baseball teams
and their affiliates with regard to how the parent group's brand
image is used to maximize company value during the different stages of
the postseason.
Using this unique system for sponsoring KBO teams and the
KBO's postseason structure, our research questions are: (1) How
does a team's outcome (victory or defeat) in a postseason series
affect (positively or negatively) the stock prices of the companies
affiliated with its sponsoring chaebol? (2) How is the appearance of the
sponsoring chaebol's name associated with variations in stock
market reactions for the companies affiliated with a sponsored team?
Answering these questions will extend existing research on the
relationship between sport finance, such as sport sponsorship, and the
value of corporate sponsors in the international context. Perhaps the
closest research to this study is that of Chen and Chen (2012), which
showed that a parent company owning a franchise in the Nippon
Professional Baseball League (NPB) could improve its value by qualifying
for and winning the championship. Although they utilized the similar
organizational structures of Japan, their focus was not on the impact of
postseason performance on the subsidiaries nor the heterogeneity across
affiliated subsidiaries but rather the parent company and the industry
effect of the parent company.
To test the research questions, we investigate abnormal returns
(AR) via an event study approach with a market model, which follows
Mikkelson and Partch (1986), to identify the impact of companies'
sport sponsorships on their market values. In particular, we normalize
the cumulative abnormal returns (CARs) using standard deviations in
market models following Aybar and Ficici (2009). Note that the use of
standardized cumulative abnormal returns (SCARs) controls for
cross-sectional event-induced variation among chaebol-affiliated firms
(Boehmer et al., 1991). The data are collected on the stock prices of
the listed companies affiliated with the KBO teams (N = 724) for the
event study.
Literature Review and Hypothesis Development
Sport Sponsorship and Firm Performance
Previous studies have examined the correlation between corporate
sport sponsors and the value of the sponsoring firm, constituting the
different streams of the literature. One stream shows that sport
sponsorship has a positive effect on the value of sponsoring companies
because of its positive effect on corporate equity, brand value
(Cornwell, Roy, & Steinard, 2001), and media exposure (Tripodi,
2001). On the other hand, the other stream shows a negative or
insignificant relationship between corporate sport sponsorship and the
sponsoring firm's value in the cases of Olympic sponsorship (Farrel
& Frame, 1997) or professional tennis and golf tournaments (Clarket
et al., 2009).
The first mentioned literature stream explains the positive impact
of sponsorship on firm value and covers global sports competitions, auto
racing, annual tournaments, and professional sports leagues. Among
global sporting events, the Olympic Games are by far the leading (and
most commercialized) sports competition, and show positive announcement
returns around sponsorship announcements due to the synergistic effects
between sport sponsorship and business goals (Agrawal & Kamakura,
1995; Clark et al., 2009; Miyszaki & Morgan, 2001; Samitas et al.,
2008). Similarly, Pruitt et al. (2004) analyze the impact of the
National Association for Stock Car Auto Racing's (NASCAR)
sponsorship announcements on the stock prices of sponsors. Cornwell et
al. (2005) also show the positive reactions of stock markets around the
sponsorship announcements of "official products" for major
sports leagues such as the National Football League (NFL), Major League
Baseball (MLB), the National Hockey League (NHL), the National
Basketball Association (NBA), and the Professional Golfers'
Association (PGA). Their findings are meaningful since theirs is the
first empirical test of the impact of the sponsorship of official major
league sports products on stock returns in the US. They contributed to
the literature by showing increased sales and a positive influence on
stock prices.
In addition, Ashton et al. (2003) identify the stock market
reactions to the outcomes of the matches of the English national
football team, finding a positive return after a win and a negative
return after a loss. Similarly, Dobson and Goddard (2011) find a
positive correlation between the match results of domestic football
teams, such as England's Manchester United and Italy's
Juventus (both publicly traded companies), and stock market performance.
In line with this finding, Chen and Chen (2012) examine the effects of
sports sponsorship on the stock prices of corporate sponsors during the
Nippon Professional Baseball (NPB) championship. The results of their
event study indicate that corporate sponsors engaged in the retail
industry show significantly positive CARs, especially if their sponsored
team advances or wins the championship. Thus, in general, stock market
investors expect the winning or losing sporting events, including
postseason performance, to have a significant effect on the value of
corporate sponsors.
However, the second stream of the literature shows that the
performance of a sponsored team has a negative or insignificant impact
on the stock returns of the sponsoring firm. For example, Farrel and
Frame (1997) show the negative abnormal returns around the announcement
of Olympic sponsorship using 1996 Atlanta Summer Olympic Games, and
suggest utilizing Olympic sponsorships in the marketing communications
mix may not be value enhancing. Clark et al. (2009), analyzing 114 title
sponsorship announcements of professional tennis and golf tournaments,
auto racing (NASCAR), and college bowl games, suggest that title
sponsorships are generally signed at market clearing prices except in
the case of NASCAR, which shows evidence of increases in share prices.
These studies use event study methodology to measure the
announcement effect of sponsorship announcements or sponsored team
performance in univariate settings. More specifically, CARs are
calculated using the market model approach based on various windows,
such as (0, +1), (-1, +1), or (-3, +3), to test whether the examined
event is a positive stock market event under a univariate setting. In
addition to the univariate analysis, multivariate analysis examines the
determinants of variations in CARs such as industry, sponsorship type,
sponsorship size, or brand level after controlling for other conditions
such as type of sport or region. For example, Reiser et al. (2012) show
that the impact of sponsorship announcements on firm value is positive
and significantly greater for brand-level sponsorship, smaller
companies, and national-level sponsorship reach.
In sum, the impact of corporate sport sponsors on the value of the
sponsoring firm is mixed and differs case by case, although the positive
cases outnumber negative ones. Therefore, this paper might have an
additional contribution by adding another empirical evidence to this
research area.
Hypothesis Development
KBO teams, whose owners are publicly listed, are different from MLB
franchise teams (which are privately owned businesses) in that every KBO
team is exclusively sponsored and managed by a chaebol-affiliated
company, which expects the team to increase the reputation of the
company's recognition and identity in the market. Although popular
sporting events can positively influence a corporate sponsor's
stock price, only a few studies, including Dobson and Goddard (2011) and
Chen and Chen (2012), have investigated whether the performance of
sponsored teams (winning/losing) yields economic benefits to corporate
sponsors. In the case of the NPB, winning a championship shows positive
economic benefits to the team's parent company (Chen & Chen,
2012). Thus, this study contributes to this stream of the literature by
examining the effect of team performance in the KBO postseason on
corporate sponsors. More specifically, among the important aspects of
the chaebols' interconnected linkages, propping and tunneling are
the main activities that characterize the chaebols (Bae, Cheon, &
Kang, 2008; Billet & Mauer, 2003; Dow & McGuire, 2009; Peng,
Wei, & Yang, 2011). According to Bae et al. (2008), propping
involves reallocating capital within a business group from higher-level
companies to lower-level companies to save a financially troubled
affiliate. In addition, propping within a business group means that an
affiliate's performance depends on the other affiliates in the
group. Tunneling within a business group involves siphoning or drawing
off affiliated companies (intragroup loans) to increase wealth or
financially support weaker companies (Gopalan, Nanda, & Seru, 2007;
Johnson, La Porta, Lopez-de-Silanes, & Shleifer, 2000). The reason
chaebols use tunneling is to prevent losses to the controlling
shareholder and consequent negative spillovers to the group. For
example, Bae et al. (2008) find that announcements of increased earnings
among South Korea's chaebols positively impact each individual
non-announcing affiliate. Han, Jo, Kwon, and Lee (2015) also show that a
change in the financial condition of a chaebol-affiliated company
significantly affects the value of other affiliates due to their common
ownership and the tight linkages among chaebol-affiliated companies.
Consequently, winning or losing in the postseason is expected to have a
spillover effect (positive or negative) on company valuation. Therefore,
we form the first research hypothesis as follows.
H1: The team that wins the KBO postseason series creates a positive
spillover effect on the stock prices of all the companies affiliated
with the sponsoring chaebol.
Therefore, the KBO offers the advantage of providing company-level
promotion, which means that the chaebol's name appears on the team
uniform, helmet, and other merchandise. More importantly, it offers the
advantage of an advertising effect that may spill over to several
brands, which can be affiliated companies in the case of the KBO.
According to the concept of image development in marketing (Meenaghan,
1999), every consumer is persuaded on two levels: intrinsic value
(consumer beliefs regarding satisfaction with product function) and
extrinsic value (added value such as brand imagery). With such image
development, it is likely that affiliated companies benefit from sharing
the business group's name, and this might cause higher stock price
reactions due to the two levels of value. According to Meenaghan (1991),
it is likely that company-level promotion allows firms to benefit from
the advertising spillover effects that result from using the
chaebol's name. Therefore, we formulate the second hypothesis as
follows.
H2: Stock price reactions to wins of the KBO postseason series for
affiliated companies are stronger if the name of the affiliated company
includes the sponsoring chaebol's name.
Data Description and Research Method
Data
This study identifies the annual list of chaebols and
chaebol-affiliated companies by searching the Korea Fair Trade
Commission (KFTC) website; this database provides the list of chaebols
every year. We also conduct manual searches regarding every final game
of every playoff series during the sample period from 2001 to 2013.
During this 13-season period, the publicly listed companies owning seven
playoff teams, out of a total of nine teams, (4) are used as our sample,
as shown in Table 1. The KBO's single-league system, which provides
the #1 seed the advantage of direct entry into the Korean Series
(championship), has a unique postseason structure because the league is
composed of only nine teams. For each postseason series, we used the
last day as the event date that determines the advancing winner. For
example, the postseason entries of 2011 were the Samsung Lions (#1
seed), Lotte Giants (#2 seed), SK Wyverns (#3 seed), and Kia Tigers (#4
seed). On October 12, 2011, the SK Wyverns won the quarterfinal against
the Kia Tigers by winning three games of the best-of-five series, and on
October 23, 2011, the SK Wyverns won the semifinal against the Lotte
Giants by winning three games of the best-of-five series. Finally, on
October 31, 2011, the Samsung Lions won the Korean Series against the SK
Wyverns by winning four games of the best-of-seven series. Therefore,
this study used October 12, 23, and 31 as event dates for the 2011
season. Consequently, the event windows for each event date do not
overlap because the dates of each event are at least eight days apart.
The data comprise the daily stock prices of all of
chaebol-affiliated companies from the Korea Composite Stock Price Index
(KOSPI) and the Korea Securities Dealers Automated Quotations (KOSDAQ).
The financial statement and stock price data come from Dataguide
(FnGuide), (5) the major financial market data provider in Korea.
Although there are only two teams in the championship series, there are
usually more than two sample companies for the calculation of CARs
because chaebols have between 4-18 affiliates. Table 1 shows the list of
affiliated companies corresponding to each chaebol group, categorized by
industry (6) and listed markets. For example, the Samsung group has a
total of 15 listed affiliated companies; of these, three firms do not
have Samsung in their names, while the remaining 12 firms do.
To isolate the confounding effect of other events (i.e., earnings
announcements) that may contaminate the announcement effect of team
performance on firm value, following MacKinlay (1997) and McWilliams and
Siegel (1997), we searched all news related to affiliated firms for
seven days (t = -3, +3) around the event date. All the news were found
using the Korea Integrated News Database System, a media portal service
website organized by the Korea Press Foundation (www.kinds.or.kr) that
covers more than 20 South Korean media-related news providers. Through
this decontamination process, we deleted 32 observations from the total
sample because of potential confounding effects related to items such as
earnings, mergers, stock repurchases, or asset sales announcements;
finally, 724 observations are used in the analysis.
Methodology
In the finance literature, the effects of important company
announcements, such as those related to mergers and acquisitions, stock
repurchases, and earnings, on company value are usually investigated
using the event study method (Bae et al., 2008; Han, Lee, & Song,
2014; Lee et al., 2008). This method enables us to assess whether
winning or losing a postseason series has a positive or negative effect
on shareholder wealth. This approach essentially measures whether an
event influences movements in the stock prices of affiliated companies.
The approach is based on the semi-strong form of the efficient market
hypothesis, which states that the current stock price fully and
instantaneously reflects all publicly available information. In many
cases, stock prices are trustworthy indicators of a company's value
because they change as soon as the market detects new material
information that tends to affect company earnings, such as a particular
corporate-sponsored team winning the championship (Agrawal &
Kamakura, 1995). Moreover, the fluctuation of the stock price after an
event relative to the prior stock price reflects the market's
unbiased estimate of the event's effect on the company's value
(Brown & Warner, 1985).
We focus on the studies that investigate the causes of the
heterogeneous impact of sponsorship on company value. The event study
method, (7) which is appropriate for evaluating the impact of
large-scale sporting events on corporate sponsors (Hill, Moore, &
Pruitt, 1991; Krueger & Kennedy, 1990), is utilized to examine the
CARs. Regarding the initial task of conducting an event study, the
events of interest are the last games of a KBO postseason series. In
most event studies, information leakage is a common concern because it
occurs when a small group of investors obtains information regarding a
relevant event prior to the official public release. Fortunately, there
is no leakage concern regarding KBO postseason game results because the
outcome is unknown until the game is over. Therefore, we used three
types of event windows for the calculation of CARs, including (t = 0,
+1), (t = 0, +2), and (t = 0, +3), focusing on the post-event date.
However, we may argue that stock market investors have expectations of
team performance before the actual event date, so we also examined two
different windows before the event date, including (t = -2, -1) and (t =
-3, -1).
It is important to compare the movement of individual stock prices
with that of the market as a whole. This relates to whether changes in
individual stock prices occur because of the market or result from
company-specific information. The difference between actual and expected
returns is captured by measuring abnormal returns (ARs), which represent
returns inconsistent with the pattern of change created by market
activity and the company's past performance (Miyazaki & Morgan,
2001). Thus, an AR can identify the portion of the return that is solely
attributable to the occurrence of the event, not assuming other
unexpected factors (Mikkelson & Partch, 1986).
The ARs of the sample companies following the final game of a
postseason series are estimated using a market model for the period from
2001 to 2012:
[AR.sub.it] = [R.sub.it] - ([[alpha].sub.i] +
[[beta].sub.i][R.sub.mt]) (1)
where [R.sub.it] is the return of company i at time t, [R.sub.mt]
is the corresponding return of the market index (e.g., the KOSPI) at
time t, and [[alpha].sub.i] and [[beta].sub.i] are the estimated market
model parameters. The CAR is the sum of ARs within the event window and
indicates the returns on the days during the event window, from day
[t.sub.0] to [t.sub.1]:
CAR([t.sub.0], [t.sub.1]) = [[[t.sub.1].summation over t=[t.sub.0]]
[AR.sub.it] (2)
The parameters of the market model are estimated using daily stock
prices for the estimation period (t = -258, -11). The CARs are estimated
for two-day windows (t = 0, +1) and (t = -2, -1), three-day windows (t =
0, +2) and (t = -3, -1), and four-day windows (t = 0, +3). The
significance of the CARs is examined across all of the sample companies;
the CARs of the winners and losers are compared in the three postseason
series, both before and after 2006, and between companies both with and
without corporate identities (i.e., those that do and do not include the
chaebols' name in the company's name). According to Coutts,
Mills, and Roberts (1995), standardized cumulative abnormal returns
(SCARs) are more appropriate when using longer event windows to correct
for serial correlation, especially for the same company, which is a
perfect fit for our data sample. In addition, the use of SCARs can
control for cross-sectional event-induced variation among
chaebol-affiliated firms (Boehmer et al., 1991). Each company's CAR
is standardized by the standard deviation, as suggested by Aybar and
Ficici (2009):
[SCAR.sub.i]([t.sub.0], [t.sub.1]) = [CAR.sub.i]([t.sub.0],
[t.sub.1])/[SD.sub.i] (3a)
[SD.sub.i] = [S.sub.i] [[square root of k + k/T] +
[[summation].sub.t=1.sup.k] [R.sub.mt] - k
[([[bar.R].sub.m]).sup.2]/[[summation].sub.t=1.sup.T] [R.sub.mt] -
[([[bar.R].sub.m]).sup.2] <3b>
where [S.sub.i] is the standard error of the market model's
regression, T is the number of observations in the estimation period,
Rmt is the return on the market portfolio for day t, [[bar.R].sub.m] is
the average return of the market portfolio for the estimation period,
and k represents the number of days in the event window. To examine
whether the SCAR is significantly different from zero, a one-sample
t-test is used.
Empirical Results and Discussion
Table 2 shows the effects of the last game of the quarterfinals,
semifinals, and finals, and also of all the games of the quarterfinals,
semifinals, and finals, on the market value of the corporate sponsors,
using the SCARs for two-day (0, +1), three-day (0, +2), and four-day (0,
+3) windows for the post-announcement returns. In addition, two-day (-2,
-1) and three-day (-3, -1) windows for the pre-announcement returns of
SCARs are calculated as reflecting the expectations of the markets.
First, as shown in Panel A of Table 2, the total SCARs of the postseason
are positive and significant at the 5% level in all three windows. More
specifically, a positive effect only occurs for a winning game, while
the effect is insignificant when a team loses. Also, the difference
between winning and losing games is statistically significant according
to independent sample t-tests for all three windows. In other words, the
impact of the postseason results might vary depending on whether a game
is won or lost. This result is consistent with Hypothesis 1 that winning
the postseason series has a positive spillover effect on the stock
prices of firms affiliated with the sponsoring chaebol. In addition,
this result is also consistent with Chen and Chen (2012) and Dobson and
Goddard (2011) in that team performance yields economic benefits to the
sponsoring or owner firms. This means that merely entering the
postseason is insufficient to achieve a positive effect on the stock
returns of affiliates; to achieve a positive effect, it is necessary to
win a game. However, the pre-announcement windows of (-3, -1) and (-2,
-1) do not show any statistically significant results. This means that
market expectations of team performance are not reflected in the stock
prices before the actual outcomes.
In addition, Panel B shows that this result is stronger for the
finals and quarterfinals, while the semifinals yield insignificant SCARs
for victories and in total; furthermore, losing teams generate a
negative spillover effect on stock returns for the (0, +1) and (0, +2)
windows. Moreover, winning the semifinals has no significant effect.
However, winning the quarterfinals and/or finals has a significant and
positive spillover effect on the affiliated firms, while losing the
quarterfinals and/or finals has no significant effect, and the
differences are statistically significant. This result shows that
investors have significant reactions to team performance in the
quarterfinals (between the #3 and #4 seed teams); this may be due to
market excitement for the start of the postseason and fans'
enthusiasm and hope that the weaker teams (#3 and #4 seed teams) could
win the finals. In other words, playoff uncertainty can have an impact
on game attendance and fans' emotions. Lee and Fort (2008) find
that playoff uncertainty has a statistically significant impact on
average MLB attendance per game. In addition, psychologists find that a
person's satisfaction regarding an outcome depends on his or her
original expectations (Atkinson, 1964; Feather, 1967, 1969). However,
due to the advantages of being the #1 seed in the KBO, the #1 seed has
won the championship every year during the past decade except in 2003,
when the #3 seed won. Thus, the insignificant results from the
semifinals reflect the gloomy expectations of market investors regarding
team performance in the semifinals.
Finally, winning the Korean Series (finals) has highly significant
and positive spillover effects for all three windows; for example,
winning the finals for the (0, +3) window shows a t-statistic of 2.563.
Therefore, the KBO's unique postseason structure has been
beneficial in a small country setting because it aims to create a
synergistic effect between the franchise baseball teams and their parent
companies' brand images to maximize firm values. Therefore, this
unique atmosphere is interesting and worthwhile to examine because it
provides a special "natural experiment" with respect to the
international aspect of the sport industry. However, we do not find any
significant results for those two pre-announcement windows for either
winning or losing the quarterfinals, semifinals, or finals, showing that
expectations of team performance are not reflected in stock prices
before the event date.
Furthermore, we also compare the SCARs before and after 2006
because the Korean national baseball team played a major role in
increasing the popularity of the KBO after it won the World Baseball
Classic tournament, and KBO game attendance has been increasing ever
since. The Hyundai Unicorns, one of the KBO teams, disbanded after 2006
because Hyundai divided itself into several small groups with different
owners. This may lead to confusion regarding how the Hyundai
Unicorns' wins would influence fans. Therefore, all data regarding
Hyundai's titles before 2006 were omitted to prevent contamination
of team performance on the spillover effect. For example, Hyundai Motors
has owned the Kia Tigers team since 2002. Panel A of Table 3 shows the
SCARs of the KBO's postseasons before and after 2006; these SCARs
are clearly more likely to be significant than those before 2006 due to
the KBO's increased popularity and attendance. After 2006,
significantly positive reactions are observed for winning a game and in
total for all three windows, while losing a game does not exhibit any
statistically significant results, and the difference in the stock
market reactions between winning and losing a game is significant for
all three post-announcement windows as well. However, there are no
significant results for pre-announcement windows.
In addition, we investigate whether including the chaebol's
name (corporate identity) in the company's name (e.g., Samsung
Electronics vs. Shilla Hotel) increases the spillover impact. The
results in Panel B show that the effects on companies with the chaebol
name are significantly positive in all of the windows ([0, +1], [0, +2],
and [0, +3]) while the effects on companies without a corporate identity
are mostly insignificant. Therefore, the results support Hypothesis 2
for the overall sample and indicate that brand name has a positive
effect on the value of sponsors' affiliates, perhaps because
consumers associate the brand image with a sports team.
For our robustness check, in Table 4, we conduct a multivariate
analysis to test the spillover effect after controlling for industry and
year effect. In Model (1) of Table 4, we find that Win Dummy variable
has a positive and significant relationship with SCARs at the 5% level
while Finals Dummy and Group Name Dummy variables do not show a
statistically significant relationship. However, in Model (2), after
2006, Win Dummy, Finals Dummy, and Group Name Dummy variables show a
statistically significant relationship with SCARs. Therefore, in the
multivariate analysis, after controlling for industry and year effect,
we find the consistent results with Hypothesis 1 and Hypothesis 2, and
the effect is more likely to be significant after year 2006, which is
consistent with the univariate analysis results in Table 3.
The empirical results of this study support Arun (2004), who argues
that sport sponsorship makes it possible to link the aspirations and
passion of a target audience to a specific sport such as KBO baseball.
Merely winning a game does not influence corporate value; however, in
the context of the KBO, winning the quarterfinals and/or the
championship increases corporate value. Therefore, winning championships
helps build strong brand names and customer loyalty and improves profit
margins.
Conclusions
In this study, we examine the spillover effects of sport
sponsorship on the value of the firms affiliated with the sponsoring
business groups using a new assessment scheme that exploits variations
within and across sponsoring chaebols, each of which has diversified
affiliated companies. Specifically, we find that the KBO teams that win
in the postseason have positive spillover effects on the firms
affiliated with their sponsoring chaebol groups. Furthermore, we find
that the chaebol's name is an important factor related to the
sponsorship effect on affiliated companies due to its stronger
association with the team's success and the financial performance
of affiliated companies.
By finding evidence that the success of sport sponsorship
strategies depends on team performance, this study can provide
managerial insights. Outstanding performance positively influences a
company's brand image via longer media exposure on television,
radio, and in the press, along with the transmission of live and
recorded matches, match reports, and positive result sequences. Given a
future upswing in the sport industry, therefore, this study also
provides implications for corporate managers in other sports leagues,
such as basketball and soccer. In particular, companies without group
names may need to find a way to expose their relevant affiliations to
the media. Corporate identity and the degree of consumer involvement are
also essential factors for corporate executives and investors planning
to manage new professional sports teams for the purpose of brand
enhancement.
However, the results of this study have a few limitations. First,
the number of KBO teams is relatively small compared with leagues in
other countries (such as the MLB and NPB). In addition, the history of
the KBO is shorter than that of the MLB and NPB, which may be
disadvantageous to our research. Using the results of our research based
on the ownership structure of South Korean chaebols, future research
should further investigate the degree of interconnection through
cross-holdings. Other than the chaebol structure, researchers might
explore and compare how other forms of ownership structure affect sport
marketing.
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Endnotes
(1) Revenues from global sports were $121.4 billion in 2010 and
expected to grow by approximately 3.7% annually from 2011-2015 (Clark,
2011).
(2) A chaebol is a business conglomerate with family-oriented
individual ownership and management; an individual owner or manager
typically has control over almost all the subsidiary companies. Under
the definition of the KFTC, a chaebol exists when an individual, or
companies controlled by the individual, owns more than 30% of a
company's shares. According to Almeida et al. (2011), chaebols in
South Korea are groups of companies under common ownership that control
many group-affiliated firms.
(3) Among those KBO teams, Nexen is not categorized as a chaebol
conglomerate by the Korea Fair Trade Commission (KFTC), but it is
regarded a business group with a couple of subsidiary companies. Thus,
the Nexen group is also expected to experience the spillover effect on
their affiliated firms as a group.
(4) From 2002-2012, the KBO fielded eight teams each year. In 2013,
the NC Dinos joined the league, and in 2015, another new franchise, the
KT Wiz, joined the league, which brings the total number of franchises
to 10.
(5) The DataGuide from the FN Guide (http://www.fnguide.com/)
provides financial information and stock return data for Korean markets.
Han, Lee, and Song (2014) and Lee, Park, and Shin (2009) use this
database for their studies on Korean markets.
(6) Industry classification is based on the Global Industry
Classification Standard.
(7) Reiser et al. (2012) used the regular market model of the event
study following Brown and Warner (1985); Pruitt et al. (2004) and
Cornwell et al. (2005) used the Scholes-William standardized
cross-sectional market model; and Chen and Chen (2011) used EGARCH
estimation based on the market model approach.
Authors' Note
This work was supported by the National Research Foundation of
Korea Grant funded by the Korean government (2014S1A3A2044459 &
2015S1A3A2046742).
Hojun Sung [1], Changi Nam [2], Minki Kim [3], and Seung Hun Han
[2]
[1] Seoul National University, Seoul, Korea
[2] Korea Advanced Institute of Science and Technology, Daejeon,
Korea
[3] Korea Advanced Institute of Science and Technology, Seoul,
Korea
Hojun Sung is a researcher and PhD student of Global Sport
Management. His research interests include finance of sports,
particularly professional baseball, effectiveness of sponsorships, and
the economic impact of professional sports teams.
Changi Nam is a professor of finance in the Department of Business
and Technology Management. His research interests include financial
analysis and management strategy.
Minki Kim is an assistant professor of marketing. With a focus on
quantitative marketing and empirical industrial organization, his
research interests are pharmaceutical R&D and marketing, and
business analytics utilizing online social network services.
Seung Hun Han is an associate professor of finance in the
Department of Business and Technology Management. His research interests
include business group analysis, corporate governance, and mergers and
acquisitions.
Table 1. Sample KBO Affiliated Firms by Group Name,
Industry, Listing Market, and Sample Size
Group Sample Company Name Size Industry Listing
Samsung N=15 Cheil Agency Service KOSPI
Hotel Shilla Service KOSPI
Credu Consumer goods KOSDAQ
Samsung Electronics Manufacturing KOSPI
Samsung SDI IT KOSPI
Samsung Electro- IT KOSPI
Mechanics
Samsung Life Finance KOSPI
Samsung Fire Finance KOSPI
Samsung Securities Finance KOSPI
Samsung Card Finance KOSPI
Samsung Heavy Material KOSPI
Industries
Samsung Construction Material KOSPI
& Trading
Samsung Engineering Material KOSPI
Samsung Techwin Material KOSPI
Samsung Fine Material KOSPI
Chemicals
LG N=15 Woori (LG) Finance KOSPI
Investment &
Securities
GS (LG) Engineering Material KOSPI
/ Construction
LS (LG) Cable & Material KOSPI
System
LS (LG) IS Material KOSPI
E1 (LG Caltex Gas) Utility KOSPI
HS (LG) Ad. Consumer Goods KOSPI
Yesco (Kukdong Utility KOSPI
City Gas)
GS Shop (LG Home Consumer Goods KOSDAQ
Shopping)
LG Innotek/Micron IT KOSDAQ
LG Telecom/U+ Telecommunication KOSDAQ
LG Chemicals Material KOSPI
LG Household & Consumer Goods KOSPI
Health Care
LG Petrochemical Material KOSPI
LG International Material KOSPI
LG Electronics Manufacturing KOSPI
Doosan N=6 Oricom Service KOSDAQ
Doosan Corporation Service KOSPI
Doosan Engineering Material KOSPI
& Construction
Doosan Heavy Material KOSPI
Industries &
Construction
Doosan Infracore Material KOSPI
Doosan Engine Manufacturing KOSPI
Lotte Chemical Material KOSPI
Lotte N=8 Lotte Midopa Consumer Goods KOSPI
Lotte Foods Consumer Goods KOSPI
(Samgang)
Lotte Shopping Consumer Goods KOSPI
Lotte Confectionery Consumer Goods KOSPI
Lotte Chilsung Consumer Goods KOSPI
Beverage
Lotte (Hyundai) IT KOSDAQ
Information
Technology
Lotte Insurance Finance KOSPI
Nexen N=4 KNN (Korea New Service KOSDAQ
Network)
Nexen Tech Manufacturing KOSDAQ
Nexen Tire Manufacturing KOSPI
Nexen Corporation Manufacturing KOSPI
SK N=18 Loen Entertainment Service KOSDAQ
Busan Gas Utility KOSPI
UB Care Health KOSDAQ
Silicon File IT KOSDAQ
Ko-One Energy & Utility KOSPI
Service
SK Service KOSPI
SK Innovation Energy KOSPI
SK C&C IT KOSPI
SKC Material KOSPI
SKC Solmics Material KOSDAQ
SK Gas Utility KOSPI
SK Networks Service KOSPI
SK Broadband Telecommunication KOSDAQ
SK Communications IT KOSDAQ
SK Chemicals Material KOSPI
SK Telecom Telecommunication KOSPI
SK Hynix IT KOSPI
SK Securities Finance KOSPI
SK Gas Utility KOSPI
SK Networks Service KOSPI
SK Broadband Telecommunication KOSPI
Hanwha N=5 Hanwha Material KOSPI
Hanwha Galleria Consumer Goods KOSPI
Time World
Hanwha Chemical Material KOSPI
Hanwha Insurance Finance KOSPI
Hanwha Securities Finance KOSPI
Note: The overall sample size is 71, including all
the subsidiary companies in the seven groups.
Table 2. SCARs: Mean Standardized Cumulative Abnormal
Returns for the KBO Postseason Series
Panel A. SCARs of Quarterfinals, Semifinals, and Finals in total
Event Windows N (0, +1) (0, +2) (0,+3)
Postseason Win 394 0.206 *** 0.215 *** 0.187 ***
total (3.164) (3.272) (3.055)
Lose 330 0.008 -0.034 0.015
(0.118) (-0.487) (0.227)
Total 724 0.115 ** 0.101 ** 0.109 **
(2.511) (2.104) (2.404)
Difference -- -0.198 ** -0.249 *** -0.172 *
(-2.154) (-2.585) (-1.896)
Panel B. SCARs of Quarterfinals, Semifinals, and Finals
Event N (0, +1) (0, +2) (0, +3)
Windows
Quarter- Win 109 0.317 ** 0.356 ** 0.348 **
finals (2.064) (2.27) (2.601)
Lose 104 0.219 ** 0.283 ** 0.214 *
(2.15) (2.467) (1.915)
Total 213 0.269 *** 0.32 *** 0.283 ***
(2.899) (3.28) (3.231)
Difference -- -0.098 -0.073 -0.134
(-0.524) (-0.37) (-0.766)
Semifinals Win 137 0.045 0.069 0.025
(0.507) (0.66) (0.236)
Lose 80 -0.3 ** -0.379 ** -0.211
(-2.173) (-2.047) (-1.216)
Total 217 -0.082 -0.096 -0.062
(-1.07) (-0.996) (-0.675)
Difference -- -0.345 ** -0.448 ** -0.236
(-2.194) (-2.272) (-1.235)
Finals Win 148 0.272 *** 0.245 *** 0.219 **
(2.691) (2.809) (2.563)
Lose 146 0.025 -0.071 -0.002
(0.259) (-0.831) (-0.029)
Total 294 0.149 ** 0.088 0.109 *
(2.126) (1.421) (1.8)
Difference -- -0.247 * -0.317 ** -0.221 *
(-1.763) (-2.583) (-1.836)
Panel A. SCARs of Quarterfinals, Semifinals, and Finals in total
Event Windows N (-3,-1) (-2, -1)
Postseason Win 394 -0.044 -0.074
total (-0.756) (-1.2)
Lose 330 0.014 -0.035
(0.207) (-0.501)
Total 724 -0.018 -0.056
(-0.402) (-1.215)
Difference -- 0.058 0.039
(0.656) (0.417)
Panel B. SCARs of Quarterfinals, Semifinals, and Finals
Event N (-3, -1) (-2, -1)
Windows
Quarter- Win 109 0.172 0.06
finals (1.299) (0.456)
Lose 104 0.103 -0.056
(0.887) (-0.406)
Total 213 0.138 0.004
(1.569) (0.04)
Difference -- -0.07 -0.116
(-0.394) (-0.609)
Semifinals Win 137 -0.114 -0.129
(-1.558) (-1.457)
Lose 80 0.002 0.029
(0.014) (0.203)
Total 217 -0.071 -0.07
(-0.916) (-0.913)
Difference -- 0.116 0.158
(0.723) (0.989)
Finals Win 148 -0.138 -0.122
(-1.409) (-1.183)
Lose 146 -0.043 -0.056
(-0.505) (-0.576)
Total 294 -0.091 -0.089
(-1.399) (-1.261)
Difference -- 0.095 0.066
(0.73) (0.468)
Note: i-statistics are within parentheses.
* p < 0.1; ** p < 0.05; *** p < 0.01.
Table 3. SCARs: Mean Standardized Cumulative Abnormal Returns Before
and After 2006 & Companies With and Without Group Name
Panel A. Total SCARs before and after 2006
Event Windows N (0, +1) (0, +2) (0,+3)
Postseason Win 120 0.101 0.126 0.1
series (0.83) (0.937) (0.855)
before 2006 Lose 117 0.051 0.043 0.114
(0.584) (0.473) (1.255)
Total 237 0.076 0.085 0.107
(1.017) (1.044) (1.443)
Difference -- -0.05 -0.083 0.014
(-0.336) (-0.508) (0.097)
Postseason Win 274 0.251 *** 0.253 *** 0.226 ***
series (3.277) (3.44) (3.139)
after 2006 Lose 213 -0.016 -0.077 -0.039
(-0.187) (-0.795) (-0.43)
Total 487 0.134 ** 0.109 * 0.11 *
(2.327) (1.832) (1.93)
Difference -- -0.267 ** -0.33 *** -0.265 **
(-2.309) (-2.769) (-2.316)
Panel B. Total SCARs after 2006 with
and without group name
Event Windows N (0, +1) (0, +2) (0, +3)
With group Win 202 0.265 *** 0.277 *** 0.275 ***
name after (3.114) (3.528) (3.858)
2006 Lose 156 0.042 -0.024 -0.008
(0.436) (-0.226) (-0.084)
Total 358 0.168 *** 0.146 ** 0.151 **
(2.621) (2.249) (2.538)
Difference -- -0.223 * -0.302 ** -0.283 **
(-1.73) (-2.319) (-2.369)
Without Win 72 0.214 0.186 0.088
group name (1.263) (1.07) (0.469)
after 2006 Lose 57 -0.176 -0.22 -0.123
(-0.935) (-1.054) (-0.609)
Total 129 0.042 0.007 -0.005
(0.33) (0.049) (-0.04)
Difference -- -0.39 -0.406 -0.21
(-1.538) (-1.506) (-0.762)
Panel A. Total SCARs before and after 2006
Event Windows N (-3,-1) (-2, -1)
Postseason Win 120 -0.047 -0.053
series (-0.44) (-0.474)
before 2006 Lose 117 0.047 0.073
(0.45) (0.743)
Total 237 -0.001 0.01
(-0.007) (0.129)
Difference -- 0.094 0.126
(0.629) (0.847)
Postseason Win 274 -0.043 -0.083
series (-0.614) (-1.122)
after 2006 Lose 213 -0.005 -0.095
(-0.053) (-1.009)
Total 487 -0.026 -0.088
(-0.479) (-1.509)
Difference -- 0.038 -0.012
(0.349) (-0.098)
Panel B. Total SCARs after 2006 with
and without group name
Event Windows N (-3, -1) (-2, -1)
With group Win 202 -0.075 -0.071
name after (-0.967) (-0.924)
2006 Lose 156 0.115 -0.012
(1.155) (-0.11)
Total 358 0.008 -0.045
(0.129) (-0.698)
Difference -- 0.19 0.059
(1.53) (0.448)
Without Win 72 0.047 -0.117
group name (0.313) (-0.642)
after 2006 Lose 57 -0.331 ** -0.32 *
(-2.03) (-1.87)
Total 129 -0.12 -0.207
(-1.073) (-1.631)
Difference -- -0.379 * -0.203
(-1.694) (-0.793)
Note: t-statistics are within parentheses.
* p < 0.1; ** p < 0.05; *** p < 0.01.
Table 4. Multivariate Analysis
Total Sample After 2006
Dependent variable: Model (1) Model (2)
SCAR (0,+1)
Win Dummy 0.237 ** 0.315 ***
(2.490) (2.664)
Finals Dummy 0.114 0.208 *
(1.163) (1.719)
Group Name Dummy 0.066 0.270 *
(0.525) (1.660)
Industry Dummy YES YES
Year Dummy YES YES
Adj [R.sup.2] 0.0046 0.0208
N 724 487
Note: t-statistics are within parentheses.
* p<0.1; ** p<0.05; *** p<0.01.
