This item is only available as the following downloads:
1 D ETERMINANTS OF T ICKET P RICE F LUCTUATION IN THE S ECONDARY M ARKET : THE C ASE OF M AJOR L EAGUE B ASEBALL E VENTS By D ONGHO Y OO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF TH E REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 201 2
2 2012 Dongho Yoo
3 To my family
4 ACKNOWLEDGMENTS I would like to show my sincere gratitude to my advisor, Dr. Yong Jae Ko, for his guidance throughout the whole process of thesis. He taught me not only research skills but also perseverance. The perseverance that I learned from him keep s myself working hard e ven though I have encountered brick wall s several ti mes during the thesis process. Als o, all of my committee members including Dr. Kelly Semrad, Dr. Shannon Kerwin, and Dr. Michelle Harrolle they offered me time and the constructive criticisms during the thesis process, and their guidance has made my research theoretically solid. I would also like to thank my friends and colleagues ( Matt, Taeho Youngmin, Jihoon, Wonseok, and Heeyoun) in the department of Tourism Recreation and Sport Management for their friendship and advice I feel very fortunate that I have studied at the Department of Tourism, Recreation, and Sport Management (TRSM) of the University of Florida (UF). This institute has offered me the welcomed environment along with the substantial academic resource to complete my thesis. Finally, without my parents, this fantastic oppor tunity to study at TRSM department of the UF would not happen. They always have supported me much I appreciate for all the support they have provided.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 7 LIST OF FIGURES ................................ ................................ ................................ ......................... 8 ABSTRACT ................................ ................................ ................................ ................................ ..... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 11 Significance of the Study ................................ ................................ ................................ ........ 14 Statement of Problem ................................ ................................ ................................ ............. 15 Purpose of Study ................................ ................................ ................................ ..................... 17 2 LITERATURE REVIEW ................................ ................................ ................................ ....... 18 Significance of the Secondary Market ................................ ................................ .................... 18 Determinants of Demand in MLB Games ................................ ................................ .............. 20 Deter minants of Ticket Price in the Secondary Market ................................ .......................... 24 Ground W ork for T his R esearch ................................ ................................ ............................. 27 Stadium Occupancy in MLB Franchises ................................ ................................ ................ 28 Hypotheses Development ................................ ................................ ................................ ....... 30 3 METHOD ................................ ................................ ................................ ............................... 36 Statistical Design ................................ ................................ ................................ .................... 36 The Variables in the Research Model ................................ ................................ ..................... 36 Dependent Variable ................................ ................................ ................................ ......... 36 Independent Variables ................................ ................................ ................................ ..... 37 Data Collection ................................ ................................ ................................ ....................... 38 Data and the Empirical Specification ................................ ................................ ..................... 38 Model Specification ................................ ................................ ................................ ................ 39 4 RESULTS ................................ ................................ ................................ ............................... 42 Preliminary Analysis ................................ ................................ ................................ .............. 42 Descriptive Analyses ................................ ................................ ................................ .............. 42 Regression Analysis ................................ ................................ ................................ ................ 43 Group Difference by the Team Attendance Level ................................ ................................ .. 45
6 5 DISCUSSION ................................ ................................ ................................ ......................... 53 Results Analysis ................................ ................................ ................................ ...................... 53 Conclusion ................................ ................................ ................................ .............................. 57 Implication ................................ ................................ ................................ .............................. 58 Limitation and Future Research ................................ ................................ .............................. 59 LIST OF REFERENCES ................................ ................................ ................................ ............... 61 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ......... 67
7 LIST OF TABLES Table page 2 1 Division rivalries ................................ ................................ ................................ ................ 34 2 2 ................................ ................................ .................. 35 4 1 Correlation analysis for each variable ................................ ................................ ................ 48 4 2 Descriptive stat istics for each variable ................................ ................................ .............. 49 4 3 Regression analysis results for seven hypotheses ................................ .............................. 49 4 4 Group d ifference of day of the week vari able on ticket prices ................................ .......... 50 4 5 Group difference of rivalry variable on ticket prices ................................ ......................... 50 4 6 Group difference of interleague variab le on ticket prices ................................ .................. 51 4 7 Group difference of opponent quality variable on ticket prices ................................ ........ 51 4 8 Group difference of league stan ding variable on ticket prices ................................ .......... 51 4 9 Group difference of promotion variable on ticket prices ................................ ................... 51 4 10 Group difference of squad variable on ticket prices ................................ .......................... 52
8 LIST OF FIGURES Figure page 5 1 b uy p age ................................ ................................ ................................ ............ 60
9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science D ETERMINANTS OF T ICKET P RICE F LUCTUATION IN THE S ECONDARY M ARKET : THE C ASE OF M AJOR L EAGUE B ASEBALL E VENTS BY Dongho Yoo August 201 2 Chair: Yong Jae Ko Major: Sport Management Each Major League Baseball (MLB) team h as 81 home games in each season and they are not 81 units of the same product but 81 different products. Fans value differently each of 81 home ga mes, but what factors fans consider in this valuation have not been investigated much. N umerous studies have focused on factors that motivate fans to come to the game in general, but not many studies have focused on factors influencing game by game fluctua tion within a season. The purpose of this study is to examine factors found from the previous literature influencing the secondary market ticket price fluctuation for individual MLB games. In this research, the author used the ticket prices of the secondar y market as dependent variables. The secondary market is the platform for buying and selling tickets that previously were obtained from the franchise team (the primary market). The ticket prices of the primary market are relatively con stant throughout the season but the ticket prices of the secondary market radical ly fluctuate according to fans willingness to pay for games. Therefore, the secondary market can be a good indicator reflecting fans perceived value fluctuation game by game After the factors were found from the literature, they were statistically analyzed through regression analysis to see if they had a significant effect on the ticket price fluctuations. Additionally, MLB teams were divided into three groups by
11 CHAPTER 1 INTRODUCTION Major League Baseball (MLB) has experienced a decrease in demand in the past four years since 2008 (CNBC, 2011). There are several fundamental challe nges in major spectator sports which directly influence fan attendance. They include : 1) competition for spectators amid 600 professional sports teams and 1,000 collegiate athletic programs in North America ( Howard & Crompton, 2004 ) 2) that i ncreased tick et prices make working class and middle class spectators unable to attend the game and 3) that there exist negative impacts on MLB attendance as created by t he emergence of other entertainment alternatives and new media technology (e.g., HD TV and I phone applications) The competition created by v ideo games, movies, and TV shows has become more intense (Howard & Crompton ). In addition, as the popularity of high definition television (HDTV) increases fans are unsure why they should attend the game when th ey can watch the game on HDTV in rich, vivid detail money more intense (Boyd & Krehbiel, 2003). Attracting sport spectators to the stadium has always been a critical revenue source for MLB teams. Although the amount of media revenues has increas ed ticketing revenues have decreased in from 76% in 1950 to 62% in 1975 and to 38.9% in 2001 However, ticketing revenues still remain the largest revenue source for MLB te ams (Pappas, 2001; Rosentraub, 1997; Tainsky & Winfree 2008). Maintaining high attendance is important, not only because it is the largest revenue generator for MLB teams, but also because it leads to increases in game day concessions, merchandising, and parking (Kim & Trail, 2010). Further prior studies indicated that the marginal cost of selling an extra seat in MLB is almost zero (Rascher et al., 2007) because all the bleachers, concession stands, and other
12 equipment already exist in the stadium. Clea rly, h aving more fans attend the game is highly profitable to the franchise teams in a variety of ways Therefore, it seems appropriate that MLB franchises apply their best efforts toward having as many spectators as possible. Of all professional sports i n the US, MLB franchises ha ve the largest number of home games: 81 games in each season. Thus, baseball fans have more discretion on deciding which games to attend. MLB teams have learned that their 81 home games are not 81 units of the sam e product but ra ther are 81 unique units differentiated by game characteristics such as the day of the week and quality of the opponent (Rascher et al., 2007). These game characteristics lead to changes in willingness to pay for tickets unique differentiating charac teristic of each game thereby allowing MLB franchises to optimize revenue performance via fluctuating ticket prices MLB franchises have adopted variable ticket pricing in the primary market which refers to changing the ticket price according to the leve l of expected demand for the tickets. Aside from the day of the week and quality of the opponent, other researchers identified numerous factors affecting demand for the games including (a) the uncertainty of outcome (Demmert, 1973; Drever & MacDonald, 1981 ; Jones, 1969; Knowles et al.,1992; Lemke et al., 2009; Meehan et al., 2007; Noll, 1974; Rascher, 1999; Rottenberg, 1956), (b) 2009; Denaux et al., 2011; Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Hill et al. 1982; Levin et al., 2000; Meehan et al., 2007; Noll, 1974; Rascher, 1999; Scully, 1974), (c) rivalry (Fillingham, 1977; Lemke et al., 2009; Levin et al., 2000), (d) a record breaking performance (Fillingham, 1977; Lemke et al., 2009), ( e ) the presence of outstanding athletes (Davis, 2009; Denaux et al., 2011; Fillingham, 1977; Hausman & Leonard, 1997; Kahn & Sherer, 1988; Levin et al., 2000; Meehan et al., 2007; Rascher, 1999; Schmidt & Berri, 2001; Schofield, 1983), and ( f ) squad of game (Rascher, 1999).
13 It is necessary to gather valid data to fully understand the influence of the listed factors on willingness to pay toward individual MLB game s The secondary market is the platform where the seller resells previously purchased tickets and where the seller is not necessarily affiliated with the league or team associated with the event (Happel & Jennings, 2002). Ti cket prices in the primary market are typically set using a cost oriented strategy (Howard & Crompton, 2004; Kotier, 2003), so the prices ge nerally reflect the revenue needs of the for individual MLB games. In addition, the ticket prices of the primary market do not fluctuate throughout the season. Unlike the primary market, the ticket pricing of the secondary market is mostly demand oriented (Drayer et al., 2008). How the pricing process works in the secondary market is consistent with what Rishe and Mondello (2003) stated That is, fans are willing to pay more for more desirable games Because the secondary market is truly a free and open market, sellers have the freedom of determining the price of tickets (Drayer et al., 2008). The sellers in the secondary market typically set the ticket price by the ticket pri ces in the secondary market may more accurately reflect differences in perceived value for the MLB games than the ticket price of the primary market. To date, only a few studies have attempted to identify how the application of differentiating game characteristics (e.g. quality of the opponent) infuences ticket price fluctuations on the secondary market. According to Wann and Branscombe (1990), teams have fans in different identification levels. Fo r example, s ome teams have a long history of being the underdog (e.g., the Chicago Cubs in professional baseball), but many fans still remain loyal and continue to pay for tickets in support of these teams. According to related literature (Manhony et al., 2000), sports fans allegiance or psy chological attachment to a team varies, and each team
14 may be an important moderator So, learning how much fans identify with a team may be important in understanding how different identification levels in fluence ticket price fluctuations. This information may be crucial to the process of developing marketing strategies to attract fans to the stadium. However, the value of using this information for purposes of developing marketing strategies has not been e mpirically assessed ( Guttmann, 1986; Mann, 1979; Schurr, Ruble, & Ellen, 1985; Schurr, Wittig, Ruble, & Ellen, 1988; Smith, 1983 ; Zillmann, Bryant, & Sapolsky, 1979). Significance of the Study Each of the MLB franchise teams has 81 home games per season. Since each MLB franchise team has many games, fans have more discretion to choose which games they will attend. As mentioned previously 81 home games are not 81 units of the same product but rather are 81 unique products dif ferentiated by characteristics such as the day of the week and quality of the opponen t (Rascher et al., 2007) Fans perceive the value of games differently according to potential factors. Although n umerous studies have b een conducted to identify factors o r motivation for attending game s it had not been examined how those factors alter fans willingness to pay for individual games. In other words, few researchers have attempted to identify factors that cause game by game difference. To identify the factors perceived value of game we first needed to obtain information indicating how much fans are willing to pay for each game. The ticket price of the secondary market provides a list of ticket prices for individual games. As the sellers in the secondary market have the freedom of determining the ticket price for the secondary market, they perceived value of tickets (Drayer et al., 2008). Moreover, MLB franchise teams have different fan bases. When it comes to popular fan base teams such as the Boston Red Sox and the Chicago Cubs, their fans come to the stadium
15 performance s The reasons for those fans to purchase tickets and attend the games might be different from those of fans who do not identify wit h the team as highly or in the same manner. A substantial amount of research has been conducted to investigate the factors that make a franchise popular; but on the other hand, only a few researchers have attempted affects the price fluctuation of the game s. Statement of Problem In the field of sport management and marketing there has been much research identifying motivation al factor s to attend games (e.g., Funk, Ridinger, & Moorman, 2003; Sloan, 1989 ; Trail & James, 2001; Wann, 1995). For example, Sport Fan Motivation Scale (SFMS) that was developed by Wann in 1995 which has been modified and used by many researchers to Although the initial scale made sig nificant impact on sport consumer research, the specific factors in the scale reflect general reasons (psychological domain) for attending the sports games, thus, this scale is insufficient in research associated with game examined to date. In the context of professional sports, Rishe and Mondello (2003) investigated the ticket price determinants of NFL f ranchise teams T hey identif i ed factors that influenced changes in seasonal demand such as the win records of the previous year and star players. The authors used the ticket prices of the primary market as a dependent variable, and they argued that the sea sonal change of ticket price reflects the expected changes in demand for a given team (Rishe & Mondello, 2003). They also assumed the increase in the ticket prices of the primary market reflects increased expected demand of tickets, and vice versa. However they could not determine
16 which factors would influence changes in demand from game to game, due in part to the limitation of using the ticket price of the primary market as a dependent variable. The primary market, as its name entails, is where professio nal franchise teams sell their tickets directly to customers. Ticket prices of the primary market are usually set before the season starts, and do not change throughout the season (Drayer & Shapiro, 2009). Furthermore ticket prices of the primary market a re typically set using a cost oriented strategy (Howard & Crompton, 2004). To identify factors influencing day to day perceived value changes, the secondary market ticket price needs to be carefully examined in addition to the ticket price of the primary m arket Specific information of the secondary market reflects the willingness to pay for each game within a season. In the secondary market, unlike the primary market, ticket transactions occur through reselling, so the secondary market ticket prices are not fixed for each of the scheduled games. As previously mentioned, in the ticket pricing mechanism of the secondary market, sellers decide ticket prices according to the demand of tickets. In addition, sellers can change the price of tickets a ny time before the game starts. Sellers can consider the factors such as weather and temperat ure, which can affect the value of the game until the last minute before the ga me starts. Therefore, the perceived value of the game is well reflected in the ticke t price of the second ary market, and it can be an appropriate dependent variable to identify which factors are affecting changes in the perceived value of games Numerous studies (e.g., Denaux et al., 2011; Greenstein & Marcum, 1981; Hansen & Gauthier, 19 89 ; Lemke et al., 2009) focused on game attendance and identified major determinants such as star players and win records (Hansen & Gauthier, 1989). However, only a
17 tion for the games. Even if the ticket prices of the secondary market c ould be a n effective indicator of the perceived value of the game, few studies have used the ticket prices of the secondary market as a dependent variable. In addition, the influence of the level of attendance of the team Thus, this study used the secondary market as a dependent variable and identified the factors that cause changes in the ti cket pri attendance level on the relationship between the identified determinants and ticket price of the secondary market was examined. Purpose of Study The purpose of this study wa s to examine factors that influence ticket price fluctuation in the secondary market of MLB events. Those factors include 1) day of the week ; 2) rivalry; 3) interleague; 4) league standing; 5) the quality of opponent; 6) promotion; and 7) the quality of squ ad. Extensive literature review was first conducted to identify potential factors. The factors found in this initial step include were statistically analyzed to test their relative effects on the ticket price fluctuations in the secondary market Next, MLB teams were divided by attendance level of teams and used to examine its moderating effect on the determinants and the ticket price of the secondary market. Having the comprehensive analysis of th e factors that affect the ticket price fluctuations will assist the MLB marketers in developing and implementing their market and pricing strategies.
18 CHAPTER 2 LITERATURE REVIEW Significance of the Secondary Market The secondary market has developed from primitive scalping to corporate level. The current orga nized secondary market started from ticket scalpers in the past. The concept of ticket scalpers first came out in the late nineteenth century as people who sold railroad tickets without authorization of the railroad company (Benitah, 2005) From the railro ad industry, ticket scalping expanded its boundaries to entertainment when similar businesses started scalping tickets to sold out theater productions; scalpers bought vast amount of tickets and then tried to sell them outside the theater, acquiring a prem ium (Benitah, 2005). The practice of ticket scalping was technically illegal, but it was a lucrative business. Ticket scalping then evolved into a legal practice of reselling tickets through ticket brokers. Ticket brokers first emerged in the early twentie th century as remote outlets for theaters and ballparks, where customers could buy tickets from convenient locations. Brokers were authorized personnel to increase ticket sales, and they returned unsold tickets to the box office while retaining a small ser vice fee. Although ticket brokers legally resold tickets, cooperating with promoters, illegal ticket scalpers still existed (Benitah, 2005). As a measure to prevent illegal ticket scalpers, anti scalping legislation ha d been enacted, aimed at prevent ing ha rm to the event promoters and allow ing promoters to control the distribution of tickets. Promoters typically set reasonable ticket prices in an effort to obtain sellouts (Benitah, 2005). However, preventing the resale of tickets was almost impossible to en force. Online secondary ticket sites such as e Bay post state laws on their site, but most users do not post their real name or contact information (Drury, 2002). In addition, it can be argued that there is no victim in the act of ticket resale, because bot h parties consent to buy and sell
19 tickets (Benitah, 2005). Moreover, online secondary ticket companies began lobbying state legislatures to overturn anti scalping laws (Nocera, 2008). Currently, various websites such as Stub h ub, TicketsNow and RazorGator o ffer platforms for customers to buy and resell sport tickets (Short, 2005). The progress of the internet has made those websites larger and more efficient. Online secondary ticket marketplaces created a new business model by taking some portion of the prof its from transactions made between buyers and sellers. For instance, the popular online secondary ticket marketplace Stub h ub takes 25 percent of each transaction ( F ree market Fleecing, 2006). Online secondary ticket marketplaces were soon acquired by large r brokering sites, due to the value of the online secondary ticket market. In 2008, TicketsNow was bought by Ticketmaster for $265 million, while in 2007, Stub h ub was acquired by EBay for $300 million (Nocera, 2008). Using secondary market data is more ap propriate considering the size, technology, and legitimacy of the secondary market. In America alone, the online secondary ticket market is worth over $10 billion, according to Jeffrey Fluhr, co founder and former CEO of StubHub (Cozart, 2010). Stubhub pro vides a platform for buying and selling of tickets of many types of events in sports and entertainment. Even though several teams in MLB have openly criticized the use of the secondary market Stubhub, in the past, the growth of the online secondary ticket market and the potential for revenue has made owners and professional leagues set aside initial objections. The online secondary ticket market was useful not only to fans but also to teams or franchises where there was the potential to receive a part of the revenues (Nocera, 2008). When fans utilize the sites to resell unused tickets, individual teams and organizations can take the commission benefit from secondary market ticket transactions (Moore, 2010). Buyers at StubHub.com pay a 10 percent
20 fee, while sellers are charged a 15 percent commission. For example, i f a baseball ticket sells for $100, the buyer pays $110 and the seller receives $85, so Stubhub and the baseball teams share $25 profit (USA today, 2007). Also, organizations f ound that fans are more likely to buy season tickets if they know they will be able to recoup their losses by reselling unusable tickets (Nocera, 2008). F ans are more accepting of the online secondary ticket market since the secondary ticket market provides a means for fans to attend already sold out games and gives them a chance to sit in the prestigious seats which are not usually available for casual fans (Moore, 2010). s to buyers concerns about fraudulent tickets but also allows a last minute sale by digitally transferring tickets to the ballpark box office with the name of buyer. Team ticket sales usually decrease within 72 hours of the game, but this system allows ticket sales up to the g ame time. Since 2006, Stubhub has provided a clickable map of each stadium showing the availability and prices of tickets according to each seating section (Sweeting, 2008). Acknowledging the growing strength of Internet ticket exchanges and the increase i n potential revenue stream, MLB has finally entered into a revenue sharing agreement with Stub h ub. Under the five year deal, all 30 baseball team Web sites and MLB.com will direct fans who want to sell their tickets or buy tickets from other fans to Stubhu b.com (New York Times, 2011). As Stubhub has become an official secondary market for MLB, the secondary market in MLB has begun playing a huge role in MLB ticket sales. Determinants of Demand in MLB Games As mentioned previously, t he sellers in the secon dary market typically set the ticket price according to (Drayer et al., 2008). Ticket prices of the secondary market are considered to be an indicator of the fans perceived value of games and the perceived value is directly related to their perceived game quality. According to Ko and his colleagues
21 (2011), quality of game is one of the main factors determining major spectator sport events. G product of g ame performance (Hansen & Gauthier, 1989) In this study, factors related to this dimension will be the focus and the study will investigate potential variables associated with this dimension and their influence on the ticket price in the secondary market. In the sport literature, t he scholarly efforts to identify factors affecting market demand for sporting events goes back to the mid 1970s by Noll (1974) and Hart et al. (1975). Explanatory price and income), but with an emphasis on uncertainty of outcome. Later, scholars identified several key determinants of game attendance. They can be categorized into factors related to game attractiveness and environmental condition. Specifically, the ga me attractiveness factors are directly related to the game performance and they include (a ) n ature of sports (e. g. strategy and dynamic ; Hansen & Gauthier, 1989) ; (b ) t he vicarious pleasure of relating to a winner (Hansen & Gauthier, 1989) ; (c ) t he curren Hansen & Gauthier, 1989; Hill et al., 1982; Levin et al., 2000; Meehan et al., 2007; Noll, 1974; Rascher, 1999; Scully, 1974) ; (d ) r ivalry (Fillingham, 1977; Lemke et al., 2 009; Levin et al., 2000) ; (e ) t he record breaking performance (Fillingham, 1977; Lemke et al., 2009) ; (f ) t he presence of outstanding athletes (Davis, 2009; Denaux et al., 2011; Fillingham, 1977; Hausman & Leonard, 1997; Kahn & Sherer, 1988; Levin et al., 2000; Meehan et al., 2007; Rascher, 1999; Schmidt & Berri, 2001; Schofield, 1983) ; (g ) s quad of game (Rascher, 1999) the ticket price fluctuation. For example, a rivalry match affects fans willingness to pay for games. Although a record breaking performance seemingly affects the ticket price, this kind of
22 incident rarely happens. Therefore, this factor is not enough to be considered one of the game attractiveness factors (Fillingham, 1977; Lemke et al., 2009) For t he presence of outstanding athletes (Davis, 2009; Denaux et al., 2011; Fillingham, 1977; Hausman & Leonard, 1997; Kahn & Sherer, 1988; Levin et al., 2000; Meehan et al., 2007; Rascher, 1999; S chmidt & Berri, 2001; Schofield, 1983) having star players on the team is very likely to affect the ticket price. However, as the team roster remains constant throughout the year, this factor would not reflect day to day fluctuation. Therefore, this will not be regarded as a potential factor. In terms of the s quad of game factor, even though the overall team roster remains constant throughout the season, the most important defensive position in the game of baseball, starting pitcher, changes every game (Ra scher, 1999) The quality of starting pitcher will represent the squad of game factor. In terms of the environmental factors, scholars examined numerous factors of game attendance including ( a) t icket price (Bird, 1982; Borland, 1987; Demmert 1973; Fill ingham, 1977; Garcia & Rodriguez, 2002; Noll, 1974; Siegfried & Eisenberg, 1980 ; Simmons, 1996; Whitney, 1988); ( b) p er capita income (Bird, 1982; Denaux, 2011; Hansen & Gauthier, 1989; Hart et al., 1975; Lemke et al., 2009; Meehan et al., 2007; Noll, 1974 ; Siegfried & Zimbalist, 2000) ; ( c) su bstitute forms of entertainment (Demmert, 1973; Fillingham, 1977; Hart et al., 1975; Hay & Thueson, 1986; Hill et al., 1982; Medoff, 1976; Noll, 1974) ; ( d) t elevision effects (Demmert, 1973; Drever & MacDonald, 1981; H ill et al., 1982; Siegfried & Zimbalist, 2000) ; (e) o n site promotion (Lemke et al., 2009; Levin et al., 2000) ; (f) p opulation size of ar ea (Fillingham, 1977; Hansen & Gauthier, 1989; Hart et al., 1975; Hay & Thueson, 1986; Hill et al, 1982; Levin et al., 2000; Medoff, 1976; Noll. 1974; Siegfried & Eisenberg, 1980; Rascher, 1999 ) ; (g) g eography, accessibility to the stadium (Demmert, 1973; Drever & MacDonald. 1981;
23 Fillingham, 1977; Greenstein & Marcum, 1981; Hay & Thueson, 1986; Hill et al., 1982; Medoff, 1976; Noll. 1974; Scully, 1974; Siegfried & Eisenber g, 1980 Winfree et al., 2004); (h ) c limate related to sport, weather conditions (Bird, 1982; Davis, 2009; Denaux et al., 2011; Drever & McDonald, 1981; Hansen and Gauthier, 1989; Hill et al, 1982; Meehan et al. 2007; Noll. 1974 ; Siegfried & Eisenberg, 1980); (i ) s cheduling of games (Noll 1974; Hill et al. 1982; Depken 2000; Siegfried and Zimbalist 2000; Schmidt and Berri 2001; Winfree et al. 2004; Meehan et al. 2007; Lemke et al. 2009; Davis, 2009) ; (j ) o pening games and end of season (Drever & MacDonald. 1981; Fillingham. 1977; Hay & Thueson. 1986; Hill et al., 1982; Siegfried & Eisenberg, 1980) ; (k ) n ewly built stadiums (Demmert, 1973; Depken ,2006; Fillingham, 1977; Greenstein & Marcum, 1981; Hay & Thue son, 1986; Hill et al., 1982; Medoff. 1976; Noll, 1974; Scully, 1974; Siegfried & Eisenberg, 1980) ; (l ) t he history of a franchise (Demmert, 1973; Siegfried & Eisenberg, 1980) In selecting potential factors from the list of environmental factors, the majo rity of factors have been taken out depending on their relevancy to its study. Factors such as per capita income, substitute forms of entertainment, television effects, population size of the area, accessibility to the stadium, the age of the stadium, and the history of the franchise will not vary game by game but influence constantly throughout the season. Therefore, these factors will not be considered. The ticket price factor will not be chosen as a factor because it will be used as a dependent variabl e. On site promotion such as fireworks and giveaway t shirts will be one of the potential factors since these events frequently happen on specific dates, and they are considered to affect the ticket price. about weekend games and the games in the specific month. The climate factor will not be included as one of the factors. Since
24 fans are not able to know the actual weather of the game day before the game starts, this weather effect cannot be reflected on th Determinants of Ticket Price in the Secondary Market In this section, the potential factors that are chosen through the selection process will be explained and will indicate how the factors will be coded. According to Lem ke et al (2009), they have divided separate dummy variables for each day, Monday through Sunday, plus a variable for playing a day game during the week. Six more variables have been used as control s for the month. They also included Federal holidays such a s Memorial Day, the Fourth of July, or Labor Day and the vacations of the largest public school district (Lemke et al., 2009). T his study will try to investigate whether the month, day of the week, or holida ys affect the ticket price These factors are imp ortant to take into account as families are more likely to attend games when children are not in school or when people are off work for a holiday (Lemke et al., 2009) In this study, the author will divide the scheduling of games into three categories: 1) day of the week ; 2) month of the year ; and 3 ) whether or not it is a holiday, and will try to investigate how much these factors would infl uence the ticket price O n site promotions can be a potential factor in this study According to previous research, promotions and special events are positively related to the demand for the games (Hill et al., 1982; Jones, 1984; Siegfried & Eisenberg, 1980). Almost 31% of games were associated with a giveaway/promotion such as cap day or bobble head day; 11 home games and 6% of games were followed by a fireworks display (Lemke et al., 2009). On the Stubhub website, they indicate whether on site promotions take place on a particular game day, so fans can use this information to d ecide if they will attend games. Rivalry has been included as a potential factor in this study. Fillingham (1977) insisted that strong rivalries between teams are a contributing factor of demand. Rivalries between teams have
25 been decided based on the report by local sports reporters and sports e conomists (Lemke et al., 2009). The assignment of division rivals was not necessarily mutual. The Orioles consider the Red Sox and Yankees both to be division rivals because of not only their proximity to those teams but also to their recent success es. Ho wever, the Red Sox and Yankees do not consider the Orioles to be their rival. Additionally, the Red Sox and the Yankees do regard each other as a only these tea ms, a n all inclusive list of rivals is given in Table 2 1. Division rivalry games account for 9.4% of all games, and these rivalry games will be closely examined as to whether or not these rivalrie s affect the ticket price. Interleague games also need to be considered as a possible variable. This will be included along with a variable indicating the home team playing an interleague rival. Particularly, MLB schedules ten interleague rivalry match ups each year based on geography: Baltimore vs. Washington, the Chicago White Sox vs. Cubs, Cleveland vs. Cincinnati, Kansas City vs. St. Louis, the Los Angeles Angels vs. Dodgers, Minnesota vs. Milwaukee, the New York Yankees vs. Mets, Oakland vs. San Francisco, Tampa Bay vs. Florida, and Texas vs. Houston. Interl eague games account for 11.1% of all games played, and interleague rivalry games account for 2.7% (Lemke et al., 2009). Squads of the game will certainly be considered as one of the potential factors. In MLB games, offensive squads are relatively consisten t throughout the season. However, starting pitc hers always change game to game. The starting pitcher is the most influential player of the sport of baseball. Evidently the game played by a quality starting pitcher who is in a low er ERA on the team is like ly to influence more positively for the fans willingness to pay for the ticket of
26 games than the games whose starting pitcher is in a higher ERA Therefore, we will consider the squads of the game as a factor and specifically look at the starting pitchers as a potential factor. The win loss record of the current years will be one of the potential factors such as team placement in the standings (Bird, 1982; Hart et al., 1975; Hay & Thueson, 1986; Hill et al., 1982; Siegfried & Eisenberg, 1980). Numer ous studies revealed the most important factor to influence demand condition (Reese & Middlestaedt, 2001; Drayer & Shapiro, 2009). Most studies of the factors of demand fluctuation use some type of historical measure s of team performance as explanatory variables. Standard measures used are winning percentage during the current season, league standing in the current season, or a measure of performance over recent seasons. A strong and consistent finding from these stud ies is that home team performance (current and lagged) has a positive effect on attendance. According to the study by Drayer and Shapiro (2009), fans prefer to pay more money to watch a better team that is more likely to have success. Ultimately, it seems that fans place a higher value on successful teams and are more likely to attend next game (Drayer & Shapiro, 2009). Therefore, current year s of team performacnes will be potential factors. The quality of the opponent will be regarded a s a p otential factor. Perceived value of the game likely depends on which team is visiting (e.g., a playoff team from last season or a team with a superstar like Alex Rodriguez ; Lemke et al., 2009). Some teams are thought to have loyal road fans that travel wi th th eir teams or home team fans who just come to the game to see a particular visiting team compete with their teams. Some of these matches might be repetitive to a rivalry factor section, but it is not necessarily repetitive. Th e quality of opponent sect ion will be left separate from the rivalry section. The Cubs, Red Sox, and Yankees were the examples of those teams that surpass an attendance of 36,000 per road game. To investigate the leverage of
27 this factor, the author will try to learn the correlation between the league standing of opponents and the ticket price for the games. Because this research is trying to look at game by game fluctuation, the static factors of influence throughout the year will not be considered. Nature of sports factor will not be considered, because this factor involves why baseball is attractive as a sport and does not involve attending the games. Furthermore, the vicarious pleasure of relating to a winner will not be one of factors in this research as it explains why people as sociate with teams rather than explaining ticket price fluctuation. Ground W ork for T his R esearch Conceptually, the most directly related ground work for this research is the work by Reese and Mittelstaedt (2001). Their research was exploratory, attempting to investigate factors in establishing NFL ticket prices. In their research, they asked the management what they consider the most when they price NFL tickets. According to the research, the managements take expected demand for the NFL tickets into accoun t, and they answered factors that they think might influence the expected demand. The list of factors in order of importance is: 1) Team performance ; 2) Revenue needs ; 3) Public relations ; 4) Market toleration; 5) Fan identification; 6) Average league pric e; 7) Economic factors; 8) Facility capacity; 9) Competing entertainment; 10) Average income; 11) Facility condition; 12) Population; 13) TV/Media coverage; 14) Accessibility; and 15) Star playe rs (Reese & Mittelstaedt, 2001). Mondello and Rische (2003) de veloped this further maintain ing the concept that the expected demand determines ticket prices but using more quantitative approach to discover factors. According to their research, the number of wins from the previous year, income of the fans, population, and playing in a new stadium turned out to be positively correlated with the ticket price increase, and the size of payroll does not significantly impact the ticket prices (Rische & Mondello, 2003).
28 The most recent study of factors affecting demand for sp orts games is the work by Drayer and Shapiro (2009). They also used ticket prices of the secondary market as a dependent variable. on the pricing pattern of s difference in the perceived game quality game by game in terms of directly game related factors. They suggested 17 potential predictors, and they used OLS regression to reduce and cross validate pot ential factors. They found eight factors to be significantly related to the demand. Those factors include: 1) the closer to the ch ampionship round of the playoff; 2) face value of tickets; 3) tot al number of bids in the market; 4) the uncertainty of outcom e ; in the current year ; ; 7) population ; and 8) income. Stadium Occupancy in MLB Franchises The sports marketing field has made it imperative for franchises to utilize information on c onsumers to create profiles of attitudinal difference and behavior towards teams as well as towards advertised products (Neale & Funk, 2006). In order for a team to exist, there must be a fan base that purchases tickets and buy s team related merchandise pr oducts, and that demonstrates continued support for a team. In MLB, Scully (1989) insisted that the potential importance of fan loyalty and fan base has different level s of loyalty across tea ms located in different cities. James (1997) insisted that r ecogn izing that customers have varying levels of interest in a product or service underscores the importance of understanding what influences people to engage in repeat ed purchase According to related literature (Mahony et al 2000), psychological attachment to a team varies, and the degree of identification with a team may be an important moderator. In other words, psychological attachment to a team or the popularity of the team can affect variables that fans like. So learning
29 how mu ch a fan identifies with a team is important in developing marketing strategies to attract fans to the stadium This study will divide all 30 MLB teams into three categories by the stadium occupancy rate. Hypothetically, three groups of teams will differ i n variable preference by fans. Some researchers including Wakefield, Sloan, and Depken regard this kind of categorization as loyalty, and suggested different methods of categorizing by loyalty. Wakefield and Sloan (1995) address fan loyalty in college foo tball using direct surveys to obtain self revealed levels of fan loyalty. Depken (2000) suggested that o ne alternative measure of fan loyalty may be the level of team oriented merchandise purchased by consumers, either nationwide or in the host city. However, these data are not easy to acquire and would be, at best, a measure of fan loyalty. Depken (2000) measures fan loyalty as being how unresponsive the fans are to changes in quality (measured by team success) or price. In this study, the author decided to regard this categorization as categorization by the attendance level of teams, rather than loyalty, because of the following: loyalty cannot be fully measu red without considering the attitudinal dimension of loyalty, but the stadium occupancy only represents the behavioral dimension of loyalty. F ranchise owners insisted that the most important manifestation of fan loyalty is displayed by stadium attendance ( Depken, 2000). However, measuring it; and the use of direct surveys is not possible in the current context. Therefore, the author operationally defined the occupancy rate o attendance level, and each of three groups was labeled as high, normal, and low attendance.
30 Table 2 1 describes the MLB 2011 season averaged stadium occupancy rate for each franchise team in MLB. Its ranking is consi stent with empirical findings. Franchise teams that are known for popularity such as the Boston Red Sox, the New York Yankees, and the Philadelphia Phillies are highly ranked. As mentioned previously, all teams were divided into three categories: 1) high a ttendance teams, 2) normal attendance teams, and 3) low attendance teams. Out of all 30 teams, the MLB teams of average stadium occupancy rate higher than 80% have been labeled as high attendance teams. Those include the Philadelphia Phillies, the Boston R ed Sox, the San Francisco Giants, the Minnesota Twins, the Chicago Cubs the Milwaukee Brewers, the NY Yankees the St. Louis Cardinals, and the LA Angels The MLB teams of average stadium occupancy rate between 60% and 80% have been labeled as normal att endance teams. Those include Detroit Tigers, Texas Rangers, NY Mets Colorado Rockies, LA Dodgers Cincinnati Reds, Pittsburgh Pirates, Houston Astros, San Diego Padres, Chicago White Sox and Atlanta Braves. The MLB teams of average stadium occupancy rate lower than 60% have been labeled as low attendance teams. Those include Washington Nationals, Kansas City Royals, Tampa Bay Rays, Arizona Diamond Backs, Cleveland Indians, Oakland Athletics, Seattle Mariners, Florida Marlins, Baltimore Orioles, and Toront o Blue Jays. H ypotheses Development This study is looking at the fluctuation of the ticket price of the secondary market. Hypotheses were developed factor by factor (7 factors), and the group difference by attendance level for each of these hypothe ses w as examined as well. The expected group difference by concept of loyalty has b een also utilized to supplement the reasoning. In this reasoning, the
31 concept of loyalty and the level of attendance were utilized interchangeably. According to Depken (2000), stadium attendance is the most important manifestation of fan loyal ty (Depken, 2000). In addition, the ticket price of the secondary market and the perceived value of games were utilized interchangeably as well. value of games (Nol l 1974; Hill et al. 1982; Depken 2000; Siegfried and Zimbalist 2000; Schmidt and Berri 2001; Winfree et al. 2004; Meehan et al. 2007; Lemke et al. 2009; Davis, 2009) Convenience of schedule is the first thing to consider when fans decide to attend MLB gam es. Therefore, it will significantly affect the ticket price of the secondary market. H 1: Day of the week is significantly related with the ticket price of the secondary market The ticket price of weekend games will be significantly higher than weekday games. Several researchers found rivalry to be a significant factor (Fillingham, 1977; Lemke et al., 2009; Levin et al., 2000) According to Rascher and his fellow colleagues (2007), a rivalry game positively affects demand for MLB games. However, not eve ry fan will have the same emotional arousal to rivalry games. Fans of highly attended teams will care less about the rivalry match and the concept of rivalry will be weak as well. H2: Rivalry variable is significantly related to ticket price of the second ary market This study considers interleague games as variables, similar to previous attendance research (Lemke et al., 2009; Meehan, Nelson, & Richardson, 2007). Interleague game has less seaso n play (Butler, 2002), so highly attended team fans will care less about the interleague games. However, interleague games account for only 11% of all games, so it has more novelty effect than intraleague games. This novelty would influence casual fans.
32 H 3: Interleague game is significantly related with the ticket price of the secondary market Many researchers insist that the perceived value of the game likely depends on which team is visiting (e.g., a playoff team from last season or a team with a supe rstar like Barry Bonds or Alex Rodriguez ; Rascher et al., 2007; Lemke et al., 2009). The desire to attend games with good H4: Quality of opponent is significantly and positively related with the ticket price of the secondary market Loyal fans are regarded as more constant than non loyal fans, and their value of games do es not fluctuate much. H5 address es BIRGing and CORFing effect Because BIRGing and CORFing effect is more prevalent amon g non loyal fans (Wann and Branscombe, 1990) it is expected that s will have different effects among different groups of fans. H5: significantly and positively related with the ticket price of the secondary market According to long time C ubs broadcaster, Harry Car ay, casual fans care less for the game on the field than loyal fans but care more about having fun at the ballpark (Butler, 2002). On site promotion can enhance the entertainment value of the games, which might contribute toward low attendance team value of games but not much toward low attendance team fans. H 6: On site promotion is significantly related with the ticket price of the secondary market. The squad variable means the exceptional starting pitchers. Several researchers insisted that vicarious achievement seeking toward individual players does exist among fans (Funk et al., 2002; Trail et al., 2000; Trail et al., 2003), so this factor is expected to increase th e perceived value of games. Especially, l oyal baseball fans tend to be more knowledgeable about the baseball
33 rules starting lineups, and player statistics (Butler, 2002). Therefore, highly attended team fans know the starting line up well ahead of games a nd value the games accordingly. H7: Squad of the game is significantly related with the ticket price of the secondary market.
34 Table 2 1. Division r ivalries Home Team Division Rivals Baltimore Orioles Boston Red Sox New York Yankees Boston Red So x New York Yankees New York Yankees Boston Red Sox Tampa Bay Rays None Toronto Blue Jays New York Yankees Cleveland Indians Detroit Tigers Chicago White Sox Cleveland Indians Detroit Tigers Detroit Tigers Cleveland Indians Kansas City Royals None Minnesota Twins Cleveland Indians Los Angeles Angels Oakland Athletics Oakland Athletics Los Angles Angels Seattle Mariners None Texas Rangers None Atlanta Braves New York Mets Florida Marlins Florida Marlins Atlanta Braves New York Mets Atlanta Braves Philadelphia Phillies Philadelphia Phillies New York Mets Washington Nationals None Chicago Cubs Milwaukee Brewers St. Louis Cardinals Cincinnati Reds None Houston Astros None Milwaukee Brewers Chicago Cubs Pittsburgh Pirates None St. Lou is Cardinals Chicago Cubs Arizona Diamondbacks Colorado Rockies Colorado Rockies Arizona Diamondbacks Los Angeles Dodgers San Francisco Giants San Diego Padres None San Francisco Giants Los Angeles Dodgers
35 Table 2 2 upancy rate Team Stadium Occupancy Rate Philadelphia Philles 104.1 Boston Red Sox 101.7 San Francisco Giants 99.8 Minnesota Twins 99 Chicago Cubs 90.5 Milwaukee Brewers 90.5 NY Yankees 89.7 St. Louis Cardinals 86.9 LA Angels 86.1 Detroit Tigers 7 9.1 Texas Rangers 74 NY Mets 72 Colorado Rockies 71.1 LA Dodgers 64.7 Cincinnati Reds 64.6 Pittsburgh Pirates 63.2 Houston Astros 62.3 San Diego Padres 62 Chicago White Sox 60.8 Atlanta Braves 60.4 Washington Nationals 59.9 Kansas City Royals 5 6.2 Tampa Bay Rays 55.4 Arizona Diamondbacks 53.4 Cleveland Indians 52.3 Oakland Athletics 52 Seattle Mariners 48.9 Florida Marlins 48.8 Baltimore Orioles 48.3 Toronto Blue Jays 45.6
36 CHAPTER 3 METHOD This chapter describes the statist ical desi gn and procedures that were used to investigate wh ich factors affect the fluctuation of ticket prices in the secondary market. The methodology chapter of this stu dy consist s of the following sections: (1) Statistical Design, (2) Procedures, and (3) Model S pecification. Statistical Design A standard multiple li near regression equation was adopted to examine the relationship between the seven variables identified in previou s research and MLB ticket prices in the secondary market through online auction. Due t o the non existence of a model that examined demand fluctuation of MLB games, the model for this research was created through an evaluation of previous literature in the areas of ticket price determinants in the primary market (Reese & Middlestaedt, 2001; Rishe & Mondello, 2003, 2004). The Variables in the Research Model A total of seven variables were used to investigate the f actors affecting MLB ticket prices of the secondary market This study is unique in that it us es ticket prices of the secondary mar ket as opposed to the primary ticket market that is assessed in other studies. The following section describes each of the explora tory variables consisting of the model. Dependent Variable Ticket prices for MLB games t he average secondary market ticket p rices that are sold through online auction during the 2011 season. ticket price in the secondary market was used. Each MLB game has approximately 3,000 listed ticket prices of the secondary market. The author used the average price for each game as a representative price for each game.
37 Independent Variables 1. Day of the Week (GDAY) This variable means that t he day that a specific game is being held on (e.g. Monday through Sunday and indicating whether it is a holiday or not) This variable coded as dummy variable. The day for the game has been coded as 1, and all other days have been coded as 0. 2. Current League Standing (STANDING) division at the time of the ga me for the home team during the current season Each of divisions h as five to six teams, so it has be en coded as 0 to 5. The lower absolute value of the place means higher quality team. Therefore, the number will be coded reversely. 3. Interleague game (IN TER) A variable indicating whether it is an interleague game. Interleague games played between teams from the National and American Leagues If it is an interleague game, it has be en coded as 1. The other cases have be en coded as 0. 4. Quality of the opponent (OP PONENT) The league standing within an opponent at the time of the game for the road team duri ng the current season. This has be en 5. Rivalry (RIV) A variable indicating whether rivalries exist between the competing two teams, d etermi ned by baseball experts. If it wa s an interleague game, it has be en coded as 1. The other cases have be en coded as 0. 6. Squad of the game (SQUAD) This variable shows the appe arance of the Cy young award nominee starters. If Cy Young award nominee pitche rs appear in the game, this has be en coded as 1. The other cases have be en coded as 0. 7. On site promotion (PROMO) A variable indicating if on site promotions (e.g. firewor ks, giveaways, etc.) take place If it was a promotional day, it has been coded as 1, and all other days have been coded as 0.
38 Data Collection Data on the variables in the seco ndary ticket price model were collected from a variety of sources. Team succes s and spread data were collected from ESPN.com. The game related data in this study were from http://www.baseball reference.com The ticket prices data were collected from Stubhub.com using print screen for all MLB games during 2011 MLB season. Data from the play off were not collected because the unique nature of this particular game would potentially skew the results. A total of 2,430 games complet co llected during this time per iod. T he auctions for every game provid e the sale of multiple tickets. Multiple prices for an individual game were averaged, and the averaged price represented for each game. Data and the Empirical Specification Following the existing empirical research i n the sport economics literature (Rische & Mondello, 2003; Lemke et. al, 20 09) the ticket price of the secondary market for baseball game may be estimated as follows: Ticket Price = Where: represents team; is home g ame played; is explanatory variables that take place on a particular game, and is the error term. Fixed effects and random effect s are employed for estimation. The fixed effects model takes into account certain unobserved team sp ecific variables, which are constant for each game and correlated with other explanatory variables. Under this assumption, is added to take into account the team specific variables. Instead of treating the team specific variable as fixed cons tants over each game played, the random effects model regard that team specific terms are randomly distributed across sectional units. So, the error term
39 is often assumed to consist of the team specific ( ) and the combined time specific and te am specific volatility ( respectively. Under a random effect specification, the error term above may be describe d as follows: where Where is a team and time specific effects combined (Hsiao, 1986; Ho ndroyiannis, 2009). The random effects model is appropriate for estimation purposes (Haylan et. al, 1997). Model Specification Ticket Price = The Bs represent s the Beta coefficients assigned to each of the independent variables during the r egression analysis. The equation above is used to estimate the secondary ticket market for the MLB Following the existing literature on the factors affecting the demand for game attendance, a var iety of game characteristics were included in the model. Equ ation 1 may be re written as follows: where: i is the team; t is the game played. Ticket price, the dependent variable for this analysis, is defined as an average ticket price in the secondary mark et To control for the time of the game and the day of the week the game is played, the study uses several dummies. The separate dum my variable for each day, TUESDAY through SUNDAY wa s included to see if the day of the week influenced overall game day attendance (Meehan et al., 2007). In order t o examine whether t he demand fluctuation, the (CUR_WIN)
40 was used as variables. (OPP_WIN) wa s included as a variable to see if how much the quality of opponent would affe ct the secondary market ticket price for the game, and the same approach as performance measurement was used. MLB has two big leagues: American League (AL) and National League (NL). Each of these leagues has three divisions, so MLB has total six divisions. Four divisions: AL East, AL Central, NL East, NL West has five teams within their division each. AL West has four teams, and NL Central has six teams within their divisions. The highest place is the first place, and the lowest plac e is the sixth place. The lower the absolute value of the place is, the better the team is, so this study coded numbers for place=2, the fifth place=1, and the sixth place=0). Out of all independent variables, only these two independent variables, Quality of opponent (OPP_WIN) and League standing (CUR_WIN) are codes as continuous variables. All other independent variables including day of the week, rivalry, int erleague, promotion, and squad variable are coded as dummy variable, Rivalry (RIV) variable has been added to analyze its e ffect on demand. Rivalry is determined by rivalry list made by MLB experts. With regard to rivalry factor, this study mentioned in th e literature review part, rivalry is not necessarily mutual. Rivalry was coded as 1 when a home team competes with the team that they consider as a rival. Team roster (SQUAD) variable has been included to capture the demand variation by the quality of sta rting pitchers. Squad variable means that the Cy young award nominee starting pitchers appeared on games as a starter. If one of them started the game, that game would be coded as 1, and other games would be coded as 0. 2011 Cy young award nominee starters include 18 pitchers: Justin Verl ander (Detroit Tigers), Jered Weaver ( Los Angeles Angels ),
41 James Shields ( Tampa Bay Rays ), CC Sabathia ( New York Yankees ), C.J. Wilson ( Texas Rangers ), Dan Haren ( Los Angeles Angels ), Josh Beckett ( Boston Red Sox ), Ricky R omero ( Toronto Blue Jays ), Clayton Kershaw (Los Angeles Dodgers), Roy Halladay ( Philadelphia Phillies ), Cliff Lee ( Ph iladelphia Phillies), Ian Kennedy ( Arizona Diamondbacks ), Cole Hamels (Philadelphia Phillies), Tim Lincecum ( San Francisco Giants ), Yovani Gallardo ( Milwaukee Brewers ), Matt Cain ( San Francisco Giants ), Madison Bumgarner ( San Francisco Giants ), Ryan Vogelsong ( San Francisco Giants ). Promotion (PROMO) variable has been included to obtain the information about the effectiveness of promotion. Promotional information from MLB.com was used to determine promotion days. Out of all promotional methods, three methods including fireworks, giveaway, and concerts have been regarded as promotion, because those are promotion information that Stubhub post ed on the website with the ticket price section. And the promotion days were coded as 1, and other days were coded as 0.
42 CHAPTER 4 RESULTS T he results of the data analyses are described in this chapter. First, descriptive statistics (e.g., number of case s and mean values) for dependent and independent variables may be found in Table 4 2. Second, correlation analysis was conducted to see if each of the variables has discriminate validity, and this analysis is shown in Table 4 1. Third, regression analysis for all cases was conducted and may be found in Table 4 3. This analysis will show the significance of each independent variable. Fourth, the regression analysis for three different groups was conducted, and results are shown in Table 4 4 through Table 4 1 0. Each of the variables for the three different groups was combined together and then compared to the corresponding hypotheses that were developed in the previous thesis section. Preliminary Analysis C omparisons of multiple correlations among construc ts also were employed for discriminate validity. Kline (2005) suggested that discriminate validity may be established if correlations among constructs are less than .85. The correlation matrix is presented in Table 4 1 Descriptive Analyses In the total of ticket price data are missing due to no transaction were the game was rained out. Therefore, the 2,411 ticket price data have been used for the following analysis. Each team has appr oximately 80 games included in the data to calculate the average ticket price. Descriptive statistics and the data source for all variables used in the analysis are reported in Table 4 2 The average ticket price of all MLB games is $55.23. The difference s in the average prices for each team are presented in Table 4 2 as well Not surprisingly, high
43 between the normal and low attendance teams is not very st rong Independent variables including day of the week, rivalry, interleague, promotion, and squad are coded as dummy variables, so sum values of those dummy variables can show how frequently each of those variables occurs. In terms of the day of the week varia ble, sum values for each day is similar, aside from Monday and Thursday. This means that that all games are similarly distributed throughout seven days, except Monday and Thursday. Monday and Thursday have often become off days for the travel time for the teams, and that is why these two days have the least number of games. Rivalry games happened 239 times out of all MLB games, approximately 10% of all games. Interleague games happened 250 times, and this variable accounts for about 10% of all games. Promot ions were conducted for 36% of home games, which happened 878 times according to information about the 2011 promotional schedule obtained from MLB.com. Regular promotions happening every week and promotions related to the price of tickets (e.g., family pac because the Stubhub website indicates promotion events for the specific game days, and those price promotion and routinely happening promotion information are not shown on the website. Squad variable means that the Cy Young award nominee starting pitcher appeared in games as a starter. This happened 11% of all games, which is equivalent to 284 games. Regression Analysis R egression analysis was employed t o test the seven hypotheses developed in the se cond chapter. Regression analysis was used to test the general relationship betw een independent variables (i.e., day of the week, holiday, rivalry, interleague, quality of opponents, league standing, promotion, and quality of squad) and dependent variable (i.e., ticket prices of the secondary market). The regression analysis for hypotheses is presented in Table 4 3. Hypothesis
44 1 states that Day of the week is positively related with the ticket price of the secondary market Thus, t he ticket price of weekend games will be significantly higher than weekday games. For the day of the week variable, the separate dummy variable for each day, TUESDAY through SUNDAY is included to control the day of the week effect on the ticket prices (Meehan et al., 2007). MONDAY has been excluded to avoid the potential linear combinations among the variables so MONDAY was placed as a dummy variable The other variables are coded as just 0/1. As expected, the estima ted coefficients for the weekend dummy variables are positive sug gesting the ticket prices for the FRIDAY 93 p < .05 ) SATURDAY 29 p < .05 ) SUNDAY 2 p < .05) and HOLIDAY 7 p < .05) games are gr eater than those for the week days (Denaux et al. 2011). Hypothesis 2 states that Rivalry va riable is positively related with ticket price of the secondary market. Rivalry variable 5 p < .05) shows a significantly positive impact on the ticket price of the secondary market. The result supports the second hypothesis. Hypothesis 3 state s that Interleague game is positively related with the ticket price. The regression analysis for interleague variable 69 p < .05) shows that this variable is significantly positive to the ticket price of the secondary market. Therefore, the result supports hypothesis 3. Hypothesis 4 states that Quality of opponent is positively related with the ticket price. The regression analysis for this hypothesis shows that Quality of opponent variable < .05 ) is significant and positive to the ti cket price of the secondary market. Thus, the result supports hypothesis 4. related with the ticket price. The regression analysis for this hypothesis shows that league
45 st anding .07 5 p < .05) is significant and positive to the ticket price of the secondary market. Thus, the result supports hypothesis 5. Hypothesis 6 states that Promotion is positively related with the ticket price. The regression analysis for this hypothesi s shows that Promotion ( .1 18 p < .05) is significant but negative to the ticket price of the secondary market. The result does not support hypothesis 6. Thus, we fail to reject the null hypothesis 6. Hypothesis 7 states that Squad is positively rela ted with the ticket price. The regression analysis for this hypothesis shows that Squad 21 p < .05) is significant and posi tive to the ticket price of the secondary market. Therefore, the result supports hypothesis 7. Group Difference by the Team Attendance Level After conducting analysis for hypothesis testing, this study identified that most independent variables are significant and positive to the ticket price of the secondary market. In this part, this study investigate d nce level influences independent variables. In other words, which variables are perceived to be important according to the attendance level of the teams. Hypothesis 1 states that Day of the week is positively related with the ticket price of the secondary market ( t he ticket price of weekend games will be significantly higher than weekday games). The moderating effect of the team attendance level for hypothesis 1 is presented in Table 4 4. The results show that the ticket price of the secondary market of gam es for weekend days are significantly higher than those for the weekday games except Sunday and Holiday for normal attendance team fans. Hypothesis 2 states that Rivalry variable is positively related with ticket price of the secondary market The moderat ing effect of the team attendance level for hypothesis 2 is presented in Table 4 5. The results indicated rivalry factor for high attendance teams ( < .05 ), normal attendance teams 3 p < .05) and low attendance teams 8 p
46 < .05 ) Rivalry factor is significant for all three groups, but it is more influential for high attendance and low attendance team fans than normal attendance team fans. Hypothesis 3 states that Interleague game is positively related with the ticket price. The moderating effect of the team attendance level for hypothesis 3 is presented in Table 4 6. Interleague variable for high attendance teams are normal attendance teams 00 p < .05) and low attendance teams 58 p > .05). Interleague variable for popular teams and unpopular teams is not significant, but this variable is significant for normal attendance teams = .1 00 p < .05). Hypothesis 4 states that Quality of opponent is positively related with the ticket price. The moderating effect of the team attendance level for hypothesis 4 is presented in Table 4 7. Quality of opponent factor influences the perceived value of games for high attendance teams p < .05), normal attendance teams 5 p < .05), and low attendance teams ( < .05). related with the ticket price. The moderating effect of the team attendance level for hypothesis 5 is presented in Table 4 8. high attendance teams and low attendance teams .04 6 p > .05), but League standing variabl e for normal teams was significant Hypothesis 6 states that Promotion is positively related with the ticket price. The moderating effect of the team attendance level for hypothesis 6 is shown in Table 4 9. On site promotion variables for high attendance teams .18 0 p > .05) and low attendance teams = .08 9 p > .05) show a significant relationship with the perceived value of a game but this variable has a rather negative influence on the perceived value of the game for those two groups
47 of fans. However, this promotion variable was positively significant for the normal attendance teams 7 p < .05) Hypothesis 7 states that Squad is positively related with the ticket price. The moderating effect of the team attendance level for hypothesis 7 is presented in Table 4 10. The results indicate squad of the game affects low attendance teams 78 p < .05), but did not show significance for high attendance teams and normal attendance teams .00 4 p > .05).
48 Table 4 1 Correlation analysis for each variable Price Rival Interleague Opponents Standing Promotion Squad Price 1 .000 .206** .069** .100** .097** 0.003 .141** Rival .206** 1 .000 .113** .134** 0.039 0.019 0.013 Interleague .069** 113** 1 0.011 0.016 .076** 0.006 Opponents .100** .134** 0.011 1 .107** .043* 0.001 Standing .097** 0.039 0.016 .107** 1 0.024 .266** Promotion 0.003 0.019 .076** .043* 0.024 1 0.015 Squad .141** 0.013 0.006 0.001 .266** 0.015 1 Day_Tue .08 6** 0.007 .042* 0.009 0.01 .148** 0.003 Day_Wed .110** 0.013 0.036 0.001 0.001 .227** 0.021 Day_Thu .041* 0.008 .058** 0.008 0.016 .106** 0.031 Day_Fri .078** 0.008 .060** 0.007 0.009 .234** 0.011 Day_Sat .139** 0.003 .055** 0.008 0 .18 5** 0.005 Day_Sun .066** 0.005 .057** 0.006 0.006 .172** 0.011 Holiday 0.018 0.015 .047* 0 0.006 .041* 0.012 Table 4 1. Continued Day_Tue Day_Wed Day_Thu Day_Fri Day_Sat Day_Sun Holiday Price .086** .110** .041* .078** .139** .066** 0.018 Rival 0.007 0.013 0.008 0.008 0.003 0.005 0.015 Interleague .042* 0.036 .058** .060** .055** .057** .047* Opponents 0.009 0.001 0.008 0.007 0.008 0.006 0 Standing 0.01 0 0.001 0.016 0.009 0 0.006 0.006 Promotion .148** .227** .106** .23 4** .185** .172** .041* Squad 0.003 0.021 0.031 0.011 0.005 0.011 0.012 Day_Tue 1 .181** .145** .182** .185** .184** .058** Day_Wed .181** 1 .146** .183** .186** .185** .059** Day_Thu .145** .146** 1 .146** .149** .148** .047* D ay_Fri .182** .183** .146** 1 .000 .188** .186** .059** Day_Sat .185** .186** .149** .188** 1 .190** .060** Day_Sun .184** .185** .148** .186** .190** 1 .060** Holiday .058** .059** .047* .059** .060** .060** 1 Note. ***p < .001; **p < .01; *p < .05. Day_Mon is coded as a dummy variable.
49 Table 4 2 Descriptive statistics for each variable Table 4 3 Regression analysis results for s even hypotheses Unstandardized B eta Std. Error Standardized Beta t R Square (Constant) 34.768 2.706 12.988*** Day_Tue 1.051 2.512 0.012 0.418 Day_Wed 1.337 2.508 0.015 0.533 Day_Thu 3.905 2.72 0 0.037 1.436 Day_Fri 17.011 2.59 0 0.193 6.567 ** Day_Sat 19.955 2.557 0.229 7.803 *** Day_Sun 15.08 2.559 0.172 5.894 *** Holiday 13.523 4.863 0.057 2.781 ** 0.0 49 Rival 20.923 2.079 0.195 10.066 *** 0.0 42 Interleague 7.226 2.022 0.069 3.573 *** 0.00 4 Opponents 1.81 0 0.419 0.084 4.322 *** 0.005 Sta nding 1.621 0.432 0.075 3.752 *** 0.01 0 Promotion 7.829 1.408 0.118 5.561 *** 0 .012 Squad 11.955 1.954 0.121 6.118 *** 0.014 Note. ***p < .001; **p < .01; *p < .05. Sum Mean Skewness Std. Error Kurtosis Std. Error Dependent Variable Price All teams 133001.07 55.2266 2.878 0.05 15.879 0.1 H igh 69.4006 Normal 48.3406 Low 49.8996 Independent Variables Day of the Week Played on Tuesday 369 0.1527 1.929 0.05 1.721 0.1 Wednesday 371 0.154 0 1.92 0 0.05 1.686 0.1 Thursday 251 0.1045 2.594 0.05 4.734 0.1 Fr iday 375 0.1561 1.902 0.05 1.619 0.1 Saturday 386 0.1598 1.855 0.05 1.442 0.1 Sunday 382 0.159 0 1.871 0.05 1.503 0.1 Holiday 45 0.0187 7.118 0.05 48.7 00 0.1 Rivalry games 239 0.0986 2.685 0.05 5.211 0.1 Interleague games 250 0.104 0 2.602 0.05 4.772 0.1 Quality of opponent 7229 2.9954 0.151 0.05 1.101 0.1 League standing 7329 2.958 0 0.196 0.05 1.068 0.1 Promotion 878 0.3645 0.565 0.05 1.682 0.1 Quality of s quad 284 0.1182 2.372 0.05 3.63 0 0.1
50 Table 4 4 Group d ifference of day of the week variable on ticket prices Day of the U nstandardized Standardized Week Beta Std. Error Beta t R Square High Day_Tue 0.889 6.062 0.008 0.147 Day_Wed 2.414 6.016 0.021 0.401 Day_Thu 7.785 6.535 0.057 1.191 0. 091 Day_Fri 25.6 00 6.135 0.217 4.173 *** Day_Sat 34.622 6.068 0.29 9 5.706 *** Day_Sun 27.016 6.156 0.23 0 4.388 *** Holiday 15.084 11.184 0.05 0 1.349 Normal Day_Tue 0.048 2.823 0.001 0.017 Day_Wed 1.69 0 2.83 0 0.027 0.597 Day_Thu 1.491 3.166 0.019 0.471 0.0 68 Day_Fri 8.429 3.001 0.137 2.809 ** Day_Sat 8 .771 2.907 0.143 3.017 ** Day_Sun 5.399 2.927 0.088 1.844 Holiday 7.53 0 6.273 0.04 0 1.2 00 Low Day_Tue 1.97 0 3.513 0.029 0.561 Day_Wed 0.11 0 3.511 0.002 0.031 Day_Thu 2.143 3.719 0.028 0.576 0.0 19 Day_Fri 10.229 3.63 0 0.151 2.818 ** Da y_Sat 11.846 3.631 0.175 3.263 ** Day_Sun 7.856 3.558 0.117 2.208 Holiday 13.415 6.264 0.079 2.142 Note. ***p < .001; **p < .01; *p < .05. Table 4 5 Group difference of rivalry variable on ticket prices Rivalry Unstandardized Standardize d Variable Beta Std. Error Beta t R Square High 24.401 4.385 0.192 5.565*** 0.0 37 Normal 6.478 2.564 0.08 3 2.503* 0.01 2 Low 25.783 3.141 0.278 8.208 *** 0.0 81 Note. ***p < .001; **p < .01; *p < .05.
51 Table 4 6 Group difference of interleagu e variable on ticket prices Interleague Unstandardized Standardized Variable Beta Std. Error Beta t R Square High 9.114 4.981 0.064 1.83 0 0.0 04 Normal 7.267 2.343 0.1 00 3.104** 0.01 1 Low 4.64 0 2.684 0.058 1.729 0.00 3 Note. ***p < .001; **p < .01; *p < .05. Table 4 7 Group difference of opponent quality variable on ticket prices Opponent Unstandardized Standardized Variable Beta Std. Error Beta t R Square High 3.284 0.997 0.114 3.293** 0.00 9 Normal 1.589 0.496 0.10 5 3.216** 0.0 07 Low 1.298 0.57 0 0.077 2.278 0.007 Note. ***p < .001; **p < .01; *p < .05. Table 4 8 Group difference of league standing variable on ticket prices Standing Unstandardized Standardized Variable Beta Std. Error Beta t R Square High 0.195 1.064 0.007 0.184 0 Normal 2.127 0.49 0 0.143 4.348*** 0.02 Low 0.901 0.659 0.046 1.367 0.00 2 Note. ***p < .001; **p < .01; *p < .05. Table 4 9 Group difference of promotion variable on ticket prices Standing Unstandardized Standardized Variable Beta Std. Error Beta t R Square High 16.567 3.281 0.18 0 5.05 0 *** 0.0 30 Normal 5.325 1.743 0.11 7 3.085** 0.0 09 Low 4.624 1.958 0.089 2.362 0 .007 Note. ***p < .001; **p < .01; *p < .05.
52 Table 4 10 Group difference of squa d variable on ticket prices Standing Unstandardized Standardized Variable Beta Std. Error Beta t R Square High 4.336 3.488 0.045 1.243 0.002 Normal 0.398 3.229 0.004 0.122 0 Low 18.076 3.382 0.178 5.346 *** 0.031 Note. ***p < .001; **p < .01 ; *p < .05.
53 CHAPTER 5 DISCUSSION This chapter interprets the statistical results and provides research and practical implications. Then, contributions to the literature and sports marketing are discussed. Finally, the author concludes this secti on with limitations and future suggestions. Results Analysis Hypothesis 1 was that Day of the week is positively related to the ticket price of the secondary market The result shows weekdays (Tuesday, Wednesday, and Thursday) are not significant to the ti cket price of the secondary market. The moderating effect on the team attendance level was not very significant. However, the result shows that weekend affects significantly the ticket price of games, except Sunday and Holiday p > .05) for normal attendance teams. It seems that Sunday and Holiday are less desirable for normal attendance team fans in the secondary market than Friday and Saturday Hypothesis 2 states that the Rivalry var iable is positively related to the ticket price of the secondary market. The results indicated that the rivalry factor was significant and positive to the ticket price of the secondary market, and the m oderating effect on t he team attendance level was sig nificant as well. The regression result of the Rivalry variable for high attendance teams = .192, p < .05) is significant. This result was expected because high attendance team fans are more likely to identify with their teams than low attendance team fans. The Rivalry factor was revealed as significant for normal attendance team fans 082, p < .05), but its coefficient is much smaller than high attendance team fans. Finally, low attendance team coefficient value for Rivalry factor was the highest among the three groups of fans. This was surprising as numero us studies indicated that low attendance team fans care more about having fun at the ballpark and care less about the game itself than high attendance team fans
54 (Butler, 2002). The author interpreted that low attendance team fans perceive highly the rivalr y games as they love the high tension and hostile game environment that rivalry games arouse. Hypothesis 3 states that Interleague game is positively related to the ticket price. The moderating effect of the team attendance level on this variable was sign ificant. The Interleague variable was only significant for normal attendance teams but not for the other two groups: high attendance team fans and low attendance team fans = .089, p > .05). This finding is consisten t with the literature stating that fans who highly identify with teams care less about an interleague game because of its relatively small impact on season play (Butler, 2002). However, interleague games account for only 11% of all games; its novelty effect makes normal fans perceive higher for these games than i ntra league games. As far as low attendance teams are concerned, interleague games are not affecting the ticket price of games significantly. L ow a ttendance team fans tend to be not very knowledgeable about the schedule of MLB and the league composition. According to Bristow & Sebastian (2000), die hard MLB fans tend to have greater levels of knowledge about MLB rules and schedule than less loyal fan s due to their greater involvement with and exposure to the team. Therefore, low attendance team fans are not much driven by the i nterleague variable. Hypothesis 4 states that the Quality of opponent is positively related to the ticket price. The moderat ing effect of the team attendance level on this variable was not significant. The Quality of opponent factor enhances the perceived value of games for all three groups: high attendance team fans normal attendance team fans, p < .05), and low attendance team fans It is consistent with the literature (Lemke et al., 2009; Rascher et al., 2007).
55 Hypothesis 5 related to the ticket pric e. Hypothesis 5 is confirmed by the regression analysis. The moderating effect of the team attendance level on this variable was significant. The standing factor turned out to be not significant for high attendance team fans > .05). This is consistent with the literature: high attendance team fans are likely to be die hard and support their team regardless of how well the team is currently performing (Wann & Branscombe, 1990). The League standing variable for normal attendanc e team fans was significant This is also consistent with the previous findings. Normal attendance team fans are likely to be fair weather fans, and they go through the BIRGing and CORFing process (Wann & Branscombe, 1990). Therefore, normal team fans perceive th e games higher when their teams are in a good shape than when their teams are not. It was somewhat unexpected that the league standing factor for low attendance team fans .044, p > .05) was not significant. This author interpreted that low attendanc e team fans do not strongly attendance team fans do. Hypothesis 6 predicted that Promotion is positively related to the ticket price. The result did not support hypothesis 6, but the moderating effect of the on site promotion variable was significant. This variable for high attendance team fans .18, p > .05) and low attendance team fans .084, p > .05) was significant and negatively affect ed the perceived value of games The author interpreted that watering down effect might have caused the negative relationship. MLB franchise team i nvolved with 38% of promotion. If we include the price promotion including family package day, ladies day, and senior day, the frequency of promotion would well be over 50%. If the franchise team has involved with too many promotion, the
56 consumers will ge t used to it and less likely to react the promotion (Mcdonald & Rascher, 2000). One potential reason for this negative relationship may be due to the recorded promotion days within the data set. Routinely or frequently occurring promotional events (e.g. we night) were not included within the assessment for promotion. Furthermore, price promotions such as family packages were also not been taken into account, since these prices are not related to the secondary market ticket price but the ticket p rice of the primary market. In addition, these promotion methods are not Stubhub does not offer these kinds of promotion information on their website. These two things that are not considered as promotion might have cause d the negative promotion result for two groups of fans. However, this promotion variable was positively significant to the normal attendance teams ). Hypothesis 7 states that Squad is positively related with the ticket price. Hypothesis 7 is confirmed by the regression an alysis, and the moderating effect of this variable was significant as well. The results indicated squad of the game affects low attendance team fans < .05) significantly but not high attendance team fans and normal attendan ce team fans .005, p > .05) This variable represents exceptional starting pitchers. Not many researchers have discussed this squad variable. It is fairly understandable that this variable affects the ticket price of the secondary market substantial ly for low attendance team fans. Fans of low attendance teams are not strongly identified with their teams, but they love to have fun at the ballpark and watch outstanding athletes. They typically identify with the exceptional players rather than their tea ms because their teams are usually in underdog status. However, normal and high attendance team fans identify enough with their teams, so it is less important for them who is a starter for the game than for low attendance team fans.
57 Conclusion The current results show that these specific individual variables : 1) day of the week; 2) rivalry; 3) interleague; 4) the quality of opponent; 5) current league standing; 6) promotion; and 7) the quality of squad are positively related with the ticket price of the se condary market except Promotion variable. Also, the moderating effect of the team attendance level is very significant. Each of the three groups reacts to the factors differently. High attendance team fans are resistant to the change caused by independent variables. This finding is consistent with the previous literature (Wann & Branscombie, 1990) According to Depken (2000), loyal fans are relatively unresponsive to factors such as team performance and ticket price increase. This indicates high attendance team fans highly identify with their teams and support them no matter what. Normal attendance team fans bond with their current performance and promotion. Low att endance team fans do not have a strong relationship with their teams. They want to watch something outstanding and are not necessarily rooting for their teams, so they value high games when exceptional pitchers are starting or great opponents are visiting. different strategies for each of fan groups. This study is unique in terms of two points. First, numerous studies have been conducted to find factors affecting the quali ty of games or demand for sports, but not many researchers have focused on factors influencing day to day fluctuation in the quality of game or demand for the games. Second, a substantial amount of research has tried to determine what factors make teams po according to the attendance level of the franchise has not been the center of attention to researchers. This study can contribute to the literature by giving clues for these two questions.
58 Implication The results of this study can produce several important implications both academically and practically. In terms of the academic field, this research can provide the empirical and quantitative support for independent varia bles found in the previous literature and supplementary explanation for the each of variables. This study also demonstrated validity of the ticket price of market as a dependant variable by cross validating the relationship between independent and dependan t variables. To date, similar studies regarding day to day demand fluctuation only utilized attendance as a dependant variable. Using the secondary ticket market can supplement the blind spot of attendance as a dependant variable. Attendance as a dependant variable is not appropriate to explain fluctuation of extremely popular teams (e.g., the Boston Red Sox and the Philadelphia Phillies), because their atten dance rate is very likely to be 100% for the entire season. Secondly, this study tries to determine the inverse relationship between the attendance level and factors, and give researchers an idea of how the level of attendance of teams affects their preference in independent variables. For practitioners, this study will give them ideas for devising cust omized marketing strategies for each group of fans b Some of the marketing strateg ies recruiting exceptional pitchers and scheduling Interleague games and having good opponent s for the low attendance team home games, but MLB commissioners or managers of franchise teams can a lso use this information to attract more spectators to their games As far as the sports marketing industry is concerned this study can give sports ma rkete rs ideas for customized marketing strategies for the different attendance level of teams. Also, this study can help the administrative staffs for franchise teams set the ticket price more accurately reflecting the close perceived value of games by fans.
59 Limitation and Future Research There are several limitations in the current research that need to be addressed. First, t his study has used only factors that are related to day to day fluctuation This delimitation might have caused the model to explain on ly a small amount of the variability The R square value is 136 so this variable explains only 13.6% variance in the total ticket value. I dentifying factors affecting throughout the season might be studied by looking at the averaged ticket price fluctuat ion year by year. For example, it might be intriguing to see how much it affected the averaged ticket price having Albert Pujols on the team or having a new stadium for the Miami Marlins. Second, the research model did not include such variables (e.g., une xpected incidents ) such as historic record chase, sudden appearance of super rookies, and many other game specific factors that are difficult to measure quantitatively. They can be included in the future study to fully understand the ticket price fluctuati ons. In the future studies, it is worthy to investigat e a correlation between stadium attendance and secondary market ticket price. Each attendance record for all MLB games is readily available. It might be an interesting study and would explain how the pr icing in the secondary market works. In the current study, ticket price in the secondary market was the only dependent variable. Future studies can also examine the influence of the selected determinants on the attendance itself in addition to the ticket p rice.
60 Figure 5 1 b uy p age
61 LIST OF REFERENCES Benitah, J C (2005). Anti scalpin g Laws: Should they be f orgotten? Texas Review of Entertainment and Sports Law, 6 (1), 55 78. Bird, P. (1982). The demand for league football. Ap plied Economics, 14 637 649. Borland, J. (1987). The demand for Australian rules football. The Economic Record, 63 220 230. Boyd, T. C., & Krehbiel, T. C. (2003) Promotion ti ming in Major League Baseball and the stacking effects of factors that increase game attractiveness. Sport Marketing Quarterly 12 (3), 173 183. Brady, M.K., & Cronin, J. Jr (2001) Some new thoughts on conceptualizing perceived service quality: A hierarchical appro ach. Journal of Marketing, 65 (3), 34 49. Butler, M. R. (2002). Inter league play and baseball attendance. Journal of Sports Economics, 3 (4), 320 334. Clapp C. M., & Hakes J. K. (2005) How Long a Honeymoon? The Effect of New Stadiums on Attendance in Major League Baseball. Journal of Sports Economics, 6 ( 3 ) 237 263 CNBC ( 2011, April 21). Does MLB have an attendance problem? Retr ie ved from http://sports.yahoo.com/mlb/news?slug=yscnbc_does_baseball_have_attendance_problem _042111 Cozart E. S. (2010) The Relationship Between the Online Secondary Ticket Market and College Ath letics Czepiel, J. A., Solomon, M. R. & Surprenant, C. F. (1985), The Service Encounter, Lexington, Lexington, MA. Danielle M. (2010) T he Times T hey A re a Changing: Secondary Ticket Market Moves from Taboo to Ma instream Texas Review of Entertainment & Sports Law Spring 11 ( 2 ), 295 307 Davis, M. C. (2009) Analyzing the Relationship Between Team Success and MLB Attendance With GARCH Effects. Journal of Sports Economics, 10 ( 1 ), 44 58 Demmert, H.G. (1973). The eco nomics of professional team sports Lexington, MA: D. C Heath. Denaux, Z.S. Denaux, D. A., & Yalcin, Y. (2011) Factors Affecting Attendance of Major League Baseball: Revisited International Atlantic Economic Society Journal, 39 117 127 Depken, C. A., I I. (2000). Fan loyalty and stadium funding in pr ofessional baseball. Journal of Sports Economics, 1 (2), 124 138. Donovan, R J. & Rossiter, J. R. (1982), Store atmosphere: A n environmental psychology approach, Journal of Retailing, 58 34 57.
62 Drever, P., & MacDonald, J. (1981). Attendances at South Australian football games. Inte rn ational Review of Sport Sociology, 16 (2), 103. Drayer, J., & Shapiro, S. L. (2009) Value determination in the secondary ticket market: A Quantitative Analysis of the NFL Playoff Sport Marketing Quarterly 18 (1), 5 13. Drayer, J., Stodar, D. K., & Irwin, R. L. (2008). Tradition vs. trend: A case study of team response to the secondary ticket market Sport Marketing Quarterly 17(4), 235 240. Drury, A. (2002, October 7). When it co USA Today. Retrieved from http://www.usatoday.com. Fillingham, E.J. (1977). Major league hockey: An industry study. Master's thesis. Universi ty of Alberta. Free market fleecing; ticket touting. (2006, Januar y 5). The Economist. Retrieved from http://www.economist.com/node/5367772 Funk, D. C, Mahony, D F., & Ridinger. L L (2002). Characterizing consumer motivation as individual difference factors: Augmenting t he S port I nterest Inventory (SII) to explain level o f sport. Sport Marketing Quarterly, 11 33 43. Garcia, J., & Rodriguez, P. (2002). The determinants of football match attendance revisited: Empirical evidence from the Spanish Football League. Journal of Sports Economics, 3 (1), 18 38. Greenstein, T. N., & Marcum, J. P. (1981). Factors affecting attendance of major league baseball: Team performance. Review of Sport and Leisure, 6 (2), 21. Journal of Marketing, 18 (4), 36 44. Guttmann, A. ( 1986 ) Sports spectators. New York: Columbia University Press. Hansen, H., & Gauthier, R. (1989). Factors affecting attendance at professional sport events. Journal of Sport Management, 3, 15 32. Happel, S. K., & Jennings, M M. (2002). Creating a futures market for major event tickets: Problems and prospects. Cato J ournal, 21 443 461. Retrieved March 6, 2006, from Business Source Premier database. Hart, R. A., Hutton, J., & Sharot. T. (1975). A statistical analysis of association footba ll attendances. Applied Statistics, 24(1), 17. Hausman, J. A., & Leonard, G. K. (1997) Superstars in the National Basketball Association: Economic Value and Policy. Journal of Labor Economics, 15 (4), 586 624.
63 Hay, R. D., & Thueson, N. C (1986). High schoo l attendance and related factors. Paper presented at Sport Administration Conference. University of Alberta. Haylan, T., Lage, M. J., & Treglia, M. (1997). Institutional change and invariance of behaviour in Major League Baseball. Applied Economics Letters 4 (5), 311 314. Hill, J.R.. Madura, J., & Zuber, R.A. (1982). The short run demand for major league baseball. Atlantic Economic Journal, 10(2), 31. Hondroyiannis, G. (2009). Fertility determinants and economic uncertainty: An assessment using European P anel Data, Working Papers 96, Bank of Greece. Howard, D. R., & C rompton, J. L., (2004). Tactics used by sports organizations in the United States to increase ticket sales. Managing Leisure (9) 87 95. James, J. D. (1997). Becoming a Sports Fan: Understandi ng Cognitive Development and Socialization in the Development of Fan Loyalty. (Doctoral dissertation, Ohio State University, 1997 ). Retrieved from Proquest Jones, J. C. H. (1969 ). The economics of the N.H.L. Canadian Journal of Economics, 2 3. Jones. J. C. H. (1984). Winners, losers and hosers: Demand and survival in the National Hockey League. Atlantic Economic Journal, XII (3), 54. Kahn, L. M. & Sherer P D. ( 1988 ). Racial Differences in Professional Ba sketball Players' Compensation. Journal of Labor Ec onomics. 6 ( 1 ), 40 61. Knowles, G., Sherony, K., & Haupert, M. (1992). The Demand for Major League Baseball: A Test of the Uncertainty of Outcome Hypothesis. The American Economist, 36, 72 80. Kim, Y. K., & Trail, G.T. (2010) Constraints and Motivators: A New Model to Explain Sport Consumer Behavior. Journal of Sport Management, 24, 190 210. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford Press. Ko, Y. J., Zhang, J., Cattani, K., & Pastore, D. L. (2011) Asses sment of event quality in major spectator sports Managing Service Quality, 21 (3), 304 322 Koenigstorfer, J., Groeppel Klein, A., & Kunkel, T. (2010). The Attractiveness of National and International Football Leagues: European Sport Management Quarterly, 10 ( 2 ) 127 163. Kwon, H. H., Trail, G., & James J. D. (2007). The Mediating Role of Perceived Value: Team Identification and Purchase Intention of Team Licensed Apparel Journal of Sport Management, 21 540 554
64 Lemke, R. J., Leonard, M., & Tlhokwane, K. (2009). Estimating Attendance at Maj or League Baseball Games for the 2007 Season. Journal of Sports Economics 11 (3), 316 348. Levin, R. C., Mitchell, G. J.,Volcker, P. A., & Will, G. F. (2000). The report of the independent members Major League Baseball. Lucking Reiley, D., Bryan, D., Prasad, N., & Reeves, D. (2007). Pennies from eBay: The determinants of price in online auction s. The Journal of Industrial Economics, 55 (2), 223 233 Mahony, D. F., Madrigal, R., and Howard, D. (2000). Using the Psychological Commitment to Team (PCT) Scale to Segment Sport Consumers Based on Loyalty Sport Marketing Quarterly, 9 (1), 15 25. Mann, L ( 1979 ) Sports C rowds V iewed f rom the P erspective of C ollective Behavior. Sports, games, and play, 337 368 Medoff, M. H. (1976) On Mono ps onistic Exploitation in Professional B aseball. Quarterly Review of Economics and Business 16(2), 113 1 21. Meehan, J W. Jr. Nelson, R. A., & Richardson, T. V. (2007). Competitive balance and game attendance in Major League Baseball. Journal of Sports Economics, 8 563 580. Neale, L., & Funk, D. C. (2006). Investigating motivation, attitudinal loyalty and attendance be haviour with fans of Australian Football. International Journal of Sports Marketing and Sponsorship, 7 307 317. Nocera, J (2008, January 19). Internet puts a sugarcoat on scalping. The New York Times. Retrieved from http://www.thenewyorktimes.com Noll, R G. (1974). Gove rn ment and the sports business (chap. 4). Washington; The Brookings Institute. Pappas, D. (2001). Society of American Baseball Research Business of Baseball Committee MLB Consolidated Industry Statement [Data file]. Retrieved from http: //roadsidephotos.com/baseball/2001consolidatedindustryforecast.xls Rascher, D. (1999). A Test of the Optimal Positive Production Network E xternality in Major League Baseball. In J. Fizel, E. Gustafson, & L. Hadley (Eds.), Sports Economics : Current Research (pp. 27 45). Westport, CT: Praeger. Rascher, D. A., McEvoy, C. D., Nagel, M. S., & Brown, M. T., (2007) Variable Ticket Pricing in Major League Baseball Journal of Sport Management, 21 407 437 Reese, J., & Mittelstaedt, R. (2001). An exploratory study of the criteria used to establish NFL ticket prices. Sport Marketing Quarterly 10 (4), 223 230.
65 R ishe, P. J., & Mondello, M. J. (2003). Ticket price determination in the National Football League: A quantitative approach. Sport Marketing Quarterly 12 (2) 72 79. for it. New York: Basic Books. Schmidt, M. B., & Berri, D. J. (2001). Competitive balance and attendance: The case of major league baseball. Journal of Sports Economics, 2 145 167. Schofield, J. A. (1983). Performance and attendance at professional team sports. Journal of Sport Behavior. 6 (4), 196. Schurr, K. T., Wittig, A. F., Ruble, V., & Ellen, A. (1987). Demographic and P ersonality Characteristics A ssociate d with P ersistent, O ccasional, and Non A ttendance of U niversity M ale B asketball G ames by College Students. Journal of Sport Behavior. 11 3 17. Scully, G. W. (1974). Pay and performance in major league baseball. American Economic Review, LXIV (6), 915. Sieg fried, J. J., & Eisenberg, J. D. (1980). The demand for minor league baseball. Atlantic Economic Journal, 8 59. Siegfried, J., &Zimbalist, A. (2000 ). The economics of sports facilities and their communities. Journal of Economic Perspectives, 14 95 114. S immons, R. (1996). The demand for English League Football: A club level analysis. Applied Economics, 28 139 155. Sweeting, A. of mimeo, Duke University Tainsky, S. & Winfree, J. A. (2008) Financial Incentives and League Policy: The Example of European Sport Management Quarterly, 8 ( 1 ) 67 81. Trail, G. T., & James, J. D. (2001). The motivati on scale for sport consumption: Assessment of the scale's psychometric properties. Journal of Sport Behavior, 24 (1), 108 127. Trail, G. T., Anderson, D. F., & Fink, J. S. (2000). A theoretical model of sport spectator consumption behavior. Internatio nal Journal of Sport Management, 1 154 180. Trail, G. T., Anderson, D. F., & Fink, J. S. (2005). Consumer satisfaction and identity theory: A model of sport spectator conative loyalty. Sport Marketing Quarterly, 14 98 112. Trail, G. T., Robinson, M. J. Dick, R. J., & Gillentine, A. J (2003). Motives and points of attachment: F ans versus spectators in intercollegiate athletics. Sport Marketing Quarterly, 12 (4), 217 227
66 Wakefield, K. L., Blodgett J. G. & Sloan, H. J. (1996), Measurement and managemen t of the Sportscape Journal of Sports Management, 10 ( 3 ), 15 31. Wakefield K. L., & Sloan, H. J. (1995) The Effects of Team Loyalty and Selected Stadium Factors on Spectator Attendance Journal of Sport Management, 9 153 172 Wann, D. L. (1995). Prelimi nary Validation of the Sport Fan Motivation Scale. Journal of Sport & Social Issues 19 (4), 377 396. Winfree, J., McCluskey, J., Mittelhammer, R., & Fort, R. (2004). Location and attendance in Major League B aseball. Applied Economics, 36 2117 2124. Whitne y, J. D. (1988). Winning games versus winning championships: The economics of fan interest and team performance. Economic Inquiry, 26, 703 724.
67 BIOGRAPHICAL SKETCH Mr. Dongho Yoo is expected to earn his Master of Science degree in the Col lege of Health and Human Performance (Sport Management) from the University of Florida in August 2012. He received his Bachelor of Science degree from sport & leisure studies at Yonsei University, Seoul, Korea in February 2010. His research interest is to identify factors affecting demand in the professional sports through contents analysis. His interest in this field has been materialized in the thesis manuscript investigating determinants of ticket price fluctuation in the secondary market. His research will continue by extending to the different context of professional sports and year by year fluctuation in the secondary market. Mr. Yoo has a half year of industry experience in the sport marketing field with the Gainesville Sports Commission (GSC). He participated in operating a variety of sports events in and around the Gainesville community, and investigated the economic impacts that each of sports events brought with GSC. In the near future, Mr. Yoo will work for the sports marketing industry and att empt to have practical and realistic research idea through work experience.