Introduction
As in any profitable enterprise, sport organizations must know the kinds of goods and services preferred by their customers to succeed in attracting spectators. One approach to understanding consumer behavior is through the elements of the traditional marketing mix--product, promotion, place, and price (Kotler & Armstrong, 2004). Specifically, the price paid for a particular good or service is often a significant factor in a consumer's decision-making process (Meir & Arthur, 2007).
With particular regard to sport organizations, ticket pricing is an important variable in marketing research. Previous studies have explored how variations in ticket pricing influence spectator attendance. For example, Rishe and Mondello (2003) quantitatively examined the cross-sectional differences in ticket prices across teams in the National Football League in the US as well as the reasons for increases in the size and direction of seasonal price. Further, Rishe and Mondello (2004) performed the same empirical investigation for the four major sport leagues in the US.
The measurement of changes in the reaction of sport consumers to ticket price is called price elasticity of demand, which is used in the present study to assess how sensitive consumers are to price fluctuations (Shank, 2009). In general, an increase in price leads to a decrease in demand (Fullerton, 2007), although this decline is contingent upon a variety of factors, such as the team performance, opposition team, and players in professional sports. In fact, price elasticity is used to estimate Major League Baseball season ticket demand (Hakes & Hutmaker, 2011) and football match attendance (Garcia & Rodriguez, 2002).
On the other hand, in professional sports, high ticket prices do not necessarily indicate a decrease in the demand (Pan, Zhu, Gabert, & Brown, 1999). Some studies have suggested that the demand is price inelastic in professional sports (Siegfried & Eisenberg, 1980; Bird, 1982; Shapiro & Dryer, 2012). The literature on professional sports finds that the demand for sports team tickets is price inelastic (Krautmann & Berri, 2007).
The studies estimated the demand by analyzing secondary data of spectator attendance and ticket prices using the economic model of regression analysis. However, such models do not specify how sensitive spectators are to price fluctuations. Thus, this study examines the price elasticity of ticket demand by analyzing primary data on spectators' ticket preferences by using the conjoint model. Conjoint analysis is used for measuring trade-offs in analyzing consumers' preferences and intentions to buy. It is a method for simulating how consumers might react to changes in current products (Green, Krieger, & Wind, 2001). Thus, the author uses conjoint analysis in this study for simulating how spectators react to price fluctuations.
In recent years, conjoint analysis has been used in studies on professional sport marketing for examining fan preferences (Aiken & Koch, 2009), ticket pricing strategies (Lee & Kang, 2011), and price sensitivities (Daniel & Johnson, 2004). These studies have successfully identified the role of management in professional sports. While previous studies on price elasticity have examined US sports, very few have explored the Japanese sport market. Thus, the focus of this study is the professional basketball league in Japan. The Japan Basketball League organization (JBL), whose members are non-professional teams sponsored by corporations, was founded in 1967. The JBL was called the Super League in 2001 and, with competence and popularity, became a top league in Japan. A few years later, two teams that aimed at professionalization left the JBL, and launched the bj-league as a new professional basketball league in 2005. As for the current state of Japanese basketball, the bj-league and the National Basketball League (NBL), whose name was changed from JBL to NBL, exist as the two top leagues. The author took up the Kyoto Hannaryz team from the professional bj-league as a case study. Kyoto Hannaryz entered the league in 2009 and started playing during the 2009-2010 season. In this season, the bj-league was divided into the Eastern conference and Western conference, with 19 participating teams. The average spectator numbers per game were 1,712 within the whole league, and 1,469 for Kyoto Hannaryz. The management is responsible for determining the ticket price at which the number of spectators decreases, with particular attention on the price elasticity of ticket demand. Thus, Kyoto Hannaryz's general manager (GM) wanted to determine the price elasticity of demand when the ticket price for each type of seat (reserved seat A, reserved seat B, and non-reserved seat) was increased.
Therefore, the author uses conjoint analysis to clarify the ticket preferences of bj-league spectators and simulate ticket purchase rates by operationalizing ticket prices. Further, it analyzes the price elasticity of ticket demand to suggest suitable marketing strategies for ticket pricing. For this purpose, ordinal data on purchases of virtual tickets for the three seat types are first collected for the analysis of customers' ticket preferences. Next, based on the findings regarding ticket preferences, a simulation analysis of the opposition team, game day, and pricing is conducted, and ticket purchase rates are predicted. Finally, the author analyzes the ticket price elasticity of demand at different pricing levels.
Literature Review
Demand for Professional Sports
Authors who have examined demand (Bird, 1982; Siegfried & Eisenberg, 1980) have analyzed secondary data by using the economic model and various economic variables for spectator participation and ticket price. Siegfried and Eisenberg (1980) estimated the demand in minor league baseball in terms of spectator attendance. They found that per capita income and winning have little effect on the attendance and that the quality and excitement of the game are important to sport fans, thus showing that the demand is negatively related to price. Bird (1982) explained the price elasticity of demand in football in terms of both admission and travel costs, although admission cost variables are considered more important. The author found that, for the Football League as a whole, the price elasticity with respect to total admission and travel costs was -.20, showing that the demand for football is relatively price inelastic. These authors indicated ticket pricing to be an important factor affecting the participation of spectators in professional sports, yet state that within professional sports the demand is price inelastic. The rise of admission prices does not necessarily bring about a decline in the demand.
In contrast, Garcia and Rodriguez (2002) estimated price elasticities having an absolute value of less than 1, but these estimates show substantial differences depending on the functional form and consideration of the potential endogeneity of prices. The results seem to support a nearly elastic demand for a significant proportion of football clubs (Spanish Football League). Using geographically scaled data for season ticket demand in Major League Baseball, Hakes and Hutmaker (2011) estimated the price elasticity of demand for season ticket packages to be larger in magnitude than those in the previous studies.
Ticket Pricing in Professional Sports
In their case study of Major League Baseball, Rascher, McEvoy, Nagel, and Brown (2007) indicated that ticket pricing varies depending on factors such as the quality of the opposition team, day of the week, and month of the year, and around special events such as Opening Day, Memorial Day, and Independence Day. On the other hand, Shapiro and Dryer (2012) examined the relationship between fixed ticket prices, dynamic ticket prices, and secondary market ticket prices for comparable seats in Major League Baseball. Seat location and price changes over time were examined to identify potential effects on ticket price in the primary and secondary market. They found that time was a significant influence on ticket price.
Reese and Mittelstaedt (2001) found in their study of ticket pricing strategies in the National Football League that team performance was the most significant factor influencing the price. The remaining influencing factors, in order of importance, were the revenue needs of the organization, public relations issues, sensitivity of the market towards price increases, fan identification, and average ticket price across the league. However, since such influencing factors were measured individually, the trade-offs among them in the evaluation of ticket price conditions were not considered. Using a decompositional approach like conjoint analysis allows us to examine the trade-off among these factors that affect how fans choose ticket conditions.
Conjoint Analysis for Sports Marketing
Conjoint analysis of sports behavior can be classified into three categories. The first category is preferences towards sport activities, such as windsurfing behavior (Ninomiya & Kikuchi, 2004) and the selection of ski and golf destinations (Won, Bang, & Shonk, 2008; Won, Hwang, & Kleiber, 2009). The second category is purpose-of-marketing research regarding the repositioning of recreational facilities (Toy, Rager, & Guadagnolo, 1989), marketing decision-making regarding public leisure services (Jones, 1991), and market segmentation among downhill skiers (Carmichael, 1996). The third category includes the testing of predictive models, such as comparing a multi-attribute attitude model with a conjoint measuring method (Timmermans, 1987), the prediction validity of choice behavior about travel destinations (Louviere & Timmermans, 1992), prediction accuracy (Jeng & Fesenmaier, 1996), and expression modes and estimation methods (Dellaert, Borgers, & Timmermans, 1997). Thus, after being applied to theoretical and substantive issues, conjoint analysis has proved to be a useful technique for analyzing sports behavior.
Recently, conjoint analysis has been employed by authors on marketing for sports management in professional sports. For example, Aiken and Koch (2009) used conjoint analysis to examine fan preferences in football, baseball, and basketball. They showed that fans were influenced by the following factors: winning percentage, presence of high-profile "all-star" players, geographic association, social affiliation, and team history within a league. However, ticket pricing was not considered. On the other hand, Lee and Kang (2011) examined the ticket pricing strategies of South Korean professional football teams for the following target markets: adults, college students, and high school students. Four broad attributes for determining ticket prices were selected for the conjoint analysis: players, coupons, points, and price. It was found that supporters regard "players having hometown backgrounds" as the most important attribute, followed by price, coupons, and points. However, the high school student segment considered price to be the most influential attribute, followed by coupons and points. Furthermore, Daniel and Johnson (2004) surveyed the price sensitivity for three seat types: Standard Members; Full Club Members, concourse seating; and Full Club Members, grandstand seating. Standard Members were found to be the most price sensitive of the three membership categories, although they were prepared to upgrade to Full Members, concourse seating. This study showed that the optimal member price could be explored using conjoint analysis. The results of the conjoint analysis for fan preferences, ticket pricing strategies, and price sensitivity are useful for sports marketers.
Research Method
Research Framework
A review of relevant literature on professional sports indicated that the demand is inelastic (Siegfried & Eisenberg, 1980; Bird, 1982; Shapiro & Dryer, 2012). Therefore, it can be inferred that an increase in admission prices does not lead to a decline in the demand (Pan, Zhu, Gabert, & Brown, 1999). Moreover, the price elasticity of ticket demand changes with seat types. Previous research supposes that the purchasers of low-priced tickets are sensitive to price change (Daniel & Johnson, 2004). Therefore, this study proposes two research questions regarding the price elasticity of demand when ticket prices increase.
RQ1: Is the demand for tickets inelastic when the ticket price increases for virtual tickets in the bj-league?
RQ2: For virtual tickets in the bj-league, is the demand for low-priced tickets more elastic than that for high-priced tickets?
Data Collection
This study focused on spectators in the public sports center where the home games of Kyoto Hannaryz were held in the 2009-2010 bj-league season. Data were collected over two weekends in 2009--November 28-29 and December 12-13. Thirty-six investigators used three kinds of questionnaires for reserved seats A, reserved seats B, and non-reserved seats. In each questionnaire, the question regarding virtual tickets is for the seat type where the respondent is sitting down. Data were collected from 98 spectators in reserved seats A, 62 spectators in reserved seats B, and 240 spectators in non-reserved seats. Of the 400 questionnaires received, a final sample size of 368 was useable after omitting incomplete responses.
Figure 1. Nine profiles of virtual tickets for Reserved Seat A. Tiket A Game day: Sunday Opposition Team: Five Arrows Ticket Price: [yen] 3,000 Ranking Tiket B Game day: Friday Opposition Team: Lake Stars Ticket Price: [yen] 4,000 Ranking Tiket C Game day: Sunday Opposition Team: Evessa Ticket Price: [yen] 4,000 Ranking Tiket D Game day: Friday Opposition Team: Five Arrow Ticket Price: [yen] 3,500 Ranking Tiket E Game day: Saturday Opposition Team: Five arrow Ticket Price: [yen] 4,000 Ranking Tiket F Game day: Sunday Opposition Team: Lake Stars Ticket Price: [yen] 3,500 Ranking Tiket G Game day: Saturday Opposition Team: Lake Star Ticket Price: [yen] 3,500 Ranking Tiket H Game day: Saturday Opposition Team: Evessa Ticket Price: [yen] 3,500 Ranking Tiket I Game day: Friday Opposition Team: Evessa Ticket Price: [yen] 3,000 RankingConjoint Analysis
Conjoint models predict an individual's preference for a stimulus object to clarify the relative importance of each attribute by decomposing the overall preference measure. Conjoint analysis thus allows the researcher to determine the individual's part-worth utility for each attribute under study. Conjoint models can be divided into rating-based and choice-based models. The traditional rating-based conjoint analysis has been a mainstay of marketing research for 30 years (Green, Krieger, & Wind, 1990). Although Moore (2004) compared the validity of these two models, he could not find any compelling empirical evidence to choose choice-based over rating-based conjoint models. This study analyzed the collated data by employing the full-profile approach and the PASW Statistics 18.0 software as follows.
The creation of attributes and levels. Kyoto Hannaryz's GM was consulted while selecting the design of attributes and levels. The following factors influencing the ticket purchase rate were considered: game day (Rascher, McEvoy, Nagel, & Brow, 2007), opposition team (Rascher, McEvoy, Nagel, & Brown, 2007), ticket price (Bird, 1982), players (Aiken & Koch, 2009), and team performance (Reese & Mittelstaedt, 2001; Aiken & Koch, 2009). However, players and team performance were excluded by the author because they are indefinite variables. Therefore, three variables with three levels each were established: game day (Friday, Saturday, Sunday), opposition team (Takamatsu Five Arrows, Shiga Lake Stars, Osaka Evessa), and ticket price (present price, present price plus 500 yen, present price plus 1,000 yen). Set-up of the ticket price was actually considered as the possible price increase by consulting with the GM. However, following a pre-test with 15 college students, the importance of low-priced tickets was demonstrated to a disproportionate degree. Therefore, we reclassified the ticket prices as follows: ticket price for seat A spectators (3,000 yen, 3,500 yen, 4,000 yen), ticket price for seat B spectators (2,000 yen, 2,500 yen, 2,800 yen), and ticket price for non-reserved seating (1,000 yen, 1,500 yen, 1,800 yen).
Construction of profiles. A profile is constructed by combining each level of the attributes. The Generate Orthogonal Design in SPSS creates an orthogonal array to ease respondents' burden. Nine profiles based on a rectangular plan were generated for each seat type. Figure 1 shows the virtual profiles that the spectators of reserved seats A were shown. Another virtual profile in which only the ticket price difference was shown to the spectators of reserved seats B and spectators of non-reserved seats.
Collection of ordinal data. Ordinal data were collected using a ranking method based on these nine profiles. In the survey, the following question was asked: "When observing another game featuring the Kyoto Hannaryz, which conditions would be important to your ticket purchase?" Nine cards representing each of the nine virtual profiles for all seat types were then shown to the respondents, and the overall rankings of their choices were calculated.
Estimation of part-worth utilities. In conjoint analysis, an algorithm is used for presuming the part-worth utilities assigned to the attribute level from the ordinal data. Part-worth utilities were estimated through the traditional individual-level conjoint model using the least-squares method. According to Andrews, Ansari, and Currim (2002), who compared the conjoint analysis models, individual-level partworths are useful for mass customization and the simulation of market outcomes for alternatives.
Calculation of relative importance. Part-worth scores can be combined to assess the overall importance of each attribute. Relative importance values are then calculated by examining the range of part-worth utilities for each attribute and then standardizing the obtained value by the total range of all part-worth values.
Simulation of ticket purchase rate. Based on these ticket price rankings, the change in demand for ticket can be simulated by using the algorithm of a maximum utility model. In this case, demand is represented by the purchase rate of each ticket under the assumption that a respondent chooses the ticket of maximum utility value. Each person was assigned a probability [P.sub.i] for each simulation i. These probabilities were then computed based on the predicted score ([[??].sub.i]) for that product. The probabilities are computed as follows:
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In a simulation of this study, the part-worth utilities for every person to the three attributes in each profile was calculated, and a profile with the highest total-utility value was chosen. For example, in a simulation of opposition and pricing, three kinds of profiles were set up for each opposition team, present ticket price, and game day of Sunday. The purchase rate of each ticket was calculated by the part-worth utilities for every person being supplied to each attribute level. Subsequently, the ticket purchasing rate was calculated for each profile's ticket price.
Price Elasticity of Ticket Demand Price elasticity of demand is the ratio of the rate of change of demand to that of ticket price; when ticket price rises from P0 to P1, it shows the rate at which demand decreases from Q0 to Q1. It can be calculated as follows:
The price elasticity of (Q1 - Q0)/{(Q1 + Q0)/2} demand = (P1 - P0)/{(P1 + P0)/2}
where Q0 = purchase rate before the price change, Q1 = purchase rate after the price change, P0 = price before the change, and P1 = price after the change.
This allows us to determine whether price elasticity is large and elastic (i.e., whether the absolute value of price elasticity is larger than 1) or small and inelastic (i.e., whether the absolute value of price elasticity is smaller than 1).
The price elasticity of ticket demand explains spectators' reaction to change in price. Elastic demand refers to a small change in ticket price producing a large change in ticket purchase rate. Inelastic demand means that changes in ticket price have little or no impact on ticket purchase rate. The value of price elasticity is elastic if it is larger than 1 and inelastic if it is smaller than 1.
Results
Individual Attributes of Spectators by Seat Type
Data were collected from seat A spectators (89 participants), seat B spectators (57), and non-reserved seat spectators (222). Men comprised 56.2% of seat A spectators (average age: 36.5 years), 54.4% of seat B spectators (average age: 31.4 years), and 61.3% of non-reserved seating spectators (average age: 35.4 years).
Ticket Preferences of Spectators by Seat Type
The relative importance and part-worth utility values of each ticket pricing level are shown in Table 1 by seat type. The Pearson's R and Kendall's tau statistics displayed in Table 1 represent the correlations between the observed ranking value and estimated parameters. Compared with previous studies examining the validity of the conjoint measurement model (Timmermans, 1987; Louviere & Timmermans, 1992), this analysis showed a higher correlation coefficient and value of rank correlation. The conformity of the model and reliability of its point estimates were high, while the collected preference order data were correctly reproduced as a preference structure.
Seat A spectators. They considered game day an important attribute (40.3%) and preferred Saturday (.609) and Sunday (.579) games. The second important attribute was opposition team (35.2%); the popular team is Osaka Evessa (.598), followed by Shiga Lake Stars (.272), while Takamatsu Five Arrows (-.870) was less popular.
Seat B spectators. They also considered game day an important attribute (38.7%) and clearly preferred Sunday games (.725) to Saturday (.444) and Friday (1.170) games. Similar to seat A spectators, they considered opposition team the second most important attribute (32.6%), with the popular team being Osaka Evessa (.673), followed by Shiga Lake Stars (.152) and Takamatsu Five Arrows (-.825).
Non-reserved seat spectators. The importance of game day was the highest (37.6%), with weekend games (Saturday (.697) and Sunday (.588) preferable to Friday games (-1.285). They were content with the present price of 1,000 yen (1.185); however, they would accept a price rise to 1,500 yen (.095) but not to 1,800 yen (-1.280).
In summary, all spectators considered game day important and prefer that the games be held on Saturdays and Sundays rather than Fridays. The second important attribute for non-reserved seating spectators was ticket price, while that for reserved seat spectators is opposition team.
Simulation of Opposition Team and Pricing
Figures 2 to 4 show the sensitivity of spectators to ticket price fluctuations for each opposition team when the ticket price for Sunday games increases. The game day was fixed (Sunday) so that a simulation can be carried out under the same conditions.
Results for reserved seat A segment. (Figure 2) An increase in the ticket price from 3,000 yen to 3,500 yen led to a higher purchase rate than that for opposition teams' tickets priced at 3,000 yen, since the ticket price of an Osaka Evessa game had a comparatively small price elasticity (1.15). On the other hand, the ticket purchase rate of a Shiga Lake Stars game decreased from 30.9% to 17.2% (price elasticity: 1.99), making it lower than that for a price rise to 3,500 yen, and that of an Osaka Evessa game decreased from 50.0% to 34.1% (price elasticity: 1.32). Although the ticket purchase rate of popular teams was not sensitive to changes in ticket price in this segment, the price elasticity of ticket demand was relatively elastic in all the opposition teams.
Results for reserved seat B segment. (Figure 3) When ticket prices rose from 2,000 yen to 2,500 yen, the ticket purchase rate of a Takamatsu Five Arrows game decreased from 22.5% to 12.9% (price elasticity: 2.44), that of a Shiga Lake Stars game decreased from 29.5% to 18.1% (price elasticity: 2.16), and that of an Osaka Evessa game decreased from 48.0% to 42.7% (price elasticity: 0.53). When the price was increased to 2,800 yen, all tickets showed the lowest purchase rate and a large price elasticity. As reported, when the ticket price of a popular team increased by 500 yen, the ticket purchase rate did not change and the price elasticity of ticket demand remained relatively inelastic.
Results for non-reserved seat segment. (Figure 4) When ticket prices rose from 1,000 yen to 1,500 yen, the ticket purchase rate of a Takamatsu Five Arrows game decreased from 14.2% to 6.5% (price elasticity: 1.86), that of a Shiga Lake Stars game decreased from 31.1% to 15.9% (price elasticity: 1.62), and that of an Osaka Evessa game decreased from 54.7% to 37.1% (price elasticity: 0.96). When the ticket price of a popular team increased by 500 yen, the ticket purchase rate in this segment did not change and the price elasticity of ticket demand remained relatively inelastic.
Simulation of Game Day and Pricing
Figures 5 to 7 illustrate the same scenario but by game day instead of ticket price. The opposition team was fixed (Shiga Lake Stars) so that a simulation could be carried out under the same conditions.
Results for reserved seat A segment. (Figure 5) When ticket prices rose from 3,000 yen to 3,500 yen, the purchase rate on Friday decreased from 12.0% to 7.3% (price elasticity: 3.17), implying high price elasticity. When ticket prices rose from 3,000 yen to 4,000 yen, ticket sales on Friday decreased from 12.0% to 6.7% (price elasticity: 1.98). Further, the purchase rate of Saturday tickets, which have high price elasticity, decreased from 45.1% to 22.5% (price elasticity: 2.34) and that of Sunday tickets decreased from 42.9% to 30.9% (price elasticity: 1.14). Moreover, when ticket prices increased for Friday and Saturday games, the purchase rate did not change and the price elasticity of demand was relatively elastic.
Results for reserved seat B segment. (Figure 6) When ticket prices rose from 2,000 yen to 2,800 yen, Friday tickets had the same purchase rate as that for a price rise to 2,500 yen, although the purchase rate decreased from 12.9% to 10.5% (price elasticity: 0.62). The purchase rate of Saturday tickets decreased from 40.9% to 21.9% (price elasticity: 1.82) and that for Sunday tickets decreased from 46.2% to 34.2% (price elasticity: 0.90). Although the price elasticity of ticket demand for this segment on Saturday was relatively elastic, that on Friday and Sunday was relatively inelastic.
Results for non-reserved seat segment. (Figure 7) When ticket prices rose from 1,000 yen to 1,800 yen, although the purchase rate for Friday tickets decreased from 9.9% to 5.5% (price elasticity: 1.00), it remained equal to that for a price rise to 1,500 yen. The purchase rate of Saturday tickets decreased from 47.3% to 21.5% (price elasticity: 1.31) and that for Sunday tickets fell from 42.8% to 22.7% (price elasticity: 1.07). Moreover, when ticket prices on Friday increased by 800 yen, the price elasticity of ticket demand was unit elastic and that for other tickets is relatively elastic.
Discussion
The author used conjoint analysis in this study to investigate the price elasticity of ticket demand in professional sports. The sensitivity of ticket purchase rate to fluctuations in the price of the bj-league's virtual tickets was simulated through the application of the estimated part-worth utility value.
The simulation that fixed game day and operationalized ticket price showed that even when the ticket price rose by 500 yen, the ticket purchase rate for popular teams was less sensitive for reserved seat B and non-reserved seat spectators. In an empirical study of Major League Baseball, Rascher, McEvoy, Nagel, and Brown (2007) point out that the quality of the opposition team affects the ticket price. Similarly, the ticket purchase rate in the simulation of popular teams does not change, and the price elasticity of ticket demand is relatively inelastic. However, when the price increased by 800 or 1,000 yen, regardless of the opposition team, the ticket purchase rate fell greatly for all seat types. Therefore, it was inferred that ticket purchase rate is unsustainable when it increased by two times.
The simulation that fixed opposition team and operationalized ticket price showed that the ticket purchase rate on Fridays and Sundays was less sensitive for seat B spectators, even when the ticket price rose by 500 yen and 800 yen. In a study of Major League Baseball, Pan, Zhu, Gabert, and Brown (1999) indicated that high ticket prices do not necessarily indicate a decrease in demand. Similarly, the ticket purchase rate in the simulation on Fridays and Sundays is relatively inelastic.
Thus, this study showed that the price elasticity of ticket demand is relatively elastic for high-priced tickets (reserved seat A) and relatively inelastic for low-priced tickets (reserved seat B and non-reserved seat). Previous research in professional sports, such as studies regarding the Spanish Football League (Garcia & Rodriguez, 2002) and Major League Baseball (Hakes & Hutmaker, 2011), assumes that demand is price elastic.
Other research regarding minor league baseball (Siegfried & Eisenberg, 1980), Football League (Bird, 1982), and Major League Baseball (Shapiro & Dryer, 2012), supposes that the demand is price inelastic. Contrary to the results of above studies, the author found the demand of high-priced tickets to be elastic and the demand for low-priced ticket to be inelastic. Daniel and Johnson (2004) suggested that purchasers of low-priced tickets are sensitive to price change. The cause may be that the low-priced tickets (reserved seat B and non-reserved seat) were cheap compared with the high-priced tickets (reserved seat A) in the 2009-2010 bj-league season. Hannaryz, a bj-league team, has set a low ticket price; thus, even if the price of tickets were to rise, the spectators would accept it. Actually, the ticket prices of reserved seats were raised by 3,000-3,500 yen from 2,000-3,000 yen, and non-reserved seats were raised by 1,800 yen from 1,000 in the 2014-2015 season.
Since this study used the segmentation of spectators based on seat type to simulate ticket purchase rate, it provides important implications for marketers. Based on our results, the Hannaryz's GM know how much an increase in the ticket prices will lead to a decrease in the demand for tickets in the three segments of spectators, depending on the opposition team and game day. In the segment of reserved seat A, if the ticket price is increased from 3,000 yen to 3,500 yen when a popular team is the opposition team, the rate of decrease in ticket purchase will exceed the rate of increase in price. In the segment of reserved seat B, when a popular team is the opposition team, despite an increase in the ticket price from 2,000 yen to 2,500 yen, the rate of decrease in ticket purchase will remain less than the rate of increase in price. Further, if the game is held on Friday and Sunday, despite an increase in the ticket price from 2,000 yen to 2,800 yen, the rate of decrease in ticket purchase will remain less than the rate of increase in price. In the segment of non-reserved seat, when a popular team is the opposition team, despite an increase in the ticket price from 1,000 yen to 1,500 yen, the rate of decrease in ticket purchase will remain less than the rate of increase in price. Based on the above discussion, it is recommended that managers take into account the opposition team and the game day when they decide on the ticket price.
Although the demand for tickets is relatively inelastic, when increasing the ticket prices, value adds like food and drink services and merchandise should be provided to protect the demand, as this is important while executing the plan that targets the reserved seat B spectators of Friday and Sunday games. Moreover, in games that involve a popular team, additional events for fans must be organized as reinforcement. This study offers practical recommendations for administrators of professional sport teams. Sport team managers can apply the findings to determine ticket purchase rates while increasing ticket prices and calculate the price elasticity of demand to revise ticket-pricing strategies.
Thus, price elasticity of demand was clarified using conjoint analysis in order to offer practical recommendations to the administrators of professional sports teams of new entry. Recently, conjoint analysis has been employed by studies on marketing in professional sports. The results of conjoint analysis for fan preferences (Aiken & Koch, 2009), ticket pricing strategies (Lee & Kang, 2011), and price sensitivity (Daniel & Johnson, 2004) are useful for sport marketing. Conjoint analysis has proved to be a useful technique for analyzing spectators' behaviors for professional sports.
A limitation is that this study took up the newly established bj-league rather than the NBL, and made a case study of the newly entered team, Kyoto Hannaryz. Therefore, compared with other teams, the spectators of Kyoto Hannaryz may have low attachment to the team, and may become sensitive to ticket prices. In addition, the price levels considered here can estimate fluctuations in the ticket purchase rate only when the ticket price increases by 500 yen or 1,000 yen. Since the rate of increase in ticket prices was very high in the pretest, the respondents reacted negatively to fluctuations in the price of low-priced tickets. Thus, to set realistic price levels, the rate of increase in the price of low-priced tickets was reduced. It cannot be denied that the range of the price levels is likely to influence the estimation of part-worth utilities in conjoint analysis. Also, simulations through conjoint analysis can only make predictions by operationalizing the levels of the examined attributes. Thus, future research should consider alternative attributes such as the result of a game, player profiles, or service quality. Finally, although ticket pricing has been discussed, the author is not taking into consideration the pricing of food and drinks, goods for sale, parking, and so on, which the spectators will pay for. Therefore, the author cannot examine the revenue of the team.
Hiroaki Ninomiya
Hiroaki Ninomiya, PhD, is a professor in the Faculty of Health and Sports Science at Doshisha University in Japan. His research interests include sport marketing and consumer behavior.
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Figure 2. Simulation of Reserved Seat A. [yen] 3,000 [yen] 3,500 [yen] 4,000 Five Arrows 19.1% 10.3% 10.3% Lake Stars 30.9% 23.4% 17.2% Evessa 50.0% 41.9% 34.1% Figure 3. Simulation of Reserved Seat B. [yen] 2,000 [yen] 2,500 [yen] 2,800 Five Arrows 22.5% 12.9% 11.1% Lake Stars 29.5% 18.1% 16.4% Evessa 48.0% 42.7% 33.0% Figure 4. Simulation of Non-Reserved Seat. [yen] 1,000 [yen] 1,500 [yen] 1,800 Five Arrows 14.2% 6.5% 5.0% Lake Stars 31.1% 15.9% 11.3% Evessa 54.7% 37.1% 29.7% Note: Table made from line graph. Figure 5. Simulation of Reserved Seat A. [yen] 3,000 [yen] 3,500 [yen] 4,000 Friday 12.0% 7.3% 6.7% Saturday 45.1% 34.8% 22.5% Sunday 42.9% 36.0% 30.9% Figure 6. Simulation of Reserved Seat B. [yen] 2,000 [yen] 2,500 [yen] 2,800 Friday 12.9% 10.5% 10.5% Saturday 40.9% 32.5% 21.9% Sunday 46.2% 41.2% 34.2% Figure 7. Simulation of Non-Reserved Seat. [yen] 1,000 [yen] 1,500 [yen] 1,800 Friday 9.9% 5.5% 5.5% Saturday 47.3% 30.0% 21.5% Sunday 42.8% 27.9% 22.7%COPYRIGHT 2015 Fitness Information Technology Inc.
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