Examining consumer perceptions of demand-based ticket pricing in sport.
Shapiro, Stephen L. ; Drayer, Joris ; Dwyer, Brendan 等
Introduction
The practice of dynamic ticket pricing (DTP), where ticket prices
fluctuate daily based on market demand factors, is currently being used
in some capacity by more than two-thirds of Major League Baseball (MLB)
organizations (Sachdev, 2013). Additionally, the National Basketball
Association (NBA), National Hockey League (NHL), college athletic
departments, and football bowl game organizers have implemented this
real-time approach to pricing (Drayer, Shapiro, & Morehead, 2014;
Shea, 2014). This strategy is a direct response to the proliferation of
the secondary ticket market through platforms such as StubHub. DTP
implementation has resulted in revenue increases of 510% in low-demand
situations and up to 30% in highdemand situations (Rishe, 2012).
According to Vincent Ircandia, vice president of business analytics
and ticket operations for the Portland Trailblazers, "Fans today
place different values on each and every game and we work hard to align
our prices with fan perceptions" (Brettman, 2013, para. 2).
However, questions remain about this strategy due to challenges
associated with fan perceptions of fairness, potential alienation of
season ticket holders, the growth of the secondary ticket market, and
matching price with consumer value for a ticket (Drayer, Shapiro, &
Lee 2012).
DTP is based on the theory of revenue management (RM), in which
prices for perishable products purchased in advance fluctuate to better
reflect market demand over time (Kimes, 1994). RM has been investigated
extensively as a pricing strategy within service industries such as
airlines, hotels, and restaurants (Chiang, Chen, & Xu, 2007; Kimes,
1989, 1994; Ng, 2007; Wirtz & Kimes, 2007). Research in these
industries has focused on pricing trends, managerial strategies, and
consumer perceptions related to the practice.
RM theory has also been applied to spectator sport, which shares
many common characteristics with traditional RM industries. Previous
research has examined the RM framework within the context of sport
ticket pricing (Drayer et al., 2012; Moe, Fader, & Kahn, 2011;
Shapiro & Drayer 2012, 2014). However, consumer perceptions of RM
strategies such as DTP have not been examined within sport. The current
study focused on this deficiency in the sport pricing literature.
Examining consumer perceptions of DTP, and RM in general, is
important for two reasons. First, RM can be perceived as unfair by
consumers, which can lead to negative attitudes, dissatisfaction,
changes in perceived value of a product, and lower purchase intentions
(Campbell, 2007; Haws & Bearden, 2006; Wirtz, Kimes, Theng, &
Patterson, 2003; Wirtz & Kimes, 2007). Perceptions of price
inequities or price gouging can have a negative impact on firm profits,
specifically in customer-oriented industries (Kahneman, Knetsch, &
Thaler, 1986; Wirtz et al., 2003). Second, the implementation of DTP
impacts the consumer purchase process. Consumers now have the ability to
check for ticket price changes over time. This is a common practice in
traditional RM industries such as airlines and hotels. Consumers can
decide to purchase tickets months prior to a game or watch for changes
in price and availability as the game draws near (Dwyer et al., 2013).
This practice may have an impact on the perceived value of a ticket and
ultimately purchase intentions.
Additionally, there are factors with respect to the spectator sport
consumer that may play a role in the viability of DTP. Tickets to
sporting events can be resold, creating two separate markets in which a
consumer can evaluate prices. The resale market does not exist in
traditional industries using RM. Furthermore, team performance plays a
significant role in demand for sporting events (Borland & MacDonald,
2003) and may impact perceptions of real-time price changes.
Therefore, the purpose of the current study was to examine the
impact of consumer perceptions of price fairness, price changes over
time, and team performance expectations on perceived value and purchase
intentions for sport event tickets. The current study was conducted
during a professional sport organization's initial implementation
of DTP. Additionally, market-based factors such as time, ticket market,
and seat location, which have been explored in previous DTP studies
(Shapiro & Drayer 2012, 2014), were considered in conjunction with
consumer perceptions.
The current study was unique in its design. The goal was to examine
consumer perceptions of ticket price after an initial exposure (prior to
a season) and over time as the game draws near. Therefore, consumers
were exposed to a professional sport organization's ticket prices
for various games before the season and at varying points leading up to
the day of a game. This design provided an opportunity to assess
perceptions of ticket prices in both markets and to place participants
in a natural setting where it is common for consumers to assess
real-time price fluctuations over a number of days before making an
advanced purchase decision (Dwyer et al., 2013).
Review of Literature
Revenue Management Theory
RM strategy, often used in the hotel and airline industries,
consists of setting prices according to predicted levels of demand to
meet the needs of different consumer groups (Kimes, Chase, Choi, Lee,
& Ngonzi, 1998). RM is defined as "the application of
information systems and pricing strategies to allocate the right
capacity to the right customer at the right place at the right
time" (Kimes et al., 1998, p. 33). RM is based on the concept of
price discrimination, which is a price-setting strategy built on the
notion that customers have different needs and price sensitivity.
Therefore, it is in the best interest of organizations to fluctuate
price in order to meet those needs (Kimes & Wirtz, 2003). Kimes
(1989) developed a framework to assess whether RM strategy is applicable
in certain industries, identifying factors such as the need for market
segmentation, perishable inventory, advanced sales, low marginal sales,
high marginal production costs, and fluctuating demand. RM has been
successfully implemented in various industries in addition to airlines
and hotels, including restaurants, movie theaters, concert halls, and
theme parks (Goulding & Leask, 1997; Hartley & Rand, 1997;
Kimes, 1994; Kimes & Wirtz, 2003).
Even with this success, many organizations have been slow to adopt
RM due to concern over negative customer perceptions (Wirtz & Kimes,
2007). Researchers and practitioners in areas where advanced purchase of
perishable inventory is common have argued that RM practices can be
perceived as unfair by consumers and lead to decreases in satisfaction
and purchase intentions (Choi & Mattila, 2004; Kahneman et al.,
1986; Wirtz et al., 2003).
RM and Demand-Based Pricing in Sport Professional sport
organizations have embraced demand-based pricing over the past 10-15
years. Although pricing strategy has evolved over this time period,
research regarding initial transitions to demand-based ticket pricing in
sport is limited. In what was most likely the first investigation of
demand-based pricing, Heilmann and Wendling (1976) examined a limited
price discounting strategy implemented by the Milwaukee Bucks during the
1974-1975 season. Researcher findings showed discounted games increased
attendance and led to more sellouts. This strategy was not adopted at
the time, but led to variable ticket pricing (VTP), a more complex
differential pricing strategy in which ticket prices are set in advance
based on factors such as opponent, day and time of the game, promotions,
and holidays (Rascher, McEvoy, Nagel, & Brown, 2007). Rascher et al.
(2007) explored VTP further and found that teams could increase ticket
revenue around 7% through use of this strategy.
Due to the growth in ticket resale, several researchers began
exploring transactions on the secondary market where prices change in
real-time to reflect actual changes in consumer demand (Drayer &
Shapiro, 2009; Drayer, Rascher, & McEvoy, 2012; Sweeting, 2012;
Wantanabe, Soebbing, & Wicker, 2013). Researcher findings suggest
when sport organizations do not price optimally, a considerable amount
of consumer surplus is captured by the resale market. Additionally,
partnerships between sport organizations and secondary market platforms,
such as StubHub's agreement with MLB, have positively influenced
price dispersion as teams are making more of an effort to vary ticket
prices (Wantanabe et al., 2013)
Another organizational response to the proliferation of the resale
market has been the implementation of DTP. DTP, where price
discrimination occurs on a daily basis, was the first true RM strategy
is sport (Drayer et al., 2012). Shapiro and Drayer (2012) extended
previous research through the examination of both DTP and secondary
market prices throughout an MLB season. The authors concluded DTP
captured some of the consumer surplus from ticket resale, but price
restrictions still exist in the primary market. Additional research has
focused on factors that impact DTP and the relationship between DTP and
the secondary ticket market (Moe et al., 2011; Paul & Weinbach,
2013; Shapiro & Drayer, 2014). Findings from these studies highlight
ticket price determinants such as team and individual player
performance, time, market, and seat location.
The previous work on demand-based pricing and the implementation of
DTP is limited due to the relative infancy of the practice in sport and
the tremendous growth of the online ticket resale market, which
generates revenues of $5 billion annually (Peoples, 2014). Each of the
aforementioned demand studies examined factors that affect attendance
and/or price, but did not consider consumer attitudes or behaviors.
Spectator sport provides a unique context in which to examine consumer
response to RM strategy. First, similar to airlines and hotels, sport
events have a specified duration (Wirtz & Kimes, 2007).
Organizations can use this predictable duration to alter prices at
certain times and under certain conditions. However, research suggests
consumers' preferences impact perceived value of the product and
that those preferences change over time (Dwyer et al., 2013; Kimes et
al., 1998). Additionally, a unique component of sport is team
performance. Previous researchers have suggested team performance
impacts ticket price (Rishe & Mondello 2003, 2004; Shapiro &
Drayer, 2014), yet the extent to which consumer expectations of
performance influences perceived value and/or purchase intention is not
known.
More research is needed regarding consumer attitudes towards DTP
and secondary market ticket prices in addition to variables such as
time, ticket market, and seat location to provide a holistic view of the
demand-based pricing landscape in sport and the impact of RM strategies
within this environment. Although DTP is becoming more popular in major
sport leagues, some organizations are hesitant to implement this
strategy due to the potential for negative customer response (Garno,
2013). The following sections focus on consumer attitude literature
related to RM practices. These attitudes include perceived fairness,
consumer perceptions of price changes over time, and perceived value.
Fairness
When it comes to the consumer decision-making process, perceived
fairness can be an influential component. Rabin (2004) suggested that
consumer behavior is a direct result of how individuals feel they are
being treated. If individuals believe they are being treated fairly,
they will reciprocate in a consistent manner. As a result, organizations
measure and assess these perceptions when pricing products and services
(Kahneman et al., 1986).
Fairness perceptions can have a great impact in RM industries,
where prices fluctuate on a daily basis (Kimes, 1994). RM strategies
have been perceived as unfair by consumers in certain conditions, which
negatively impacts purchase intentions (Kahneman et al., 1986; Wirtz et
al., 2003). Haws and Bearden (2006) found fairness perceptions played a
significant role in consumer satisfaction and purchase intention. In
particular, the characteristics of the seller and length of time prior
to the transaction had the greatest influence on fairness. These
fairness judgments are a direct method for assessing feelings of
individual exploitation, which can be easily developed when a seller
dynamically prices products and services (Lind, 2002). The impact of
fairness perceptions is magnified in customer-oriented industries (Wirtz
et al., 2003).
Sport organizations that incorporate DTP, in particular, should be
cognizant of consumer perceived fairness, as Tripathi (2013) suggests
the difference between dynamic pricing and price gouging resides in the
mind of the consumer. Citing the dual entitlement principle in which
consumers understand that a firm is entitled to a profit just as a
consumer is entitled to a fair price,
Tripathi (2013) alluded to the potential for DTP to erode fan
loyalty if perceived fairness is not monitored. Within the sport
marketing literature, fairness perceptions of ticket price have not been
thoroughly explored (Courty, 2003). As DTP becomes more prevalent, there
is a need to understand the impact of fairness perceptions related to RM
pricing strategies.
Impact of Time
The impact of time within the sport consumer decision-making
process requires additional inquiry.
Dwyer et al. (2013) applied the generic advanced-booking decision
model (Schwartz, 2000, 2006) to professional hockey through a
quasi-experimental design. In general, advance-booking consumers, sport
or otherwise, have several options when quoted a price. Schwartz (2008)
was the first to include time as a variable within the advanced booking
model. Schwartz found that holding all other factors constant, the
decision to reserve a hotel (or buy a ticket) depends somewhat on how
far out the purchase decision was from the date of stay (or game day).
For the most part, the literature on advanced selling comes almost
exclusively from the field of travel and tourism, where price
discrimination and RM strategies have been found to provide competitive
advantages for gaining market share, ensuring capacity fulfillment, and
creating profitability (Gale & Holmes, 1992; Shugan & Xie,
2000). Several similarities exist in the experiences of sport consumers
and tourists with respect to product and service consumption (Drayer et
al., 2012). For instance, similar to staying in a hotel, attending a
sporting event is a perishable experience driven by the intersection of
tickets available (hotel rooms available), ticket price (room rate), and
consumer demand. Second, purchasing a ticket or reserving a room in
advance have similar uncertainties related to availability as limited
information about alternatives is readily accessible.
Dwyer et al. (2013) specifically targeted professional hockey fans
to examine the impact of time within the advanced purchasing process.
The authors found that the impact of time was an influential variable
within the sport consumer decision process--specifically, the perceived
likelihood of ticket availability and finding a lower priced ticket
increased as the date of the game drew closer. The findings were the
first for the field of sport marketing, yet represent only the beginning
as the authors suggested the inclusion of several points of time to
provide more insight to the specific influence of time.
The current study examined the impact of consumer perceptions of
price changes in a similar RM environment to that of Dwyer et al.
(2013). However, the cur rent study was conducted during the initial
implementation of DTP and considers other consumer attitudes and price
attributes in conjunction with perceptions of price changes over time.
The impact of time is not known within this context.
Perceived Value and Willingness to Pay The behavioral outcome most
often associated with consumer research is the decision to purchase.
However, there are sport-related studies that have focused on the
maximum price consumers are willing to pay (WTP) (Carmon & Ariely,
2000; Drayer & Shapiro, 2011; Rosas & Orazem, 2014).
Understanding consumers' WTP point is helpful for marketers as they
attempt to set prices to better reflect perceived value of a product
(Drayer & Shapiro, 2011).
Often through experimental or survey methods, researchers attempt
to determine a respondents' WTP point through the contingent
valuation method (CVM). Though the exact question format can vary, CVM
research engages each respondent in a transaction and asks them directly
to identify the highest price they would pay for the product.
Additionally, CVM can be used to show the difference between WTP and
price in an effort to measure perceived value for a product or service
(Herath & Kennedy, 2004). This concept is called consumer surplus,
and it has been conceptualized as the economic measure of consumer
satisfaction (Marshall, 1980). As a means to assess value, consumer
surplus through CVM has been utilized to evaluate the environment,
tourism, and outdoor recreation.
Within the context of sport, Carmon and Ariely (2000) found that
game significance, television viewership, financial considerations, and
other environmental factors significantly impacted Duke University
basketball fans' WTP for tickets. Drayer and Shapiro (2011)
conducted a similar study and found that "fans who have stronger
team identification or loyalty are willing to pay more to see the team
play" (p. 397). Further, they split buyers into two groups in which
one group saw the printed face value of the ticket and the other did
not. This simple change in product characteristics had a significant
impact on consumers' WTP (almost a $14 difference in WTP between
the two groups on average). Ultimately, changes in WTP may be due to
changing product attributes within the context of an individual offer.
In sport, this is critically important as product attributes (i.e., team
and player performance) change almost daily.
Despite its importance in describing perceived value, consumer
surplus as a form of CVM has yet to be used to value spectator sport
pricing (Kanagal, 2013). Eggert and Ulaga (2002) determined that
consumer satisfaction was an important substitute for perceived value
within certain business markets, and the economic measure of consumer
satisfaction has been quantified as the difference between one's
maximum WTP point and price or consumer surplus (Marshall, 1980). Thus,
operationalizing perceived value as a fan's consumer surplus was a
logical extension for this examination of dynamic-based ticketing.
In summary, there has been a significant shift from traditional
pricing to demand-based pricing in professional sport. Demand-based
pricing can be applied within the context of sport through the RM
framework, which was developed in the hotel and airline industries. The
literature on this topic in sport ticket pricing is underdeveloped as
the proliferation of the secondary market and strategies such as DTP are
relatively new. Specifically, the research on consumer perceptions of
demand-based pricing (i.e., fairness, expectations of price changes,
team performance expectations) is limited and warrants further
investigation. Additionally, the impact of consumer attitudes on
perceived value and purchase intentions in conjunction with previously
studied factors such as time, ticket market, and seat location is
unknown.
Given the infancy of DTP in the primary market and price volatility
in the secondary ticket market, it is critically important to gauge the
impact of consumers' perceptions of prices over time in both
markets. Additionally, consumer perceptions of price (after the initial
exposure to tickets in both markets) were assessed to understand impacts
on perceived value and purchase intention. These factors have been
identified in the previous RM literature, but have not been examined in
spectator sport, with a vibrant resale market and team performance
impacts. Therefore, the following research questions were developed to
examine consumer perceptions of ticket price in a demand-based pricing
environment:
RQ1: Is there a difference in consumers' perceived value for
sport event tickets based on time, ticket market, and/or seat location?
RQ2: To what extent do consumer perceptions of price fairness,
likelihood of price changes, and expectations of team performance
influence perceived value of sport event tickets in both the primary and
secondary markets?
RQ3: To what extent do consumer perceptions of ticket value, price
fairness, likelihood of price changes, and expectations of team
performance influence likelihood to purchase sport event tickets in both
the primary and secondary markets?
Method
Participants and Procedure Through a partnership with an ESPN MLB
beat reporter, fans were invited to participate in a study about ticket
prices during an MLB team's inaugural implementation of DTP. A
total of 81 fans agreed to participate in the study, with each being
assigned one home game for the team being examined. A total of 30
participants completed multiple data collections over the course of the
season and provided usable data sets totaling 720 unique observations
for this study. This accounted for 38% of the team's home games in
2012, covering every month of the season. Sample demographics and
purchase behavior results are available in Table 1. According to the
survey results, the sample appears to be a relatively highly attached
group of baseball fans with team and sport attachment scores of 6.411
(SD = .852) and 5.289 (SD = 1.768) on a seven-point scale, respectively.
In addition, the sample was moderately familiar with ticket purchasing
from both primary and secondary markets with scores of 3.983 (SD = .737)
and 4.133 (SD = .928) on a five-point scale, respectively.
Participants were asked to collect prices on four dates prior to
their assigned game. The first date was three weeks prior to the season
and consistent across all participants. The three remaining dates were
15, five, and one day prior to a participant's assigned game.
Participants were directed to collect prices from both the team's
website and StubHub for three different seat locations (a low, mid-tier,
and premium seat). Participants were then asked attitudinal and
behavioral questions related to the tickets each time they observed a
ticket price.
Each participant was given a personalized spreadsheet that had
columns for all price data and attitudinal and behavioral questions.
Additionally, participants were provided an instruction sheet and were
reminded by email a week before each data collection date. Since each
participant collected 24 ticket prices (two markets, three seat
locations, over four time periods) and responded to perception questions
for each ticket price, the total number of observations for analysis was
720.
Instrumentation
Four single-item attitudinal/behavioral measures were developed for
this study, and included likelihood to purchase, likelihood the price
will change, fairness perceptions, and likelihood that the team would
win the game. Each item was measured on five-point Likert-type scales
(Not at All Likely to Very Likely and Extremely Unfair to Extremely
Fair). The single-item fairness measure was adapted from previous work
in which fairness perceptions were examined to identify a potential
impact on consumer attitudes and behavior (Kimes, 1994; Kimes &
Wirtz, 2003). The likelihood to purchase item was adapted from previous
consumer-based sport ticket pricing studies (Dwyer et al., 2013;
Shapiro, Dwyer, & Drayer, 2013). Likelihood of ticket price change
and likelihood of team winning measures were developed for this specific
study. Finally, the one-item WTP measure was open ended, in which
participants were asked to provide the highest price they would be
willing to pay for a given ticket at the time of the price observation.
Single-item measures such as the items used in the current study
may not be appropriate in all research settings. However, in the case of
this study, single items were used to simplify the process for
participants who were asked to provide individual perceptions of six
different ticket prices on four different occasions.
Previous research has highlighted the value of using single-item
scales, particularly in cases in which simplicity is paramount
(Bergkvist & Rossiter, 2007).
Data Analysis
To answer RQ1, a mixed ANOVA was conducted on the data collected 15
days, five days, and one day before the game. The dependent variable was
perceived value. Perceived value was operationalized by taking the
difference between an observed ticket price and a participant's
willingness to pay for that ticket (P-WTP). Perceived value, defined by
Holbrook (1994), is the difference between the benefits of a product and
the costs. McDougall and Levesque (2000) conceptualized it as
consumers' cognitive evaluation of what they have received for what
they have given. Given these definitions and recommendations from Murray
and Howat (2002) and Cronin, Brady, and Hult (2000) that price must be
part of the perceived value equation, P-WTP was utilized to measure the
difference between the maximum price a consumer was willing to pay and
the actual cost of attendance. Time was the within-subjects variable and
ticket market and seat location were the between-subjects variables.
To answer RQ2 and RQ3, two separate multiple linear regression
models were developed to examine the specific cross-section data
collected three weeks before the MLB season. The first regression
examined factors influencing perceived value for tickets. Independent
variables in the model included seat location, market (primary or
secondary), perceived fairness, perceived likelihood of price change,
days before the game, and perceived likelihood the team would win. The
second regression model examined factors influencing likelihood to
purchase. An ordinal or PLUM (Polytomous Universal Model) regression was
conducted due to the scaled nature of the dependent variable.
Independent variables in this model were the same as above except
perceived value was included.
Results
Descriptive statistics for price data and attitudinal/behavioral
measures are provided in Table 1. Average ticket price in the primary
market was $94.80 (SD = 75.30) and average ticket price in the secondary
market was $91.44 (SD = 90.38). When broken down by price tier, average
ticket prices for lower-tier seats were $29.51 (SD = 13.63) in the
primary market and $22.14 (SD = 17.55) in the secondary market. Average
ticket prices for mid-tier seats were $73.57 (SD = 30.22) in the primary
market and $72.40 (SD = 35.11) in the secondary market. Finally, average
ticket prices for upper-tier seats were $181.32 (SD = 60.86) in the
primary market and $186.77 (SD = 97.47) in the secondary market.
On average, the WTP was about the same for primary market tickets
($61.54, SD = 47.63) compared to secondary market tickets ($61.37, SD =
50.66). WTP did not appear to be influenced by market price for
comparable seats. Overall, participants' likelihood to purchase
tickets at a given price was minimal (M = 2.17, SD = 1.25).
Additionally, likelihood of price changes over time (M = 2.60, SD =
.782) and perceptions of fairness (M = 2.76, SD = 1.11) were moderate.
Participants felt the team was generally likely to win the game in
question (M = 3.13, SD = .993).
With regard to RQ1, the mixed design ANOVA model in which perceived
value was the dependent variable provided statistically significant
results for Time (F[2,173] = 3.263, p = .041, [[eta].sub.p.sup.2] =
.024) and the interaction of time and seat location (F [4,348] = 4.161,
p = .003, [[eta].sub.p.sup.2] = .06). The within-subjects results for
time indicated a statistically significant increase in perceived value
between 15 days and five days before the game; however, the effect size
was generally small. In terms of time and seat location interaction, the
significant perceived value change occurred between 15 days and five
days, but only for the premium seats, with a moderate effect size. The
remaining interaction results were not statistically significant.
With regard to RQ2, examining perceived value based on the initial
exposure to ticket prices before the season only, the perceived value
for tickets was $41 below the observed price in both the primary and
secondary market, on average. The regression model for RQ2 was
statistically significant (F [7,172] = 23.414, p < .001) and the
[R.sup.2] value was .488. Detailed independent variable results are
available in Table 2. For every positive unit change in the likelihood
of the team winning (on a five-point scale), perceived value of the
ticket increased by approximately 40%. Additionally, for every positive
unit change in fairness perceptions (on a five-point scale), perceived
value of the ticket increased by approximately 50%. The perceived
likelihood of price change and days before the game were not
statistically significant.
The ordinal regression model utilized to answer RQ3, which examined
purchase intention based on the initial exposure to ticket prices before
the season only, was also statistically significant ([X.sup.2] [8] =
174.00, p < .001). The Nagelkerke [R.sup.2] value equaled .655,
suggesting a good fit to the data. The independent variable results are
available in Table 3. Perceived fairness had the strongest positive
relationship with purchase intentions. Additionally, both seat location
and likelihood of price change appeared to play a significant role in
purchase intentions. Purchase intention for lower-tier seats was
highest, followed by mid-tier seats and premium seats. In terms of price
change, the higher the likelihood of a price change the more likely a
participant was to purchase a ticket. Finally, perceived value had a
significant relationship with purchase intention. This relationship was
slightly negative.
Discussion
As sport ticket pricing strategy continues to shift toward a
demand-based focus and consumers become more accustomed to real-time
price changes on both the primary and secondary market, it is important
to understand attitudes and behaviors related to those price changes
during the course of a season. Sport consumer behavior as it relates to
ticket pricing becomes more important within the context of an RM
framework, in which optimal pricing is a result of understanding shifts
in consumer demand over time in advanced purchase environments.
The current examination supports the work of Drayer et al. (2012),
in which the RM framework is adapted to fit the spectator sport
environment. This study extends the RM theory by investigating consumer
perceptions of real-time pricing strategy within sport, similar to
research that has been conducted in the airline, hotel, and restaurant
industries (Kimes, 1994; Kimes & Wirtz, 2003; Wirtz & Kimes,
2007). This study also extends our knowledge of consumer perceptions of
ticket price within the sport industry, which has received limited
attention (Drayer & Shapiro, 2011; Dwyer et al., 2013). Spectator
sport provides a unique environment in which to examine RM, due to a
viable ticket resale market and the impact of team performance on demand
for sporting events.
The current findings are consistent with previous literature
highlighting the important roles of time, team performance, and
attitudes such as fairness perceptions and likelihood of ticket price
changes as a sporting event draws near (Drayer & Shapiro, 2009;
Dwyer et al., 2013; Moe et al., 2011; Shapiro & Drayer 2012).
However, the current study helps to extend the RM literature by not only
examining consumer perception of the practice in a new environment, but
by combining variables such as time, market, and seat location with
consumer perceptions of fairness, time changes, and performance
expectations into one study. Additionally, the study design was new to
sport, allowing participants to collect prices in two markets
simultaneously and provide attitudes and intentions at the time of
exposure, mimicking the current ticket purchase process. The following
sections highlight the theoretical and practical implications of these
findings.
The Importance of Time
RQ1 focused on the impact of time on perceived value for sport
tickets in a demand-based environment with two market options.
Consumers' perceived value significantly changed over time, but
only between two weeks and five days before the game. Additionally, the
interaction between perceived value and seat location was significant,
indicating a difference in premium seat prices around two weeks prior to
the game.
Figure 1 provides a closer examination of concurrent price and WTP
changes over time. The price changes for dynamically priced tickets and
the secondary ticket market look very similar to Shapiro and
Drayer's (2012) findings with limited DTP price changes, but with
more dramatic secondary market price reductions as the game draws near.
Interestingly, WTP values more closely mirror the DTP prices compared to
secondary market prices. It appears as if DTP pricing is more reflective
of what fans are willing to pay compared to secondary market prices,
most notably prior to the season. The secondary market prices shift
towards WTP as the game draws near, as expected in a demand-based
pricing environment with no restrictions.
Additionally, in the current study, perceived value was represented
by the difference between the asking price of the sellers and the
consumers' WTP. Subsequently, any change in perceived value may be
due to either changes in prices or changes in WTP. Indeed, according to
Figure 1, the change in perceived value could be driven by sellers who
are lowering their asking prices, especially in the secondary market.
These dramatic secondary market price changes were quite clear in the
analysis by Shapiro and Drayer (2012), whose research considered only
the sellers' asking price and not consumers' perception of
value.
On the other hand, the study by Dwyer et al. (2013) examined how
some consumer perceptions change over time (e.g., perceptions of ticket
availability and likelihood of price reductions). The authors also
suggested that consumers' attitudes began to change significantly
approximately two weeks prior to the event; however, they did not
examine how these changing attitudes influenced perceived value. With
the current study it appears that the changing seller behaviors is what
is driving the change in perceived value and not consumers' WTP,
which remains relatively consistent. So, while some consumer attitudes
may change over time, their perception of what the ticket is worth may
not respond as dramatically. For consumers, understanding that
sellers' asking prices begin to fall approximately two weeks prior
to an event is valuable information in the ongoing struggle to obtain
the lowest possible price.
In terms of seat location, only premium seat prices significantly
differed over time. This finding is also consistent with Shapiro and
Drayer (2012), who found the most dramatic price shifts occur in the
premium seat locations. One explanation for this is the considerably
high premium seat prices set by the secondary ticket market. Resellers
appear to inflate the premium seat prices, which generally drop
substantially as the game draws near (similar to the overall trend
represented in Figure 1). The premium seat location is more volatile in
terms of price due to the fact that ticket brokers are looking at these
seats as a revenue priority (Sauter, Poltrack, & Allen, 2012).
For RQ2 and RQ3 the time variable as a whole did not have a
significant influence on perceived value or purchase intentions. This
finding further demonstrates that certain time intervals are playing a
more significant role in consumers' perception of value than
others, most notably around two weeks prior to a game taking place.
Although the current results are consistent with previous research
suggesting time is an important factor regarding consumer perceptions of
ticket price in demand-based markets, more research is needed to further
explain that role.
Fairness
Perceptions of fairness have a significant influence on perceived
value of tickets and purchase intentions. The impact of fairness in the
current study is consistent with the general marketing literature on the
role of fairness (Kahneman et al., 1996; Kimes, 1994; Kimes & Wirtz,
2003) and within the context of sport event tickets (Shapiro et al.,
2013). In a demand-based pricing environment, where prices fluctuate
daily, consumers will assess the level of fairness regarding those price
changes and respond accordingly. Fairness can be a powerful component of
consumer behavior and these perceptions have an influence on future
purchase intentions (Kahneman et al., 1986). Therefore, price changes
cannot be based solely on an RM formula adjusting price at the
appropriate place and time.
Those responsible for setting price, regardless of market, must be
sensitive to consumer responses to those changes. The importance of
considering fairness becomes magnified with respect to the sport
organizations due to the fact that DTP prices generally increase over
time (Shapiro & Drayer, 2012). Additionally, unlike most resellers,
who can hide beyond the anonymity provided by platforms such as StubHub,
sport organizations are susceptible to public reaction to price changes
that may be considered unfair. Finally, sport organizations must take
fairness perceptions into consideration as consumers now have two
markets to choose from when making a ticket purchase. Perceptions of
fairness could be a differentiating factor when deciding whether to buy
a ticket directly from the team or on the resale market.
Perceived Value vs. Purchase Intentions Fan expectations of team
performance, fairness perceptions, seat location, and ticket market
influenced perceived value of the ticket. The significance of these
factors is consistent with the demand literature (Rascher, 1999; Welki
& Zlatoper, 1994) and pricing literature (Drayer & Shapiro,
2011; Shapiro & Drayer, 2012). Interestingly, these variables were
not completely consistent in the likelihood to purchase model. For
example, fairness and seat location significantly influenced purchase
intention, but market and fan expectations of team performance did not.
Consumer behavior researchers have consistently found a strong
positive correlation between one's perceived value and purchase
intentions (Kwon, Trail, & James, 2007; Teas & Agarwal, 2000).
Thus, it was surprising that the same statistically significant factors
that influenced perceived value did not influence purchase intentions at
a statistically significant level. A few mitigating factors exist that
may explain this unique finding. For instance, advertising researchers
Grewal, Monroe, and Krishnan (1998) found that acquisition value
mediated the relationship between perceived value and purchase
intentions. Acquisition value was defined as a more universal and
enduring type of value that accounts for both price and quality (Grewal
et al., 1998). In addition, travel and tourism studies have uncovered
the importance of customer satisfaction as a mediator between perceived
value and purchase intentions (Ryu, Han, & Kim, 2008; Tarn, 1999).
With respect to the current study's context and results,
perceived value was measured in part through WTP. This metric has been
found to be an important determinant of value, but it may be too narrow
when one considers the larger prospect of attending an MLB game
including constraints such as traffic, weather, and in-home viewership.
Thus, factors such as global or acquisition value and customer
satisfaction may be creating noise within our investigation,
specifically when one thinks of the enduring nature of team fandom. In
other words, team performance may impact perceived value in the short
run, but highly identified fans are swayed less by on-field performance
as their attachment has been found to be more stable over time
(Matsuoka, Chelladurai, & Harada, 2003). Regardless, further
investigation of the perceived value/purchase intention relationship in
a demand-based ticket pricing environment is warranted.
Ticket Market Effects
Ticket market had a significant influence on perceived value of
tickets prior to the season, but did not influence perceived value over
time as respondents were repeatedly exposed to price changes.
Additionally, market did not influence purchase intentions based on
ticket prices prior to the start of the season. There have been mixed
results in terms of market influence in relation to sport ticket
pricing. Drayer (2011) and Shapiro and Drayer (2012) argue that with the
proliferation and legitimization of the secondary ticket market and the
use of DTP by sport organizations, the primary and secondary ticket will
begin to merge and consumers will be comfortable purchasing in either
market. The current findings partially support this sentiment. When
first exposed to ticket prices at the beginning of the season,
participants valued secondary tickets $29 higher compared to primary
tickets. Although perceived value was generally below ticket prices in
both markets, it appears as if the initial secondary market prices
inflated perceived value for those tickets. The tickets for both markets
were for identical seat locations, but the higher initial price in the
secondary market may have influenced respondent value of those tickets.
This finding is consistent with the literature on face value and the
anchoring effect of pricing (Drayer & Shapiro, 2011).
However, over the course of the season, participants were exposed
to price changes over time. When examining the data over the course of
multiple observations, participants valued tickets in both markets
similarly. Perhaps the exposure to ticket prices on both markets for
multiple periods allowed the participants to assess price changes
concurrently, resulting in similar value perceptions across markets. The
inconsistent findings regarding market and perceived value warrant
further investigation, as previous literature supports the notion that
perceived value has an influence on purchase intentions (Kwon et al.,
2007; Teas & Agarwal, 2000). Additionally, market did not influence
purchase intentions directly. Perhaps perceived value acts as a
mediating variable between exposure to price and purchase intention,
which is supported within different contexts in the consumer behavior
literature (Kwon et al., 2007; Kwon, Pyon, & Choi, 2014).
As DTP gains more popularity it will be important to examine the
relationship between the primary and secondary ticket market from both a
consumer and managerial perspective. Consumers are becoming more
comfortable with shopping for tickets in either market, so managerial
decisions will need to reflect these attitudes. One example of this is
Ticketmaster's TM+ initiative, in which primary and secondary
ticket listings will be located on a single event page (Fisher, 2013).
WTP-Price Gap
The substantial deficit between a consumer's WTP point and the
price of the ticket (M=$41) is an interesting result though not
necessarily novel. This phenomenon is essentially why and how the
secondary ticket market has exploded. There are numerous factors sports
fans consider before attending a game, and the result of these variables
is fan perceived value. The considerable difference between the two
totals could be the result of the DTP system not being sensitive enough
to account for daily variable change, a potential study bias where we
forced respondents to evaluate a specific game, or a substitution
effect. MLB games are a relatively elastic sport product with 81 home
games. Regardless of the reason, the current study was exploratory, and
this result warrants further investigation.
Limitations and Future Research
The current study was delimited to one MLB team.
This was done in an effort to study the consumer response to the
initial year of DTP implementation. However, consumers of different
teams and leagues may react differently to demand-based pricing,
particularly as the pricing strategy becomes more familiar to consumers.
Future research should extend this exploratory effort by examining
multiple teams that have implemented DTP.
Second, the researchers attempted to collect consumer data for all
home games during the course of one season. Participants were asked to
collect data on four different occasions throughout the season. This
data included six different price points per date and participants were
asked to provide individual opinions and attitudes for each price. The
nature of this data collection led to respondent attrition, which
reduced our number of participants from 81 to 30. Although this sample
of participants is low, each participant was responsible for 24
observations (N=720). Still, the low sample size may cause sampling
error, which could have impacted some of the findings. Future research
should attempt to simplify the process or find more willing respondents
(perhaps with an incentive) in order to increase sample size and cover
more games.
Finally, while valuable in understanding day-to-day shifts in
demand, this study (and previous studies) are limited due to the fact
that only a certain number of tickets are available for sale at any
given point in time, and this number is never truly known as changes in
demand will affect the number of people willing to sell their tickets.
One of the difficult challenges with data collection of this kind is
access to ticket supply and transactional data. Future studies, perhaps
through a partnership with a professional sport organization, should
attempt to ascertain ticket supply and transactional data to expand upon
our knowledge of demand-based ticket pricing and consumer response to
the current pricing environment.
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Stephen L. Shapiro, Joris Drayer, and Brendan Dwyer
Stephen L. Shapiro, PhD, is an associate professor in the
Department of Human Movement Sciences at Old Dominion University. His
research interests include consumer behavior, sport finance, sport
marketing, and ticket pricing in the primary and secondary markets.
Joris Drayer, PhD, is an associate professor in the School of
Tourism and Hospitality Management at Temple University. His research
interests include consumer behavior and sport pricing.
Brendan Dwyer, PhD, is an associate professor and the director of
research and distance learning in the Center for Sport Leadership at
Virginia Commonwealth University. His research interests include
consumer behavior, sport marketing, and sport pricing.
Author Correspondence
Stephen L. Shapiro
Old Dominion University
Student Recreation Center #2012
Norfolk, VA 23529
Email: sshapiro@odu.edu
Table 1
Sample Demographics and Purchase Behavior
AGE
Mean 29.679
St. Dev. 7.045
GENDER n %
Female 3 10.0%
Male 27 90.0%
INCOME n %
Less than $25,000 3 10.0%
$25,000-49,999 4 13.3%
$50,000-74,999 3 10.0%
$75,000-99,999 4 13.3%
$100,000-149,999 11 36.7%
More than $150,000 5 16.7%
EDUCATION n %
High School / GED 1 3.3%
Some College 9 30.0%
4-year College Degree 14 46.7%
Master's Degree 6 20.0%
PURCHASE FREQUENCY n %
1-2 times/year 8 26.7%
3-5 times/year 12 40.0%
6-10 times/year 4 13.3%
11 or more times/year 6 20.0%
ETHNICITY n %
Caucasian 26 86.7%
Other 4 13.3%
Table 2 Descriptive Statistics
Variable Mean SD
Ticket Price
Primary Market (PM) Overall 94.80 75.30
PM Lower-tier 29.52 13.63
PM Mid-tier 73.57 30.22
PM Premium-tier 181.32 60.86
Secondary Market (SM) Overall 91.44 90.38
SM Lower-tier 22.14 17.54
SM Mid-tier 72.40 35.11
SM Premium-tier 186.77 97.47
Willingness to Pay
Overall 62.15 48.97
PM 61.55 47.63
SM 61.37 50.66
Purchase Intention
Overall 2.30 1.36
PM 2.17 1.25
SM 2.45 1.44
Price Change
Overall 2.47 .836
PM 2.60 .782
SM 2.39 .875
Fairness
Overall 2.93 1.24
PM 2.76 1.11
SM 3.16 1.31
Likelihood of Team Win
Overall 3.13 .992
Table 3 OLS Regression Results--DV = Perceived Value of Ticket
Variable U-Beta (a) t-value p-value
Days before the game -.019 -.241 .810
Perceived change in price -2.82 -.476 .635
Perceived fairness of price -23.77 -4.92 <.001
Perceived likelihood team win -17.53 -3.11 .002
Market (a) 29.07 3.45 .001
Seat Location 1 (b) -53.40 -3.92 <.001
Seat Location 2 (b) -60.23 -5.24 <.001
(a) Unstandardized Beta coefficients
(b) Beta changes for low and mid-tier seat locations compared
to a premium seat location
Table 4 Ordinal Regression Results--DV = Likelihood to Purchase
Variable Estimate Wald p-value
Days before the game -.003 .985 .321
Perceived change in price .633 7.559 .006
Perceived fairness of price 1.260 28.416 <.001
P-WTP -.018 4.802 .028
Perceived likelihood team win -.083 .134 .715
Market (a) .199 .337 .562
Seat Location 1 (b) 1.344 4.773 .029
Seat Location 2 (b) .747 1.722 .1898
(a) Odds changes for secondary market compared to primary market
(b) Odds changes for low and mid-tier seat locations compared
to a premium seat location
Figure 1. Ticket prices and WTP broken down by market.
DTP Mets StubHub WTP--Primary WTP--Secondary
Pre-season $117.83 $92.69 $84.21 $76.79
15 Days $96.59 $95.61 $93.38 $93.63
5 Days $66.34 $61.91 $60.92 $60.56
1 Day $62.27 $61.14 $61.73 $60.34
Note: Table made from line graph.
