Proceed to checkout? The impact of time in advanced ticket purchase decisions.
Dwyer, Brendan ; Drayer, Joris ; Shapiro, Stephen L. 等
Introduction
In an environment where several variables could impede a consumer
from attending a sporting event (e.g., weather, mood, team performance),
advanced ticket sales provide a sport organization with the security of
guaranteed revenue. And while revenue from multimedia rights are at an
all-time high, event attendance remains the largest revenue source for
several professional leagues including Major League Baseball (MLB) and
the National Hockey League (NHL; Fisher, 2010). As a result, advance
selling has become a distinct marketing objective and ticketing strategy
for many organizations looking to combat consumer sovereignty, uncertain
event outcomes, and a highly competitive marketplace (Hendrickson,
2012).
The proliferation of the secondary ticket market, which provides
consumers with multiple purchase options, has also been a function of
the growing importance of advanced ticket sales in sport. Where ticket
sources were once limited to the organization or ticket scalpers, the
contemporary sport consumer now has several advance ticket purchase
options. For instance, one can obviously still purchase directly from
the sport organization (primary market). However, if a sellout occurs,
or even if tickets are still available directly from the team, secondary
market platforms such as StubHub or eBay provide potential consumers
additional purchase options. As a result, organizations in the primary
market must adapt to an evolving marketplace and develop appropriate
marketing strategies to ensure advanced purchases.
Further complicating advanced sales, and consumer behavior in
general, are the varying levels of attachment associated with sport
consumers (Koo & Hardin, 2008). Several researchers have suggested
that sport organizations intentionally underprice their tickets at least
in part to ensure that consumers maintain their positive feelings about
the team (Coates & Humphreys, 2007; Fort, 2004; Krautmann &
Berri, 2007). However, Drayer and Shapiro (2011) found that "fans
who have stronger team identification or loyalty are willing to pay more
to see the team play" (p. 396). Additionally, highly identified
sport consumers are less affected by fluctuations in team performance
(Branscombe & Wann, 1991; Wann & Branscombe, 1993). That said,
less-identified sport consumers are equally important to sport
organizations, and despite more dramatic demand fluctuations based on
team performance, teams must continually recruit and retain this group
of potential consumers (Whitney, 1988). In the end, despite the
difficulty in establishing different marketing strategies based on team
identification, it has been established as a primary market segmentation
strategy. Empirical research related to team identification and advance
ticketing strategies is lacking.
In addition to ticket source and team identification, perhaps the
most important variable in the advance sales equation is time. With
several options from which to purchase, varying levels of team interest,
and ultimately, multiple market factors related to ticket supply and
demand, sport consumers are forced to speculate before acting. It is
challenging for consumers to speculate how ticket prices and seat
availability will change over time, not to mention the difficulty of
speculating on the consumer utility factors listed above (e.g., weather,
mood, and team record). Previous research on demand-based pricing in
sport has provided evidence of price shifts based on both time and
availability of tickets (Drayer & Shapiro, 2009; Shapiro &
Drayer, 2012). However, consumer perceptions of these influences and how
they affect purchase decisions within the context of sporting events
have not been explored. Thus, guided by the generic advanced-booking
decision model (Schwartz, 2000; 2006), this study systematically
explored the role of time in an advanced-purchasing setting. In
addition, the study examined ticket source (primary or secondary market)
and team identification as potential moderators of the advanced sport
ticketing process. Given the direct relationship between a
consumer's advanced purchasing behavior and an organization's
pricing decisions, it is believed that a more comprehensive
understanding of the sport consumer decision making process in an
advanced-sales setting will aid sport organizations in implementing more
effective pricing and revenue management tactics.
Review of Literature
Ticketing Strategies and Sources Traditional ticket pricing
strategies used seat location as the primary factor in determining price
differences between tickets. Around the turn of the millennium, several
professional sports franchises introduced variable ticket pricing (VTP)
which allowed them to use additional factors in setting prices such as
opponent and day of the week. However, as these prices were set before
the start of the season, they still ignored the changes in consumer
demand for these events over the course of the season. Subsequently, in
2009, the San Francisco Giants introduced dynamic ticket pricing (DTP)
where prices changed daily based on fluctuating demand conditions. Over
half of the teams in MLB along with several more in the NHL and National
Basketball Association (NBA) have now adopted some form of DTP.
While understanding what factors to consider when setting prices on
a daily basis is a difficult task, a wealth of previous literature has
examined changes in consumer demand. For example, when considering the
quality of the game, several researchers have focused on changes in
attendance based on the expected outcome. Using a variety of measures
including betting odds (Welki & Zlatoper, 1999), difference in
league ranking (Garcia & Rodriguez, 2002), average number of games
behind the first place team (Noll, 1974), and differences in games won
(Price & Sen, 2003), these researchers examined how outcome
(un)certainty may influence consumer demand for an event. There is no
shortage of research on the factors influencing consumer demand (see
Borland & McDonald, 2003, for a summary of such studies). However,
these studies examined consumer demand based on fluctuations in
attendance and did not consider how these factors may influence consumer
attitudes and/or their willingness to pay for tickets. Further, DTP
considers how these factors change over time suggesting that perhaps the
importance of these factors may be influenced by time itself.
The evolution of primary market pricing strategies from a
seat-location-based approach to VTP, and eventually DTP, coincides with
the growth of the secondary market. With the ability of the Internet to
quickly and conveniently facilitate transactions, this resale market has
evolved into a legitimate, multi-billion dollar industry (Drayer &
Martin, 2010). In this transparent, free-market environment, research
has been conducted which has further illuminated customer preferences
for tickets. For example, Drayer and Shapiro (2009) examined online
auctions on eBay and determined that several factors, including home and
visiting team performance, population, and day of the week influenced
the amount customers were willing to bid for tickets. Additionally, they
found the number of days before the game affected final auction prices.
In other words, prices decreased as the event drew closer.
Shapiro and Drayer (2012) also compared dynamic prices in the
primary market to secondary market prices on StubHub. They determined
sellers in the primary market steadily increase prices over time while
secondary market sellers were more inclined to lower prices over time.
In this case, sellers' consideration of the effect of time on
consumers' willingness to pay is quite different. This suggests the
phenomenon is in need of further examination.
The influence of time was also apparent in the work of Moe, Fader,
and Kahn (2011), who found ticket sales were influenced by constantly
fluctuating factors such as team performance and days before the game.
As attractiveness of teams fluctuates based on performance, and the game
date nears, ticket sales and seat location choices change. The authors
concluded data-driven pricing decisions based on consumer demand are
most likely to capture true value of the ticket as prices can change
based on outcome uncertainty.
Of course, consumers' perceptions of the source of these
tickets may also influence consumers' perception of the ticket
being offered. The studies mentioned previously focus primarily on
sellers' price setting strategies and ignore how consumer
perceptions of the product may influence these consumption decisions.
There is a wide array of research suggesting that consumers'
perception of a product goes beyond the extrinsic characteristics and
may be influenced by other intrinsic attributes such as perceived
trustworthiness of the seller and experience with similar transactions
in the past (i.e., reference transactions). For example, Xia, Monroe,
and Cox (2004) found that transaction similarity and buyer-seller
relationship influenced consumer attitudes. Within the tourism and
hospitality literature, several studies have considered consumers'
perceptions of the seller. However, given that the product is guaranteed
to be the same across all platforms, third party websites'
formalized relationships with hotels. Several studies have focused
primarily on the fairness of sellers' pricing strategies (Choi
& Mattila, 2005; Kimes, 2003; Wirtz & Kimes, 2007). There are,
however, differences between tourism and sport. Specifically, third
party sellers (i.e., secondary market sellers) are not supplied with
tickets from sport organizations through any contractual relationship,
meaning that consumers may be uncertain about the authenticity of the
ticket. One of the unique features of the secondary market is that
perceptions of the industry have been affected by previous instances of
unethical business practices and the existence of laws in many states
that makes ticket resale illegal (Drayer & Martin, 2010). Thus,
consumers' perceptions may be affected by not only the price of the
ticket but also their perception of the source. However, to date, no
research has explored how ticket source affects consumers'
attitudes and purchase intentions. In the advanced ticket purchase
setting, ticket source may be an intriguing and timely variable as sport
consumers are no longer strictly limited to a team's pricing
structure.
Team Identification
Identification refers to the roles an individual plays within a
network of social relationships (Stryker & Burke, 2000). Identities
are organized and conceptualized through social interactions, and these
identities can influence behavior (Stryker, 1968, 1980). Social
interactions not only affect the development of identification, but
these interactions impact the salience of these identities. This is the
foundation of identity theory proposed by Stryker (1968, 1980).
Various facets of identity theory have been examined in
social-science research providing significant evidence of the role
identity plays in the decision making process. Within the context of
sport marketing, there is a wealth of literature on the role of
identification and its relationship with other aspects of sport consumer
behavior (Lock, Taylor, Funk, & Darcy, 2012; Fink, Trail, &
Anderson, 2002; Trail, Anderson, & Fink, 2000; Wann &
Branscombe, 1993; Zillman, Bryant, & Sapolsky, 1989). According to
Wann and Branscombe (1993), team identification can be used not only to
understand the interaction between sport consumers and teams, but to
gauge the level of consumer behavior. In essence, team identification
can help predict sport consumption.
Some of the early research on sport and identification claimed
spectator sport provides an opportunity for individuals to establish a
sense of belonging and develop relationships with other sport consumers
who identify highly with a team (Zillman et al., 1989). High levels of
identification with a team have been shown to enhance one's
allegiance to that team regardless of performance (Branscombe &
Wann, 1991). This is an important point of emphasis, as the literature
on demand in sport has consistently identified a positive relationship
between team performance and attendance (Hansen & Gauthier, 1989;
Lemke, Leonard, & Tlhokwane, 2010; Noll, 1974; Whitney, 1988). In
general, consumers attend fewer games when a team does not perform well.
In the case of highly identified consumers, however, team performance is
less of a factor (Branscombe & Wann, 1991; Wann & Branscombe,
1993).
Additionally, team identification has been shown to influence
consumption within a variety of contexts beyond event attendance. Wann
and Branscombe (1993) found highly identified sport consumers tend to
invest more time and resources into their favorite team. Subsequent
examinations have supported these findings. Wakefield (1995) found a
positive relationship between team identification and re-patronage,
providing some of the first evidence that a consumer's attachment
to team can have an effect on future intentions. Trail et al. (2000) and
Trail, Fink, and Anderson (2003) found highly identified sport consumers
are more likely to attend games. Additionally, Trail et al. (2003)
discovered these consumers purchase more team-related merchandise.
In terms of team identification moderating various attitudes and
behavior within the context of sport, the research is limited. Trail et
al. (2012) examined whether team identification moderated the
relationship between vicarious achievement and basking in reflected
glory (BIRGing) or cutting off reflective failure (CORFing). There were
no interacting effects found for the moderating models. However, Wann
and Branscombe (1993) suggest this moderated relationship could exist
based on the fact that individuals with high team identification would
be more likely to support the team and less likely to reject them
regardless of outcome. The extent to which a relationship between time
and ticket purchase decisions might be influenced by attachment to a
team is unknown.
Although there is strong support for the relationship between team
identification and consumption, little is known regarding the level of
team identity and the ticket purchase process. In the current
demand-based pricing environment, where the price and number of tickets
available are constantly changing, it is important to understand the
role identification may play in a sport consumer's decision to
purchase a ticket at a given time before a game. This information
becomes more important as consumers begin to fully understand the
process of DTP and the secondary ticket market, where prices may
fluctuate daily making it difficult to determine the optimal purchase
time and/or price. The role of team identification in this process
should not be understated.
[FIGURE 1 OMITTED]
Theoretical Background
The generic advanced-booking decision model (Schwartz, 2000; 2006)
served as the theoretical foundation for this study as it is grounded in
the consumer decision making process. This model was developed and
validated in the field of travel and tourism with the particular aim at
understanding the process of hotel reservations. In general, the
literature on advanced selling comes almost exclusively from the field
of travel and tourism (e.g., airlines & hotels) where price
discrimination and yield management strategies have been found to
provide competitive advantages for gaining market share, ensuring
capacity fulfillment, and ultimately, creating profitability (Gale &
Holmes, 1992; Shugan & Xie, 2000; 2005; Xie & Shugan, 2001).
However, the extension of this particular theory to the field of
sport marketing is both logical and needed (Gibson, 1998). First,
several similarities exist in the experiences of sport consumers and
tourists with respect to product and service consumption. 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. Lastly, due the similarities between
the tourist and sport consumer experiences, there is a growing need to
bridge theoretical gaps between the two fields (Gibson, 1998).
[FIGURE 2 OMITTED]
According to the model, prospective consumers have four different
generic decision options as they respond to a price quoted by a hotel:
(1) reserve the hotel room, (2) reserve the room and continue searching
for a better rate, (3) not reserve and continue searching for a better
rate, or (4) disregard the hotel entirely and consider alternatives.
Placed on an expected utility-rate plane, as depicted in Figure 1, one
can see three strategic switching points where one must choose between
the rate quoted by the hotel and the other options. The model assumes
risk neutral consumers that choose the action in which their expected
utility is maximized. Several variables are at play in the determination
of the switching points including the search cost, the discount the
consumer expects the hotel will offer in the future, the probability the
hotel will sell out, the probability that a discounted rate will be
offered after a given number of periods of search, and the penalty for
canceling the reservation. From the hotel's perspective, it is
preferable that the consumer choose option one followed by option two,
three, and four.
Clearly, the options available to sport consumers are not exactly
the same as it is not an accepted practice to reserve a ticket while
searching for alternatives. Most ticket transactions are final. However,
with the emergence of the secondary ticket market, opportunities exist
to resell tickets purchased in advance to recoup some or all of the
cost. In the case of a high-demand event, selling a ticket on secondary
ticket market may even result in a substantial profit. Regardless, the
specific options available to sport consumers as compared to hotel
consumers are not of particular importance in this context. In general,
advance-booking consumers, sport or otherwise, have several options when
quoted a price, and it is the incorporation of timing within the
advanced-booking model that makes the extension cogent.
In 2008, Schwartz extended the generic advanced-booking model to
include time as a variable claiming the options available to consumers
are not static over time. In other words, holding all other factors
constant, the decision to reserve a hotel depends somewhat on how far
out the purchase decision was from the date of stay. Two specific
variables in the model were identified as important factors in the
decision making process as they relate to time. First, to ensure
occupied rooms, it has become common for hotels to change prices as the
date of stay nears based on supply and demand. As mentioned above, DTP
strategies that account for fluctuating demand have emerged in
professional sports as a means to more effectively manage revenue. Thus,
the probability that a discounted price will be offered in the future
(Expected Lower Rate [ELR]) is an important variable in the
advanced-booking decision process. Second, the supply of hotel rooms and
sporting event tickets is limited; thus, the probability the hotel or
game will sell out (Expected Ticket Availability [ETA]) is an important
variable in the purchasing process.
[FIGURE 3 OMITTED]
Schwartz (2008) argued for the testing of this time-related
extension of the advanced-booking model. It was proposed that testing of
the impact of time in a booking decision would provide organizations a
better understanding of the time-related shifts in consumer perception
and propensity to book. As a result, organizations could practice more
effective revenue management strategies. Chen and Schwartz (2008b)
tested the impact of time on ELR and ETA related to hotel booking
decisions and found that consumer perceptions and expectations about
variables related to advanced booking changed as the date of stay
neared. The change patterns were more complicated than hypothesized, and
as a result, the authors suggested further research. In particular, the
authors recommended investigations should focus on the final 21 days
before the intended hotel stay.
Similar empirical research in the field of sport management and
marketing is lacking despite the fact that understanding time-related
shifts in demand would provide vital revenue management information.
Thus, the current study explored the role of time in the advanced-ticket
purchasing decisions by first measuring the impact of time on a sport
consumer's expectations of ticket availability (ETA) and finding
lower priced tickets (ELR) with respect to a given professional sporting
event. Second, given the potential importance of team identification and
ticket source, these variables were examined as moderators of the time,
ELR, and ETA relationship. Moderating relationships were hypothesized
for team identification and ticket source because relationships between
time, ELR, and ETA have been established in the travel and tourism
literature, and similar relationships were hypothesized for this study.
Figure 2 provides the conceptual model for the first aim of the
study, and Figure 3 provides the conceptual model for the second. The
solid lines denote relationships examined in the current study where the
dotted lines were established by Schwartz (2000) or Chen and Schwartz
(2008a). The following research questions were developed to guide the
research:
RQ1: Does a consumer's expectation of ticket availability with
respect to an upcoming professional sporting event differ over time?
RQ2: Is the relationship between consumer expectation of ticket
availability and days before the event moderated by ticket source and/or
team identification?
RQ3: Does a consumer's expectation of finding lower priced
tickets with respect to an upcoming professional sporting event differ
over time?
RQ4: Is the relationship between consumer expectation of lower
priced tickets and days before the event moderated by ticket source
and/or team identification?
Method
Sample and Procedures
Through a partnership with the Philadelphia Inquirer, the research
team had access to a panel of over 2,300 Philadelphia area sports fans.
As a result, a Philadelphia Flyers' home game against the Montreal
Canadiens was chosen as the context for the investigation. Participants
were solicited electronically via three date-specific email blasts prior
to a March 24th game. Within each email, a brief message and link were
provided to an online questionnaire. The online questionnaire was hosted
by Qualtrics. An incentive was provided to entice participation.
Subjects who agreed to participate were provided one of two written,
imaged-enhanced scenarios: (1) an opportunity to purchase a ticket from
the Flyers website, or (2) an opportunity to purchase the same ticket
from StubHub.com, the largest secondary ticket market website (see
appendix). According to the scenario, the participant and a friend
decided to attend the Saturday evening game, and the participant
volunteered to find tickets. They (according to the scenario) went
directly to the Flyers' website or StubHub.com and found a pair of
lower level tickets for $165 each. The arena seating chart was provided
as was an image of the view from the seat. After reading the scenario,
the subjects were asked to answer two questions estimating the
probability of future events (the two dependent variables). Team
identification and demographic information was collected as well.
Variables
Independent variable. The independent variable was time,
specifically the number of days before the hockey game. Three levels
were chosen based on previous travel research and secondary data
provided by StubHub. While purchasing tickets in advance may occur any
time after the season schedule is released, the volume of secondary
market transactions that occurred within the last three weeks leading up
to the event was substantial enough to warrant a shorter range of dates.
In addition, the time related work of Chen and Schwartz (2008b) resulted
in greater variability within this range. As a result, six, 13, and 19
days prior to the game were selected. Participants were randomly
assigned to one of the three treatments via email solicitation.
Dependent variables. The two dependent variables for this study
were
ETA, a participant's assessment of the expected availability of
the same or similar ticket between the scenario date and the game, and
ELR, a participant's assessment of finding a similar priced ticket
between the scenario date and the game. Both variables were measured by
percentage expectation between 0 and 100. Similar measures were used in
Chen and Schwartz's (2008b) study of hotel room rates and time.
Moderating variables. Ticket source, either primary (Flyers.com) or
secondary (StubHub.com), was added as a moderating variable given the
possibility that the secondary ticket market may influence consumer
behavior (Carter, 2012). Subjects were randomly assigned one of two
ticket sources and grouped as such. In addition, team identification was
examined as a potential moderator to investigate the importance of team
fandom as a function of time, ETA, and ELR. Team identification was
assessed through Trail, Robinson, Dick, and Gillentine's (2003)
team attachment items from their larger Points of Attachment Index. The
three item scale used a seven point Likert type (7=strongly agree;
1=strongly disagree). Participants were placed into one of two groups
(high and low) based on their mean attachment score. A score of less
than four was deemed low and four or greater was deemed high.
Statistical Tests
A multivariate analysis of variance (MANOVA) was conducted to
determine the overall differences in the mean likelihoods between
groups. A MANOVA is the appropriate statistical test to conduct when
there are multiple dependent variables that are moderately correlated
(Tabachnick & Fidell, 2007). Two 3x2x2 factorial analyses of
variance (ANOVA) were then conducted to determine if there were any
differences in the mean assessments for each treatment. The main effects
results were analyzed for time to answer research questions one and
three while the interaction effects were assessed for ticket source and
time and, team identification and time, and ticket source, team
identification, and time to answer research questions two and four. A
post hoc test (Tukey) was also conducted to see which time treatment
differed from the others. Additionally, due to the use of the same
dependent variables in two separate procedures, a Bonferonni adjustment
was made. The significance value was set at .025 for all main effects.
Results
A total of 415 Philadelphia area sports fans responded to the email
solicitation with 389 fully completing the survey resulting in a
response rate of 16.9%. Table 1 provides demographic information for the
sample. Table 2 shows the number of observations in each of the three
time treatments as well as the averages and standard deviations for each
of the two dependent variables (ELR and ETA). Respondents who were
solicited 19 days before the hockey game estimated the likelihood of the
same or similar tickets being available sometime during the next 18 days
to be 35.9%. At 13 days, the respondents estimated the ticket
availability to be 47.1%, and the respondents at six days estimated the
availability to be 52.5%. With regard to lower ticket prices, the
respondents at 19 days estimated the likelihood of finding the same or
similar tickets at a lower price during the next 18 days to be 31.1%.
The respondents at 13 days out estimated the probability to be 43.6%,
and at six days, the respondents estimated the probability to be 48.6%.
In general, as the game drew closer, the respondents' perceived
probability of both ticket availability and finding lower ticket prices
increased with the biggest jump occurring between 19 days out and 13.
The MANOVA test was significant F(4,770) = 6.25, p<.001
suggesting the participants in each time period differed in regard to
their assessments of ETA and ELR. Based on the MANOVA results, the
subsequent factorial ANOVAs were conducted. The main effects results of
the ETA factorial ANOVA with regard to time suggest that the
respondents' estimate of ticket availability differed between the
treatments F(2,377) = 13.50, p<.001. The Tukey HSD post hoc test
indicated that the respondents' estimate of ticket availability at
19 days was significantly lower than the respondents at 13 and six days.
No difference existed between the groups at 13 and six days. The main
effects results of ETA with regard to ticket source F(1,377) = 30.05,
p<.001 and team identification F(1,377) = 6.69; p = .010 also
resulted in statistically significant differences;. Respondents provided
with the StubHub.com scenario felt the probability the same or similar
ticket would be available between the scenario date and the date of the
game was higher than those provided with the Flyers.com scenario.
Meanwhile, those with a higher level of team identification felt there
was a better probability the same or similar ticket would be available
between the scenario date and the date of the game than those with a
lower level of team identification. Tables 3 and 4 provide the main
effects results for ticket source and team identification.
The interaction effect results with regard to moderating influence
of ticket source and time was significant F(2,377) = 3.20; p = .008. As
can be seen in Table 5, 19 days before the game, respondents provided
with the StubHub.com purchasing scenario felt there was a higher
probability the same or a similar ticket would be available in the days
leading up to the game compared to those provided with the Flyer's
website scenario. The same interaction effect was true for the
respondents presented with the differing scenarios 13 days out and six
days out. The other possible moderators of ETA (time x team
identification, source x team identification, time x source x team
identification) did not result in a statistically significant
interaction effect.
The main effects results of the ELR factorial ANOVA with regard to
time was also statistically significant indicating a difference between
the treatments F(2,377) = 5.58; p =.004. Similar to the ETA results, the
post hoc findings indicate the respondents' estimate of finding
lower ticket prices 19 days out was lower than the groups at 13 and six
days. No difference resulted between the groups at 13 and six days prior
to the game. Statistically significant differences resulted for the main
effects of ticket source F(1,377) = 36.44, p<.001 and team
identification F(1,377) = 9.23, p = .003 with regard to ELR. Once again
similar to the ETA results, respondents provided with the StubHub.com
scenario felt the probability of finding a similar ticket at a lower
price between the scenario date and the date of the game was higher than
those provided with the Flyers.com scenario. In addition, those with a
higher level of team identification felt there was a better probability
of finding a lower priced ticket compared to those with lower
identification levels.
The interaction effect between time and ticket source was once
again statistically significant F(2,377) = 2.33 p = .023 suggesting
respondents provided the StubHub.com scenario at each time interval felt
there was better probability of finding a similar ticket for a lower
price than those provided the Flyer website scenario. The other possible
moderators of ELR (time x team identification, source x team
identification, time x source x team identification) did not result in a
statistically significant interaction effect.
Discussion
The purpose of this study was to systematically investigate the
impact of time in the advanced-booking setting of a professional hockey
game. Ticket source and team identification were also examined as
potential moderating variables between time before the event and a
consumer's estimation of ticket availability and finding lower
priced tickets. The results suggest that as time before the event
decreased, a consumer's estimation of ticket availability and
finding a lower ticket price increased significantly. In addition,
respondents provided with the secondary ticket source (StubHub) had a
higher estimation of ticket availability and finding a lower ticket
price than those presented with the primary source (Flyers'
website) scenario. Respondents with a higher level of team
identification also had higher estimations for both dependent variables
than the respondents with a lower level attachment to the Flyers.
With respect to previous applications of the generic
advanced-booking decision model, the results appear to mildly parallel
the impact of time on consumer perceptions of availability and price
(Chen & Schwartz, 2008b). However, the results soundly confirm the
impact of time as an influential variable within the consumer decision
process, as statistically significant differences existed with respect
to consumer probability over time (Chen & Schwartz, 2008a; 2008b).
Therefore, the future application of the theoretical model in the field
of sport marketing should include data from different points in time.
Preferably, the inclusion of several points of time may provide more
insight to the specific influence of time. In addition, a more complete
understanding as to why consumers perceive ticket prices will decrease
and availability will increase or stay constant as time before the event
decreases is needed. Howard and Crompton (2004) suggested the sport
industry was headed towards more consumer focused pricing strategies as
opposed to previous regimes' aim at covering organizational costs.
In that case, further empirical research related to the impact of time,
consumer perceptions, and the advanced booking process is strongly
suggested. A more complete understanding of consumer perceptions of
ticket price and availability over time will provide for more effective
pricing strategies, revenue management tactics, and ultimately, less
empty seats in the stadium.
Additionally, these findings are consistent with previous sport
literature stating the effect of time on price, which has focused on
consumer demand for tickets (Moe et al., 2011; Shapiro & Drayer,
2012). Moe et al. (2011) found that in addition to team performance,
time played a significant role in ticket sales numbers. Ticket sales
appear to fluctuate more rapidly as the game draws closer. This finding
was also supported by Shapiro and Drayer's (2012) examination of
San Francisco Giants ticket prices during the first full year of DTP
implementation. In the primary market ticket prices gradually increased
as the game drew closer, where in the secondary market, ticket prices
rose initially (approximately a month before the game) and then dropped
considerably leading up to game time. These examinations focused on
actual ticket sales and price data. The current study supports the
impact of time in terms of the consumer's perception of price and
availability, suggesting a global influence from both the organization
and consumer perspective.
The distinct impact of ticket source as both a main effects and
moderating effect on a consumer's perception of price and
availability is a finding new to both the tourism and sport literature.
Obviously, the number of studies in this area is small, but
statistically significant differences in consumer estimations by ticket
source were present. Similarly, the differences between the primary
source-participants expanded as time before the game decreased, as the
respondents with the primary source scenario felt less likely to find
lower priced tickets and less likely the seats would remain available.
Several possible explanations may exist as to why this phenomenon is
occurring. For instance, per haps consumers perceive the prices offered
on StubHub are more fluid than prices offered by the Flyers, or perhaps
the same perception exists with regard to the number of options for
similar tickets on StubHub compared to the team's website. It could
also be a function of the general consumer's lack of awareness of
DTP from a team's perspective. Obviously, these are just suggested
possibilities, as the answers to these propositions go far beyond the
scope of the current results, but it is important to note that the
probabilities were different based on ticket source and ticket source
and time; thus, several questions remain. For instance, what do these
results have to do with the popularity of secondary ticket market and/or
consumer familiarity with these platforms? In addition, are these
results unique to sport or is it unique to secondary markets? Further
research in this area is highly-advised.
Team identification was not found to be a moderating variable as
hypothesized, but it was determined to be an influential variable within
the advanced-purchasing process. The inclusion of this variable was
based partly on the uniqueness of sport in eliciting a one-of-a-kind
connection between a consumer and the product. Previous research had
already established the importance of team identification in association
with consumption and event attendance (Trail et al., 2000; Trail et al.,
2003; Wakefield, 1995). Therefore, as a result of this bond, there was a
possibility highly-identified consumers would behave irrationally with
respect to time and estimations of price and availability. However, the
results suggested no significant relationship with time and the
dependent variables existed, and highly-identified consumers actually
indicated higher estimations of ticket availability and finding a lower
price. Perhaps highly-identified consumers are not only more attached to
the team, but also more knowledgeable of the advanced ticketing process.
That is, these consumers may be more aware of the ticket market and
price fluctuations through DTP and the secondary market. Research
related to team identification and consumer knowledge is sparse. Wann
and Branscombe (1995) found a relationship between team identification
and objective/subjective knowledge of the sports team, but the study
focused more on fandom than consumer knowledge. Thus, there is an
opportunity for more empirical research in this area, as well.
From a practitioner's perspective, the results related to time
and advanced purchases are essential especially with the supreme
importance of advanced sales for revenue management (Hendrickson, 2012).
There are practical considerations related to time and market
segmentation. Target markets are typically segmented based on simple
descriptors such as gender, age, geography, and frequency of purchase.
However, one the most unique features of DTP is that it allows the
seller to consider changes in consumer demand over time. In previous
research, time has been an important factor in predicting final sale
prices (Drayer & Shapiro, 2009; Moe et al., 2011). The results of
the current study indicate that, similar to the tourism and hospitality
industries, sport marketers may be able to segment consumers based on
time. The findings of the current study suggest that greater uncertainty
exists the further back the ticket sales pitch occurs. Sport marketers
may be able to capitalize on this sense of urgency and continue to push
customers to purchase tickets well in advance of an event.
Interestingly, Drayer and Shapiro (2009) found that secondary
market prices in an auction environment (where consumers determined the
ultimate sale price) tended to decline over time. Further, Drayer,
Shapiro, and Lee (2012) suggested that consumers who were educated about
DTP might ultimately be able to manipulate the market by waiting for
prices to fall over time. In this case, sport properties can be
reassured that there still exists a sense of urgency over time. Although
this phenomenon is still in need of further examination, DTP creates an
opportunity for sport marketers to segment consumers based on time.
Shapiro and Drayer (2012) found the dynamic ticket prices in the primary
market slowly increased over time. While sport organizations may do this
in order to protect the integrity of their ticket prices and encourage
advanced sales, this strategy may ignore consumers' expected
evaluation of the ticket market over time. Ultimately, sport marketers
must continue to balance traditional pricing strategies with an
understanding of consumer response to specific pricing stimuli.
Limitations & Future Research
While the study was grounded in sound theory, it was essentially an
exploratory study within the field of sport. As a result, the findings
only compare differences in consumer estimation at specific points in
time. It does not, however, explain how or why these patterns formed. As
indicated, more research is needed examining time as a variable within
sport consumer decision making. For instance, as suggested by Chen and
Schwartz (2008a), more research within the final 21 days before the
event may be beneficial. The current study only examined three distinct
points within this period, but perhaps a less restrictive investigation
of several points or perhaps even all points of time between day 21 and
game day would provide additional insight about this volatile segment of
time.
Another potential limitation of this study was the context of
professional hockey. While still considered a mainstream sport by many,
it is obviously less popular than the National Football League, the
National Basketball Association, and Major League Baseball, to the
average consumer. The population selection of general Philadelphia area
sports fans as opposed to only Flyers or even only hockey enthusiasts
helps the study's generalizability, but it also makes it hard to
assess the impact of the context on the variables under examination. For
instance, would the results differ from an investigation of another
league? In addition, the researchers selected a Saturday evening game
late in the season against a somewhat premium opponent (Montreal
Canadiens). How much did these specifics of the game impact the results?
The study also included real-life, time-specific details in the
scenarios provided to the participants in an attempt to create a
quasi-experimental research setting. As a result, several constraints
could have limited a participant's interest in attending the Flyers
game. For example, participants may have already had plans for the
weekend or perhaps even already had tickets to the game.
Along the same line, there is a need in the field for additional
longitudinal studies on sport consumers. Too often, a cross section of
individual attitudes and behaviors are studied with respect to a given
phenomenon. However, as this study shows, consumers are dynamic and
fluid. Thoughts and actions change over time, and while methods (as
employed in this study) accounting for the influence of time may require
more work upfront, the potential for more impactful results subsist.
Lastly, as mentioned throughout the discussion section, several
possible extensions of this study exist for future examination. For
instance, a closer look at how and why participants believed ticket
prices would decrease, yet availability would increase as time before
the event decreased is a logical follow-up. In addition, inquiry related
to team identification and other forms of consumer knowledge would be an
enticing extension. Investigating other sport-related factors that are
likely to interact with time within the advanced-purchasing process
would also be fruitful. For example, stadium location, team and opponent
quality, or even number of seats needed could interact with the time
variable. Consumer familiarity with both primary and secondary markets
in conjunction with time, perceived fairness, and perceived value would
also be an interesting line of research. In all, the examination these
distinct attitudinal patterns over time is of great importance to the
field, as they may provide a more clear understand of consumer behavior
in an advanced-purchasing setting.
Appendix
Flyers.com Scenario
Consider the following scenario: a good friend suggested going to a
Philadelphia Flyers game on Saturday, March 24th, 2012 (7 p.m.) where
the Flyers play the Montreal Canadiens. You went directly to the
Flyers' website (www.flyers.nhl.com) and found two tickets in the
middle of section 102 (see Seating Chart and View from the Section) for
$165 each.
[ILLUSTRATION OMITTED]
Please answer the following questions after carefully considering
all of the facts outlined in this scenario.
I believe the chance the same or very similar tickets will be
available between tomorrow (DATE) and Saturday, March 24th is --%.
(Please indicate a number between 0 and 100).
I believe the chance that I could find the same or very similar
tickets somewhere else at a price lower than $165 each between tomorrow
(DATE) and Saturday, March 24th is --%. (Please indicate a number
between 0 and 100).
StubHub.com Scenario
Consider the following scenario: a good friend suggested going to a
Philadelphia Flyers game on Saturday, March 24th, 2012 (7 p.m.) where
the Flyers play the Montreal Canadiens. You went directly to the StubHub
website (www.stubhub.com) and found two tickets in the middle of section
102 (see Seating Chart and View from the Section) for $165 each.
[ILLUSTRATION OMITTED]
WELLS FARGO CENTER--SEATING CHART
Please answer the following questions after carefully considering
all of the facts outlined in this scenario.
I believe the chance the same or very similar tickets will be
available between tomorrow (DATE) and Saturday, March 24th is --%.
(Please indicate a number between 0 and 100).
I believe the chance that I could find the same or very similar
tickets somewhere else at a price lower than $165 each between tomorrow
(DATE) and Saturday, March 24th is --%. (Please indicate a number
between 0 and 100).
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Table 1
Sample Demographics
Age 33.674, Mean
11.792, SD
Ethnicity 92.0%, Caucasian
5.7%, Other
2.3%, Did not specify
Education 6.4%, High School
41.1%, Bachelor's Degree
18.0%, Graduate Degree
16.5%, Professional Degree
7.7%, Other
9.0%, Did not specify
Gender 79.2%, Male
14.9%, Female
5.9%, Did not specify
Household Income 12.3%, Less than $50K
30.1%, $50K-$99K
24.7%, $100K-$150K
6.1%, More than $150K
16.8%, Did not specify
Table 2
Expected ticket availability (ETA) and expected lower rate
(ELR) by days before the event
Days Before Number of Expected Expected
the Game Observations Ticket Lower Rate
Availability
Average SD Average SD
(%) (a) (%) (b)
6 119 52.5 24.7 48.6 26.5
13 135 47.1 30.5 43.6 28.2
19 135 35.9 27.4 31.3 28.8
(a) Main effects result, p < .001
(b) Main effects result, p < .001
Table 3
Expected ticket availability (ETA) and expected lower rate
(ELR) by ticket source
Ticket Source Number of Expected Expected
Observations Ticket Lower Rate
Availability
Average SD Average SD
(%) (a) (%) (b)
Flyers Website 193 36.7 26.5 32.5 27.5
StubHub.com 196 52.7 27.2 49.9 27.6
(a) Main effects result, p < .001
(b) Main effects result, p < .001
Table 4
Expected ticket availability (ETA) and expected lower rate
(ELR) by team identification
Level of Team Number of Expected Expected
Identification Observations Ticket Lower Rate
Availability
Average SD Average SD
(%) (a) (%) (b)
Low 171 40.7 26.0 36.6 28.0
High 218 47.7 29.1 44.8 28.8
(a) Main effects result, p = .01
(b) Main effects result, p = .003
Table 5
Expected lower rate (ELR) and expected availability (EA) by days
before the event and ticket source interaction
Days Before Ticket Number of Expected Ticket
the Game Source Observations Availability
Average (%) (a) SD
6 Flyers Website 57 42.8 23.9
StubHub.com 62 56.3 23.9
13 Flyers Website 68 35.1 26.5
StubHub.com 67 57.1 30.1
19 Flyers Website 68 32.2 28.2
StubHub.com 67 42.7 25.5
Days Before Expected Lower Rate
the Game
Average (%) (b) SD
6 37.5 24.6
54.5 26.5
13 35.2 26.7
54.4 28.2
19 22.6 26.6
39.1 28.8
(a) Main effects result, p = .041
(b) Main effects result, p = .018
